Legal Assessment Tool (LAT) - interactive help for data sharing

Institute of Information Systems (HES-SO)

There exist several privacy frameworks for cancer research or biobanking (e.g. ACGT, GenoMatch, caBIG). But most existing privacy frameworks apply the most stringent approach to their data flow and interpret “anonymisation” in a restrictive way. A more flexible approach is needed to guarantee privacy of patient data, but at the same time enable unhindered research. We developed an easy model to display policies and rules for data privacy; it employs the novel concept of "privacy zones for research data flows". The zone model can be used for all important research scenarios. The Privacy Zone Model is built upon the concept of three zones (Care Zone, Non-care Zone and Research Zone) habouring databases, data transformation operators, such as data linkers and privacy filters. Using our model, a risk gradient for moving data from a zone of high risk for patient identification to a zone of low risk can be created for each data flow.

ScienceDirectAvailable online at www.sciencedirect.com

daniatrappit

ScienceDirect Available online at www.sciencedirect.com Procedia Computer Science 113 (2017) 73–80 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2017.08.292 10.1016/j.procs.2017.08.292 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. 1877-0509 Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 00 (2017) 000–000 www.elsevier.com/locate/procedia 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017) Big data security and privacy in healthcare: A Review Karim ABOUELMEHDIa 1, Abderrahim BENI-HSSANEa, Hayat KHALOUFIa, Mostafa SAADIb aDepartment of computer science Laboratory LAMAPI and LAROSERI Chouaib doukali University El Jadida Morocco bEcole Nationale des Sciences Appliquée(ENSA) de Khouribga laboratry IPOSI Université Hassan 1er - Settat, Morocco Abstract The ever-increasing integration of highly diverse enabled data generating technologies in medical, biomedical and healthcare fields and the growing availability of data at the central location that can be used in need of any organization from pharmaceutical manufacturers to health insurance companies to hospitals have primarily make healthcare organizations and all its sub-sectors in face of a flood of big data as never before experienced. While this data is being hailed as the key to improving health outcomes, gain valuable insights and lowering costs, the security and privacy issues are so overwhelming that healthcare industry is unable to take full advantage of it with its current resources. Managing and harnessing the analytical power of big data, however, is vital to the success of all healthcare organizations. It is in this context that this paper aims to present the state- of-the-art security and privacy issues in big data as applied to healthcare industry and discuss some available data privacy, data security, users' accessing mechanisms and strategies. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. Keywords: Big Data, Privacy and security , Big data in healthcare , privacy preserving 1Corresponding author [email protected] Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 00 (2017) 000–000 www.elsevier.com/locate/procedia 1877-0509 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017) Big data security ...

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Aetionomy project

Data Science Institute - Imperial College London

Dr Marc Zimmermann (Fraunhofer SCAI) on the AETIONOMY project. AETIONOMY is a consortium brought together to tackle the problem of the classification of neuro-degenerative diseases. It is a Big Data Approach: A knowledge base comprising curated, re-annotated and well-organised data Modelling and Mining Workflows suited for the generation of new mechanism-hypotheses with a special focus on early dysregulation events A set of testable hypotheses on disease mechanisms of which a subset has been validated in a clinical study. The clinical study links us to the EPAD project A first version of a mechanism-based taxonomy for AD and PD providing the high level structure for a future taxonomy of neurodegenerative diseases http://www.aetionomy.eu/

Regulations, privacy, security for data bridges - Kuchinke

The presentation is addressing the complexity of data sharing from two view points: (1) Ethical, legal and regulatory challenges, (2) Data sharing between Research Infrastructures. A comprehensive analysis of the regulatory landscape of data bridges is provided, including: Data Protection Directive, relevant national data protection acts, Good Clinical Practice (GCP), Animal protection laws, security rules for biosamples, data ownership regulations (intellectual ownership, database laws), and others. Basis was the concepts of Open Data and Open Science, which aims to make scientific research output (publications, data, biosamples, and alogrithms) accessible to all levels of an inquiring including research bey citicen scientists. This includes transparent and accessible knowledge that is shared and developed through collaborative networks and practices such as publishing open research, campaigning for open access and data sharing. But several mechanisms restrict access to data or reuse of data: copyright, patents, database rights, time-limited access rules, political, commercial or legal rules and interests. Researchers are confronted with the question, whether, on what basis and with what limitations, human data can be used freely and made available to support open research and open science. We conducted an analysis of the legal landscape for data sharing, employing concepts from requirements engineering, like the definition and collection of legal requirements for data bridges, which were based on access rules of many database providers. We defined “legal interoperability” of data sharing as interoperability that forms conditions where a combination of rules allows the exchange of data between different data providers. The basis was the creation of legal “requirement clusters” defining applicable rules, roles and policies used by database owners (data controllers). Such "requirement clusters” can act as a kind of „filter" between different data sources to allow for compliant data transfer. To create "requirement clusters” data sharing usage scenarios were built consisting of real-world examples of interaction between data providers during data sharing. Finally, the legal analysis based on five Usage Scenarios and the development of Requirements Clusters for data protection, data security, intellectual property, security of biosamples and animal protection providing constraints and recommendations for legally sound data bridges and the implementation of “legal filters“ for complinat data flow.

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Regulations, privacy and security requirements - Legal interoperability for d...

We are addressing the complexity generated by different ethical, legal and regulatory requirements resulting from data sharing needs between Research Infrastructures. We conducted a comprehensive analysis of the regulatory landscape for creating data bridges: including GDPR (EU Data Protection Directive), relevant national data protection acts, Good Clinical Practice (GCP), animal protection laws, security rules for biosamples, intellectual property regulations. The legal analysis was based on five Usage Scenarios and used for the development of Legal Requirements Clusters for data protection, data security, intellectual property, security of biosamples and animal protection. We intended to create conditions for legal interoperability for data sharing that may be used for the automation of data exchange processes.

eROSA Stakeholder WS1: Ensembl, ELIXIR and engineering interconnections

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Standards for clinical research data - steps to an information model (CRIM).

Standards for clinical research data: Introduction to CDISC standards CDASH, SHARE, PRM and BRIDG and their evaaluation to create a Information model for clinical research (CRIM). In particular, CRIM should allow the integrative usage of medical care data together with clinical research data; it should support the processes of the Learning Health System (LHS). CDASH is Clinical Data Acquisition Standards Harmonization; it identifies the basic data collection fields needed from a clinical, scientific and regulatory perspective to enable more efficient data collection at the Investigator sites. SHARE is a globally accessible electronic library built on a common information model, which enables precise and standardized data element definitions that can be used in studies and applications to improve biomedical research. SHARE is intended to be a healthcare‐biomedical research enriched data dictionary. The Protocol Representation Model (PRM) focuses on the characteristics of a clinical study and the definitions and association of activities within the protocols and defines over 100 common protocol elements. The BRIDG Model is an instance of the Domain Analysis Model. The dynamic component of BRIDG defines the various processes and dynamic behaviour of the domain; the static component describes the concepts, attributes, and relationships of the static constructs which collectively define a domain-of-interest. The CRIM was developed based on activity models and use cases. CRIM specifies the necessary information objects, their relationships and associated activities. It is required to fully support the development of TRANSFoRm project's tools for the Learning Health System. All activity objects of the workflows were defined and characterized according to their data requirements and information needs and mapped to the concepts of established information models including the above mentioned CDISC standards. The best mapping results were achieved with PCROM and it was decided to use PCROM as basis for the development of CRIM. The comparison of PCROM with BRIDG found a significant overlap of concepts but also several areas important to research that were either not yet represented or represented quite differently in BRIDG. Adaption of PCROM to the needs of CRIM was acchieved by adding 14 information object types from BRIDG, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area).

Ontologies for Clinical Research - Assessment and Development

Ontologies for Clinical Research. Ontologies are representations with names and categories, properties and relations between concepts and entities of a domain of knowledge; they are showing the properties of a subject area and how they are related. The purpose of an ontology is to limit the complexity of information and to organize data into information and knowledge. The topic of my presentation deals with Ontology-Based Data Integration for clinical research. Data from clinical trials need to be reused and shared for secondary research purposes. Integrated clinical terminologies are necessary for an efficient clinical trial system. Following ontologies for clinical research were assessed: Clinical Trial Ontology (CTO), Ontology of Clinical Research (OCRe), Ontology for Biomedical Investigations (OBI), Cochrane PICO Ontology, ACGT Master Ontology, Basic Formal Ontology (BFO). The aim of our efforts was to find ontologies to enable and simplify data reuse and data sharing between pre-clinical research, clinical phase I – phase III clinical trials and health data research. Existing ontologies are insufficient for this purpose and it exists a need for an ontology for clinical trial data integration. We want to create such an ontology by joining PICO, OCRe and OBI to form a new, more comprehensive ontology.

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More from Wolfgang Kuchinke

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Temporal relations in queries of ehr data for research

Temporal Relations in Queries of Electronic Patient Records. The main usage scenario for queries covers patient identification and recruitment process for clinical trials. For this purpose an extension of the EHR4CR workbench to support patient recruitment was created. This workbench covers following requirements: Need for built-in privacy protection. Patient identification and recruitment tracking tools have been made available to the clinical sites in the form of the workbench. Each participating clinical site has its own installation only used locally (patient data don't leave the hospital). One important requirement for the workbench is the ability to generate queries with temporal relations and constraints for eligibility criteria. Queries in EHRs often have a temporal component. But available user interfaces allow only simple queries with simple temporal expressions. Time points and time intervals are the main concepts that must be considered, being related to instantaneous events (e.g. a single myocardial infarction), or to situations lasting for a time span (e.g. a drug therapy for 2 weeks). Intervals can be represented using time points by their upper and lower temporal boundaries: the start and end time points. Temporal relations (e.g before, after) can be expressed via anchors. The dates of these anchor events can be retrieved and event dates relative to this anchor events can be calculated. EHR4CR decided to build the workbench upon a simple, time-stamp database. To each patient’s attribute a time-stamp, which corresponds to the time of the attribute’s occurrence was assigned. The processing of temporal intervals was necessary since many questions dealing with inclusion / exclusion criteria often involve complex temporal periods. A graphical interface to use boxes for querying with temporal relations was created to simplify query generation. We think that the easiest way to specify temporal operators is with an user interface based on the combination of boxes. Temporal operators based on Allen’s algebra were included. Expressions are displayed as graphic boxes and combined by operators. Events are specified and a temporal operator selected from a predefined list.

Requirements engineering scenario based software requirement specification

Requirements Engineering – Writing the Software Requirements Specification (SRS). A CRI Group Workshop. The requirements engineering approach employed successfully in the EHR4CR process is shown and discussed in order to extract lessons learned and to use it for new projects. Requirements engineering is the process of eliciting stakeholder needs and desires and develope them into an agreed set of detailed requirements. It serves as basis for all subsequent software development activities. In general, a project begins with the requirement acquisition phase and ends with the specification of requirements in form of the Software Requirement Specification (SRS). Requirements specification may even be used to manage the consistency of the entire system. Learning from the Requirements Engineering process in the the EU project EHR4CR. Especially the topics of Requirements Scenarios in the process of requirement gathering and the iterative writing and validation of software requirements specification (SRS) document can be applied to new projects. The Requirements Process consists of 4 steps: Requirements Elicitation – the art to receive meaningful requirements. Requirements Analysis – iterative improvement of quality of requirements. Writing the Requirements Specification document (Software Requirement Specification) and Requirements Validation - this is also done iteratively with several workshops. Novel is the introduction of an iterative process for requirements engineering. Start with only a subset of software requirements, iterate the collection and validation until the full system is implemented. In each iteration, design modifications are made and new functional capabilities are added. Following tools for requirements gathering were used: Use Cases, Descriptions of current situation and workflow, Context diagram, Stakeholder interviews, Scenarios and Use Case workshops. A novel scenario based approach for requirements engineering is being introduced: The domain scenario is used to estimate probable effects (situation analysis and long-range planning). The domain scenario is broken down into high-level "Usage Scenarios". Usage Scenarios describe critical business interactions and their anticipated operations; they serve as context for the use cases and the generation of requirements; they make sure requirements are complete. Development of the SRS with involvement of scenarios: 1. Begin with Domain Scenarios; 2. Development of Usage Scenarios; 3. Software Requirements Specification document. Several round of change management were employed during writing the SRS. This possibility for correction and improvement ensured that the requirements are of high quality and applicability.

Computer validation of e-source and EHR in clinical trials-Kuchinke

Clinical Trials in the Learning Health System (LHS): Computer System Validation of eSource and EHR Data. The question that was addressed: How to make a clinical trial data management system that uses EHR data, Patient Reported Outcome (PRO) and eSource data as part of the Learning Health System compliant with regulations and with Good Clinical Practice (GCP)? The Learning Health System (LHS) connects health care with translational and clinical research. It generates new medical knowledge as a by-product of the care process and its aim is to improve health and safety of patients. The LHS generates and applies knowledge. For this purpose, clinical research, which is research involving humans, must be part of the LHS. Two general types of research exists: observational studies and clinical trials. Clinical data drive the LHS, because results from randomized controlled trials are seen as “gold standard” for medical evidence. For this reason the concept of using data gathered directly from the patient care environment has enormous potential for accelerating the rate at which useful knowledge is generated. All computer systems involved in clinical trials must undergo Computer System Validation (CSV). For this process, a legal framework for the TRANSFoRm project was developed. It was used for data privacy analysis of the data flow in two research use cases: an epidemiological cohort study on Diabetes and a randomised clinical trial about different GORD treatment regimes. Computerized system validation is the documented process to produce evidence that a computerized system does exactly what it is designed to do in a consistent and reproducible manner. The validation of electronic source data in clinical trials presents many challenges because of the blurring of the border between care and research. Here we present our approach for the validation of eSource data capture and the developed documentation for the CSV of the complete data flow in the LHS developed by the TRANSFoRm project. An important part hereby played the GORD Valuation Study.

Personalized medicine tools for clinical trials - Kuchinke

Tools for personalised medicine in clinical trials. The implementation of clinical trials in personalized medicine is a different way of doing clinical research, compared to the standard way of large clinical trials aiming for statistical significance. Personalized medicine uses a medical model that separates people into different groups with medical decisions, practices, drugs, interventions being tailored to the individual patient based on their predicted response. Basis for this approach is the progress of the study of the human genome and its variation over the last two decades. Especially advancements in automated DNA sequencing, PCR technologies and the use of expressed sequence tags (ESTs), cDNAs, antisense molecules, small interfering RNAs (siRNAs). But the adoption of personalized medicine requires an active and flexible and highly integrated infrastructure, which must allow the joining of many different competences and technologies. We asked the question: can the tools developed for personalized medicine in the p-pedicine project be employed effectively in a clinical trials network to support personalised clinical trials? We conducted an analysis of tool integration and the evaluation of tool usage requirements. Based on the survey results, the tendency for the clinical trial network ECRIN is to use software as a service in the form of SaaS or ASP. ECRIN data centres will (probably) not install and employ p-medicine tools in one of their data centres. A robust business model for the provision of services and the implementation and employment of tools does not yet exist. How can the personalized medicine infrastructure p-medicine and the clinical trials network ECRIN gain from each other to allow the conduct of personalized clinical trials? We suggest a business model, in which personalized medicine infrastructures and clinical trials networks exchange their services to gain jointly from each other. An integration of networks by reciprocal exchange of services may be the solution. Not only software as a service will be exchanged, but also knowledge, personnel and staff trainings.

Kuchinke Personalized Medicine tools for clinical research networks

Personalized medicine for clinical trials networks. The p-medicine project is presented. It deals with the creation of an integrative infrastructure for Personalised Medicine, which aims to accelerate personalized medicine and personal clinical research. For this purpose p-medicine developed a comprehensive set of software tools, including ObTiMA data management system, Patient Empowerment Tool, data mining, data warehousing, biobank access, decision support, image annotation (DrEye) and simulation (Oncosimulator). Here we show the evaluation of some of the p-medicine tools for their suitablity to perform clinical trials. Is their usage conform with regulations and standards (GCP, GDPR, GAMP, computer system validation)? Can these tools be integrated into the existing systems (IT infrastructure / organisational framework) of an international clinical trials network (ECRIN)? To perform clinical trials, a legal and ethical framework based on international requirements and approved concepts for data security must be adopted. GCP (Good Clinical Practice) is such an international ethical and scientific quality standard for designing, recording and reporting trials that involve the participation of human subjects. Evaluation of usability of p-medicine software tools for clinical trials was done with two surveys: (1) survey of p-medicine tools in the ECRIN network and (2) p-medicine developer survey. The tool integration topics contained questions about the employment of the right Clinical Data Management System (CDMS) at the many ECRIN centres. There is competition between different solutions, like VISTA (EORTC) MACRO, secuTrial, RAVE, OpenClinica. CDMS should be usable for all types of trials and the usability in clinical trials must be demonstrated by integration of biobank access / safety functions. Only ObTiMA is able to specifically address the challenges of personal medicine clinical trials. The results of the evaluation was that there exists some compliance gaps for quality management during software development, no complete GCP compliance yet and the missing of a robust business model for software sustainability. To address the latter, a Reciprocal Integration approach was developed to integrate p-medicine tools into clinical research networks.

Kuchinke Clinical Trials Networks supported by tools and services

Clinical Trials Networks supported by Tools and Services from Infrastructure Projects. International clinical trials are a challenge to management. Though, the number of clinical trials worldwide is increasing by around 10% per year, approvals for new molecule entities and biomedical licenses show little long-term increase. Main challenges are the need to recruit and retain sufficient numbers of patients and the successful implementing e-Clinical Trials technologies, especially for trials incorporating ePRO (patient reported outcome) and eRecruitment services. We suggest that clinical trials networks should cooperate with infrastructure projects to enable the implementation of eTrials and patient-centric trials. Clinical trials systems can be optimised by coordination through information sharing and collaboration and by building networks. Here infrastructures can function as enablers by the provision of software tools, especially patient centric trials, ePRO (Patient Reported Outcome) and data collection and recruitment using EHRs (Electronic Health Records) and the implementation of nessessary data protection, privacy protection and identity management. As example for a clinical trials network ECRIN is addressed. ECRIN is a public, non-profit organisation that links scientific partners and networks across Europe to facilitate multinational clinical research. We suggest the integration of clinical research at ECRIN with several infrastructure services developed by BBMRI, EATRIS, EUDAT, TransForm, p-medicine, BioMedBridges, etc., resulting in an increase in interoperability of clinical data management, biobanking, genetic databases, Electronic Health Records (EHR), query systems, data warehouses, data repositories and imaging data.

Kuchinke-The Learning Health System (LHS) Introduction and meeting 1fpdf

We present the results of the conference: The Learning Health System (LHS) in Europe - A conference held in Brussels. A comparison between the US and the European approach to implementing a LHS was achieved. The perspective from the US was given by Charles Friedman; the European point of view by Federico Paoli. C. Friedman also introduced the concept of Learning by virtious cycles. An example for the implementing the LHS in Europe was demonstrated in detail: the TRANSFoRm project, which aims to develop and demonstrate methods, models, standards and a digital infrastructure for three specific components of the LHS: – Genotype-phenotype epidemiological studies – Randomised Clinical Trials (RCTs) with both data and trial processes embedded within the functionality of EHRs – Decision support for diagnosis, based on clinical prediction rules Other topics presented were: Sustainability and Business Development ofthe LHS. Knowledge Management & Data Standards in the LHS. Data privacy and security in the LHS (EHR4CR solutions for privacy protection, HIPAA - US experiences with privacy protection, TRANSFoRm Zone Model for data privacy protection, IMI code of practice for the secondary use of health data). Knowledge Translation and Decision Support in the LHS (Decision support for interventions in the LHS, TRANSFoRm Decision support for diagnosis, TRANSFoRm Extensible model for diagnostic evidence, CareWell - A learning integrated care system, Improving quality of care with routine data: the perspective of a statutory health insurer, The impact on LHS). CDISC and clinical trial standards in the LHS, pan-European platforms for the reuse of EHRs, TRANSFoRm and CDISC standards, Provenance and GCP for real world clinical trials, Using Health Data for Innovative Trial Designs, ...

Importance of data standards and system validation of software for clinical r...

We present our evaluation of existing data standards for clinical trials. For this purpose a survey about the importance of data standards for clinical trials centers and EDC software companies were conducted. Electronic data capture in clinical trials uses a computerized system designed for the collection of clinical data in electronic form in Case Report Forms (CRF). It also covers medical data captured during clinical trials, safety data related to clinical trials, and patient reported outcome. The degree of implementation of standards, like CDISC ODM in available EDC software products was evaluated. Failure to establish data standards will make it difficult or impossible to connect data between different systems for efficient clinical study execution. The next step after purchasing a software solution is the computer system validation. Validation is about bringing computerized systems into regulatory compliance and making them compliant with GCP, GLP and GMP and other regulations (e.g. data protection). The basis standard for validation is provided by the GAMP Good Practice Guide, which provides a framework of best practices to ensure that computer systems are suitable for use and compliant with the legislation. The newest version uses a risk-based approach to computer system validation A system is evaluated and assigned to a predefined category based on its intended use and complexity. For validation one should define how all elements of the computer system are supposed to work (functional requirements), develop corresponding scripts and test routines to validate it is functioning as it should.

Legal and Ethical Issues of International Clinical Trials

An analysis of regulatory, ethical and international aspects of clinical trials is presented, covering all relevant regulatory and ethical requirements for the conduct of international clinical trials. Our analysis is extended by discussing certain legal and ethical issues that are of importance for personalised medicine, especially for the use of software tools dealing with sensitive patient data and supporting patients in their decision making. Following ethical issues connected to the use of personalised medicine tools are discussed: Tools that have been completely developed and tested for use in a medical environment and are GCP compliant, tools that capture patient data that have to be accurate, reliable and correct. Compliant clinical data management systems (CDMS) that are based on computer system validation. The ethical environment for GCP compliance in data management (audits, subject identification codes, importance of retention of sponsor-specific essential documents). Security of the portal so that unauthorised persons do not have access; the issue of personal data used in clinical trials and “directly" or "indirectly" identifiable data; legal and ethical issues arising by the deep integration of tools for clinical trials in personalised medicine; and issues caused by software that falls under the medical device law. An important aspect is to ensure patient’s autonomy during use of integrated tools for Patient Empowerment.

Computer System Validation - The Validation Master Plan

Computer System Validation (CSV) is the process used to ensure and document that a computerbased system is operating according to predefined requirements. CSV is necessary when replacing paper records, like Case Report Forms for clinical trials, with an electronic system within the highly regulated data zone that impacts public health and safety. Necessary validation documents are for example the Standard Operating Procedures (SOPs), which outline how the computer system should be used. Here, we describe in detail the System Validation Master Plan, the most important document in Computer System Validation. In contains topics, like: Validation Policy, Definition of Validation, Rules and Regulations in CSV, Legal basis, FDA 21 CFR Part 11, FDA Guidance for industry, ICH Guideline GCP, Annex 11 EU-GMP, Validation Philosophy, Organisation validation document, Audit Reports, Organisation guidelines, Organisation quality management handbook, etc. The steps of the Validation Life Cycle are: 1. System Specification, 2. System Classification, 3. Validation Planning, 4. Establishing of the validated state, 5. Maintaining the validated state, 6. System Retirement.

Evaluation of the importance of standards for data and metadata exchange for ...

Electronic Data Capture (EDC) in clinical research can ensure high-quality, clean data capture. Especially easy is the capture of repeating measurement like Adverse Events. Here, a project is describes that evaluates the importance of data and metadata exchange for clinical research with the focus on EDC systems, standards that are relevant and the Computer System Validation (CSV) of EDC systems. The project creats process descriptions, as well as necessary documents and checklists for any GCP-compliant system validation (Computer System Validation, CSV), like the Traceability Matrix. Research networks are made aware of the necessity and importance of using EDC systems and conducting the computer system validation for regulatory compliance. The important role of CDISC standard, the standard for collection, exchange, submission to authorities and archiving of data from clinical studies is discussed in detail.

More from Wolfgang Kuchinke (13)

Standards for clinical research data - steps to an information model (CRIM).

Ontologies for Clinical Research - Assessment and Development

Temporal relations in queries of ehr data for research

Requirements engineering scenario based software requirement specification

Computer validation of e-source and EHR in clinical trials-Kuchinke

Personalized medicine tools for clinical trials - Kuchinke

Kuchinke Personalized Medicine tools for clinical research networks

Kuchinke Clinical Trials Networks supported by tools and services

Kuchinke-The Learning Health System (LHS) Introduction and meeting 1fpdf

Importance of data standards and system validation of software for clinical r...

Legal and Ethical Issues of International Clinical Trials

Computer System Validation - The Validation Master Plan

Evaluation of the importance of standards for data and metadata exchange for ...

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Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.

role of pramana in research.pptx in science

sonaliswain16

NuGOweek 2024 Ghent - programme - final version

pablovgd

Body fluids_tonicity_dehydration_hypovolemia_hypervolemia.pptx

Nistarini College, Purulia (W.B) India

RNA INTERFERENCE: UNRAVELING GENETIC SILENCING

AADYARAJPANDEY1

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 .

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.

Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...

Sérgio Sacani

Since volcanic activity was first discovered on Io from Voyager images in 1979, changes on Io’s surface have been monitored from both spacecraft and ground-based telescopes. Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images show that a plume deposit from a powerful eruption at Pillan Patera has covered part of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive optics at visible wavelengths.

The ASGCT Annual Meeting was packed with exciting progress in the field advan...

Health Advances

Nucleic Acid-its structural and functional complexity.

Seminar of U.V. Spectroscopy by SAMIR PANDA

SAMIR PANDA

Hemostasis_importance& clinical significance.pptx