text
stringlengths 2
132k
| source
dict |
|---|---|
of the propagator in the case of the thermal Green function, defined as G ( x , τ ∣ x ′ , τ ′ ) = ⟨ T ψ ( x , τ ) ψ ¯ ( x ′ , τ ′ ) ⟩ . {\displaystyle {\mathcal {G}}(\mathbf {x} ,\tau \mid \mathbf {x} ',\tau ')=\langle T\psi (\mathbf {x} ,\tau ){\bar {\psi }}(\mathbf {x} ',\tau ')\rangle .} Due to translational symmetry, it is only necessary to consider G ( x , τ ∣ 0 , 0 ) {\displaystyle {\mathcal {G}}(\mathbf {x} ,\tau \mid \mathbf {0} ,0)} for τ > 0 {\displaystyle \tau >0} , given by G ( x , τ ∣ 0 , 0 ) = 1 Z ∑ α ′ e − β E α ′ ⟨ α ′ ∣ ψ ( x , τ ) ψ ¯ ( 0 , 0 ) ∣ α ′ ⟩ . {\displaystyle {\mathcal {G}}(\mathbf {x} ,\tau \mid \mathbf {0} ,0)={\frac {1}{\mathcal {Z}}}\sum _{\alpha '}e^{-\beta E_{\alpha '}}\langle \alpha '\mid \psi (\mathbf {x} ,\tau ){\bar {\psi }}(\mathbf {0} ,0)\mid \alpha '\rangle .} Inserting a complete set of eigenstates gives G ( x , τ ∣ 0 , 0 ) = 1 Z ∑ α , α ′ e − β E α ′ ⟨ α ′ ∣ ψ ( x , τ ) ∣ α ⟩ ⟨ α ∣ ψ ¯ ( 0 , 0 ) ∣ α ′ ⟩ . {\displaystyle {\mathcal {G}}(\mathbf {x} ,\tau \mid \mathbf {0} ,0)={\frac {1}{\mathcal {Z}}}\sum _{\alpha ,\alpha '}e^{-\beta E_{\alpha '}}\langle \alpha '\mid \psi (\mathbf {x} ,\tau )\mid \alpha \rangle \langle \alpha \mid {\bar {\psi }}(\mathbf {0} ,0)\mid \alpha '\rangle .} Since | α ⟩ {\displaystyle |\alpha \rangle } and | α ′ ⟩ {\displaystyle |\alpha '\rangle } are eigenstates of H − μ N {\displaystyle H-\mu N}
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
, the Heisenberg operators can be rewritten in terms of Schrödinger operators, giving G ( x , τ | 0 , 0 ) = 1 Z ∑ α , α ′ e − β E α ′ e τ ( E α ′ − E α ) ⟨ α ′ ∣ ψ ( x ) ∣ α ⟩ ⟨ α ∣ ψ † ( 0 ) ∣ α ′ ⟩ . {\displaystyle {\mathcal {G}}(\mathbf {x} ,\tau |\mathbf {0} ,0)={\frac {1}{\mathcal {Z}}}\sum _{\alpha ,\alpha '}e^{-\beta E_{\alpha '}}e^{\tau (E_{\alpha '}-E_{\alpha })}\langle \alpha '\mid \psi (\mathbf {x} )\mid \alpha \rangle \langle \alpha \mid \psi ^{\dagger }(\mathbf {0} )\mid \alpha '\rangle .} Performing the Fourier transform then gives G ( k , ω n ) = 1 Z ∑ α , α ′ e − β E α ′ 1 − ζ e β ( E α ′ − E α ) − i ω n + E α − E α ′ ∫ k ′ d k ′ ⟨ α ∣ ψ ( k ) ∣ α ′ ⟩ ⟨ α ′ ∣ ψ † ( k ′ ) ∣ α ⟩ . {\displaystyle {\mathcal {G}}(\mathbf {k} ,\omega _{n})={\frac {1}{\mathcal {Z}}}\sum _{\alpha ,\alpha '}e^{-\beta E_{\alpha '}}{\frac {1-\zeta e^{\beta (E_{\alpha '}-E_{\alpha })}}{-i\omega _{n}+E_{\alpha }-E_{\alpha '}}}\int _{\mathbf {k} '}d\mathbf {k} '\langle \alpha \mid \psi (\mathbf {k} )\mid \alpha '\rangle \langle \alpha '\mid \psi ^{\dagger }(\mathbf {k} ')\mid \alpha \rangle .} Momentum conservation allows the final term to be written as (up to possible factors of the volume) | ⟨ α ′ ∣ ψ † ( k ) ∣ α ⟩ | 2 , {\displaystyle |\langle \alpha '\mid \psi ^{\dagger }(\mathbf {k} )\mid \alpha \rangle |^{2},} which confirms the expressions for the Green functions in the spectral representation. The sum rule can be proved by considering
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
the expectation value of the commutator, 1 = 1 Z ∑ α ⟨ α ∣ e − β ( H − μ N ) [ ψ k , ψ k † ] − ζ ∣ α ⟩ , {\displaystyle 1={\frac {1}{\mathcal {Z}}}\sum _{\alpha }\langle \alpha \mid e^{-\beta (H-\mu N)}[\psi _{\mathbf {k} },\psi _{\mathbf {k} }^{\dagger }]_{-\zeta }\mid \alpha \rangle ,} and then inserting a complete set of eigenstates into both terms of the commutator: 1 = 1 Z ∑ α , α ′ e − β E α ( ⟨ α ∣ ψ k ∣ α ′ ⟩ ⟨ α ′ ∣ ψ k † ∣ α ⟩ − ζ ⟨ α ∣ ψ k † ∣ α ′ ⟩ ⟨ α ′ ∣ ψ k ∣ α ⟩ ) . {\displaystyle 1={\frac {1}{\mathcal {Z}}}\sum _{\alpha ,\alpha '}e^{-\beta E_{\alpha }}\left(\langle \alpha \mid \psi _{\mathbf {k} }\mid \alpha '\rangle \langle \alpha '\mid \psi _{\mathbf {k} }^{\dagger }\mid \alpha \rangle -\zeta \langle \alpha \mid \psi _{\mathbf {k} }^{\dagger }\mid \alpha '\rangle \langle \alpha '\mid \psi _{\mathbf {k} }\mid \alpha \rangle \right).} Swapping the labels in the first term then gives 1 = 1 Z ∑ α , α ′ ( e − β E α ′ − ζ e − β E α ) | ⟨ α ∣ ψ k † ∣ α ′ ⟩ | 2 , {\displaystyle 1={\frac {1}{\mathcal {Z}}}\sum _{\alpha ,\alpha '}\left(e^{-\beta E_{\alpha '}}-\zeta e^{-\beta E_{\alpha }}\right)|\langle \alpha \mid \psi _{\mathbf {k} }^{\dagger }\mid \alpha '\rangle |^{2}~,} which is exactly the result of the integration of ρ. ==== Non-interacting case ==== In the non-interacting case, ψ k † ∣ α ′ ⟩ {\displaystyle \psi _{\mathbf {k} }^{\dagger }\mid \alpha '\rangle } is an eigenstate with (grand-canonical) energy E α ′ + ξ k {\displaystyle E_{\alpha '}+\xi _{\mathbf {k} }}
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
, where ξ k = ϵ k − μ {\displaystyle \xi _{\mathbf {k} }=\epsilon _{\mathbf {k} }-\mu } is the single-particle dispersion relation measured with respect to the chemical potential. The spectral density therefore becomes ρ 0 ( k , ω ) = 1 Z 2 π δ ( ξ k − ω ) ∑ α ′ ⟨ α ′ ∣ ψ k ψ k † ∣ α ′ ⟩ ( 1 − ζ e − β ξ k ) e − β E α ′ . {\displaystyle \rho _{0}(\mathbf {k} ,\omega )={\frac {1}{\mathcal {Z}}}\,2\pi \delta (\xi _{\mathbf {k} }-\omega )\sum _{\alpha '}\langle \alpha '\mid \psi _{\mathbf {k} }\psi _{\mathbf {k} }^{\dagger }\mid \alpha '\rangle (1-\zeta e^{-\beta \xi _{\mathbf {k} }})e^{-\beta E_{\alpha '}}.} From the commutation relations, ⟨ α ′ ∣ ψ k ψ k † ∣ α ′ ⟩ = ⟨ α ′ ∣ ( 1 + ζ ψ k † ψ k ) ∣ α ′ ⟩ , {\displaystyle \langle \alpha '\mid \psi _{\mathbf {k} }\psi _{\mathbf {k} }^{\dagger }\mid \alpha '\rangle =\langle \alpha '\mid (1+\zeta \psi _{\mathbf {k} }^{\dagger }\psi _{\mathbf {k} })\mid \alpha '\rangle ,} with possible factors of the volume again. The sum, which involves the thermal average of the number operator, then gives simply [ 1 + ζ n ( ξ k ) ] Z {\displaystyle [1+\zeta n(\xi _{\mathbf {k} })]{\mathcal {Z}}} , leaving ρ 0 ( k , ω ) = 2 π δ ( ξ k − ω ) . {\displaystyle \rho _{0}(\mathbf {k} ,\omega )=2\pi \delta (\xi _{\mathbf {k} }-\omega ).} The imaginary-time propagator is thus G 0 ( k , ω ) = 1 − i ω n + ξ k {\displaystyle {\mathcal {G}}_{0}(\mathbf {k} ,\omega )={\frac {1}{-i\omega _{n}+\xi _{\mathbf {k} }}}} and the retarded propagator is G 0 R
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
( k , ω ) = 1 − ( ω + i η ) + ξ k . {\displaystyle G_{0}^{\mathrm {R} }(\mathbf {k} ,\omega )={\frac {1}{-(\omega +i\eta )+\xi _{\mathbf {k} }}}.} ==== Zero-temperature limit ==== As β → ∞, the spectral density becomes ρ ( k , ω ) = 2 π ∑ α [ δ ( E α − E 0 − ω ) | ⟨ α ∣ ψ k † ∣ 0 ⟩ | 2 − ζ δ ( E 0 − E α − ω ) | ⟨ 0 ∣ ψ k † ∣ α ⟩ | 2 ] {\displaystyle \rho (\mathbf {k} ,\omega )=2\pi \sum _{\alpha }\left[\delta (E_{\alpha }-E_{0}-\omega )\left|\left\langle \alpha \mid \psi _{\mathbf {k} }^{\dagger }\mid 0\right\rangle \right|^{2}-\zeta \delta (E_{0}-E_{\alpha }-\omega )\left|\left\langle 0\mid \psi _{\mathbf {k} }^{\dagger }\mid \alpha \right\rangle \right|^{2}\right]} where α = 0 corresponds to the ground state. Note that only the first (second) term contributes when ω is positive (negative). == General case == === Basic definitions === We can use 'field operators' as above, or creation and annihilation operators associated with other single-particle states, perhaps eigenstates of the (noninteracting) kinetic energy. We then use ψ ( x , τ ) = φ α ( x ) ψ α ( τ ) , {\displaystyle \psi (\mathbf {x} ,\tau )=\varphi _{\alpha }(\mathbf {x} )\psi _{\alpha }(\tau ),} where ψ α {\displaystyle \psi _{\alpha }} is the annihilation operator for the single-particle state α {\displaystyle \alpha } and φ α ( x ) {\displaystyle \varphi _{\alpha }(\mathbf {x} )} is that state's wavefunction in the position basis. This gives G α 1 … α n | β 1 … β n ( n ) ( τ 1 … τ n | τ 1 ′ … τ n ′ ) = ⟨ T ψ α
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
1 ( τ 1 ) … ψ α n ( τ n ) ψ ¯ β n ( τ n ′ ) … ψ ¯ β 1 ( τ 1 ′ ) ⟩ {\displaystyle {\mathcal {G}}_{\alpha _{1}\ldots \alpha _{n}|\beta _{1}\ldots \beta _{n}}^{(n)}(\tau _{1}\ldots \tau _{n}|\tau _{1}'\ldots \tau _{n}')=\langle T\psi _{\alpha _{1}}(\tau _{1})\ldots \psi _{\alpha _{n}}(\tau _{n}){\bar {\psi }}_{\beta _{n}}(\tau _{n}')\ldots {\bar {\psi }}_{\beta _{1}}(\tau _{1}')\rangle } with a similar expression for G ( n ) {\displaystyle G^{(n)}} . === Two-point functions === These depend only on the difference of their time arguments, so that G α β ( τ ∣ τ ′ ) = 1 β ∑ ω n G α β ( ω n ) e − i ω n ( τ − τ ′ ) {\displaystyle {\mathcal {G}}_{\alpha \beta }(\tau \mid \tau ')={\frac {1}{\beta }}\sum _{\omega _{n}}{\mathcal {G}}_{\alpha \beta }(\omega _{n})\,e^{-i\omega _{n}(\tau -\tau ')}} and G α β ( t ∣ t ′ ) = ∫ − ∞ ∞ d ω 2 π G α β ( ω ) e − i ω ( t − t ′ ) . {\displaystyle G_{\alpha \beta }(t\mid t')=\int _{-\infty }^{\infty }{\frac {d\omega }{2\pi }}\,G_{\alpha \beta }(\omega )\,e^{-i\omega (t-t')}.} We can again define retarded and advanced functions in the obvious way; these are related to the time-ordered function in the same way as above. The same periodicity properties as described in above apply to G α β {\displaystyle {\mathcal {G}}_{\alpha \beta }} . Specifically, G α β ( τ ∣ τ ′ ) = G α β ( τ − τ ′ ) {\displaystyle {\mathcal {G}}_{\alpha \beta }(\tau \mid \tau ')={\mathcal {G}}_{\alpha \beta }(\tau -\tau ')} and G α β ( τ ) = G α β ( τ + β ) , {\displaystyle {\mathcal {G}}_{\alpha \beta }(\tau )={\mathcal {G}}_{\alpha \beta }(\tau
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
+\beta ),} for τ < 0 {\displaystyle \tau <0} . === Spectral representation === In this case, ρ α β ( ω ) = 1 Z ∑ m , n 2 π δ ( E n − E m − ω ) ⟨ m ∣ ψ α ∣ n ⟩ ⟨ n ∣ ψ β † ∣ m ⟩ ( e − β E m − ζ e − β E n ) , {\displaystyle \rho _{\alpha \beta }(\omega )={\frac {1}{\mathcal {Z}}}\sum _{m,n}2\pi \delta (E_{n}-E_{m}-\omega )\;\langle m\mid \psi _{\alpha }\mid n\rangle \langle n\mid \psi _{\beta }^{\dagger }\mid m\rangle \left(e^{-\beta E_{m}}-\zeta e^{-\beta E_{n}}\right),} where m {\displaystyle m} and n {\displaystyle n} are many-body states. The expressions for the Green functions are modified in the obvious ways: G α β ( ω n ) = ∫ − ∞ ∞ d ω ′ 2 π ρ α β ( ω ′ ) − i ω n + ω ′ {\displaystyle {\mathcal {G}}_{\alpha \beta }(\omega _{n})=\int _{-\infty }^{\infty }{\frac {d\omega '}{2\pi }}{\frac {\rho _{\alpha \beta }(\omega ')}{-i\omega _{n}+\omega '}}} and G α β R ( ω ) = ∫ − ∞ ∞ d ω ′ 2 π ρ α β ( ω ′ ) − ( ω + i η ) + ω ′ . {\displaystyle G_{\alpha \beta }^{\mathrm {R} }(\omega )=\int _{-\infty }^{\infty }{\frac {d\omega '}{2\pi }}{\frac {\rho _{\alpha \beta }(\omega ')}{-(\omega +i\eta )+\omega '}}.} Their analyticity properties are identical to those of G ( k , ω n ) {\displaystyle {\mathcal {G}}(\mathbf {k} ,\omega _{n})} and G R ( k , ω ) {\displaystyle G^{\mathrm {R} }(\mathbf {k} ,\omega )} defined in the translationally invariant case. The proof follows exactly the same steps, except that the two matrix elements are no longer complex conjugates. ==== Noninteracting case ==== If the particular single-particle
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
states that are chosen are 'single-particle energy eigenstates', i.e. [ H − μ N , ψ α † ] = ξ α ψ α † , {\displaystyle [H-\mu N,\psi _{\alpha }^{\dagger }]=\xi _{\alpha }\psi _{\alpha }^{\dagger },} then for | n ⟩ {\displaystyle |n\rangle } an eigenstate: ( H − μ N ) ∣ n ⟩ = E n ∣ n ⟩ , {\displaystyle (H-\mu N)\mid n\rangle =E_{n}\mid n\rangle ,} so is ψ α ∣ n ⟩ {\displaystyle \psi _{\alpha }\mid n\rangle } : ( H − μ N ) ψ α ∣ n ⟩ = ( E n − ξ α ) ψ α ∣ n ⟩ , {\displaystyle (H-\mu N)\psi _{\alpha }\mid n\rangle =(E_{n}-\xi _{\alpha })\psi _{\alpha }\mid n\rangle ,} and so is ψ α † ∣ n ⟩ {\displaystyle \psi _{\alpha }^{\dagger }\mid n\rangle } : ( H − μ N ) ψ α † ∣ n ⟩ = ( E n + ξ α ) ψ α † ∣ n ⟩ . {\displaystyle (H-\mu N)\psi _{\alpha }^{\dagger }\mid n\rangle =(E_{n}+\xi _{\alpha })\psi _{\alpha }^{\dagger }\mid n\rangle .} We therefore have ⟨ m ∣ ψ α ∣ n ⟩ ⟨ n ∣ ψ β † ∣ m ⟩ = δ ξ α , ξ β δ E n , E m + ξ α ⟨ m ∣ ψ α ∣ n ⟩ ⟨ n ∣ ψ β † ∣ m ⟩ . {\displaystyle \langle m\mid \psi _{\alpha }\mid n\rangle \langle n\mid \psi _{\beta }^{\dagger }\mid m\rangle =\delta _{\xi _{\alpha },\xi _{\beta }}\delta _{E_{n},E_{m}+\xi _{\alpha }}\langle m\mid \psi _{\alpha }\mid n\rangle \langle n\mid \psi _{\beta }^{\dagger }\mid m\rangle .} We then rewrite ρ α β ( ω ) = 1 Z ∑ m , n 2 π δ ( ξ α − ω ) δ ξ α ,
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
ξ β ⟨ m ∣ ψ α ∣ n ⟩ ⟨ n ∣ ψ β † ∣ m ⟩ e − β E m ( 1 − ζ e − β ξ α ) , {\displaystyle \rho _{\alpha \beta }(\omega )={\frac {1}{\mathcal {Z}}}\sum _{m,n}2\pi \delta (\xi _{\alpha }-\omega )\delta _{\xi _{\alpha },\xi _{\beta }}\langle m\mid \psi _{\alpha }\mid n\rangle \langle n\mid \psi _{\beta }^{\dagger }\mid m\rangle e^{-\beta E_{m}}\left(1-\zeta e^{-\beta \xi _{\alpha }}\right),} therefore ρ α β ( ω ) = 1 Z ∑ m 2 π δ ( ξ α − ω ) δ ξ α , ξ β ⟨ m ∣ ψ α ψ β † e − β ( H − μ N ) ∣ m ⟩ ( 1 − ζ e − β ξ α ) , {\displaystyle \rho _{\alpha \beta }(\omega )={\frac {1}{\mathcal {Z}}}\sum _{m}2\pi \delta (\xi _{\alpha }-\omega )\delta _{\xi _{\alpha },\xi _{\beta }}\langle m\mid \psi _{\alpha }\psi _{\beta }^{\dagger }e^{-\beta (H-\mu N)}\mid m\rangle \left(1-\zeta e^{-\beta \xi _{\alpha }}\right),} use ⟨ m ∣ ψ α ψ β † ∣ m ⟩ = δ α , β ⟨ m ∣ ζ ψ α † ψ α + 1 ∣ m ⟩ {\displaystyle \langle m\mid \psi _{\alpha }\psi _{\beta }^{\dagger }\mid m\rangle =\delta _{\alpha ,\beta }\langle m\mid \zeta \psi _{\alpha }^{\dagger }\psi _{\alpha }+1\mid m\rangle } and the fact that the thermal average of the number operator gives the Bose–Einstein or Fermi–Dirac distribution function. Finally, the spectral density simplifies to give ρ α β = 2 π δ ( ξ α − ω ) δ α β , {\displaystyle \rho _{\alpha \beta }=2\pi \delta (\xi _{\alpha }-\omega )\delta _{\alpha \beta },} so that the thermal Green function is G α β ( ω n ) = δ α β − i ω n + ξ β {\displaystyle
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
{\mathcal {G}}_{\alpha \beta }(\omega _{n})={\frac {\delta _{\alpha \beta }}{-i\omega _{n}+\xi _{\beta }}}} and the retarded Green function is G α β ( ω ) = δ α β − ( ω + i η ) + ξ β . {\displaystyle G_{\alpha \beta }(\omega )={\frac {\delta _{\alpha \beta }}{-(\omega +i\eta )+\xi _{\beta }}}.} Note that the noninteracting Green function is diagonal, but this will not be true in the interacting case. == See also == Fluctuation theorem Green–Kubo relations Linear response function Lindblad equation Propagator Correlation function (quantum field theory) Numerical analytic continuation == References == === Books === Bonch-Bruevich V. L., Tyablikov S. V. (1962): The Green Function Method in Statistical Mechanics. North Holland Publishing Co. Abrikosov, A. A., Gorkov, L. P. and Dzyaloshinski, I. E. (1963): Methods of Quantum Field Theory in Statistical Physics Englewood Cliffs: Prentice-Hall. Negele, J. W. and Orland, H. (1988): Quantum Many-Particle Systems AddisonWesley. Zubarev D. N., Morozov V., Ropke G. (1996): Statistical Mechanics of Nonequilibrium Processes: Basic Concepts, Kinetic Theory (Vol. 1). John Wiley & Sons. ISBN 3-05-501708-0. Mattuck Richard D. (1992), A Guide to Feynman Diagrams in the Many-Body Problem, Dover Publications, ISBN 0-486-67047-3. === Papers === Bogolyubov N. N., Tyablikov S. V. Retarded and advanced Green functions in statistical physics, Soviet Physics Doklady, Vol. 4, p. 589 (1959). Zubarev D. N., Double-time Green functions in statistical physics, Soviet Physics Uspekhi 3(3), 320–345 (1960). == External links == Linear Response Functions in Eva Pavarini, Erik Koch, Dieter Vollhardt, and Alexander Lichtenstein (eds.): DMFT at 25: Infinite Dimensions, Verlag des Forschungszentrum Jülich, 2014 ISBN 978-3-89336-953-9
|
{
"page_id": 7864709,
"source": null,
"title": "Green's function (many-body theory)"
}
|
Hemerochory (Ancient Greek ἥμερος, hemeros: 'tame, ennobled, cultivated, cultivated' and Greek χωρίς choris: separate, isolated), or anthropochory, is the distribution of cultivated plants or their seeds and cuttings, consciously or unconsciously, by humans into an area that they could not colonize through their natural mechanisms of spread, but are able to maintain themselves without specific human help in their new habitat. Hemerochory is one of the main propagation mechanisms of a plant. Hemerochoric plants can both increase and decrease the biodiversity of a habitat. == Categorisation == Hemerochoric plants are classified according to the manner of introduction into, for example: Ethelochory: the conscious introduction by seed or young plants. Speirochory: the unintentional introduction by contaminated seed. Examples are the true chamomile and the cornflower. Agochory: the introduction by unintentional transport with, among other things, ships, trains and cars. These plants are common in port areas, roadsides, stations and railways. === Division === Chronologically the hemerochoric plants are divided in: Archaeophytes: plants that were introduced before the onset of world trade around the year 1500, or before the year 1492 (discovery of America). Neophytes: plants that were introduced later. === Related terms === Anthropochory is often used synonymously but does not mean exactly the same. Anthropochory is the spread by humans. The spread through domestic animals does not belong to the anthropochoric, but to the hemerochoric, because domestic animals belong to the human culture. Strictly speaking, anthropochoric means the spread through humans as a transport medium. These can also be native species that were either adapted from the outset to locations created by human cultural activity or have adapted to them afterwards; As a result, their area of distribution has often, but not always, increased. == History == Hemerochorous spread of plants through human cultural activity very likely already happened in
|
{
"page_id": 68551049,
"source": null,
"title": "Hemerochory"
}
|
the Stone Age, but demonstrably at the latest in antiquity, namely along old trade routes. Fruits such as apples and pears gradually made their way along the Silk Road from the area around the Altai Mountains to Greece and from there to the gardens of the Romans, who in turn brought these cultivated plants to Central Europe, and some of these plants were eventually able to survive outside the culture. Many useful plants, such as tomato, potato, pumpkin and French bean did not reach Central Europe until the 16th century, after the American continent was discovered, and are now grown worldwide. In the last 400 to 500 years the spread has expanded through trade and military campaigns, through explorers and missionaries. The latter brought countless plants with them from their travels both out of an interest in exotic plants, which were often included in the plant collections of princely courts, and for purely scientific purposes. In the context of botanical studies, the interest was often in the possible healing effects of these plants, but also in the expansion of botanical knowledge, or the plants were only used for collecting (herbaria). Some ornamental plants also came to Europe because they promised a lucrative business. This applies, for example, to the camellias, one of which is also grown as a tea plant in Japan and China. While this species turned out to be not cultivable in Central Europe, people very quickly discovered the aesthetic appeal of the other camellia species as an ornamental plant. Botanical gardens played a major role in the acclimatization of such plants from distant habitats. == Forms == === Agochory === Agochoric plants are those that are spread through accidental transport. Unlike speirochoric plants, they are usually not sown on human-prepared soil. On land, agochoric plants used to
|
{
"page_id": 68551049,
"source": null,
"title": "Hemerochory"
}
|
be common in harbors, at train stations or along railway lines. However, mainly aquatic plants are spread through agochory. Ballast water plays a major role in the agochoric spread of aquatic plants. Around the world, around ten billion tons of seawater and the organisms it contains are shipped in this way. Exporting countries in particular are affected by the spread of organisms through ballast water. The ships arrive at the ports with empty cargo hold, but fully pumped ballast tanks. In the draining of this ballast water, these ports receive thousands of cubic meters of seawater brimming with alien creatures now in a new environment. The seaweed Undaria pinnatifida, which is native to the Japanese coast, reached the Tasmanian coast via ballast water and has formed dense kelp forests along the coast since 1988, displacing the native flora and fauna. Caulerpa taxifolia is one of those plants that are often spread by ballast water. It is also spread by the fact that ships tear off parts of the algae with their anchors. Australia was the first country to introduce a ballast water policy back in 1990 and is now the most determined to address this problem. Ships were asked not to take in ballast water in shallow and polluted bays and not to refuel with ballast water during the night, since then many marine organisms that are otherwise on the seabed rise to the surface of the water. Ships should also exchange their ballast water 200 kilometers away from the coastal waters, so that on the one hand the offshore species are not introduced into the more sensitive coastal waters and, on the other hand, no inhabitants of the coastal zone are transported to other continents. === Ethelochory === Ethelochory is intentional transportation of plants or seeds to different regions
|
{
"page_id": 68551049,
"source": null,
"title": "Hemerochory"
}
|
for agricultural and gardening purposes. Numerous crops that are important for human nutrition have been willingly spread by humans. Wheat, barley, lentil, beans, flax and poppy seeds, for example, are not typical plants for Central Europe, although they are all archaeotypes. People brought them after the beginning of the Neolithic (about 6,500 years ago) gradually from the eastern Mediterranean to central Europe and the rest of the world through the upcoming centuries. In central Europe, it is especially Cyperus esculentus which has been classified since the 1980s among the invasive species, because their tubers have been spread en masse, by sticking to vehicles or machines. Many of the old cultivated plants have spread around the world, primarily through emigrants from Europe. Grown for at least 4,000 years, wheat was introduced to America in the 16th century and Australia in the 19th century. Orange, lemons, apricots and peaches were originally native to China. They probably came via the Silk Road as early as the 3rd century BC. In Asia Minor and from there through the Romans to the Mediterranean. European settlers, in turn, used these species to grow fruit in suitable regions of America. From the 16th century, ornamental plants were grown more and more. Species native to Europe were first introduced as garden plants. These include, for example, the gladioli, the ornamental onion, European bluebell, the snowdrop native to southeast Europe and the common clematis. Ornamental plants from more distant regions were added later. From East Asia in particular, a number of plants were introduced to Europe as exotic or for economic reasons. === Speirochory === Some plants were unintentionally introduced in this process; this unwanted hemerochory as a seed companion is called speirochory. Since every seed also contains seeds of the herbs of the field from which it comes,
|
{
"page_id": 68551049,
"source": null,
"title": "Hemerochory"
}
|
their competitors, the "weeds", were also sold through the trade in the seeds of the useful plant. The real chamomile is one of the plants that were unintentionally spread as a companion to seeds. Speirochoric plants are sown on human-prepared soil and are competitors of the crops. Plants that are considered to be archaeophytes, such as the poppy, native to the Mediterranean area, the real chamomile, the cornflower and field buttercup, spread through the seeds with the grain in Central Europe. In the meantime, the seeds are cleaned more thoroughly using modern methods and the cultivation is hardly contaminated by pesticides or other control techniques. In spite of this, Cuscuta campestris, which is classified as a problematic weed in Australia, was accidentally imported into the country together with basil seeds in 1981, 1988 and 1990. == See also == Assisted colonization Escaped plant Volunteer plant Adventive plant == References ==
|
{
"page_id": 68551049,
"source": null,
"title": "Hemerochory"
}
|
The following timeline starts with the invention of the modern computer in the late interwar period. == 1930s == John Vincent Atanasoff and Clifford Berry create the first electronic non-programmable, digital computing device, the Atanasoff–Berry Computer, that lasted from 1937 to 1942. == 1940s == Nuclear bomb and ballistics simulations at Los Alamos National Laboratory and Ballistic Research Laboratory (BRL), respectively. Monte Carlo simulation (voted one of the top 10 algorithms of the 20th century by Jack Dongarra and Francis Sullivan in the 2000 issue of Computing in Science and Engineering) is invented at Los Alamos National Laboratory by John von Neumann, Stanislaw Ulam and Nicholas Metropolis. First hydrodynamic simulations performed at Los Alamos National Laboratory. Ulam and von Neumann introduce the notion of cellular automata. == 1950s == Equations of State Calculations by Fast Computing Machines introduces the Metropolis–Hastings algorithm. Also, important earlier independent work by Berni Alder and Stan Frankel. Enrico Fermi, Ulam and John Pasta with help from Mary Tsingou, discover the Fermi–Pasta–Ulam-Tsingou problem. Research initiated into percolation theory. Molecular dynamics is formulated by Alder and Tom E. Wainwright. == 1960s == Using computational investigations of the 3-body problem, Michael Minovitch formulates the gravity assist method. Glauber dynamics is invented for the Ising model by Roy J. Glauber. Edward Lorenz discovers the butterfly effect on a computer, attracting interest in chaos theory. Molecular dynamics is independently invented by Aneesur Rahman. Walter Kohn instigates the development of density functional theory (with L.J. Sham and Pierre Hohenberg), for which he shared the Nobel Chemistry Prize (1998). Martin Kruskal and Norman Zabusky follow up the Fermi–Pasta–Ulam problem with further numerical experiments, and coin the term "soliton". Kawasaki dynamics is invented for the Ising model. Loup Verlet (re)discovers a numerical integration algorithm, (first used in 1791 by Jean Baptiste Delambre, by
|
{
"page_id": 35520902,
"source": null,
"title": "Timeline of computational physics"
}
|
P. H. Cowell and A. C. C. Crommelin in 1909, and by Carl Fredrik Störmer in 1907, hence the alternative names Störmer's method or the Verlet-Störmer method) for dynamics, and the Verlet list. == 1970s == Computer algebra replicates the work of Boris Delaunay in Lunar theory. Martinus Veltman's calculations at CERN lead him and Gerard 't Hooft to valuable insights into renormalizability of electroweak theory. The computation has been cited as a key reason for the award of the Nobel Physics Prize that has been given to both. Jean Hardy, Yves Pomeau and Olivier de Pazzis introduce the first lattice gas model, abbreviated as the HPP model after its authors. These later evolved into lattice Boltzmann models. Kenneth G. Wilson shows that continuum quantum chromodynamics (QCD) is recovered for an infinitely large lattice with its sites infinitesimally close to one another, thereby beginning lattice QCD. == 1980s == Italian physicists Roberto Car and Michele Parrinello invent the Car–Parrinello method. Swendsen–Wang algorithm is invented in the field of Monte Carlo simulations. Fast multipole method is invented by Vladimir Rokhlin and Leslie Greengard (voted one of the top 10 algorithms of the 20th century). Ullli Wolff invents the Wolff algorithm for statistical physics and Monte Carlo simulation. == See also == Timeline of scientific computing Computational physics Important publications in computational physics == References == == External links == The Monte Carlo Method: Classic Papers Monte Carlo Landmark Papers
|
{
"page_id": 35520902,
"source": null,
"title": "Timeline of computational physics"
}
|
SHEEP is one of the earliest interactive symbolic computation systems. It is specialized for computations with tensors, and was designed for the needs of researchers working with general relativity and other theories involving extensive tensor calculus computations. SHEEP is a freeware package (copyrighted, but free for educational and research use). The name "SHEEP" is pun on the Lisp Algebraic Manipulator or LAM on which SHEEP is based. The package was written by Inge Frick, using earlier work by Ian Cohen and Ray d'Inverno, who had written ALAM - Atlas LISP Algebraic Manipulation in earlier (designed in 1970). SHEEP was an interactive computer package whereas LAM and ALAM were batch processing languages. Jan E. Åman wrote an important package in SHEEP to carry out the Cartan-Karlhede algorithm. A more recent version of SHEEP, written by Jim Skea, runs under Cambridge Lisp, which is also used for REDUCE. == See also == GRTensorII == Notes == == External links == SHEEP download directory at Queen Mary, University of London Some sources of info on Sheep Review article by M.A.H.MacCallum in "Workshop on Dynamical Spacetimes and Numerical Relativity" edited by Joan Centrella
|
{
"page_id": 2687374,
"source": null,
"title": "SHEEP (symbolic computation system)"
}
|
The Enrico Fermi Prize, first awarded in 2001, is given by the Italian Physical Society (Società Italiana di Fisica). It is a yearly award of €30,000 honoring one or more Members of the Society who have "particularly honoured physics with their discoveries." == Recipients == == See also == List of physics awards == References ==
|
{
"page_id": 4850065,
"source": null,
"title": "Enrico Fermi Prize"
}
|
The molecular formula C3H3N3 may refer to: Aminomalononitrile Triazine
|
{
"page_id": 23921045,
"source": null,
"title": "C3H3N3"
}
|
In engineering, a factor of safety (FoS) or safety factor (SF) expresses how much stronger a system is than it needs to be for its specified maximum load. Safety factors are often calculated using detailed analysis because comprehensive testing is impractical on many projects, such as bridges and buildings, but the structure's ability to carry a load must be determined to a reasonable accuracy. Many systems are intentionally built much stronger than needed for normal usage to allow for emergency situations, unexpected loads, misuse, or degradation (reliability). Margin of safety (MoS or MS) is a related measure, expressed as a relative change. == Definition == There are two definitions for the factor of safety (FoS): The ratio of a structure's absolute strength (structural capability) to actual applied load; this is a measure of the reliability of a particular design. This is a calculated value, and is sometimes referred to, for the sake of clarity, as a realized factor of safety. A constant required value, imposed by law, standard, specification, contract or custom, to which a structure must conform or exceed. This can be referred to as a design factor, design factor of safety or required factor of safety. The realized factor of safety must be greater than the required design factor of safety. However, between various industries and engineering groups usage is inconsistent and confusing; there are several definitions used. The cause of much confusion is that various reference books and standards agencies use the factor of safety definitions and terms differently. Building codes, structural and mechanical engineering textbooks often refer to the "factor of safety" as the fraction of total structural capability over what is needed. Those are realized factors of safety (first use). Many undergraduate strength of materials books use "Factor of Safety" as a constant value intended
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
as a minimum target for design (second use). == Calculation == There are several ways to compare the factor of safety for structures. All the different calculations fundamentally measure the same thing: how much extra load beyond what is intended a structure will actually take (or be required to withstand). The difference between the methods is the way in which the values are calculated and compared. Safety factor values can be thought of as a standardized way for comparing strength and reliability between systems. The use of a factor of safety does not imply that an item, structure, or design is "safe". Many quality assurance, engineering design, manufacturing, installation, and end-use factors may influence whether or not something is safe in any particular situation. === Design factor and safety factor === The difference between the safety factor and design factor (design safety factor) is as follows: The safety factor, or yield stress, is how much the designed part actually will be able to withstand (first usage from above). The design factor, or working stress, is what the item is required to be able to withstand (second usage). The design factor is defined for an application (generally provided in advance and often set by regulatory building codes or policy) and is not an actual calculation, the safety factor is a ratio of maximum strength to intended load for the actual item that was designed. Factor of safety = yield stress working stress {\displaystyle {\text{Factor of safety}}={\frac {\text{yield stress}}{\text{working stress}}}} The design load is the maximum load the part should ever see in service. By this definition, a structure with an FoS of exactly 1 will support only the design load and no more. Any additional load will cause the structure to fail. A structure with an FoS of 2 will fail
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
at twice the design load. === Margin of safety === Many government agencies and industries (such as aerospace) require the use of a margin of safety (MoS or MS) to describe the ratio of the strength of the structure to the requirements. There are two separate definitions for the margin of safety so care is needed to determine which is being used for a given application. One usage of MS is as a measure of capability like FoS. The other usage of MS is as a measure of satisfying design requirements (requirement verification). Margin of safety can be conceptualized (along with the reserve factor explained below) to represent how much of the structure's total capability is held "in reserve" during loading. MS as a measure of structural capability: This definition of margin of safety commonly seen in textbooks describes what additional load beyond the design load a part can withstand before failing. In effect, this is a measure of excess capability. If the margin is 0, the part will not take any additional load before it fails, if it is negative the part will fail before reaching its design load in service. If the margin is 1, it can withstand one additional load of equal force to the maximum load it was designed to support (i.e. twice the design load). Margin of safety = failure load design load − 1 {\displaystyle {\text{Margin of safety}}={\frac {\text{failure load}}{\text{design load}}}-1} Margin of safety = factor of safety − 1 {\displaystyle {\text{Margin of safety}}={\text{factor of safety}}-1} MS as a measure of requirement verification: Many agencies and organizations such as NASA and AIAA define the margin of safety including the design factor, in other words, the margin of safety is calculated after applying the design factor. In the case of a margin of 0, the
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
part is at exactly the required strength (the safety factor would equal the design factor). If there is a part with a required design factor of 3 and a margin of 1, the part would have a safety factor of 6 (capable of supporting two loads equal to its design factor of 3, supporting six times the design load before failure). A margin of 0 would mean the part would pass with a safety factor of 3. If the margin is less than 0 in this definition, although the part will not necessarily fail, the design requirement has not been met. A convenience of this usage is that for all applications, a margin of 0 or higher is passing, one does not need to know application details or compare against requirements, just glancing at the margin calculation tells whether the design passes or not. This is helpful for oversight and reviewing on projects with various integrated components, as different components may have various design factors involved and the margin calculation helps prevent confusion. The design safety factor is provided as a requirement. Margin of safety = failure load design load × design safety factor − 1 {\displaystyle {\text{Margin of safety}}={\frac {\text{failure load}}{\text{design load × design safety factor}}}-1} Margin of safety = realized factor of safety design safety factor − 1 {\displaystyle {\text{Margin of safety}}={\frac {\text{realized factor of safety}}{\text{design safety factor}}}-1} For a successful design, the realized safety factor must always equal or exceed the design safety factor so that the margin of safety is greater than or equal to zero. The margin of safety is sometimes, but infrequently, used as a percentage, i.e., a 0.50 MS is equivalent to a 50% MS. When a design satisfies this test it is said to have a "positive margin", and, conversely, a "negative
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
margin" when it does not. In the field of nuclear safety (as implemented at US government-owned facilities) the margin of safety has been defined as a quantity that may not be reduced without review by the controlling government office. The US Department of Energy publishes DOE G 424.1-1, "Implementation Guide for Use in Addressing Unreviewed Safety Question Requirements" as a guide for determining how to identify and determine whether a margin of safety will be reduced by a proposed change. The guide develops and applies the concept of a qualitative margin of safety that may not be explicit or quantifiable, yet can be evaluated conceptually to determine whether an increase or decrease will occur with a proposed change. This approach becomes important when examining designs with large or undefined (historical) margins and those that depend on "soft" controls such as programmatic limits or requirements. The commercial US nuclear industry utilized a similar concept in evaluating planned changes until 2001, when 10 CFR 50.59 was revised to capture and apply the information available in facility-specific risk analyses and other quantitative risk management tools. === Reserve factor === A measure of strength frequently used in Europe is the reserve factor (RF). With the strength and applied loads expressed in the same units, the reserve factor is defined in one of two ways, depending on the industry: RF = proof strength proof load {\displaystyle {\text{RF}}={\frac {\text{proof strength}}{\text{proof load}}}} RF = ultimate strength ultimate load {\displaystyle {\text{RF}}={\frac {\text{ultimate strength}}{\text{ultimate load}}}} The applied loads have many factors, including factors of safety applied. == Yield and ultimate calculations == For ductile materials (e.g. most metals), it is often required that the factor of safety be checked against both yield and ultimate strengths. The yield calculation will determine the safety factor until the part starts to deform
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
plastically. The ultimate calculation will determine the safety factor until failure. In brittle materials the yield and ultimate strengths are often so close as to be indistinguishable, so it is usually acceptable to only calculate the ultimate safety factor. == Choosing design factors == Appropriate design factors are based on several considerations, such as the accuracy of predictions on the imposed loads, strength, wear estimates, and the environmental effects to which the product will be exposed in service; the consequences of engineering failure; and the cost of over-engineering the component to achieve that factor of safety . For example, components whose failure could result in substantial financial loss, serious injury, or death may use a safety factor of four or higher (often ten). Non-critical components generally might have a design factor of two. Risk analysis, failure mode and effects analysis, and other tools are commonly used. Design factors for specific applications are often mandated by law, policy, or industry standards. Buildings commonly use a factor of safety of 2.0 for each structural member. The value for buildings is relatively low because the loads are well understood and most structures are redundant. Pressure vessels use 3.5 to 4.0, automobiles use 3.0, and aircraft and spacecraft use 1.2 to 4.0 depending on the application and materials. Ductile, metallic materials tend to use the lower value while brittle materials use the higher values. The field of aerospace engineering uses generally lower design factors because the costs associated with structural weight are high (i.e. an aircraft with an overall safety factor of 5 would probably be too heavy to get off the ground). This low design factor is why aerospace parts and materials are subject to very stringent quality control and strict preventative maintenance schedules to help ensure reliability. A usually applied Safety Factor
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
is 1.5, but for pressurized fuselage it is 2.0, and for main landing gear structures it is often 1.25. In some cases it is impractical or impossible for a part to meet the "standard" design factor. The penalties (mass or otherwise) for meeting the requirement would prevent the system from being viable (such as in the case of aircraft or spacecraft). In these cases, it is sometimes determined to allow a component to meet a lower than normal safety factor, often referred to as "waiving" the requirement. Doing this often brings with it extra detailed analysis or quality control verifications to assure the part will perform as desired, as it will be loaded closer to its limits. For loading that is cyclical, repetitive, or fluctuating, it is important to consider the possibility of metal fatigue when choosing factor of safety. A cyclic load well below a material's yield strength can cause failure if it is repeated through enough cycles. == History == According to Elishakoff the notion of factor of safety in engineering context was apparently first introduced in 1729 by Bernard Forest de Bélidor (1698-1761) who was a French engineer working in hydraulics, mathematics, civil, and military engineering. The philosophical aspects of factors of safety were pursued by Doorn and Hansson. == See also == Engineering tolerance – Permissible limit or limits of variation Limit state design – Design method in structural engineering Probabilistic design – Discipline within engineering design Redundancy (total quality management) – Approach to business improvementPages displaying short descriptions of redirect targets Sacrificial part – Component engineered to fail first to protect the rest of the device Statistical interference – When two probability distributions overlap Verification and validation – Methods for checking conformance to requirements == Notes == == Further reading == Lalanne, C., Specification Development
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
- 2nd Ed., ISTE-Wiley, 2009
|
{
"page_id": 262553,
"source": null,
"title": "Factor of safety"
}
|
The molecular formula C18H37NO2 (molar mass: 299.49 g/mol, exact mass: 299.2824 u) may refer to: Ammonium oleate Palmitoylethanolamide (PEA) Sphingosine
|
{
"page_id": 24838559,
"source": null,
"title": "C18H37NO2"
}
|
Metabolic flexibility is the capacity to alter metabolism in response to exercise or available fuel (especially fats and carbohydrates). Metabolic inflexibility was first described as the ability to generate energy through either aerobic or anaerobic respiration or as the inability of muscle to increase glucose oxidation in response to insulin. An organism can also be said to have metabolic flexibility if it is capable of metabolizing either carbohydrate or fat efficiently, depending on availability of those fuels. By this definition, metabolic flexibility can be quantified using respiratory quotient. This form of metabolic flexibility is reduced by insulin resistance. With aging there is a decrease in metabolic flexibility due to a decline in pyruvate dehydrogenase activity which results in pyruvate increasingly being anaerobically converted to lactate rather than aerobically converted to acetyl-CoA. Similarly, a virus-induced cytokine storm can compromise metabolic flexibility by inactivating the pyruvate dehydrogenase complex and other enzymes. == See also == Insulin resistance == References ==
|
{
"page_id": 69534115,
"source": null,
"title": "Metabolic flexibility"
}
|
A g-factor (also called g value) is a dimensionless quantity that characterizes the magnetic moment and angular momentum of an atom, a particle or the nucleus. It is the ratio of the magnetic moment (or, equivalently, the gyromagnetic ratio) of a particle to that expected of a classical particle of the same charge and angular momentum. In nuclear physics, the nuclear magneton replaces the classically expected magnetic moment (or gyromagnetic ratio) in the definition. The two definitions coincide for the proton. == Definition == === Dirac particle === The spin magnetic moment of a charged, spin-1/2 particle that does not possess any internal structure (a Dirac particle) is given by μ = g e 2 m S , {\displaystyle {\boldsymbol {\mu }}=g{e \over 2m}\mathbf {S} ,} where μ is the spin magnetic moment of the particle, g is the g-factor of the particle, e is the elementary charge, m is the mass of the particle, and S is the spin angular momentum of the particle (with magnitude ħ/2 for Dirac particles). === Baryon or nucleus === Protons, neutrons, nuclei, and other composite baryonic particles have magnetic moments arising from their spin (both the spin and magnetic moment may be zero, in which case the g-factor is undefined). Conventionally, the associated g-factors are defined using the nuclear magneton, and thus implicitly using the proton's mass rather than the particle's mass as for a Dirac particle. The formula used under this convention is μ = g μ N ℏ I = g e 2 m p I , {\displaystyle {\boldsymbol {\mu }}=g{\mu _{\text{N}} \over \hbar }{\mathbf {I} }=g{e \over 2m_{\text{p}}}\mathbf {I} ,} where μ is the magnetic moment of the nucleon or nucleus resulting from its spin, g is the effective g-factor, I is its spin angular momentum, μN is the nuclear
|
{
"page_id": 6095269,
"source": null,
"title": "G-factor (physics)"
}
|
magneton, e is the elementary charge, and mp is the proton rest mass. == Calculation == === Electron g-factors === There are three magnetic moments associated with an electron: one from its spin angular momentum, one from its orbital angular momentum, and one from its total angular momentum (the quantum-mechanical sum of those two components). Corresponding to these three moments are three different g-factors: ==== Electron spin g-factor ==== The most known of these is the electron spin g-factor (more often called simply the electron g-factor) ge, defined by μ s = g e μ B ℏ S , {\displaystyle {\boldsymbol {\mu }}_{\text{s}}=g_{\text{e}}{\frac {\mu _{\text{B}}}{\hbar }}\mathbf {S} ,} where μs is the magnetic moment resulting from the spin of an electron, S is its spin angular momentum, and μB = eħ/2me is the Bohr magneton. In atomic physics, the electron spin g-factor is often defined as the absolute value of ge: g s = | g e | = − g e . {\displaystyle g_{\text{s}}=|g_{\text{e}}|=-g_{\text{e}}.} The z component of the magnetic moment then becomes μ z = − g s μ B m s , {\displaystyle \mu _{\text{z}}=-g_{\text{s}}\mu _{\text{B}}m_{\text{s}},} where ℏ m s {\displaystyle \hbar m_{\text{s}}} are the eigenvalues of the Sz operator, meaning that ms can take on values ± 1 / 2 {\displaystyle \pm 1/2} . The value gs is roughly equal to 2.002319 and is known to extraordinary precision – one part in 1013. The reason it is not precisely two is explained by quantum electrodynamics calculation of the anomalous magnetic dipole moment. ==== Electron orbital g-factor ==== Secondly, the electron orbital g-factor gL is defined by μ L = − g L μ B ℏ L , {\displaystyle {\boldsymbol {\mu }}_{L}=-g_{L}{\frac {\mu _{\text{B}}}{\hbar }}\mathbf {L} ,} where μL is the magnetic moment resulting from the orbital
|
{
"page_id": 6095269,
"source": null,
"title": "G-factor (physics)"
}
|
angular momentum of an electron, L is its orbital angular momentum, and μB is the Bohr magneton. For an infinite-mass nucleus, the value of gL is exactly equal to one, by a quantum-mechanical argument analogous to the derivation of the classical magnetogyric ratio. For an electron in an orbital with a magnetic quantum number ml, the z component of the orbital magnetic moment is μ z = − g L μ B m l , {\displaystyle \mu _{z}=-g_{L}\mu _{\text{B}}m_{l},} which, since gL = 1, is −μBml. For a finite-mass nucleus, there is an effective g value g L = 1 − 1 M , {\displaystyle g_{L}=1-{\frac {1}{M}},} where M is the ratio of the nuclear mass to the electron mass. ==== Total angular momentum (Landé) g-factor ==== Thirdly, the Landé g-factor gJ is defined by | μ J | = g J μ B ℏ | J | , {\displaystyle |{\boldsymbol {\mu }}_{J}|=g_{J}{\frac {\mu _{\text{B}}}{\hbar }}|\mathbf {J} |,} where μJ is the total magnetic moment resulting from both spin and orbital angular momentum of an electron, J = L + S is its total angular momentum, and μB is the Bohr magneton. The value of gJ is related to gL and gs by a quantum-mechanical argument; see the article Landé g-factor. μJ and J vectors are not collinear, so only their magnitudes can be compared. === Muon g-factor === The muon, like the electron, has a g-factor associated with its spin, given by the equation μ = g e 2 m μ S , {\displaystyle {\boldsymbol {\mu }}=g{e \over 2m_{\mu }}\mathbf {S} ,} where μ is the magnetic moment resulting from the muon's spin, S is the spin angular momentum, and mμ is the muon mass. That the muon g-factor is not quite the same as the electron g-factor is
|
{
"page_id": 6095269,
"source": null,
"title": "G-factor (physics)"
}
|
mostly explained by quantum electrodynamics and its calculation of the anomalous magnetic dipole moment. Almost all of the small difference between the two values (99.96% of it) is due to a well-understood lack of heavy-particle diagrams contributing to the probability for emission of a photon representing the magnetic dipole field, which are present for muons, but not electrons, in QED theory. These are entirely a result of the mass difference between the particles. However, not all of the difference between the g-factors for electrons and muons is exactly explained by the Standard Model. The muon g-factor can, in theory, be affected by physics beyond the Standard Model, so it has been measured very precisely, in particular at the Brookhaven National Laboratory. In the E821 collaboration final report in November 2006, the experimental measured value is 2.0023318416(13), compared to the theoretical prediction of 2.00233183620(86). This is a difference of 3.4 standard deviations, suggesting that beyond-the-Standard-Model physics may be a contributory factor. The Brookhaven muon storage ring was transported to Fermilab where the Muon g–2 experiment used it to make more precise measurements of muon g-factor. On April 7, 2021, the Fermilab Muon g−2 collaboration presented and published a new measurement of the muon magnetic anomaly. When the Brookhaven and Fermilab measurements are combined, the new world average differs from the theory prediction by 4.2 standard deviations. == Measured g-factor values == The electron g-factor is one of the most precisely measured values in physics. == See also == Anomalous magnetic dipole moment Electron magnetic moment Landé g-factor == Notes and references == == Further reading == CODATA recommendations 2006 == External links == Media related to G-factor (physics) at Wikimedia Commons Gwinner, Gerald; Silwal, Roshani (June 2022). "Tiny isotopic difference tests standard model of particle physics". Nature. 606 (7914): 467–468. doi:10.1038/d41586-022-01569-3.
|
{
"page_id": 6095269,
"source": null,
"title": "G-factor (physics)"
}
|
PMID 35705815. S2CID 249710367.
|
{
"page_id": 6095269,
"source": null,
"title": "G-factor (physics)"
}
|
Dangerous Things is a Seattle-based cybernetic microchip biohacking implant retailer formed in 2013 by Amal Graafstra, following a crowdfunding campaign. Dangerous Things built the first personal publicly available implantable NFC compliant transponder in 2013. In September 2020, Dangerous Things began another highly successful crowdfunding campaign to realize the world's first titanium encased fully bio-compatible sensing magnet, named the Titan. == References ==
|
{
"page_id": 65405351,
"source": null,
"title": "Dangerous Things"
}
|
In electrochemistry, electrosynthesis is the synthesis of chemical compounds in an electrochemical cell. Compared to ordinary redox reactions, electrosynthesis sometimes offers improved selectivity and yields. Electrosynthesis is actively studied as a science and also has industrial applications. Electrooxidation has potential for wastewater treatment as well. == Experimental setup == The basic setup in electrosynthesis is a galvanic cell, a potentiostat and two electrodes. Typical solvent and electrolyte combinations minimizes electrical resistance. Protic conditions often use alcohol-water or dioxane-water solvent mixtures with an electrolyte such as a soluble salt, acid or base. Aprotic conditions often use an organic solvent such as acetonitrile or dichloromethane with electrolytes such as lithium perchlorate or tetrabutylammonium salts. The choice of electrodes with respect to their composition and surface area can be decisive. For example, in aqueous conditions the competing reactions in the cell are the formation of oxygen at the anode and hydrogen at the cathode. In this case a graphite anode and lead cathode could be used effectively because of their high overpotentials for oxygen and hydrogen formation respectively. Many other materials can be used as electrodes. Other examples include platinum, magnesium, mercury (as a liquid pool in the reactor), stainless steel or reticulated vitreous carbon. Some reactions use a sacrificial electrode that is consumed during the reaction like zinc or lead. Cell designs can be undivided cell or divided cell type. In divided cells the cathode and anode chambers are separated with a semiporous membrane. Common membrane materials include sintered glass, porous porcelain, polytetrafluoroethene or polypropylene. The purpose of the divided cell is to permit the diffusion of ions while restricting the flow of the products and reactants. This separation simplifies workup. An example of a reaction requiring a divided cell is the reduction of nitrobenzene to phenylhydroxylamine, where the latter chemical is
|
{
"page_id": 6160807,
"source": null,
"title": "Electrosynthesis"
}
|
susceptible to oxidation at the anode. == Reactions == Organic oxidations take place at the anode. Compounds are reduced at the cathode. Radical intermediates are often invoked. The initial reaction takes place at the surface of the electrode and then the intermediates diffuse into the solution where they participate in secondary reactions. The yield of an electrosynthesis is expressed both in terms of the chemical yield and current efficiency. Current efficiency is the ratio of Coulombs consumed in forming the products to the total number of Coulombs passed through the cell. Side reactions decrease the current efficiency. The potential drop between the electrodes determines the rate constant of the reaction. Electrosynthesis is carried out with either constant potential or constant current. The reason one chooses one over the other is due to a trade-off of ease of experimental conditions versus current efficiency. Constant potential uses current more efficiently because the current in the cell decreases with time due to the depletion of the substrate around the working electrode (stirring is usually necessary to decrease the diffusion layer around the electrode). This is not the case under constant current conditions, however. Instead, as the substrate's concentration decreases the potential across the cell increases in order to maintain the fixed reaction rate. This consumes current in side reactions produced outside the target voltage. === Anodic oxidations === A well-known electrosynthesis is the Kolbe electrolysis, in which two carboxylic acids decarboxylate, and the remaining structures bond together: A variation is called the non-Kolbe reaction when a heteroatom (nitrogen or oxygen) is present at the α-position. The intermediate oxonium ion is trapped by a nucleophile, usually solvent. Anodic electrosynthesis oxidize primary aliphatic amine to nitrile. Amides can be oxidized to N-acyliminium ions, which can be captured by various nucleophiles, for example: This reaction type
|
{
"page_id": 6160807,
"source": null,
"title": "Electrosynthesis"
}
|
is called a Shono oxidation. An example is the α-methoxylation of N-carbomethoxypyrrolidine Oxidation of a carbanion can lead to a coupling reaction for instance in the electrosynthesis of the tetramethyl ester of ethanetetracarboxylic acid from the corresponding malonate ester α-amino acids form nitriles and carbon dioxide via oxidative decarboxylation at AgO anodes (the latter is formed in-situ by oxidation of Ag2O): Cyanoacetic acid from cathodic reduction of carbon dioxide and anodic oxidation of acetonitrile. Selective electrochemical oxidation have been developed in the last decades for nitrile preparation form amines. Propiolic acid is prepared commercially by oxidizing propargyl alcohol at a lead electrode.. === Cathodic reductions === In the Markó–Lam deoxygenation, an alcohol could be almost instantaneously deoxygenated by electroreducing its toluate ester. In concept, adiponitrile is prepared from dimerizing acrylonitrile: 2 CH2=CHCN + 2 e− + 2 H+ → NC(CH2)4CN In practice,the cathodic hydrodimerization of activated olefins is applied industrially in the synthesis of adiponitrile from two equivalents of acrylonitrile : The cathodic reduction of arene compounds to the 1,4-dihydro derivatives is similar to a Birch reduction. Examples from industry are the reduction of phthalic acid: and the reduction of 2-methoxynaphthalene: The Tafel rearrangement, named for Julius Tafel, was at one time an important method for the synthesis of certain hydrocarbons from alkylated ethyl acetoacetate, a reaction accompanied by the rearrangement reaction of the alkyl group: The cathodic reduction of a nitrile to a primary amine in a divided cell; the cathodic reduction of benzyl cyanide to phenethylamine is shown: Cathodic reduction of a nitroalkene can give the oxime in good yield. At higher negative reduction potentials, the nitroalkene can be reduced further, giving the primary amine but with lower yield. Azobenzene is prepared in industrial electrosynthesis using nitrobenzene. An electrochemical carboxylation of a para-isobutyl benzyl chloride to Ibuprofen is
|
{
"page_id": 6160807,
"source": null,
"title": "Electrosynthesis"
}
|
promoted under supercritical carbon dioxide. Cathodic reduction of a carboxylic acid (oxalic acid) to an aldehyde (glyoxylic acid, shows as the rare aldehyde form) in a divided cell: Originally phenylpropanoic acid could be prepared from reduction of cinnamic acid by electrolysis. An electrocatalysis by a copper complex helps reduce carbon dioxide to oxalic acid; this conversion uses carbon dioxide as a feedstock to generate oxalic acid. It has been reported that formate can be formed by the electrochemical reduction of CO2 (in the form of bicarbonate) at a lead cathode at pH 8.6: HCO−3 + H2O + 2e− → HCO−2 + 2OH− or CO2 + H2O + 2e− → HCO−2 + OH− If the feed is CO2 and oxygen is evolved at the anode, the total reaction is: CO2 + OH− → HCO−2 + 1/2 O2 === Redox reactions === Cathodic reduction of carbon dioxide and anodic oxidation of acetonitrile afford cyanoacetic acid. An electrosynthesis employing alternating current prepares phenol at both the cathode and the anode. === Electrofluorination === In organofluorine chemistry, many perfluorinated compounds are prepared by electrochemical synthesis, which is conducted in liquid HF at voltages near 5–6 V using Ni anodes. The method was invented in the 1930s. Amines, alcohols, carboxylic acids, and sulfonic acids are converted to perfluorinated derivatives using this technology. A solution or suspension of the hydrocarbon in hydrogen fluoride is electrolyzed at 5–6 V to produce high yields of the perfluorinated product. == See also == Electrochemical engineering == External links == Electrochemistry Encyclopedia Link == References ==
|
{
"page_id": 6160807,
"source": null,
"title": "Electrosynthesis"
}
|
The molecular formula C9H9NO2 (molar mass: 163.17 g/mol, exact mass: 163.0633 u) may refer to: 2,6-Diacetylpyridine Phenyl-2-nitropropene (P2NP)
|
{
"page_id": 23921063,
"source": null,
"title": "C9H9NO2"
}
|
This page provides supplementary chemical data on barium nitrate. == Material Safety Data Sheet == The handling of this chemical may incur notable safety precautions. It is highly recommend that you seek the Material Safety Datasheet (MSDS) for this chemical from a reliable source and follow its directions. SIRI MSDS from BARIUM AND CHEMICALS INC in the SDSdata.org database Science Stuff == Structure and properties == == Thermodynamic properties == == Spectral data == == References ==
|
{
"page_id": 8323499,
"source": null,
"title": "Barium nitrate (data page)"
}
|
Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways (such as glycolysis and the citric acid cycle) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology. In general, the process to build a reconstruction is as follows: Draft a reconstruction Refine the model Convert model into a mathematical/computational representation Evaluate and debug model through experimentation The related method of flux balance analysis seeks to mathematically simulate metabolism in genome-scale reconstructions of metabolic networks. == Genome-scale metabolic reconstruction == A metabolic reconstruction provides a highly mathematical, structured platform on which to understand the systems biology of metabolic pathways within an organism. The integration of biochemical metabolic pathways with rapidly available, annotated genome sequences has developed what are called genome-scale metabolic models. Simply put, these models correlate metabolic genes with metabolic pathways. In general, the more information about physiology, biochemistry and genetics is available for the target organism, the better the predictive capacity of the reconstructed models. Mechanically speaking, the process of reconstructing prokaryotic and eukaryotic metabolic networks is essentially the same. Having said this, eukaryote reconstructions are typically more challenging because of the size of genomes, coverage of knowledge, and the multitude of cellular compartments. The first genome-scale metabolic model was generated in 1995 for
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
Haemophilus influenzae. The first multicellular organism, C. elegans, was reconstructed in 1998. Since then, many reconstructions have been formed. For a list of reconstructions that have been converted into a model and experimentally validated, see http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms. == Drafting a reconstruction == === Resources === Because the timescale for the development of reconstructions is so recent, most reconstructions have been built manually. However, now, there are quite a few resources that allow for the semi-automatic assembly of these reconstructions that are utilized due to the time and effort necessary for a reconstruction. An initial fast reconstruction can be developed automatically using resources like PathoLogic or ERGO in combination with encyclopedias like MetaCyc, and then manually updated by using resources like PathwayTools. These semi-automatic methods allow for a fast draft to be created while allowing the fine tune adjustments required once new experimental data is found. It is only in this manner that the field of metabolic reconstructions will keep up with the ever-increasing numbers of annotated genomes. ==== Databases ==== Kyoto Encyclopedia of Genes and Genomes (KEGG): a bioinformatics database containing information on genes, proteins, reactions, and pathways. The ‘KEGG Organisms’ section, which is divided into eukaryotes and prokaryotes, encompasses many organisms for which gene and DNA information can be searched by typing in the enzyme of choice. BioCyc, EcoCyc, and MetaCyc: BioCyc Is a collection of 3,000 pathway/genome databases (as of Oct 2013), with each database dedicated to one organism. For example, EcoCyc is a highly detailed bioinformatics database on the genome and metabolic reconstruction of Escherichia coli, including thorough descriptions of E. coli signaling pathways and regulatory network. The EcoCyc database can serve as a paradigm and model for any reconstruction. Additionally, MetaCyc, an encyclopedia of experimentally defined metabolic pathways and enzymes, contains 2,100 metabolic pathways and 11,400 metabolic
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
reactions (Oct 2013). ENZYME: An enzyme nomenclature database (part of the ExPASy proteonomics server of the Swiss Institute of Bioinformatics). After searching for a particular enzyme on the database, this resource gives you the reaction that is catalyzed. ENZYME has direct links to other gene/enzyme/literature databases such as KEGG, BRENDA, and PUBMED. BRENDA: A comprehensive enzyme database that allows for an enzyme to be searched by name, EC number, or organism. BiGG: A knowledge base of biochemically, genetically, and genomically structured genome-scale metabolic network reconstructions. metaTIGER: Is a collection of metabolic profiles and phylogenomic information on a taxonomically diverse range of eukaryotes which provides novel facilities for viewing and comparing the metabolic profiles between organisms. ==== Tools for metabolic modeling ==== Pathway Tools: A bioinformatics software package that assists in the construction of pathway/genome databases such as EcoCyc. Developed by Peter Karp and associates at the SRI International Bioinformatics Research Group, Pathway Tools has several components. Its PathoLogic module takes an annotated genome for an organism and infers probable metabolic reactions and pathways to produce a new pathway/genome database. Its MetaFlux component can generate a quantitative metabolic model from that pathway/genome database using flux-balance analysis. Its Navigator component provides extensive query and visualization tools, such as visualization of metabolites, pathways, and the complete metabolic network. ERGO: A subscription-based service developed by Integrated Genomics. It integrates data from every level including genomic, biochemical data, literature, and high-throughput analysis into a comprehensive user friendly network of metabolic and nonmetabolic pathways. KEGGtranslator: an easy-to-use stand-alone application that can visualize and convert KEGG files (KGML formatted XML-files) into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g., MIRIAM annotations) beyond the scope of the KGML document, and amends missing components
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator converts these files to SBML, BioPAX, SIF, SBGN, SBML with qualitative modeling extension, GML, GraphML, JPG, GIF, LaTeX, etc. ModelSEED: An online resource for the analysis, comparison, reconstruction, and curation of genome-scale metabolic models. Users can submit genome sequences to the RAST annotation system, and the resulting annotation can be automatically piped into the ModelSEED to produce a draft metabolic model. The ModelSEED automatically constructs a network of metabolic reactions, gene-protein-reaction associations for each reaction, and a biomass composition reaction for each genome to produce a model of microbial metabolism that can be simulated using Flux Balance Analysis. MetaMerge: algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. CoReCo: algorithm for automatic reconstruction of metabolic models of related species. The first version of the software used KEGG as reaction database to link with the EC number predictions from CoReCo. Its automatic gap filling using atom map of all the reactions produce functional models ready for simulation. ==== Tools for literature ==== PUBMED: This is an online library developed by the National Center for Biotechnology Information, which contains a massive collection of medical journals. Using the link provided by ENZYME, the search can be directed towards the organism of interest, thus recovering literature on the enzyme and its use inside of the organism. === Methodology to draft a reconstruction === A reconstruction is built by compiling data from the resources above. Database tools such as KEGG and BioCyc can be used in conjunction with each other to find all the metabolic genes in the organism of interest. These genes will be compared to closely related organisms that have already developed reconstructions to find homologous genes and reactions. These homologous genes and
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
reactions are carried over from the known reconstructions to form the draft reconstruction of the organism of interest. Tools such as ERGO, Pathway Tools and Model SEED can compile data into pathways to form a network of metabolic and non-metabolic pathways. These networks are then verified and refined before being made into a mathematical simulation. The predictive aspect of a metabolic reconstruction hinges on the ability to predict the biochemical reaction catalyzed by a protein using that protein's amino acid sequence as an input, and to infer the structure of a metabolic network based on the predicted set of reactions. A network of enzymes and metabolites is drafted to relate sequences and function. When an uncharacterized protein is found in the genome, its amino acid sequence is first compared to those of previously characterized proteins to search for homology. When a homologous protein is found, the proteins are considered to have a common ancestor and their functions are inferred as being similar. However, the quality of a reconstruction model is dependent on its ability to accurately infer phenotype directly from sequence, so this rough estimation of protein function will not be sufficient. A number of algorithms and bioinformatics resources have been developed for refinement of sequence homology-based assignments of protein functions: InParanoid: Identifies eukaryotic orthologs by looking only at in-paralogs. CDD: Resource for the annotation of functional units in proteins. Its collection of domain models utilizes 3D structure to provide insights into sequence/structure/function relationships. InterPro: Provides functional analysis of proteins by classifying them into families and predicting domains and important sites. STRING: Database of known and predicted protein interactions. Once proteins have been established, more information about the enzyme structure, reactions catalyzed, substrates and products, mechanisms, and more can be acquired from databases such as KEGG, MetaCyc and NC-IUBMB. Accurate
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
metabolic reconstructions require additional information about the reversibility and preferred physiological direction of an enzyme-catalyzed reaction which can come from databases such as BRENDA or MetaCyc database. == Model refinement == An initial metabolic reconstruction of a genome is typically far from perfect due to the high variability and diversity of microorganisms. Often, metabolic pathway databases such as KEGG and MetaCyc will have "holes", meaning that there is a conversion from a substrate to a product (i.e., an enzymatic activity) for which there is no known protein in the genome that encodes the enzyme that facilitates the catalysis. What can also happen in semi-automatically drafted reconstructions is that some pathways are falsely predicted and don't actually occur in the predicted manner. Because of this, a systematic verification is made in order to make sure no inconsistencies are present and that all the entries listed are correct and accurate. Furthermore, previous literature can be researched in order to support any information obtained from one of the many metabolic reaction and genome databases. This provides an added level of assurance for the reconstruction that the enzyme and the reaction it catalyzes do actually occur in the organism. Enzyme promiscuity and spontaneous chemical reactions can damage metabolites. This metabolite damage, and its repair or pre-emption, create energy costs that need to be incorporated into models. It is likely that many genes of unknown function encode proteins that repair or pre-empt metabolite damage, but most genome-scale metabolic reconstructions only include a fraction of all genes. Any new reaction not present in the databases needs to be added to the reconstruction. This is an iterative process that cycles between the experimental phase and the coding phase. As new information is found about the target organism, the model will be adjusted to predict the metabolic and
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
phenotypical output of the cell. The presence or absence of certain reactions of the metabolism will affect the amount of reactants/products that are present for other reactions within the particular pathway. This is because products in one reaction go on to become the reactants for another reaction, i.e. products of one reaction can combine with other proteins or compounds to form new proteins/compounds in the presence of different enzymes or catalysts. Francke et al. provide an excellent example as to why the verification step of the project needs to be performed in significant detail. During a metabolic network reconstruction of Lactobacillus plantarum, the model showed that succinyl-CoA was one of the reactants for a reaction that was a part of the biosynthesis of methionine. However, an understanding of the physiology of the organism would have revealed that due to an incomplete tricarboxylic acid pathway, Lactobacillus plantarum does not actually produce succinyl-CoA, and the correct reactant for that part of the reaction was acetyl-CoA. Therefore, systematic verification of the initial reconstruction will bring to light several inconsistencies that can adversely affect the final interpretation of the reconstruction, which is to accurately comprehend the molecular mechanisms of the organism. Furthermore, the simulation step also ensures that all the reactions present in the reconstruction are properly balanced. To sum up, a reconstruction that is fully accurate can lead to greater insight about understanding the functioning of the organism of interest. == Metabolic stoichiometric analysis == A metabolic network can be broken down into a stoichiometric matrix where the rows represent the compounds of the reactions, while the columns of the matrix correspond to the reactions themselves. Stoichiometry is a quantitative relationship between substrates of a chemical reaction. In order to deduce what the metabolic network suggests, recent research has centered on a few
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
approaches, such as extreme pathways, elementary mode analysis, flux balance analysis, and a number of other constraint-based modeling methods. === Extreme pathways === Price, Reed, and Papin, from the Palsson lab, use a method of singular value decomposition (SVD) of extreme pathways in order to understand regulation of a human red blood cell metabolism. Extreme pathways are convex basis vectors that consist of steady state functions of a metabolic network. For any particular metabolic network, there is always a unique set of extreme pathways available. Furthermore, Price, Reed, and Papin, define a constraint-based approach, where through the help of constraints like mass balance and maximum reaction rates, it is possible to develop a ‘solution space’ where all the feasible options fall within. Then, using a kinetic model approach, a single solution that falls within the extreme pathway solution space can be determined. Therefore, in their study, Price, Reed, and Papin, use both constraint and kinetic approaches to understand the human red blood cell metabolism. In conclusion, using extreme pathways, the regulatory mechanisms of a metabolic network can be studied in further detail. === Elementary mode analysis === Elementary mode analysis closely matches the approach used by extreme pathways. Similar to extreme pathways, there is always a unique set of elementary modes available for a particular metabolic network. These are the smallest sub-networks that allow a metabolic reconstruction network to function in steady state. According to Stelling (2002), elementary modes can be used to understand cellular objectives for the overall metabolic network. Furthermore, elementary mode analysis takes into account stoichiometrics and thermodynamics when evaluating whether a particular metabolic route or network is feasible and likely for a set of proteins/enzymes. === Minimal metabolic behaviors (MMBs) === In 2009, Larhlimi and Bockmayr presented a new approach called "minimal metabolic behaviors" for the
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
analysis of metabolic networks. Like elementary modes or extreme pathways, these are uniquely determined by the network, and yield a complete description of the flux cone. However, the new description is much more compact. In contrast with elementary modes and extreme pathways, which use an inner description based on generating vectors of the flux cone, MMBs are using an outer description of the flux cone. This approach is based on sets of non-negativity constraints. These can be identified with irreversible reactions, and thus have a direct biochemical interpretation. One can characterize a metabolic network by MMBs and the reversible metabolic space. === Flux balance analysis === A different technique to simulate the metabolic network is to perform flux balance analysis. This method uses linear programming, but in contrast to elementary mode analysis and extreme pathways, only a single solution results in the end. Linear programming is usually used to obtain the maximum potential of the objective function that you are looking at, and therefore, when using flux balance analysis, a single solution is found to the optimization problem. In a flux balance analysis approach, exchange fluxes are assigned to those metabolites that enter or leave the particular network only. Those metabolites that are consumed within the network are not assigned any exchange flux value. Also, the exchange fluxes along with the enzymes can have constraints ranging from a negative to positive value (ex: -10 to 10). Furthermore, this particular approach can accurately define if the reaction stoichiometry is in line with predictions by providing fluxes for the balanced reactions. Also, flux balance analysis can highlight the most effective and efficient pathway through the network in order to achieve a particular objective function. In addition, gene knockout studies can be performed using flux balance analysis. The enzyme that correlates to the
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
gene that needs to be removed is given a constraint value of 0. Then, the reaction that the particular enzyme catalyzes is completely removed from the analysis. === Dynamic simulation and parameter estimation === In order to perform a dynamic simulation with such a network it is necessary to construct an ordinary differential equation system that describes the rates of change in each metabolite's concentration or amount. To this end, a rate law, i.e., a kinetic equation that determines the rate of reaction based on the concentrations of all reactants is required for each reaction. Software packages that include numerical integrators, such as COPASI or SBMLsimulator, are then able to simulate the system dynamics given an initial condition. Often these rate laws contain kinetic parameters with uncertain values. In many cases it is desired to estimate these parameter values with respect to given time-series data of metabolite concentrations. The system is then supposed to reproduce the given data. For this purpose the distance between the given data set and the result of the simulation, i.e., the numerically or in few cases analytically obtained solution of the differential equation system is computed. The values of the parameters are then estimated to minimize this distance. One step further, it may be desired to estimate the mathematical structure of the differential equation system because the real rate laws are not known for the reactions within the system under study. To this end, the program SBMLsqueezer allows automatic creation of appropriate rate laws for all reactions with the network. === Synthetic accessibility === Synthetic accessibility is a simple approach to network simulation whose goal is to predict which metabolic gene knockouts are lethal. The synthetic accessibility approach uses the topology of the metabolic network to calculate the sum of the minimum number of steps
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
needed to traverse the metabolic network graph from the inputs, those metabolites available to the organism from the environment, to the outputs, metabolites needed by the organism to survive. To simulate a gene knockout, the reactions enabled by the gene are removed from the network and the synthetic accessibility metric is recalculated. An increase in the total number of steps is predicted to cause lethality. Wunderlich and Mirny showed this simple, parameter-free approach predicted knockout lethality in E. coli and S. cerevisiae as well as elementary mode analysis and flux balance analysis in a variety of media. == Applications of a reconstruction == Several inconsistencies exist between gene, enzyme, reaction databases, and published literature sources regarding the metabolic information of an organism. A reconstruction is a systematic verification and compilation of data from various sources that takes into account all of the discrepancies. The combination of relevant metabolic and genomic information of an organism. Metabolic comparisons can be performed between various organisms of the same species as well as between different organisms. Analysis of synthetic lethality Predict adaptive evolution outcomes Use in metabolic engineering for high value outputs Reconstructions and their corresponding models allow the formulation of hypotheses about the presence of certain enzymatic activities and the production of metabolites that can be experimentally tested, complementing the primarily discovery-based approach of traditional microbial biochemistry with hypothesis-driven research. The results these experiments can uncover novel pathways and metabolic activities and decipher between discrepancies in previous experimental data. Information about the chemical reactions of metabolism and the genetic background of various metabolic properties (sequence to structure to function) can be utilized by genetic engineers to modify organisms to produce high value outputs whether those products be medically relevant like pharmaceuticals; high value chemical intermediates such as terpenoids and isoprenoids; or biotechnological outputs
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
like biofuels, or polyhydroxybutyrates also known as bioplastics. Metabolic network reconstructions and models are used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the immune system by lysing macrophages, then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferation inside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. These models can also look at the minimal genes necessary for a cell to maintain virulence. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to discover novel biological functions such as drug-engineering and drug delivery techniques. == See also == Computational systems biology Computer simulation Flux balance analysis Fluxomics Metabolic control analysis Metabolic flux analysis Metabolic network Metabolic pathway Biochemical systems equation Metagenomics == References == == Further reading == Overbeek R, Larsen N, Walunas T, D'Souza M, Pusch G, Selkov Jr, Liolios K, Joukov V, Kaznadzey D, Anderson I, Bhattacharyya A, Burd H, Gardner W, Hanke P, Kapatral V, Mikhailova N, Vasieva O, Osterman A, Vonstein V, Fonstein M, Ivanova N, Kyrpides N. (2003) The ERGO genome analysis and discovery system. Nucleic Acids Res. 31(1):164-71 Whitaker, J.W., Letunic, I., McConkey, G.A. and Westhead, D.R. metaTIGER: a metabolic evolution resource. Nucleic Acids Res. 2009 37: D531-8. == External links == ERGO GeneDB KEGG PathCase Case Western Reserve University BRENDA BioCyc and Cyclone - provides an open source Java API to the pathway tool BioCyc to extract Metabolic graphs. EcoCyc MetaCyc SEED ModelSEED ENZYME SBRI Bioinformatics Tools and Software TIGR Pathway Tools metaTIGER
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
Stanford Genomic Resources Pathway Hunter Tool IMG The Integrated Microbial Genomes system, for genome analysis by the DOE-JGI. Systems Analysis, Modelling and Prediction Group at the University of Oxford, Biochemical reaction pathway inference techniques. efmtool provided by Marco Terzer SBMLsqueezer Cellnet analyzer from Klamt and von Kamp Copasi gEFM A graph-based tool for EFM computation
|
{
"page_id": 3408308,
"source": null,
"title": "Metabolic network modelling"
}
|
Peptone water is a microbial growth medium composed of peptic digest of animal tissue and sodium chloride. The pH of the medium is 7.2±0.2 at 25 °C and is rich in tryptophan. Peptone water is also a non-selective broth medium which can be used as a primary enrichment medium for the growth of bacteria. == References ==
|
{
"page_id": 52167095,
"source": null,
"title": "Peptone water"
}
|
This is a list of genetic algorithm (GA) applications. == Natural Sciences, Mathematics and Computer Science == Bayesian inference links to particle methods in Bayesian statistics and hidden Markov chain models Artificial creativity Chemical kinetics (gas and solid phases) Calculation of bound states and local-density approximations Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption. Computer architecture: using GA to find out weak links in approximate computing such as lookahead. Configuration applications, particularly physics applications of optimal molecule configurations for particular systems like C60 (buckyballs) Construction of facial composites of suspects by eyewitnesses in forensic science. Data Center/Server Farm. Distributed computer network topologies Electronic circuit design, known as evolvable hardware Evolutionary image processing Feature selection for Machine Learning Feynman-Kac models File allocation for a distributed system Filtering and signal processing Finding hardware bugs. Game theory equilibrium resolution Genetic Algorithm for Rule Set Production Scheduling applications, including job-shop scheduling and scheduling in printed circuit board assembly. The objective being to schedule jobs in a sequence-dependent or non-sequence-dependent setup environment in order to maximize the volume of production while minimizing penalties such as tardiness. Satellite communication scheduling for the NASA Deep Space Network was shown to benefit from genetic algorithms. Learning robot behavior using genetic algorithms Image processing: Dense pixel matching Learning fuzzy rule base using genetic algorithms Molecular structure optimization (chemistry) Optimisation of data compression systems, for example using wavelets. Power electronics design. Traveling salesman problem and its applications Stopping propagations, i.e. deciding how to cut edges in a graph so that some infectious condition (e.g. a disease, fire, computer virus, etc.) stops its spread. A bi-level genetic algorithm (i.e. a genetic algorithm where the fitness of each individual is calculated by running another genetic algorithm) was used due to the ΣP2-completeness of the
|
{
"page_id": 28311992,
"source": null,
"title": "List of genetic algorithm applications"
}
|
problem. == Earth Sciences == Climatology: Estimation of heat flux between the atmosphere and sea ice Climatology: Modelling global temperature changes Design of water resource systems Groundwater monitoring networks == Finance and Economics == Financial mathematics Real options valuation Portfolio optimization Genetic algorithm in economics Representing rational agents in economic models such as the cobweb model the same, in Agent-based computational economics generally, and in artificial financial markets == Social Sciences == Design of anti-terrorism systems Linguistic analysis, including grammar induction and other aspects of Natural language processing (NLP) such as word-sense disambiguation. == Industry, Management and Engineering == Audio watermark insertion/detection Airlines revenue management Automated design of mechatronic systems using bond graphs and genetic programming (NSF) Automated design of industrial equipment using catalogs of exemplar lever patterns Automated design, including research on composite material design and multi-objective design of automotive components for crashworthiness, weight savings, and other characteristics Automated planning of structural inspection Container loading optimization Control engineering, Marketing mix analysis Mechanical engineering Mobile communications infrastructure optimization. Plant floor layout Pop music record production Quality control Sorting network Timetabling problems, such as designing a non-conflicting class timetable for a large university Vehicle routing problem Optimal bearing placement Computer-automated design == Biological Sciences and Bioinformatics == Bioinformatics Multiple Sequence Alignment Bioinformatics: RNA structure prediction Bioinformatics: Motif Discovery Biology and computational chemistry Building phylogenetic trees. Gene expression profiling analysis. Medicine: Clinical decision support in ophthalmology and oncology Computational Neuroscience: finding values for the maximal conductances of ion channels in biophysically detailed neuron models Protein folding and protein/ligand docking Selection of optimal mathematical model to describe biological systems Operon prediction. == General Applications == Neural Networks; particularly recurrent neural networks Training artificial neural networks when pre-classified training examples are not readily obtainable (neuroevolution) == Physics == Optimization of beam dynamics in accelerator
|
{
"page_id": 28311992,
"source": null,
"title": "List of genetic algorithm applications"
}
|
physics. Design of particle accelerator beamlines == Other Applications == Clustering, using genetic algorithms to optimize a wide range of different fit-functions. Multidimensional systems Multimodal Optimization Multiple criteria production scheduling Multiple population topologies and interchange methodologies Mutation testing Parallelization of GAs/GPs including use of hierarchical decomposition of problem domains and design spaces nesting of irregular shapes using feature matching and GAs. Rare event analysis Solving the machine-component grouping problem required for cellular manufacturing systems Stochastic optimization Tactical asset allocation and international equity strategies Wireless sensor/ad-hoc networks. == References ==
|
{
"page_id": 28311992,
"source": null,
"title": "List of genetic algorithm applications"
}
|
The Global 200 is the list of ecoregions identified by the World Wide Fund for Nature (WWF), the global conservation organization, as priorities for conservation. According to WWF, an ecoregion is defined as a "relatively large unit of land or water containing a characteristic set of natural communities that share a large majority of their species dynamics, and environmental conditions". For example, based on their levels of endemism, Madagascar gets multiple listings, ancient Lake Baikal gets one, and the North American Great Lakes get none. The WWF assigns a conservation status to each ecoregion in the Global 200: critical or endangered; vulnerable; and relatively stable or intact. Over half of the ecoregions in the Global 200 are rated endangered. == Background == The WWF has identified 867 terrestrial ecoregions across the Earth's land surface, as well as freshwater and marine ecoregions. The goal of this classification system is to ensure that the full range of ecosystems will be represented in regional conservation and development strategies. Of these ecoregions, the WWF selected the Global 200 as the ecoregions most crucial to the conservation of global biodiversity. The Global 200 list actually contains 238 ecoregions, made up of 142 terrestrial, 53 freshwater, and 43 marine ecoregions. Conservationists interested in preserving biodiversity have generally focused on the preservation of tropical moist broadleaf forests (commonly known as tropical rainforests) because it is estimated that they harbor one half of Earth's species. On the other hand, the WWF determined that a more comprehensive strategy for conserving global biodiversity should also consider the other half of species, as well as the ecosystems that support them. Several habitats, such as Mediterranean forests, woodlands, and scrub biome, were determined to be more threatened than tropical rain forests, and therefore require concerted conservation action. WWF maintains that "although conservation
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
action typically takes place at the country level, patterns of biodiversity and ecological processes (e.g., migration) do not conform to political boundaries", which is why ecoregion-based conservation strategies are deemed essential. === Classification === Historically, zoologists and botanists have developed various classification systems that take into account the world's plant and animal communities. Two of the worldwide classification systems most commonly used today were summarized by Miklos Udvardy in 1975. The Earth's land surface can be divided into eight biogeographic realms (formerly called kingdoms, and which the BBC calls ecozones) that represent the major terrestrial communities of animals and plants, and are a synthesis of previous systems of floristic provinces and faunal regions. The biome system classifies the world into ecosystem types (i.e. forests, grasslands, etc.) based on climate and vegetation. Each biogeographical realm contains multiple biomes, and biomes occur across several biogeographical realms. A system of biogeographical provinces was developed to identify specific geographic areas in each biogeographical realm that were of a consistent biome type, and shared distinct plant and animal communities. The WWF system represents a further refinement of the system of biomes (which the WWF calls "major habitat types"), biogeographical realms, and biogeographical provinces (the WWF scheme divides most biogeographical provinces into multiple smaller ecoregions). === Selection process === Based on a comprehensive list of ecoregions, The Global 200 includes all major habitat types (biomes), all ecosystem types, and species from every major habitat type. It focuses on each major habitat type of every continent (such as tropical forests or coral reefs). It uses ecoregions as the unit of scale for comparison. WWF say ecoregions could be considered as conservation units at regional scale because they meet similar biological communities. Some ecoregions were selected over other ecoregions of the same major habitat type (biome) or realm.
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Selection of the Global 200 relied on extensive studies of 19 terrestrial, freshwater, and marine major habitat types. Selection of the ecoregions was based on analyses of species richness, species endemism, unique higher taxa, unusual ecological or evolutionary phenomena, and global rarity of major habitat type. Global 200 ecoregion list is most helpful to conservation efforts at a regional scale: local deforestation, destruction of swamp habitats, degradation of soils, etc. However, certain phenomena, such as bird or whale migration, depend on more complex parameters not used to define the current database, such as atmospheric currents and dynamic pelagic ecosystems. These would require gathering more information, and co-ordination of efforts between multiple ecoregions. However, the Global 200 ecoregions can help these efforts by identifying habitat sites and resting sites for migratory animals. It may also help identify the origin of invasive species, and offer insights for slowing down or stopping their intrusion. == Global 200: Terrestrial == === Tropical and subtropical moist broadleaf forests === ==== Afrotropical ==== Guinean moist forests AT0111 Eastern Guinean forests AT0114 Guinean montane forests AT0130 Western Guinean lowland forests Congolian coastal forests AT0102 Atlantic Equatorial coastal forests AT0107 Cross–Sanaga–Bioko coastal forests AT0127 São Tomé, Príncipe, and Annobón forests Cameroon Highlands forests AT0103 Cameroonian Highlands forests AT0121 Mount Cameroon and Bioko montane forests Northeastern Congolian lowland forests AT0124 Northeastern Congolian lowland forests Central Congo Basin Moist Forests AT0104 Central Congolian lowland forests AT0110 Eastern Congolian swamp forests Western Congo Basin Moist Forests AT0126 Northwestern Congolian lowland forests AT0129 Western Congolian swamp forests Albertine Rift montane forests AT0101 Albertine Rift montane forests East African Coastal Forests AT0125 Northern Zanzibar–Inhambane coastal forest mosaic AT0128 Southern Zanzibar–Inhambane coastal forest mosaic Eastern Arc Montane Forests (Kenya, Tanzania) AT0109 Eastern Arc forests Madagascar lowlands and subhumid forests AT0117 Madagascar lowland forests AT0118
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Madagascar subhumid forests Seychelles and Mascarene Islands moist forests AT0113 Granitic Seychelles forests AT0120 Mascarene forests ==== Australasia ==== Sulawesi moist forests AA0123 Sulawesi lowland rain forests AA0124 Sulawesi montane rain forests Moluccas moist forests (Indonesia) AA0106 Halmahera rain forests AA0118 Seram rain forests Southern New Guinea lowland forests AA0122 Southern New Guinea lowland rain forests AA0128 Vogelkop-Aru lowland rain forests New Guinea montane forests AA0105 Central Range montane rain forests AA0107 Huon Peninsula montane rain forests AA0120 Southeastern Papuan rain forests AA0127 Vogelkop montane rain forests Solomons–Vanuatu–Bismarck moist forests AA0101 Admiralty Islands lowland rain forests AA0111 New Britain–New Ireland lowland rain forests AA0112 New Britain–New Ireland montane rain forests AA0119 Solomon Islands rain forests AA0126 Vanuatu rain forests Queensland tropical rain forests AA0117 Queensland tropical rain forests New Caledonia moist forests AA0113 New Caledonia rain forests Lord Howe–Norfolk Islands forests AA0109 Lord Howe Island subtropical forests ==== Indomalaya ==== South Western Ghats montane rain forests and moist deciduous forests IM0150 South Western Ghats moist deciduous forests IM0151 South Western Ghats montane rain forests Sri Lanka moist forests IM0154 Sri Lanka lowland rain forests IM0155 Sri Lanka montane rain forests Northern Indochina Subtropical moist forests IM0137 Northern Indochina subtropical forests Southeast China-Hainan moist forests IM0149 South China–Vietnam subtropical evergreen forests IM0169 Hainan Island monsoon rain forests Taiwan montane forests IM0172 Taiwan subtropical evergreen forests Annamite Range moist forests (Cambodia, Laos, Vietnam) IM0136 Northern Annamites rain forests IM0152 Southern Annamites montane rain forests Sumatran Islands lowland and montane forests IM0157 Sumatran freshwater swamp forests IM0158 Sumatran lowland rain forests IM0159 Sumatran montane rain forests IM0160 Sumatran peat swamp forests Philippines moist forests IM0114 Greater Negros–Panay rain forests IM0122 Luzon montane rain forests IM0123 Luzon rain forests IM0128 Mindanao montane rain forests IM0129 Mindanao–Eastern Visayas rain forests IM0130 Mindoro rain forests IM0156
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Sulu Archipelago rain forests Palawan moist forests IM0143 Palawan rain forests Kayah-Karen/Tenasserim moist forests IM0119 Kayah–Karen montane rain forests IM0163 Tenasserim–South Thailand semi-evergreen rain forests Peninsular Malaysian lowland and montane forests IM0144 Peninsular Malaysian montane rain forests IM0145 Peninsular Malaysian peat swamp forests IM0146 Peninsular Malaysian rain forests Borneo lowland and montane forests IM0102 Borneo lowland rain forests IM0103 Borneo montane rain forests IM0104 Borneo peat swamp forests Nansei Shoto Archipelago forests (Japan) IM0170 Nansei Islands subtropical evergreen forests Eastern Deccan Plateau moist forests (India) IM0111 Eastern highlands moist deciduous forests Naga-Manupuri-Chin hills moist forests (Bangladesh, India, Myanmar) IM0109 Chin Hills–Arakan Yoma montane forests IM0120 Lower Gangetic Plains moist deciduous forests IM0131 Mizoram–Manipur–Kachin rain forests Cardamom Mountains moist forests IM0106 Cardamom Mountains rain forests Western Java montane forests IM0167 Western Java montane rain forests Maldives–Lakshadweep–Chagos Archipelago tropical moist forests IM0125 Maldives–Lakshadweep–Chagos Archipelago tropical moist forests ==== Neotropic ==== Greater Antillean moist forests NT0120 Cuban moist forests NT0127 Hispaniolan moist forests NT0131 Jamaican moist forests NT0155 Puerto Rican moist forests Talamancan-Isthmian Pacific forests NT0167 Talamancan montane forests Choco–Darien moist forests NT0115 Chocó–Darién moist forests Northern Andean montane forests NT0145 Northwestern Andean montane forests Coastal Venezuela montane forests NT0147 Orinoco Delta swamp forests NT0169 Tepuis NT0171 Trinidad and Tobago moist forests Guianan moist forests NT0125 Guianan moist forests Napo moist forests NT0142 Napo moist forests Rio Negro - Juruá moist forests NT0132 Japurá–Solimões–Negro moist forests NT0133 Juruá–Purus moist forests NT0158 Rio Negro campinarana Guayana Highlands moist forests NT0124 Guayanan Highlands moist forests Central Andean yungas NT0105 Bolivian Yungas NT0153 Peruvian Yungas Southwestern Amazonian moist forests NT0166 Southwest Amazon moist forests Atlantic forests NT0103 Bahia coastal forests NT0151 Pernambuco coastal forests NT0160 Serra do Mar coastal forests ==== Oceania ==== South Pacific Islands forests (American Samoa - United States, Cook Islands - New
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Zealand, Fiji, French Polynesia - France, Niue - New Zealand, Samoa, Tonga, Wallis and Futuna Islands - France) OC0102 Central Polynesian tropical moist forests OC0103 Cook Islands tropical moist forests OC0104 Eastern Micronesia tropical moist forests OC0105 Fiji tropical moist forests OC0112 Samoan tropical moist forests OC0114 Tongan tropical moist forests OC0117 Western Polynesian tropical moist forests Hawaii moist forests OC0106 Hawaiian tropical rainforests === Tropical and subtropical dry broadleaf forests === ==== Afrotropic ==== Madagascar dry deciduous forests AT0202 Madagascar dry deciduous forests ==== Australasia ==== Nusa Tenggara Dry Forests (Indonesia) AA0201 Lesser Sundas deciduous forests AA0203 Sumba deciduous forests AA0204 Timor and Wetar deciduous forests New Caledonia dry forests AA0202 New Caledonia dry forests ==== Indomalaya ==== Indochina dry forests IM0202 Central Indochina dry forests Chhota - Nagpur dry forests IM0203 Chota Nagpur dry deciduous forests ==== Neotropic ==== Mexican dry forests NT0204 Bajio dry forests NT0205 Balsas dry forests NT0227 Sierra de la Laguna dry forests Tumbesian - Andean valleys dry forests (Colombia, Ecuador, Peru) NT0201 Apure–Villavicencio dry forests NT0214 Ecuadorian dry forests NT0221 Magdalena Valley dry forests NT0223 Marañón dry forests NT0232 Tumbes–Piura dry forests Chiquitano dry forests NT0212 Chiquitano dry forests Atlantic dry forests NT0202 Atlantic dry forests ==== Oceania ==== Hawaii dry forests OC0202 Hawaiian tropical dry forests === Tropical and subtropical coniferous forests === ==== Nearctic ==== Sierra Madre Oriental and Occidental pine-oak forests NA0302 Sierra Madre Occidental pine–oak forests NA0303 Sierra Madre Oriental pine–oak forests ==== Neotropic ==== Greater Antillean pine forests NT0304 Cuban pine forests NT0305 Hispaniolan pine forests Mesoamerican pine–oak forests (El Salvador, Guatemala, Honduras, Mexico, Nicaragua) NT0308 Sierra Madre de Oaxaca pine–oak forests NT0309 Sierra Madre del Sur pine–oak forests NT0310 Trans-Mexican Volcanic Belt pine–oak forests === Temperate broadleaf and mixed forests === ==== Australasia ==== Eastern Australia temperate
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
forests AA0402 Eastern Australian temperate forests Tasmanian temperate rain forests AA0413 Tasmanian temperate rain forests New Zealand temperate forests AA0403 Fiordland temperate forests AA0404 Nelson Coast temperate forests AA0405 Northland temperate forests AA0406 Northland temperate kauri forests AA0407 Rakiura Island temperate forests AA0410 Southland temperate forests AA0414 Westland temperate forests ==== Indomalaya ==== Eastern Himalayan broadleaf and conifer forests IM0401 Eastern Himalayan broadleaf forests Western Himalayan temperate forests IM0403 Western Himalayan broadleaf forests ==== Nearctic ==== Appalachian and mixed mesophytic forests NA0402 Appalachian mixed mesophytic forests ==== Neotropic ==== Valdivian temperate rain forests - Juan Fernández Islands NT0401 Juan Fernández Islands temperate forests NT0404 Valdivian temperate forests ==== Palearctic ==== Southwest China temperate forests PA0417 Daba Mountains evergreen forests PA0434 Qin Ling Mountains deciduous forests PA0437 Sichuan Basin evergreen broadleaf forests Russian Far East temperate forests PA0426 Manchurian mixed forests PA0443 Ussuri broadleaf and mixed forests === Temperate coniferous forests === ==== Nearctic ==== Pacific temperate rain forests NA0510 Central Pacific coastal forests NA0512 Eastern Cascades forests NA0520 Northern Pacific coastal forests Klamath - Siskiyou forests NA0516 Klamath-Siskiyou forests Sierra Nevada forests NA0527 Sierra Nevada forests Southeastern coniferous and broadleaf forests NA0529 Southeastern conifer forests ==== Palearctic ==== European - Mediterranean montane mixed forests PA0501 Alps conifer and mixed forests PA0513 Mediterranean conifer and mixed forests Caucasus-Anatolian-Hyrcanian temperate forest (Armenia, Azerbaijan, Bulgaria, Georgia, Iran, Russia, Turkey, Turkmenistan) PA0407 Caspian Hyrcanian mixed forests PA0408 Caucasus mixed forests PA0507 Elburz Range forest steppe PA0515 Northern Anatolian conifer and deciduous forests Altai - Sayan montane forests PA0502 Altai montane forest and forest steppe PA0519 Sayan montane conifer forests Hengduan Shan coniferous forests PA0509 Hengduan Mountains subalpine conifer forests === Boreal forests/taiga === ==== Nearctic ==== Muskwa / Slave Lake boreal forests NA0610 Muskwa–Slave Lake forests Canadian Boreal Forests NA0606 Eastern Canadian Shield taiga
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
==== Palearctic ==== Ural Mountains taiga PA0610 Urals montane tundra and taiga East Siberian taiga PA0601 East Siberian taiga Kamchatka taiga and grasslands PA0603 Kamchatka–Kurile meadows and sparse forests PA0604 Kamchatka–Kurile taiga === Tropical and subtropical grasslands, savannas, and shrublands === ==== Afrotropic ==== Horn of Africa acacia savannas AT0715 Somali Acacia–Commiphora bushlands and thickets East African acacia savannas AT0711 Northern Acacia–Commiphora bushlands and thickets Central and Eastern miombo woodlands AT0704 Central Zambezian miombo woodlands AT0706 Eastern miombo woodlands Sudanian savannas AT0705 East Sudanian savanna AT0722 West Sudanian savanna ==== Australasia ==== Northern Australia and Trans-Fly savannas AA0701 Arnhem Land tropical savanna AA0702 Brigalow tropical savanna AA0703 Cape York tropical savanna AA0704 Carpentaria tropical savanna AA0705 Einasleigh upland savanna AA0706 Kimberley tropical savanna AA0708 Trans-Fly savanna and grasslands ==== Indomalaya ==== Terai-Duar savannas and grasslands IM0701 Terai–Duar savanna and grasslands ==== Neotropic ==== Llanos savannas NT0709 Llanos Cerrado woodlands and savannas NT0704 Cerrado === Temperate grasslands, savannas, and shrublands === ==== Nearctic ==== Northern prairie NA0810 Northern mixed grasslands NA0811 Northern short grasslands NA0812 Northern tall grasslands ==== Neotropic ==== Patagonian steppe NT0805 Patagonian steppe ==== Palearctic ==== Daurian steppe PA0804 Daurian forest steppe === Flooded grasslands and savannas === ==== Afrotropic ==== Sudd - Sahelian flooded grasslands and savannas (Cameroon, Chad, Ethiopia, Mali, Niger, Nigeria, Sudan, Uganda) AT0903 Inner Niger Delta flooded savanna AT0904 Lake Chad flooded savanna AT0905 Saharan flooded grasslands Zambezian flooded savannas AT0907 Zambezian flooded grasslands ==== Indomalaya ==== Rann of Kutch flooded grasslands IM0901 Rann of Kutch seasonal salt marsh ==== Neotropic ==== Everglades flooded grasslands NT0904 Everglades Pantanal flooded savannas NT0907 Pantanal === Montane grasslands and shrublands === ==== Afrotropic ==== Ethiopian Highlands AT1007 Ethiopian montane grasslands and woodlands AT1008 Ethiopian montane moorlands Southern Rift montane woodlands AT1015 Southern Rift montane forest–grassland mosaic East
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
African moorlands AT1005 East African montane moorlands Drakensberg montane shrublands and woodlands AT1003 Drakensberg alti-montane grasslands and woodlands AT1004 Drakensberg montane grasslands, woodlands and forests ==== Australasian ==== New Guinea Central Range subalpine grasslands AA 1002 Central Range subalpine grasslands ==== Indomalaya ==== Kinabalu montane shrublands IM1001 Kinabalu montane alpine meadows ==== Neotropic ==== Northern Andean páramo NT1006 Northern Andean páramo Central Andean dry puna NT1001 Central Andean dry puna ==== Palearctic ==== Tibetan Plateau steppe PA1020 Tibetan Plateau alpine shrublands and meadows Middle Asian montane steppe and woodlands (Afghanistan, China, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan) PA1011 North Tibetan Plateau–Kunlun Mountains alpine desert PA1015 Qilian Mountains subalpine meadows PA1013 Ordos Plateau steppe Eastern Himalayan alpine meadows PA1003 Eastern Himalayan alpine shrub and meadows === Tundra === ==== Nearctic ==== Alaskan North Slope coastal tundra (Canada, United States) NA1103 Arctic coastal tundra NA1104 Arctic foothills tundra NA1108 Brooks–British Range tundra Canadian low arctic tundra (Canada) NA1114 Low Arctic tundra NA1116 Ogilvie–MacKenzie alpine tundra NA1118 Torngat Mountain tundra ==== Palearctic ==== Fenno - Scandia alpine tundra and taiga (Finland, Norway, Russia, Sweden) PA1106 Kola Peninsula tundra PA1110 Scandinavian montane birch forest and grasslands Taimyr and Siberian coastal tundra PA1107 Northeast Siberian coastal tundra PA1111 Taimyr–Central Siberian tundra Chukote coastal tundra (Russia) PA1104 Chukchi Peninsula tundra === Mediterranean forests, woodlands, and scrub === ==== Afrotropic ==== Fynbos AT1202 Lowland fynbos and renosterveld AT1203 Montane fynbos and renosterveld ==== Australasia ==== Southwestern Australia forests and scrub AA1201 Coolgardie woodlands AA1202 Esperance mallee AA1209 Southwest Australia savanna AA1210 Southwest Australia woodlands Southern Australia mallee and woodlands AA1203 Eyre and York mallee AA1206 Mount Lofty woodlands AA1208 Naracoorte woodlands ==== Nearctic ==== California chaparral and woodlands NA1201 California coastal sage and chaparral NA1202 California interior chaparral and woodlands NA1203 California montane chaparral and woodlands ==== Neotropic
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
==== Chilean Matorral NT1201 Chilean Matorral ==== Palearctic ==== Mediterranean forests, woodlands, and scrub PA1214 Mediterranean woodlands and forests === Deserts and xeric shrublands === ==== Afrotropic ==== Namib - Karoo - Kaokoveld deserts (Angola, Namibia, South Africa) AT1310 Kaokoveld desert AT1314 Nama Karoo AT1315 Namib desert AT1322 Succulent Karoo Madagascar spiny thicket AT1311 Madagascar spiny thickets Socotra Island desert (Yemen) AT1318 Socotra Island xeric shrublands Arabian Highland woodlands and shrublands (Oman, Saudi Arabia, United Arab Emirates, Yemen) AT1320 Southwestern Arabian foothills savanna AT1321 Southwestern Arabian montane woodlands ==== Australasia ==== Carnavon xeric scrub AA1301 Carnarvon xeric shrublands Great Sandy - Tanami deserts AA1304 Great Sandy-Tanami desert ==== Nearctic ==== Sonoran - Baja deserts NA1301 Baja California desert NA1310 Sonoran Desert Chihuahuan - Tehuacan deserts NA1303 Chihuahuan Desert ==== Neotropic ==== Galápagos Islands scrub NT1307 Galápagos Islands xeric scrub Atacama - Sechura deserts NT1303 Atacama Desert NT1315 Sechura Desert Brazilian Atlantic Dry Forests NT1304 Caatinga ==== Palearctic ==== Central Asian deserts (Kazakhstan, Kyrgyzstan, Uzbekistan, Turkmenistan) PA1310 Central Asian northern desert PA1312 Central Asian southern desert === Mangroves === ==== Afrotropic ==== East African mangroves AT1402 East African mangroves Gulf of Guinea mangroves AT1403 Guinean mangroves Madagascar mangroves AT1404 Madagascar mangroves ==== Australasia ==== New Guinea mangroves AA1401 New Guinea mangroves ==== Indomalaya ==== Greater Sundas mangroves IM1405 Sunda Shelf mangroves Sundarbans mangroves IM1406 Sundarbans mangroves ==== Nearctic ==== Northwest Mexican coast mangroves NA1401 Northwest Mexican coast mangroves ==== Neotropic ==== Guianan - Amazon mangroves NT1401 Alvarado mangroves NT1402 Amapá mangroves NT1406 Belizean reef mangroves NT1411 Guianan mangroves NT1427 Pará mangroves Panama Bight mangroves NT1414 Gulf of Panama mangroves NT1409 Esmeraldas–Pacific Colombia mangroves NT1418 Manabí mangroves NT1413 Gulf of Guayaquil–Tumbes mangroves == Global 200: Freshwater ecoregions == === Large rivers === ==== Afrotropic ==== Congo River and flooded forests (Angola, Democratic
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Republic of Congo, Republic of Congo) ==== Indomalaya ==== Mekong River (Cambodia, China, Laos, Myanmar, Thailand, Vietnam) ==== Nearctic ==== Colorado River (Mexico, United States) Lower Mississippi River (United States) ==== Neotropic ==== Amazon River and flooded forests (Brazil, Colombia, Peru) Orinoco River and flooded forests (Brazil, Colombia, Venezuela) ==== Palearctic ==== Yangtze River and lakes (China) === Large river headwaters === ==== Afrotropic ==== Congo basin piedmont rivers and streams (Angola, Cameroon, Central African Republic, Democratic Republic of Congo, Gabon, Republic of Congo, Sudan) ==== Nearctic ==== Mississippi piedmont rivers and streams (United States) ==== Neotropic ==== Upper Amazon rivers and streams (Bolivia, Brazil, Colombia, Ecuador, French Guiana (France), Guyana, Peru, Suriname, Venezuela) Upper Paraná rivers and streams (Argentina, Brazil, Paraguay) Brazilian Shield Amazonian rivers and streams (Bolivia, Brazil, Paraguay) === Large river deltas === ==== Afrotropic ==== Niger River delta (Nigeria) ==== Indomalaya ==== Indus River Delta (India, Pakistan) ==== Palearctic ==== Volga River Delta (Kazakhstan, Russia) Mesopotamian delta and marshes (Iran, Iraq, Kuwait) Danube River delta (Bulgaria, Moldova, Romania, Ukraine, Yugoslavia) Lena River delta (Russia) === Small rivers === ==== Afrotropic ==== Upper Guinea rivers and streams (Côte d'Ivoire, Guinea, Liberia, Sierra Leone) Madagascar freshwater (Madagascar) Gulf of Guinea rivers and streams (Angola, Cameroon, Democratic Republic of Congo, Equatorial Guinea, Gabon, Nigeria, Republic of Congo) Cape rivers and streams (South Africa) ==== Australasia ==== New Guinea rivers and streams (Indonesia, Papua New Guinea) New Caledonia rivers and streams (New Caledonia) Kimberley rivers and streams (Australia) Southwest Australia rivers and streams (Australia) Eastern Australia rivers and streams (Australia) ==== Indomalaya ==== Xi Jiang rivers and streams (China, Vietnam) Western Ghats Rivers and Streams (India) Southwestern Sri Lanka rivers and streams (Sri Lanka) Salween River (China, Myanmar, Thailand) Sundaland rivers and swamps (Brunei, Malaysia, Indonesia, Singapore) ==== Nearctic ====
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Southeastern rivers and streams (United States) Pacific Northwest coastal rivers and streams (United States) Gulf of Alaska coastal rivers and streams (Canada, United States) ==== Neotropic ==== Guianan freshwater (Brazil, French Guiana, Guyana, Suriname, Venezuela) Greater Antillean freshwater (Cuba, Dominican Republic, Haiti, Puerto Rico) ==== Palearctic ==== Balkan rivers and streams (Albania, Bosnia and Herzogovina, Bulgaria, Croatia, Greece, Macedonia, Turkey, Yugoslavia) Russian Far East rivers and wetlands (China, Mongolia, Russia) === Large lakes === ==== Afrotropic ==== Rift Valley lakes (Burundi, Democratic Republic of Congo, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania, Uganda, Zambia) ==== Neotropic ==== High Andean lakes (Argentina, Bolivia, Chile, Peru) ==== Palearctic ==== Lake Baikal (Russia) Lake Biwa (Japan) === Small lakes === ==== Afrotropic ==== Cameroon crater lakes (Cameroon) ==== Australasia ==== Lakes Kutubu and Sentani (Indonesia, Papua New Guinea) Central Sulawesi lakes (Indonesia) ==== Indomalaya ==== Philippines freshwater (Philippines) Inle Lake (Myanmar) Yunnan lakes and streams (China) ==== Neotropic ==== Mexican highland lakes (Mexico) === Xeric basins === ==== Australasia ==== Central Australian freshwater (Australia) ==== Nearctic ==== Chihuahuan freshwater (Mexico, United States) ==== Palearctic ==== Anatolian freshwater (Syria, Turkey) == Global 200 Marine ecoregions == === Polar === ==== Antarctic Ocean ==== Antarctic Peninsula & Weddell Sea ==== Arctic Ocean ==== Bering Sea (Canada, Russia, United States) Barents-Kara Sea (Norway, Russia) === Temperate shelves and seas === ==== Mediterranean Sea ==== Mediterranean (Albania, Algeria, Bosnia and Herzegovina, Croatia, Cyprus, Egypt, France, Greece, Israel, Italy, Lebanon, Libya, Malta, Monaco, Morocco, Serbia & Montenegro, Slovenia, Spain, Syria, Tunisia, Turkey) ==== Temperate Northern Atlantic ==== Northeast Atlantic Shelf Marine (Belgium, Denmark, Estonia, Finland, France, Germany, Ireland, Latvia, Lithuania, Netherlands, Norway, Poland, Russia, Sweden, United Kingdom) Grand Banks (Canada, St. Pierre and Miquelon (France), United States) Chesapeake Bay (United States) ==== Temperate Northern Pacific ==== Yellow Sea (China,
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
North Korea, South Korea) Sea of Okhotsk (Japan, Russia) ==== Southern Ocean ==== Patagonian Southwest Atlantic (Argentina, Brazil, Chile, Uruguay) Southern Australian Marine (Australia) New Zealand Marine (New Zealand) === Temperate upwelling === ==== North Temperate Indo-Pacific ==== California Current (Canada, Mexico, United States) ==== South Temperate Atlantic ==== Benguela Current (Namibia, South Africa) ==== South Temperate Indo-Pacific ==== Humboldt Current (Chile, Ecuador, Peru) Agulhas Current (Mozambique, South Africa) === Tropical upwelling === ==== Central Indo-Pacific ==== Western Australian Marine (Australia) ==== Eastern Indo-Pacific ==== Panama Bight (Colombia, Ecuador, Panama) Gulf of California (Mexico) Galápagos Marine (Ecuador) ==== Eastern Tropical Atlantic ==== Canary Current (Canary Islands, Gambia, Guinea-Bissau, Mauritania, Morocco, Senegal, Western Sahara) === Tropical coral === ==== Central Indo-Pacific ==== Nansei Shoto (Ryukyu Islands) (Japan) Sulu-Sulawesi Seas (Indonesia, Malaysia, Philippines) Bismarck-Solomon Seas (Indonesia, Papua New Guinea, Solomon Islands) Banda-Flores Sea (Indonesia) New Caledonia Barrier Reef (New Caledonia) Great Barrier Reef (Australia) Lord Howe-Norfolk Islands Marine (Australia) Palau Marine (Palau) Andaman Sea (Andaman and Nicobar Islands (India), Indonesia, Malaysia, Myanmar, Thailand) ==== Eastern Indo-Pacific ==== Tahitian Marine (Cook Islands, French Polynesia) Hawaiian Marine (Hawaii) Rapa Nui (Easter Island) Fiji Barrier Reef (Fiji) ==== Western Indo-Pacific ==== Maldives, Chagos, and Lakshadweep atolls (Chagos Archipelago (United Kingdom), India, Maldives, Sri Lanka) Red Sea (Djibouti, Egypt, Eritrea, Israel, Jordan, Saudi Arabia, Sudan, Yemen) Arabian Sea (Djibouti, Iran, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, United Arab Emirates, Yemen) East African Marine (Kenya, Mozambique, Somalia, Tanzania) West Madagascar Marine (Comoros, Madagascar, Mayotte and Iles Glorieuses (France), Seychelles) ==== Western Tropical Atlantic ==== Mesoamerican Barrier Reef System (Belize, Guatemala, Honduras, Mexico) Greater Antillean Marine (Bahamas, Cayman Islands, Cuba, Dominican Republic, Haiti, Jamaica, Puerto Rico, Turks and Caicos Islands, United States) Southern Caribbean Sea (Aruba, Colombia, Netherlands Antilles, Panama, Trinidad and Tobago, Venezuela) Northeast Brazil Shelf Marine
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
(Brazil) == Global Priority Places == WWF has identified 35 global priority places around the world (terrestrial, freshwater and marine) as either being home to irreplaceable and threatened biodiversity, or representing an opportunity to conserve the largest and most intact representative of their ecosystem. African Rift Lakes Region - Include the 3 largest lakes in Africa: Victoria, Tanganyika and Malawi, as well as lakes Turkana, Albert, Edward, Kivu and others. Altai-Sayan Montane Forests - One of the last remaining untouched areas of the world Amazon Guianas - World's largest tropical rain forest and river basin with a mosaic of mountains, coniferous forests, steppe and alpine meadows. Amur-Heilong - Refuge for Amur leopard and tiger. Arctic Seas & Associated Boreal/Tundra - Protecting Arctic Environments Atlantic Forests - Forest stretches from the Atlantic coast of Brazil, south along the Brazilian Atlantic coastline and inland into northeast Argentina and eastern Paraguay. Borneo and Sumatra - Priceless forests harbor untold species Cerrado-Pantanal Chihuahuan Desert - Protecting the balance of a desert Chocó–Darién Coastal East Africa - Improving livelihoods by conserving nature Congo Basin - Protecting Africa's tropical forests Coral Triangle - Home to the world's most abundant variety of corals and sea life Eastern Himalayas - Empowering communities to protect sacred lands Fynbos The Galápagos - The world's most treasured islands Greater Black Sea Basin Lake Baikal Madagascar - Safeguarding one of Earth's most captivating islands Mediterranean Sea Mekong Complex - Protecting the river of life from source to sea Miombo woodlands Namib-Karoo-Kaokoveld New Guinea & Offshore Islands Northern Great Plains Orinoco River & Flooded Forests Southeastern Rivers and Streams Southern Chile - A land of ancient forests and abundant oceans Southern Ocean Southwest Australia Southwest Pacific Sumatra West Africa Marine Western Ghats Yangtze Basin - Sustaining a valley of life == Gallery ==
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
== See also == Biodiversity hotspots Megadiverse countries Arid Forest Research Institute (AFRI) == References == == External links == A-Z of Areas of Biodiversity Importance: Global 200 Ecoregions Archived 2013-10-24 at the Wayback Machine Map of the Global 200 Conservation status map of the global 200 Archived 2006-06-14 at the Wayback Machine List of the Global 200 Map of Ecoregions Global Priority Places Archived 2018-05-04 at the Wayback Machine
|
{
"page_id": 197049,
"source": null,
"title": "Global 200"
}
|
Compatibility is a term used by geochemists to describe how elements partition themselves in the solid and melt within Earth's mantle. In geochemistry, compatibility is a measure of how readily a particular trace element substitutes for a major element within a mineral. Compatibility of an ion is controlled by two things: its valence and its ionic radius. Both must approximate those of the major element for the trace element to be compatible in the mineral. For instance, olivine (an abundant mineral in the upper mantle) has the chemical formula (Mg,Fe)2SiO4. Nickel, with very similar chemical behaviour to iron and magnesium, substitutes readily for them and hence is very compatible in the mantle. Compatibility controls the partitioning of different elements during melting. The compatibility of an element in a rock is a weighted average of its compatibility in each of the minerals present. By contrast, an incompatible element is one that is least stable within its crystal structure. If an element is incompatible in a rock, it partitions into a melt as soon as melting begins. In general, when an element is referred to as being “compatible” without mentioning what rock it is compatible in, the mantle is implied. Thus incompatible elements are those that are enriched in the continental crust and depleted in the mantle. Examples include: rubidium, barium, uranium, and lanthanum. Compatible elements are depleted in the crust and enriched in the mantle, with examples nickel and titanium. Compatibility is commonly described by an element's distribution coefficient. A distribution coefficient describes how the solid and liquid phases of an element will distribute themselves in a mineral. Current studies of Earth's rare trace elements seek to quantify and examine the chemical composition of elements in the Earth's crust. There are still uncertainties in the understanding of the lower crust and
|
{
"page_id": 5571005,
"source": null,
"title": "Compatibility (geochemistry)"
}
|
upper mantle region of Earth's interior. In addition, numerous studies have focused on looking at the partition coefficients of certain elements in the basaltic magma to characterize the composition of oceanic crust. By having a way to measure the composition of elements in the crust and mantle given a mineral sample, compatibility allows relative concentrations of a particular trace element to be determined. From a petrological point of view, the understanding of how major and rare trace elements differentiate in the melt provides deeper understanding of Earth's chemical evolution over the geologic time scale. == Quantifying compatibility == === Distribution (Partition) coefficient === In a mineral, nearly all elements distribute unevenly between the solid and liquid phase. This phenomenon known as chemical fractionation and can be described by an equilibrium constant, K {\displaystyle K} which sets a fixed distribution of an element between any two phases at equilibrium. A distribution constant K D {\displaystyle K_{D}} is used to define the relationship between the solid and liquid phase of a reaction. This value is essentially a ratio of the concentration of an element between two phases, typically between the solid and liquid phase in this context. This constant is often referred to as D {\displaystyle D} when dealing with trace elements, where K D = C S C L = X i s o l i d X i l i q u i d {\displaystyle K_{D}={\frac {C_{S}}{C_{L}}}={\frac {X_{i}^{solid}}{X_{i}^{liquid}}}} K D = D {\displaystyle K_{D}=D} for trace elements The equilibrium constant is an empirically determined value. These values depend on temperature, pressure, and composition of the mineral melt. D {\displaystyle D} values differ considerably between major elements and trace elements. By definition, incompatible trace elements have an equilibrium constant value of less than one because trace elements have higher concentrations in
|
{
"page_id": 5571005,
"source": null,
"title": "Compatibility (geochemistry)"
}
|
the melt than solids. This means that compatible elements have a value of D > 1 {\displaystyle D>1} . Thus, incompatible elements are concentrated in the melt, whereas compatible elements tend to be concentrated in the solid. Compatible elements with D ≫ 1 {\displaystyle D\gg 1} are strongly fractionated and have very low concentrations in the liquid phase. === Bulk distribution coefficient === The bulk distribution coefficient is used to calculate the elemental composition for any element that makes up a mineral in a rock. The bulk distribution coefficient, D ¯ i {\displaystyle {\overline {D}}_{i}} , is defined as D ¯ i = Σ W A D i A {\displaystyle {\overline {D}}_{i}=\Sigma W_{A}D_{i}^{A}} where i {\displaystyle i} is the element of interest in the mineral, and W A {\displaystyle W_{A}} is the weight fraction of mineral A {\displaystyle A} in the rock. D i A {\displaystyle D_{i}^{A}} is the distribution coefficient for the element in mineral A {\displaystyle A} . This constant can be used to describe how individual elements in a mineral is concentrated in two different phases. During chemical fractionation, certain elements may become more or less concentrated, which can allow geochemists to quantify the different stages of magma differentiation. Ultimately, these measurements can be used to provide further understanding of elemental behavior in different geologic settings. == Applications == One of the main sources of information about the Earth's composition comes from understanding the relationship between peridotite and basalt melting. Peridotite makes up most of Earth's mantle. Basalt, which is highly concentrated in the Earth's oceanic crust, is formed when magma reaches the Earth's surface and cools down at a very fast rate. When magma cools, different minerals crystallize at different times depending on the cooling temperature of that respective mineral. This ultimately changes the chemical composition
|
{
"page_id": 5571005,
"source": null,
"title": "Compatibility (geochemistry)"
}
|
of the melt as different minerals begin to crystallize. Fractional crystallization of elements in basaltic liquids has also been studied to observe the composition of lava in the upper mantle. This concept can be applied by scientists to give insight on the evolution of Earth's mantle and how concentrations of lithophile trace elements have varied over the last 3.5 billion years. === Understanding the Earth's interior === Previous studies have used compatibility of trace elements to see the effect it would have on the melt structure of the peridotite solidus. In such studies, partition coefficients of specific elements were examined and the magnitude of these values gave researchers some indication about the degree of polymerization of the melt. A study conducted in East China in 1998 looked at the chemical composition of various elements found in the crust in China. One of the parameters used to characterize and describe the crustal structure in this region was compatibility of various element pairs. Essentially, studies like this showed how compatibility of certain elements can change and be affected by the chemical compositions and conditions of Earth's interior. Oceanic volcanism is another topic that commonly incorporates the use of compatibility. Since the 1960s, the structure of Earth's mantle started being studied by geochemists. The oceanic crust, which is rich in basalts from volcanic activity, show distinct components that provides information about the evolution of the Earth's interior over the geologic timescale. Incompatible trace elements become depleted when mantle melts and become enriched in oceanic or continental crust through volcanic activity. Other times, volcanism can produce enriched mantle melt onto the crust. These phenomena can be quantified by looking at radioactive decay records of isotopes in these basalts, which is a valuable tool for mantle geochemists. More specifically, the geochemistry of serpentinites along the
|
{
"page_id": 5571005,
"source": null,
"title": "Compatibility (geochemistry)"
}
|
ocean floor, specifically subduction zones, can be examined using compatibility of specific trace elements. The compatibility of lead (Pb) into zircons under different environments can also be an indication of zircons in rocks. When observing levels of non-radiogenic lead in zircons, this can be a useful tool for radiometric dating of zircons. == References ==
|
{
"page_id": 5571005,
"source": null,
"title": "Compatibility (geochemistry)"
}
|
The molecular formula C18H32O2 (molar mass: 280.44 g/mol) may refer to: Chaulmoogric acid Conjugated linoleic acid Laballenic acid, rare fatty acid Linoleic acid Linoelaidic acid Malvalic acid Rumenic acid, bovinic acid Stearolic acid, acetylenic fatty acid Tariric acid, acetylenic fatty acid Taxoleic acid, saturated fatty acid
|
{
"page_id": 23593405,
"source": null,
"title": "C18H32O2"
}
|
A feral child (also called wild child) is a young individual who has lived isolated from human contact from a very young age, with little or no experience of human care, social behavior, or language. Such children lack the basics of primary and secondary socialization. The term is used to refer to children who have suffered severe abuse or trauma before being abandoned or running away. They are sometimes the subjects of folklore and legends, often portrayed as having been raised by animals. While there are many cases of children being found in proximity to wild animals, there are no eyewitness accounts of animals feeding human children. == Description == Feral children lack the basic social skills that are normally learned in the process of enculturation. For example, they may be unable to learn to use a toilet, have trouble learning to walk upright after walking on all fours their whole lives, or display a complete lack of interest in the human activity around them. They often seem mentally impaired and have almost insurmountable trouble learning a human language. The impaired ability to learn a natural language after having been isolated for so many years is often attributed to the existence of a critical period for language learning, and taken as evidence in favor of the critical period hypothesis. There is little scientific knowledge about feral children. One of the best-documented cases has supposedly been that of sisters Amala and Kamala, described by Reverend J. A. L. Singh in 1926 as having been "raised by wolves" in a forest in India. French surgeon Serge Aroles, however, has persuasively argued that the case was a fraud, perpetrated by Singh in order to raise money for his orphanage. Child psychologist Bruno Bettelheim states that Amala and Kamala were born mentally and physically
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
disabled. Yet other scientific studies of feral children exist, such as the case of Genie. == History == Prior to the 1600s, feral and wild children stories were usually limited to myths and legends. In those tales, the depiction of feral children included hunting for food, running on all fours instead of two, and not knowing language. Philosophers and scientists were interested in the concept of such children, and began to question if these children were part of a different species from the human family. The question was taken seriously as science tried to name and categorize the development of humans, and the understanding of the natural world in the 18th and 19th century. Around the 20th century, psychologists were attempting to differentiate between biological behavior and culture. Feral children who lived in isolation or with animals provided examples of this dilemma. == Journalistic accounts of children raised by animals == === Raised by primates/monkeys === Lucas, a native South African boy who was nurtured by a group of baboons. The boy was found in 1903. Marina Chapman claimed to have lived with weeper capuchin monkeys in the Colombian jungle from the age of five to about nine, following a botched kidnapping in about 1954. Unusual for feral children, she went on to marry, have children and live a largely normal life with no persisting problems. John, a boy who was discovered in 1974 in a jungle of Burundi with a troop of grey monkeys. Robert Mayanja (1982) lost his parents in the Ugandan Civil War at the age of three, when Milton Obote's soldiers raided their village, around 50 miles (80 km) from Kampala. Robert then survived in the wild, presumably with vervet monkeys, for three years until he was found by soldiers of National Resistance Army. Saturday Mthiyane
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
(or Mifune) (1987), a boy of around five, was found after spending about a year in the company of monkeys in KwaZulu-Natal, South Africa. He was given the name Saturday after the day he was found, and Mthiyane was the name of the headmistress of the Special School which took him in. At the age of around 17, he could still not talk, and still walked and jumped like a monkey. He never ate cooked food and refused to share or play with other children. In 2005 he was killed in a fire. John Ssebunya, from Uganda, was a toddler when his father killed his mother and hanged himself. Instead of going into a care facility, he went to live with vervet monkeys. For two years he learned how to forage and travel. The monkeys protected him in the wild. When he was around seven years old, he was brought back to civilization. According to a local villager, when he was a child, the only forms of communication he was capable of were crying and demanding food, and he was a "wild boy" whom everyone feared. He has since gained full speech abilities and has also twice competed in the Special Olympics for Uganda. === Raised by wolves === Hessian wolf-children: 15–7 (1304, 1341 and 1344) lived with the Eurasian wolf in the forests of Hesse: The first boy (1304) was taken by wolves at age 3 and found when 7 or 8 by Benedictine monks, the wolves having cared for him by "surrounding him in cold weather, and fed him the best meat from the hunt." He was later sent to the court of Prince Henry, and became accustomed to human society but said he preferred the wolves. The second boy (1341) resisted capture, biting and scratching; he continued
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
to run on all four limbs and hid under a bench. He refused any food given to him and died shortly after being found. The third boy (1344) was found in the winter near the farm Echtzel in Wetterau. He had lived with wolves in a dense wooded area known as "the Hart" for 12 years. He was found by nobles who used the area as hunting grounds. He eventually lived to the age of 80, but has also been reported as having died shortly after discovery. The Hasunpur wolf boy (1843), wandered into the town at around age 12, apparently raised by wolves. He was dark-skinned, covered in short hair, and ate both cooked and raw meat. He soon learned to walk and understand hand signs but never spoke; he often sat at a shop in the town bazaar, where his parents recognized him and took him back. His further fate is unknown. (Case reported by the Rajah of Hasunpoor Bundooa.) Dina Sanichar, discovered among wolves in a cave in Sikandra (near Agra) in Uttar Pradesh, India, in 1872, at the age of 6. He went on to live among humans for over twenty years, including picking up smoking, but never learned to speak and remained seriously impaired for his entire life. Marcos Rodríguez Pantoja (c. 1946, Sierra Morena, Spain) lived for 12 years with wolves in the mountains of Southern Spain. He was discovered at age 19. Rodríguez's story was depicted in the 2010 Spanish-German film Entrelobos. For his portrayal of Rodríguez, young actor Manuel Camacho received a Best New Actor nomination at the 2011 Goya Awards. === Raised by dogs === Oxana Malaya was an eight-year-old Ukrainian girl who lived with Black Russian Terriers for six years. She was found in a kennel with dogs in 1991.
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
She was neglected by her parents, who were alcoholics. The three-year-old, looking for comfort, crawled into the farm and snuggled with the dogs. Her behavior imitated dogs more than humans. She walked on all fours, bared her teeth, and barked. She was removed from her parents' custody by social services. As she lacked human contact, she did not know any words besides yes and no. Upon adulthood, Oxana has been taught to subdue her dog-like behavior. She learned to speak fluently and intelligently and works at the farm milking cows, but remains somewhat intellectually impaired. Ivan Mishukov, a six-year-old boy born in Reutov, Russia, was rescued by the police in 1998 from wild dogs, with whom he lived for two years. He ran from his mother and her abusive alcoholic boyfriend at the age of four. He earned the dogs' trust by giving them food and in return the dogs protected him. The boy had risen to being "alpha male" of the pack. When the police found him, they set a trap for him and the dogs by leaving food in a restaurant kitchen. Because he had lived among the dogs for only two years, he relearned language fairly rapidly. He studied in military school and served in the Russian Army. A 10-year-old Chilean boy (Dog Boy) was rescued in 2001, after living with street dogs for two years. At the age of five, the boy was abandoned by his parents. After fleeing a subsequent child care facility, he roamed the streets with 15 stray dogs. He spent his time with them living in a cave and searching for food, sometimes finding leftovers in garbage cans. In 2001, his situation was brought to the attention of the police. Upon a rescue attempt, the boy tried to escape by jumping into
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
frigid ocean water. However, he was caught and hospitalized. He exhibited depression and aggressive tendencies and, although he could speak, he would rarely do so. Traian Căldărar, Romania (found in 2002) also known as "the Romanian Dog Boy" or "Mowgli". From the ages of four to seven, Traian lived without his family. The boy was found at the age of seven and was described as a three-year-old due to undernutrition. His mother had left her home because of domestic violence, and Traian ran from home sometime after his mother left. He lived in the wild and took shelter in a cardboard box. He suffered from infected wounds, having poor circulation, and a children's disease caused by vitamin D deficiency. Traian was found by Manolescu Ioan, who had been walking across the country after his car broke down. In the surrounding area, a dog that had been eaten was also found. Many assume that the boy was eating the dog to stay alive. When Traian was being cared for, he would usually sleep under the bed and wanted to eat all the time. In 2007, Traian was being taken care of by his grandfather and was doing well in 3rd grade at school. Andrei Tolstyk (2004) was raised by dogs in a remote part of Siberia from the age of three months to 7 years. He was neglected by his parents because he had speaking and hearing problems. Social workers who found the boy were curious about why the boy was not admitted to his local school. This boy was not able to talk as he lacked human interaction and had many dog-like characteristics including walking on all fours, biting people, and sniffing his food before eating. Madina was a three-year-old girl when she was found in Russia in 2013. Madina
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
lived with dogs from birth until she was three years old. She slept with them in the cold, ate food with them, and played with them. Madina's father left her after she was born, after which Madina's mother became an alcoholic and neglected the child as well. When found by social workers in 2013, Madina was completely nude and engaged in dog-like behavior, including the chewing of bones. Afterwards doctors confirmed that she was still mentally and physically capable despite being neglected for nearly her entire life. === Raised by bears === In 1619, a 14 or 15-year-old Danish boy was found to have been "living with bears in a wooded area" and reported upon as being "distinguish[able] from [the bears] but by his shape." He later learned to speak but claimed to have no memory of his time with the bears. The three Lithuanian bear-boys (1657, 1669, 1694): 21–28 – Serge Aroles shows from the archives of the Queen of Poland (1664–1688) that these are false. There was only one boy who lived in the forests of Lithuania with the Eurasian brown bear; he was found in the spring of 1663 and then brought to Poland's capital.: 196 The boy, named Joseph, was one of two boys seen by hunters living in the woods with the bears. He was captured at about age 9, learning to walk upright and eat cooked meat, but disliked clothes and never spoke well. The other boy was never captured; the two may have been accidentally abandoned by their families trying to avoid Tatar raids. === Raised by sheep === An Irish boy brought up by sheep, reported by Nicolaes Tulp in his book Observationes Medicae (1672).: 20–1 The boy reportedly avoided capture for some time and, after being caught at age 16 in
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
1672 and taken to Amsterdam, refused to eat normal food, endured extreme temperatures, and still avoided other humans. Serge Aroles gives evidence that this boy was severely disabled and was exhibited for money.: 199–201 A 14-year-old boy, also known as the sheep boy (2009), was found in Kyrgyzstan living in a sheep flock. He was raised by sheep for 8 years. He had no communication skills and could not use the toilet. His parents left to find work and he was left with his grandmother. His grandmother took care of him until she died. === Raised by cattle === The Bamberg boy reportedly grew up among cattle (late 16th century): 18–9 in the region's mountains, and was later brought to the Prince of Bamberg's court. He initially continued his wild behavior, such as chasing and fighting dogs on all fours, but eventually accustomed to human society and later married. === Raised by goats === Daniel, called the Andes Goat Boy (1990), lived in the wild for about 8 years. He was discovered in the mountains of Peru and was raised by goats or llamas. He walked and ran on all fours with the mountain goats. He drank goat's milk and ate berries and roots. === Other cases === Jean de Liège. Described by natural philosopher Sir Kenelm Digby in his book Two Treatises (1644). Jean, at age five, hid in the woods with his fellow villagers during a religious war. After the fighting left the area, Jean remained in the woods for another 16 years without human contact; during this time his senses became incredibly sharp. He was captured at age 21, "naked, 'all overgrown with hair,' and incapable of speech"; he reintegrated to human society and learned to speak, but lost his acute senses. Anna Maria Jennaert, (1717) caught
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
at Kranenburg near Zwolle. She was born in Antwerp in 1698 and had been kidnapped in 1700 at 16 months old. She was taken by the mistress of a recently deceased merchant who had willed 5,000 dollars to the mistress's (also deceased) baby; the mistress took Jennaert to pass off as her own to collect the money. A large group had to be used to capture her, and she was found unable to speak and surviving on leaves and grass. In January 1718, she was identified after newspapers covered her story and reunited with her mother. She never learned to speak but recognized her mother and re-accustomed herself to human society. The girl of Oranienburg (1717).: 29–31 The two Pyrenean boys (1719).: 32 The boys were reported to "run along the mountains on all-fours, and leaped from one rock to another like the chamois." They were found on the French side of the range. Very little details are available of their case. They were seen, but not captured. The girl of Issaux (1719?) was lost in the woods of the Pyrenees with friends at age eight and found at age 16 by a group of shepherds. She did not like human society and wished to return to the woods. A wild man in the Pyrenees (1723). He was caught by some hunters but escaped before reintegrating into human society. Peter the Wild Boy of Hamelin (1724): 32–41 – Mentally disabled boy with Pitt-Hopkins Syndrome. Another man in the Pyrenees (1774). Found by shepherds in forest of Yvary, he "inhabited the rocks" and was described as a "mild character" and a "solitary, but cheerful creature," harmlessly visiting the cottages of nearby workers. He never attempted to harm any animals, and always outran the shepherds' dogs when they were sent after him.
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
While at one of the workers' cottages he was briefly caught, but "laughed heartily" and escaped. He appeared to be around 30, and was suspected to have been lost as an infant and "subsisted on herbs." Another boy in the Pyrenees (1775). Victor of Aveyron (1800) – Victor was a feral child in the forests of Aveyron for twelve years. The subject is treated with a certain amount of realism in François Truffaut's 1970 film L'Enfant Sauvage (UK: The Wild Boy, US: The Wild Child), where a scientist's efforts in trying to rehabilitate a feral boy meet with great difficulty. Marie-Angélique Memmie Le Blanc was a famous feral child of the 18th century in France who was known as The Wild Girl of Champagne, The Maid of Châlons, or The Wild Child of Songy. Marie-Angélique survived for ten years living wild in the forests of France, between the ages of nine and 19, before she was captured by villagers in Songy in Champagne in September 1731. She was likely born in 1712 as a Native American of the Meskwaki (or "Fox") people, and brought to France in 1720; or she was born in an unknown location in 1712. Marie died in Paris in 1775. Documents show that she learned to read and write as an adult, thus making her unique among feral children. Hany Istók (a.k.a. Steve of the Marsh) of Kapuvár, Hungary (1749). According to documents stored at the Catholic parish of Kapuvár, an abandoned child was once found in a marshy lakeside forest by fishermen. He was brought to the town of Kapuvár, where he was christened and received the name István. The local governor took him to his castle and tried to raise him, but the boy eventually escaped and ran back to the forest. Later, numerous
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
folk tales developed around his character, depicting him as a "half fish, half human creature" who lived in a nearby lake. Kaspar Hauser (early 19th century), portrayed in the 1974 Werner Herzog film The Enigma of Kaspar Hauser (Jeder für sich und Gott gegen alle), who existed but whose account of his own early isolation may have been a hoax. Ramachandra (1970s and 1980s) – First reported in 1973 in the Indian state of Uttar Pradesh, at roughly 12 years old, and as living an amphibian lifestyle in the Kuwano River. He was rescued in 1979 and taken to a nearby village. He only partly adapted to a conventional lifestyle, still preferring raw food, walking with an awkward gait, and spending most of his time alone in nearby rivers and streams. He died in 1982 after approaching a woman who was frightened by him, and who badly scalded Ramachandra with boiling water. Historian Mike Dash speculates that Ramachandra's uncharacteristically bold approach to the woman was sparked by a burgeoning sexual attraction coupled with his ignorance of cultural morals and taboos. Cambodian jungle girl (2007) – Alleged to be Rochom P'ngieng, who lived 19 years in the Cambodian jungle. Other sources questioned these claims. In August 2016, after immigration officials spent two weeks reviewing the case, the woman left Cambodia with her family and returned to Vietnam. Vietnamese media reported that her birth father discovered her through photographs on Facebook. The woman never learned to speak while living with her adoptive family in Cambodia, and according to her Vietnamese birth family, she has been that way since birth. Ng Chhaidy, Theiva near Saiha, Mizoram, India (2012) – She went missing in a jungle aged four, returning 38 years later. When she was first seen, she was naked, long-haired, and with long
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
fingernails, which caused her to be seen as a "wild woman". In 2012, her vocabulary was limited to a few words. Ho Van Lang (2013) was found in Quang Ngai, Vietnam. His father, Ho Van Thanh, took him into the jungle (leaving behind a brother, Ho Van Tri) to flee from the Vietnam War, where he was raised for four decades in isolation. Upon discovery, he barely spoke a few words of the local dialect from the Cor minority. According to his brother, he was developmentally stunted like a child and could not distinguish good from bad. He died of liver cancer on 7 September 2021, at age 52. == Alleged cases of feral children == === Ancient reports === The historian Herodotus wrote that Egyptian pharaoh Psammetichus I (Psamtik) sought to discover the origin of language, and prove Egypt was the oldest people on Earth by conducting an experiment with two children. Allegedly, he gave two newborn babies to a shepherd, with the instructions that no one should speak to them, but that the shepherd should feed and care for them while listening to determine their first words. The hypothesis was that the first word would be uttered in the root language of all people. When both of the children cried βεκος (bekos) with outstretched arms, the shepherd concluded that the word was Phrygian because that was the sound of the Phrygian word for bread. Thus, they concluded that the Phrygians were an older people than the Egyptians. === Modern reports === Victor of Aveyron. The Lobo Wolf Girl of Devil's River (1845) – A figure in Texas folklore, was captured in 1846, but escaped. She was last spotted at age 17 in 1852. She was born to trapper couple Mollie and John Dent in May 1835, John leaving
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
Mollie to get help with her delivery at a nearby Mexican goat ranch. Upon returning, Mollie was found dead and the baby missing, presumably eaten by wolves. In 1845, however, she was spotted helping a wolf pack attack a herd of goats, and the next year eating raw goat meat. She was briefly held in a ranch but the ranch was attacked by wolves, and in the confusion she escaped. She was last seen in 1852 suckling two wolf cubs along a river near El Paso, but disappeared. Vicente Caucau (1948) – Chilean boy found in a savage state at age 12, allegedly raised by pumas. == Raised in confinement == Isabelle (1938) was almost seven years old when she was discovered. She had spent the first years of her life isolated in a dark room with her mother, who was deaf and unable to speak, as her only contact. Only seven months later, she had learned a vocabulary of around 1,500 to 2,000 words. She is reported to have acquired normal linguistic abilities. Anna (1938) was six years old when she was found, having been kept in a dark room for most of her life. She was born in March 1932 in Pennsylvania, United States. She was her mother's second illegitimate child. Her mother had tried to give Anna up for several months but no agency was willing to take the financial burden, as this was during the Great Depression. Anna was kept in a store room at least until she was five and a half, out of the way of her disapproving grandfather, who was infuriated by her presence. Her mother also resented her, considering her troublesome. She was tied to a broken chair which was too small for her, and is believed to have also been tied
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
to a cot for long periods of time. She was mostly fed milk and was never bathed, trained, or caressed by anyone. When she was found, she was suffering from malnutrition as well as muscle atrophy. She was immobile, expressionless, and indifferent to everything. She was believed to be deaf as she did not respond to others (later it was found that her deafness was functional rather than physical). She could not talk, walk, feed herself, or do anything that showed signs of cognition. Once she was taken away and placed in a foster home, she showed signs of improvement. At the age of 9, she began to develop speech. She had started to conform to social norms and was able to feed herself, though only using a spoon. Her teachers described her as having a pleasant disposition. Anna died on 6 August 1942, at the age of 10 of hemorrhagic jaundice. Genie (1970) is the pseudonym given to a feral girl born in 1957 in Los Angeles. Confined to one room without external stimulation of any kind, Genie was strapped to a child's toilet and restrained in a makeshift harness for up to 13 hours per day and immobilized in a crib overnight. It was also theorized that Genie's father would beat her with a wooden plank kept in the room if Genie vocalized at all, and that he would growl like a dog outside of her door. This abuse continued from the age of 20 months until approximately 13 years and 7 months. Attempts were made to teach Genie language, but results were limited. After the first five years in which Genie received treatment, her living situation was mostly unstable, often moving between foster homes and hospitals, as her mother proved unable to care for her by herself.
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
Sujit Kumar (1979), named the "Chicken Boy of Fiji" by the media, was born with cerebral palsy and epilepsy. Sujit's mother committed suicide when he was a toddler and his father left him confined under the house to live with the chickens. Sujit was rescued while still a boy and committed to the Samabula Old People's Home, where he was confined to his room and tied to his bed. He could not speak, and his only verbalisation was clucking; his only interaction with people consisted of outbursts. Sujit remained at the old people's home for 20 years until he was found by Elizabeth Clayton, a wealthy Australian businesswoman who founded the Happy Home Trust to care for Sujit and other at-risk Fijian children. Sujit's behaviour has improved, but it is assumed that he will never learn to speak, and he remains profoundly disabled. Danielle Crockett (2005), of Plant City, Florida, United States, had been locked in her room and deprived of any human interaction for the first seven years of her life, causing a variety of severe developmental delays. She was found and adopted, and, as of 2017, she lives in a group home. She has not learned how to speak but can now let people touch her, look others in the eyes, swim, and has made progress acclimating to human conditioning. Vanya Yudin (2008), the "Russian bird boy," is a seven-year-old boy who spent his entire life living in a tiny two bedroom apartment surrounded by birds. His mother never spoke to him and treated him as a pet, and when found he was unable to communicate except for chirping and flapping his arms like wings. Natasha (2009), born in Chita, Zabaykalsky Krai, is a five-year-old girl who spent her entire life locked in a room with cats and
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
dogs, and no heat, water, or sewage system. When she was found, she could not speak, would jump at the door and bark as caretakers left, and had "clear attributes of an animal." Sasha T. (2012) is a two-year-old Russian boy who was kept in a room with goats his entire life by his mother. Without any human contact, he had not learned how to speak, and weighed only about two-thirds as much as a typical child his age when he was discovered by Russian social workers. == Hoaxes == Following the 2008 disclosure by Belgian newspaper Le Soir that the bestselling book Misha: A Mémoire of the Holocaust Years and movie Survivre avec les loups ('Surviving with Wolves') was a media hoax, the French media debated the credulity with which numerous cases of feral children have been unquestioningly accepted. Although there are numerous books on these children, almost none of them have been based on archives; the authors instead have used dubious second- or third-hand printed information. According to the French surgeon Serge Aroles, who wrote a general study of feral children based on archives (L'Enigme des Enfants-loups or The Enigma of Wolf-children, 2007), many alleged cases are totally fictitious stories: The teenager of Kronstadt (1781): 49–55 – According to the Hungarian document published by Serge Aroles, this case is a hoax: the boy, mentally disabled, had a goitre and was exhibited for money. Syrian Gazelle Boy (1946) – A boy aged around 10 was reported to have been found in the midst of a herd of gazelles in the Syrian desert in the 1950s, and was only rescued with the help of an Iraqi army jeep, because he could run at speeds of up to 50 km/h (30 mph). However, it was supposedly a hoax, as are several
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
other gazelle-boy cases. Amala and Kamala – Claimed to have been found in 1920 by missionaries near Midnapore, Calcutta region, India, later proved to be a hoax to gain charity for Rev. Singh's orphanage.: 104–113 Scholars from Japan and France launched a new inquiry about Amala and Kamala, and validated the discoveries and conclusions done by Serge Aroles 20 years before: the story was a hoax. Lucknow, India, (1954) – A girl named Ramu, taken by a wolf as a baby, and raised in the jungle until the age of seven. Aroles made inquiries on the scene and classifies this as another hoax. The Bear-girl of Krupina, Slovakia (1767): 48–9 – Serge Aroles found no traces of her in the Krupina archives. == Legend, fiction, and popular culture == Myths, legends, and fiction have depicted feral children reared by wild animals such as wolves, apes, monkeys, and bears. Famous examples include Romulus and Remus, Ibn Tufail's Hayy, Ibn al-Nafis' Kamil, Rudyard Kipling's Mowgli, Edgar Rice Burroughs's Tarzan, George of the Jungle and the legends of Atalanta and Enkidu. Roman legend has it that Romulus and Remus, twin sons of Rhea Silvia and Mars, were suckled by a she-wolf. Rhea Silvia was a priestess, and when it was found that she had been pregnant and had children, King Amulius, who had usurped his brother's throne, ordered her to be buried alive and for the children to be killed. The servant who was given the order set them in a basket on the Tiber river instead, and the children were taken by Tiberinus, the river god, to the shore where a she-wolf found them and raised them until they were discovered as toddlers by a shepherd named Faustulus. He and his wife Acca Larentia, who had always wanted a child but never
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
had one, raised the twins, who would later feature prominently in the events leading up to the founding of Rome (named after Romulus, who eventually killed Remus in a fight over whether the city should be founded on the Palatine Hill or the Aventine Hill). Legendary and fictional children are often depicted as growing up with relatively normal human intelligence and skills and an innate sense of culture or civilization, coupled with a healthy dose of survival instincts. Their integration into human society is made to seem relatively easy. One notable exception is Mowgli, for whom living with humans proved to be extremely difficult. The book Knowledge of Angels involves a feral girl found on a fictional island based upon Mallorca. She is the subject of an experiment to see if the knowledge of God is learned or innate. Placed in a convent, while she is there the nuns are instructed not to teach her about God or even mention him in front of her. This is to see whether an atheist who washed up there should be condemned or not. The Earthsea series by Ursula K. Le Guin mentions a brother and sister who were abandoned on a remote island as children, and thus grew up as feral children; in A Wizard of Earthsea, Ged washes up on their island and is unable to communicate much with them, as they only know a few words in their native language (which he did not speak at the time). They were both elderly and very frightened of him, but the sister gives him one of her few possessions when he leaves. Later in The Tombs of Atuan, Ged tells Tenar about the sister and brother (named Anthil and Ensar respectively), and Tenar explains their names, lineage, and how the abandonment was
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
known about in their (and her) home country. Tenar and Ged agree that abandonment was kinder than the murder the children would have otherwise been victims of, but Ged remarks that it was still very cruel and "They scarcely knew human speech." The 2006 novel Magic Hour by Kristin Hannah is about a six-year-old feral child living during her formative years inside a cave in the Olympic National Forest. The girl wanders one day into the fictional small town of Rain Valley, Washington, searching for food and carrying her pet wolf pup and unable to speak. The police chief calls in her psychiatrist sister to teach the girl how to speak and to find the girl's family. == See also == Child development Cognitive ethology Hermit Language deprivation experiments Psychogenic dwarfism Street child Wild man Critical period hypothesis Tarzanesque == References and notes == == Bibliography == Sleeman, William (1888) [Single chapter of the work published in 1858 as A Journey Through the Kingdom of Oude]. "Wolves Nurturing Children In Their Dens". The Zoologist. ser.3, v.12: 87–98 + note p. 221. OCLC 173338280. Armen, Jean-Claude (1974) [1971 in French]. Gazelle-Boy: A Child Brought Up by Gazelles in the Sahara Desert. London: Bodley Head. ISBN 0370102843. For the first opportune critical approach based on archives : Aroles, Serge (2007). L'Enigme des enfants-loups [The Enigma of wolf-children] (in French). Editions Publibook. ISBN 978-2-7483-3909-3. Gale Encyclopedia of Psychology (2nd ed.). Gale Group. 2001. ISBN 978-0-7876-4786-5. Kidd, Kenneth B.; Worrell, Elijah (2004). Making American Boys: Boyology and the Feral Tale. Minneapolis: University of Minnesota Press. ISBN 0-8166-4295-8. Luchte, James (2012). Of the Feral Children. London: Createspace. ISBN 978-1-4792-9488-6. McCrone, John (1993). The Myth of Irrationality – The Science of the Mind from Plato to Star Trek. London: Macmillan. ISBN 0-333-57284-X. Michael, Newton (2002). Savage
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
Boys and Wild Girls: A History of Feral Children. London: Faber and Faber. ISBN 0-571-21460-6. Reynolds, Cecil R.; Fletcher-Janzen, Elaine, eds. (2004), Concise Encyclopedia of Special Education: A Reference for the Education of the Handicapped and Other Exceptional Children and Adults (2 ed.), Hoboken, New Jersey: John Wiley & Sons, pp. 428–429, ISBN 978-0-471-65251-9, OCLC 46975017. == External links == Media related to Feral children at Wikimedia Commons
|
{
"page_id": 19005888,
"source": null,
"title": "Feral child"
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.