This document summarizes a workshop on health and social development analytics using big data. It discusses how data sources are becoming larger, more diverse and used for multiple purposes. This presents opportunities to better understand issues but also challenges around privacy, bias and data quality. The workshop aims to identify partnership opportunities and prototype projects using integrated data to address health and social issues. Case studies from various institutions are presented using combined data sources like medical records, surveys and environmental factors.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
SDAL addresses social science in new ways that will transform how we understand the world. Among our goals: creating smart and resilient cities, combatting homelessness, understanding the spread of disease and developing effective public health responses, identifying innovation drivers, and meeting the demand for educated graduates in the field.
Sdal pires, bianica, riots in an urban slum 140813kimlyman
In order to understand the relationship between people, physical space, and future change, a diverse set of methods is used that focuses around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA). The intersection between these research areas can be represented through computational social science (CSS), which lies at the foundation of this research as it represents the interdisciplinary science that uses computational modeling and related techniques to study complex social systems. A computational model of the riots that broke-out in an urban slum after the 2007 Kenyan presidential election is used to demonstrate the value of integrating these research areas. Characteristics such as poverty, overpopulation, and a growing youth bulge put urban slums at greater risk for violence. Using empirical data for which to build the landscape and provide agents with unique attributes, an ABM is integrated with SNA and GIS to simulate the outbreak of riots. The model investigates the role individual identity, group identity, and social influence played on the occurrence and intensity of riots. Model results find that the cyclical nature in the emergence and dissolution of rioting is due to positive reinforcement, an effect that can be largely attributed to the agents’ social networks, and thus their interactions and influences through these networks. Riots arise from the interactions between individuals with unique attributes, all within a connected social network over a physical environment. In order to gain a better understanding of the macro-level patterns that emerge, the nonlinear and reinforcing nature of this system is modeled from the bottom-up.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
SDAL addresses social science in new ways that will transform how we understand the world. Among our goals: creating smart and resilient cities, combatting homelessness, understanding the spread of disease and developing effective public health responses, identifying innovation drivers, and meeting the demand for educated graduates in the field.
Sdal pires, bianica, riots in an urban slum 140813kimlyman
In order to understand the relationship between people, physical space, and future change, a diverse set of methods is used that focuses around three main research areas: agent-based modeling (ABM), geographical information science (GIS) and social network analysis (SNA). The intersection between these research areas can be represented through computational social science (CSS), which lies at the foundation of this research as it represents the interdisciplinary science that uses computational modeling and related techniques to study complex social systems. A computational model of the riots that broke-out in an urban slum after the 2007 Kenyan presidential election is used to demonstrate the value of integrating these research areas. Characteristics such as poverty, overpopulation, and a growing youth bulge put urban slums at greater risk for violence. Using empirical data for which to build the landscape and provide agents with unique attributes, an ABM is integrated with SNA and GIS to simulate the outbreak of riots. The model investigates the role individual identity, group identity, and social influence played on the occurrence and intensity of riots. Model results find that the cyclical nature in the emergence and dissolution of rioting is due to positive reinforcement, an effect that can be largely attributed to the agents’ social networks, and thus their interactions and influences through these networks. Riots arise from the interactions between individuals with unique attributes, all within a connected social network over a physical environment. In order to gain a better understanding of the macro-level patterns that emerge, the nonlinear and reinforcing nature of this system is modeled from the bottom-up.
Ethical Priniciples for the All Data RevolutionMelissa Moody
A presentation by Stephanie Shipp, from the Research Highlights session at the 2019 Women in Data Science Charlottesville Conference. Hosted by the UVA Data Science Institute.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Gather evidence to demonstrate the impact of your researchIUPUI
This workshop is the 3rd in a series of 4 titled "Maximize your impact" offered by the IUPUI University Library Center for Digital Scholarship. Faculty must provide strong evidence of impact in order to achieve promotion and tenure. Having strong evidence in year 5 is made easier by strategic dissemination early in your tenure track. In this hands-on workshop, we will introduce key sources of evidence to support your case, demonstrate strategies for gathering this evidence, and provide a variety of examples. These sources include citation metrics, article level metrics, and altmetrics as indicators of impact to support your narrative of excellence.
Citizen Sensor Data Mining, Social Media Analytics and ApplicationsAmit Sheth
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Can medical education take advantage of Learning Analytics techniques? How? Where? In this presentation a study is analyzed pinpointing three areas in which Medical Education needs to invest and all three are related to Learning Analytics.
People & Organizational Issues in Health IT Implementation (February 24, 2021)Nawanan Theera-Ampornpunt
Presented at the 11th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on February 24, 2021
Research process and research data management. Many universities are looking at how they can better serve the needs of researchers. Ken Chad Consulting worked with the University of Westminster to look the needs and attitudes of researchers and admin staff in terms of research data management (RDM). The result led the University to look first at the whole lifecycle and workflows of research administration. This in turn led to the innovative, rapid development of a system to support researchers and admin staff. Presented by Suzanne Enright (University of Westminster) and Ken Chad at the annual UKSG conference in April 2014
INSPIRE @ IMSH 2016 in San Diego, CA was a hit for newcomers and prior attendees. Learn about the growth and progress of INSPIRE, simulation-based research, and new projects down the pipeline.
Presentation on Data4Impact methodology & results in the workshop on the use ...Data4Impact
The workshop on the use of big data technologies for advanced research assessment was part of a two day event, co-organised by OpenAIRE and Data4Impact, with support of Science Europe, explored mechanisms for research policy monitoring and indicators, and how to link these to infrastructure and services. The first day was focused on open science indicators as these emerge from national and EU initiatives, while the second day explored more advanced aspects of indicators for innovation and societal impact.
The presentation of the second workshop day includes the introduction to Data4Impact, presents our conceptual framework, and discusses the development of a series of indicators on the performance and societal impact of 40+ research programmes in the health domain.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
Gather evidence to demonstrate the impact of your researchIUPUI
This workshop is the 3rd in a series of 4 titled "Maximize your impact" offered by the IUPUI University Library Center for Digital Scholarship. Faculty must provide strong evidence of impact in order to achieve promotion and tenure. Having strong evidence in year 5 is made easier by strategic dissemination early in your tenure track. In this hands-on workshop, we will introduce key sources of evidence to support your case, demonstrate strategies for gathering this evidence, and provide a variety of examples. These sources include citation metrics, article level metrics, and altmetrics as indicators of impact to support your narrative of excellence.
Citizen Sensor Data Mining, Social Media Analytics and ApplicationsAmit Sheth
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
Can medical education take advantage of Learning Analytics techniques? How? Where? In this presentation a study is analyzed pinpointing three areas in which Medical Education needs to invest and all three are related to Learning Analytics.
People & Organizational Issues in Health IT Implementation (February 24, 2021)Nawanan Theera-Ampornpunt
Presented at the 11th Healthcare CIO Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on February 24, 2021
Research process and research data management. Many universities are looking at how they can better serve the needs of researchers. Ken Chad Consulting worked with the University of Westminster to look the needs and attitudes of researchers and admin staff in terms of research data management (RDM). The result led the University to look first at the whole lifecycle and workflows of research administration. This in turn led to the innovative, rapid development of a system to support researchers and admin staff. Presented by Suzanne Enright (University of Westminster) and Ken Chad at the annual UKSG conference in April 2014
INSPIRE @ IMSH 2016 in San Diego, CA was a hit for newcomers and prior attendees. Learn about the growth and progress of INSPIRE, simulation-based research, and new projects down the pipeline.
Presentation on Data4Impact methodology & results in the workshop on the use ...Data4Impact
The workshop on the use of big data technologies for advanced research assessment was part of a two day event, co-organised by OpenAIRE and Data4Impact, with support of Science Europe, explored mechanisms for research policy monitoring and indicators, and how to link these to infrastructure and services. The first day was focused on open science indicators as these emerge from national and EU initiatives, while the second day explored more advanced aspects of indicators for innovation and societal impact.
The presentation of the second workshop day includes the introduction to Data4Impact, presents our conceptual framework, and discusses the development of a series of indicators on the performance and societal impact of 40+ research programmes in the health domain.
IngwallKöket made to measure quality shutters that fit all kitchen frames.It also have kitchen cabinets, if someone choose to completely rebuild and renovate your kitchen.
Perusahaan anak memiliki lebih dari satu jenis atau golongan saham yang beredar
Laba/Rugi dari transaksi antar anak perusahaan yang berafiliasi (inter-company profit)
Pembelian saham langsung dari perusahaan anak
Saham bonus (Stock Deviden) dari perusahaan anak
Pemilikan obligasi (surat berharga lainnya) antar perusahaan berafiliasi
Financil Contracts (FCs) specify rights and obligations that parties are legally
bind.Hence effective management of FCs is vital.Domain Specific Language (DSL)
approach provides a method of defining rights and obligations of contracts using fixed
and precisely defined set of combinators and observables.As a result, any contract can
be composed using fixed set of symbols, the contract management becomes efficient and effective.The Haskell Contract Combinator Library (HCCL) is the driving forcebehind the DSL approach in finance sector
Joint Venture (JV) tidak banyak berbeda dengan persekutuan, yaitu kerja sama beberapa pihak untuk menyelenggarakan usaha bersama dalam jangka waktu tertentu. Kerja sama tersebut akan berakhir setelah tujuan tercapai atau pekerjaan selesai.
Perbedaan pokok antara joint venture dengan persekutuan pada umumnya terletak pada umurnya, karena umur joint venture lebih pendek daripada umur persekutuan
Det sies at man skal gi råd i kun to situasjoner: 1. Når det er snakk om liv og død - 2. Når de blir etterspurt.
Situasjon 1 oppstår heldigvis svært sjelden. Situasjon 2 oppstår dessverre også ganske sjelden.
Jeg er neppe den hvasseste gründeren i landet, selv om jeg har startet fire selskap på fire år. Men mine 20+ år i arbeidslivet har allikevel hele tiden dreid seg om oppbygging av avdelinger, divisjoner og selskaper.Jeg tar sjansen på å dele essensen av opp- og nedturer med dere her.
Financil Contracts (FCs) specify rights and obligations that parties are legally
bind.Hence effective management of FCs is vital.Domain Specific Language (DSL)
approach provides a method of defining rights and obligations of contracts using fixed
and precisely defined set of combinators and observables.As a result, any contract can
be composed using fixed set of symbols, the contract management becomes efficient and effective.The Haskell Contract Combinator Library (HCCL) is the driving forcebehind the DSL approach in finance sector
Why do some reconstruction programs reduce political instability while others do not? Conflict studies literature provides limited insight into this policy problem by failing to see the unique complexities behind infrastructure as a public good. Key is acknowledging that although infrastructure tends to remain stationary, its consequences are anything but fixed. I argue that infrastructure’s consequences shifts as times passes with both short and long term consequences. To analyze the effect of infrastructure in post-conflict situations, this research is a geospatial study on 33 cases where post-conflict reconstruction was needed. This research uses a unique longitudinal dataset built using GIS that combines data in PRIO-grid format on conflict event and infrastructure proximity and access. Supplementing the quantitative results is a case study into Ethiopia’s reconstruction following their Civil War. This research demonstrates how conflict morphs after infrastructure provision and how disconnected peripheries increase conflict risks over time--both of which have policy implications in regards to reconstruction, foreign aid, national infrastructure policy, and urban planning. In conclusion, this research advocates for a new theoretical approach that incorporates infrastructure's long-term qualities and role in shaping society alongside the normative goal of political stability.
Precision and Participatory Medicine - Medinfo 2015 Panel on big data. Includes the proposal to use the term Expotype to characterise the Exposome of an individual. Electronic expo typing would refer to the automatic construction of individual expo types from electronic clinical records and other sources of environmental risk factor and exposure data.
Improving health care outcomes with responsible data scienceWessel Kraaij
Keynote presentation by Wessel Kraaij at the Dutch pattern recognition and impage processing society (NVPBV) 29/5/2018, Eindhoven.
This talk discusses
1. trends in health care and respondible data science and their intersection
2. Secure federated analytics on distributed data repositories
3. Generating clinically relevant hypotheses from patient forum discussions.
Presentation by Prof. Fernando MArtin-Sanchez, Director of the Health and Biomedical Informatics Centre (HaBIC) of the University of Melbourne at at the Panel on Big Data in Health and Biomedical Research, at the annual AMIA 2013 Conference, 19th November, Washington DC
The Uneven Future of Evidence-Based MedicineIda Sim
An Apple ResearchKit study enrolled 22,000 people in five days. A
study claims that Twitter can be used to identify depressed patients. A computer program crunches genomic data, the published literature, and electronic health record data to guide cancer treatment. The pace, the data sources, and the methods for generating medical evidence are changing radically. What will — what should — evidence-based medicine look like in a faster, personalized, data-dense tomorrow?
- Presented as the 3rd Annual Cochrane Lecture, October 2015 in Vienna, Austria.
Personal Data for the Public Good: New opportunities to enrich understanding ...Matthew Bietz
views
Individuals are tracking a variety of health-related data via a growing number of wearable devices and smartphone apps. More and more data relevant to health are also being captured passively as people communicate with one another on social networks, shop, work, or do any number of activities that leave “digital footprints.” Self-tracking data can provide better measures of everyday behavior and lifestyle and can fill in gaps in more traditional clinical or public health data collection, giving us a more complete picture of health.
We at the Health Data Exploration project are creating a Network of innovators in PHD to catalyze the use of personal data for the public good. This Network will bring together companies, researchers, and strategic partners to strategize, coordinate, and experiment with using PHD to understand health.
Wake up Pharma and look into your Big data Yigal Aviv
The vast volumes of medical data collected offers pharma the opportunity to harness the information in big data sets
Unlocking the potential in these data sources can ultimately lead to improved patients outcomes
This presentation describes consideration how to maximize the impact of Big Data.
its methodology, practical challenges and implications.
Why should we care about integrating data? What should we be trying to achieve? Population Health. The Softer, Human Side of Being “Data Driven” not “Driven By Data." The New Era of Decision Support in Healthcare. Top 10 Challenges To Integrating External Data.
Peter Embi: Leveraging Informatics to Create a Learning Health SystemPAÍS DIGITAL
Presentación del Dr. Peter Embi, Presidente y CEO del Regenstrief Institute, en el marco del Primer Simposio Salud: Nuevas Tecnologías, Avances y Desafíos, realizado en Santiago de Chile los días 18 y 19 de julio, 2017
From the event "Specimen Science: Ethics and Policy Implications," held at Harvard Law School on November 16, 2015.
This event is a collaboration between The Center for Child Health and Policy at Case Western Reserve University and University Hospitals Rainbow Babies & Children’s Hospital; the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School; the Multi-Regional Clinical Trials Center of Harvard and Brigham and Women's Hospital; and Harvard Catalyst | The Harvard Clinical and Translational Science Center. It is supported by funding from the National Human Genome Research Institute and the Oswald DeN. Cammann Fund at Harvard University.
For more information, visit our website at http://petrieflom.law.harvard.edu/events/details/specimen-science-ethics-and-policy
April 3, 2019
Digital innovation is transforming health care, and the amount of digital health care data being generated will likely have increasing research utility over time. Despite the seemingly logical and inevitable application of health care data from deceased persons for research and health care both now and in the future, the issue of how best to manage posthumous medical records is currently unclear, including elements of resource governance, issues of law, and infrastructural challenges.
This presentation explored current issues surrounding how to manage the medical records of the dead, integrating evidence from the field of body donation to inform and guide the discussion on the utilisation of posthumous medical information. It also delivered results from a year-long study on posthumous health care data utility that explored the views of the general population on the use of posthumous medical records, which showed a centrally collated and government-governed resource of posthumous health care data was almost universally supported, with varying caveats around how such a resource should be utilized.
The Digital Health @ Harvard series features speakers from Harvard as well as collaborators and colleagues from other institutions who research the intersection between health and digital technology. The series is cosponsored by the Berkman Klein Center for Internet & Society at Harvard University and the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School. The goal of the series is to discuss ongoing research in this research area, share new developments, identify opportunities for collaboration, and explore the digital health ecosystem more generally.
Learn more on the website: https://petrieflom.law.harvard.edu/events/details/digital-health-harvard-april-2019
From personal health data to a personalized adviceWessel Kraaij
Invited talk at the health track of ICT.OPEN 2018, 20-3-2018
1. Related Data science challenges to Digital Health trends
2. Designing an infrastructure to support secure learning from distributed health data repositories, for personalized health advice
3. Supporting patients with rare diseases with patient driven research and the generation of new hypotheses based on patient experiences.
This discussion, covened by the Dubai Future Foundation, focusses on identifying the significance of the concept of well-being for social-science and policy; and the opportunities to measure it at scale.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Sdal air health and social development (jan. 27, 2014) final
1. Health & Social Development Analytics
and Big Data –
A Joint AIR and Virginia Tech Workshop
SALLIE KELLER, DIRECTOR
SOCIAL AND DECISION ANALYTICS LABORATORY
VIRGINIA BIOINFORMATICS INSTITUTE AT VIRGINIA TECH
Social and Decision Analytics Laboratory
2. Starting the Journey
“In attempting to arrive at the truth, I have applied everywhere for information,
but scarcely an instance have I been able to obtain hospital records fit for
any purpose of comparison. If they could be obtained, they would enable us to
decide many other questions besides the ones alluded to. They would show
subscribers how their money was spent, what amount of good was really
being done with it, or whether their money was not doing mischief rather than
good.”
Florence Nightingale (1864)
Social and Decision Analytics Laboratory
3. Social and Decision Analytics Laboratory
• Pressures & Opportunities
of Today
• Big data
– Why important?
– What about privacy?
• Health & Social
Development analytics
– What makes it big data?
– How does big data change
current approaches?
• Selected examples
• Methodology challenges
Outline
4. Health and Social Development Pressures
Source: Congressional Budget Office.
Social and Decision Analytics Laboratory
• Health as a percent of
GDP
– 5% in 1960 to 18% in 2012
• Changing demographics
– Increasing minority
populations
– Rapidly aging populations
– Rural vs. urban living
– Increasing inequality
• Focus on the patient
– Health outcomes
4
5. Health Care Analytics Opportunities
• Drivers behind health care costs
– Technology, infectious and chronic diseases
• Workforce demand
– Care givers, biomedical researchers, IT specialists
• Prevention and personalization
– Changing demographics and lifestyles
Social and Decision Analytics Laboratory
6. Social Development Analytics Opportunities
Social and Decision Analytics Laboratory
• Understanding and anticipating
– Changes in population growth, aging and diversity
– Adapting to increasing urbanization
– Building individual and community resiliency
• Tailoring programs and policies by defined subpopulations
7. Big Data - Doesn’t matter what its called, only
matters what you do with it
Social and Decision Analytics Laboratory
• Big data
– Structured & unstructured
– Collections
• Designed
• Observational/convenience
• Statistics / analytics
– Replication, reproducibility,
representativeness
– Description, association, causation
• prediction ≠ correlation
• Cost drivers
– Analytics and informatics, NOT data collection
8. Now Big Data is Changing Social Sciences
• Social science research
– Traditionally informed by
surveys and statistically
designed experiments
– Clean, well-controlled, limited
in scale (~103)
• Bringing “Big data” to bear
for social policy
– Data informed computational
social science models
– Quantitative social science
methods & practice at scale
Social and Decision Analytics Laboratory
9. Methodological Issues
Social and Decision Analytics Laboratory
New methods and tools are
needed to ensure
– Data access
– Data quality
– Representativeness
– Replication
– Reproducibility
– Characterization of noisy
data
• Managing biases
– Selection bias
– Measurement bias
National Research Council 2013
11. Social and Decision Analytics Laboratory
• European Council 1995/1996:
– “… any information relating to an
identified or identifiable natural
person; an identifiable person
is one who can be identified
(data subject), directly or indirectly,
in particular by reference to an
identification number or to one
or more factors specific to his
physical, physiological, mental,
economic, cultural or social identity.”
• World Economic Forum 2011:
– “… digital data created by and
about people.”
11
Personal Data - New Asset Class
12. World Economic Forum 2013
Social and Decision Analytics Laboratory
Yesterday
• Definition of personal data is
predetermined and binary
• Individual provides legal
consent but not truly engaged
• Policy framework focuses on
minimizing risk to individual
Today
• Definition of personal data is
contextual and dependent on
social norms
• Individual engaged and
understands how data is used
and value created
• Policy needs to focus on
balancing protection with
innovation and economic
growth
12
13. Further Privacy Thoughts
• Will people voluntarily give up their data if they can see a
personal or societal benefit?
• Are norms/expectations changing with generations?
• What are technical fixes for multi-level privacy/
classification?
• What is the optimal level of privacy for studies of interest?
Social and Decision Analytics Laboratory
14. Can we table privacy for the duration of
the workshop?
• Deserves serious, devoted conversation
• We should be leaders in this conversation
• Will need to specifically address as projects develop
Social and Decision Analytics Laboratory
15. Changing Landscape of Health Data
Social and Decision Analytics Laboratory
• Electronic Health Records
• Interoperability challenges
• Public choices
– 23andME
– Google Health
– Health Vault
P. Breugel, Tower of Babel (1563)
16. Personal Health Data
Social and Decision Analytics Laboratory
• Today
– medical history
– lab results
– imaging results (X-ray,
MRI)
– medication records
– Allergies
– vaccination records
– demographic data
– billing information
• Tomorrow
– genome sequence
– Epigenome
– Transcriptome
– Proteome
– Metabolome
– Immunome
– Microbiome
– survey data
– health monitor data
17. Omics
Social and Decision Analytics Laboratory
"Omics" datasets are large,
require sophisticated
interpretation, and will have to
be reinterpreted over time as
knowledge and standard of care
change
• Tomorrow
– Genome sequence
– Epigenome
– Transcriptome
– Proteome
– Metabolome
– Immunome
– Microbiome
– Survey data
– Health monitor data
18. Self Reported Data
Social and Decision Analytics Laboratory
These self-reported data will
vary widely in quality and utility for
research, but will be an important
source of phenotype information
• Tomorrow
– genome sequence
– Epigenome
– Transcriptome
– Proteome
– Metabolome
– Immunome
– Microbiome
– survey data
– health monitor data
19. Tomorrow is Today
• Infrastructure is being created to enable large longitudinal
studies that combine:
– Comprehensive electronic health records
– Behavioral and environmental factors (survey information)
– Genetic information (partial or complete genome sequence)
NIH - Electronic Medical Records and Genomics Network
Wellcome Trust - UK Biobank
Vanderbilt University - BioVU
Kaiser Permanente – Research Genes, Enviro., & Health
Veterans Administration - Million Veteran Program
Social and Decision Analytics Laboratory
20. Tomorrow is Today
• Began collecting DNA in 2007; now has 167,250 samples
• Opt-out program; relatively few patients opt out
• Samples are matched with deidentified EHRs
• Use is restricted to Vanderbilt researchers
NIH - Electronic Medical Records and Genomics Network
Wellcome Trust - UK Biobank
Vanderbilt University - BioVU
Kaiser Permanente – Research Genes, Enviro., & Health
Veterans Administration - Million Veteran Program
Social and Decision Analytics Laboratory
21. Additional Characteristics that Make the Data Big
• Multi-sourced
• Observational
• Noisy
• Multi-purposed
Social and Decision Analytics Laboratory
22. Multi-Sourced Data
Health and social development occurs within context
• Individual and family history and experiences
• Environment
• Access to care, programs, and facilities
• Local, state, and national health and welfare systems
• Political and economic factors
Information communication technology opens opportunity to
capture meta data and provenance of the information
Challenge: integration and interpretation of data captured
under such varied circumstances
Social and Decision Analytics Laboratory
23. Observational Data
• Can come from every stakeholder, source, or technology
that interacts with the patient, care giver, or facility
• Little discrimination on what is captured
– Internet medical surveys, on-line disease tracking, prevention
activities, attitudes on blogs, etc.
• On-demand data from multiple systems
– Social networks, education records, work history, medical
records, extramural activities, etc.
Presents opportunity to study the health and development
processes as the naturally occur
Challenge: manage biases, data quality, and data linkage
Social and Decision Analytics Laboratory
24. Social and Decision Analytics Laboratory
Meanwhile, if the quantity of
information is increasing by
2.5 quintillion bytes per day,
the amount of useful
information almost certainly
isn’t. Most of it is just noise,
and the noise is increasing
faster than the signal.
Nate Silver, 2013
Challenge: uncertainty quantification
Noisy data
25. Multi-Purposed Data
• Individual health and well being versus the population
• Data reuse for multiple purposes
– Macro-level: regional, state, national, and international
– Meso-level: institution-wide
– Micro-level: individuals, cohorts, and groups
An opportunity to more fully use data
Challenge: What is optimal for an individual may not be
optimal for the population and vice versa
Social and Decision Analytics Laboratory
Source: Buckingham Shum, S. (2012)
26. Case Studies from VT Colleagues and
Collaborators
• Bureau of Economic Analysis Health Accounts
• Out of Hospital Cardiac Arrest
• EMBERS
• Mild Cognitive Impairment
• Synthetic Information
Social and Decision Analytics Laboratory
27. Household Consumption Expenditures for Medical Care:
An Alternate Presentation
Ana Aizcorbe, Eli B. Liebman, David M. Cutler, and
Allison B. Rosen
• Health care predicted to reach 20% of GDP by 2020
• Health care expenditures increased ~29% (2002-2006)
• Developing a satellite account on medical care spending
• Data include public and private sources
Survey of Current Business
June 2012:34-47
http://www.bea.gov/scb/pdf/2012/06%20June/0612_healthcare.pdf
32. Open Source Indicators for Forecasting
ILI Case Counts and Rare Disease Outbreaks
Naren Ramakrishnan (PI) – involves large multi-institutional team
• EMBERS: Early Model-based Event Recognition using
Surrogates
• Fully automated processing of data and delivery of warnings
Source
https://www.cs.vt.edu/node/6565
33. Google Flu Trends Google Search Trends Healthmap Weather Twitter OpenTable Parking Lot Imagery
EMBERS Prediction
Pipeline
33
35. Family Triad Perceptions of Mild Cognitive Impairment (MCI)
Karen A. Roberto, Rosemary Blieszner and Tina Savla
• Age-related decline in memory and executive functioning
• 10-20% of individuals aged 65+ have MCI
• Data Sources
– Memory clinics, churches, senior housing
– Family-level data: Elder with MCI age 60+, Primary care partner ,
Secondary care partner
Journal of Gerontology: Social Sciences
2011(6): 756-768
36. reasoning,
planning,
speech,
movement
emotions,
problem-solving
vision perception of
touch, pressure,
temperature,
pain
perception
and
recognition of
auditory
stimuli,
memory
*Executive Function*
Brain Functioning
37. Benefits of Multiple Informants
Complete
Acknowledgement
Families
Partial
Acknowledgement
No
Acknowledgement
Passive
Acknowledgement
38. Synthetic Information – Disease (Pandemic) Evolution
Stephen Eubank, Bryan Lewis, and many others
• Age-related decline in memory and executive functioning
• 10-20% of individuals aged 65+ have MCI
• Data Sources
– Memory clinics, churches, senior housing
– Family-level data: Elder with MCI age 60+, Primary care
partner , Secondary care partner
Source
: Roberto, Blieszner, McCann, & McPherson 2011
FIX
http://supercomputing.vbi.vt.edu/
43. Goals for the Workshop
• Imagine a different world –case studies are examples
• Look for synergistic capabilities to build partnerships
• Assess opportunities to integrate multiple sources of data
and approaches to comprehensively understand health
and social development issues
• Propose prototype projects to work on together to set the
stage for future projects
Social and Decision Analytics Laboratory