scholarly journals Screening performance of abbreviated versions of the UPSIT smell test

2018 ◽  
Author(s):  
Theresita Joseph ◽  
Stephen D. Auger ◽  
Luisa Peress ◽  
Daniel Rack ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Cost Effective ◽  
Data Driven ◽  
Screening Performance ◽  
Smell Test ◽  
Identification Test ◽  
Data Driven Approach ◽  
Smell Identification ◽  
Smell Tests

ABSTRACTBackgroundHyposmia features in several neurodegenerative conditions, including Parkinson’s disease (PD). The University of Pennsylvania Smell Identification Test (UPSIT) is a widely used screening tool for detecting hyposmia, but is time-consuming and expensive when used on a large scale.MethodsWe assessed shorter subsets of UPSIT items for their ability to detect hyposmia in 891 healthy participants from the PREDICT-PD study. Established shorter tests included Versions A and B of both the 4-item Pocket Smell Test (PST) and 12-item Brief Smell Identification Test (BSIT). Using a data-driven approach, we evaluated screening performances of 23,231,378 combinations of 1-7 smell items from the full UPSIT.ResultsPST Versions A and B achieved sensitivity/specificity of 76.8%/64.9% and 86.6%/45.9% respectively, whilst BSIT Versions A and B achieved 83.1%/79.5% and 96.5%/51.8% for detecting hyposmia defined by the longer UPSIT. From the data-driven analysis, two optimised sets of 7 smells surpassed the screening performance of the 12 item BSITs (with validation sensitivity/specificities of 88.2%/85.4% and 100%/53.5%). A set of 4 smells (Menthol, Clove, Gingerbread and Orange) had higher sensitivity for hyposmia than PST-A, -B and even BSIT-A (with validation sensitivity 91.2%). The same 4 smells also featured amongst those most commonly misidentified by 44 individuals with PD compared to 891 PREDICT-PD controls and a screening test using these 4 smells would have identified all hyposmic patients with PD.ConclusionUsing abbreviated smell tests could provide a cost-effective means of screening for hyposmia in large cohorts, allowing more targeted administration of the UPSIT or similar smell tests.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

scholarly journals mRNA-Based Vaccines

Vaccines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 390
Author(s):  
Frank Kowalzik ◽  
Daniel Schreiner ◽  
Christian Jensen ◽  
Daniel Teschner ◽  
Stephan Gehring ◽  
...  
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Vaccine Development ◽  
Effective Means ◽  
Cost Effective ◽  
Clinical Applications ◽  
Cell Mediated Immunity ◽  
Healthy Individuals ◽  
Emerging Pathogens ◽  
Cell Genome

Increases in the world’s population and population density promote the spread of emerging pathogens. Vaccines are the most cost-effective means of preventing this spread. Traditional methods used to identify and produce new vaccines are not adequate, in most instances, to ensure global protection. New technologies are urgently needed to expedite large scale vaccine development. mRNA-based vaccines promise to meet this need. mRNA-based vaccines exhibit a number of potential advantages relative to conventional vaccines, namely they (1) involve neither infectious elements nor a risk of stable integration into the host cell genome; (2) generate humoral and cell-mediated immunity; (3) are well-tolerated by healthy individuals; and (4) are less expensive and produced more rapidly by processes that are readily standardized and scaled-up, improving responsiveness to large emerging outbreaks. Multiple mRNA vaccine platforms have demonstrated efficacy in preventing infectious diseases and treating several types of cancers in humans as well as animal models. This review describes the factors that contribute to maximizing the production of effective mRNA vaccine transcripts and delivery systems, and the clinical applications are discussed in detail.

scholarly journals RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009315
Author(s):  
Ardalan Naseri ◽  
Junjie Shi ◽  
Xihong Lin ◽  
Shaojie Zhang ◽  
Degui Zhi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Scale Up ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Inference Method ◽  
Genome Wide ◽  
Familial Relationship ◽  
Kinship Coefficients ◽  
Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

scholarly journals Variable Porous Electrode Compression for Redox Flow Battery Systems

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 53 ◽  
Author(s):  
Nicholas Gurieff ◽  
Victoria Timchenko ◽  
Chris Menictas
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Porous Electrode ◽  
Cost Effective ◽  
Electrical Energy ◽  
Limiting Current ◽  
Great Promise ◽  
Full Potential ◽  
Uniform Compression ◽  
Novel Concept

Vanadium redox flow batteries (VRFBs) offer great promise as a safe, cost effective means of storing electrical energy on a large scale and will certainly have a part to play in the global transition to renewable energy. To unlock the full potential of VRFB systems, however, it is necessary to improve their power density. Unconventional stack design shows encouraging possibilities as a means to that end. Presented here is the novel concept of variable porous electrode compression, which simulations have shown to deliver a one third increase in minimum limiting current density together with a lower pressure drop when compared to standard uniform compression cell designs.

scholarly journals Interlaboratory Collaboration for Optimized Screening for Urinary Tract Infection

2015 ◽  
Vol 54 (1) ◽  
pp. 93-98 ◽  
Author(s):  
Anne Russcher ◽  
Elske Kusters ◽  
Ron Wolterbeek ◽  
Ed J. Kuijper ◽  
Christa M. Cobbaert ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Positive Culture ◽  
Cost Effective ◽  
Objective Evaluation ◽  
University Hospital ◽  
Rapid Screening ◽  
Gram Stain ◽  
Sample Quality ◽  
Positive Culture Result

As the majority of urine samples submitted for culture yields a negative result, rapid screening that accurately predicts culture outcome benefits clinicians by reducing the time to result and improves the efficiency of the microbiological laboratory. Automated urinalysis using the IRIS Diagnostics iQ200 Elite (iQ200) analyzer permits just such a fast and large-scale screening. We aimed to predict and thus to reduce negative cultures with a screening algorithm based on iQ200 urinalysis in a tertiary university hospital. In parallel, we evaluated the performance of the iQ200 screen compared to that of Gram stain for sample quality. We screened 1,442 samples submitted for bacterial culture using the iQ200 analyzer; of these samples, 357 (24.8%) had a positive culture result. We identified the absence of microorganisms in the iQ200 screen as the strongest solitary predictor for a negative culture, with a sensitivity of 90.5% (323/357). The algorithm was further improved by performing logistic regression on leukocyte counts, which gave a cutoff of 65 leukocytes/μl to obtain the desired sensitivity of >95% (95.2%; 95% confidence interval [CI], 92.5 to 97.0), a negative predictive value of 97.3% (95% CI, 95.7 to 98.3), and an anticipated culture workload reduction of 44% (95% CI, 41 to 46). Concordance between sample quality based on Gram stain and iQ200 screening was only 72%, which was probably a result of interobserver effect in evaluation of the Gram stain. In conclusion, in our setting, screening by iQ200 proved to be a safe and cost-effective means to provide faster culture results, and it has the added benefit of a more objective evaluation of sample quality.

scholarly journals Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

2018 ◽  
Vol 115 (37) ◽  
pp. 9300-9305 ◽  
Author(s):  
Shuo Wang ◽  
Erik D. Herzog ◽  
István Z. Kiss ◽  
William J. Schwartz ◽  
Guy Bloch ◽  
...  
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Data Driven ◽  
Electrical Networks ◽  
Chemical Oscillators ◽  
Complex Interactions ◽  
Dynamic Topology ◽  
Estimation Problems ◽  
Data Driven Approach ◽  
Fast Chemical

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.

scholarly journals A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

2021 ◽  
Vol 12 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

scholarly journals CN-Probase: A Data-Driven Approach for Large-Scale Chinese Taxonomy Construction

2019 ◽  
Author(s):  
Jindong Chen ◽  
Ao Wang ◽  
Jiangjie Chen ◽  
Yanghua Xiao ◽  
Zhendong Chu ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Driven Approach

Automatic Detection and Analysis of the “Game Hack” Scam

2020 ◽  
Author(s):  
Emad Badawi ◽  
Guy-Vincent Jourdan ◽  
Gregor Bochmann ◽  
Iosif-Viorel Onut
Keyword(s):  
Social Media ◽  
Market Research ◽  
Large Scale ◽  
Personal Information ◽  
Data Driven ◽  
Search Queries ◽  
Vast Array ◽  
Low Profile ◽  
Data Driven Approach ◽  
Insight Into

The “Game Hack” Scam (GHS) is a mostly unreported cyberattack in which attackers attempt to convince victims that they will be provided with free, unlimited “resources” or other advantages for their favorite game. The endgame of the scammers ranges from monetizing for themselves the victims time and resources by having them click through endless “surveys”, filing out “market research” forms, etc., to collecting personal information, getting the victims to subscribe to questionable services, up to installing questionable executable files on their machines. Other scams such as the “Technical Support Scam”, the “Survey Scam”, and the “Romance Scam” have been analyzed before but to the best of our knowledge, GHS has not been well studied so far and is indeed mostly unknown. In this paper, our aim is to investigate and gain more knowledge on this type of scam by following a data-driven approach; we formulate GHS-related search queries, and used multiple search engines to collect data about the websites to which GHS victims are directed when they search online for various game hacks and tricks. We analyze the collected data to provide new insight into GHS and research the extent of this scam. We show that despite its low profile, the click traffic generated by the scam is in the hundreds of millions. We also show that GHS attackers use social media, streaming sites, blogs, and even unrelated sites such as change.org or jeuxvideo.com to carry out their attacks and reach a large number of victims. Our data collection spans a year; in that time, we uncovered 65,905 different GHS URLs, mapped onto over 5,900 unique domains.We were able to link attacks to attackers and found that they routinely target a vast array of games. Furthermore, we find that GHS instances are on the rise, and so is the number of victims. Our low-end estimation is that these attacks have been clicked at least 150 million times in the last five years. Finally, in keeping with similar large-scale scam studies, we find that the current public blacklists are inadequate and suggest that our method is more effective at detecting these attacks.

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