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13,064 results for “predictions”
Constructing the hierarchy of predictive auditory sequences in the marmoset brain
<p>Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up g and top-down b oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.</p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Predicting changes in molluscan spatial distributions in mangrove forests in response to sea-level rise
<p class="MsoNormal"><span>Molluscs are an important component of the mangrove ecosystem, and the vertical distributions of molluscan species in this ecosystem are primarily dictated by tidal inundation. Thus, sea-level rise (SLR) may have profound effects on mangrove mollusc communities. Here, we used dynamic empirical models, based on measurements of surface elevation change, sediment accretion, and molluscan zonation patterns, to predict changes in molluscan spatial distributions in response to different sea-level rise rates in the mangrove forests of Zhenzhu Bay (Guangxi, China). The change in surface elevation was 4.76–9.61 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span> during the study period (2016–2020), and the magnitude of surface-elevation change decreased exponentially as original surface elevation increased. Based on our model results, we predicted that mangrove molluscs might successfully adapt to a low rate of SLR (2.00–4.57 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>) by 2100, with molluscs moving seaward and those in the lower intertidal zones expanding into newly available zones. However, as SLR rate increased (4.57–8.14 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>), our models predicted that surface elevations would decrease beginning in the high intertidal zones and gradually spread to the low intertidal zones. Finally, at high rates of SLR (8.14–16.00 mm yr</span><sup><span>−</span></sup><sup><span>1</span></sup><span>), surface elevations were predicted to decrease across the elevation gradient, with molluscs moving landward and species in higher intertidal zones blocked by landward barriers. Tidal inundation and the consequent increases in interspecific competition and predation pressure were predicted to threaten the survival of many molluscan groups in higher intertidal zones, especially arboreal and infaunal molluscs at the landward edge of the mangroves, resulting in a substantial reduction in the abundance of original species on the landward edge. Thus, future efforts to conserve mangrove floral and faunal diversity should prioritize species restricted to landward mangrove areas and protect potential species habitats.</span></p>
Comorbid-phenome prediction and phenotype risk scores enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder
<p>GWAS summary statistics. See ReadMe for column descriptions.</p>
GJI:Learning source, path, and site effects: CNN-based Onsite Intensity Prediction for Earthquake Early Warning
<p>Dataset used for the study. Submitting to GJI. Wish me luck.</p>
Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables</p>
Predicting Software Refactoring - CSV Cache
<p>Dataset contains cache used for machine learning solution of our paper.</p>
Dataset for UAV velocity prediction_pt2
<p>This file contains a dataset for UAV velocity prediction only in the Tropical Forest (Landscape Pro 2.0) map.</p>
Results from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>This zipped archive contains the result files from running the pipeline described in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p>
Input data from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>These files are a 1GB fragments of a compressed archive containing the data used in the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance".</p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat input_data* > input_data.tar.gz`</p> <p>All data used is either publicly available or generated by this project. The subsets of the Recount3 and GTEx that we used are not owned by us, so putting them in this creative commons repository should not be construed as re-licensing them. </p> <p> </p>
Model weights from "The Effects of Nonlinear Signal on Expression-Based Prediction Performance"
<p>This file stores a representative set of saved models from the manuscript "The Effects of Nonlinear Signal on Expression-Based Prediction Performance". </p> <p>To simplify the uploading process, the file was split into chunks using the `split` utility in Linux. They can be joined back together with the command `cat model_weights* > model_weights.tar.gz`</p> <p>These saved files correspond to the weights and optimizer state of models trained on various biological tasks. They can be "rehydrated" using the `load_model` function of the associated model class from this repo: https://github.com/greenelab/linear_signal</p>
Data Set of Publication on Accurate Performance Predictions with Component-based Models of Data Streaming Applications
<p>This is the data set for the article "Accurate Performance Predictions with Component-based Models of Data Streaming Applications" by Dominik Werle, Stephan Seifermann and Anne Koziolek which appears in the proceedings of the 16th European Conference on Software Architecture (ECSA).</p> <p>The data set contains measurements of the evaluation system, models of the system, simulation results, derived analysis results and code for running the simulation.</p> <p>This work was supported by KASTEL Security Research Labs and by the German Research Foundation (DFG) under project number 432576552, HE8596/1-1 (FluidTrust).</p>
Traits and ecological space availability predict avian densities at the country scale of the Czech Republic
<p><span>Species geographical distributions and abundances are a central focus of current ecological research. Although multiple studies have been conducted on their elucidation, some important information are still missing. One of them is the knowledge of ecological traits of species responsible for the population density variations across geographical (i.e. total physical area) and ecological spaces (i.e. suitable habitat area). This is crucial for understanding how ecological specialisation shapes the geographical distribution of species, and provides key knowledge about the sensitivity of species to current environmental challenges. Here, we precisely describe habitat availability for individual species using fine-scale field data collected across the entire Czech Republic. In the next step, we used this information to test the relationships between bird traits and country-scale estimates of population densities assessed in both geographical and ecological space.</span><span> We did not find any effect of habitat specialisation on avian density in geographical space. But when we recalculated densities for ecological space available, we found a positive correlation with habitat specialization. Specialists occur at higher densities</span><span> in suitable habitats. Moreover, birds with arboreal and </span><span>hole-nesting strategies showed higher densities in both geographical and ecological spaces. However, we found no significant </span><span>effects of morphological (body mass, structural body size) and reproductive (position along </span><span>the slow-fast life-history continuum)</span><span> traits on avian densities in either geographical or ecological space. Our findings suggest that ecological space availability is a strong determinant of avian abundance and highlight the importance of precise knowledge of species-specific habitat requirements. Revival of this classical but challenging ecological topic of habitat-specific densities is needed for both proper understanding of pure ecological issues and practical steps in </span><span>the conservation of nature.</span></p>
Data from: Validity of bioelectrical impedance analysis in predicting total body water and adiposity among Senegalese school-aged children
Introduction: Childhood obesity is currently a serious public health challenge in developing countries. Therefore, an accurate assessment of adiposity is required. The objective of this study was to validate BIA prediction equations for the assessment of total body water and adiposity or percentage of body fat for the first time in Senegalese school-aged children. Methods: One-hundred-fifty-one (151) pupils who were 8-11 years old were randomly selected from four public schools in Dakar. The body composition measured by deuterium dilution method (DDM) was used as the reference method and compared to that predicted by BIA using a multi-frequency analyser. Stepwise backward multiple linear regression was performed to calculate TBW and %BF in a subsample, which were then validated it in the rest of the sample. The Bland and Altman approach was used to assess the agreement between the two methods (bias and limits of agreement). Results: FFM was higher in boys (24.6±6.9 kg) compared to girls (21.2±3.3 kg; P<0.001), and %BF was lower in boys (5.1±4.6 kg) compared to girls (5.3±3.8 kg; P<0.05). Overall, 11.3% of children were obese (%BF ≥25% in boys, and ≥30% in girls) and 4.6% were overweight/obese according to their BMI-z score (>+1). The equations developed were as follows: TBW=0.376(Height²/Z50)-0.470 (sex) +0.076(weight) +0.065(height)-2.28. %BF= -1.10(height²/Z50) +3.14(sex)+1.57(weight)-4.347. These specific equations showed good precision and a low and non-significant mean bias (0.11 kg, P=0.279; and 0.19 kg, P=0.764) for TBW and %BF, respectively. Conclusion: The newly developed equations can be used as an accurate and alternative tool for screening for obesity among African school-aged children in various settings
Cell Anomaly Localisation using Structured Uncertainty Prediction Networks
<p>Fluorescent and brightfield datasets for "Cell Anomaly Localisation using Structured Uncertainty Prediction Networks", part of MIDL 2022.</p>
Build Prediction in Continuous Integration Using Textual Analysis of Source Code
<p>The data-set comprises of software metrics that characterize build outcomes in CI using traditional and token frequency metrics.</p>
Data from: Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study
<p class="MsoNormal">Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU.</p> <p>Design: A retrospective observational cohort study.</p> <p>Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD).</p> <p>Primary outcome: In-hospital mortality.</p> <p>Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The C-index of the nomogram (0.767, 95%CI: 0.759–0.779) was <a>superior</a> to that of the traditional SOFA, APSIII and GWTGHF score, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits.</p> <p>Conclusion: The twenty independent risk factors for in-hospital mortality of CHF patients were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, <a>mechanical</a> <a>ventilation</a>, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW, and WBC. The nomogram that included these factors accurately predicted the in-hospital mortality of CHF patients. The novel nomogram has the potential to be a clinical practice aided predictive tool for predicting and assessing mortality in CHF patients in the ICU.</p>
Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by nest space
<p><strong>Overview</strong></p> <p>Data used for the manuscript: Nest shape influences colony organization in ants: spatial distribution and connectedness of colony members differs from that predicted by random movement and is affected by available space</p> <p><strong>Purpose of the study</strong></p> <p>Investigating how nest shape influences how <em>Temnothorax rugatulus</em> colonies spatially organize in their nests. This includes physical location of colony members and their distances from the entrance, mobile colony member distance to the brood center, worker distance to the physical center of the nest, and comparing worker distributions with those predicted by a random walk model.</p> <p><strong>Structure of the data</strong></p> <p>EMPIRICAL DATA</p> <p>WORKERS: FullDataCoordWorkers.csv, FullDataCoordWorkersRD2.csv</p> <p>Raw experimental data with worker x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>ColorID: The unique color marking assigned to an individual worker's head, thorax, abdomen1, abdomen2 (i.e., Yellow, White, Green, Green = Y,W,G,G)</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>BROOD / QUEENS: FullDataCoordBrood.csv, FullDataCoordBroodRD2.csv; FullDataCoordQueen.csv, FullDataCoordQueenRD2.csv</p> <p>Raw experimental data with brood (OR) queen x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>Density: The density treatment (High / Low)</li> </ul> <p>ALATES: FullDataCoordAlate.csv</p> <p>Raw experimental data with alate (winged reproductive individuals) x and y position in nests</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Day: The experimental day that the observation was collected on</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> <li>SexID: The unique sex assignment and number given to an individual alate: Sex, SexNumber, TotalNumber (i.e., the first male alate observation that came after three queen alates making it the fourth total observation = M,1,4)</li> </ul> <p>NETLOGO SIMULATIONS: ArchitectureMoveModelFull.csv</p> <p>Raw netlogo simulation data with agent x and y positions in nests</p> <ul> <li>RunNumber: The simulation number - 1 to 4000 - there are 1000 simulations for each combination of nest shape and size</li> <li>NestSize: The size of the nest area that agents were allowed to move throughout (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>TimeStep: The duration of each simulation (should be 50000)</li> <li>xcor: a list of every agent x coordinate position at the end of the simulation</li> <li>ycor: a list of every agent y coordinate position at the end of the simulation</li> </ul> <p>REFERENCE DATA </p> <p>NEST BINS: Empirical</p> <p>BinsNullFull.csv</p> <p>Null data sheet with eight bins for tube and circle nests in every colony</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordFull.csv</p> <p>Reference binning coordinates to group empirical coordinates into nest sections</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> <li>ScaledX: X-axis coordinate, scaled from original (px) to (cm) in the software Fiji (Schindelin et al., 2012)</li> <li>ScaledY: Y-axis coordinate, scaled from original (px) to (cm) in the software Fiji</li> </ul> <p>NEST BINS: Netlogo Simulations</p> <p>BinsNullNetlogo.csv</p> <p>Null data sheet with eight bins for tube and circle nests in each simulation treatment</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Bin: Nest section identifier (1-8)</li> </ul> <p>BinCoordNetlogo.csv</p> <p>Reference binning coordinates to group Netlogo simulation coordinates into nest sections</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>ScaledX: X-axis coordinate</li> <li>ScaledY: Y-axis coordinate</li> <li>CoordID: The unique coordinate identifier within each colony and nest combination</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFull.csv</p> <p>Whether a nest section has a corner or not</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>CORNERS: Empirical</p> <p>CornerFullSim.csv</p> <ul> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Bin: Nest section identifier (1-8)</li> <li>Corner: Presence of a corner (Y / N)</li> </ul> <p>REFERENCE DATA </p> <p>DISTANCES IN THE NEST: Empirical</p> <p>DistBinsFull.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>DISTANCES IN THE NEST: Netlogo Simulations</p> <p>DistBinsFullNetlogo.csv</p> <p>Reference coordinates for the entrance of nest sections (Bin) front-to-back and shortest distance to the entrance from each nest section entrance</p> <ul> <li>NestSize: The size treatment for simulations (Small / Large)</li> <li>Nest: The nest shape treatment (Tube / Circle)</li> <li>Distance: Reference shortest distance from a nest section to the entrance</li> <li>Bin: Nest section identifier (1-8)</li> <li>BinX: X-axis reference coodinate for a nest section entrance</li> <li>BinY: Y-axis reference coodinate for a nest section entrance</li> <li>Xmax: Max X-axis coordinate possible within the nest</li> <li>Ymax: Max Y-axis coordinate possible within the nest</li> <li>MaxDist: Max possible shortest distance from the nest entrance</li> <li>TubeRatio: Ratio of shortest distance to the nest entrance in circle nest / tube nest</li> </ul> <p>REFERENCE DATA </p> <p>WORKER SITE FIDELITY (SPATIAL FIDELITY & OCCURRENCE ZONE SIZES), ALSO RELATING SIZES TO DISTANCES IN THE NEST</p> <p>ColorRefFull.csv</p> <p>Reference of all possible unique color identifiers paint marked workers</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Head: Head color mark</li> <li>Thorax: Thorax color mark</li> <li>Abd1: Left side abdomen mark</li> <li>Abd2: Right side abdomen mark</li> </ul> <p>NestAreaFull.csv</p> <p>Reference for colony size (number of workers in the colony) and nest area</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Number.ants: Number of workers in the colony after painting</li> <li>Diameter: The diameter of the circle nest</li> <li>Area: The area of the nest</li> </ul> <p>ScalingCircleSFZ.csv</p> <p>Reference to scale the radius of circle nests to make coordinates representing fidelity zone bins</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Scaling: The scaling factor that is applied to the radius of each circle nest</li> </ul>
data for drought prediction using deep learning algorithms
<p>This dataset is monthly SPEI from 1901 to 2015 with 0.5 spatial resolution. It is standardized to [0,1] as inputs/outputs of neural network.</p>
Serum osmolality predicts mortality
<p>Supplementary Material 1: ICD-9 codes of patients included in this study.</p> <p>Supplementary Material 2: Table S1:Univariate logistics regression analysis of all patients on ICU and in-hospital mortality.Table S2:Univariate Cox regression analysis of all patients on 30-day and 90-day mortality.</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.