Statement of need Collec'ng, aggrega'ng, and analyzing data to produce metrics is easier than ever before. In the realm of academic research, data on the digital distribu'on of scholarly content provides new opportuni'es to understand the publishing ecosystem. A range of stakeholders currently generates and collects this data, o?en as products of content usage or sales. There is growing concern about the ownership of, access to, and analysis of this data for “predic've bibliometrics.” Despite the growing demand for rich data and predic've analy'cs, the high costs of providing or purchasing data-‐ related services are reinforcing inequi'es in the academic publishing landscape. Furthermore, the challenges associated with gathering, integra'ng, interpre'ng, and repor'ng usage data limit the ability of individual publishers, libraries, and other stakeholders to iden'fy— much less predict—important usage trends and opportuni'es through which these organiza'ons might extend their impact. Based on ins'tu'onal contribu'ons that would be used to seed-‐fund this project (at $1K and $5K levels), Project Meerkat will gather a diverse community of scholarly communica'on stakeholders, including authors and editors, to jointly develop governance, sustainability, and ethical frameworks for how usage data is gathered, analyzed, and shared, building upon the NISO Privacy Principles. The project team will also scope and develop analy'c and benchmark reports to meet the iden'ﬁed needs of libraries, publishers, aggregators, and other stakeholders. The resul'ng Publishing Analy'cs Data Alliance will work to sustain the outcomes of Project Meerkat, providing its members with shared governance of collected data. Member organiza'ons will agree to a code of prac'ce as a condi'on of membership. Developing tools and protocols rela5ng to the collec5on, aggrega5on, and dissemina5on of usage data about digital monograph publica5ons A Publishing Analy'cs Data Alliance We invite publishers, libraries, research centers, socie7es, funders, aggregators, and other scholarly communica7on stakeholders to join Project Meerkat to develop guidelines and standards for digital scholarly monograph usage data and to construct a neutral organiza7onal apparatus for the ongoing collec7on and aggrega7on of data about these scholarly publica7ons. Current conveners for Project Meerkat Chris7na Drummond (Educopia Ins'tute) Mar7n Paul Eve (Birbeck, University of London & Open Library of Humani'es) Kevin Hawkins (University of North Texas) Sarah Melton (Emory University) Lucy Montgomery (Cur'n University & Knowledge Unlatched) Cameron Neylon (Cur'n University) Lisa Schiﬀ (California Digital Library) Katherine Skinner (Educopia Ins'tute) Es7mated budget Project Meerkat (2017–2020): $480,500 To cover the costs of project facilita'on, development, and in-‐person convenings of experts to complement virtual working groups. Publishing Analy7cs Data Alliance (2020 forward) On-‐going revenue streams will be established to cover the costs associated with con'nued support of shared infrastructure and services.


U.S. Quantum Information Science School Micro-scale Types of Models Superfluidity Mathematical Analysis of Models of Superfluids Pranava Chaitanya Jayanti Department of Mathematics, University of Southern California Formerly: Department of Physics, University of Maryland Conversion between normal and superfluid phases with changing temperature. Adapted from [Vinen 2004]. Phase diagram of He-4. “Liquid II” = Superfluid. Adapted from [Young 1975]. D = integral length scale; l = inter-vortex spacing; 𝛏 = vortex core radius. Taken from [Jayanti 2022] Macro-scale • D A Young. Phase diagrams of the elements. Technical report, Lawrence Livermore National Lab, 1975. • W F Vinen. The physics of superfluid helium. Technical report, CERN, 2004. • P C Jayanti. Analysis of models of superfluidity. Doctoral dissertation, University of Maryland, 2022. • P C Jayanti, K Trivisa. Local existence of solutions to a Navier-Stokes-Nonlinear-Schrödinger model of superfluidity. J. Math. Fluid Mech., 2022. • P C Jayanti, K Trivisa. Uniqueness in a Navier-Stokes-Nonlinear-Schrödinger model of superfluidity. Nonlinearity, 2022. • J Jang, P C Jayanti, I Kukavica. Small-data global existence of solutions for the Pitaevskii model of superfluidity. arXiv, 2023. • J Jang, P C Jayanti, I Kukavica. Small-data global existence for the Pitaevskii model of superfluidity in 3D. In prep, 2023. • P C Jayanti, K Trivisa. Global Regularity of the 2D HVBK Equations. Journal of Nonlinear Science, 2021. q Most fluids are described by the Navier-Stokes equations (NSE) q Exhibit viscosity: microscopic roots in the entropic drive for particles to exchange momentum and achieve equilibrium, manifesting as dissipation at macroscopic length scales q Superfluids like Helium-4 are fundamentally inviscid: all the particles move in unison q Bose Einstein condensation below 𝑇! ≈2.17𝐾 for He-4 q Superfluid vorticity – discrete filaments, quantized circulation q He-4 (bosonic) and He-3 (fermionic) both display remarkable properties: critical velocity, film flow, fountain pressure q 𝑇= 0: Pure superfluid; 𝑇> 0: Part of normal liquid condenses into the superfluid q Essential to model and quantify the retarding interactions between phases Pitaevskii 𝜕!𝜓+ 𝜆𝐵𝜓= −1 2𝑖Δ𝜓+ 𝜇 𝑖𝜓"𝜓 𝐵= 1 2 −𝑖∇−𝑢# + 𝜇𝜓" 𝜕!𝜌+ ∇⋅𝑢𝜌= 2𝜆ℜ𝜓𝐵𝜓 𝜕! 𝜌𝑢+ ∇⋅𝜌𝑢⊗𝑢+ ∇𝑞= 𝜈Δ𝑢−𝛼𝜌𝑢−2𝜆ℑ∇𝜓𝐵𝜓 ∇⋅𝑢= 0 Hall-Vinen-Bekarevich-Khalatnikov ∇⋅𝑢$ = 0, ∇⋅𝑢% = 0 𝜕!𝑢$ + 𝑢$ ⋅∇𝑢$ + ∇𝑝$ = 𝜈$Δ𝑢$ + 𝜌% 𝜌𝐹 𝜕!𝑢% + 𝑢% ⋅∇𝑢% + ∇𝑝% = 𝜈%Δ𝑢% −𝜌$ 𝜌𝐹 𝐹= −𝐵 2 𝜔% 𝑢$ −𝑢% + 𝐵& 2 𝜔% 𝑢$ −𝑢% ' q Proposed by Pitaevskii in 1959 to model interacting dynamics between superfluid (nonlinear Schrödinger) and normal fluid (Navier-Stokes) q Order parameter 𝜓 describes superfluid; density 𝜌 and velocity 𝑢 characterize normal fluid q Both phases interact through bidirectional, nonlinear coupling operator 𝐵, consisting of relative kinetic energy and potential self-interaction q Normal fluid velocity taken to be divergence-free: incompressible, inhomogeneous fluid q For small data, global-in-time existence of weak solutions established for both 2D and 3D when 𝑝< 4, and exponentially/polynomially long existence time for 2D when 𝑝≥4 [Jang, Jayanti, Kukavica 2023] q Weak-strong uniqueness of solutions also studied [Jayanti, Trivisa 2022] q Main difficulty in analysis stems from sign-indefinite source in the continuity equation – converts superfluid into normal fluid (on average) q Ignore vortex dynamics and relaxation – continuous vorticity field q Assume both fluids classical, and add a nonlinear coupling q Useful when high density of vortex filaments are aligned (2D) q Studies on instability of superfluid Taylor-Couette flow, and quantum turbulence q Dealing with high frequencies: suppress in 𝑢", subgrid modeling like LES, or artificial viscosity q For 2D, system is globally-in-time well-posed: unique, smooth solutions with continuous dependence on initial data [Jayanti, Trivisa 2021] q Current work is on investigating system for analytic-in-time, Gevrey-in-space solutions (subset


PathoScope: a multi-omic approach to characterize microbiomes and identify pathogens Castro-Nallar E.1, Shen Y.2, Freishtat R.J.3, Pérez-Losada M.1, Manimaran S.2, Liu G.2, Spira A.2, Johnson W.E.2, and Crandall K.A.1 1Computational Biology Institute, George Washington University, Ashburn, VA 20147; 2Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118; 3Division of Emergency Medicine, Children’s National Medical Center, Washington, DC 20010 INTRODUCTION • The human microbiome plays a key role in a variety of human health issues from obesity to respiratory disease (Huang et al., 2013). • Assessing taxonomic and functional diversity of microbiomes across time, individuals, and tissues within individuals is crucial to understand infection and disease. • The affordable costs of high-throughput sequencing (HTS) and the limitations of single gene inferences, are pushing microbiome studies towards shotgun metagenomics. • Identifying causal pathogens and characterize microbiomes in metagenomic studies is a daunting task due to the large volume of data generated. • A variety of techniques have been developed to isolate potential pathogens for HTS targets using molecu lar biological approaches. However, this limits the inferences with respect to the host response. • We developed a computational solution combining metatranscriptomic microbial identification and host differential gene expression signatures. • We applied this approach to identify pathogens associated with pediatric asthma. METHODS Sample Collection Nasal epithelial cells from the nares were collected from 8 children with asthma and 6 controls. Participants were part of the Asth MaP (Asthma Severity Modifying Polymorphisms) Project at Children’s National Medical Center. Total RNA was extracted using Trizol (Life Technologies) reagent and puri fied in silica columns (Ambion, Carls bad CA). Total RNA was subjected to RiboZ ero ribosomal RNA reduction prior to library preparation using Illumina TrueSeq Stranded Total RNA kit (San Diego, CA) and sequenced on a HiS eq 2500 instrument on two separate ‘Rapid’ flow cells. Analyses Reads were preprocessed using PrinSeq-Lite and PathoQC (trim ming reads and bases < 25 PHRED). Target and filter genome libraries were built in PathoLib. Reads were aligned using Bowtie2 (Langmead and Salzberg, 2012) and filtered against the human genome (hg19) using PathoMap. The microbial communities in each patient were then characterized in PathoID. Raw counts identified in Pa thoScope were normalized using regularized-log transformation in DESeq2. Taxa abundance was tested us ing a negative binomial Wald test also as in DESeq2. Plots were created in R using Phyloseq, ggplot2, gplot packages. Gene differential expression analysis were performed in TopHat (Kim et al., 2013) and Cufflinks (Trapnell et al., 2012). Host response gene expression signatures between groups were compared in PathoSig using an adaptive Bayesian factor analysis model. CASE STUDY: ASTHMA IN CHILDREN Metatranscriptomic microbial identification • RNA-Seq runs generated an average of 41.4 million single-end 100bp sequencing reads per sample: 90- 95% human and 5-10% microbial. • Five of the 8 cases showed a high prevalence of the bacterial species Moraxella catarrhalis and Moraxella nonliquefaciens, while controls showed low prevalence. • These findings confirm previous metagenomic surveys which found large proportions of Proteobacteria in cases but not controls, speculating that this could be explained by Moraxella spp. and Hemophilus spp. (Hilty et al., 2010). PathoLib Raw Sequencing Reads (fastq) Quality Controlled Reads (fastq) NCBI nucleotide database (fasta) Host, Filter, and Target Genome Libraries (fasta) Filtered Read Alignments (sam) Reassigned Read Alignments (sam) Detailed Report (xml) 2. PathoMap 4. PathoID 5. PathoReport 6. PathoDB 1. PathoQC 3. PathoDB Core Module Optional Module Modules 1. PathoDB A mysql database con


CSSI Frameworks: Re-engineering Galaxy for Performance, Scalability and Energy Efficiency Award #: OAC-1931531 NSF CSSI PI Meeting, Seattle, WA, Feb. 13-14, 2020 II. Our Objective Re-engineer the Galaxy framework to enable GPUs and accelerators like FPGAs as "first-class" compute engines. Enlarge the Galaxy community by bringing GPU and FPGA supported tools. Enable Galaxy tools to take better advantage of emerging cluster scheduling capabilities. Achieve significant improvements in performance scalability and energy efficiency. I. What is the Problem? Galaxy is an open source, web-based framework that is extensively used by more than 20,000 researchers world-wide in many areas. Current galaxy implementation does not support GPU and accelerators like FPGAs. There is no support for dynamic resource scheduling and management in Galaxy. Galaxy Centers Galaxy Popularity IV. Current Developments Identified existing GPU based tools such as Racon. Integrated a GPU based implementation of RACON, a genomic consensus tool to Galaxy Toolshed . Integrated a GPU-version of Smith- Waterman sequencing tool to Galaxy. Deployed Galaxy on a Kubernetes managed cluster. V. Next Steps Develop GPU based implementation for existing ML tools for RNN sequencing, using Keras framework. Use GPU plugin in Kubernetes to enable GPU access for Galaxy pods. Expose GPU metrics to develop resource utilization-aware scheduling policies. Develop a Reinforcement-based Machine Learning scheduling framework. Develop a distributed data-store using Rook, to enable near data placement based scheduling policies. https://galaxyproject.org Get in Touch: mtk2@psu.edu Acknowledgments: Galaxy project is also supported by NSF grant #1661497 and other NIH grants. III. Our Vision for Galaxy 300k page views Deployed in 128 platforms 8915 publications > 7 million Research funds > 7800 supported tools 10000+ online subscribers 8000+ twitter followers Learn More PI: Mahmut Taylan Kandemir *, Co-PIs: Chita R. Das *, Anton NekrutenkoΨ, Paul Medvedev * * Dept. of Computer Science and Engineering, Ψ Dept. of Biochemistry and Molecular Biology Institution: The Pennsylvania State University, University Park


Fairness wird mit Fairness-Metriken gemessen, die auf einer binären Einteilung beruhen. Um Fairness zu messen, muss Techie entscheiden, welche Gruppen verglichen werden sollen. Techie tut dies, indem sensible Merkmale (sensitive attributes) definiert und die Testdaten in zwei Gruppen aufgeteilt werden: privilegiert und nicht privilegiert. So kann Techie feststellen, ob sich das KI-Modell für verschiedene Gruppen unterschiedlich verhält. für vordefinierte Merkmale wie Geschlecht, Ethnie und sexuelle Orientierung. Sensible Merkmale Privilegiert (A) z.B. Menschen, die als männlich, weiß, nicht-queer gelesen werden Nicht-privilegiert (B) Ich fühle mich unwohl, Geschlecht, Ethnie und sozio- ökonomischen Status von Menschen auf Basis ihrer Daten anzunehmen... gruselig! Aber ich sehe keine andere Möglichkeit, um systemische Ungerechtigkeit zu erkennen... Für jedes Attribut erstellt Techie eine Tabelle für die privilegierten (A) und die nicht-privilegierten (B) Gruppen. Sie zeigt, wo das Modell korrekt prognostiziert hat und wo nicht. Durch den Vergleich dieser Zahlen wird deutlich, ob das Modell für privilegierte oder nicht-privilegierte Gruppen genauer ist. Fairness durch Unawarness Fairness bedeutet, dass sensible Merkmale bei der Entscheidungsfindung nicht explizit berücksichtigt werden. Treatment Equality Das KI-Modell ist fair, wenn: Das KI-Modell ist fair, wenn: Biases und Unfairness sind überall – wie kann Techie sicherstellen, dass deren KI-Modell fair ist? Fairness wird als Gleichbehandlung verschiedener Gruppen beschrieben. Das kann jedoch unterschiedlich interpretiert werden. Fairness durch Awareness Fairness bedeutet, dass ähnliche Personen unabhängig von ihrer Gruppe ähnlich eingeordnet werden. Demographic Parity Was denkst du ist fair? Was empfindest du als fair? Welcher der Ansätze hat dich überzeugt? Stimme mit einem Sticker ab! Intersektionaler Ansatz: Fairness bedeutet, dass wir Machtverhältnisse verschieben und Prozesse teilen. Sie zu quantifizieren ist weder möglich noch hilfreich. Es gibt viele verschiedene Fairness Metriken - welche ist die beste? Hier sind einige Beispiele: Fairness bedeutet, dass beide Gruppen dieselbe Wahr- scheinlichkeit haben, akzeptiert zu werden: 40% der Bewerber*innen sind weiblich – 40 % der neu Eingestellten sollten weiblich sein! Das Verhältnis zwischen den fälschlicherweise abgelehnten und den fälschlicherweise eingestellten Personen ist in etwa gleich groß. Fairness bedeutet, dass sich Fehler des KI-Modells auf beide Gruppen in ähnlichem Maße auswirken. Fairness-Metriken basieren auf binären Kategorien und erschweren die Berücksichtigung der Nuancen verschiedener Lebensrealitäten. Es gibt zwei Ansätze für Kritik an ihnen: Genauigkeitsbezogener Ansatz: Individuenbezogene Fairness metriken Feels fair? Wirken sich Fehler stärker auf eine Gruppe aus? Gruppenbezogene fairness metriken Bias zu reduzieren, bedeutet, Datensätze zu verändern. Diese Veränderungen könnten zu mehr Fairness, aber auch zu geringerer Genauigkeit führen. Es muss einen Abwägungsprozess geben, welche Kriterien angestrebt werden. Der Versuch, Bias zu verringern, macht KI- Modelle weniger genau. Genauigkeit Fairness Die Verwendung von Metriken zur Messung von Fairness ist zu eindimensional. Klasse, Geschlecht oder Ethnie sind nicht binär, sie überschneiden und überlappen sich. Fairness – die Gleichbehandlung verschiedener Gruppen – ist nicht immer der richtige Ansatz. Wenn es um Gerechtigkeit geht, dann muss Gerechtigkeit im Sinne gleicher Zugangsmöglichkeiten (Equity) eine Rolle spielen. Wichtiger als Metriken ist Anerkennung: Haben die betroffenen Menschen ein Mitspracherecht in den Prozessen? Alle Positiven Alle Alle Positiven Alle ≈ Gruppe A: Gruppe B: Falsch Negativ Falsch Positiv Falsch Negativ Falsch Positiv ≈ Gruppe A: Gruppe B: z.B. Menschen, die als nicht- männlich, nicht-weiß, queer gelesen werden Fairness entzieht sich Metriken wahr POSITIV FALSch NEGATIV FALSch POSITIV Wahr NEGATIV KI prognostiziert: einst


Multiphase Fluid-Structure Interaction Software Infrastructure to Enable Applications in Medicine, Biology, and Engineering Boyce E. Grifﬁth1, Amneet P. S. Bhalla2, M. Gregory Forest1, Matthew G. Knepley3, and Neelesh A. Patankar4 1University of North Carolina at Chapel Hill, 2San Diego State University, 3University at Buffalo, and 4Northwestern University NSF Awards OAC 1931516 (UNC-Chapel Hill), OAC 1931372 (Northwestern), OAC 1931368 (SDSU), and OAC 1931524 (UB) ibamr.github.io OVERVIEW Coupled ﬂuid-structure systems are ubiquitous in biology, physiology, and medicine, and these systems occur across a broad range of spatial scales: • writhing and coiling of DNA; • beating and pumping motions of cilia and ﬂagella; • ﬂow of blood in the heart and throughout the circulation; • swimming bacteria and ﬁsh, and ﬂying birds and insects; • ﬂuttering of leaves and dispersal of seeds and pollen by the wind. Fluid-structure interaction is also common in engineering applications. The immersed boundary (IB) method is an approach to to ﬂuid-structure inter- action (FSI) that uses: • an Eulerian description of the momentum and incompressibility of the ﬂuid-structure system; and • a Lagrangian description of the structural stresses, forces, and deformations. The IB approach to FSI: • facilitates the use of fast Cartesian grid solvers; • reduces or eliminates the need for dynamic regridding; and thereby • enables simulations with very large deformations/displacements. IBAMR is a C++ framework for FSI models based on the IB method. IBAMR provides support for Cartesian grid (structured) adaptive mesh reﬁne- ment (AMR). Support for Lagrangian (unstructured) AMR is planned. Cartesian grid AMR allows high spa- tial resolution to be deployed only where needed (e.g. near the immersed bodies): IBAMR also enables numerical methods research, including work on Lagrangian- Eulerian coupling schemes, time-stepping algorithms, and fast solvers. IBAMR IBAMR provides modular structural de- scriptions to enable a range of models: • systems of springs and beams; • nonlinear elasticity; • peridynamics; • elastic rods; and • rigid structures and bodies with prescribed kinematics. This project is extending IBAMR’s multiphase ﬂuids capabilities and en- hancing its Lagrangian-Eulerian coupling schemes, geometrical model representa- tions, and parallel performance. Recent methodological enhancements include: • efﬁcient Brinkman penalization-based coupling; • polymeric ﬂuid models; • one-sided IB coupling operators; • immersed interface method (IIM) coupling; • IIM-based schemes for rigid-body and ﬂexible-body FSI; and • cut cell-based methods for advection-reaction-diffusion models in moving domains. IBAMR project milestones include: • six-month release cycle since 2018; • ≥460 unit and integration tests; • new CMake-based build system; • improved support for CI to automatically run tests on multiple platforms; • autoibamr: automatically compile and install IBAMR and its dependencies; • new algorithms for parallel IB data partitioning and load balancing; and • used in ≥130 peer-reviewed publications, ≥30 graduate and undergraduate theses. 2018 2019 2020 2021 2022 2023 0 100 200 300 400 500 600 700 800 900 1000 Total Execution Time, s libMesh deal.II Benchmark performance of IBAMR’s IBFE nonlinear elasticity FSI scheme with different ﬁnite element libraries. MAJOR ENHANCEMENTS Advection and diffusion of chemical species in moving subdomains with Robin boundary conditions that convert from one concentration to the other along the moving interface. Melting and solidiﬁcation of an aluminum block with trapped gas. Porosity defect in metal 3D printing. Colors: yellow (solid Al), blue (molten Al), white (gas). Light rigid sphere rising in a water column via a new IIM-based sharp-interface FSI methodology. Contours: Q-criterion. Contact between rigid and elastic bodies via a new IIM-based sharp-interface FSI methodology. Colors: ﬂow speed (red=fast, blue=slow). APPLICATION


TORCH Digitization Hub: Streamlining Digitization Workflows for Natural History Collections Mr. Jason Best1, Mr. Diego Barroso1, Dr. Brian Witte2 1Botanical Research Institute Of Texas, Fort Worth, United States, 2Software Developer, , United States The TORCH Digitization Hub is a web-based platform designed to support specimen digitization workflows for multiple participants of a larger digitization group as well as workflows of individual organizations. The Hub bridges the gap between initial image capture and dissemination of images and metadata through specimen data portals. It is designed to provide collection managers, data managers, and digitization technicians with tools to simplify the processes of image upload, quality control, data extraction, and derivative image generation. The hub is a platform built on a foundation of open source components, allowing it to be freely distributed and modified and helping to accelerate the digitization workflows of natural history collections. Although many natural history collections have existing digitization workflows, these may not scale well internally or when multiple institutions must collaboratively digitize specimens across multiple collections. The Hub addresses some of these challenges by providing a solution for participants to upload specimen images into a central workflow platform where images and specimen data undergo multiple steps to ensure image quality, conformity, and completeness. Then, derivative images for dissemination are generated, and the complete end-product is uploaded to a web hosting platform to be used by the specimen portals. The Hub provides a web-based interface for managing workflows, monitoring progress and performance, and generating reports. It can be hosted in the cloud to support distributed digitization projects, or hosted locally to support workflows within a single institution. While the Hub was designed for the herbarium digitization workflows of the Texas Oklahoma Regional Consortium of Herbaria (TORCH) Thematic Collections Network, it can be adapted for other types of collections and workflows and, due to being open source, can be extended with other image management tools and technologies such as OCR and computer vision. The TORCH Digitization Hub project code and documentation is available at https://github.com/TORCH-TCN/torch_hub.


OICR - MaRS Centre, West Tower 661 University Avenue, Suite 465 Toronto, Ontario, Canada http://oicr.on.ca/ EMBL-EBI Wellcome Trust Genome Campus Hinxton, Cambridgeshire, CB10 1SD, UK http://www.ebi.ac.uk NYU Langone Medical Center 550 1st Ave, New York, NY 10016, United States http://www.med.nyu.edu/ http://www.reactome.org Iden%ﬁers submission interface Pathway Analysis Tool Suite Fabregat A1, Weiser J2, Jupe S1, Garapati P1, Forner O1, Porras P1, Haw P2, D'Eustachio P3, Stein L2, Hermjakob H1 (1) European Bioinformatics Institute, Hinxton (UK) (2) Ontario Institute for Cancer Research, Toronto (Canada) What is Reactome? Reactome is a free, open-source, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its goals is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modeling, systems biology and education. (3) NYU Langone Medical Center, New York (US) New Pathway Analysis The performance has been boosted by creating a data structure to model Reactome objects and relationships which is directly placed in memory. The data structure is composed of a radix tree (1) for the identifiers used as cross-references to the target entities, a graph (2) to model these entities and the relationships between them (like the species orthology) and double-linked trees (3) to model the hierarchy. What does the Tool Suite provide? The analysis tool suite contains an enrichment analysis, an expression data analysis and a species comparison tool. Results are presented in the Pathway Browser in tabular form within the details panel, as an overlay on pathway diagrams and hierarchy. Besides improving stability, performance, reliability and scalability to cope with future demands, we have also extended functionality. Reactome currently annotates 7,460 human proteins and 1,462 chemical compounds. The analysis tool provides an enrichment analysis for a dataset of 1,215 protein identifiers in ca. 2 seconds. The tool suite is available via a RESTFul service (http://www.reactome.org/AnalysisService/) so the different analysis can be easily integrated in third party software. 1 7 1 9 0 1 2 6 3 8 7 2 P 0 2 6 9 2 5 P10620 P10635 P10636 P11086 P60468 P60484 P05480 P10639 2 9 3 8 9 1 0 5 4 8 0 P05386 P12931 0 3 8 6 6 N E T 4 0 8 6 4 8 6 7 P12938 P12939 P11087 P10622 P29172 P60467 Mus Musculus RaDus Norvegicus Saccharomyces Cerevisae Bos Taurus Homo Sapiens Canis Familiaris Homo Sapiens Mus Musculus RaDus Norvegicus Saccharomyces Cerevisae Bos Taurus Canis Familiaris 1 2 3 1. RADIX TREE Space-optimized data structure to rapid lookup whether the target identifier is present in the Reactome data. When a identifier is found the result is a pointer(s) to the related graph node(s). 3. DOUBLE-LINKED TREE Represents the pathways and their parent-child relationships for each species in Reactome. Once the tree leaf is reached, the entity is flagged as found in each node from it to the root (unless it was flagged before). 2. ENTITIES GRAPH Represents orthology relationships between proteins of different species. Used for the projection option in order to speed up the analysis (no queries to the database are needed). ANALYSIS EXAMPLE Green arrows show use cases where the identifier is analysed in its own species. Red arrows show a use case where the identifier is projected to human during the analysis. Enrichment analysis results Expression data analysis results Contact Reactome Acknowledgements This work was supported by the US National Institutes of Health (P41 HG003751), Ontario Research Fund, the European Molecular Biology Laboratory Core Funding and the Centre for Therapeutic Target Validation (CTTV). Help Desk: help@reactome.org URL: http://www.reactome.org


 Simplified 2D Cavity Model  Flow Field Characteristics of the Simple 2D Cavity I. Introduction IV. Results • Atrial fibrillation (AFib) is the most common cardiac arrhythmia. • The risk of stroke is increased approximately 5-fold in patients with AFib. • In 91% of patients, atrial thrombi develop within the left atrium and appendage. • It is still not fully understood how the AFib-induced flow would change the hemodynamic conditions in the LA • In this study, we performed an in vitro tomographic Particle Image Velocimetry study to quantitatively characterize the pulsatile flow in the left atrium under regular (double peak) and simulated AFib (loss of A wave) waveforms. V. Conclusions CAD Model of Left Atrium • A 2D side-wall cavity model (depth-to-width ratio is 1.5) was used to examine the vortex dynamics under normal and AFib waveforms. Left atrial flow waveforms: simulated vs clinical data  3D Flow Streamlines for Normal Double-Peak LA Flow  The Cardiovascular Flow Simulator  Tomographic Particle Image Velocimetry (Tomo-PIV) II. Methodology Fluid Dynamics of the Human Left Atrium under Simulated Normal and Atrial Fibrillation Conditions: A in vitro Tomographic PIV Study Sifat K. Chowdhury, Al Habib Ullah, Zachary Posch, Yan Zhang* Department of Mechanical Engineering, North Dakota State University *Speaker; Corresponding Author: yan.zhang.4@ndsu.edu, +1(701)-231-9217 • Phase-locked planar and Tomographic Particle Image Velocimetry (PIV) were used to quantify the flow characteristics in a 2D cavity model and in a realistic LAA model, respectively • The PIV system consists of o Double-pulsed Nd:YAG lasers o Synchronizing timing unit o Four CCD cameras o Imaging processing software • A closed-loop cardiovascular flow simulator was constructed with a pulsatile flow generator to mimic the heart, under both normal and AFib waveforms, III. Test Model and Conditions  3D Left Atrium Silicone Phantom Pulmonary Veins Left Ventricle Chamber (simplified outlet) Appen dage • The refractive index (RI) matching: RI = 1.43 of the silicone model. Mixture of water-glycerin (40 % and 60 % respectively by volume)  Regular and AFib Flow Waveforms • A lump-parameter numerical model was used • The regular LA flow boundary condition features a double-peak flow (E-wave and A-wave), representing the early LV suction and the late atrial pumping effects • The simulated AFib flow was generated by smoothing out the A-wave (2nd peak). • Flow rate: 5 Liters/min • Heart rate: 75 beats per minutes Velocity vectors and out-of-plane vorticity fields of pulsatile flow past the simple cavity (a) the normal LA mitral flow and AFib flow waveforms used for experiments; (b - e) the flow fields under normal double-peak LA waveform reveals higher momentum of vortical flow inside cavity throughout the cycle; (f - i) the flow fields under AFib waveform reveals the cavity vortical flow dissipates faster after the E-wave. VI. Acknowledgement We would like to acknowledge the American Heart Association for the funding support under the AHA Institutional Research Enhancement Award (AIREA) #19AIREA34470008. • The Simple Cavity experiment results indicate that compared with the AFib waveform, the “double-peak” of the normal LA flow helps maintain the momentum of vortical flow and reduce flow stasis inside the appendage cavity. • Tomographic PIV results of LA flow suggest: • The clock-wise rotating helical flow (posterior view) is a predominant feature for LA flow, regardless of normal or AFib conditions • The secondary peak of flow under normal condition feeds into the momentum/energy of the helical flow in the LA chamber, potentially reducing flow stasis compared to Afib • The results emphasize the important role of normal atrial pulsation. Cross-Section for Tomographic PIV Measurement Volume (Volume Thickness ~10mm) Left Right Anterior Wall Posterior Wall  3D Flow Streamlines for AFib Single-Peak LA Flow Velocity Magnitude (m/s) Specific Kinetic Energy (𝒎𝟐/𝒔𝟐) Veloc


@ F A C EITArcti c @ F A C EITArcti c @ fa c e _i t_arc tic @ The F A C E -IT Pro j e ct www.face-it-project.eu FACE-IT has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 869154. Five categories were defined, each containing two or three drivers of change: • Cryosphere: Sea-ice, Glacier mass balance, Terrestrial runoff • Physics: Seawater temperature, Salinity, Light • Chemistry: Carbonate system, Nutrients • Biology: Primary production, Biomass, Species richness • Social: Governance, Tourism, Fisheries Each of these drivers of change are themselves an important facet of the overall socio-ecological fjord system, and most drivers have multiple, often complex interactions with others. Some of which, such as light penetration into fjord waters, are both very important to understand, and yet very difficult to project into the future. Arctic fjords serve as a point of confluence between land and sea. They are also the point at which the highest levels of human habitation and natural productivity intersect. The Arctic is however changing extremely rapidly, requiring that the monitoring and reporting of these changes keep pace. But what exactly is it that is changing? Or more complexly, which changes are affecting what? To answer these questions a broad team of experts was assembled under the FACE-IT consortium to investigate the drivers of change to socio-ecological systems in Arctic fjords. A survey was conducted amongst the experts within FACE-IT on what types of data (social and natural) were most important when studying change in Arctic fjords. This process created a long list of potentially important drivers (QR right), which were used to guide the review of available literature in the space. Finding the drivers of change We are providing a framework for future studies on any of the facets of change within Arctic fjord socio-ecological systems. The review publication (QR right) provides the necessary literature background to explain and prioritise the importance of the many drivers of change. The connected dataset provides researchers with the necessary data foundation to begin studying the historic state of the primary drivers presented here. After multiple rounds of deliberation the key drivers of change were refined down to a manageable length, and all in situ sampled data available for these drivers were sourced and amalgamated. This dataset was published open access and may also be queried via a user interface constructed by FACE-IT (QR right). Fig. 1 The interconnections between different drivers of change for socio-ecological Arctic fjord processes according to the available literature. The centre colour of the dots show the category of the drivers, and the border colour shows the direction of the long-term change. Arrows between dots show the direction of the relationship, and the colour shows if these are driving increases or decreases (or uncertain). Arctic fjord socio-ecological systems: Drivers of change Robert Schlegel1& all authorsofSchlegel et al. 2023 1Sorbonne University, CNRS, Laboratoire d’Océanographie de Villefranche, Villefranche-sur-mer, France robert.schlegel@imev-mer.fr Making all data available Which changes are affecting what?Cryosphere Physics Chemistry Biology Social Get Schlegel et al. 2023


Combining NMR docking data with EPR distances and in silico calculations for a more complete model of colicin protein-protein interactions Morgan Bye, A. Stratford, C. Teutloff, G. Moore, F. MacMillan Henry Wellcome Unit for Biological EPR, University of East Anglia, Norwich, UK. School of Chemistry, University of East Anglia, Norwich, UK.  (Currently) Fachbereich Physik, Freie Universitat Berlin, Berlin, Germany morgan.bye@uea.ac.uk morganbye.net Work supported by [1] Ferentz et al, Rev Biophys, 33, 29 (2000) [2] Palma et al, Proteins, 39, 372 (2000) [3] de Vries et al, Proteins, 69, 726 (2007) [4] MMM – http://www.epr.ethz.ch/software [5] HADDOCK – http://www.nmr.chem.uu.nl/haddock [6] Cascales et al, MMBR, 71, 1 (2007) Currently atomic resolution structures of protein-protein interactions are commonly limited to X-ray crystallography or NMR, developments in these fields have allowed for more accurate and biologically relevant structures to be developed [1] however ab initio information still remains a developing field [2,3]. Site-directed spin labelling (SDSL) in combination with electron paramagnetic resonance (EPR) pulsed electron double resonance (PELDOR) allows for the precise measurement of intra and intermolecular distances. New in silico methods allow for comparison to the experimental [4]. SDSL at several sites within a complex allows a three dimensional model to be built up using distance constraints, which when combined with NMR docking data gives a comprehensive model [5]. Using the well-characterised system, of colicin E9 and its cognate inhibitor, Im9 [6] a comprehensive solution is being developed to combine such NMR docking models, with PELDOR distance (and angle) constraints and in silico computations which are comparable to X-ray crystallographic models. The biology During times of stress it is evolutionarily beneficial to have an advantage over the competition, resulting in E. coli having developed colicins which act as bactericides to other species [1]. Here we focus upon the DNase colicin E9. To provide immunity to the host it is co- expressed with an immunity protein, Im9. A simple diagram of the E9 action. The E9 DNase domain (C domain) is in red, whilst the E9 T and R domains are in blue, Im9 in green Spin labelling Initial spin labelling efficiencies were < 1% using standard procedures. A change to buffer, aerobic conditions and the addition of EDTA, acceptable labelling efficiencies were obtained. E9 S3C 56% E9 S30C 25% E9 S80C 27% Im9 C23 33% X-band cw-EPR Here it can be seen (by lineshape, width and position of peaks) that the binding of the cognate protein slightly influences the cw spectra. All experiments here were run with the same parameters, with the same protein concentrations. References X – band PELDOR (above) Colicin E9 DNase domain in grey with residues S3C, S30C and S80C MTSL spin labelled. Colicin Immunity Protein, Im9, in cyan with residue C23 MTSL spin labelled (left) E9 S80C position showing all 95 spin label rotamers at 298K, electron location shown as a purple sphere. Rotamer opacity and sphere volume is proportional to rotamer population (above) E9 S30C PELDOR trace prediction at various temperatures, highlighted is the 80K prediction for comparison to below. E9 S30C cw spectra comparison at 80K (top) and 298K (bottom). In blue E9S30C*, green E9S30C*- Im9 red E9S30C*-Im9* cyan E9S30C-Im9* magenta Im9*. Where * indicates a spin label. 298K 80K Complete the PELDOR with other mutants Complete the data set at other frequencies Develop technique to accurately model Expanded to characterise other ill- defined non-cognate binding pairs of colicin E9 with Im2, Im7 and Im8. Pump Detect Field Swept Echo Fourier Transformation Distance Distribution Pulse Sequence PELDOR (using the 4 pulse technique shown left) has been conducted on the E9 S30C* - Im9 C23* complex at 50K. The result of this shows the main distance distribution to lie at 38.1 Å. This compares well with the 38.2 Å pre


Introducing extremely red quasars. Models predict that massive black holes mostly grow in obscurity inside a dusty starburst until a blowout of gas and dust quenches star formation and reveal a visibly luminous quasar. Dust-reddened quasars are believed to be in the transition phase and still partially embedded. At the peak epoch of galaxy formation and quasar activity, feedback should have the greatest impact. We discovered a remarkable population of “extremely red quasars” at z ~ 2–3 in the Baryon Oscillation Sky Survey (Hamann+17). ERQs have extremely red colors from rest-frame UV to mid-IR, high bolometric luminosities, and normal radio emission. They have exceptionally broad and blueshifted [OIII]5007 reaching speeds >6000 km s−1 (Perrotta+19). [OIII]5007 emissions in ERQs are compact. We obtained adaptive-optics integral-ﬁeld spectra of 10 ERQs (Vayner+21; Lau+23). We identify physically distinct [OIII] kinematic components that are bimodal, and respectively trace ERQ-driven outﬂows and dynamically quiescent interstellar media. The outﬂows are ~1 kpc scales whereas the quiescent gas extends to few kpc. Compared to normal quasars the extremely fast ERQ-driven outﬂows are compact, supporting that ERQs are in a young evolution stage and represent unique physical conditions beyond orientation diﬀerences. The kinematically quiescent emissions are spatially- resolved but less extended than normal quasars, which can be explained by patchy obscuration. The hint of ionization cones suggests patchy torus. A deep survey for even redder, fainter ERQs. The extreme outﬂow properties are tied to their extreme red i−W3 (or z−W3) colors. We are searching for ERQs that are even redder to explore the possibility of more extreme physical properties tied perhaps to an even earlier evolution stage. We select candidates from DESI+WISE photometry based on the unique spectral energy distribution that is ﬂat across rest-frame UV, and conﬁrm with optical spectroscopy. If you study red quasars, I invite you to compare our selection techniques and the characteristic properties. Probing Quasar-Galaxy Evolution via Extremely Red Quasars / Probing Inﬂows onto Quasar Hosts via Associated Absorbers Marie Wingyee Lau (wingyeel@ucr.edu; University of California, Riverside), Fred Hamann 16000 16200 16400 16600 0 20 40 60 80 100 Average SB (10−17 cgs arcsec−2) 0.0" ≤ distance < 0.1" Data Fits 16000 16200 16400 16600 0 5 10 15 Average SB (10−17 cgs arcsec−2) 0.1" ≤ distance < 0.2" Data Fits 16000 16200 16400 16600 0 1 2 3 4 5 Average SB (10−17 cgs arcsec−2) 0.2" ≤ distance < 0.4" Data Fits 16000 16200 16400 16600 0.0 0.5 1.0 1.5 2.0 Average SB (10−17 cgs arcsec−2) 0.4" ≤ distance < 0.6" Data Fits Wavelength (Å) 16000 16200 16400 16600 0.0 0.5 1.0 1.5 2.0 Average SB (10−17 cgs arcsec−2) 0.6" ≤ distance < 1.0" Data Fits Wavelength (Å) Wavelength (Å) 16000 16200 16400 16600 0.0 0.5 1.0 1.5 2.0 Average SB (10−17 cgs arcsec−2) 1.0" ≤ distance < 1.4" Data Fits Wavelength (Å) Wavelength (Å) Wavelength (Å) −1 0 1 −1 0 1 (arcsec) (arcsec) 0.0 0.2 0.4 0.6 0.8 1.0 SB (10−14 cgs arcsec−2) J0006+1215 Hn3 Residual Relative Velocity (km s−1) Relative Velocity (km s−1) SiIV at z = 1.716 −400 −200 0 +200 +400 −2600 −2400 −2200 −2000 CIV 1548 CIV 1548 CIV 1550 CIV 1550 NV 1238 NV 1238 NV 1242 NV 1242 SiIV 1393 SiIV 1393 SiIV 1402 SiIV 1402 SiIII 1206 SiIII 1206 HI 1215 HI 1215 Left: Spectra extracted from nuclear and annular apertures of the reddest ERQ J0006+1215, and the best fits. We overplot the individual continuum, Hβ, broad and narrow [OIII] components. Right: Residual surface brightness map of J0006+1215 after subtracting the point spread function. The residuals are dominated by the narrow [OIII] component. Right: AB color vs. observed wavelength for candidates compared to median SED of known ERQs, Type 1 QSO with and without reddening, hot DOGs, highly reddened Type 1, Type 2 QSO, and starburst galaxy NGC 6240. Left: A newly-discovered ERQ. We see the characteristic s


Evolution of the HD 163296 Jet Axis Andrew Miller1, Aisling Murphy1, Christian Schneider2, Emma Whelan1, Catherine Dougados3, Jochen Eislöffel4 1Department of Experimental Physics, Maynooth University 2Hamburger Sternwarte, University of Hamburg 3IPAG, University of Grenoble 4Thüringer Landessternwarte Tautenburg, Tautenburg, Germany Abstract HD 163296 is a Herbig Ae star which drives a bipolar knotty jet with a total length of ~6000 au. Strong evidence exists that the disk of HD 163296 harbors planets which could affect the morphology of the jet, and ‘wiggling’ models have been proposed as a method to infer the presence of companions in other stars. Using the VLT/MUSE spectrograph, we investigate the morphology and proper motions of the HD 163296 jet with the aim of searching for evidence of its planetary system. In particular, we analyze the jet width and jet axis position with distance from the star and compare to ‘wiggling’ models to explore the possibility of orbital motion or precession in the jet axis. We conclude that there is no signature of this wiggle in the jet, and our results allow us to constrain the parameters of possible companions and further assess the robustness of the wiggling jet model as a method for detecting companions. 1. Introduction ●Astrophyiscal jets are ubiquitous in young stellar objects (YSOs), and accretion/outflow connection impacts circumstellar disk and potentially planet formation (Frank et al., 2014). ●Jets display complex structures, and sometimes present ‘wiggles’ in jet trajectory: potential signature of binary companions close to jet source (Masciadri & Raga., 2002; Murphy et al., 2021). ●Strong evidence for multiple planets embedded in HD 163296 disk (Teague et al., 2018; Pinte et al., 2018), and potential periodicity of jet ejection events may suggest a close-in companion (Ellerbroek et al., 2014) ●Using MUSE observations taken in 2018, we examine jet axis of HD 163296 to seek signatures of orbital motion or jet precession, and test the applicability of jet wiggling models to the case of planetary companions. 6. References 4. Results 3. Data Analysis ●Integrated spectro-images of forbidden emission lines (FELs) extracted from MUSE data cubes, focused on primarily on Hα, [N II]λ6583, and [S II]λλ6716,6731 (See Figure 1). ●Gaussian fitting along outflow axis yields jet FWHM and axis centroid position as a function of distance from the star. Deviation of centroids from axis center are examined for ‘wiggling’ signature. ●If wiggling motion is due to precession in jet axis, the motion can be expressed in terms of jet velocity (vj), precession period (ν), and half-opening angle (β) by: ●If wiggling is due to orbital motion of the jet source, the motion can be expressed in terms of orbital velocity (vo), jet velocity, orbital radius (ro), and phase angle () by: ●Each model is first fit with only vj constrained, then fit using proposed planets as function parameters. Figure 5: Parameter space of possible companion ranges expected to produce an observable ‘wiggle’ in the jet axis. Gray hatches indicated regions excluded by the MUSE observations. The green on the left indicates companions with unde- tectable effects. The top blue and bottom teal indicate periods too long or short to be observed. Figure 3: A plot of the [S II] centroids with free-fitted orbital and precession components over-laid. The distances are deprojected from the plane of the sky with x’ = xt / cos i. Only the jet velocity is held constant. The fitted parameters suggest a 0.2 M☉ companion at 8 au for an orbital motion model, while a precession model suggests a 48 MJup companion at < 0.35 au. The largest proposed planet in the HD 163296 disk is 2 Mjup, and the closest planet is located at approximately 83 au. These observations were collected with MUSE at the Very Large Telescope on Cerro Paranal (Chile), operated by the European Southern Observatory (ESO), under Program ID 099.C-0214(A). Figure 2: Co-added centroid (to


2018 LESSON PLAN Neelam Bharti Senior Librarian, Chemistry Matt Marsteller Principal Librarian, Engineering & Science Mike Melville Data Science Research Associate, Eberly Center Huajin Wang Librarian, Biology and Computer Science Sarah Young Senior Librarian, Public Policy Melanie Gainey Librarian, Biological Sciences University Libraries The Design and Implementation of Integrated and Interdisciplinary Information Literacy Instruction Project Design In 2017, we were invited to teach a one week unit on information literacy in large (180-250 students) MCS classes, EUREKA! and PROPEL. In 2018, we introduced a pre- and post-learning assessment. Our research question is how much do first-year science students learn from a one-week unit on information literacy? Lessons Learned Project Evaluation We compared students’ knowledge of information literacy concepts before and after a week of instruction in EUREKA! in the fall of 2018. We thank Ken Hovis for the opportunity to teach in the EUREKA! and PROPEL courses and for conversations about lesson planning. Insert Presenter’s picture here LECTURE • 1 hour lecture with 5 interactive Direct Poll questions and 2 think- pair-share exercises RECITATION • “Along the Graphene Trail” is a 7- step digital scavenger hunt that gives students the opportunity to navigate through several science information resources. • Recitations were led by teaching faculty. We supplied a lesson plan that included notes on strategy and pain points for each step of the exercise. • This exercise is easily adaptable for other disciplines We are developing interdisciplinary active learning lesson plans to teach information literacy in two large required science classes, EUREKA! and PROPEL. Our goal is to use learning gain assessments to systematically assess the effectiveness of different active learning exercises and target areas of weakness with interventions. INFO SCAVENGER HUNT at a glance • The learning gains assessment was administered on Canvas. • We used counter-balanced design to control for differences in the difficulty of the pre- and post-assessments and order effects. • Students were assigned version A or B depending on their recitation section and took one version at the beginning of lecture and the other at the end of recitation. • Students were given immediate feedback on the answers. • Results showed no significant difference between version A and B. THE SCENARIO You are browsing the June 2018 issue of Scientific American online and an article catches your eye “Quirky Graphene” by Prachi Patel. EXAMPLE STEPS STEP 1: As you read over the short news article, part of the article discusses electronic ID tags. Use the clues given in the article to locate the original work that they are describing. STEP 2: You have found the original article in ACS Nano. What clues do you have that this is a scholarly article? • Student engagement is quite variable across active learning exercises and recitation sections. • In spite of uneven engagement, 74% of students in 2017 reported in an attitudes post-assessment that they found a role-playing exercise to be useful for learning information literacy concepts. • Active learning exercises had to be fairly structured to be successful in these large classes. • Our implementation of Think-Pair-Share needs to be optimized. Students used the time to chat. • Group exercises worked better if the groups were limited to a few students. • The exercises often took a bit longer than the time we budgeted for them. • In 2018, we gained access to C@CMU and aimed to create lesson plans that built upon, rather than duplicated, that content. This allowed us to focus on more advanced concepts. • The learning assessment was really useful for understanding pre- existing knowledge and the effectiveness of our instruction and will guide lesson planning in future semesters. Students exhibited a significant learning gain from pre-test to post-test, t (181) = 10.71, p <.001, Cohen’s d = 1.04. Al


Restoring the West 2014 Comparing Channel Classiﬁcation Frameworks to Better Inform Watershed Management Alan Kasprak1, Nate Hough-Snee1,2, Reid Camp1, Tim Beechie3, Gary Brierley4, Martha L. Jensen1, Joseph M. Wheaton1,2, Hiroo Imaki3, Gary O’Brien1 1 Utah State University, Department of Watershed Sciences, Logan UT 2 Utah State University, Ecology Center, Logan UT 3 NOAA Fisheries, Northwest Science Center, Seattle WA 4 University of Auckland, Auckland, New Zealand Introduction ▶The diversity of channel forms across a basin complicates watershed management Channel Classiﬁcation: ▶Links watershed/local processes to channel form ▶Allows an understanding of watershed characteristics ▶May aid in prioritizing and planning stream restoration Study Setting: Middle Fork John Day River, Oregon ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 0 10 20 30 40 Kilometers $ ! CHaMP Reaches Elevation (m) High: 2479 Low: 639 ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !!! ! Basin Characteristics ▶Located in northeastern Oregon, USA ▶117 km long, drains 2051 km2 ▶Physiographically-diverse meta-volcanic basin ▶Semi-arid watershed, large climatic and precipitation variability across elevations ▶Threatened native steelhead population ▶Ongoing intensive site-based ﬁsh and geomorphic monitoring, basin-wide channel classiﬁcation eﬀorts Methods: River Styles and Natural Channel Classification The River Styles Framework ▶Developed by Brierley and Fryirs [2005] ▶Hierarchical reach-scale classiﬁcation using watershed- and local-scale variables ▶Number of River Styles is basin-dependent Natural Channel Classiﬁcation ▶Developed by Beechie and Imaki [2014] ▶Predicts pre-disturbance planform ▶Streams < 8 m width using Montgomery and Buﬃngton [1994] Straight Meandering Anabranching Braided Channel Slope 2-Year Discharge Valley Confinement Basin Forest Cover Basin Geology Data Sources Used: Channels < 3 m Classified via Slope: Pool-Riffle S < 0.02 Plane Bed 0.02 < S < 0.03 Step-Pool 0.03 < S < 0.08 Cascade S > 0.08 Valley Setting >90% of channel abuts conﬁning margin Presence/absence extent of ﬂoodplain Planform Instream Geomorphic Units low sinuosity (superimposed meandering outline) Bed Material Texture River Style riﬄes, runs, cascades, steps plunge pools bedrock, steps, riﬄes, cascades riﬄes, runs, plane bed, bars cobble, gravel, sand boulder, cobble, gravel cobble, gravel ocsl. boulder STEEP PERENNIAL HEADWATER (Forced Meander) (Plane Bed Canyon) STEP-CASCADE cascades 2ndry Channel steps, pools bedrock boulder cobble low sinuosity STEEP EPHEMERAL HILLSLOPE CONFINED VALLEY BEDROCK RIVER riﬄes, runs, point bars, rapids cutbanks, anabranch step-pool cascade boulder, cobble, gravel, sand sand, gravel, cobble, boulders Laterally Conﬁned occasional ﬂoodplain pockets ﬂoodplain absent recurring ﬂoodplain straight, slightly sinuous Floodplain Geomorphic Units boulder berms, terraces ﬁne-grained ﬂoodplain, terrrace CONFINED VALLEY W/ FLOODPLAIN POCKETS ENTRENCHED BEDROCK CANYON ﬁne-grained ﬂoodplain, terrrace Structural Elements* Valley Alignment CS woody debris CS and bar-forced woody debris BA woody debris CS and bar-forced woody debris BA woody debris CS = channel spanning BA = Bank Attached Methods: Natural Channel Design and Clustering Note: NCD and Clustering use data from the Columbia Habitat Monitoring Program (www.champmonitoring.org.) Natural Channel Design ▶Developed by Rosgen [1994] ▶Hierarchical reach-scale classiﬁcation using local variables Statistical Clustering ▶Automated grouping with user-deﬁned input data ▶Number of classes attempts to balance separating reaches with minimal reach overlap -50 0 50 100 -50 0 50 Middle Fork John Day Reaches by Cluster Membership PC 1 (86%) PC 2 (11%) Bankfull Width Profile Gradient D16 D50 D84 Wetted Width Width:Depth Ratio Cluster Membership 1 2 3 4 Predictor Variables Bankfull Width Sinuosity Gradient D16, D50, D84 Width-Depth Ratio Results: River Styles and Natur


www.postersession.com www.postersession.com Stars are thought to be commonly born and found in binary/ multiple systems. The diverse orbital configurations and rich dynamics in planet-bearing binary systems provide valuable conditions to test various theories and models of planet formation and evolution Conclusions We build our planet sample by retrieving data (on 04-March- 2021) from two online catalogues. i.e., (1) the catalog of exoplanet-hosting binaries with separations up to 500 AU[1], (2) the catalogue of exoplanets in binary star systems [2]. Furthermore, in order to have a unified criteria to define our sample, we adopted an upper mass limit (20 mJ) for planetary objects and a lower mass limit (50 mJ) for stellar objects. Finally, we obtain 110 planets in 80 systems, 61 (19) of which are single (multiple) planet systems. Introduction *To whom corresponding maybe addressed. Email: jwxie@nju.edu.cn Demographics of Exoplanets in Binaries (DEB). I. Architecture of S-Type Planetary Systems Revealed by the RV Sample Xiang-Ning Su1,2，Ji-Wei Xie1,2*，Ji-Lin Zhou1,2，Philippe Thebault3 1 School of Astronomy and Space Science, Nanjing University, Nanjing 210023, People’s Republic of China 2 Key Laboratory of Modern Astronomy and Astrophysics, Ministry of Education, Nanjing 210023, People’s Republic of China 3 LESIA, Observatoire de Paris, Meudon Principal Cedex, France orbital configurations of planets in binaries Most planets in binaries are currently found in S-type, there are 211confirmed S-type planets. Among them, 116 are from radial velocity (RV) surveys, 89 from transit surveys and 6 from others. S-type: planets orbit one of the binary stars; P-type: planets orbit both the binary stars. sample heterogeneity and observational bias The sample heterogeneity and observational bias are obstacles toward a clear figure of exoplanet demographics in the binary environment. We only focus on S-type planet systems from RV surveys to reduce the sample complexity and thus better take into potential observational bias. Sample and Method Results quantifying the RV detection constraints We adopt RV signal injection-recovery test to quantify observational bias: • we collect the RV data of each system and extract the RV residual. • we inject a test RV signale, and generate a set of simulated RV data • we evaluate whether the injected planet signal can be recovered from the simulated RV data. single planets VS. multiple planets Multiple-planet systems are more prevalent in wide binaries whereas single-planet ones are, on average, found in tighter binaries. There are no difference between single and multiple planet systems in the statistics of planetary mass and eccentricity distributions after considering the effect of detection limit. But singles and multiples seem to differ significantly in the 2-D distribution of the period-mass diagram. There is a rectangular- shaped gap (period = 10-300 days and Msini = 160-640 )in the period-mass diagram of single-planet systems but not for multiples. Close VS. Wide Binaries The period-mass gap identified for single-planet systems is also preferentially found in close-binaries. There is a rising upper envelope planets in the period-mass diagram for wide binaries (and single star systems) which is not found in the close-binary subsample. 1. Single (multiple) planetary systems are mostly found in close (wide) binaries with separation aB< (>) 100- 300 AU. 2. In binaries, single and multiple-planet systems are similar in 1-D distributions of mass and period as well as eccentricity (in contrast to the "eccentricity dichotomy" found in single-star systems) but different in the 2-D period-mass diagram. Specifically, there is a rectangular-shaped gap in the period-mass diagram of single-planet systems but not for multiples. This gap also depends on binary separation and is more prominent in close binaries. 3. There is a rising upper envelope in the period-mass diagram for planets in wide binaries as well as in


Ontology Explorer: An Ontology-Based Visual Analytics System for Exploring Time Series Data in Oil and Gas Well-founded ontologies are a powerful tool to make apparent the seman- tics of the data that supports the decision-making process in surveillance en- vironments. Optimizing oil and gas production requires the right-time analysis of a large amount of data produced by several distinct data providers that adopt their conventions and formats. The main contributions of this work are: ▪Ontology Explorer, a new Visual Analytics system that leverages the con- cept of Ontologies in the interactive exploration of time series in digital twins. ▪A case study that demonstrates an application of the Ontology Explorer in the oil and gas digital twins. SUMMARY ONTOLOGY EXPLORER SELECTED REFERENCES ACKNOWLEDGMENT REQUIREMENTS & ARCHITECTURE CONCLUSIONS AND FUTURE WORK • This work presents the Ontology Explorer, a visual analytics system for time series data in the oil and gas ﬁeld, enhanced by the O3PO ontology to enable semantic interactive exploration of data. • Leveraging the semantics of the domain, the tool allows users to ﬁlter data in terms that closely reﬂect the core business, relieving the extra burden of thinking about the way information is stored. • Future work will focus on the ontological treatment of processes related to petroleum production (e.g., the event of injection loss), aiming to pro- vide a similar semantic exploration of them. We plan to release a general version of the tool, allowing users to plug in their ontologies, provided they meet certain requirements, along with necessary documentation. Also, regarding the larger landscape of the PeTwin project (petwin.org), the prototype has been validated and is being installed for evaluation and improvement at the corporate partner while also being integrated with a data analytics & machine learning module. 1. Nicolau Santos, Jonathan Rivera, Rafael Petry, Fabrício Rodrigues, Givanildo Nascimento, João Comba, Mara Abel. Ontology Explorer : An Ontology-Based Visual Analytics System for Exploring Time Series Data in Oil and Gas. Formal Ontology in Information Systems: Proceedings of the Fourteenth International Con- ference (FOIS 2023). IOS Press, 2023. 2. Nicolau O. Santos, Mara Abel, Fabrício Henrique Rodrigues, and Daniela Schmidt. Towards an ontology of oﬀshore petroleum production equipment. In Emilio M. Sanﬁlippo, Mohamed-Hedi Karray, Dimitrios Kyritsis, and Arkopaul Sarkar, editors, Proceedings of the 12th International Workshop on Formal Ontologies meet Industry (FOMI 2022) Co-located with workshops about the Industrial Ontology Foundry (IOF) and the European project OntoCommons (EU H2020 proj- ect), Tarbes, France, September 12-15, 2022, volume 3240 of CEUR Workshop Proceedings. CEUR-WS.org, 2022. 3. Nicolau Santos, Fabrício Rodrigues, Daniela Schmidt, Régis Romeu, Givanildo Nascimento and Mara Abel. O3PO: A Domain Ontology for Oﬀshore Petroleum Production Plants. Available at SSRN: https://ssrn.com/abstract=4280151 or http://dx.doi.org/10.2139/ssrn.4280151. Nicolau O. Santos, Jonathan C. Rivera, Rafael H. Petry, Fabrício H. Rodrigues, Givanildo S. Nascimento, João Comba, Mara Abel Lorem ipsum R1. Display the time series associated with sensor data as line graphs, supporting individual plots, or composed plots that allow comparisons; R2. Allow the user to deﬁne temporal ﬁlters to narrow the analysis to speciﬁc peri- ods (months, days, and hours); R3. Display the taxonomy of the ontology, allowing the user to hide, expand and inspect certain paths from the root of the taxonomy; R4. Support a text query from the user that highlights in the taxonomy the class nodes that match the input query, summarizing at each node the count of chil- dren nodes returned by the query; R5. Support selection of class nodes and inspection of their instances; R6. Support navigation through semantically non-hierarchical associations such as components or properties; R7. Support select


Lorraine Johnson JD|MBA*, Mira Shapiro MSc** *Principal Investigator, MyLymeData; **Biostatistician, Analytic Designers LLC Delayed Diagnosis Linked to Persistent Lyme Disease NAM Diagnostic Errors Schematic and Lyme Diagnostic Errors in Early Lyme Disease Most Patient Symptoms are Not Counted by CDC CDC Does Not Count Most Symptoms Acknowledgements We thank Dr. Elizabeth Maloney for her insightful comments, Allison Childs for graphic design, and the patients participating in MyLymeData, without whom this research could not have been done. Suggested Citation: Johnson, L.; Shapiro, M. (2019): Delayed Diagnosis Linked to Persistent Lyme Disease. figshare. Poster. https://doi.org/10.6084/m9.figshare.9947105 Contact lbjohnson@lymedisease.org | mira.shapiro@analyticdesigners.com Conclusion Most patients with persistent Lyme disease are not diagnosed until six or more months after symptom onset. Early diagnosis and treatment of acute Lyme disease usually prevents the progression to persistent Lyme disease. Diagnostic barriers include: using inaccurate geographic risk maps, failing to consider the most common early manifestations of the disease, and over reliance on imperfect lab tests. Remedying these identified diagnostic errors may significantly improve patient outcomes by providing patients with early diagnosis and treatment, which is associated with higher treatment success. References Newman-Toker, D. et al. Diagnosis 2019 Jul 11. doi:10.1515/dx-2019-0019 Khanna, G. AHRQ Blog. 2019 May 6. https://www.ahrq.gov/news/blog/ahrqviews/ahrqs-road-ahead.html NAM. 2015. Improving Diagnosis in Medicine. doi:10.17226/21794. Lee-Lewandrowski, E. et al. American Journal of Clinical Pathology. 2019 Jul. doi:10.1093/ajcp/aqz030 CDC, Lyme Case Report. 2009. https://www.cdc.gov/lyme/resources/LymeDiseaseCaseReportForm.pdf Introduction Lyme disease is the most common vector-borne disease in the United States. Diagnostic errors are believed to be common. Errors stemming from delayed or inaccurate diagnosis are generally the number one cause of serious harms among medical errors. (Newman-Toker 2019) The Agency for Healthcare Research and Quality has made reduction of diagnostic errors one of its three strategic priorities. (Khanna 2019) The National Academy of Medicine (NAM) has developed a schematic to identify points in the medical care system where diagnostic errors occur. (NAM 2015) It deﬁnes diagnostic medical error as “the failure to (a) establish an accurate and timely explanation of the patient's health problem(s) or (b) communicate that explanation to the patient." NAM looks at errors that occur when the patient ﬁrst encounters the healthcare system, when clinical history is taken, during physical exam, and when diagnostic testing is being conducted. They then look at the consequences and costs of the diagnostic errors. This study analyzes patient responses from the MyLymeData Patient Registry using the NAM schematic to identify points of diagnostic errors in persistent Lyme disease. Sample Determination Methods and Results Lyme Lab Test Positivity Rates in 2016 The MyLymeData patient registry, developed by LymeDisease.org, enables patient healthcare data to be pooled. Since its launch in November 2015, over 12,000 patients have enrolled. This study included 3,903 US residents clinically diagnosed with Lyme disease who completed the Phase I Survey, which ran from November 2015-2016. Most participants (79%) have late or persistent Lyme disease. Questions included time to diagnosis, early symptoms, inaccurate lab tests, and whether their diagnosis relied on perception of Lyme in their state. The NAM schematic applied to acute Lyme disease diagnosis illustrates sources of errors including: inaccurate geographic exclusions, failure to consider full symptom range, and over reliance on inaccurate diagnostic tests. Diagnostic delays occur when clinicians restrict their diagnosis using the five CDC surveillance criteria: rash, neuropathy, Bell’s


Poster template courtesy Faculty & Curriculum Support (FACS), Georgetown University School of Medicine Zevatars; the future of personalized cancer medicine Shaila Mudambi Ph.D; Anna Zdunek; Mary Pasquale B.S; Kamden Gray; Seray Er; Ceylan Metin; Paige Burkhardt, B.S; Saumya Kasliwal B.S; Russell Sillmon M.S; Stephen Byers Ph.D; Michael Pishvaian M.D; Ph.D Eric Glasgow Ph.D Department of Oncology, Lombardi Comprehensive Cancer Centre, Georgetown University, Washington D.C. Background Figure 1. Differential drug response in cryopreserved liver metastasis biopsy (primary pancreatic cancer): A (a,c,e): Images of implanted tumor (patient ID : 1-01302019-A) on Day 1 of either FOLFIRINOX (4.2mM Fluorouracil, 1mM folinic acid, 0.08mM irinotecan, 0.08mM oxaliplatin) or Gemcitabine (0.1mM) + abraxane (0.005mM) treatment. A(b,d,f): Images of tumors 3 days post treatment. (B) Percentage of fish that survived after drug treatment (C) Change in area of the tumor between Day 1 and Day 3 in each embryo in each group (D) Average change in area between treatment groups. Error bars represent standard deviation (E) Percentage of fish with metastasis after drug treatment. Recapitulating patient drug responses in the clinic using Zevatars Humanizing zebrafish embryos Hematopoietic chimeras and immune tolerance in cmyb+/- zebrafish embryos (Refer to oral presentation 104) Zevatars determine patient tumor drug response Conclusions and Future directions Figure 2- Summary of patient drug responses; change in tumor size, fish with metastasis and fish survival after 3-5 days of drug treatment: (A-C) Primary PDAC biopsies from 6 patients treated with 2 doses of gemcitabine (D-F) Liver metastasis from 7 patients with either primary pancreatic or colorectal cancer treated with gemcitabine, abraxane or folfirinox. Bars represent maximum-minimum interval range with mean. Interestingly, response varies greatly from one patient biopsy to another, recapitulating patient tumor behavior observed in the clinic SSC-A FSC-A Blood from engrafted fish for flow cytometry to detect hematopoietic cell populations Table 1 : Cell lineages of engrafted RFP-expressing cells in 6 cmyb+/- transplanted fish Human blood cells persisted in injected fish for at least 15 days. Two larvae injected at 2dpf were dissociated after 12 days and Dye I positive blood cells were observed by flow cytometry Zevatars • Zevatars involve implanting fresh or cryopreserved patient tumor pieces from biopsy samples into zebrafish and screening for best drug response before treating patients in the clinic • The main advantage of using zevatars is rapid drug response screening and ability to assess tumor proliferation, angiogenesis, and metastasis by imaging and finally by implanting tumor pieces takes into account the tumor microenvironment as compared to current methods involving injecting dissociated tumor cells • In this study we describe the application of Zevatars for personalized medicine in pancreatic adenocarcinoma and colorectal cancer. Humanizing zebrafish • Currently we cannot use Zevatars to test immunotherapy so therefore we aimed to generate humanized fish • cmyb mutant fish fail to develop definitive hematopoietic cells and therefore provides us a way to completely replace fish system with a human hematopoietic system 2 1 • Zebrafish embryos exhibit immune tolerance when whole kidney marrow is transplanted prior to development of adaptive immune response • Human peripheral blood was collected and red blood cells (RBCs) were lysed and removed. RBC free blood was then labelled with DiI. • Stage of development is important for immune tolerance so DiI labelled blood was injected into circulation of wild type zebrafish embryos at different stages of development (2dpf-7dpf) • Human blood engrafted and fish survived at least until 14 days post injection when injected into 2dpf embryos • Zevatars can used to screen for the best treatment and hence applied to patients to improve personalized can


Inicio de vida sexual EDAD PORCENTAJE 12 años 1.7% 13 años 7.3 % 14 años 18.7 % 15 años 36% 16 años 19.3% 17 a 19 años 17% El embarazo en la adolescencia es un problema causado por multiples factores sociales. A pesar de la educacion en salud sexual y reproductiva, los embarazos adolescentes continúan siendo una preocupación en America Latina. Este estudio se enfoca en analizar factores subyacentes y mejorar los programas de prevención. INTRODUCCIÓN • Estudio en base a revisión bibliográfica de 11 artículos científicos en bases de datos como UpToDate, PubMed, Scielo, Elsevier, Journal y Google Scholar. • Términos de búsqueda solos o combinados con los operadores booleanos “AND” u “OR” de palabras clave embarazo adolescente, factores de riesgo, educación sexual y reproductiva, madres adolescentes y embarazos en Ecuador • Criterios de inclusión: idioma ingles-español, calidad de los artículos; criterios de exclusión investigaciones con más de 5 años de antigüedad. MATERIAL Y MÉTODO RESULTADOS • Estas adolescentes no tenían antecedentes de consumo de sustancias adictivas y su embarazo no era producto de una violación. El embarazo adolescente es un desafío importante en salud sexual y reproductiva, con implicaciones para el bienestar materno-infantil. Se requiere abordar los diversos factores que contribuyen al embarazo adolescente, con énfasis en la educación, la prevención y el acceso a servicios de salud adecuados. CONCLUSIÓN Guanoliquin Sonia, Pilatasig Erika, Tarco Estefanía edad porcentaje 14 a 19 años 62% 20 a 44 años 38% EDAD DE LA PAREJA DECISIÓN DE LA GESTANTE PORCENTAJE ABORTOS 55% CONTINUA LA GESTACION 45% Análisis de los Factores Subyacentes que Contribuyen a los Embarazos Adolescentes a Pesar de la Promoción y Educación en Salud Sexual y Reproductiva TOTAL CASO MUJERES 10-14 AÑOS 15-18 AÑOS 19-24 AÑOS 22239 7587 6053 8599 EMBARAZOS POR NO USO DE METODO ANTICONCEPTIVO 71,6% 58,0% 59,3%


Binaural Audio Tools for the Creative Industries MORE INFORMATION www.binci.eu @BINCI_EU NEW CULTURAL EXPERIENCES THROUGH 3D AUDIO www.binci.eu The project has received funding from the EU’s Horizon 2020 programme. Grant Nº 732130. 3D AUDIO IN MUSEUMS & CULTURAL SITES ewesemann@antennainternational.com PROJECT COORDINATOR info@binci.eu All partners of the BINCI project have been working collaboratively to develop an integrated market-ready software and hardware solution to ease the production, postproduction and distribution of binaural or 3D audio content. Sound designers, producers and artists will be able to create 3D audio content for interactive applications in the fields of music, virtual reality and cinema more easily. BINCI is a EU-funded project aimed at providing audio technology solutions to the European Creative Industries in order to push and pull the European market offer of innovative audio contents. THE PROJECT


NANOAOD: a new compact event data format in CMS Karl Ehatäht (NICPB, Tallinn) on behalf of the CMS collaboration karl.ehataht@cern.ch Future prospects References • Promote the format to achieve target adoption of 50-70% of all CMS physics analyses • Develop customized NANOAOD formats for specialized tasks such as automation of object calibration workflow • Explore the possibility to add support for alternate choices of generator information and parton distribution functions which are currently expressed as event weights but do not fit within the typical NANOAOD event size • Increase the frequency of producing new NANOAOD Ntuples [1] "The NanoAOD event data format in CMS", M. Peruzzi, G. Petrucciani, A. Rizzi, ACAT 2019 [2] "A further reduction in CMS event data for analysis: the NANOAOD format", M. Peruzzi, G. Petrucciani, A. Rizzi, EPJ Web Conf. Volume 214, 2019 AK4 jets, 382, 23% Generator-level particles, 310, 19% Electrons, 117, 7% Generator-level AK4 jets, 91, 6% Vertices, 87, 5% Hadronic taus, 84, 5% Muons, 75, 5% Trigger objects, 73, 4% Others, 422, 26% 1-3 MB 500 kb 50 kb 1-2 kb RAW AOD MINIAOD NANOAOD Event size Introduced in year 2009 / 2010 Run1 2011 / 2012 LS1 2013 / 2014 End of Run2 2018 / 2019 Typically run twice a year ~1 month runtime, run every few months 1-2 week runtime Motivation ROOT Ntuple ‒ nObject ‒ Object_var[i] ‒ Object_otherIdxQualifier[i] ‒ ... Self-descriptive collection name (example: Electron, Muon, Tau, Jet) Size of the collection (range of [i]) (example: nMuon, nPhoton) Link / cross-reference to associated collection (example: Muon_jetIdx, GenPart_genPartIdxMother) Event-level variables such as: MET (MET_pt, MET_phi), PDF weights (n/LHEPdfWeight), trigger bits (HLT_*), … i-th object in the collection Attribute of the object (example: Tau_eta, Jet_btagDeepB) Provenance tracking TTree::GetEntry() to access the event content ROOT::TTree ROOT::TBranch Figure 2. Breakdown of a simulated 𝑡ҧ𝑡event by average storage size of its high level objects (in bytes). Figure 1. Evolution of data tiers used in CMS. Figure 4. Structure of NANOAOD Ntuple [2]. ‒ Events ‒ LuminosityBlocks ‒ Runs • Standardized access to complex high level physics objects – simplifies automation of physics analyses • No need for CMS software to perform data analysis • Lower barrier of entry for new students • Enables open data access for people outside CMS • Documentation of physics objects embedded in NANOAOD Ntuples • Increases integrity and quality of physics analyses • Modular structure of the NANOAOD format promotes parallel and continuous development • Time between data recording and publication shortened • No need for custom Ntuple production by individual analysis groups • Typical thresholds used in physics analyses applied to high level physics objects (𝜇, 𝑒/𝛾, 𝜏ℎ, jets) • Storing the output of 𝑒/𝛾, 𝜏ℎand 𝑏-jet identification algorithms, rather than the input variables to these algorithms • Not storing redundant information that can be later derived from the event content (such as systematic variations of jet energy scale) • Not storing 32-bit floating point numbers at full precision because experimental measurements are not performed at this high precision • File size further reduced with LZMA compression algorithm • Production fast at 10 events / second / core • Reading faster than kHz with decompression • Required storage in data tiers projected to reduce by a factor of two (more than 2 exabytes) if 50% of the analyses switch from MINIAOD to NANOAOD during HL-LHC [1] • No tracks or individual particle • No details about detector configuration candidates, store jets instead • No object cleaning and skimming applied to maintain flexibility on an analysis level 0 20 40 60 80 100 120 09/2017 03/2018 09/2018 03/2019 09/2019 Number of forks Centrally maintained companion tool available to perform analysis-specific steps after NANOAOD production: • Computation of systematic variations of physics observables, data to simulation c


TERAHERTZ COMMUNICATION: A STORY OF BANDWIDTH AND SILICON PHOTONIC INTEGRATION PHOTONICS RESEARCH GROUP & IDLAB, DEPARTMENT OF INFORMATION TECHNOLOGY (INTEC) Dennis Maes, Emilien Peytavit, Bart Kuyken, Sam Lemey Contact dennis.maes@ugent.be linkedin.com/in/dennis-maes/ Wireless networks are hungry for bandwidth The need for higher wireless data rates makes the radio spectrum congested. In addition to spatial multiplexing and spectral optimization, higher carrier frequencies with more available bandwidth can be used to accommodate this need. The mmWave and terahertz (THz) spectrum (100 GHz and beyond) offer this much needed bandwidth. (IEEE 802.15.3d- 2017) >50 Gbit/s - 300 GHz 69 GHz (1), 13 GHz(5) 5G NR (3GPP rel.15) sub-6 GHz mmWave THz 4G) (LTE) 300 Mbit/s - 800, 1800, 2600 MHz BE 20 MHz (10s) WiFi 5 (IEEE 802.11ac) 6.9 Gbit/s - 5 GHZ 160 MHz (2)EU WiFi 4 (IEEE 802.11n) 900 Mbit/s - 2.4, 5 GHz 40 MHz (2+12)EU WiGig (IEEE 802.11ad) 7 Gbit/s - 60 GHz 2 160 MHz (6) 4G+ (LTE-A) 1 Gbit/s - 800, 1800, 2600 MHz BE 100 (5x20) MHz (10s) WiFi 6E (IEEE 802.11ax) 9.6 Gbit/s - 2.4, 5, 6 GHz 160 MHz (0,3,3)EU Max. data rate - Carrier frequency Max. channel bandwidth (supported # of channels) > 10 Gbit/s - 0.7, 3.8, 26, 36 GHz BE 100 MHz (<10), 400 MHz (<10) Phased Antenna Array (PAA) Array of integrated opto-electronic antenna elements Low-loss optical Si3N4 waveguides Optical beam forming network (OBFN) On-chip modulatorIntegrated amplifiers Integrated laser sources Ultra fast photodetector Laser 2 Laser 1 Modulator νopt λ1 > λ2 Optical waveguides Electrical waveguide fRF fTHz fTHz THz signals can be efficiently generated by on-chip photomixing Beating two optical signals on a photodetector (photomixing) will result in a radio signal at their offset frequency. The only limitation is the conversion efficiency of the photodetector, a very high bandwidth photodiode is thus needed. Beamforming improves signal strength but requires a low-loss feed network A phased antenna array (PAA) is used to generate narrow, high-gain beams. Beamforming not only improves signal strength to overcome higher path loss at THz frequencies, but it also allows more spectral reuse. # of antenna elements Gain [dB] 1 2 5 10 20 50 100 200 500 20 17 13 10 7 3 0 -3 -7 -10 -13 -17 Optical power splitterPRF∽Popt2Feed network (CMOS) Feed network (III/V) Feed network (SiPh) Optical (SiPh full-passive)Distributed amplifier Optical (SiPh + III/V optical amplifiers) Electrical (CMOS)Power splitter Electrical (III/V)More antennas+10 dB/dec -10dB/dec -20dB/dec Array gain A Silicon Photonic (SiPh) beamforming chip leverages the low-loss waveguides for efficient RF signal routing and allows distributed optical amplification to overcome power splitting losses. A high-speed photodiode on a low-loss SiPh platform enables data rates beyond 100 Gbit/s at 280 GHz 16-QAM at 35 GBaud (140 Gbit/s) 11% EVM 32-QAM at 25 GBaud (125 Gbit/s) 7% EVM By means of micro transfer printing, we have heterogeneously integrated a Uni- Traveling-Carrier photodiode (UTC-PD) on a Silicon Nitride (SiN) waveguide. This ultra-fast photodetector has a bandwidth of 155 GHz. We demonstrated a back-to-back THz link at 280 GHz achieving data rates beyond 100 Gbit/s. Electrical contact pads to extract THz signal SiN optical grating coupler SiN low-loss waveguide Waveguide-coupled photodiode 100 µm 10 µm PD coupon just after transfer-printing Conclusion Immense bandwidths are available in the THz spectrum Photonic chips offer compact solutions for beamforming Heterogenous integration of UTC-PDs on SiN waveguides results in ultra-fast detectors needed for photomixing These detectors enable super-fast THz links at 280 GHz with a data rate of more than 100 Gbit/s More information? D. Maes et al., “High-Speed Photodiodes on Silicon Nitride with a Bandwidth beyond 100 GHz“, 2022 Conference on Lasers and Electro-Optics (CLEO), 2022


Qualität von Montessori-Kindergärten und traditionellen Kindergärten und ihre Effekte auf die kindliche Entwicklung Antonia Baumeister, Heiner Rindermann, Technische Universität Chemnitz Kerstin Steinhauser, Karl-Franzens-Universität Graz Stichprobe Raum Graz: 8 Kindergärten; 3 Kindergruppen; 1 Kinderhaus Alter der Kinder: 4;2 – 7;3 Jahre (M = 5;10 J.; SD = 0;6 J.) Montessori-Kindergartenpädagogik „Hilf mir, es selbst zu tun“ nach Maria Montessori (1870 - 1952) Besuchsdauer: mind. 6 Monate – 4 Jahre (M = 2;5 Jahre; SD = 8 Monate) Aufenthaltsdauer pro Woche: mind 15 Std – max 46 Std ( ) Grundsatz: Förderung der Entwicklung, Selbständigkeit & Unabhängigkeit des Kindes, z.B.: Sinnesmaterialien Sprachförderung Mathematische Materialien Übungen des tägl. Lebens http://www.einsatzzylinder.de/ Ausrichtung Betreuungsstätte Anzahl Kinder weiblich männlich traditionell 7 52 27 25 Montessori 5 45 29 16 N 12 97 56 41 Aufenthaltsdauer pro Woche: mind. 15 Std. max. 46 Std. (M = 30 Std. 37 Min.; SD = 7 Std. 49 Min.) Ergebnisse Höhere Qualität von Montessori-Kindergärten (β = .68, p < .01; d = 1.36) Höhere Qualität des Kindergartens sagt höhere kognitive Fähigkeiten (β = .36, p < .01) und bessere hli h E t i kl h (β 37 01) Untersuchungsfragen Weisen Montessori-Kindergärten eine höhere Qualität auf als traditionelle Kindergärten? Hat die Qualität des Kindergartens einen Einfluss auf die kognitiven und sprachlichen Fähigkeiten sowie die sprachfreie Intelligenz? h sprachliche Entwicklung vorher (β = .37, p < .01); kein Zusammenhang mit sprachfreier Intelligenz (β = .20, p > .10) Höhere Bildung der Eltern sagt höhere kognitive Fähigkeiten (β = .49, p < .01), bessere sprachliche Entwicklung (β = .42, p < .01) sowie höhere sprachfreie Intelligenz (β = .36, p < .01) vorher - 3 - Hat die Bildung der Eltern einen Einfluss auf die kognitiven und sprachlichen Fähigkeiten sowie die sprachfreie Intelligenz? - 1 - Methode Variable Funktion Messung 3 Diskussion 1) Höhere Qualität von Montessori-Kindergärten durch: - mathematische, motorische & sprachliche Förderung - höhere Qualifikation der Kindergartenpädagoginnen (z.B. Zusatzausbildungen in: Mototherapie, Malspieltherapie, Interkultureller Pädagogik, Waldpädagogik, Variable Funktion Messung Inhaltliche Ausrichtung (traditionell vs. Montessori) Prädiktor Befragung Schul- & Berufsausbildung der Eltern Prädiktor Befragung Kindergartenqualität (Rahmenbedingungen 1.) Kriterium 2.) Prädiktor Kindergarten-Skala (KES-R; Tietze, g g , p g g , Suchtprävention im Kindesalter) - bessere Rahmenbedingungen (z. B. Größe des Innenraumes) - pädagogische Arbeit & Elternarbeit eventuell zielgerichteter als in traditionellen Kindergärten 2) Bildung der Eltern weiterhin sehr wichtig für Entwicklung der Kinder. (Rahmenbedingungen, Ausbildung, Elternarbeit, pädagogische Arbeit, Förderung sprachlicher / motorischer Entwicklung & mathematischer Kompetenzen) 2.) Prädiktor ( ; , Schuster, Grenner & Roßbach, 2007) (Skalenauswahl) Sprachliche Entwicklung Kriterium Wiener Entwicklungstest (WET; Kastner-Koller & Deimann 2002) Literatur Kastner-Koller, U. & Deimann, P. (2002). Wiener Entwicklungstest: Ein Verfahren zur Erfassung des allgemeinen Entwicklungsstandes bei Kindern von 3 bis 6 Jahren. 2. Aufl. Göttingen: Hogrefe. Schmidtke, A., Schaller, S., & Becker, P. (1980). CPM Raven-Matrizen-Test. Weinheim: Beltz. Tietze, W., Schuster, K.-M., Grenner, K. & Roßbach, H.-G. (2007). Kindergarten-Skala (KES-R) Feststellung und Unterstützung pädagogischer Gesamtwert kognitiver Fähigkeiten Deimann, 2002) (Skalenauswahl) Sprachfreie Intelligenz Kriterium Coloured Progressive Matrices (CPM; Raven & Raven Jr., 1976; Schmidtke, Schaller & 1 < Fakultät/Bereich/Professur > Kontakt: antonia.baumeister@psychologie.tu-chemnitz.de 47. Kongress der Deutschen Gesellschaft für Psychologie (DGPs), Bremen, 26.-30. September 2010 Kindergarten-Skala (KES-R). Feststellung und Unterstützung pädagogischer Qualität in Kindergärten. 3. Auflage. Berlin


Introduction There are over 7000 rare diseases and most of these do not have a treatment or a cure. The general public is barely aware that they exist unless a member of their family or a friend has the disease. We are already seeing pharmaceutical companies eyeing rare diseases as a potential money machine with some treatments costing >$300,000 per year/ per patient. We are increasingly seeing a shift to more companies, institutes and researchers openly sharing data on the internet. Alongside this there are increasing efforts by researchers to publish in open access journals and release data into open or free databases. Researchers working on these diseases are generally spread throughout the globe and in developing countries. Many of the rare diseases attract little funding and therefore less research, so how can we draw attention to them? How can we possibly connect all of this data and impact research? We believe scientific mobile apps [1] may have a role to play in collaboration and aggregating the available data for different diseases. This lead us to develop a free app called Open Drug Discovery Teams (ODDT) for the sharing of scientific data initially focused on neglected and rare disease drug discovery. These tools could also be used as part of a drug repurposing [2, 3] or other strategy for rare or orphan disease research [4]. We have created a user interface via the ODDT app, for iOS-based devices (iPhone, iPod and iPad) that is "Flipboard or magazine-like". The user initially selects from a list of topics, and from there can flip through recently posted content. The app was launched April 12 2012 and is free for anyone to use, and provides content-consumption features as its primary purpose. We are capturing content on a server and make use of Twitter as the primary source (as a proof of concept), which is regularly polled and assimilated into the data collection. The service provides an API for accessing ODDT topics and content. As the project evolves, the server will be gradually augmented to recognize particular data sources and information streams, and provide value added functionality. Currently it is able to recognize chemical data such as molecular structures, reactions and datasheets. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Methods Using the hashtags #huntingtons, #sanfilipposyndrome and #hhf4gan we capture Twitter feeds for Huntingtons Disease, Sanfilippo syndrome and Giant Axonal Neuropathy, respectively. We can also inject chemistry related and other content into the App. Results Discussion In order to expand ODDT further we have launched an IndieGoGo crowdfunding campaign to fund support of the server for 3 years and increase the number of sources of data integrated (http://www.indiegogo.com/projects/122117). In return donors get to select additional diseases which we will add into the App. This will enable the community to decide which rare diseases can be followed in the same way as those currently (Fig 1). References [1] A. J. Williams, S. Ekins, A. M. Clark, J. J. Jack, R. L. Apodaca, Drug Disc Today 2011, 16, 928. [2] S. Ekins, A. J. Williams, M. D. Krasowski, J. S. Freundlich, Drug Disc Today 2011, 16, 298. [3] S. Ekins, A. J. Williams, Pharm Res 2011, 28, 1786. [4] C. L. Beaulieu, S. Ekins, M. Samuels, K. M. Boycott, A. MacKenzie, Orphanet J Rare Dis 2012, In Press. Acknowledgements We kindly acknowledge Jill Wood and Lori Sames for supplying the images for Sanfillipo Syndrome and GAN and we thank our alpha testers. Open Drug Discovery Teams: Sharing Data for Rare Diseases Sean Ekins1 and Alex M. Clark2 1 Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, U.S.A. 2 Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1. We have defined a small number of rare orphan disease


101 102 Eν [GeV] 0.0 0.2 0.4 0.6 0.8 1.0 P (νµ →νµ) cos(θ) = −1.00 (L = 12737 km) No sterile ∆m2 41 = 10 eV2 ∆m2 41 = 0.1 eV2 ∆m2 41 = 10−2 eV2 ∆m2 41 = 10−3 eV2 ∆m2 41 = 10−4 eV2 SK (2015): Phys. Rev. D 91, 052019 IC (2017): Phys. Rev. D 95, 112002 IC (2020): Phys. Rev. D 102, 052009 ANTARES (2019): J. HEP 2019, 113 MINOS+(2020): PRL 125, 071801 ORCA115: J. HEP 2021, 180 (2021) Sensitivity of KM3NeT/ORCA6 to light sterile neutrino mixing parameters Louis Bailly-Salinsa on behalf of the KM3NeT collaboration aUniversité de Caen Normandie, ENSICAEN, CNRS/IN2P3, LPC Caen UMR 6534, F-14000 Caen, France KM3NeT/ORCA Cherenkov neutrino telescope under construction in the Mediterranean sea. Atmospheric ν 1 - 100 GeV. Light Sterile Neutrino Analysis workflow ORCA6 510 days, 433 kton-year Expected number of events: Loss of sensitivity <10-2 eV2 1) Short BaseLine (SBL) anomaly: Δm2 41~ 1 eV2 MSW resonance > 1 TeV: hard to see with ORCA ORCA main feature on P(νµ→ νµ) at 1st osc. maximum (E~25 GeV for vertical upgoing νµ) θ24 & θ34 change amplitude; when both ≠0, change position depending on δ24 value Sensitivity Results 2) Dependency on Δm2 41 Wide range of E & L: can scan wide Δm2 41 range including very-low sterile (Δm2 41 <10-2 eV2) mass, not yet constrained by cosmology. E~25 GeV feature disappears for very low-mass, instead effects at ≤ 10 GeV (L/E > 1000 km/GeV) 101 102 103 104 Eν [GeV] 0.0 0.2 0.4 0.6 0.8 1.0 P (νµ →νµ) cos(θ) = −1.00 (L = 12737 km) No sterile sin2 θ24 = 0.1 sin2 θ34 = 0.1 sin2 θ24 = sin2 θ34 = 0.1 sin2 θ24=0.1 Determine parameters of interest x through Maximum Likelihood Estimator: (Poisson + Gaussian for constrained nuisance parameters η’) Std osc. params: NuFit 5.0 w/o SK + Daya Bay for Δm2 31 HONDA 2014 @ Fréjus, solar minimum GENIE 3 classes: low VS high-purity (LP / HP) tracks VS showers (BDT) Pν y→ν x(Et ,θt) σν x(Et) ϕatm ν y (Et,θt) Ri(Et,θt,ν x, Er ,θr) ni( Er,θr) × × × ⇒ ϕatm ν y (Et,θt) M eff ν x (Et) × νhor/νver skew νe/νe skew νµ/νµ skew νµ/νe skew Spectral index Overall norm. ντ-CC norm. NC norm. Energy scale HE light sim. HP track norm. Shower norm. Muon norm. Δm2 31 θ23 Systematics 1 Detection Unit (DU) = 18 DOMs 115 DUs when complete ⇒ 7 Mton instrumented water 3-D array of photomultiplier tubes: 1 Digital Optical Module (DOM) = 31 PMTs Constrained No prior Compare measured ni,j VS predicted µi,j 2D (Er, cos θr) reconstructed event distributions for each class i l (x , η)=2 ∑ i=1 N classes ∑ j =1 N bins [μi , j (x , η)−ni , j +ni , j ln( ni, j μi , j(x , η))]+∑ k=1 N priors ( ηk' −⟨ηk' ⟩ σk ) 2 ● Asimov data set in null hypothesis, both true mass ordering tested ● Assume Wilk’s theorem: Δl ∼χ2 2 ● No sensitivity to θ14 ⇒ θ14 = δ14 = 0 δ24 free unless stated otherwise HP tracks LP tracks Showers Total 1870 2002 1959 5831 Δm2 41- dependent sensitivities to θ24 and θ34 Sensitivity to Uµ4 and Uτ4 with Δm2 41 = 1 eV2 ORCA6 ≠ ORCA115 (21 Mt-y) ⇒ mostly from statistics ORCA6 not competitive for θ24 Close to SK and IC for θ34 Competitive for Uτ4 δ24 is important Δm2 41=1 eV2 Main goal: Neutrino Mass Ordering Here ORCA6 (6 DUs), operated Jan. 2020- Nov. 2021 References 10−3 10−2 10−1 100 sin2 θ24 10−4 10−3 10−2 10−1 100 101 ∆m2 41 [eV2] KM3NeT/ORCA6 Preliminary, 433 kt-y 99% CL sensitivity ORCA6 NO ORCA6 IO ORCA115 NO ORCA115 IO 99% CL upper limit SK (2015) IC (2020) MINOS+ (2020) 0.0 0.2 0.4 0.6 0.8 1.0 sin2 θ34 10−4 10−3 10−2 10−1 100 101 ∆m2 41 [eV2] KM3NeT/ORCA6 Preliminary, 433 kt-y 99% CL sensitivity ORCA6 NO, θ24 free ORCA6 IO, θ24 free ORCA6 NO, θ24 = 0 ORCA115 NO ORCA115 IO 99% CL upper limit IC (2017) SK (2015) 0.00 0.05 0.10 0.15 0.20 0.25 0.30 |Uµ4|2 = sin2 θ24 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 |Uτ4|2 = cos2 θ24 sin2 θ34 KM3NeT/ORCA6 Preliminary, 433 kt-y 99% CL sensitivity ORCA6 NO, δ24 free ORCA6 IO, δ24 free ORCA6 NO, δ24 = 0 ORCA115 NO ORCA115 IO 99% CL upper limit ANTARES (2019) IC (2017) SK (2015)


Kyung-Ae Park1,2 and Eun-Young Lee1 1 Department of Earth Science Education, Seoul National University, Seoul, Korea 2 Research Institute of Oceanography, Seoul National University, Seoul, Korea Abstract Study Area and Data Conclusion Comparison with Numerical Model Simulation Spatial Distribution of Extreme SSTs Extreme value analysis (EVA) has been extensively used to understand and predict long-term return extreme values. This study provides the first approach to EVA using satellite-observed sea surface temperature (SST) data over the past decades. Representative EVA methods were compared to select an appropriate method to derive SST extremes of the East/Japan Sea (EJS). As a result, the peaks-over- threshold (POT) method showed better performance than the other methods. The Optimum Interpolation Sea Surface Temperature (OISST) database was used to calculate the 100- year-return SST values in the EJS. The calculated SST extremes were 1.60–3.44 °C higher than the average value of the upper 5th-percentile satellite-observed SSTs over the past decades (1982–2018). The monthly distribution of the SST extremes was similar to the known seasonal variation of SSTs in the EJS, but enhanced extreme SSTs exceeding 2 °C appeared in early summer and late autumn. The calculated 100-year-return SSTs were compared with the simulation results of the Coupled Model Intercomparison Project 5 (CMIP5) climate model. As a result, the extreme SSTs were slightly smaller than the maximum SSTs of the model data with a negative bias of –0.36 °C. This study suggests that the POT method can improve our understanding of future oceanic warming based on statistical approaches using SSTs observed by satellites over the past decades. ◀Figure. (A) Shape parameter (k) and (B) modified scale parameter (σ*) with 95% confidence intervals for every value of the threshold (μ). The red dot indicates the 98th percentile of SSTs. Thresholds for POT • In this study, the 98th-percentile value was selected as the threshold by checking the change and significant interval of the shape (k) and scale (σ) parameters according to the threshold setting. • We selected the thresholds marked by red dots. These values have also been used in previous studies (Parey et al., 2007; Laurent and Parey, 2007; Cheng et al., 2014), which compute the extreme values of surface air temperatures. A similar approach was applied to calculate the extreme values of precipitation (Cheng and AghaKouchak, 2014). Monthly Distribution of Extreme SSTs ▲Figure. Examples for the computed result of the monthly extreme SST. (A-L) Distribution of the probability of monthly SST values, where the red line represents the fitted distribution for the computation of the extreme SST by the POT method and (M) Q-Q plot for all months. ▲ Figure. Spatial distribution of (A) monthly 100-year return SSTs using the POT method and (B) temperature differences between the computed SST extremes and mean values of satellite-observed SSTs (1982 – 2018) within the upper 5th percentile. ▲Figure. (A) Bathymetry of the Northwestern Pacific, where the red box shows the study area, and (B) spatial distribution of sea surface temperatures (SSTs) averaged for the period from 1982 to 2018 in the study area. Satellite-observed Sea Surface Temperature Data • The daily Optimal Interpolation SST (OISST) data • provided by the National Oceanic and Atmospheric Administration (NOAA)/Oceanic and Atmospheric Research (OAR)/Earth System Research Laboratory (ESRL) Physical Sciences Lab (PSL) • based on NOAA/AVHRR data the longest period from 1981 onwards • The data used in this study covered the period from 1982 to 2018. Simulated Sea Surface Temperature Data of Climate Model • The Coupled Model Intercomparison Project 5 (CMIP5) monthly data on single-level • extracted from global ocean data from Dec. 2005 to Nov. 2099 • obtained from the Representative Concentration Pathway (RCP) 4.5 experiment using the HadGEM2- ES model (UK Met Office, UK) St