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701 results for “factor analysis”
Analysis files for MSC Marc finite element software, Article: The analysis of shrink-fit connection – the methods of heating and the factors influencing the distribution of residual stresses
<p>This archive contains model files for Finite Element Analysis of the shrink-fit connection in crankshaft and the files for charts in GNUPlot.</p>
TABLE 2. — Principal factor analysis giving factors higher than 1 in Amphibians of Togo: taxonomy, distribution and conservation status
<p>TABLE 2. — Principal factor analysis giving factors higher than 1, their individual variance and the cumulative variance of these six components.</p><table><tbody><tr><th><b>Factor</b></th></tr></tbody><tbody><tr><th><b>Variable</b></th><td><b>1</b></td><td><b>2</b></td><td><b>3</b></td><td><b>4</b></td><td><b>5</b></td><td><b>6</b></td></tr><tr><th>RSVL</th><td>–0.299</td><td>0.765</td><td>0.335</td><td>–0.089</td><td>–0.059</td><td>–0.080</td></tr><tr><th>RHW</th><td>0.673</td><td>0.500</td><td>0.356</td><td>–0.136</td><td>0.175</td><td>–0.097</td></tr><tr><th>RHL</th><td>0.898</td><td>0.013</td><td>0.196</td><td>0.106</td><td>0.000</td><td>–0.016</td></tr><tr><th>RMN</th><td>0.854</td><td>0.018</td><td>0.097</td><td>0.219</td><td>–0.042</td><td>–0.032</td></tr><tr><th>RMFE</th><td>0.838</td><td>0.290</td><td>0.163</td><td>0.016</td><td>0.015</td><td>–0.149</td></tr><tr><th>RMBE</th><td>0.572</td><td>–0.062</td><td>0.210</td><td>–0.273</td><td>–0.118</td><td>–0.062</td></tr><tr><th>RIFE</th><td>0.766</td><td>–0.146</td><td>–0.068</td><td>0.289</td><td>0.209</td><td>–0.228</td></tr><tr><th>RIBE</th><td>0.786</td><td>0.119</td><td>0.212</td><td>–0.004</td><td>0.289</td><td>–0.039</td></tr><tr><th>RIN</th><td>0.613</td><td>0.138</td><td>0.041</td><td>0.187</td><td>0.245</td><td>–0.360</td></tr><tr><th>REN</th><td>0.265</td><td>–0.181</td><td>–0.160</td><td>0.776</td><td>0.026</td><td>0.156</td></tr><tr><th>REL</th><td>0.812</td><td>0.043</td><td>–0.136</td><td>0.059</td><td>0.275</td><td>0.074</td></tr><tr><th>RTYD</th><td>0.243</td><td>0.761</td><td>0.448</td><td>–0.160</td><td>0.057</td><td>0.037</td></tr><tr><th>RTYE</th><td>–0.071</td><td>–0.796</td><td>0.157</td><td>–0.337</td><td>–0.048</td><td>–0.168</td></tr><tr><th>RIUE</th><td>0.118</td><td>0.052</td><td>0.027</td><td>0.827</td><td>0.039</td><td>0.017</td></tr><tr><th>RUEW</th><td>0.672</td><td>0.416</td><td>0.198</td><td>–0.356</td><td>0.250</td><td>–0.111</td></tr><tr><th>RFLL</th><td>0.483</td><td>–0.203</td><td>0.107</td><td>0.432</td><td>0.007</td><td>–0.534</td></tr><tr><th>RHAL</th><td>0.396</td><td>0.279</td><td>0.680</td><td>–0.050</td><td>0.263</td><td>–0.043</td></tr><tr><th>RTFL</th><td>0.251</td><td>0.145</td><td>0.765</td><td>0.105</td><td>0.046</td><td>–0.133</td></tr><tr><th>RTL</th><td>–0.154</td><td>–0.588</td><td>0.156</td><td>0.604</td><td>–0.089</td><td>0.071</td></tr><tr><th>RFOL</th><td>0.272</td><td>0.465</td><td>0.713</td><td>–0.158</td><td>0.169</td><td>0.122</td></tr><tr><th>RFTL</th><td>0.014</td><td>0.031</td><td>0.635</td><td>–0.028</td><td>0.070</td><td>0.168</td></tr><tr><th>RTFOL</th><td>–0.144</td><td>–0.712</td><td>–0.210</td><td>0.535</td><td>–0.189</td><td>0.105</td></tr><tr><th>RIMT</th><td>0.263</td><td>0.672</td><td>0.468</td><td>–0.265</td><td>0.163</td><td>–0.212</td></tr></tbody></table>
Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital
<p><strong>Figure S1:</strong> Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.<strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community services]. </p> <p><strong>Figure S2:</strong> Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. <strong>[A. </strong>Complications/comorbidities prolonging discharge, <strong>B.</strong> Healthcare-associated infection, <strong>C</strong>. Frailty, falls and/or integrated rehabilitation needs, <strong>D</strong>. Patient-centred needs, <strong>E</strong>. Community Services].</p>
Incidence and influencing factors of occupational pneumoconiosis: A systematic review and meta-analysis
<p><span><strong>Objectives</strong>: </span><span>To determine the incidence of pneumoconiosis worldwide and its influencing factors. </span></p> <p><span><strong>Design</strong>:</span><span> Systematic review and meta-analysis. </span></p> <p><span><strong>Setting</strong>:</span><span> Cohort studies on occupational pneumoconiosis.</span></p> <p><span><strong>Participants</strong>:</span><span> PubMed, Embase, the Cochrane Library, and Web of Science were searched until November 2021. Studies were selected for meta-analysis if they involved at least one variable investigated as an influencing factor for the incidence of pneumoconiosis and reported either the parameters and 95% confidence intervals (CIs) of the risk fit to the data, or sufficient information to allow for the calculation of those values. </span></p> <p><span><strong>Primary outcome measures</strong>:</span><span> The </span><span>pooled incidence of pneumoconiosis and risk ratio (RR) and 95% CIs of influencing factors. </span></p> <p><span><strong>Results</strong>:</span><span> Our meta-analysis included 19 studies with a total of 335,424 participants, of whom 29,972 developed pneumoconiosis. The pooled incidence of pneumoconiosis was 0.093 (95% CI: 0.085~0.135). We identified the following influencing factors: (1) male (RR=3.74; 95%CI 1.31–10.64; P=0.01), (2) smoking (RR=1.80; 95%CI 1.34</span><span>–</span><span>2.43; P=0.0001), (3) tunneling category (RR=4.75; 95%CI 1.96</span><span>–</span><span>11.53; P<0.0001), (4) helping category (RR=0.07; 95%CI 0.13</span><span>–</span><span>0.16; P<0.0001), </span><span>(5) age (the highest incidence occurs between the ages of 50 and 60), </span><span>(6) duration of dust exposure ((RR=4.59, 95% CI 2.41</span><span>–</span><span>8.74, P<0.01), (7) cumulative total dust exposure (CTD) (RR=34.14, 95% CI 17.50</span><span>–</span><span>66.63, P<0.01). A dose-response analysis revealed a significant positive linear dose-response association between the risk of pneumoconiosis and duration of exposure and CTD (P-nonlinearity=0.10, P-nonlinearity=0.16; respectively). The Pearson correlation analysis revealed that silicosis incidence was highly correlated with CSE (r=0.794, P<0.001).</span></p> <p><span><strong>Conclusion</strong>:</span><span> The incidence of pneumoconiosis in occupational workers was 0.093 and seven factors were found to be associated with the incidence, providing some insight into the prevention of pneumoconiosis.</span></p> <p><span>PROSPERO registration number: CRD42022323233.</span></p> <p><span>Abbreviations:</span><span> NOS: Newcastle Ottawa Scale, CWP: Coal Worker's Pneumoconiosis, CTD: cumulative total dust exposure, CSE: cumulative silica exposure.</span></p>
Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India
<p>This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".</p> <p>Data</p> <p>The data is organized in the form of tables.</p> <p>hypothesis-test-data</p> <p>This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.</p> <pre><code>* Region * Male_Deaths - Number of male COVID-19 deaths in region. * Female_Deaths - Number of female COVID-19 deaths in region. * Male_cases - Number of male COVID-19 positive in region. * Female_cases - Number of female COVID-19 positive in region. </code></pre> <p>lasso-covid19India</p> <p>This table contains data used for analysis on cases throughout India.</p> <p>Columns from COVID-19 India data</p> <pre><code>* State_Code * State * District * Confirmed * Active * Recovered * Deceased </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_ratio_of_the_total_population_females_per_1000_males * Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>lasso-KA+TN-bulletin</p> <p>This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.</p> <p>Data from Media Bulletin</p> <pre><code>* District * Total_Positives * total_deaths * male_deaths * female_deaths * Male_cases_in_data * Female_cases_in_data </code></pre> <p>Calculated Data</p> <pre><code>* Estimated_Male_cases - Estimated male cases using total positives column and existing case data * Estimated_Female_Cases - Estimated female cases using total positives column and existing case data * Male_Mortality - Estimated Male Cases / male_deaths * Female_Mortality - Estimated Female Cases / female_deaths </code></pre> <p>Columns taken from NFHS data</p> <pre><code>* Sex_Ratio_females_every_1000_males * State Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm214_ * Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI__185_kgm2_ * Women_who_are_overweight_or_obese_BMI__250_kgm214_ * Men_who_are_overweight_or_obese_BMI__250_kgm2_ * All_women_age_1549_years_who_are_anaemic_ * Men_age_1549_years_who_are_anaemic_130_gdl_ * Women_Blood_sugar_level__high_140_mgdl_ * Men_Blood_sugar_level__high_140_mgdl_ * Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ * Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_ </code></pre> <p>Code</p> <p>The code is available at this <a href="https://github.com/harishpb26/Sex-disaggregated-Analysis-of-Risk-Factors-of-COVID-19-Mortality-Rates-in-India">Github Repository</a>.</p>
Types of Intracellular Bacteria in Atherosclerotic Plaques and Analysis of Risk Factors
ClinicalTrials.gov study NCT06935279. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Why do youths initiate to smoke? A data mining analysis on tobacco advertising, peer, and family factors for Indonesian youths
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Location specific risk factors for intracerebral hemorrhage: Systematic review and meta-analysis
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Meta-analysis of the effects of abiotic factors on plant microbes
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Global analysis of environmental and socioeconomic factors associated with human burden of environmentally mediated pathogens
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Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?
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Influence of polymorphisms in the vascular endothelial growth factor gene on allograft rejection after kidney transplantation: a meta-analysis
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A multiscale analysis of factors influencing Blackpoll Warbler occupancy and abundance during the non-breeding season in eastern Colombia
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Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
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Incidence and influencing factors of occupational pneumoconiosis: A systematic review and meta-analysis
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Data from: A meta-analysis of factors influencing the strength of mate choice copying in animals
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Analysis of factors influencing the network teaching effect of college students in a medical school during the COVID-19 epidemic
<p><strong>Analysis of factors influencing the network teaching effect of college students in a medical school during the COVID-19 epidemic</strong></p>
Data from: The influence of ecological and life history factors on ectothermic temperature-size responses: analysis of three Lycaenidae butterflies (Lepidoptera)
Body size has been shown to decrease with increasing temperature in many species, prompting the suggestion that it is a universal ecological response. However, species with complex life cycles, such as holometabolous insects, may have correspondingly complicated temperature-size responses. Recent research suggests that life history and ecological traits may be important for determining the direction and strength of temperature-size responses. Yet, these factors are rarely included in analyses. Here, we aim to determine if the size of the bivoltine butterfly, Polyommatus bellargus, and the univoltine butterflies, Plebejus argus and Polyommatus coridon, change in response to temperature and whether these responses differ between the sexes, and for P. bellargus, between generations. Forewing length was measured using digital specimens from the Natural History Museum, London (NHM), from one locality in the UK per species. The data were initially compared to annual and seasonal temperature values, without consideration of life history factors. Sex and generation of the individuals and mean monthly temperatures, which cover the growing period for each species, were then included in analyses. When compared to annual or seasonal temperatures only, size was not related to temperature for P. bellargus and P. argus, but there was a negative relationship between size and temperature for P. coridon. When sex, generation and monthly temperatures were included, male adult size decreased as temperature increased in the early larval stages, and increased as temperature increased during the late larval stages. Results were similar but less consistent for females, while second generation P. bellargus showed no temperature-size response. In P. coridon, size decreased as temperature increased during the pupal stage. These results highlight the importance of including life history factors, sex and monthly temperature data when studying temperature-size responses for species with complex life cycles.
Data from: Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence
BACKGROUND: Heroin dependence is a debilitating psychiatric disorder with complex inheritance. Since the dopaminergic system has a key role in rewarding mechanism of the brain, which is directly or indirectly targeted by most drugs of abuse, we focus on the effects and interactions among dopaminergic gene variants. OBJECTIVE: To study the potential association between allelic variants of dopamine D2 receptor (DRD2), ANKK1 (ankyrin repeat and kinase domain containing 1), dopamine D4 receptor (DRD4), Catechol-O-methyl transferase (COMT) and dopamine transporter (SLC6A3) genes and heroin dependence in Hungarian patients. METHODS: 303 heroin dependent subjects and 555 healthy controls were genotyped for 7 single nucleotide polymorphisms (SNPs): rs4680 of the COMT gene; rs1079597 and rs1800498 of the DRD2 gene; rs1800497 of the ANKK1 gene; rs1800955, rs936462 and rs747302 of the DRD4 gene. Four variable number of tandem repeats (VNTRs) were also genotyped: 120 bp duplication and 48 bp VNTR in exon 3 of DRD4 and 40 bp VNTR and intron 8 VNTR of SLC6A3. We also provide a multivariate model for the associations among them implying Bayesian networks in Bayesian multilevel analysis. FINDINGS AND CONCLUSIONS: In single marker analysis the TaqIA (rs1800497) and TaqIB (rs1079597) variants were associated with heroin dependence. Moreover, -521 C/T SNP (rs1800955) of the DRD4 gene showed nominal association with a possible protective effect of the C allele. After applying the Bonferroni correction TaqIB was still significant suggesting that the minor (A) allele of the TaqIB SNP is a risk component in the genetic background of heroin dependence. The findings of the additional multiple marker analysis are consistent with the results of the single marker analysis, but this method was able to reveal an indirect effect of a promoter polymorphism (rs936462) of the DRD4 gene and this effect is mediated through the -521 C/T (rs1800955) polymorphism in the promoter.
Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
<p>This work presents an advanced source apportionment approach that estimates the uncertainties of the positive matrix factorization model. The selection of the environmentally meaningful solutions is challenging in such models and a systematic approach to identify and sort the factors is important. We applied this method to a novel data set covering one yearly cycle at three sites in Estonia. The main results showed that biomass burning dominates in winter, while biogenic sources prevail in summer.</p>
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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)
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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.