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670 results for “Influence factors”
Experimental Factors Influence Diversity Metrics of the Gut Microbiome in Laboratory Mice
<p>Abstract<br> Introduction</p> <p>Gut microbiome studies often overlook experimental factors that could influence gut microbiome diversity and could impact findings. Large-scale studies investigating these experimental factors are lacking. Thus, we aimed to determine which experimental factors influence the gut microbiome diversity in pre-clinical animal model studies.</p> <p><br> Methods</p> <p>We extracted DNA and sequenced the V4 region of the 16S rRNA gene of a total of 538 samples from various sections of the gastrointestinal tract of 303 young and aged male and female C57BL/6J mice of three different genotypes on five diets from three animal house facilities. As a proof-of-concept in a disease model, some mice were treated with sham or angiotensin II, a commonly studied agent used as a hypertension model. Some samples were sequenced twice as a matched-comparison group.</p> <p>Results</p> <p>Using over 17 million sequencing reads, we found that experimental factors such as animal house facility, genotype, diet, age, sex, sampling site, and technical factor (i.e., sequencing batch) affected both α- and β-diversity (weighted and unweighted UniFrac), and were associated with compositional changes in the microbiome at varying magnitude, with diet and sampling site having the largest effect. After adjustment by these factors, treatment with angiotensin II had no impact on α-diversity and was only significant in unweighted UniFrac (presence/absence of bacteria) analyses.</p> <p><br> Conclusion</p> <p>Our data identified several key experimental and technical factors that affect the gut microbiome in laboratory mice. Our findings support that not accounting or adjusting for these factors may lead to false-positive discoveries and non-biologically relevant findings in the gut microbiome field.</p>
Factors Influencing Aboveground Carbon Storage in Mixed Oak-Pine Forests: USDA FIA Data from Southeastern U.S. (2009-2019)
This study explores factors affecting aboveground carbon (AGC) storage in mixed oak-pine forests across the Southeastern United States. Utilizing USDA Forest Inventory and Analysis (FIA) data from 2009 to 2019, the research spans nine states: Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, Louisiana, and Virginia. Data processing in R included converting units to the metric system and calculating structural diversity using Shannon diversity indices. Climate data from the PRISM Climate Group were integrated with FIA data using longitude and latitude. The research aims to uncover how various factors influence AGC storage and contribute to informed forest management practices.
Factors influencing decomposition of leaves for five plant species at El Verde
We evaluated the influences of leaf quality, climate and microsite on the decomposition of leaves of five tropical tree species. Single-species litterbags were used to determine weight loss during the first three months of decomposition in the Luquillo Experimental Forest, Puerto Rico. Significant differences were found in decomposition rates among leaf species (Inga fagifolia < I. vera < Manilkara bidentata < C-roton poecilanthus << Sapium laurocerasus), but only S. laurocerasus differed significantly from the other species. Lignin had a suggestive negative correlation with leaf decomposition while carbon content and the lignin:N ratio were significantly correlated with mass loss. Content of N, P, Ca, and polyphenol were not significantly correlated with mass loss, but several of the litter quality variables were correlated with each other. Leaf species decomposed faster under canopies of their source trees than in a common plot where the source species were absent. Decomposition in two species in the Euphorbiaceae, S. laurocerasus and C. poecilanthus, was significantly affected by microsite. Leaching losses during the first three weeks were greater under source trees than in the common plot, and may have been associated with differences in canopy structure and throughfall. Differences in detrital communities, however, could have contributed to the differences in decomposition between microsites. Leaves of all species decomposed significantly faster in the wet than in the dry period (P = 0.001) despite little climatic variation in this subtropical wet forest type. This suggests that decomposition of tropical leaf litter might be sensitive to microclimatic changes on the forest floor resulting from either global climate change, or from natural or anthropogenic disturbances that open the canopy. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA, 2013 - 2018
Centaurea stoebe (Asteraceae; spotted knapweed) is an emerging invader in northeast US, and is a major invasive plant in the northern Midwest and western USA. Although it has been present in New York State (NYS) for over 100 years, its apparent recent population increases and spread provide a rare opportunity to study a plant in the early stages of invasion. Therefore, a study was carried out understand how distinct environmental factors influence the distribution, density and change in density C. stoebe at different spatial scales within its novel range in the northeastern USA. First, we collected field data on the occurrence, density and change in density of this species in North Eastern United States, from 2013 to 2014. Then, using species distribution models, we assessed the potential influence of environmental factors on the invasion of spotted knapweed in northeast US. Within different parts of C. stoebe‘s range, different factors explained its occurrence, density and change in density over 2 years. Across northeast US, climate and soil factors were the most influential predictors explaining C. stoebe‘s distribution, while within Long Island in southeastern NYS and the Adirondack Mountains in northern NYS, precipitation and disturbance respectively were the most important. These results are published in the paper titled The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA (Akin-Fajiye and Gurevitch, 2018).
Environmental and social factors influencing median income and BMI in the State of Geneva
<p>Hectometric grid (100m x 100m) covering the inhabited areas of the State of Geneva. It contains informations relative to the bmi and median income (GIREC) within the cells, together with a series of environmental and social factors, with which a correlation can be sought.</p>
Figure 2 in Factors influencing the level of infestation of Ixodes ricinus (Acari: Ixodidae) on Lacerta agilis and Zootoca vivipara (Squamata: Lacertidae)
Figure 2 Mean number (± SE) of ticks per body size class (I to IV), and sex-age category (juv: juvenile; F: female, M: male), found in (A) transformed conditions and (B) natural conditions.
Figure 1 in Factors influencing the level of infestation of Ixodes ricinus (Acari: Ixodidae) on Lacerta agilis and Zootoca vivipara (Squamata: Lacertidae)
Figure 1 Mean number (± SE) of ticks per body size class (I to IV) and Sex-age category (juv: juvenile; F: female, M: male), found on (A) Zootoca viviparaand (B)Lacerta agilis.
Figure 3 in Factors influencing the level of infestation of Ixodes ricinus (Acari: Ixodidae) on Lacerta agilis and Zootoca vivipara (Squamata: Lacertidae)
Figure 3 Mean number of ticks found on different locations on the lizards bodies (± standard errors).
Factors influencing the likelihood of accessing healthcare during the COVID-19 pandemic in Ireland: lessons for the future
<p>This is an adapted version of the original National Household Survey - Wave 1 whereby existing variables were recoded to create new variables for the purpose of a new analysis.</p>
Understanding Organizational Commitment and its Factors Influencing the Nurse's Job Satisfaction in Hospitals- A Systematic Literature Review and Further Research Agendas
<div> <p><em><span>Organizational commitment is a crucial concept when it comes to human resource management and Organizational behavior. It has to do with how much a worker commits to and identifies with the goals, values, and purposes of their company. Elevated levels of Organizational commitment are associated with enhanced job satisfaction, less attrition, and better performance. The expected ideal condition, status, and research deficit are all included in this article. A research agenda is determined by applying the ABCD framework to qualitatively analyze the identified research gap.The paper documents the topic and provides helpful information about it, which will aid future scholars. </span></em></p> </div>
What factors influence the rediscovery of lost tetrapod species? Appendix G: Variables tested for their influence on rediscovery
<p>For the study associated with this dataset, we created a database of lost and rediscovered tetrapod species and identified patterns in their distribution and factors influencing rediscovery. This appendix provides a list of the lost and rediscovered species and all data used to calculate 11 variables (V):</p> <ul> <li>V1: Taxonomic status - class, order, family, species name, common name;</li> <li>V2: Countries / islands occupied by each species;</li> <li>V3: The cumulative number of lost and rediscovered species - four columns, (i) last seen date, (ii) rediscovered date, (iii) for rediscovered species, the number of years lost, (iv) for lost species, the number of years lost;</li> <li>V4: Time lost (the number of years each species has been lost for);</li> <li>V5: Adult body mass (g) of each species;</li> <li>V6: Habitat breadth - the number of broad habitat types occupied by each species;</li> <li>V7: Habitat type - the broad habitat types occupied by each species;</li> <li>V8: Small island / mainland - whether a species occupies a small island (< 20,000 km<sup>2</sup>) or a mainland location (including islands > 20,000km<sup>2</sup>) (0 = mainland, 1 = small island);</li> <li>V9: Threats - the different threats associated with each species;</li> <li>V10: Human development - the highest level of human development across the range of each species, measured using the Human Development Index (HDI);</li> <li>V11: Survey effort - the level of effort invested in searching for each species (1 = low, 2 = medium, 3 = high, 4 = very high). See Supporting Information (Table S1) for the methods used to calculate this variable.</li> </ul>
What factors influence the rediscovery of lost tetrapod species? Appendix E: Lost and rediscovered species
<p>For the study associated with this dataset, we created a database of lost and rediscovered tetrapod species and identified patterns in their distribution and factors influencing rediscovery. This appendix provides a list of the lost and rediscovered tetrapod species used in the analysis. It includes data on:</p> <ul> <li>taxonomy (class, order, family, species, common name, and whether the species is a subspecies);</li> <li>location (continent, country, region);</li> <li>each species threat status (as published on the IUCN Red List of Threatened Species: <a href="https://www.iucnredlist.org/">https://www.iucnredlist.org/</a>).</li> </ul> <p>The appendix also indicates:</p> <ul> <li>whether each species was included in another list of lost tetrapod species published by the organisation Re:wild (<a href="https://www.rewild.org/">https://www.rewild.org/</a>);</li> <li>whether each species was used to construct the phylogenetic trees used for analysis.</li> </ul>
What factors influence the rediscovery of lost tetrapod species? Appendix F: Excluded species
<p>For the study associated with this dataset, we created a database of lost and rediscovered tetrapod species and identified patterns in their distribution and factors influencing rediscovery. Our lost species list included many of the species on another published list of lost species compiled by Re:wild (<a href="https://www.rewild.org/)">https://www.rewild.org/)</a> in collaboration with the International Union for Conservation of Nature (IUCN) (<a href="https://www.iucn.org/">https://www.iucn.org/</a>). However, we did not include some of the species included on the Re:wild/IUCN list. This appendix provides a list of those species. It includes the following information for each species:</p> <ul> <li> <p>taxonomy - class, order, scientific name, common name;</p> </li> <li> <p>threat status (as published on the IUCN Red List of Threatened Species);</p> </li> <li> <p>location - continent / island, range / country.</p> </li> </ul>
Changes in the factors influencing forest floor BVOC emissions during forest succession
<p>The files have been uploaded to comply with AGU and journal requirements, particularly the "Open Research" section, which provides links to the data and analytical code necessary for the peer review process. This initiative aims to support transparent and reproducible science. </p> <ul> <li>Data analysis and the ploting of Figure2 in manuscript, along with Figure S1-S3 and Table S1-S4 in supporting information, were conducted using R Studio. The file "Forest floor BVOC emissions_analyses and plots.R" and datasets "ForestFloor.csv", "boxplot_BVOC_ca.csv", "boxplot_BVOC_fi.csv", "boxplot_BVOC_ru.csv", "SamplingSite_1.csv" were utilized for this purpose.</li> <li>To generate Figure 3 in the manuscript, the file "SIMCA 18 for Fig 3.dox" and dataset "ForestFloor.xlsx" were used. The word file provide the the trial software link. </li> <li>For the analysis and ploting of Figure 4 in the manuscript, the file "PLS_PM.R" and dataset "BVOC_PLSR_PM.csv" were employed. </li> <li>The file "For Fig S4.xlsx" was used to create Figure S4 in the supporting information. </li> </ul> <p>Abstract in article</p> <p><span>The boreal forest floor is a crucial source of diverse biogenic volatile organic compounds (BVOCs) emitted into the atmosphere. Climate change is increasing in the frequency of wildfires in the boreal forest, major disturbances with lasting impacts on the ecosystem, particularly the forest floor. Wildfires changed BVOC sources and emissions, influencing aerosol formation during forest succession across various age classes. This study quantified BVOC emissions from the forest floor and characterized microenvironmental conditions, including abiotic factors (air temperature, soil temperature, soil moisture, light intensity) and biotic factors (ground vegetation composition, species coverage, soil respiration). Our objective was to understand how abiotic and biotic factors influence the forest floor BVOC emissions during forest succession. Path models revealed direct influences of ground vegetation composition on isoprene and monoterpene emissions. Sesquiterpene emissions were mainly regulated by abiotic factors, while isoprene and monoterpene emissions were influenced both directly and indirectly by abiotic factors. The indirect impact of abiotic factors was manifested through biotic factors, including vegetation and soil processes. Effect sizes of influencing factors varied across different forest age areas, with temperature exerting a larger impact in earlier burned areas compared to recently burned areas. The influence of soil moisture on BVOC emissions diminished with forest age. Our findings indicated the importance of identifying influencing factors and their relationship with forest floor BVOC emissions during different stages of forest succession for predicting the effect of post-wildfire forest succession on the BVOC emission patterns and, consequently, their impact on climate.<span> </span></span></p>
Fig. 1 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 1. Analysis of the population of the potato ladybird beetle with the species-specific PCR-markers of the gene COI mtDNA. А – species-specific marker for H. vigintioctopunctata, 400 b.p.; Б – species-specific marker for H. vigintioctomaculata, 406 b.p.; М – marker of the lengths of fragments 100 b.p. ladder; 1–3 – Primorsky krai: Chuguevsky district; 4–6 – Amurskaya oblast; 7–17 – Primorsky krai: Timiryazevsky.
Fig. 3 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 3. Sinergetic activity of the protainases of trypsin type (in an insect) and trypsin inhibitors (in a plant) in the course of feeding on different potato varieties.
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching
<p>The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.</p>
Fig. 1 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 1. Spatial distribution of ignitions in 2010–2014 on studied area (dotted line is ATO zone's limits in 1.06– 30.09.2014).
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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)
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.
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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.
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