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).
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
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.
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
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.
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.
Presentaion for NetBio SIG 2013 by Robin Haw, Scientific Associate and Outreach Coordinator, Ontario Institute for Cancer Research. “Reactome Knowledgebase and Functional Interaction (FI) Cytoscape Plugin”
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.
The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
Presentation for NetBio SIG 2013 by Martina Kutmon, PhD Researcher in the BiGCaT Bioinformatics Dept at the University of Maastricht in the Netherlands. “Building Biological Regulatory Networks in Cytoscape Using CyTargetLinker”
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.
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.
Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”
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”
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
With the improvement of high-throughput technology, the dramatic increase of large-scale data in both biomolecular concentration and biomolecular interactions has resulted in many biological networks, such as protein interaction networks, gene regulatory networks, and metabolic networks. Although functional analysis is the fundamental step of better understanding biological networks, utilizing vast wealth of data and huge amount of knowledge to annotate and analyze the function of biological networks is still challenging in nowadays bioinformatics. Many software tools are available to visualize and analyze function-derived biological networks, but most of them are isolated with simple functions. One challenge faced by these visualization tools is how to make sense of such networks often represented as massive “hairballs.” Many network analysis algorithms filter or partition networks based on topological features, or mathematically model networks rely on their statistical properties, sidestepping the issue of making sense of the network itself altogether. On other hand, traditional functional enrichment analysis methods regard a network as a list of genes, and annotate networks with gene set enrichment methods. However, it does not consider the topological dynamics of network which might lead to the different functions under different conditions. Therefore, it is necessary to consider molecular interactions to correctly and specifically annotate biological networks.
As one of the most successful open source frameworks in bioinformatics, Cytoscape is a powerful network visualization platform that actively supports independent plugin development. By integrating model-view-controller design pattern and Cytoscape techniques, it makes possible an integrated ontology-annotated biological network visualization and analysis platform. In the first stage of the project, we successfully developed two interactive plugins -- Mosaic (http://nrnb.org/tools/mosaic) and NOA (http://nrnb.org/tools/noa) -- to address both visualization and analysis respectively. Mosaic supports interactive network annotation and visualization that includes partitioning, layout and coloring based on biologically-relevant ontologies. It shows slices of a given network in the visual language of biological pathways, which are familiar to any biologist and are ideal frameworks for integrating knowledge, and also provides researchers with an interactive tool to evaluate biological interactions within the context of well-defined processes, functions and cellular localization while retaining all original network information. NOA first introduced link ontology that assigns functions to interactions based on the known annotations of joint genes via optimizing two novel indexes ‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference sets to statistically rank the enriched functional terms for a given biological network. It has been proved to be more efficient not only in...
Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”
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”
Community Finding with Applications on Phylogenetic Networks [Extended Abstract]Luís Rita
[Master Thesis Extended Abstract]
With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics.
The aim of this thesis was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks – Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js, and, finally, to make it all available online (mscthesis.herokuapp.com).
Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
With the improvement of high-throughput technology, the dramatic increase of large-scale data in both biomolecular concentration and biomolecular interactions has resulted in many biological networks, such as protein interaction networks, gene regulatory networks, and metabolic networks. Although functional analysis is the fundamental step of better understanding biological networks, utilizing vast wealth of data and huge amount of knowledge to annotate and analyze the function of biological networks is still challenging in nowadays bioinformatics. Many software tools are available to visualize and analyze function-derived biological networks, but most of them are isolated with simple functions. One challenge faced by these visualization tools is how to make sense of such networks often represented as massive “hairballs.” Many network analysis algorithms filter or partition networks based on topological features, or mathematically model networks rely on their statistical properties, sidestepping the issue of making sense of the network itself altogether. On other hand, traditional functional enrichment analysis methods regard a network as a list of genes, and annotate networks with gene set enrichment methods. However, it does not consider the topological dynamics of network which might lead to the different functions under different conditions. Therefore, it is necessary to consider molecular interactions to correctly and specifically annotate biological networks.
As one of the most successful open source frameworks in bioinformatics, Cytoscape is a powerful network visualization platform that actively supports independent plugin development. By integrating model-view-controller design pattern and Cytoscape techniques, it makes possible an integrated ontology-annotated biological network visualization and analysis platform. In the first stage of the project, we successfully developed two interactive plugins -- Mosaic (http://nrnb.org/tools/mosaic) and NOA (http://nrnb.org/tools/noa) -- to address both visualization and analysis respectively. Mosaic supports interactive network annotation and visualization that includes partitioning, layout and coloring based on biologically-relevant ontologies. It shows slices of a given network in the visual language of biological pathways, which are familiar to any biologist and are ideal frameworks for integrating knowledge, and also provides researchers with an interactive tool to evaluate biological interactions within the context of well-defined processes, functions and cellular localization while retaining all original network information. NOA first introduced link ontology that assigns functions to interactions based on the known annotations of joint genes via optimizing two novel indexes ‘Coverage’ and ‘Diversity’. Then, NOA generates two alternative reference sets to statistically rank the enriched functional terms for a given biological network. It has been proved to be more efficient not only in...
The Functional and Pathway Analysis talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was designed to introduce wet-lab researchers to using web-based tools for doing functional analysis of gene lists, such as from microarray experiments.
Systems biology & Approaches of genomics and proteomicssonam786
This presentation provides the basic understanding of varous genomics and proteomics techniques.Systems biology studies life as a system .It includes the study of living system using various omic technologies .
Short tutorials on how to use the web-based tool DAVID - Database for Annotation, Visualization and Integrated Discovery) - http://david.abcc.ncifcrf.gov/
DAVID provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.
A full picture of -omics cellular networks of regulation brings researchers closer to a realistic and reliable understanding of complex conditions. For more information, please visit: http://tbioinfopb.pine-biotech.com/
T-Bioinfo is a comprehensive bioinformatics platform that allows the user to navigate NGS, Mass-Spec and Structural Biology data analysis pipelines using consistent interface. Analysis and integration of such data allows for better and faster discovery and optimization of personalized and precision treatment of complex diseases and understanding of medical conditions. For more information, go to pine-biotech.com
Presentation pathway extensions using knowledge integration and network approaches presented at the Systems Biology Institute in Luxembourg on November 28 2012.
Semantic Web for Health Care and Biomedical InformaticsAmit Sheth
Amit Sheth, "Semantic Web for Health Care and Biomedical Informatics," Keynote at NSF Biomed Web Workshop, Corbett, Oregon, December 4-5, 2007.
http://www.biomedweb.info/2007/
LIMS in Modern Molecular Pathology by Dr. Perry MaxwellCirdan
This presentation was delivered by Dr Perry Maxwell, Queen's University Belfast at Pathology Horizons 2017 in Cairns, Australia.
Pathology Horizons is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information on the event at www.pathologyhorizons.com
Forum on Personalized Medicine: Challenges for the next decadeJoaquin Dopazo
Bioinformatics and Big Data in the era of Personalized Medicine
10th Anniversary Instituto Roche Forum on Personalized Medicine: Challenges for the next decade.
Santiago de Compostela (Spain), September 25th 2014
EnrichNet: Graph-based statistic and web-application for gene/protein set enr...Enrico Glaab
EnrichNet is a web-application and web-service to identify and visualize functional associations between a user-defined list of genes/proteins and known cellular pathways. As a complement to classical overlap-based enrichment analysis methods, the EnrichNet approach integrates a novel graph-based statistic with a new interactive visualization of network sub-structures to enable a direct molecular interpretation of how a set of genes or proteins is related to a specific cellular pathway. Available at: http://www.enrichnet.org
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
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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.
Follow us on: Pinterest
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
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
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
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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.
Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
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
- 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
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
1. Enrichment Network Analysis
and Visualization (ENViz)
global program that offers
student developers stipends to
Anya Tsalenko write code for various open
Allan Kuchinsky source projects.
Agilent Laboratories
July 13, 2012
Agilent Confidential
2. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps
3. Integrative Biology
Primary Analysis
NMR
Proteins
Genomic Workbench GeneSpring MassHunter
Workstation Public Data
LC/MS Integrated Biology
Metabolites GC/MS Informatics
Microarrays
DNA / RNA Target Enrichment
Network Biology Integrated Analysis Genome Browser
miRNA
Microfluidics
Hypothesis, experiment, model
4. Example: breast cancer study
“miRNA-mRNA integrated
analysis reveals roles for miRNAs
in primary breast tumors”, 2011
• Cancer dataset from Anne-Lise
Børresen-Dale Lab in Norwegian
Radium Hospital, Oslo
• 100 breast tumor samples with
various characteristics
• Matched miRNA and mRNA data,
Agilent microarrays
5. Correlation of miRNA and mRNA expression,
miR-150
Sorted expression of miRNA -150
Genes most correlated to miR-150
across 100 breast cancer samples
6. Enrichment analysis of genes correlated to
miR-150
mHG p-value<E-147
GO terms enrichment analysis in the top of the list of genes ordered by
correlation to miR-150 based on minimum Hypergeometric Statistics
(Eden et al, PLoS CB 2007)
7. Biological validation
GO enrichment
Association between miR-19a for genes
and the cell-cycle module was correlated to
substantiated as an association miR-19a
to proliferation.
Further validated using high-
throughput transfection assays
where transfection of miR-19a
to MCF7 cell lines resulted in
increased proliferation.
8. Generic 3 matrices enrichment software
Two different types of
measurements in the same set of
samples:
mRNA and miRNA expression (or
Annotation
other non-coding RNAs)
Roy Navon
mRNA expression and quantitative
clinical phenotype
mRNA expression and metabolites
levels
mRNA expression and copy
number
Analysis is based on statistical
enrichment in lists ranked by
correlation
Enrichment can be calculated based on
any other annotation such as GO,
pathway or disease ontology
9. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps
10. Cytoscape at a glance Shannon et al. Genome Research 2003
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)
• Ultimate goal: provide tools to facilitate all aspects of
network assembly, annotation, and use in biomedicine.
Downloaded approximately 3000 times per month, ~137
plugins (1st June 2011)
Signaling, metabolic pathways Genetic regulatory networks
http://www.cytoscape.org
Host pathogen Functional enrichment Linked structural,
Genetic and protein Subnetworks active in maps networked data
interactions
interaction networks disease
11. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps
12. ENViz: what it is
Enrichment Network Visualization (ENViz): a Cytoscape plugin
for integrative statistical analysis and visualization of multiple sample matched
data sets
13. Control Panel
Use the main control panel to:
• Specify input primary data, pivot, and
annotation files
• Run analysis
• Set thresholds that control the size of
the enrichment network to visualize
• Run the visualization
Separate sub-panels can be collapsed or
expanded by clicking on their handles
(collapsible subpanels, Bader Lab, U
Toronto)
Interactive Legend:
• graphical overview of the workflow.
• click on labeled boxes for file prompt.
• drag and drop a file reference onto a
labeled box.
14. Enrichment Network
• Example of enrichment network built from mRNA and miRNA data from Enerly et al, using
WikiPathway annotation.
• Results are represented as bi-partite graph: nodes = pathways (green) and miRNAs (grey).
• Edge (i,j) represents enrichment of pathway j in the set of genes whose expression correlate the
expression pattern of miRNA i. red = positive correlation, blue = negative correlation
• Double-click on edge to load its pathway into Cytoscape.
15. Enrichment Network Zoom:
• Zoom in to see details around selected nodes and edges
• See zoomed-in network in the context of the whole network on the bottom left
16. Pathway visualization in WikiPathways
• Click on selected edge shows corresponding WikiPathway
• All gene nodes in the mRNA processing pathway that map to primary data
elements are color coded (blue -> red) for correlation score between the primary
data element (mRNA) and the pivot data element for the clicked edge (hsa-miR-
92a) • thick borders and high opacity those genes above
correlation threshold that were included in the gene set
used for enrichment analysis.
17. Tiling Pathway views
• Double-click on a pathway Node to loads multiple WikiPathways, each one colored by correlation
with the specific pivot datum for an Edge, connected to the Node, up to a user-configurable limit
• Network views are tiled in a ‘small multiples’ view that accentuates contrasts between correlations
for different pivot data.
18. Gene Ontology visualization
• enrichment networks built from Enerly et al. mRNA and miRNA data and Gene Ontology
annotation.
• left = bi-partite graph for GO terms (yellow -> red scale) and miRNA (grey)
• edge (i,j) is enrichment of GO term j in in the set of genes that correlate with miRNA i.
• right = GO summary network for GO terms in the left enrichment network. Each GO nodes
color-coded (yellow to red) by maximum enrichment score for its set of pivot nodes.
• parent terms are added, to complete the GO hierarchy view.
19. miR-150 - oriented GO Terms
• Double-click on an pivot node in the enrichment network to show GO terms in the GO Summary
network that have significant enrichment values for the pivot datum.
• Enrichments for GO terms and genes correlated to miR-150 are color-coded yellow -> red.
20. Agenda
• Introduction to integrative analysis
• Cytoscape at a glance
• ENViz walkthrough
• Next steps
21. Next steps
• Working on performance, completeness, robustness
• Extend support for other organisms beyond Homo sapiens, Mus
Musculus, mycobacterium tuberculosis
• Extend the range of database id mappings
• beta-release tentatively planned for end of Summer 2012
• Possible future features: heatmap view, sample grouping, more annotation
types (TFs, disease ontologies), crosstalk visualization
22. Acknowledgements
• Agilent Technologies
– Roy Navon, Zohar Yakhini, Michael Creech
• Technion
– Israel Steinfeld
• Collaborators
– Norwegian Radium Hospital, Oslo: Espen Enerly, Kristine
Kleivi, Vessela N. Kristensen, Anne-Lise Børresen-Dale
– UCSF/Gladstone: Alex Pico, Nathan Salomonis, Kristina
Hanspers, Bruce Conklin, Scooter Morris
– Maastricht University: Thomas Kelder, Martijn van Iersel, Chris
Evelo
– Cytoscape core developers and PIs: Trey Ideker, Chris Sander,
Gary Bader, Benno Schwikowski, Mike Smoot, Peng Liang, Kei Ono,
Leroy Hood, Ben Gross, Ethan Cerami
