The document summarizes a study that used RNA sequencing data from over 500 brain samples to construct gene coexpression networks and identify modules of genes associated with Alzheimer's disease traits like cognitive decline and amyloid pathology. They prioritized one module (m109) associated with cognitive decline and validated two genes (INPPL1 and PLXNB1) within that module that reduced amyloid levels in vitro. However, these genes did not fully account for the effects of the entire module, suggesting more work is needed to identify additional driver genes related to cognitive decline in Alzheimer's disease.
Raj Lab Meeting presentation (05/01/19)
by Katia Lopes and Ricardo Vialle
Discussing the paper "The impact of rare variation on gene expression across tissues" - Li et al. Nature (2017)
Functional Genomics Journal Club presentation on the following publication:
Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010–13015. https://doi.org/10.1073/pnas.1323099111
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
Raj Lab Meeting presentation (05/01/19)
by Katia Lopes and Ricardo Vialle
Discussing the paper "The impact of rare variation on gene expression across tissues" - Li et al. Nature (2017)
Functional Genomics Journal Club presentation on the following publication:
Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010–13015. https://doi.org/10.1073/pnas.1323099111
Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past ten years. GWAS continues to be highly relevant as a scientific method. Over 2,000 human GWAS reports now appear in scientific journals. Our free eBook aims to explain the basic steps and concepts to complete a GWAS experiment.
MAGIC :Multiparent advanced generation intercross and QTL discovery Senthil Natesan
MAGIC or multiparent advanced generation inter-crosses is an experimental method that increases the precision with which genetic markers are linked to quantitative trait loci (QTL). This method was first introduced by (Mott et al., 2000) in animals as an extension of the advanced intercrossing (AIC) approach suggested by (Darvasi and Soller , 1995)for fine mapping multiple QTLs for multiple traits. Advanced Intercrossed Lines (AILs) are generated by randomly and sequentially intercrossing a population initially originating from a cross between two inbred lines.
MAGIC involves multiple parents, called founder lines, rather than bi-parental control. AILs increase the recombination events in small chromosomal regions for the purpose of fine mapping. These lines are then cycled through multiple generations of outcrossing. Each generation of random mating reduces the extent of linkage disequilibrium (LD), allowing the QTL to be mapped more accurately.
Detecting clinically actionable somatic structural aberrations from targeted ...Ronak Shah
Structural aberrations including deletions, insertions, inversions, tandem duplications, translocations, and more complex rearrangements constitute a frequent type of alteration in human tumors. Here, we sought to explore the potential to discover such events from targeted DNA sequence data in our CLIA-compliant molecular diagnostics laboratory. To detect somatic structural aberrations in individual tumors, we have developed an analytic framework in Perl & Python to detect these events in data generated by a hybridization capture-based, targeted sequencing clinical assay (MSK-IMPACT), which can reveal structural rearrangements as small as 500bp.
Status and prospects of association mapping in crop plantsJyoti Prakash Sahoo
Polygenic inheritance of agronomic traits - controlled by multiple genes whose expression is affected by many factors. Hence phenotypic selection becomes tedious job.
Family mapping (Limitations- Biparental population, Low resolution, Analysis of only 2 alleles, time consuming).
Population or Association mapping (I) increased mapping resolution, (ii) reduced research time, and (iii) greater allele number (Yu and Buckler, 2006).
The IMPACT of INDEL realignment: Detecting insertions and deletions longer th...Ronak Shah
Cancer is a disease of the genome –most of its forms result from a buildup of genetic alterations that, directly or indirectly, allow the patient’s cells to proliferate without restraint. For decades, identifying and targeting cancer mutations for treatment was impractical due to the limitations of sequencing technology. However, the rise of high-throughput next-generation sequencing (NGS) tools has allowed researchers to rapidly and cheaply sequence large, targeted regions of DNA. MSK-IMPACT(Memorial Sloan Kettering-IntegratedMutation Profiling of Actionable Cancer Targets), a sequencing platform with an associated computational pipeline, takes advantage of improvements in sequencing technology to analyzetumor specimensfor clinically actionable variants in341 cancer-associatedgenes.Criticalto IMPACT’s efficacy is the detection of somatic DNAalterationslike INDELs, which are insertions or deletions of nucleotides. Current sequence aligners have difficulty accuratelymapping reads (short, overlapping DNA sequences) containing morethan a single base change, let alone reads containing INDELs. This flaw necessitates the use of INDEL realigners, whichrearrange reads inregions where INDELs might exist in order to identify them more easily. Currently, the INDEL realignment software associated withMSK-IMPACT’scomputational pipeline, the Genome Analysis Toolkit’s IndelRealigner (GATK), canonly efficiently resolveINDELsshorter than 30 base pairs, which limits theplatform’sreliability forINDELdetection. Thus, wetested and compared the performance of a new INDEL realigner called ABRA (Assembly BasedRe-Aligner) to that of GATK’s IndelRealigner.
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
GWAS of Resistance to Stem and Sheath Diseases of Uruguayan Advanced Rice Bre...CIAT
Speaker: Lic. JUAN ROSAS, (MSc.) Programa de Arroz INIA-Uruguay y estudiante de Doctorado en Ciencias Agrarias de la Universidad de la República de Uruguay
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Comparing Genetic Evolutionary Algorithms on Three Enzymes of HIV-1: Integras...CSCJournals
In this work, we utilized Quantitative Structure-Activity Relationship (QSAR) techniques to develop predictive models for inhibitors of the HIV-1 enzymes Integrase, HIV-Protease, and Reverse Transcriptase. Each predictive model was composed of quantitative drug characteristics that were selected by genetic evolutionary algorithms, such as Genetic Algorithm (GE), Differential Evolutionary Algorithm (DE), Binary Particle Swarm Optimization (BPSO), and Differential Evolution with Binary Particle Swarm Optimization (DE-BPSO). After characteristic selection, each model was tested with machine-learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Multi-Layer Perceptron neural networks (MLP/ANN). We found that a combination of DE-BPSO combined with Multi-Layer Perceptron produced the most accurate predictive models as measured by R2, the statistical measure of proportion of variance in prediction values, and root-mean-square-error (RMSE) of prediction values compared to observed values. As for the models themselves: the best predictors for Integrase inhibitor included mass-weighted centred Broto-Moreau autocorrelation values, Moran autocorrelations, and eigenvalues of Burden matrices weighted by I-states; the best predictors for HIV-Protease inhibitors included the second Zagreb index value, the normalized spectral positive sum from Laplace matrix, and the connectivity-like index of order 0 from edge adjacency mat; and the best predictors for Reverse Transcriptase inhibitors included the number of hydrogen atoms, the molecular path count of order 7, the centred Broto-Moreau autocorrelation of lag 2 weighted by Sanderson electronegativity, the P_VSA-like on ionization potential, and the frequency of C – N bonds at topological distance 3.
DNA Amplification is a Ubiquitous Mechanism of Oncogene Activation in Lung an...Shryli Shreekar
Chromosomal translocation is the best-characterized
genetic mechanism for oncogene activation. However, there
are documented examples of activation by alternate
mechanisms, for example gene dosage increase, though
its prevalence is unclear. Here, we answered the fundamental question of the contribution of DNA amplification
as a molecular mechanism driving oncogenesis. Comparing
104 cancer lines representing diverse tissue origins
identified genes residing in amplification ‘hotspots’ and
discovered an unexpected frequency of genes activated by
this mechanism. The 3431 amplicons identified represent
B10 per hematological and B36 per epithelial cancer
genome. Many recurrently amplified oncogenes were
previously known to be activated only by disease-specific
translocations. The 135 hotspots identified contain 538
unique genes and are enriched for proliferation, apoptosis
and linage-dependency genes, reflecting functions advantageous to tumor growth. Integrating gene dosage with
expression data validated the downstream impact of the
novel amplification events in both cell lines and clinical
samples. For example, multiple downstream components of
the EGFR-family-signaling pathway, including CDK5,
AKT1 and SHC1, are overexpressed as a direct result of
gene amplification in lung cancer. Our findings suggest that
amplification is far more common a mechanism of
oncogene activation than previously believed and that
specific regions of the genome are hotspots of amplification.
MAGIC :Multiparent advanced generation intercross and QTL discovery Senthil Natesan
MAGIC or multiparent advanced generation inter-crosses is an experimental method that increases the precision with which genetic markers are linked to quantitative trait loci (QTL). This method was first introduced by (Mott et al., 2000) in animals as an extension of the advanced intercrossing (AIC) approach suggested by (Darvasi and Soller , 1995)for fine mapping multiple QTLs for multiple traits. Advanced Intercrossed Lines (AILs) are generated by randomly and sequentially intercrossing a population initially originating from a cross between two inbred lines.
MAGIC involves multiple parents, called founder lines, rather than bi-parental control. AILs increase the recombination events in small chromosomal regions for the purpose of fine mapping. These lines are then cycled through multiple generations of outcrossing. Each generation of random mating reduces the extent of linkage disequilibrium (LD), allowing the QTL to be mapped more accurately.
Detecting clinically actionable somatic structural aberrations from targeted ...Ronak Shah
Structural aberrations including deletions, insertions, inversions, tandem duplications, translocations, and more complex rearrangements constitute a frequent type of alteration in human tumors. Here, we sought to explore the potential to discover such events from targeted DNA sequence data in our CLIA-compliant molecular diagnostics laboratory. To detect somatic structural aberrations in individual tumors, we have developed an analytic framework in Perl & Python to detect these events in data generated by a hybridization capture-based, targeted sequencing clinical assay (MSK-IMPACT), which can reveal structural rearrangements as small as 500bp.
Status and prospects of association mapping in crop plantsJyoti Prakash Sahoo
Polygenic inheritance of agronomic traits - controlled by multiple genes whose expression is affected by many factors. Hence phenotypic selection becomes tedious job.
Family mapping (Limitations- Biparental population, Low resolution, Analysis of only 2 alleles, time consuming).
Population or Association mapping (I) increased mapping resolution, (ii) reduced research time, and (iii) greater allele number (Yu and Buckler, 2006).
The IMPACT of INDEL realignment: Detecting insertions and deletions longer th...Ronak Shah
Cancer is a disease of the genome –most of its forms result from a buildup of genetic alterations that, directly or indirectly, allow the patient’s cells to proliferate without restraint. For decades, identifying and targeting cancer mutations for treatment was impractical due to the limitations of sequencing technology. However, the rise of high-throughput next-generation sequencing (NGS) tools has allowed researchers to rapidly and cheaply sequence large, targeted regions of DNA. MSK-IMPACT(Memorial Sloan Kettering-IntegratedMutation Profiling of Actionable Cancer Targets), a sequencing platform with an associated computational pipeline, takes advantage of improvements in sequencing technology to analyzetumor specimensfor clinically actionable variants in341 cancer-associatedgenes.Criticalto IMPACT’s efficacy is the detection of somatic DNAalterationslike INDELs, which are insertions or deletions of nucleotides. Current sequence aligners have difficulty accuratelymapping reads (short, overlapping DNA sequences) containing morethan a single base change, let alone reads containing INDELs. This flaw necessitates the use of INDEL realigners, whichrearrange reads inregions where INDELs might exist in order to identify them more easily. Currently, the INDEL realignment software associated withMSK-IMPACT’scomputational pipeline, the Genome Analysis Toolkit’s IndelRealigner (GATK), canonly efficiently resolveINDELsshorter than 30 base pairs, which limits theplatform’sreliability forINDELdetection. Thus, wetested and compared the performance of a new INDEL realigner called ABRA (Assembly BasedRe-Aligner) to that of GATK’s IndelRealigner.
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
GWAS of Resistance to Stem and Sheath Diseases of Uruguayan Advanced Rice Bre...CIAT
Speaker: Lic. JUAN ROSAS, (MSc.) Programa de Arroz INIA-Uruguay y estudiante de Doctorado en Ciencias Agrarias de la Universidad de la República de Uruguay
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Comparing Genetic Evolutionary Algorithms on Three Enzymes of HIV-1: Integras...CSCJournals
In this work, we utilized Quantitative Structure-Activity Relationship (QSAR) techniques to develop predictive models for inhibitors of the HIV-1 enzymes Integrase, HIV-Protease, and Reverse Transcriptase. Each predictive model was composed of quantitative drug characteristics that were selected by genetic evolutionary algorithms, such as Genetic Algorithm (GE), Differential Evolutionary Algorithm (DE), Binary Particle Swarm Optimization (BPSO), and Differential Evolution with Binary Particle Swarm Optimization (DE-BPSO). After characteristic selection, each model was tested with machine-learning algorithms such as Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Multi-Layer Perceptron neural networks (MLP/ANN). We found that a combination of DE-BPSO combined with Multi-Layer Perceptron produced the most accurate predictive models as measured by R2, the statistical measure of proportion of variance in prediction values, and root-mean-square-error (RMSE) of prediction values compared to observed values. As for the models themselves: the best predictors for Integrase inhibitor included mass-weighted centred Broto-Moreau autocorrelation values, Moran autocorrelations, and eigenvalues of Burden matrices weighted by I-states; the best predictors for HIV-Protease inhibitors included the second Zagreb index value, the normalized spectral positive sum from Laplace matrix, and the connectivity-like index of order 0 from edge adjacency mat; and the best predictors for Reverse Transcriptase inhibitors included the number of hydrogen atoms, the molecular path count of order 7, the centred Broto-Moreau autocorrelation of lag 2 weighted by Sanderson electronegativity, the P_VSA-like on ionization potential, and the frequency of C – N bonds at topological distance 3.
DNA Amplification is a Ubiquitous Mechanism of Oncogene Activation in Lung an...Shryli Shreekar
Chromosomal translocation is the best-characterized
genetic mechanism for oncogene activation. However, there
are documented examples of activation by alternate
mechanisms, for example gene dosage increase, though
its prevalence is unclear. Here, we answered the fundamental question of the contribution of DNA amplification
as a molecular mechanism driving oncogenesis. Comparing
104 cancer lines representing diverse tissue origins
identified genes residing in amplification ‘hotspots’ and
discovered an unexpected frequency of genes activated by
this mechanism. The 3431 amplicons identified represent
B10 per hematological and B36 per epithelial cancer
genome. Many recurrently amplified oncogenes were
previously known to be activated only by disease-specific
translocations. The 135 hotspots identified contain 538
unique genes and are enriched for proliferation, apoptosis
and linage-dependency genes, reflecting functions advantageous to tumor growth. Integrating gene dosage with
expression data validated the downstream impact of the
novel amplification events in both cell lines and clinical
samples. For example, multiple downstream components of
the EGFR-family-signaling pathway, including CDK5,
AKT1 and SHC1, are overexpressed as a direct result of
gene amplification in lung cancer. Our findings suggest that
amplification is far more common a mechanism of
oncogene activation than previously believed and that
specific regions of the genome are hotspots of amplification.
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
Assessing the clinical utility of cancer genomic and proteomic data across tu...Gul Muneer
Molecular profiling of tumors promises to advance the clinical
management of cancer, but the benefits of integrating
molecular data with traditional clinical variables have not been
systematically studied. Here we retrospectively predict patient
survival using diverse molecular data (somatic copy-number
alteration, DNA methylation and mRNA, microRNA and protein
expression) from 953 samples of four cancer types from The
Cancer Genome Atlas project. We find that incorporating
molecular data with clinical variables yields statistically
significantly improved predictions (FDR < 0.05) for three
cancers but those quantitative gains were limited (2.2–23.9%).
Additional analyses revealed little predictive power across
tumor types except for one case. In clinically relevant genes,
we identified 10,281 somatic alterations across 12 cancer types
in 2,928 of 3,277 patients (89.4%), many of which would
not be revealed in single-tumor analyses. Our study provides
a starting point and resources, including an open-access
model evaluation platform, for building reliable prognostic and
therapeutic strategies that incorporate molecular data
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.
Next Generation Sequencing and its Applications in Medical Research - Frances...Sri Ambati
The so-called “next-generation” sequencing (NGS) technologies allows us, in a short time and in parallel, to sequence massive amounts of DNA, overcoming the limitations of the original Sanger sequencing methods used to sequence the first human genome. NGS technologies have had an enormous impact on biomedical research within a short time frame. This talk will give an overview of these applications with specific examples from Mendelian genomics and cancer research. #h2ony
As increasing numbers of people choose to have their genomes sequenced and made available for research, more genomic data is available for analysis by machine learning approaches. Single Nucleotide Polymorphisms (SNPs) are known to be a major factor influencing many physical traits, diseases and other phenotypes. Using publicly available data and tools we predict phenotype from genotype using SNP data (1 to 2 million SNPs). We utilize data analysis and machine learning approaches only, no domain knowledge, so that our automated approach may be generally used to predict different phenotypes from genotype. In the first application of our method we predicted eye color with 87% accuracy.
Altered proliferation and networks in neural cells derived from idiopathic au...Masuma Sani
Autism Spectrum Disorders; heterogeneous nature of genetic and brain pathology in ASD– which makes it difficult to produce relevant animal and cell models
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Med...Ryan Squire
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Medicine as presented at the Ohio State University Medical Center Personalized Health Care National Conference.
Leroy Hood, MD, PhD, is the president and founder of the Institute of Systems Biology. Dr. Hood is a member of the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences, the Institute of Medicine and the National Academy of Engineering. His professional career began at Caltech where he and his colleagues pioneered four instruments — the DNA gene sequencer and synthesizer and the protein synthesizer and sequencer — which comprise the technological foundation for contemporary molecular biology. In particular, the DNA sequencer played a crucial role in contributing to the successful mapping of the human genome during the 1990s.
http://www.systemsbiology.org/Scientists_and_Research
A Network View on Parkinson’s Disease Elsevier webinar 15 jan 2015Ann-Marie Roche
Professor D.Bonchev shows an in-depth look at how a systems biology approach was used to identify some of the critical aspects Parkinson's disease: molecular players, drug targets, and underlying biological processes.
PICS: Pathway Informed Classification System for cancer analysis using gene e...David Craft
We introduce PICS (Pathway Informed Classification System) for classifying cancers based on tumor sample gene expression levels. The method clearly separates a pan-cancer dataset into their tissue of origin and is also able to sub-classify individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into sub-types. Variants of the method allow it to be used on datasets that do and do not contain non-cancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate, for pancreatic cancer signaling and metabolic pathways, and for melanoma immune system pathways are the most useful. This work suggests the utility of pathway level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis of UC was not yet been clearly elucidated, and few researches on immune-related biomarkers published.
CXCL1, CCL20, STAT1 was Identified and Validated as a Key Biomarker Related t...semualkaira
Growing evidence suggests a correlation between ulcerative colitis (UC) and immune markers. Pathogenesis
of UC was not yet been clearly elucidated, and few researches on
immune-related biomarkers published.
Similar to FunGen JC Presentation - Mostafavi et al. (2019) (20)
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
2. Affiliation
1Department of Statistics, Department of Medical Genetics, University of British
Columbia, Vancouver, BC, Canada.
2Canadian Institute for Advanced Research, Toronto, ON, Canada.
3Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA.
4Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA.
5Broad Institute, Cambridge, MA, USA.
6Center for Translational & Computational Neuroimmunology, Department of Neurology,
Columbia University Medical Center, New York, NY, USA.
7University of Sydney, Sydney, NSW, Australia.
8Harvard Medical School, Boston, MA, USA.
9Harvard T.H. Chan School of Public Health, Boston, MA, USA.
2
3. • The incidence of late-onset AD is expected to triple in the
US by 2050, yet no therapies are available to treat or
prevent the disease.
• Recent genome-wide association studies (GWAS) have
identified new potential therapeutic targets involved in
endocytosis, metabolism and inflammation.
• However, possible reasons for the continued failure of AD
trials include the biological complexity of the disease and
its phenotypic heterogeneity.
3
Introduction
4. • The authors hypothesized that RNA-seq data from the Dorsal
Lateral Prefrontal Cortex (DLPFC) would enable to identify
coherent intermediate cellular mechanisms associated with
cognitive decline and/or neuropathological changes.
• They created an approach, called gene Module Trait Network
analysis (MTN), constructs gene expression modules and
identifies those that are directly associated with cognitive
decline.
4
Introduction
• Use of the network in prioritizing amyloid and
cognition-associated genes for in vitro
validation in human neurons and astrocytes.
5. 5
Fig. 1 | Schematic of the implementation of
the module–trait network (MTN) method to
prioritize modules and genes directly related
to AD-related traits in our study.
a - Inputs to the MTN method are
(i) AD pathological traits of amyloid and tau
measurements;
(ii) slope of cognitive decline before death;
(iii) average expression of coexpressed gene
sets (modules).
b - These three inputs are combined using
conditional independence relationships (via
Bayesian networks) to identify direct
relationships among coexpression modules,
AD traits and cognitive decline (cog).
c - The disease relevance of top predicted
genes is tested experimentally in an astrocyte
and iPSC-induced neuron in vitro system.
Schema
6. • RNA was sequenced from the gray matter of DLPFC of 542 samples
• Sequencer: Illumina HiSeq
• QC with Parallelized pipeline
• Trimming of the reads
• Reads alignment: Bowtie
• Estimate expression level: RSEM
• Remove outliers samples
• Excluded 30 samples with incomplete assessment (for trait analysis)
• Data normalization:
• Quantile normalization and Combat algorithm
• Keep only genes >= 4 reads in 100 individuals (13,484 g)
• Linear regression to remove batch effects
They chose to only account for know covariates and not any hidden
covariates
6
Methods
7. • Networks:
• SpeakEasy and WGCNA
• 257 modules | 47 of them > 20 genes (98% total)
• Module Enrichment:
• All DLPFC expressed genes
• 29 of the 47 modules were significantly enriched for at least one GO
category
• Replication of gene modules:
• Module preservation (Z-summary)
• 4 datasets as validation data
(I) Microarray Zhang et. al
(II) Microarray mouse Matarin et. al.
(III) Test dataset from ROSMAP
(IV) H3K9ac
7
Methods
8. • Bayesian Network used to estimated the Directed
Acyclic Graph (DAG):
• Included 11 modules associated with at least one of the
3 traits (β-amyloid, tau tangles and cognitive decline)
• Verified cell-type-specific genes
• Chose 4 cell types - neurons (m187), astrocytes (m107),
microglia (m116) and oligodendrocytes (m123)
• Experimental validation of target genes
• iPSCs cell culture
• Perturbation with lentivirus
• qPCR
• Immunocytochemistry and microscopy
• ELISA
8
Methods
9. ROSMAP dataset:
• 478 participants, with a mean age at death of 88.7 years
• At the time of death 32% remained cognitively unimpaired
• 27% had mild cognitive impairment
• 39% had a diagnosis of AD dementia
• 2% had another form of dementia.
Pathological AD (58.6% n = 280) | Clinical AD (38.7% n = 185)
9
Results
5 phenotypic traits related to AD
Clinical measures:
• Clinical diagnosis of AD dementia proximate to death;
• Continuous measure of cognitive decline over time;
Pathology variables:
• Continuous measures of PHFtau tangle density and β-amyloid burden;
• Binary diagnosis of pathologic AD.
10. • 478 individuals;
• Average of 95 million paired-end reads for each subject;
• Normalized to account for the effects of many known biological and
technical confounding factors;
• Genes with low expression were removed, resulting in 13,484 unique genes
10
RNA-seq Analysis
TWAS study:
They identified the proportion of
genes whose expression is associated
with each pair of AD traits
3,025 genes at FDR<0.05 (Cognitive
decline)
π1 statistic, estimated that 55–90% of
the associated genes are shared
among correlated AD-related traits
1
1
1
1
1
0.55
0.95
11. Fig. 2 | Characterization of human cortical RNA-seq data and their relation with AD traits and
a) Genes associated with AD-traits
b) Module enrichment for cell-
specific signature.
c) Association strength: 47 modules & traits.
Bonferroni (p < 0.001)
d) Strength and direction of each module’s
associated with AD diagnostic.
neurons (m187)
microglia (m116)
oligodendrocytes (m123)
astrocytes (m107)
12. MTN consists of 3 steps:
12
RNA-seq Analysis
1 – Identified modules of genes and tried to validated with other
datasets:
• SpeakEasy and WGCNA algorithms.
2 – They determined which modules have direct relationships with
cognitive decline and other AD traits using Bayesian networks
5 perspectives: (i) Functional enrichment analysis, (ii) module preservation
with an independent cohort, (iii) concordance with co-regulation in
epigenomic data, (iv) concordance with brain gene expression data from
multiple AD mouse models, and (v) cell-type-specific expression.
3 – Prioritize genes for validation in vitro model systems
Criteria: Gene network connectivity, sufficient expression levels, gene-level
association with AD phenotypes and gene function. Total = 21 genes.
13. 13Sup Fig 4. Module preservation. X = z-summary | Y = modules
Strongly preserved
Kruskal Wallis test(10)
Moderately preserved
Kruskal Wallis test(2)
𝑍𝑠𝑢𝑚𝑚𝑎𝑟𝑦 =
𝑍𝑑𝑒𝑛𝑠𝑖𝑡𝑦 + 𝑍𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦
2
Preservation in 45 of the 47
modules in the Zhang study
All 47 modules preserved in
the separately-processed
ROSMAP dataset
Results
14. 14
Fig. 3 | The AD network model prioritizes
m109 as being directly associated with
cognitive decline. a) A directed acyclic
graph, obtained using Bayesian network.
Modules
Cell type
AD traits
Bayesian network
Bayesian network consisted of
18 nodes: 11 nodes
representing trait-associated
modules, 3 trait nodes, and 4
‘cell type modules’.
Module 109 (m109) was the
module most strongly
associated with cognitive
decline. It consists of 390
genes with diverse functions.
15. b) Trajectories of cognitive decline for people with low (left) or high (right) levels of m109
expression.
c) Mean expression of m109 for individuals who have no cognitive impairment (NCI; red), mild
cognitive impairment (MCI; green) or an AD diagnosis (AD; blue). d, Expression of m109 for
individuals without (red) and with (turquoise) amyloid deposition at autopsy.
Prioritizing genes in module 109 and testing their effect on extra-cellular -
amyloid levels.
16. 16
Fig. 4 | identifying specific genes within m109 for experimental follow-up. a) The estimated
gene regulatory network (Bayesian network) for 112 selected genes in m109. b) Coexpression
values for the 112 genes shown in Fig. 3a, highlighting the substructure within the
coexpression pattern of m109.
Yellow: tested in astrocytes
and iPSC derived-neurons
Blue: Tested only in astrocytes
Orange: Tested only in iPSC
neurons
Genes that are tested in wet lab
17. 17
Fig. 5 a) In iPSC-derived neurons (black dots) the outcome measure was not
altered. For astrocytes, 2 shRNA constructs targeting different genes: INPPL1 and
PLXNB1.
b) Replication study: results of INPPL1 and PLXNB1 knockdown on Aβ42 secretion
were measured in additional experiments using multiple shRNA constructs targeting
each of these genes. In these experiments, knockdown of both genes led to reduced
Based on those possible targets found using the network they knockdown 12 genes in
neurons, 14 genes in astrocytes and 11 genes on both using short hairpin RNAs. Then, they
measured the effect of each shRNA on the protein Aβ42 levels.
Bonferroni (p < 0.0012)
18. 18
c) They immunostained frontal cortex from subjects with pathologic AD and showed that
both INPPL1 and PLXNB1 were expressed at the protein level in astrocytes confirming that
these two genes were expressed in vivo in the human cell type used in the validation
experiments.
d) The proportion of variance in cognitive decline that is explained by different factors.
As shown, PLXNB1 and INPPL1 capture much but not all of the effect of m109, and
Results
Proportion of variance in cognitive decline
Green = gene marker
Red= astrocyte marker
5.5%
4.4%
5.4%
19. The authors used a network-based approach, to identify biological
processes and specific genes associated with multiple AD traits;
The central finding of this project is the existence of a robust set of
coexpressed genes, supported by other datasets;
Module m109 was associated with β-amyloid pathology;
Genes INPPL1 and PLXNB1 are intriguing candidates connected to
amyloid biology in vitro, however...
… they do not appear to account for the effects of the entire module, suggesting
that further validation work will be needed to identify additional driver genes for
m109.
In summary… they’ve illustrated the use of network in prioritizing
module and a subset of genes.
19
Conclusion