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10,356 results for “Severity”
Illness perception mediates the relationship between the severity of symptoms and perceived health status in patients with Behcet's disease.
<p><strong>Abstract</strong></p> <p><em>Objective</em>. To investigate the relationship between psychological representations of illness, perceived health status and self-assessment of symptom severity in patients with <strong>Behçet's</strong>Disease – a rare long-term incurable condition with unclear etiology.</p> <p><em>Methods</em>. Using cross-sectional survey design, data on self-administered questionnaires on illness perception, health status, symptoms severity and demographic characteristics were collected from 273 patients with <strong>Behçet's</strong>Disease (age range 18 – 65+). The data were subjected to mediation analysis to test whether cognitive and emotional components of illness perception mediate the relationship between the severity of symptoms and heath status.</p> <p><em>Results</em>. The results support our hypotheses that cognitive components of illness perception (perceived consequences and identity of the illness) mediate the link between symptom activity and pain, whereas emotional components of the illness (emotional representations about the illness) mediate the relationship between disease activity and perceived energy level.</p> <p> <em>Conclusion</em>. The robustness of these mediation effects suggests potential directions for clinical psychologists and health care practitioners in developing support programmes. We supplement our study with Open Access database containing information about type of medication, comorbid mood disorder, detailed measurement of the severity of BD symptomsfor sharing and accumulating multidisciplinary knowledge aiming to support the development of interventions. Addressing psychological aspects ofBD will help to manage complex patients effectively.</p> <p>Database of survey (complete)</p>
Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking
<p>The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives.</p> <p>Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are</p> <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> <p>These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations.</p> <p>The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing.</p> <p>Publications that describe the W-Model:</p> <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of <em>Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>)</em>, July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, <em>Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08)</em>, pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>
Fig. 1. Geographic distribution of bogidiellid amphipods. Large map: type localities of: 1, Bogidiella veneris n. sp.; 2, Bogidomma australis; 3, Xystriogidiella capricornea; and 4, Xystriogidiella juliani on the Australian continent (with kind permission of demis.nl). Insert map: global distribution of Bogidiellidae and several, possibly closely related taxa (modified after Koenemann and Holsinger, 1999).
Fig. 1. Geographic distribution of bogidiellid amphipods. Large map: type localities of: 1, Bogidiella veneris n. sp.; 2, Bogidomma australis; 3, Xystriogidiella capricornea; and 4, Xystriogidiella juliani on the Australian continent (with kind permission of demis.nl). Insert map: global distribution of Bogidiellidae and several, possibly closely related taxa (modified after Koenemann and Holsinger, 1999).
Combined high-depth Illumina+PacBio Sequencing of several samples from FDA-ARGOS
<p>The (real) sequencing data is compiled from a concatenation of sequencing runs from Database for Reference Grade Microbial Sequences (FDA-ARGOS). Specifically, the following samples were sequenced with both Illumina and PacBio. The sample accessions are shown below.</p> <pre><code> BioSample Run Platform Organism bases source <chr> <chr> <chr> <chr> <dbl> <chr> 1 SAMN06173354 SRR5409204 ILLUMINA Bacillus anthracis 1778000000 Colorado Serum Co., Anthrax Spore Vaccine 2 SAMN06173354 SRR5409205 PACBIO_SMRT Bacillus anthracis 2420000000 Colorado Serum Co., Anthrax Spore Vaccine 3 SAMN06173356 SRR5448657 ILLUMINA Bacillus circulans 2311000000 swab with brown-gray powder 4 SAMN06173356 SRR5448656 PACBIO_SMRT Bacillus circulans 242000000 swab with brown-gray powder 5 SAMN04875535 SRR4123920 ILLUMINA Elizabethkingia anophelis 1357000000 blood 6 SAMN04875535 SRR4123919 PACBIO_SMRT Elizabethkingia anophelis 2173000000 blood 7 SAMN06173306 SRR5413253 ILLUMINA Escherichia coli O157 3051000000 clinical isolate 8 SAMN06173306 SRR5413252 PACBIO_SMRT Escherichia coli O157 915000000 clinical isolate 9 SAMN06173318 SRR5413272 ILLUMINA Mycobacterium avium subsp. paratuberculosis 1032000000 feces 10 SAMN06173318 SRR5413271 PACBIO_SMRT Mycobacterium avium subsp. paratuberculosis 462000000 feces 11 SAMN07312468 SRR5879398 ILLUMINA Mycobacterium tuberculosis 1054000000 human 12 SAMN07312468 SRR5879396 PACBIO_SMRT Mycobacterium tuberculosis 1854000000 human 13 SAMN04875542 SRR4123931 ILLUMINA Neisseria gonorrhoeae 1053000000 ATCC strain 14 SAMN04875542 SRR4123930 PACBIO_SMRT Neisseria gonorrhoeae 1117000000 ATCC strain </code></pre> <p>Samples were selected with the SRA Run selector. The SraRunTable.txt file was exported containing the metadata for each sample, and fastq-dump from the SRA toolkit was used to write out fastq files for each run, with paired Illumina data being split into separate _1.fastq.gz and _2.fastq.gz files. </p>
Ultimaker 2 sensor data for several print jobs
<p>This datasets were measured the same way as <a href="https://doi.org/10.5281/zenodo.54574">https://doi.org/10.5281/zenodo.54574</a>. But instead of capturing only events, the positions of the printer head and the printer plate were measured for all print jobs of Ultimaker 2 3D printer until 2016 and combined into one file.</p>
Data and code: Outbreak and Wildfire Severity BC
<p>This is the first release of data and code for outbreak severity and wildfire severity in British Columbia.</p> <p>This file contains code and data for original research evaluating the effect of mountain pine beetle outbreaks on wildfire severity in British Columbia, Canada that was published in Ecosphere.</p> <p><br> Citation for data and code: Talucci, A. C. 2019. Data and code: Outbreaks and wildfire British Columbia. GitHUB Repository. https://github.com/taluccia/outbreak-wildfire-severity-BC/releases/tag/v1.0.0. 10.5281/zenodo.2648051</p> <p>Access Ecosphere paper: https://esajournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ecs2.2744</p>
Fig. 3 in Severe coenurosis caused by larvae of Taenia serialis in an olive baboon (Papio anubis) in Benin
Fig. 3. Detail of a protoscolex extracted from a cyst.
Figure 1 in Evolutionary relationships among several species from the genus Cricotopus (Diptera: Chironomidae): What about Turkish representatives of this genus?
Figure 1. Sampling localities of the collected Cricotopus specimens in this study.
Fig. 2 in Toxicity for control of Frankliniella schultzei and Selenothrips rubrocinctus (Thysanoptera: Thripidae) of several common synthetic insecticides
Fig. 2. Adult Selenothrips rubrocinctus.
Electroluminiscence Dataset of Photovolaic Cells of several technologies
Open the record for dataset details and reuse information.
Summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI WGS data set
<p>These are the summary statistics of the gene-based collapsing analyses for COVID-19 severity within the DeCOI Whole-Genome Sequencing data set from the European subcohort (n=1,017).</p> <p>Case - control definitions:<br>Ex (file: DeCOI_EUR_Ex_RVAS.tsv.gz): 272 cases (required mechanical ventilation or died because of COVID-19 = WHO score 6-10); 362 controls (not hospitalized = WHO score 1-3)<br>B1 (file: DeCOI_EUR_B1_RVAS.tsv.gz): 655 cases (at least hospitalized = WHO score 4-10); 362 controls (not hospitalized = WHO score 1-3)</p> <p>Column description:<br>CHROM: Chromosome of the respective gene<br>ID: Column of the format: [Ensemble Gene ID].[mask which was used to select variants].[upper alle frequency cut-off]<br>ALLELE0: The reference allele<br>ALLELE1: The alternative allele and the effect allele - here all variants which pass allele frequency and mask filters (see ID-column)<br>A1FREQ: Total frequency of Allele 1<br>A1FREQ_CASES: Frequency of Allele 1 in cases<br>A1FREQ_CONTROLS: Frequency of Allele 1 in controls<br>N: Count of individuals that were used in the analysis<br>N_CASES: Count of cases that were used in the analysis<br>N_CONTROLS: Count of controls that were used in the analysis<br>TEST: Test-mode of regenie - here an additive model was used<br>BETA: Estimated effect size given as beta<br>SE: Standard error of BETA<br>LOG10P: Negative decadic logarithm of the p-value</p> <p>Method:<br>Please refer to our accompanying manuscript for a detailed description. We conducted gene-based collapsing analysis using regenie 3.2.4 in logistic regression mode (without step 1). Covariates used were sex, age, age*age, age*sex and the first 10 principal components derived from common variants.</p> <p>Contact for further information:<br>dac_decoi_hostgenetics@listen.uni-bonn.de</p>
Clinical and exploratory blood parameters for French Bulldogs with various severity grade of BOAS
<p>This dataset is a part of the study "<span>Selected blood parameters in relation to BOAS severity grading in French Bulldogs"</span></p>
Genomic data from a pharmacogenetic study based on the efficacy of Tocilizumab in severe COVID-19 patients
<p>These data correspond to a pharmacogenetic study aimed to explore genetic associations related to the efficacy of Tocilizumab in severe COVID-19 patients. The study comprised two sets of patients, an initial subset consisted of 425 patients to whom a in-dpeth sequencing of a thermofisher sequencing panel of immune-related genes was performed, as well as a second set of 245 patients with the information of the genotyping of three selected SNPs</p>
Evaluating the Impact of Triple Compound Therapy on Relapse Frequency and Quality of Life in Children with Severe Atopic Dermatitis: A Phase II Randomised Clinical Trial
<p>Datasets and analysis from A multi-centre, randomized controlled trial, on pediatric Atopic Dermatitis, conducted at Red Cross and Walter Sisulu Hospitals. The study is entitled Evaluating the Impact of Triple Compound Therapy on Relapse Frequency and Quality of Life in Children with Severe Atopic Dermatitis: A Phase II Randomised Clinical Trial.</p>
The conflict between hosts and non-hosts changes the severity of diseases in degraded grassland plant communities
<p>load: disease severity of plant community</p> <p>richnes: species richness</p> <p>Nonhost: Non-hosts richness</p> <p>coverage: relative coverage</p> <p>RHost: relative hosts richness</p> <p>PD: Faith's phylogenetic distance</p> <p>beta: beta diversity of plant community</p> <p>SLA: CWM SLA</p> <p>LN: CWM leaf N content</p> <p>LP:CWM leaf P content</p> <p>NP: CWM leaf N:P</p> <p>group: degree of grassland degradation</p> <p>Shannon: Shannon index of plant community</p> <p>Simpson: Simpsion index of plant community</p> <p>Pielou: Pielou index of plant community</p> <p>marglef: Marglef index of plant community</p>
Minimal dataset for study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020
<p>These datasets are the minimal datasets for the study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020.</p> <p>Study DOI: https://doi.org/10.21203/rs.3.rs-4752336/v1</p>
It's a wormy world: Meta-analysis reveals several decades of change in the global abundance of the parasitic nematodes Anisakis spp. and Pseudoterranova spp. in marine fishes and invertebrates
<p>The Anthropocene has brought substantial change to ocean ecosystems, but whether this age will bring more or less marine disease is unknown. In recent years, the accelerating tempo of epizootic and zoonotic disease events has made it seem as if disease is on the rise. Is this apparent increase in disease due to increased observation and sampling effort, or to an actual rise in the abundance of parasites and pathogens? We examined the literature to track long-term change in the abundance of two parasitic nematode genera with zoonotic potential: <em>Anisakis</em> spp. and <em>Pseudoterranova</em> spp. These anisakid nematodes cause the disease anisakidosis and are transmitted to humans in undercooked and raw marine seafood. A total of 123 papers published between 1967 and 2017 met our criteria for inclusion, from which we extracted 755 host–parasite–location–year combinations. Of these, 69.7% concerned <em>Anisakis</em> spp. and 30.3% focused on <em>Pseudoterranova</em> spp. Meta-regression revealed an increase in <em>Anisakis</em> spp. abundance (average number of worms/ fish) over a 53 year period from 1962 to 2015 and no significant change in <em>Pseudoterranova</em> spp. abundance over a 37 year period from 1978 to 2015. Standardizing changes to the period of 1978–2015, so that results are comparable between genera, we detected a significant 283-fold increase in <em>Anisakis</em> spp. abundance and no change in the abundance of <em>Pseudoterranova</em> spp. This increase in <em>Anisakis</em> spp. abundance may have implications for human health, marine mammal health, and fisheries profitability.</p>
Dataset of Psychological Impact of the COVID-19 Pandemic on Families of People with Severe Mental Disorders in Brazil
<p>The objective of the study is to understand how the families of patients with severe mental disorders experienced the beginning of the pandemic in Brazil and how they fared during the period of restricted social contact.</p>
Datasets used in European Surgical Research special edition on severity assessment (2021)
<p>Rawdata used for pSAS Implementation, simulation SAS parameter study and FORID function presentation.</p> <p>Included datasets:</p> <ul> <li>rawdata_Apgar.xlsx: Raw data for pSAS Implementation</li> <li>sas_example.xlsx: Excerpt from rawdata_Apgar.xlsx to be used in simulation parameter study</li> <li>upload_example.xlsx: Example used to demonstrate data upload procedure to FORID database</li> <li>mobps_example.xlsx: Example used to demonstrate data forwarding from FORID database to MoBPS analysis tool</li> </ul>
PSYCHE-D: predicting change in depression severity using person-generated health data (DATASET)
<p>This dataset is made available under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)</a>. See LICENSE.pdf for details.</p> <p><strong>Dataset description</strong></p> <p>Parquet file, with:</p> <ul> <li>35694 rows</li> <li>154 columns</li> </ul> <p>The file is indexed on [<em>participant</em>]_[<em>month</em>], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.</p> <p>Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.</p> <p>File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (<a href="https://clinicaltrials.gov/ct2/show/NCT03421223">https://clinicaltrials.gov/ct2/show/NCT03421223</a>). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.</p> <p>The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.</p> <p>The data subset used in this work comprises the following:</p> <ul> <li>Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study</li> <li>Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities</li> <li>Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month</li> <li>Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity</li> </ul> <p>From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).</p> <p>The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.