GlyGen.org is a web portal that integrates glycomics data from multiple sources to enable exploration of glycans and glycosylation. It features tools for navigating and searching glycan data as well as detail pages on glycans and glycosylation sites. A recent update introduced enhanced search capabilities, allowing users to find glycosylation sites impacted by changes to the Asn residue. Future enhancements will incorporate additional data types and annotations as well as new search and display features to further facilitate glycoscience research.
GlyGen.org is a web portal and data store that integrates glycoscience data from various sources to enable mining and discovery of new knowledge about glycans and glycoproteins. It provides users with tools to explore glycan and protein data, map identifiers across databases, search for customized information, and view high resolution details about glycosylation sites. The goal of GlyGen is to integrate diverse glycoscience data and reveal new insights by connecting concepts across disciplinary boundaries.
GlyGen slides include the following topics:
What is GlyGen, GlyGen goals, and effort, Data Collection and Integration, GlyGen portal Homepage, Explore searches, Quick Search, Detail pages, GlyGen Data page, and Acknowledgements.
Quick Search allows users to explore glycans, proteins, glycoproteins and diseases through a search feature. Data is collected from multiple international resources and made accessible on data pages, APIs and SPARQL endpoints. GlyGen.org is funded by NIH and provides open data and source code under a permissive license. It aims to be a comprehensive resource for computational and informatics needs in glycoscience.
GlyGen.org is a website that provides computational and informatics resources for glycoscience. It contains a data store, web services API, and SPARQL endpoint for accessing glycoscience data at https://data.glygen.org, https://api.glygen.org, and https://sparql.glygen.org respectively. The site also provides contact information for Raja Mazumder and Michael Tiemeyer.
Compegence: Dr Abhinanda Sarkar - Biomarker Discovery and Big Data_Statistica...COMPEGENCE
Compegenc: Dr Abhinanda Sarkar - Biomarker Discovery and Big Data. Presented in International Indian Statistical Association Conference, Chennai (Jan 2013)
Elsevier is a global information analytics business that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. Elsevier provides digital solutions and tools in the areas of strategic research management, R&D performance, clinical decision support, and professional education; including ScienceDirect, Scopus, Scival, ClinicalKey and Sherpath. Elsevier publishes over 2,500 digitized journals, including The Lancet and Cell, more than 35,000 e-book titles and many iconic reference works, including Gray’s Anatomy. Elsevier is part of RELX Group, a global provider of information and analytics for professionals and business customers across industries. www.elsevier.com
About Reaxys
Reaxys retrieves literature, compound properties and chemical reaction data faster than any other solution and, together with RMC, offers pharma companies a ‘one stop shop’ chemistry ecosystem thanks to its integration capabilities, innovative APIs, and bioactivity data. The Reaxys platform contains over 240 years of unparalleled chemistry content, including: 119 million organic, inorganic and organometallic compounds, 46 million chemical reactions, 500 million published experimental facts, 16,000 chemistry related periodicals, and six indexing sources for a cross-disciplinary view of chemistry. RMC offers access to data from a vast repository of peer-reviewed journal articles and patents and is interoperable with Reaxys. https://www.elsevier.com/en-gb/solutions/reaxys
GlyGen.org is a web portal and data store that integrates glycoscience data from various sources to enable mining and discovery of new knowledge about glycans and glycoproteins. It provides users with tools to explore glycan and protein data, map identifiers across databases, search for customized information, and view high resolution details about glycosylation sites. The goal of GlyGen is to integrate diverse glycoscience data and reveal new insights by connecting concepts across disciplinary boundaries.
GlyGen slides include the following topics:
What is GlyGen, GlyGen goals, and effort, Data Collection and Integration, GlyGen portal Homepage, Explore searches, Quick Search, Detail pages, GlyGen Data page, and Acknowledgements.
Quick Search allows users to explore glycans, proteins, glycoproteins and diseases through a search feature. Data is collected from multiple international resources and made accessible on data pages, APIs and SPARQL endpoints. GlyGen.org is funded by NIH and provides open data and source code under a permissive license. It aims to be a comprehensive resource for computational and informatics needs in glycoscience.
GlyGen.org is a website that provides computational and informatics resources for glycoscience. It contains a data store, web services API, and SPARQL endpoint for accessing glycoscience data at https://data.glygen.org, https://api.glygen.org, and https://sparql.glygen.org respectively. The site also provides contact information for Raja Mazumder and Michael Tiemeyer.
Compegence: Dr Abhinanda Sarkar - Biomarker Discovery and Big Data_Statistica...COMPEGENCE
Compegenc: Dr Abhinanda Sarkar - Biomarker Discovery and Big Data. Presented in International Indian Statistical Association Conference, Chennai (Jan 2013)
Elsevier is a global information analytics business that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. Elsevier provides digital solutions and tools in the areas of strategic research management, R&D performance, clinical decision support, and professional education; including ScienceDirect, Scopus, Scival, ClinicalKey and Sherpath. Elsevier publishes over 2,500 digitized journals, including The Lancet and Cell, more than 35,000 e-book titles and many iconic reference works, including Gray’s Anatomy. Elsevier is part of RELX Group, a global provider of information and analytics for professionals and business customers across industries. www.elsevier.com
About Reaxys
Reaxys retrieves literature, compound properties and chemical reaction data faster than any other solution and, together with RMC, offers pharma companies a ‘one stop shop’ chemistry ecosystem thanks to its integration capabilities, innovative APIs, and bioactivity data. The Reaxys platform contains over 240 years of unparalleled chemistry content, including: 119 million organic, inorganic and organometallic compounds, 46 million chemical reactions, 500 million published experimental facts, 16,000 chemistry related periodicals, and six indexing sources for a cross-disciplinary view of chemistry. RMC offers access to data from a vast repository of peer-reviewed journal articles and patents and is interoperable with Reaxys. https://www.elsevier.com/en-gb/solutions/reaxys
Make glycomics and glyco-related information accessible
• Users inside and outside the glycomics community
• Integrate datasets from different resources allowing to answer questions that can not be answered by the individual resources
This document summarizes a report that outlines a roadmap to advance the field of glycoscience over the next 15 years. Glycoscience explores the structures and functions of sugars (glycans) which play central roles in biology but are less studied than other biomolecules. The report identifies needs for new tools and capabilities to synthesize, analyze, model, and study glycans. It proposes goals in areas like synthesizing glycans, developing analytical techniques, creating molecular models, and expanding databases and education to transform glycoscience and enable contributions to fields like healthcare, energy, and materials science.
GlySpace is a collaborative project between several universities to create GlyGen, a glycoinformatics resource. GlyGen will integrate various glycan, protein, and glycoprotein data from public databases. It will have a SPARQL endpoint and web interface to allow searching and exploring relationships between glycans, proteins, enzymes and other glycoinformatics data. The goal is to facilitate research in glycobiology and disease.
Combinatorial chemistry has produced a huge amount of chemical libraries and data banks which include prospective drugs. Despite all of this progress, the fundamental problem still remains: how do we take advantage of this data to identify the prospective nature of a compound as a vital drug? Traditional methodologies fail to provide a solution to this.
Grakn, however, provides the framework which can make drug discovery much more efficient, effective and approachable. This radical advancement in technology can model biological knowledge complexity as it is found at its core. With concepts such as hyper relationships, type hierarchies, automated reasoning and analytics we can finally model, represent, and query biological knowledge at an unprecedented scale.
Check out the following links to learn more:
Grakn: http://grakn.ai/
Drug Discovery Knowledge Graph Blog Post: https://blog.grakn.ai/drug-discovery-knowledge-graphs-46db4212777c
BioGrakn: https://github.com/graknlabs/biograkn
Mining Phenotypes: How to set up a reverse genetics experiment with an Arabid...adcobb
In this lesson, students will mine data from Araport.org to design and propose a reverse genetics experiment using a known Arabidopsis mutant. They will select a treatment to reveal phenotypic dfifferences between wild type and mutant Arabidopsis. Student handout and teacher resources are available at www.Araport.org, teacher resources. Suitable for grades 9-12 or first year undergraduate students.
Introduction to Gene Mining Part A: BLASTn-off!adcobb
In this lesson, students will learn to use bioinformatics portals and tools to mine plant versions of human genes. Student handout and teacher resource materials are available at www.Araport.org, Teaching Resources (Community tab). Suitable for grades 9-12 or first year undergraduate students.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDSSérgio Sacani
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Make glycomics and glyco-related information accessible
• Users inside and outside the glycomics community
• Integrate datasets from different resources allowing to answer questions that can not be answered by the individual resources
This document summarizes a report that outlines a roadmap to advance the field of glycoscience over the next 15 years. Glycoscience explores the structures and functions of sugars (glycans) which play central roles in biology but are less studied than other biomolecules. The report identifies needs for new tools and capabilities to synthesize, analyze, model, and study glycans. It proposes goals in areas like synthesizing glycans, developing analytical techniques, creating molecular models, and expanding databases and education to transform glycoscience and enable contributions to fields like healthcare, energy, and materials science.
GlySpace is a collaborative project between several universities to create GlyGen, a glycoinformatics resource. GlyGen will integrate various glycan, protein, and glycoprotein data from public databases. It will have a SPARQL endpoint and web interface to allow searching and exploring relationships between glycans, proteins, enzymes and other glycoinformatics data. The goal is to facilitate research in glycobiology and disease.
Combinatorial chemistry has produced a huge amount of chemical libraries and data banks which include prospective drugs. Despite all of this progress, the fundamental problem still remains: how do we take advantage of this data to identify the prospective nature of a compound as a vital drug? Traditional methodologies fail to provide a solution to this.
Grakn, however, provides the framework which can make drug discovery much more efficient, effective and approachable. This radical advancement in technology can model biological knowledge complexity as it is found at its core. With concepts such as hyper relationships, type hierarchies, automated reasoning and analytics we can finally model, represent, and query biological knowledge at an unprecedented scale.
Check out the following links to learn more:
Grakn: http://grakn.ai/
Drug Discovery Knowledge Graph Blog Post: https://blog.grakn.ai/drug-discovery-knowledge-graphs-46db4212777c
BioGrakn: https://github.com/graknlabs/biograkn
Mining Phenotypes: How to set up a reverse genetics experiment with an Arabid...adcobb
In this lesson, students will mine data from Araport.org to design and propose a reverse genetics experiment using a known Arabidopsis mutant. They will select a treatment to reveal phenotypic dfifferences between wild type and mutant Arabidopsis. Student handout and teacher resources are available at www.Araport.org, teacher resources. Suitable for grades 9-12 or first year undergraduate students.
Introduction to Gene Mining Part A: BLASTn-off!adcobb
In this lesson, students will learn to use bioinformatics portals and tools to mine plant versions of human genes. Student handout and teacher resource materials are available at www.Araport.org, Teaching Resources (Community tab). Suitable for grades 9-12 or first year undergraduate students.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDSSérgio Sacani
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
Farming systems analysis: what have we learnt?.pptx
ASBMB 2021 GlyGen Supersearch
1. https://glygen.org
https://data.glygen.org
https://api.glygen.org
Web portal:
Data store:
WS API:
https://twitter.com/gly_gen
GlyGen.org
Computational and Informatics
Resources for Glycoscience
NIH Common Fund Glycoscience Program
1U01GM125267-01 (York & Mazumder)
PI: Raja Mazumder
The George Washington University
PI: Michael Tiemeyer
CCRC, University of Georgia
CONTACT
Will York will@ccrc.uga.edu
Raja Mazumder mazumder@gwu.edu
Michael Tiemeyer mtiemeyer@ccrc.uga.edu
CONTACT
Will York will@ccrc.uga.edu
Raja Mazumder mazumder@gwu.edu
Michael Tiemeyer mtiemeyer@ccrc.uga.edu
Enhanced interface for retrieving glycan and
glycosylation data at GlyGen
Michael Tiemeyer, Sujeet Kulkarni, Robel Kashay, Rene Ranzinger, and
Raja Mazumder
2. GlyGen.org
Representing and Integrating
Glycan Diversity
• Glycoconjugates, whether
secreted from cells or found at
the surface or inside of cells,
regulate essential functions in
health and disease
• Almost all currently marketed
biologic pharmaceuticals are
glycoproteins (antibodies,
growth factors, enzyme
replacements)
• Glycan structural features
influence glycoconjugate
functions
• Where is this data captured?
Can it be harvested in a way
that is informative and that may
reveal new knowledge?
4. GlyGen.org
Navigating GlyGen Features
Introduction to GlyGen and spotlight a new feature of the most
recent GlyGen release (v1.8, Apr. 2021)
Introduction to Portal
• Navigating and orienting, Try Me query
GlyGen Super Search
• Arriving at GlyGen with only a motif in mind
• Acquiring novel answers to important questions
13. GlyGen.org
Navigating GlyGen Features
Introduction to GlyGen and spotlight a new feature of the most
recent GlyGen release (v1.8, Apr. 2021)
Introduction to Portal
• Navigating and orienting, Try Me query
GlyGen Super Search
• Arriving at GlyGen with only a motif in mind
• Acquiring novel answers to important questions
15. GlyGen.org
Navigating GlyGen Features
Undifferentiated stem cells
express a much higher
abundance of proteins that
are modified with Lewis X
glycans than differentiating
neurons
What is Lewis X?
What proteins are modified
with Lewis X?
Is Lewis X or are the
proteins that carry it
associated with any
disease?
28. GlyGen.org
Navigating GlyGen Features
Introduction to GlyGen and spotlight a new feature of the most
recent GlyGen release (v1.8, Apr. 2021)
Introduction to Portal
• Navigating and orienting, Try Me query
GlyGen Super Search
• Arriving at GlyGen with only a motif in mind
• Acquiring novel answers to important questions
37. GlyGen.org
• New data types: additional species, natural variants, phenotypes,
phosphorylation, glycan binding protein interactions, protein-protein
interactions, protein-GAG interactions, more glycosylation sites
through automated literature mining
• New annotations: PTM functional annotations, more glycosylation
subtypes, glycan names and relations (subsumption), biosynthetic
pathways mapping enzymes to glycan precursor and products
• New features: enhanced filtering and sorting options, highlight
hot-topics in glycoscience on the portal home page, new routes for
accessing data through function/biosynthesis/disease, extensive
help system using mediawiki, intuitive displays of data statistics,
improved mobile friendliness
Future plans, data integration, feature development:
Future Enhancements
Editor's Notes
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
So what data does GlyGen include:- This slide kind of a snapshot of what are data looks like, how we are connecting different resources that allow users to perform queries across different platforms. Our primary resources are UniProt- for protein and GlyTouCan for glycan. For any new data (weather its from a database or author submitted that we wmap it to either of those resources based on the data.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.
To achieve this, our major areas of focus points include: data collection, QC-QA, building a interface that targets community-wide questions, open access data, documentation and playing a vital role in promoting community research.