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265 results for “Factor model”
Datasets of the proposed models for carcinogenicity assessment based on slope factor.
<p>Datasets of the classification and regression models for carcinogenicity proposed in the paper "QSAR models for the human carcinogenicity: an assessment based on oral and inhalation slope factors" by Cosimo Toma<sup>1,2</sup>, Alberto Manganaro<sup>3</sup>, Giuseppa Raitano<sup>1</sup>, Marco Marzo<sup>1</sup>, Domenico Gadaleta<sup>1</sup>, Diego Baderna<sup>1</sup>*, Alessandra Roncaglioni<sup>1</sup>, Nynke Kramer<sup>2</sup>, Emilio Benfenati<sup>1</sup>*</p> <p><sup>1</sup> Laboratory of Chemistry and Environmental Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy</p> <p><sup>2</sup> Institute for Risk Assessment Sciences (IRAS), Utrecht University, PO Box 80177, 3508 TD Utrecht, the Netherlands</p> <p><sup>3</sup> Kode Chemoinformatics s.r.l., Pisa, Italy</p> <p><strong>*</strong> Correspondence: diego.baderna@marionegri.it</p> <p>Paper is now published on Molecules by MDPI and it is available here DOI:10.3390/molecules26010127</p> <p>S5 dataset for models based on Oral Slope Factor and S5 dataset for models based on Inhalation Slope Factor.</p> <p><strong>Models were developed under the framework of LIFE-VERMEER project (LIFE 16ENV/IT7000167).</strong></p>
QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?
<p>This dataset is described and studied in the article </p> <p>"QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?"</p> <p>published in <em>SAR and QSAR Environmental Research</em> (Taylor&Francis).</p> <p>Files description:</p> <p>SI_BCFtrainset.xlsx: a collection of 1129 chemical structures and CAS identifiers with their logBCF values extracted from various literature sources.</p> <p>SI_BCFtestset.xlsx: a collection of 204 chemical structures for which the logBCF is considered of lower reliability and used as an external test set.</p> <p>SI_FullDataset_rawdata.csv: the raw data composed of 15372 entries with the following columns: CASRN, Tissue, Duration [d], Test organism, Exposure type, Steady state, RESPONSE, RESPONSE UNIT, Media type, TakenFrom, TITLE, AUTHOR, YEAR, SOURCE, SMILES</p> <p>SI_ExcludedOutliers34.csv: 34 chemical structures that have been identified as suspicious during analysis.</p> <p> </p>
Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture
<p>Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).</p>
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet: transcription factor preferences
<p>The main output of our analysis, as a raw dataset. This data was used to create the plots depicting transcription factor preferences across our paper, including for our treemaps.</p>
Fig. 1 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 1. Potential distribution of the Common vole Microtus arvalis. White circles are georeferenced occurrences of genetically identified individuals; black indicates areas of maximum habitat suitability, white are areas of lowest suitability.
Fig. 2 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 2. Observed (bars) and expected (PMF) host infestation by Nycteribiidae bat flies: before/after (top/bottom) host mating; host females/males (left/right). No zero truncation and the used categorisation (pooled both host species and bat flies species) are the reasons of relatively bad fit to Poisson distribution.
Fig. 3 in Some Factors Behind Density Dynamics Of Bat Flies (Diptera, Nycteribiidae) - Ectoparasites Of The Boreal Chiropterans: Omitted Predictors And Hurdle Model Identification
Fig. 3. Observed (all kinds of dots) and expected (lines: y = exp (m+Acos (2pi (x–c)/36 — f) or y = b0exp (–b1x)) seasonal density dynamics of Nycteribiidae bat flies. Filled circles and thin lines — normally infested males; open circles and solid lines — normally infested females; double crosses and dashed lines — super-infested males; crosses and dashed lines — super-infested females. N o t e: Infested host only.
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).
Fig. 5 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 5. Distribution of two snake species, H. caspius and E. dione, in Ukrainian East (ATO zone is indicated by dotted line, burnt area marked inside zone).
Fig. 3 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 3. Spatial local distribution of ignitions in 2010–2014 in the outskirts of Slavyanoserbsk, Luhansk Region.
Factors influencing transferability in species distribution models
<p>Species distribution models (SDMs) provide insights into species' ecology and distributions and are frequently used to guide conservation priorities. However, many uses of SDMs require model transferability, which refers to the degree to which a model built in one place or time can successfully predict distributions in a different place or time. If a species' model has high spatial transferability, the relationship between abundance and predictor variables should be consistent across a geographical distribution. We used Breeding Bird Surveys, climate and remote sensing data, and a novel method for quantifying model transferability to test whether SDMs can be transferred across the geographic ranges of 129 species of North American birds. We also assessed whether species' traits are correlated with model transferability. We expected that prediction accuracy between modeled regions should decrease with 1) geographical distance, 2) degree of extrapolation, and 3) were affected by a 'core-boundary' effect, which assesses distances to the boundary of a distribution. Our results suggest that very few species have a high model transferability index (<em>MTI</em>). Species with large distributions, with distributions located in areas with low topographic relief, and with short lifespans are more likely to exhibit low transferability. Transferability between modeled regions also decreased with geographical distance and degree of extrapolation. We expect that low transferability in SDMs potentially resulted from both ecological non-stationarity (i.e., biological differences within a species across its range) and over-extrapolation. Accounting for non-stationarity and extrapolation should substantially increase prediction success of species distribution models, therefore enhancing the success of conservation efforts.</p>
Association of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model
<p>this dataset are “ssociation of Body Index with Fecal Microbiome in Children Cohorts with Ethnic-Geographic Factor Interaction: Accurately Using a Bayesian Zero-inflated Negative Binomial Regression Model” Supplementary Material.</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context
<p>R retrospective, P prospective, CC case – control study, MC multicenter controlled trail, PB populational based, HB hospital based, C clinical, CT computed tomographic scan, MRI cerebral magnetic resonance imaging, CUS cranial ultrasonography / cerebral ultrasound, USG ultrasonography, EEG electroencephalogram (standard), CpH cord Ph, BpH blood Ph, HT therapeutic hypothermia It can be noticed that seizure diagnosis was based on clinical grounds and functional explorations naming neuroimaging and/or EEG procedures (conventional EEG, aEEG, vEEG, CUS, MRI). The minimum number of newborns considered in these studies was 55, while the maximum was 403 with a mean of 148 (SD=86.75, median=112, IQR: (98,175)) and a total of 2226 evaluated cases.</p>
Transfer learning and DNA language models enhance transcription factor binding predictions
<p>This is the dataset for replicating the results of the paper called "Transfer learning and DNA language models enhance transcription factor binding predictions" by Ekin Deniz Aksu and Martin Vingron.</p> <p>See https://github.com/ekinda/tfbs_prediction_paper</p>
Factors Associated with Scientific Production Citations in Dentistry: Zero-inflated Negative Binomial Regression and Hurdle Modelling
<p><strong>Abstract:</strong> The global scientific literature in dentistry has shown important advances in the field, with major contributions ranging from the analysis of the basic epidemiological aspects of prevention to specialised results in the field of dental treatments. The present investigation aims to analyse the current state of the scientific literature on dentistry hosted in the Web of Science database. The methodology includes two phases in the analysis of articles and indexed reviews in all thematic areas. During the first phase, the following variables are analysed: scientific production by the publisher, the evolution of scientific output published by publishers, the factors associated with the impact of scientific production, and the modelling of the impact of scientific production on dentistry. During the second phase, associations, evolutions, and trends in the use of main keywords in the scientific literature in dentistry are analysed. In conclusion, the study shows that the most studied topics include the association of dental education and the curriculum, the association of pediatric dentistry with oral health, and dental care. The findings show that more recently emphasised topics also stand out, such as evidence-based dentistry, the pandemic, infection control, and endodontics, as well as the need for future research to expand current knowledge based on emerging topics in the scientific literature on dentistry.</p>
Factors influencing transferability in species distribution models
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Data from: Refining trophic dynamics through multi-factor Bayesian mixing models: a case study of subterranean beetles.
<p>Food web dynamics are vital in shaping the functional ecology of ecosystems. However, trophic ecology is still in its infancy in groundwater ecosystems due to the cryptic nature of these environments. To unravel trophic interactions between subterranean biota, we applied an interdisciplinary Bayesian mixing model design (multi-factor BMM) based on the integration of faunal C and N bulk tissue stable isotope data (δ<sup>13</sup>C and δ<sup>15</sup>N) with radiocarbon data (Δ<sup>14</sup>C), and prior information from metagenomic analyses. We further compared outcomes from multi-factor BMM with a conventional isotope double proxy mixing model (SIA BMM), triple proxy (δ<sup>13</sup>C, δ<sup>15</sup>N and Δ<sup>14</sup>C, multi-proxy BMM) and double proxy combined with DNA prior information (SIA+DNA BMM) designs. Three species of subterranean beetles (<i>Paroster macrosturtensis</i>, <i>Paroster mesosturtensis</i> and <i>Paroster microsturtensis</i>) and their main prey items Chiltoniidae<i> </i>amphipods (AM1: <i>Scutachiltonia axfordi</i> and AM2: <i>Yilgarniella sturtensis</i>), cyclopoids and harpacticoids from a calcrete in Western Australia were targeted. Diet estimations from stable isotope only models indicated homogeneous patterns with modest preferences for amphipods as prey items. Multi-proxy BMM suggested increased - and species-specific - predatory pressures on amphipods coupled with high rates of scavenging/predation on sister species. SIA+DNA BMM showed marked preferences for amphipods AM1 and AM2 and reduced interspecific scavenging/predation on <i>Paroster </i>species. Multi-factorial BMM revealed the most precise estimations (lower overall SD and very marginal beetles' interspecific interactions), indicating consistent preferences for amphipods AM1 in all the beetles' diets. Incorporation of genetic priors allowed crucial refining of the feeding preferences, while integration of more expensive radiocarbon data as a third proxy (when combined with genetic data) produced more precise outcomes but close dietary reconstruction to that from SIA+DNA BMM. Further multidisciplinary modelling from other groundwater environments will help elucidate the potential behind these designs and bring light to the feeding ecology of one the most vital ecosystems worldwide.</p>
Developing crown width model for mixed forests using soil, climate, and stand factors
<ol> <li>The tree crown is a useful measure of tree vigor and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focused on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected.</li> <li>Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 was located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity, and their interactions.</li> <li>We observed that the cross-species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter, and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index, and species mixture. The interaction of quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modeling than the aridity index, and structural diversity had a greater effect on crown width than species diversity.</li> <li> <em>Synthesis</em>. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.</li> </ol>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></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)
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.