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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:95253
- loss:MultipleNegativesRankingLoss
base_model: thenlper/gte-base
widget:
- source_sentence: Molecular phylogenetic resolution of the mega-diverse clade Apoditrysia
sentences:
- In a previous study of higher-level arthropod phylogeny, analyses of nucleotide
sequences from 62 protein-coding nuclear genes for 80 panarthopod species yielded
significantly higher bootstrap support for selected nodes than did amino acids.
This study investigates the cause of that discrepancy. The hypothesis is tested
that failure to distinguish the serine residues encoded by two disjunct clusters
of codons (TCN, AGY) in amino acid analyses leads to this discrepancy. In one
test, the two clusters of serine codons (Ser1, Ser2) are conceptually translated
as separate amino acids. Analysis of the resulting 21-amino-acid data matrix shows
striking increases in bootstrap support, in some cases matching that in nucleotide
analyses. In a second approach, nucleotide and 20-amino-acid data sets are artificially
altered through targeted deletions, modifications, and replacements, revealing
the pivotal contributions of distinct Ser1 and Ser2 codons. We confirm that previous
methods of coding nonsynonymous nucleotide change are robust and computationally
efficient by introducing two new degeneracy coding methods. We demonstrate for
degeneracy coding that neither compositional heterogeneity at the level of nucleotides
nor codon usage bias between Ser1 and Ser2 clusters of codons (or their separately
coded amino acids) is a major source of non-phylogenetic signal. The incongruity
in support between amino-acid and nucleotide analyses of the forementioned arthropod
data set is resolved by showing that "standard" 20-amino-acid analyses yield lower
node support specifically when serine provides crucial signal. Separate coding
of Ser1 and Ser2 residues yields support commensurate with that found by degenerated
nucleotides, without introducing phylogenetic artifacts. While exclusion of all
serine data leads to reduced support for serine-sensitive nodes, these nodes are
still recovered in the ML topology, indicating that the enhanced signal from Ser1
and Ser2 is not qualitatively different from that of the other amino acids.
- 'Recent molecular phylogenetic studies of the insect order Lepidoptera have robustly
resolved family-level divergences within most superfamilies, and most divergences
among the relatively species-poor early-arising superfamilies. In sharp contrast,
relationships among the superfamilies of more advanced moths and butterflies that
comprise the mega-diverse clade Apoditrysia (ca. 145,000 spp.) remain mostly poorly
supported. This uncertainty, in turn, limits our ability to discern the origins,
ages and evolutionary consequences of traits hypothesized to promote the spectacular
diversification of Apoditrysia. Low support along the apoditrysian "backbone"
probably reflects rapid diversification. If so, it may be feasible to strengthen
resolution by radically increasing the gene sample, but case studies have been
few. We explored the potential of next-generation sequencing to conclusively resolve
apoditrysian relationships. We used transcriptome RNA-Seq to generate 1579 putatively
orthologous gene sequences across a broad sample of 40 apoditrysians plus four
outgroups, to which we added two taxa from previously published data. Phylogenetic
analysis of a 46-taxon, 741-gene matrix, resulting from a strict filter that eliminated
ortholog groups containing any apparent paralogs, yielded dramatic overall increase
in bootstrap support for deeper nodes within Apoditrysia as compared to results
from previous and concurrent 19-gene analyses. High support was restricted mainly
to the huge subclade Obtectomera broadly defined, in which 11 of 12 nodes subtending
multiple superfamilies had bootstrap support of 100%. The strongly supported nodes
showed little conflict with groupings from previous studies, and were little affected
by changes in taxon sampling, suggesting that they reflect true signal rather
than artifacts of massive gene sampling. In contrast, strong support was seen
at only 2 of 11 deeper nodes among the "lower", non-obtectomeran apoditrysians.
These represent a much harder phylogenetic problem, for which one path to resolution
might include further increase in gene sampling, together with improved orthology
assignments. '
- 'One of the major challenges in cell implantation therapies is to promote integration
of the microcirculation between the implanted cells and the host. We used adipose-derived
stromal vascular fraction (SVF) cells to vascularize a human liver cell (HepG2)
implant. We hypothesized that the SVF cells would form a functional microcirculation
via vascular assembly and inosculation with the host vasculature. Initially, we
assessed the extent and character of neovasculatures formed by freshly isolated
and cultured SVF cells and found that freshly isolated cells have a higher vascularization
potential. Generation of a 3D implant containing fresh SVF and HepG2 cells formed
a tissue in which HepG2 cells were entwined with a network of microvessels. Implanted
HepG2 cells sequestered labeled LDL delivered by systemic intravascular injection
only in SVF-vascularized implants demonstrating that SVF cell-derived vasculatures
can effectively integrate with host vessels and interface with parenchymal cells
to form a functional tissue mimic. '
- source_sentence: Exosomes as drug delivery systems for gastrointestinal cancers
sentences:
- Gastrointestinal cancer is one of the most common malignancies with relatively
high morbidity and mortality. Exosomes are nanosized extracellular vesicles derived
from most cells and widely distributed in body fluids. They are natural endogenous
nanocarriers with low immunogenicity, high biocompatibility, and natural targeting,
and can transport lipids, proteins, DNA, and RNA. Exosomes contain DNA, RNA, proteins,
lipids, and other bioactive components, which can play a role in information transmission
and regulation of cellular physiological and pathological processes during the
progression of gastrointestinal cancer. In this paper, the role of exosomes in
gastrointestinal cancers is briefly reviewed, with emphasis on the application
of exosomes as drug delivery systems for gastrointestinal cancers. Finally, the
challenges faced by exosome-based drug delivery systems are discussed.
- Background In the myocardium, pericytes are often confused with other interstitial
cell types, such as fibroblasts. The lack of well-characterized and specific tools
for identification, lineage tracing, and conditional targeting of myocardial pericytes
has hampered studies on their role in heart disease. In the current study, we
characterize and validate specific and reliable strategies for labeling and targeting
of cardiac pericytes. Methods and Results Using the neuron-glial antigen 2 (NG2)
- Exosomes are small extracellular vesicles with diameters of 30-150 nm. In both
physiological and pathological conditions, nearly all types of cells can release
exosomes, which play important roles in cell communication and epigenetic regulation
by transporting crucial protein and genetic materials such as miRNA, mRNA, and
DNA. Consequently, exosome-based disease diagnosis and therapeutic methods have
been intensively investigated. However, as in any natural science field, the in-depth
investigation of exosomes relies heavily on technological advances. Historically,
the two main technical hindrances that have restricted the basic and applied researches
of exosomes include, first, how to simplify the extraction and improve the yield
of exosomes and, second, how to effectively distinguish exosomes from other extracellular
vesicles, especially functional microvesicles. Over the past few decades, although
a standardized exosome isolation method has still not become available, a number
of techniques have been established through exploration of the biochemical and
physicochemical features of exosomes. In this work, by comprehensively analyzing
the progresses in exosome separation strategies, we provide a panoramic view of
current exosome isolation techniques, providing perspectives toward the development
of novel approaches for high-efficient exosome isolation from various types of
biological matrices. In addition, from the perspective of exosome-based diagnosis
and therapeutics, we emphasize the issue of quantitative exosome and microvesicle
separation.
- source_sentence: Comparison of pesticide active substances in conventional agriculture
and organic agriculture in Europe
sentences:
- Total concentrations of metals in soil are poor predictors of toxicity. In the
last decade, considerable effort has been made to demonstrate how metal toxicity
is affected by the abiotic properties of soil. Here this information is collated
and shows how these data have been used in the European Union for defining predicted-no-effect
concentrations (PNECs) of Cd, Cu, Co, Ni, Pb, and Zn in soil. Bioavailability
models have been calibrated using data from more than 500 new chronic toxicity
tests in soils amended with soluble metal salts, in experimentally aged soils,
and in field-contaminated soils. In general, soil pH was a good predictor of metal
solubility but a poor predictor of metal toxicity across soils. Toxicity thresholds
based on the free metal ion activity were generally more variable than those expressed
on total soil metal, which can be explained, but not predicted, using the concept
of the biotic ligand model. The toxicity thresholds based on total soil metal
concentrations rise almost proportionally to the effective cation exchange capacity
of soil. Total soil metal concentrations yielding 10% inhibition in freshly amended
soils were up to 100-fold smaller (median 3.4-fold, n = 110 comparative tests)
than those in corresponding aged soils or field-contaminated soils. The change
in isotopically exchangeable metal in soil proved to be a conservative estimate
of the change in toxicity upon aging. The PNEC values for specific soil types
were calculated using this information. The corrections for aging and for modifying
effects of soil properties in metal-salt-amended soils are shown to be the main
factors by which PNEC values rise above the natural background range.
- There is much debate about whether the (mostly synthetic) pesticide active substances
(AS) in conventional agriculture have different non-target effects than the natural
AS in organic agriculture. We evaluated the official EU pesticide database to
compare 256 AS that may only be used on conventional farmland with 134 AS that
are permitted on organic farmland. As a benchmark, we used (i) the hazard classifications
of the Globally Harmonized System (GHS), and (ii) the dietary and occupational
health-based guidance values, which were established in the authorization procedure.
Our comparison showed that 55% of the AS used only in conventional agriculture
contained health or environmental hazard statements, but only 3% did of the AS
authorized for organic agriculture. Warnings about possible harm to the unborn
child, suspected carcinogenicity, or acute lethal effects were found in 16% of
the AS used in conventional agriculture, but none were found in organic agriculture.
Furthermore, the establishment of health-based guidance values for dietary and
non-dietary exposures were relevant by the European authorities for 93% of conventional
AS, but only for 7% of organic AS. We, therefore, encourage policies and strategies
to reduce the use and risk of pesticides, and to strengthen organic farming in
order to protect biodiversity and maintain food security.
- Herpes simplex virus 1 (HSV-1) encodes Us3 protein kinase, which is critical for
viral pathogenicity in both mouse peripheral sites (e.g., eyes and vaginas) and
in the central nervous systems (CNS) of mice after intracranial and peripheral
inoculations, respectively. Whereas some Us3 substrates involved in Us3 pathogenicity
in peripheral sites have been reported, those involved in Us3 pathogenicity in
the CNS remain to be identified. We recently reported that Us3 phosphorylated
HSV-1 dUTPase (vdUTPase) at serine 187 (Ser-187) in infected cells, and this phosphorylation
promoted viral replication by regulating optimal enzymatic activity of vdUTPase.
In the present study, we show that the replacement of vdUTPase Ser-187 by alanine
(S187A) significantly reduced viral replication and virulence in the CNS of mice
following intracranial inoculation and that the phosphomimetic substitution at
vdUTPase Ser-187 in part restored the wild-type viral replication and virulence.
Interestingly, the S187A mutation in vdUTPase had no effect on viral replication
and pathogenic effects in the eyes and vaginas of mice after ocular and vaginal
inoculation, respectively. Similarly, the enzyme-dead mutation in vdUTPase significantly
reduced viral replication and virulence in the CNS of mice after intracranial
inoculation, whereas the mutation had no effect on viral replication and pathogenic
effects in the eyes and vaginas of mice after ocular and vaginal inoculation,
respectively. These observations suggested that vdUTPase was one of the Us3 substrates
responsible for Us3 pathogenicity in the CNS and that the CNS-specific virulence
of HSV-1 involved strict regulation of vdUTPase activity by Us3 phosphorylation.
- source_sentence: Load-dependent detachment and reattachment kinetics of kinesin-1,
-2 and 3 motors
sentences:
- Bidirectional cargo transport by kinesin and dynein is essential for cell viability
and defects are linked to neurodegenerative diseases. Computational modeling suggests
that the load-dependent off-rate is the strongest determinant of which motor 'wins'
a kinesin-dynein tug-of-war, and optical tweezer experiments find that the load-dependent
detachment sensitivity of transport kinesins is kinesin-3 > kinesin-2 > kinesin-1.
However, in reconstituted kinesin-dynein pairs vitro, all three kinesin families
compete nearly equally well against dynein. Modeling and experiments have confirmed
that vertical forces inherent to the large trapping beads enhance kinesin-1 dissociation
rates. In vivo, vertical forces are expected to range from negligible to dominant,
depending on cargo and microtubule geometries. To investigate the detachment and
reattachment kinetics of kinesin-1, 2 and 3 motors against loads oriented parallel
to the microtubule, we created a DNA tensiometer comprising a DNA entropic spring
attached to the microtubule on one end and a motor on the other. Kinesin dissociation
rates at stall were slower than detachment rates during unloaded runs, and the
complex reattachment kinetics were consistent with a weakly-bound 'slip' state
preceding detachment. Kinesin-3 behaviors under load suggested that long KIF1A
run lengths result from the concatenation of multiple short runs connected by
diffusive episodes. Stochastic simulations were able to recapitulate the load-dependent
detachment and reattachment kinetics for all three motors and provide direct comparison
of key transition rates between families. These results provide insight into how
kinesin-1, -2 and -3 families transport cargo in complex cellular geometries and
compete against dynein during bidirectional transport.
- 'AP-1 and AP-2 adaptor protein (AP) complexes mediate clathrin-dependent trafficking
at the trans-Golgi network (TGN) and the plasma membrane, respectively. Whereas
AP-1 is required for trafficking to plasma membrane and vacuoles, AP-2 mediates
endocytosis. These AP complexes consist of four subunits (adaptins): two large
subunits (β1 and γ for AP-1 and β2 and α for AP-2), a medium subunit μ, and a
small subunit σ. In general, adaptins are unique to each AP complex, with the
exception of β subunits that are shared by AP-1 and AP-2 in some invertebrates.
Here, we show that the two putative Arabidopsis thaliana AP1/2β adaptins co-assemble
with both AP-1 and AP-2 subunits and regulate exocytosis and endocytosis in root
cells, consistent with their dual localization at the TGN and plasma membrane.
Deletion of both β adaptins is lethal in plants. We identified a critical role
of β adaptins in pollen wall formation and reproduction, involving the regulation
of membrane trafficking in the tapetum and pollen germination. In tapetal cells,
β adaptins localize almost exclusively to the TGN and mediate exocytosis of the
plasma membrane transporters such as ATP-binding cassette (ABC)G9 and ABCG16.
This study highlights the essential role of AP1/2β adaptins in plants and their
specialized roles in specific cell types.'
- A single kinesin molecule can move "processively" along a microtubule for more
than 1 micrometer before detaching from it. The prevailing explanation for this
processive movement is the "walking model," which envisions that each of two motor
domains (heads) of the kinesin molecule binds coordinately to the microtubule.
This implies that each kinesin molecule must have two heads to "walk" and that
a single-headed kinesin could not move processively. Here, a motor-domain construct
of KIF1A, a single-headed kinesin superfamily protein, was shown to move processively
along the microtubule for more than 1 micrometer. The movement along the microtubules
was stochastic and fitted a biased Brownian-movement model.
- source_sentence: Phylogenetic analysis of mitochondrial genes in Macquarie perch
from three river basins
sentences:
- Sedentary behavior is an emerging risk factor for cardiovascular disease (CVD)
and may be particularly relevant to the cardiovascular health of older adults.
This scoping review describes the existing literature examining the prevalence
of sedentary time in older adults with CVD and the association of sedentary behavior
with cardiovascular risk in older adults. We found that older adults with CVD
spend >75 % of their waking day sedentary, and that sedentary time is higher among
older adults with CVD than among older adults without CVD. High sedentary behavior
is consistently associated with worse cardiac lipid profiles and increased cardiac
risk scores in older adults; the associations of sedentary behavior with blood
pressure, CVD incidence, and CVD-related mortality among older adults are less
clear. Future research with larger sample sizes using validated methods to measure
sedentary behavior are needed to clarify the association between sedentary behavior
and cardiovascular outcomes in older adults.
- An improved Bayesian method is presented for estimating phylogenetic trees using
DNA sequence data. The birth-death process with species sampling is used to specify
the prior distribution of phylogenies and ancestral speciation times, and the
posterior probabilities of phylogenies are used to estimate the maximum posterior
probability (MAP) tree. Monte Carlo integration is used to integrate over the
ancestral speciation times for particular trees. A Markov Chain Monte Carlo method
is used to generate the set of trees with the highest posterior probabilities.
Methods are described for an empirical Bayesian analysis, in which estimates of
the speciation and extinction rates are used in calculating the posterior probabilities,
and a hierarchical Bayesian analysis, in which these parameters are removed from
the model by an additional integration. The Markov Chain Monte Carlo method avoids
the requirement of our earlier method for calculating MAP trees to sum over all
possible topologies (which limited the number of taxa in an analysis to about
five). The methods are applied to analyze DNA sequences for nine species of primates,
and the MAP tree, which is identical to a maximum-likelihood estimate of topology,
has a probability of approximately 95%.
- 'Genetic variation in mitochondrial genes could underlie metabolic adaptations
because mitochondrially encoded proteins are directly involved in a pathway supplying
energy to metabolism. Macquarie perch from river basins exposed to different climates
differ in size and growth rate, suggesting potential presence of adaptive metabolic
differences. We used complete mitochondrial genome sequences to build a phylogeny,
estimate lineage divergence times and identify signatures of purifying and positive
selection acting on mitochondrial genes for 25 Macquarie perch from three basins:
Murray-Darling Basin (MDB), Hawkesbury-Nepean Basin (HNB) and Shoalhaven Basin
(SB). Phylogenetic analysis resolved basin-level clades, supporting incipient
speciation previously inferred from differentiation in allozymes, microsatellites
and mitochondrial control region. The estimated time of lineage divergence suggested
an early- to mid-Pleistocene split between SB and the common ancestor of HNB+MDB,
followed by mid-to-late Pleistocene splitting between HNB and MDB. These divergence
estimates are more recent than previous ones. Our analyses suggested that evolutionary
drivers differed between inland MDB and coastal HNB. In the cooler and more climatically
variable MDB, mitogenomes evolved under strong purifying selection, whereas in
the warmer and more climatically stable HNB, purifying selection was relaxed.
Evidence for relaxed selection in the HNB includes elevated transfer RNA and 16S
ribosomal RNA polymorphism, presence of potentially mildly deleterious mutations
and a codon (ATP6'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on thenlper/gte-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [thenlper/gte-base](https://huggingface.co/thenlper/gte-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [thenlper/gte-base](https://huggingface.co/thenlper/gte-base) <!-- at revision c078288308d8dee004ab72c6191778064285ec0c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Phylogenetic analysis of mitochondrial genes in Macquarie perch from three river basins',
'Genetic variation in mitochondrial genes could underlie metabolic adaptations because mitochondrially encoded proteins are directly involved in a pathway supplying energy to metabolism. Macquarie perch from river basins exposed to different climates differ in size and growth rate, suggesting potential presence of adaptive metabolic differences. We used complete mitochondrial genome sequences to build a phylogeny, estimate lineage divergence times and identify signatures of purifying and positive selection acting on mitochondrial genes for 25 Macquarie perch from three basins: Murray-Darling Basin (MDB), Hawkesbury-Nepean Basin (HNB) and Shoalhaven Basin (SB). Phylogenetic analysis resolved basin-level clades, supporting incipient speciation previously inferred from differentiation in allozymes, microsatellites and mitochondrial control region. The estimated time of lineage divergence suggested an early- to mid-Pleistocene split between SB and the common ancestor of HNB+MDB, followed by mid-to-late Pleistocene splitting between HNB and MDB. These divergence estimates are more recent than previous ones. Our analyses suggested that evolutionary drivers differed between inland MDB and coastal HNB. In the cooler and more climatically variable MDB, mitogenomes evolved under strong purifying selection, whereas in the warmer and more climatically stable HNB, purifying selection was relaxed. Evidence for relaxed selection in the HNB includes elevated transfer RNA and 16S ribosomal RNA polymorphism, presence of potentially mildly deleterious mutations and a codon (ATP6',
'An improved Bayesian method is presented for estimating phylogenetic trees using DNA sequence data. The birth-death process with species sampling is used to specify the prior distribution of phylogenies and ancestral speciation times, and the posterior probabilities of phylogenies are used to estimate the maximum posterior probability (MAP) tree. Monte Carlo integration is used to integrate over the ancestral speciation times for particular trees. A Markov Chain Monte Carlo method is used to generate the set of trees with the highest posterior probabilities. Methods are described for an empirical Bayesian analysis, in which estimates of the speciation and extinction rates are used in calculating the posterior probabilities, and a hierarchical Bayesian analysis, in which these parameters are removed from the model by an additional integration. The Markov Chain Monte Carlo method avoids the requirement of our earlier method for calculating MAP trees to sum over all possible topologies (which limited the number of taxa in an analysis to about five). The methods are applied to analyze DNA sequences for nine species of primates, and the MAP tree, which is identical to a maximum-likelihood estimate of topology, has a probability of approximately 95%.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9449, 0.8056],
# [0.9449, 1.0000, 0.7868],
# [0.8056, 0.7868, 1.0000]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 95,253 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 19.51 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 223.97 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 51 tokens</li><li>mean: 309.24 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Sox5 modulates the activity of Sox10 in the melanocyte lineage</code> | <code>The transcription factor Sox5 has previously been shown in chicken to be expressed in early neural crest cells and neural crest-derived peripheral glia. Here, we show in mouse that Sox5 expression also continues after neural crest specification in the melanocyte lineage. Despite its continued expression, Sox5 has little impact on melanocyte development on its own as generation of melanoblasts and melanocytes is unaltered in Sox5-deficient mice. Loss of Sox5, however, partially rescued the strongly reduced melanoblast generation and marker gene expression in Sox10 heterozygous mice arguing that Sox5 functions in the melanocyte lineage by modulating Sox10 activity. This modulatory activity involved Sox5 binding and recruitment of CtBP2 and HDAC1 to the regulatory regions of melanocytic Sox10 target genes and direct inhibition of Sox10-dependent promoter activation. Both binding site competition and recruitment of corepressors thus help Sox5 to modulate the activity of Sox10 in the melano...</code> | <code>Transcripts for a new form of Sox5, called L-Sox5, and Sox6 are coexpressed with Sox9 in all chondrogenic sites of mouse embryos. A coiled-coil domain located in the N-terminal part of L-Sox5, and absent in Sox5, showed >90% identity with a similar domain in Sox6 and mediated homodimerization and heterodimerization with Sox6. Dimerization of L-Sox5/Sox6 greatly increased efficiency of binding of the two Sox proteins to DNA containing adjacent HMG sites. L-Sox5, Sox6 and Sox9 cooperatively activated expression of the chondrocyte differentiation marker Col2a1 in 10T1/2 and MC615 cells. A 48 bp chondrocyte-specific enhancer in this gene, which contains several HMG-like sites that are necessary for enhancer activity, bound the three Sox proteins and was cooperatively activated by the three Sox proteins in non-chondrogenic cells. Our data suggest that L-Sox5/Sox6 and Sox9, which belong to two different classes of Sox transcription factors, cooperate with each other in expression of Col2a1 a...</code> |
| <code>are asgard archaea related to eukaryotes</code> | <code>Asgard archaea are considered to be the closest known relatives of eukaryotes. Their genomes contain hundreds of eukaryotic signature proteins (ESPs), which inspired hypotheses on the evolution of the eukaryotic cell</code> | <code>Eukaryotes evolved from a symbiosis involving alphaproteobacteria and archaea phylogenetically nested within the Asgard clade. Two recent studies explore the metabolic capabilities of Asgard lineages, supporting refined symbiotic metabolic interactions that might have operated at the dawn of eukaryogenesis.</code> |
| <code>Fanconi Anemia in Pediatric Medulloblastoma and Fanconi Anemia</code> | <code>The outcome of children with medulloblastoma (MB) and Fanconi Anemia (FA), an inherited DNA repair deficiency, has not been described systematically. Treatment is complicated by high vulnerability to treatment-associated side effects, yet structured data are lacking. This study aims to give a comprehensive overview of clinical and molecular characteristics of pediatric FA MB patients.</code> | <code>The Sonic Hedgehog (SHH) signaling pathway is indispensable for development, and functions to activate a transcriptional program modulated by the GLI transcription factors. Here, we report that loss of a regulator of the SHH pathway, Suppressor of Fused (Sufu), resulted in early embryonic lethality in the mouse similar to inactivation of another SHH regulator, Patched1 (Ptch1). In contrast to Ptch1+/- mice, Sufu+/- mice were not tumor prone. However, in conjunction with p53 loss, Sufu+/- animals developed tumors including medulloblastoma and rhabdomyosarcoma. Tumors present in Sufu+/-p53-/- animals resulted from Sufu loss of heterozygosity. Sufu+/-p53-/- medulloblastomas also expressed a signature gene expression profile typical of aberrant SHH signaling, including upregulation of N-myc, Sfrp1, Ptch2 and cyclin D1. Finally, the Smoothened inhibitor, hedgehog antagonist, did not block growth of tumors arising from Sufu inactivation. These data demonstrate that Sufu is essential for deve...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `max_steps`: 20
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `max_steps`: 20
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 5.0.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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