Central Repository Research Articles

An Approach of Stipulation Change Management Using Cloud Computing

Every technology project's successful implementation depends on the requirements. Changes in stipulations at any point of the software development life cycle are considered a healthy operation. Nevertheless, this transition is a little simpler in a co-located setting than in a decentralized system in which participants are spread over more than one area. This presents numerous challenges, such as coordination, communication & control, effective and efficient management of changes and the management of central repositories. Cloud computing can therefore be used to mitigate these stakeholder problems. We used a case study to test the system of cloud computing.

Smart Wireless Sensor Technology for Healthcare Monitoring System Using Cognitive Radio Networks

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients’ health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Machine-Generated Colon Class Numbers: Automatic Classification of Indian Literary Works in the Wikidata Environment

Wikidata is emerging rapidly as an omnipresent information space. The main objective of Wikidata is to create a structure to represent all human knowledge in many different languages. It is a free, Linked Open Dataset (LOD) that can interact with humans and machines and act as a bridge between them. Wikidata serves as a central repository for structured data of all other Wiki-based applications (like Wikipedia, Wikibooks, and Wikinews), integrating them into a coherent unit. It is a multilingual, collaboratively editable central repository of structured and linked open data. Interestingly, Wikidata is based on a facet-based linked open data approach like, the theory of facet analysis proposed by Ranganathan. The literary works available in Wikidata are linked with the author of the work, the language of the work, and other important attributes like genre, publication year, and so on. The author of the work has properties like, date of birth, occupation, native language, writing language, and so on. These faceted linked data elements of literary works can be joined together based on the rule base of the Colon Classification (6th edition). The synthesis of the Colon class number is therefore amenable to automation in the Wikidata environment by using a set of suitable JavaScripts (these JavaScripts are called gadgets in the Wikidata environment). One such gadget is CCLitBox, developed by Stefano Bargioni (a librarian) and Carlo Bianchini (a teacher in LIS). This research study explores the applications of CCLitBox in generating CC 6th edition-based class numbers for Indian literary works.

1150-P: Improving Early Diagnosis of Presymptomatic Stage 2 Type 1 Diabetes with a Sample-Sparing Assay

Teplizumab was approved by the FDA in Nov 2022 as the first therapeutic to delay progression of type 1 diabetes from pre-symptomatic stage 2 to clinical onset stage 3. Nevertheless, individuals with stage 2 type 1 diabetes do not exist in hospital systems, and more than 85% of them do not have family history. Large scale population testing remains the only effective way of identifying eligible patients for disease interventional drugs. Thus, accurate, accessible and low cost tests are urgently needed to implement such screening programs. To that end, we have conducted a pilot retrospective screening efficacy study with blinded serum specimens from the Diabetes Prevention Trial of Type 1 Diabetes (DPT-1) study from the NIDDK central repository. A total of 97 serum specimen from subjects that either developed T1D within 5 years (i.e. 50 progressors) or had at least 5 years of follow-up (i.e. 47 progressors) were included in the study. A subject was considered high-risk for T1D progression if positive for two or more islet autoantibodies, and vice versa. Under this scheme, the multiplex ADAP assay achieved PPV and NPV of 49% and 92% for 5 years progression risks. The matching radiobinding assay (RBA) had PPV and NPV of 50% and 77% in this cohort. The improved NPV stemmed from 12 progressors tested positive for multiple islet autoantibodies by multiplex ADAP but not by RBA. Notably, 6 out of these 12 subjects tested positive for multiple islet autoantibodies by RBA in subsequent sampling events with a median delay of 2.8 years compared to multiplex ADAP. In summary, the multiplex ADAP assay could be an ideal tool for large scale identification of individuals with stage 2 type 1 diabetes due to its sample-sparing nature (i.e. 4µL serum input), non-radioactiveness, compatibility with widely-available real-time qPCR instruments and favorable risk prediction capability. Disclosure J. Tsai: Stock/Shareholder; Enable Biosciences. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R44DK124009-01)

Fifty years of collecting wild Helianthus species for cultivated sunflower improvement

Abstract Wild Helianthus species have been undeniably beneficial in sustaining the sunflower crop by providing plant breeders with a diverse genetic pool of potentially useful traits. Exploration to collect populations of wild sunflowers is one of the more difficult and challenging activities in the conservation and utilization of these valuable genetic resources. The logistics of collecting requires careful planning, locating the target species, obtaining permission to access and collect, and timing the exploration to ensure the availability of mature seed. The US Department of Agriculture, Agricultural Research Service (USDA-ARS) established the wild Helianthus seed collection in 1976 at Bushland, Texas with the goal of collecting and conserving the broadest representative genetic diversity possible and serving as a central repository of germplasm and related information. In 1985 this collection was transferred to UDSA-ARS, North Central Regional Plant Introduction Station, Ames, Iowa. Over the last half century, 37 explorations were undertaken covering 175,000 km to collect the 53 Helianthus species from their distributional ranges in the forty-eight conterminous states in the US, three Canadian Providences (Manitoba, Saskatchewan, and Alberta), Argentina and Australia. The many explorations have created a global crop wild relatives (CWR) genebank collection. The current wild CWR sunflower genebank contains 2562 accessions of 53 species with 1065 wild Helianthus annuus accessions (42 %), 617 accessions representing populations of the 13 other wild annual species (24 %), and 880 accessions representing 39 perennial species (34 %). This collection is the largest and most genetically diverse ex situ sunflower collection in the world and is vital to the conservation of wild sunflower species for the global sunflower community.

Achieving pan-microbiome biological insights via the dbBact knowledge base.

16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of experiments when interpreting their own findings in a broader context. To bridge this gap, we introduce dbBact - a novel pan-microbiome resource. dbBact combines manually curated information from studies across diverse habitats, creating a collaborative central repository of 16S rRNA amplicon sequence variants (ASVs), which are assigned multiple ontology-based terms. To date dbBact contains information from more than 1000 studies, which include 1500000 associations between 360000 ASVs and 6500 ontology terms. Importantly, dbBact offers a set of computational tools allowing users to easily query their own datasets against the database. To demonstrate how dbBact augments standard microbiome analysis we selected 16 published papers, and reanalyzed their data via dbBact. We uncovered novel inter-host similarities, potential intra-host sources of bacteria, commonalities across different diseases and lower host-specificity in disease-associated bacteria. We also demonstrate the ability to detect environmental sources, reagent-borne contaminants, and identify potential cross-sample contaminations. These analyses demonstrate how combining information across multiple studies and over diverse habitats leads to better understanding of underlying biological processes.

Indigenous data sovereignty in the context of big data

AbstractBackgroundDespite evidence that 1 of every 3 American Indian / Alaska Native (AI/AN) elders will develop dementia, they remain substantially underrepresented in Alzheimer’s disease and related dementias (ADRD) research. In our partnership with the Oneida Nation of Wisconsin, scientists from the Wisconsin Alzheimer’s Disease Research Center (ADRC) find ourselves at the intersection of two influential and opposing movements: 1) a movement to ensure Tribal data sovereignty, driven by abuse stemming from unrestricted access to AI/AN data and samples; and 2) researchers' push toward free and unfettered access to big datasets, including genetic data. National ADRD datasets include information from AI/AN participants for whom Tribal affiliation is unknown and there is little to no guidance on how to conduct research with datasets where AI/AN race is identified but Tribal affiliation is unknown. We see a need to establish guidelines for researchers utilizing ADRD big datasets and repositories containing data and samples from AI/AN of unknown Tribal affiliation.MethodWe have contacted stakeholders of ADRD research using AI/AN data of unknown Tribal affiliation, including the National Institute of Aging (NIA) Alzheimer’s Disease Research Centers Program, the NIA Office of Special Populations, the National Alzheimer’s Coordinations Center, the National Centralized Repository for Alzheimer’s Disease and Related Dementias, the National Institute of Health Tribal Health Research Office, and AI/AN scientists who are stakeholders in the use of AI/AN data. Future efforts will involve Alzheimer’s Disease genetic, biomarker sample, and data repositories, and AI/AN elders, youth, and Tribal members.ResultWe plan to develop a process through which proposed analyses that include AI/ANN data will be culturally informed and reviewed by AI/AN scientist ‐ e.g., an AI/AN Advisory Committee. This would ensure that proposals to use AI/AN data and samples from ADRD big datasets and repositories do not inflict further harm to this historically marginalized group. Moreover, the review would ensure that data sovereignty measures are addressed prior to analysis and publication.ConclusionWe propose to convene an AI/AN Advisory Committee to guide ADRD researchers on culturally responsible management of data, samples, and genetic information collected from AI/AN participants, their Tribes, and their Nations.

Global analysis of the yeast knockout phenome.

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Digital Healthcare Application

The population of the world is multiplying with each coming year and so are the diseases and health issues. There is a rise in the need for blood as the population grows. There are many potential blood donors thanks to the world's expanding population. Blood banks play an important role in the process of collecting blood and managing blood stocks, approving blood requests, updating donations, and updating available blood types. The use of digital healthcare software makes it easier for patients and healthcare professionals to communicate with one another. With the help of this function, medical professionals such as doctors and therapists can interact intimately with their patients and provide them with very individualized health information. The software system is an online healthcare management system that helps in managing various blood banks and healthcare operations effectively. The project comprises a central repository with a variety of blood deposits and related information. These particulars include blood type, storage location, and storage date. These specifics aid in preserving and keeping an eye on the blood deposits. The project is an online system that enables users to determine whether the blood bank has the required blood deposits for a specific group. Moreover, the system also has added features such as patient name and contacts, blood booking, and even use for certain blood groups provided in case of emergency. Also, the project has various other health care facilities like providing contact of hospitals with dialysis services, booking blood test schedules online scheduling of appointments, ambulance services, and ventilator services. Key Word: Flutter, SQL, Languages, Database

A Systematic Review of Blockchain-based Identity Management Solutions

The involvement of digital identity in almost all online services contributes to the growing reliance on Identity Management Systems (IDMS) that establish, verify, and manage digital identities. However, digital identities are still kept in central repositories. Which are controlled by a single authority that may have many vulnerabilities due to low security, leading attackers to exploit these vulnerabilities and causing various security breaches such as identity theft or disclosure of sensitive information. Additionally, powerful entities who have access to these repositories, could gather and abuse users' information without their knowledge or consent. The concept of Self-sovereign Identity (SSI) allows users to exert ownership of their identity and gain insight into how their data is being used. The development of Blockchain technology has made a breakthrough in achieving SSI by giving individuals the ability to be the final arbiter of who can access and use their own identity. This paper overviews the traditional identity management (IdM) models and presents the next generation of distributed IDMS using Blockchain technology that targets user-centricity and eliminates the identity provider as a trusted third party. Furthermore, It gives an analysis of the recent Blockchain-based IdM solutions, discussing their architecture, components, and features. It also, reveals their weaknesses to identify the gaps between these solutions for future secure IDMS.

Phenopacket-tools: Building and validating GA4GH Phenopackets.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

Effect of demand uncertainty on omnichannel distribution network design strategies

AbstractTo investigate the effect of uncertain demand on various omnichannel distribution network strategies and analyze the impacts of various online order fulfillment policies, a two‐staged network optimization model is built. The two stages are a deterministic model to define the optimal distribution network and a simulation model to determine the optimal network flow to maximize the total profit for a given distribution network and online order fulfillment policy. This study shows the importance of taking demand uncertainty, requested delivery time, and customer service level (CSL) into account in designing an omnichannel distribution network. The results of the simulation model indicate that shipments from warehouse (SFW) and hybrid strategies more successfully cope with variation because there is a greater profit decline in shipments from store (SFS) strategies. The results also show that applying a dynamic assignment policy plays an important role in handling variation in demand. The study shows that, as demand uncertainty and CSL increase, having products available in central repositories becomes even more important to cope with demand variation. The study also indicates SFS strategies that seem more profitable in the assumed‐to‐be deterministic model might be misleading because SFS strategies become less profitable (compared to SFW strategies) when demand uncertainty is taken into account.

Creation of a Memory Disorders Clinic Biorepository to Characterize a Clinic Population and Accurately Refer to Research Studies (P2-6.006)

<h3>Objective:</h3> 1) To build a biorepository of blood samples from a memory disorders clinic population, 2) To make samples available to Alzheimer’s Disease and Related Dementias (ADRD) researchers, 3) To further characterize the samples with plasma biomarkers <h3>Background:</h3> The Duke University Memory Disorders Clinic receives requests from researchers for participants from our clinic population and enrolls patients in clinical trials. Results of demographic and clinical information, genetic testing and plasma biomarkers will characterize patients for referral to research studies with increased accuracy. <h3>Design/Methods:</h3> We approach Duke Memory Disorders Clinic patients to contribute a blood sample for ADRC research. Staff processes and stores the sample. A portion is banked at the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD). NCRAD performs APOE testing free of charge and returns results to Duke. Staff enters patient demographics and clinical information from the EHR into a REDCap database. Information from REDCap including APOE status is gathered in a publicly viewed secure web-based platform developed at Duke called the Alzheimer’s disease RElational and iNtegrated dAtaset (ARENA). Researchers at Duke and outside institutions can request samples or participants. <h3>Results:</h3> We collected samples from 115 patients. Patients continue to be enrolled. Demographics include 85% white participants, 11% black participants, 4% other race/unknown; 56% female; 55% with family history of Alzheimer’s disease or dementia; average age 76. Diagnoses include 40% mild cognitive impairment; 27% clinical Alzheimer’s disease; 24% dementia; 14% normal cognition; 3% subjective cognitive impairment. Average MoCA score is 20. The rate of APOE 4 status is overall 53% with 5% APOE 2/4, 38% APOE 3/4, 10% APOE 4/4. <h3>Conclusions:</h3> The rate of APOE4 in the biorepository is higher than in the general population. The biorepository provides a rich source of APOE4 samples and participants for research. In the future, we will evaluate plasma-based biomarkers (NfL, Aβ40/42, p-tau). <b>Disclosure:</b> Dr. Johnson has nothing to disclose. Mr. Perry has nothing to disclose. The institution of Dr. Parker has received research support from NIH. Dr. Liu has received personal compensation in the range of $0-$499 for serving as a Consultant for Darwin Research Group. Ms. Beam has received personal compensation in the range of $500-$4,999 for serving as a Faculty for Cognitive Assessment Toolkit with AAPA/PA Foundation. Ms. Sanfilippo has nothing to disclose. The institution of Dr. Lutz has received research support from NIH. Dr. O’Brien has nothing to disclose.

Using Pathology Synoptic Reporting Data to Create Individual Dashboards for Pathologists and Surgeons.

Electronic synoptic pathology reporting using xPert from mTuitive is available to all pathologists in British Columbia, Canada. Comparative feedback reports for pathologists and surgeons were created by using the synoptic reporting software. To use data stored in a single central data repository to provide nonpunitive confidential comparative feedback reports (dashboards) to individual pathologists and surgeons for reflection on their practice and to use aggregate data for quality improvement initiatives. Integration of mTuitive middleware in 5 different laboratory information systems to have 1 software solution (xPert) sending discrete data elements to the central data repository was performed. Microsoft Office products were used to build comparative feedback reports and made the infrastructure sustainable. Two different types of reports were developed: individual confidential feedback reports (dashboards) and aggregated data reports. Pathologists have access to an individual confidential live feedback report for the 5 major cancer sites. Surgeons get an annual confidential emailed PDF report. Several quality improvement initiatives were identified from the aggregate data. We present 2 novel dashboards: a live pathologist dashboard and a static surgeon dashboard. Individual confidential dashboards incentivize use of nonmandated electronic synoptic pathology reporting tools and have increased adoption rates. Use of dashboards has also led to discussions about how patient care may be improved.

Abstract 2068: MCAP Explore: A self-service data exploration and cohort building tool for oncologists

Abstract The goal of the described work was to create a self-service tool for data exploration and cohort creation, facilitating feasibility assessments for research studies and providing insights into the primary clinical and demographic characteristics of the Moffitt patient population. The Moffitt Cancer Analytics Platform (MCAP) was designed to enable seamless end-to-end data lifecycle management, promote data democratization, and reduce data duplication by feeding diverse, typically siloed local data streams into a central data repository. Eight distinct sources including the Moffitt electronic medical record, cancer registry, billing systems and clinical trial management system feed into an Amazon S3-based data lake. Raw data are cleaned, standardized for research use, and stored in the enterprise data warehouse in Snowflake. To date, over 80 distinct tables and 2,200 data elements are captured, representing over 500,000 patients. To provide transparency into these data assets, Moffitt partnered with phData to create a custom tool for data self-service, MCAP Explore. Built on the Sigma cloud analytic platform, Explore is a highly customized interactive workbook that allows the user to filter the total patient population on a broad range of clinical and demographic characteristics to identify cohorts of interest, with applications ranging from research study feasibility assessments to operational oversight of the composition of the patient population and where disparities may exist in disease characteristics, treatment or outcomes. In addition, the user is able to view a broad range of visualizations summarizing the resulting cohort and deep-dive into the specific records available. The features that distinguish Explore from similar tools include: a) the broad range of data domains covered by the available filters (demographics, diagnoses, appointments, labs/vitals, treatment, study enrollment/consent, patient-reported, biospecimens, molecular); b) the ability to apply any number of filters in an arbitrary order, on data that is both one-to-one and many-to-one with the patient, while maintaining important linkages between record types; c) full customization of the product for our oncology-specific use cases, facilitating transparency into the underlying data model and flexibility for future expansion; d) no data being required to leave the Moffitt environment, and de-identification processes governed at an institutional level and applied within Snowflake being automatically inherited within Explore by connection to our institutional Active Directory accounts and Snowflake user roles. We will present detailed examples of tool functionality and describe the underlying data model and design decisions required to accommodate a broad range of complex oncology-specific use cases. In addition, we will present early usage metrics and next steps for expansion into new data domains including imaging. Citation Format: Rachel Howard, Phillip Reisman, Patricia Lewis, Rodrigo Carvajal, Chandan Challa, Mark Ruesink, Joe McFarren, Katrina Johnson, Mukund Sridhar, Kedar Kulkarni, Dana E. Rollison. MCAP Explore: A self-service data exploration and cohort building tool for oncologists [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2068.

MP78-04 PRO-FIBROTIC SERUM CYTOKINES ARE INCREASED IN PATIENTS WITH ANTERIOR URETHRAL STRICTURE DISEASE

You have accessJournal of UrologyCME1 Apr 2023MP78-04 PRO-FIBROTIC SERUM CYTOKINES ARE INCREASED IN PATIENTS WITH ANTERIOR URETHRAL STRICTURE DISEASE Wade Gutierrez, Yi Luo, Denise Juhr, Benjamin Breyer, Sean Elliott, Jeremy Myers, Alex Vanni, Katherine Christel, Laila Dahmoush, and Bradley Erickson Wade GutierrezWade Gutierrez More articles by this author , Yi LuoYi Luo More articles by this author , Denise JuhrDenise Juhr More articles by this author , Benjamin BreyerBenjamin Breyer More articles by this author , Sean ElliottSean Elliott More articles by this author , Jeremy MyersJeremy Myers More articles by this author , Alex VanniAlex Vanni More articles by this author , Katherine ChristelKatherine Christel More articles by this author , Laila DahmoushLaila Dahmoush More articles by this author , and Bradley EricksonBradley Erickson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003355.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Anterior urethral stricture disease (aUSD) is a heterogeneous condition with complex pathophysiology. Little is known about systemic factors predisposing to or resulting from stricture formation. To address this gap in knowledge, we assessed levels of 27 cytokines in serum from patients with and without strictures. We hypothesized that patients with strictures would have distinct serum cytokine profiles relative to controls. METHODS: Five centers prospectively enrolled patients into an NIDDK-funded study evaluating local and systemic inflammatory patterns in aUSD. Subjects underwent standard urethroplasty per surgeon routine and tissue was sent to a centralized tissue repository. Serum was collected prior to stricture repair. Levels of 27 cytokines were assessed by ELISA. H&E-stained sections of stricture tissue were examined by a single, blinded pathologist and evaluated for the presence or absence of inflammation. RESULTS: Patient-matched serum and stricture specimens were collected from 113 subjects. Strictures were grouped based on the presence or absence of inflammation (n=66 and n=47, respectively). Serum from age-matched patients without urethral strictures was used as a control (n=9). Four pro-fibrotic cytokines were significantly increased in patients with strictures: interleukin-9 (IL-9), platelet derived growth factor-BB (PDGF-BB), C-C Motif Chemokine Ligand 4 (CCL4), and C-C Motif Chemokine Ligand 5 (CCL5) [A-D]. Only IL-9 levels differed by stricture inflammatory pattern (890.4 ng/mL vs 773.0 ng/mL, p<0.05). In addition to higher levels of pro-fibrotic cytokines, interleukin-1 receptor antagonist (IL-1Ra), which has immunosuppressive functions, was increased in patients with strictures [E]. CONCLUSIONS: Patients with aUSD have unique serum cytokine profiles relative to age-matched controls. Four of these cytokines, IL-9, PDGF-BB, CCL4, and CCL5 have previously been implicated in promoting fibrosis and scarring in humans and in animal models. IL-1Ra has immunosuppressive functions and may be increased to maintain homeostasis. Further studies are needed to determine if these cytokines are increased as a result of the stricture or if their levels are higher at baseline, predisposing patients to stricture formation. Source of Funding: NIDDK 1R21DK115945-01 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1132 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Wade Gutierrez More articles by this author Yi Luo More articles by this author Denise Juhr More articles by this author Benjamin Breyer More articles by this author Sean Elliott More articles by this author Jeremy Myers More articles by this author Alex Vanni More articles by this author Katherine Christel More articles by this author Laila Dahmoush More articles by this author Bradley Erickson More articles by this author Expand All Advertisement PDF downloadLoading ...

Evaluating the hourly emissions intensity of the US electricity system

High-quality data for the greenhouse gas and air pollution emissions associated with electricity generation and consumption are increasingly important to enable effective and targeted action to decarbonize the electric grid and to inform research in a range of academic disciplines including environmental economics, lifecycle assessment, and environmental health. To inform the broadest range of use cases, such data should ideally have a high temporal and spatial resolution, be available in as close to real-time as possible, represent the complete power sector, use the highest-quality measured data, have complete historical coverage, and represent both generated and consumed emissions. To date, no published datasets have achieved all of these characteristics. This work is the first to publish a comprehensive, plant-level dataset of hourly-resolution generation, fuel consumption, and direct CO2, NOx, and SO2 emissions for the entire U.S. power sector. This data is published as part of the public and open-source Open Grid Emissions Initiative, which also includes hourly, consumption-based emissions intensities for every grid balancing area in the country. Using insights generated by this new dataset, this paper also interrogates how several of the assumptions implicit in the use of existing power sector emissions datasets may under-count or misrepresent the climate and health impacts of air emissions from the U.S. power sector. We envision the Initiative becoming a central repository of, and hub of activity for addressing open research questions related to power sector emissions data, and the go-to source for high-quality, high-resolution data for future research on grid emissions.

نضج إدارة البيانات الرئيسية في المؤسسات الحكومية: دراسة حالة لوزارة النقل والخدمات اللوجستية

ملخص تستمد الدراسة الحالية أهميتها من منطلق أن البيانات بشكل عام تعد عنصرًا مهمًّا في كل مؤسسة، وركيزة أساسية في اتخاذ القرارات، بالإضافة إلى الدور الذي يقوم به كلٌّ من البعد التنظيمي والتكنولوجي والإداري في تحسين جودة البيانات الرئيسة. وتهدف الدراسة إلى التعرف على الأبعاد الثلاثة (الإدارية – التقنية – البشرية) التي تؤثر في نضج إدارة البيانات الرئيسة في المؤسسات الحكومية بشكل عام، ووزارة النقل والخدمات اللوجستية بشكل خاص، وبناء على ذلك اعتمدت الدراسة على منهج تحليل المحتوى لتحليل الإنتاج الفكري المنشور، كما اعتمدت الدراسة على منهج دراسة الحالة بغرض الوصول إلى فهم أعمق عن مدى نضج إدارة البيانات الرئيسة في وزارة النقل والخدمات اللوجستية في المملكة العربية السعودية، واستطاعت الدراسة وضع نموذج مقترح لما ينبغي أخذه بعين الاعتبار، سواء من الباحثين عند تصميم حلول مماثلة، أو من المسؤولين عند التخطيط لتطويع إدارة البيانات الرئيسة في مؤسساتهم، بحيث تكون فعالة للغاية. أهم ما توصلت إليه الدراسة: تبنت الوزارة واجهة رسمية معتمدة لتبادل البيانات داخل مختلف الإدارات في الوزارة بشكل منظم يُسهل الحصول على البيانات عند الحاجة إليها. وتفحص الوزارة قواعد البيانات لديها باستمرار لتحسين أداء الأعمال وكفاءته، كما تخزن البيانات بطريقة فعّالة تُمكنها من الاستفادة من تلك البيانات للتنبؤ والتحليل لاتخاذ القرارات. كما تفتقر الوزارة إلى وجود مستودع مركزي يسهل الوصول إلى البيانات الرئيسة المشتركة في مختلف الإدارات العامة. وخرجت الدراسة بوضع نموذج مقترح لإدارة البيانات الرئيسة في وزارة النقل والخدمات اللوجستية، وقدمت مجموعة من التوصيات، أهمها: الحرص على وجود نظام مقياس معياري لتقويم جودة البيانات لدى الوزارة لضمان استمرار جودتها. وضرورة استحداث مستودع مركزي لتخزين البيانات الرئيسة وبثها عبر مختلف الإدارات، وتسهيل وصول الموظفين إليها لأداء الأعمال.

From multiallele fish to nonstandard environments, how ZFIN assigns phenotypes, human disease models, and gene expression annotations to genes.

Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for genetic, genomic, and phenotypic data that result from research using D. rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity.

Federated Learning for Data Analytics in Education

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices while avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, and data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to a well-known learning analytics problem: student dropout prediction. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding the concentration of all the data in a single place for training the models.