The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Network embedding in biomedical data scienceArindam Ghosh
Excerpts from the paper:
What is it?
Network embedding aims at converting the network into a low-dimensional space while structural information of the network is preserved.
In this way, nodes and/or edges of the network can be represented as compacted yet informative vectors in the embedding space.
Advantages:
Typical non-network-based machine learning methods such as linear regression, Support Vector Machine (SVM) and decision forest, which have been demonstrated to be effective and efficient as the state-of-the-art techniques, can be applied to such vectors.
Current status:
Efforts of applying network embedding to improve biomedical data analysis are already planned or underway.
Difficulties:
The biomedical networks are sparse, noisy, incomplete, heterogeneous and usually consist of biomedical text and other domain knowledge. It makes embedding tasks more complicated than other application fields.
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools developed at Rothamsted Research for integrating and visualising large biological datasets in order to accelerate gene discovery. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes or publications. The aim is to provide leads for scientists who are investigating the molecular basis for a particular trait or ways of improving the organism’s performance in some way
Masters Thesis Defense: Minimum Complexity Echo State Networks For Genome and...Christopher Neighbor
Successful Thesis Defense presentation for my Master's in Electrical Engineering: Signal Processing and Machine Learning from Portland State University in March 2020.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Presentation for Network Biology SIG 2013 by Gang Su, University of Michigan, USA. “CoolMap Cytoscape App: Flexible Multi-scale Heatmap-Driven Molecular Network Exploration”
Network embedding in biomedical data scienceArindam Ghosh
Excerpts from the paper:
What is it?
Network embedding aims at converting the network into a low-dimensional space while structural information of the network is preserved.
In this way, nodes and/or edges of the network can be represented as compacted yet informative vectors in the embedding space.
Advantages:
Typical non-network-based machine learning methods such as linear regression, Support Vector Machine (SVM) and decision forest, which have been demonstrated to be effective and efficient as the state-of-the-art techniques, can be applied to such vectors.
Current status:
Efforts of applying network embedding to improve biomedical data analysis are already planned or underway.
Difficulties:
The biomedical networks are sparse, noisy, incomplete, heterogeneous and usually consist of biomedical text and other domain knowledge. It makes embedding tasks more complicated than other application fields.
A collaborative model for bioinformatics education: combining biologically i...Elia Brodsky
Presented at the 6th Annual LA Conference on Computational Biology & Bioinformatics
Authors:
Kimberlee Mix*, Patricia Dorn*, Donald Hauber*, Scott McDermott**, Ryan Harvey** , Jack LeBien***, Sahil Sethi***, Julia Panov***, Avi Titievsky****, Elia Brodsky***
Departments of Biological Sciences*, Mathematics and Computer Science**, Loyola University New Orleans, 6363 St Charles Avenue, New Orleans, LA 70118
Pine Biotech, Inc***, 1441 Canal St. New Orleans, LA 70112
Tauber Bioinformatics Research Center****, University of Haifa Multi Purpose Building Room 225A Mount Carmel, Haifa 3498838 ISRAEL
Despite the growing impact of bioinformatics in the biological science community, integration of an on-site bioinformatics curriculum is cost prohibitive for many universities due to the necessary infrastructure and computational resources. Furthermore, many programs prioritize the technical aspects of bioinformatics over the biological concepts and logic of analyses, thus limiting the emphasis on critical thinking, problem solving, and in-depth inquiry. To address the gap in bioinformatics education and train students to approach complex biomedical problems, we present a new model for curriculum development that combines our unique online learning environment with traditional pedagogical approaches delivered through academic partnerships. The T-BioInfo platform (https://t-bio.info) allows users to combine computational analysis modules into pipelines to develop solutions for ‘omics data and machine learning problems. State-of-the-art tools for analysis, integration, and visualization of data are offered through a user-friendly interface. In parallel, online educational modules provide a theoretical framework for the analysis methods and experimental techniques. This model for bioinformatics training was implemented at Loyola University New Orleans, a liberal arts institution, for the first time in January 2018. Twelve undergraduate students and five faculty members participated in a new one-semester bioinformatics course. After completing a core set of online modules and pipelines, students conducted team research projects on topics such as patient derived xenograft (PDX) models, immune responses in cancer, and precision medicine. Gains in critical thinking and problem-solving skills were observed and participants were enthusiastic about engaging in bioinformatics research. In conclusion, our collaborative model for bioinformatics education combines best-practices in online and in-class learning with a powerful computational platform. This model could be implemented in undergraduate and graduate curricula to enhance research, build partnerships with industry, and strengthen the scientific workforce.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools developed at Rothamsted Research for integrating and visualising large biological datasets in order to accelerate gene discovery. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes or publications. The aim is to provide leads for scientists who are investigating the molecular basis for a particular trait or ways of improving the organism’s performance in some way
Masters Thesis Defense: Minimum Complexity Echo State Networks For Genome and...Christopher Neighbor
Successful Thesis Defense presentation for my Master's in Electrical Engineering: Signal Processing and Machine Learning from Portland State University in March 2020.
The Amazing Ways Artificial Intelligence Is Transforming Genomics and Gene Ed...Bernard Marr
It is predicted that artificial intelligence (AI) will transform many aspects of our life including healthcare and genomics. AI and machine learning have helped us to understand the genome of organisms and will potentially change the way we treat disease, determine effective drugs and edit genes.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
IMPORTANT: If you want to get a clear review of the Differences & Complementarities Between « Heuristic » and « Mathematical » approaches, we invite you to download our presentation given during the EPA (European Psychiatric Association) conference in 2011 that is now utilized in training programs.
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
Introduction to Cytoscape talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was design to give a broad introduction the features available in Cytoscape for wet lab researchers.
Cyberinfrastructure Day 2010: Applications in BiocomputingJeremy Yang
UNM Cyberinfrastructure Day 2010 presentation: Applications in Biocomputing, biomedical and cheminformatics research computing cyberinfrastructure issues.
Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. It is an interdisciplinary field, which harnesses computer science, mathematics, physics, and biology
Visualization and Analysis of Dynamic Networks Alexander Pico
DynNetwork development was taken up initially by Sabina Sara Pfister back in GSoC 2012. She laid out a strong foundation for dynamic network visualization in Cytoscape and my job was to extend the plugin’s functionality to help users analyse time changing networks. The two of us were mentored by Jason Montojo. We had developed a decent tool over the course of two GSoC programs to aid dynamic network analysis and our efforts culminated in DynNetwork getting accepted for an oral presentation at the International Network for Social Network Analysis (INSNA), Sunbelt 2014 which was held in St. Petersburg, FL in February.
Keynote presentation for Network Biology SIG 2013 by Esti Yeger-Lotem, Senior Lecturer in Clinical Biochemistry at The National Institute for Biotechnology in the Negev, Israel
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
3. Systematic mapping
of molecular interactions
andmolecular profiles
Metabolic networks
mRNA & protein
expression
Genetic and protein
interaction networks
Transcriptional networks
4. National Resource for Network Biology:
Mission
To provide freely available, open-source software
technology that broadly enables networkassembly,
analysis, visualization and network-based biomedical
discovery for NIH-funded researchers.
These tools are enabling researchers to assemble large-
scale biological data into models of networks and
pathways and to use these networks to better
understand how biological systems operate under
normal conditions and how they fail in disease.
5. Components of the Network Resource
• Technology Research & Development (TRD)
• Driving Biological Projects (DBP)
• Collaboration and Service (CSP)
• Training
• Dissemination
• Administration
• Core infrastructure (Cytoscape, Supercomputing)
6. Assembling and Using Networks in Biomedicine
Network-based disease
Genes and gene functions
The Working Network Map diagnosis, prognosis, and
stratification
Physical interactions Network-based prediction of
cell fate; regenerative medicine
Genetic interactions Network-based rational
drug design
Gene / proteinexpression
Network wide association
studies (NWAS)
Advanced
Network
Visualization
Assembly of molecular networks Network based applications
via data integration to disease
7. National Resource for Network Biology
Faculty
PI: Trey Ideker Exec. Director: Alex Pico Gary Bader Mike Norman
UCSD Medicine & UCSF / Gladstone U Toronto Donnelly Ctr Director, SDSC
Bioengineering for Mol&BiomolRsrch
Chris Sander James Fowler BennoSchwikowski Chairman of External
Director Bioinformatics UCSD Medicine / Systems Biology Advisory Council:
@ MSKCC Social Sciences Institut Pasteur Stephen Friend,
Sage Bionetworks
8. Our flagship tool: Shannon et al. Genome Research 2003
www.cytoscape.org Cline et al. Nature Protocols 2007
OPEN SOURCE Java platform for
integration of systems biology data
•Layout and query of networks
(physical, genetic, social, functional)
•Visual and programmatic
integration of network state data
(attributes)
•The ultimate goal is to provide
tools to facilitate all aspects of
network assembly, annotation, and
use in biomedicine.
RECENT NEWS
• Version 3.0 due July 2011
•Cytoscape ® Registered Trademark
• The Cytoscape Consortium is a 501(c)3 non-for-profit in the State of California
•Centerpiece of the NCRR-funded National Resource for Network Biology
Downloaded approximately 3000 times per month
9. Agenda
08:30 Introductions, overview and organizational structure of NRNB (Friend, Ideker)
08:45 EAC roles, goals, and responsibilities (Pico, Ideker)
09:00 Discussion of technology projects and matched driving biology (Ideker)
10:00 Break
10:15 Progress in Cytoscape 3.0 (Smoot)
10:40 Collaboration, Training, and Service (Bader, Pico)
11:30 EAC recommendations, formulation and review of NRNB policies (Pico)
12:15 Summary (Friend)
12:30 Lunch
11. EAC goals and responsibilities
Feedback on technology we’ve been
working on over first 8 months
Advice on administrative structure
Vision and guidance for future years
Short written report
Two-way discussion
•Not a presentation of annual report
• We are prepared to listen!
We will report back next year!
12. Technology Projects
Questions:
How are we doing? Strengths and weakness?
Where would you want to see these projects in several
years?
Are there key network tools that we should be
developing yet are not?
Are there network tools we are developing which are
not so exciting?
13. TRD Project A: Network-based biomarkers for
diagnosis and personalized therapy
Bandyopadhyay et al. Science (2010)
14. Visualizing and Analyzing Cancer Genomic Data in the
Context of Biological Pathways and Networks
• Algorithm Development
• Adding Network Visualization and Analysis to the cBio Portal.
16. TRD Project B: How Do Genes
Affect Behavior?
Genes Behaviors
Dopamine Health
Serotonin Happiness
Cooperation
Politics
?
Social
Networks
17.
18. TRD C: Visualization and Representation
of Biological Networks
Cellular
semantics
Interactome
Meaningful modules
Exons to protein domains Complex data views
PIs: Bader, Conklin, Pico
22. TRD D: Cytoscape network inference
Mission: Interface Cytoscape with the network inference community
23. Example inferred regulatory network
DBP: Van Dijl laboratory
•Network induced by pathway search in 418 genes
• Transcriptome experiments of selected knockouts underway
24. Dynamic management of TRDs
TRD projects
•These projects should present compelling research and
development opportunities for NRNB.
• TRDs should be coupled to new or existing DBPs.
• They should represent significant improvements to critical
tools and resources for the research community.
• We would provide (or fund) developer time to develop or
enhance a novel tool or resource, defining a 2-5 year TRD
project.
• They should be assessed competitively each cycle with
other new proposals and proposals for continuation of
ongoing TRD projects.
26. Functional Overview
• Milestone 2 release just completed.
• All core plugins except "Advanced Network Merge" have
been ported.
• Cytoscape 3 is functional, but there are lots of bugs and the
code hasn't been tuned for performance.
29. Architecture Status
• Still using OSGi for modularity.
• Still using Spring-DM to configure modules.
• Still using Maven to build and pull everything together.
• We now have a "simplified" plugin API.
o You only need to know basic Java.
o Just like in 2.X, you extend a single abstract class.
• So far, everything is behaving as planned.
o The application starts correctly, services get registered,
APIs are public, implementation is hidden, plugins start,
etc..
30. Documentation Status
• Lots of documentation written on the Wiki:
o Overview of the Architecture
o Overview of the API
o Plugin Developer's Guide
o Plugin Porting Hints
• A Plugin Developer's tutorial has been added to
OpenTutorials
31. Next Steps
• Fix known bugs.
• Add missing features.
• Analyze and address performance bottlenecks.
• Re-evaluate API.
• Ensure that proper OSGi metadata is being published.
• Refactor build system to that development code - compile -
test cycle is faster.
• Refactor build system so that release generation is easier.
• Help plugin developers port plugins.
34. Collaboration and Training
• Collaboration and training
• Win win: collaborators learn network and pathway
analysis, we learn about community needs
• 37 Collaborations and service projects (CSP)
• Project management
– Services e.g. training, dissemination
– Target # of CSPs?
• Balance of R&D and collaborations
35. MHC-I Cell Projection
Microtubule Edge type (gene-set overlap)
& Cell Motility
Cytoskeleton Between gene-sets
Centrosome enriched in deletions
From disease genes
Membrane to enriched gene-sets
Between sets enriched in
Nucleolus deletions and in disease
genes or between disease
SMC flexible hinge domain Cell Motility sets only
(stricter cluster) Cell Proliferation
Urea and amine group metabolism Positive regulation of cell proliferation
Cell cycle Intellectual
Regulation of Disability Regulation of cell proliferation
hormone levels
Aminoacid Behavior
derivative /
amine Organ Morphogenesis
metabolism
Vasculature develepment
CNS Development Palate develepment
Glycosylation Autism Kinase Activity/Regulation
LIS1 in neuronal Heart develepment
Synaptic vescicle maturation migration and
development Regulation of GTPase
Reelin pathway RHO Ras Tyrosin kinase
Zoom of CNS-Development Adhesion
Carboxyl
Cell projection Neuron Zn finger esterase
organization migration domain
domain
Cell morphogenesis Kinase regulation
Cerebral cortex
cell migration Ras signaling GTPase regulator
Negative cKIT
regulation mTor pathway GTPase/Ras
Neurite development Cell Motility of cell cycle pathway Signaling Node type (gene-set)
(stricter cluster)
CNS neuron Enriched Known Enriched only
Brain in deletions disease genes in disease genes
differentiation
development 0% ID ID
Axonogenesis
CNS Projection neuron
development FDR ASD ASD
axonogenesis
12.5% Both
Pinto et al. Functional impact of global rare copy number variation in autism
spectrum disorders. Nature. 2010 Jun 9.
36.
37. Collaboration Triage Workflow
NRNB site
Check information Discard if low quality
and process
If unrelated to
appropriately network biology
Collaboration General bioinfo / data processing
request
Web form input
•Request type
Development of tools
• General bioinfo / data processing
• Cytoscape training Scientific research collaboration Beginners
• Development of tools (ex. Plug-ins) Data provider Simple consultation by
• Scientific research collaboration
Cytoscape training E-Mail, giving some
• Data provider
•Contact info options of what they can
•Research proposal do (I & some graduate
•Publications students?)
Discussion in regular meeting - Recommendation of using
Discard if proposal is Web tutorial, Quick start
not appropriate for Work member guide.
collaboration or no assignment - Providing Cytoscape retreat
available human information
resource. Research Collaboration
Researchers, Cytoscape team,
Post-docs, graduate students
37
38. Services NRNB should provide to select CSPs
Organizational
•Impact: Measure impact of project - e.g. track relevant publications and
collaborations
•Interface: identify related resources, project and programs; facilitate new
collaborations; hold joint conferences
•Host: link to project download, documentation and tutorial pages from nrnb.org
•Advisory: provide recommendations and proposals to project leads regarding
technical development aims and organizational opportunities
Training
•Tutorials: prioritize, produce and promote tutorial materials; organize events
•Feedback: collect and organize feedback from training events to share with
developers
Community
•Events: organize, promote and staff lectures, tutorials and workshops
•News: highlight news items related to the project and project leads
•Communication: coordinate discussion lists, help desk, Facebook, Twitter, etc
•GSoC: support project ideas, mentors and students in annual Summer of Code
program sponsored by Google
•Retreat: organizing the annual retreat for project in conjunction with Cytoscape and
NRNB
39. Dynamic management of TRDs and CSPs
CSP projects
• The NRNB should continually engage in new Collaborative
and Service projects which should be highly relevant to
network biology and represent the “best in class.”
• They should have demonstrated stability and a designated
point-person for NRNB collaboration.
• There should be obvious and direct synergy with existing
NRNB projects.
• We should take on such projects as a long-term
commitment.
• We will commit to providing a set of services to the user
and development communities, ranging from organizational
to training to communications.
40. Training and Outreach
1. Launched new NRNB website
2. Redesigned Cytoscape website
3. Launched new tutorial system, Open Tutorials
4. Out training events, presentations and GSoC
41. 4. Handling requests for external
training events
What to do with requests for external training support?
We send staff, our “Roving Engineer”
We provide tutorial materials through OpenTutorials,
(including customizable online content, slideshows, and handouts)
Proposal: Sponsor travel and accommodations for a
number of external personnel to attend the annual
Cytoscape Retreat. We would use the opportunity to
“train the trainers” and thereby multiply our outreach
capability with efficient investment of resources.
43. Acknowledgments: NCRR P41 RR031228
PI: Trey Ideker Exec. Director: Alex Pico co- PI: Gary Bader, U Toronto
UCSD Medicine / Bioengineering UCSF / Gladstone Donnelly CtrMol&BiomolRsrch
Chris Sander James Fowler BennoSchwikowski Chairman of External
Director Bioinformatics UCSD Medicine / Systems Biology Advisory Council:
@ MSKCC Social Sciences Institut Pasteur Stephen Friend,
Sage Bionetworks
44. Protein networks as
biomarkers of disease
Network guided
random forests
Dutkowski et al. submitted
Previous work:
Chuang et al. MSB 2007
Lee et al. PLoS Comp Bio 2008
Ravasi et al. Cell 2010
Editor's Notes
We view these EAC meetings as an important opportunity to get feedback and guidance *from YOU* for how to run the NRNB resource. * This should NOT be a one-way report of our progress, but rather a two-way discussion * We want feedback on our performance over the first year * We have a number of proposals derived from our first year experience that we need your advice on. We’ll cover these toward the end of the afternoon. * And we want guidance in general for our second year It will be our responsibility to report back on all action items and milestones generated from this meeting, and demonstrate how important this meeting is the success of NRNB.
TRD OVERVIEW: The overall motivation for this TRD is making it easier for biologists to visualize and explore their data in the context of networks. The importance of networks and a systems biology perspective in the study of desease has already been illustrated by the prior TRDs and the growing field NRNB services. The goal of this TRD is to develop tools that make this work more integrative (from interactomes to exons; and across datatypes), more meaningful (biological semantics, both lexical and graphical), and more practical. The field is rapidly growing and more and more researchers are seeking powerful and easy-to-use network biology tools. These 5 images were made using at least 5 different software packages; but this can all be done in Cytoscape with the completion of TRD C.NEXT: The first project relates to this top row of going from networks to biologically meaningful diagrams. And the second project will focus on integrating complex data views and drilling-down to exons and protein domains.
BACKGROUND:This project addresses the problem of viewing exon-level data in the context of networks AND genomic alignments. Here we have a typical pathway with data mapped to P53. We then want to drill down and see the data aligned to gene structure. This tell us about exon-level expression and possible alternative splicing isoforms. The results of this view get passed up to the network view, telling the user that there is information worth digging into. [CLICK] This second example highlights the value of viewing multiple conditions (or timepoints) simultaneously. [CLICK]PROGRESS: So, in terms of progress, we now have Cytoscape features that address stripe and pie chart views on nodes to support the visualization of multiples AND we have extended support for grouping and heirarchical relationships between entities, which is critical to supporting how probesets relate to exons and exons relate to genes and proteins. CSP&DBP: There are many applications of these general visualization solutions. We are applying it to (1) the study of the role of alternative splicing in stem cell differentiation, (2) understanding the role of SNPs associated with Glioma brain tumors, and (3) prioritizing crystallography targets for a Protein Structure Initiative project focused on the pathways involved in stem cell pluripotency.
…discuss…and…decide.
Our current menu of services is primarily based on the support of Cytoscape. As we look to expand the reach of our support, we propose the following list of services. For each CSP under consideration for NRNB support, we would package a custom set of services from this list on a case-by-case basis: (list)
…discuss…and…decide.
We are tackling Training and Outreach on a number of fronts. I’m going to briefly review the new features and content rolled out this year as a function of NRNB.[ONLINE – Live demo] * NRNB site: home, tools, training (events, tracker, spreadsheet), outreach (collaborate form), projects (internal tracking, collaboration list). [STATS: ~100visitors/day] Tools>Cytoscape * Cytosacpe: front page redesign (sexy images, targeted sections). Documentation>users>OT * Open Tutorials: portal, new tutorial>Basic-Human (scroll, editable, slideshow, handout). [STATS: 1,000 visits in past month] * Google: “network biology resource” (Note “resources”, then back, mention adwords [STATS: >1,300 clicks per month, potential of $120k/yr], then note: main site, ncrr, gsoc) * GSoC: 10 students, paid for by Google = $55k, start coding on Monday!
We have already received a request to fund an externally-hosted training event for Cytoscape that did not directly involve any NRNB staff or investigators. We rejected this request and came up with the following alternative proposal to be applied in future cases. (Proposal)