scholarly journals Updated Sixth Water/Diamond Fork data repository

2021 ◽  
Keyword(s):  
Data Repository

Critical Review of Data Analytics techniques used in the Expanded Program on Immunization (EPI)

2020 ◽  
Vol 16 ◽  
Author(s):  
Sadaf Qazi ◽  
Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Analytics ◽  
Public Health Intervention ◽  
Cold Chain ◽  
Data Repository ◽  
Quality Of Data ◽  
Mortality And Morbidity ◽  
Healthcare Facilities ◽  
Expanded Program On Immunization

Background: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free accessibility, is still very low in developing countries. One of the primary reasons for this low coverage is the lack of analysis and proper utilization of immunization data at various healthcare facilities. Purpose: In this paper, the existing machine learning based data analytics techniques have been reviewed critically to highlight the gaps where this high potential data could be exploited in a meaningful manner. Results: It has been revealed from our review, that the existing approaches use data analytics techniques without considering the complete complexity of Expanded Program on Immunization which includes the maintenance of cold chain systems, proper distribution of vaccine and quality of data captured at various healthcare facilities. Moreover, in developing countries, there is no centralized data repository where all data related to immunization is being gathered to perform analytics at various levels of granularities. Conclusion: We believe that the existing non-centralized immunization data with the right set of machine learning and Artificial Intelligence based techniques will not only improve the vaccination coverage but will also help in predicting the future trends and patterns of its coverage at different geographical locations.

scholarly journals NoRCE: non-coding RNA sets cis enrichment tool

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gulden Olgun ◽  
Afshan Nabi ◽  
Oznur Tastan
Keyword(s):  
Expression Patterns ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Fruit Fly ◽  
Relevant Information ◽  
R Package ◽  
Data Repository ◽  
Biologically Relevant ◽  
Gene Sets ◽  
Data Files

Abstract Background While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association. Results We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. Conclusions NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users’ preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

scholarly journals Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

2021 ◽  
pp. 155335062110186
Author(s):  
Abdel-Moneim Mohamed Ali ◽  
Emran El-Alali ◽  
Adam S. Weltz ◽  
Scott T. Rehrig
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Point Of Care ◽  
Prognostic Indicators ◽  
Data Repository ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Clinical Protocol ◽  
Improved Outcomes ◽  
Current Experience

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

Data Acquisition System for Web-based Multi-modal Data Repository

2021 ◽  
Author(s):  
Munyaradzi Charles Rushambwa ◽  
Anirban Mukherjee ◽  
Maitreya Maity ◽  
Rajkumar Palaniappan ◽  
Vikneswaran Vijean ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Data Repository ◽  
Web Based ◽  
Modal Data

scholarly journals A risk index for COVID-19 severity is associated with COVID-19 mortality in New York City

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wil Lieberman-Cribbin ◽  
Naomi Alpert ◽  
Raja Flores ◽  
Emanuela Taioli
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
York City ◽  
Underserved Populations ◽  
Risk Index ◽  
Data Repository ◽  
Severe Illness ◽  
Mortality Data ◽  
Cross Sectional

Abstract Background New York City (NYC) was the epicenter of the COVID-19 pandemic, and is home to underserved populations with higher prevalence of chronic conditions that put them in danger of more serious infection. Little is known about how the presence of chronic risk factors correlates with mortality at the population level. Here we determine the relationship between these factors and COVD-19 mortality in NYC. Methods A cross-sectional study of mortality data obtained from the NYC Coronavirus data repository (03/02/2020–07/06/2020) and the prevalence of neighborhood-level risk factors for COVID-19 severity was performed. A risk index was created based on the CDC criteria for risk of severe illness and complications from COVID-19, and stepwise linear regression was implemented to predict the COVID-19 mortality rate across NYC zip code tabulation areas (ZCTAs) utilizing the risk index, median age, socioeconomic status index, and the racial and Hispanic composition at the ZCTA-level as predictors. Results The COVID-19 death rate per 100,000 persons significantly decreased with the increasing proportion of white residents (βadj = − 0.91, SE = 0.31, p = 0.0037), while the increasing proportion of Hispanic residents (βadj = 0.90, SE = 0.38, p = 0.0200), median age (βadj = 3.45, SE = 1.74, p = 0.0489), and COVID-19 severity risk index (βadj = 5.84, SE = 0.82, p <  0.001) were statistically significantly positively associated with death rates. Conclusions Disparities in COVID-19 mortality exist across NYC and these vulnerable areas require increased attention, including repeated and widespread testing, to minimize the threat of serious illness and mortality.

scholarly journals Helminth infections in the US military: from strongyloidiasis to schistosomiasis

2021 ◽  
Author(s):  
Alyssa R Lindrose ◽  
Indrani Mitra ◽  
Jamie Fraser ◽  
Edward Mitre ◽  
Patrick W Hickey
Keyword(s):  
Military Personnel ◽  
International Classification Of Diseases ◽  
Data Repository ◽  
Military Health ◽  
Intestinal Nematode ◽  
Us Military ◽  
Helminth Infections ◽  
The Us ◽  
East And Southeast Asia ◽  
The Military

Abstract Background Helminth infections caused by parasitic worms, including nematodes (roundworms), cestodes (tapeworms) and trematodes (flukes), can cause chronic symptoms and serious clinical outcomes if left untreated. The US military frequently conducts activities in helminth-endemic regions, particularly Africa, the Middle East and Southeast Asia. However, the military does not currently screen for these infections, and to date, no comprehensive surveillance studies have been completed to assess the frequency of helminth diagnoses in the military personnel and their families. Methods To determine the burden of helminth infections in the US Military Health System (MHS), we conducted a retrospective analysis of International Classification of Diseases (ICD)-9/10 diagnosis codes from all medical encounters in the MHS Data Repository (MDR) from fiscal years (FY) 2012 to 2018. Chart reviews were conducted to assign ICD diagnoses as incorrect, suspected, probable or confirmed based on the laboratory results and symptoms. Results Abstraction of MHS data revealed over 50 000 helminth diagnoses between FY 2012 and FY 2018. Of these, 38 445 of diagnoses were amongst unique subjects. After chart review, we found there were 34 425 validated helminth infections diagnosed amongst the unique subjects of US military personnel, retirees and dependents. Nearly 4000 of these cases represented infections other than enterobiasis. There were 351 validated strongyloidiasis diagnoses, 317 schistosomiasis diagnoses and 191 diagnoses of cysticercosis during the study period. Incidence of intestinal nematode infection diagnoses showed an upward trend, whilst the incidence of cestode infection diagnoses decreased. Conclusions The results of this study demonstrate that helminth infections capable of causing severe morbidity are often diagnosed in the US military. As helminth infections are often asymptomatic or go undiagnosed, the true burden of helminth infections in US military personnel and dependents may be higher than observed here. Prospective studies of US military personnel deployed to helminth-endemic areas may be indicated to determine if post-deployment screening and/or empirical treatment are warranted.

scholarly journals Multi-modal magnetic resonance imaging in a mouse model of concussion

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xuan Vinh To ◽  
Fatima A. Nasrallah
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Data Collection ◽  
Mouse Model ◽  
Meta Analysis ◽  
Data Repository ◽  
Resonance Imaging ◽  
Loss Of Righting Reflex ◽  
Post Impact ◽  
Magnetic Resonance Imaging Mri

AbstractThis data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3–4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal’s backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.

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