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13,064 results for “predictions”
Data from: Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence
Precise temporal coordination of slow oscillations (SO) and sleep spindles is a fundamental mechanism of sleep-dependent memory consolidation. SO and spindle morphology changes considerably throughout development. Critically, it remains unknown how the precise temporal coordination of these two sleep oscillations develops during brain maturation and whether their synchronization indexes the development of memory networks. Here, we use a longitudinal study design spanning from childhood to adolescence, where participants underwent polysomnography and performed a declarative word-pair learning task. Performance on the memory task was better during adolescence. After disentangling oscillatory components from 1/f activity, we found frequency shifts within SO and spindle frequency bands. Consequently, we devised an individualized cross-frequency coupling approach, which demonstrates that SO-spindle coupling strength increases during maturation. Critically, this increase indicated enhanced memory formation from childhood to adolescence. Our results provide evidence that improved coordination between SOs and spindles indexes the development of sleep-dependent memory networks.
CodiEsp Silver Standard: Participant predictions in eHealth CLEF2020 - Spanish clinical cases coded in ICD10 (CIE10)
<p><strong>Introduction</strong></p> <p>Predictions in the background set of <a href="https://temu.bsc.es/codiesp/">eHealth CLEF 2020 Task 1</a> participants.</p> <p> </p> <p><strong>Zip structure</strong></p> <p>One directory per CodiEsp subtask. Within each CodiEsp subtask directory, there is one directory per team that contains the prediction runs.</p> <p> </p> <p><strong>Format</strong><br> The text documents are distributed in plain text files, UTF-8 encoding.<br> The CodiEsp Silver Standard annotations have the following format:</p> <p>For the sub-tracks CodiEsp-Diagnostic and CodiEsp-Procedure, the file files have the following fields:</p> <pre>articleID ICD10-code </pre> <p>Tab-separated files for the sub-track CodiEsp-X (explainability) contain extra fields that provide the text-reference and its position:</p> <pre>articleID label ICD10-code text-reference reference-position</pre> <p> </p> <p><strong>Resources:</strong></p> <ul> <li><strong><a href="https://temu.bsc.es/codiesp/">Web</a></strong></li> <li><strong>Citation: </strong>Miranda-Escalada, A., Farré, E., & Krallinger, M. (2020). Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In <em>Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings</em>.</li> <li><strong><a href="https://zenodo.org/record/3837305#.X7T9KVlKg5k">Gold Standard corpus</a></strong></li> <li><strong><a href="https://doi.org/10.5281/zenodo.3878178">Annotation guidelines</a></strong></li> <li><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhC24g5dsp5eVMp8BZFWCraX"><strong>YouTube presentations</strong></a></li> <li><a href="https://temu.bsc.es/cantemist/?p=4606"><strong>Participant codes</strong></a></li> </ul> <p> </p> <p>All credit to CodiEsp participants</p>
Code and data underlying "Interictal intracranial EEG for predicting surgical success: the importance of space and time"
<p>Code and data underlying our publication "Interictal intracranial EEG for predicting surgical success: the importance of space and time" in Epilepsia 2020</p>
Validation of Time Series Technique for Prediction of Conformational States of Amino Acids
<p>Validation of Time Series Technique for Prediction of Conformational States of Amino Acids</p> <p>- a project for fulfillment of M.Sc (Master of Science) in Bioinformatics.</p>
Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces
<p>These are data corresponding to the paper '<a href="https://doi.org/10.1093/bioinformatics/btaa263">Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces</a>', published in Bioinformatics 2020. The code to run the experiments can be found on github <a href="https://github.com/vamships/PECAN">here</a>.</p>
Predicting human health from biofluid-based metabolomics using machine learning
<p>Processed input data sets as well as output files and trained health state predictive models for human diseases using metabolomics of biofluids. These data are associated with the following manuscript: 'Predicting human health from biofluid-based metabolomics using machine learning' https://doi.org/10.1101/2020.01.29.20019471</p>
Predictability of food supply modulates nocturnal hypothermia in a small passerine
<p class="MsoBodyText">The combination of short days and long cold nights in winter, in temperate regions, presents a major challenge for small diurnal birds. Small birds regularly employ heterothermy and enter rest-phase hypothermia during winter nights to conserve energy. However, we know little about how environmental conditions, such as food availability, shape these strategies.</p> <p class="MsoBodyText">Using an experimental set-up, we manipulated food availability in winter to free-living great tits <i>Parus major</i>. A "predictable" and constant food supply was provided to birds in one area of a forest, while birds in another area did not have access to a reliable supplementary food source. We found that predictability of food affected the extent of nocturnal hypothermia, but the response differed between sexes. Whereas male nocturnal body temperature was similar regardless of food availability, females exposed to a naturally "unpredictable" food supply entered deeper hypothermia at night, compared with females that had access to predictable food and compared with males in both treatment groups. We suggest that this response likely is a consequence of dominance, and the subdominant females cannot maintain sufficient energy intake, resulting in a higher demand for energy conservation at night.</p>
Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"
<p>Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"</p>
Transfer learning enables prediction of CYP2D6 haplotype function
<p>This data here were used to train the models described in the manuscript "Transfer learning enables prediction of CYP2D6 haplotype function". The deep learning model described predicts metabolic function of <em>CYP2D6</em> star alleles. It uses two pretraining steps, first with simulated data, then with sequence data collected from liver microsomes, and finally using sequence data for <em>CYP2D6</em> star alleles.</p> <p> </p> <p>simulated_cyp2d6_diplotypes.tar.gz - This file contains sequence data and labels for simulated <em>CYP2D6 </em>data used in the first training step</p> <p>dalton_2019_cyp2d6_microsomes.txt - This file contains summary statistic data for liver microsome data used in the second pretraining step (originally from <a href="https://doi.org/10.1111/cts.12695">https://doi.org/10.1111/cts.12695)</a></p> <p>star_samples.vcf - This file contains sequence data for <em>CYP2D6 </em>star alleles derived from PharmVar (https://www.pharmvar.org/gene/CYP2D6) used in the final training step.</p>
Data from: Link prediction in real-world multiplex networks via layer reconstruction method
Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed Layer Reconstruction Method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.
Data from: Brain size does not predict learning strategies in a serial reversal learning test
<p><span><span><span><span><span><span><span><span><span><span><span>Reversal learning assays are commonly used across a wide range of taxa to investigate associative learning and behavioural flexibility. In serial reversal learning, the reward contingency in a binary discrimination is reversed multiple times. Performance during serial reversal learning varies greatly at the interspecific level, as some animals adapt a rule-based strategy that enables them to switch quickly between reward contingencies. Enhanced learning ability and increased behavioural flexibility generated by a larger relative brain size has been proposed to be an important factor underlying this variation. Here we experimentally test this hypothesis at the intraspecific level. We use guppies (<i>Poecilia reticulata</i>) artificially selected for small and large relative brain size, with matching differences in neuron number, in a serial reversal learning assay. We tested 96 individuals over ten serial reversals and found that learning performance and memory were predicted by brain size, whereas differences in efficient learning strategies were not. We conclude that variation in brain size and neuron number is important for variation in learning performance and memory, but these differences are not great enough to cause the larger differences in efficient learning strategies observed at higher taxonomic levels. </span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Total Aβ42/Aβ40 ratio in plasma predict amyloid-PET status, independent of clinical AD diagnosis
Background: Research has focussed upon the identification of a blood-based biomarker to inform clinicians on the likelihood of Alzheimer's disease pathology prior to ordering the CSF and PET testing. Objective: To explore if the plasma total Aβ42/Aβ40 ratio is a reliable predictor of the amyloid-PET status we have explored the association between these two variables in a subset of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of ageing cohort. Methodology: Taking plasma samples at three separate time points, we assessed the total Aβ42/Aβ40 ratio in plasma (TP42/40) with regard to neocortical amyloid beta (Aβ) burden via PET SUVR, and investigated both association with Aβ-PET status and correlation (and agreement) with SUVR. Results: The TP42/40 plasma ratio was associated with PET status at all time points (p<0.013). Correlations with SUVR were similar across each time point, with Spearman's Rho reaching -0.64 (p<0.0001). Area under the curve (AUC) values were highly reproducible over time points, with values ranging from 0.880 at 36 months to 0.913 at 54 months. Assessing the healthy control (HC) group only, the same relationships were found. Conclusions: The current study demonstrates reproducibility of the plasma assay to discriminate between amyloid-PET positive and negative over three time points, which can help to substantially reducing the screening rate of failure (SRF) for clinical trials targeting pre-clinical or prodromal disease. Classification of evidence: This study provides Class II evidence that plasma total AB42/AB40 ratio is associated with neocortical amyloid burden as measured by PET SUVR.
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Data from: Predicting multivariate responses of sexual dimorphism to direct and indirect selection
<p>Sexual dimorphism is often assumed to result from balancing the strength of antagonistic selection in favor of dimorphism against the degree of constraint imposed by the shared genome of the sexes, reflected in the <b>B</b> matrix of genetic inter-sexual covariances. To investigate the totality of forces shaping dimorphism, we reparameterized the Lande equation to predict changes in trait averages and trait differences between the sexes. As genetic constraints on the evolution of dimorphism in response to antagonistic selection become larger, dimorphism will tend to respond more rapidly to concordant selection (which favors the same direction of change in male and female traits) than to antagonistic selection. When we apply this theory to four empirical estimates of <b>B</b> in <i>Drosophila melanogaster</i>, the indirect responses of dimorphism to concordant selection are of comparable or larger magnitude than the direct responses of dimorphism to antagonistic selection in two suites<i> </i>of traits with typical levels of inter-sex correlation. Antagonistic selection is more important in two suites of traits where the inter-sex correlations are unusually low. This suggests that the evolution of sexual dimorphism may sometimes be dominated by concordant selection, rather than antagonistic selection. </p>
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
For foveated animals, visual tracking of moving stimuli requires the synergy between saccades and smooth pursuit eye movements. Deciding to trigger a catch-up saccade during pursuit influences the quality of visual input. This decision is a trade-off between tolerating sustained position error when no saccade is triggered or a transient loss of vision during the saccade due to saccadic suppression. Although catch-up saccades have been extensively investigated, it remains unclear how the trigger decision is made by the brain. de Brouwer et al (2002) demonstrated that catch-up saccades were less likely to occur when the expected time to foveate a target using pursuit alone is between 40 and 180ms into the future, referred to as the smooth zone. However, this descriptive result lacks a mechanistic explanation for how the trigger decision is made. More recently, we proposed a decision model (Coutinho et al., 2018) that relies on a probabilistic estimation of predicted position error (PEpred) during visual tracking. To test the model predictions, we investigated how human participants combined predicted position error, retinal slip, and the uncertainty in those estimates to make trigger decisions. We found a significant effect of the pre-saccadic magnitude of PEpred on trigger time and occurrence of catch-up saccades. To test the role of uncertainty, we blurred the moving target which led to longer and more variable saccade trigger times and more smooth pursuit trials, consistent with model predictions. As predicted by our model, large PEpred (>10deg) produced early saccades regardless of the level of uncertainty while saccades preceded by small PEpred (<10deg) were significantly modulated by high uncertainty. Our model also predicted increased signal dependent noise as retinal slip increases, which resulted in longer saccade trigger times and more smooth trials. In conclusion, the data supports our hypothesized role of PEpred in deciding when to trigger a catch-up saccade during smooth pursuit while taking into account uncertainty in sensory estimates.
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
Predicting plasmid contigs from assemblies using single copy marker genes, plasmid genes, kmers
<p>Introduction: Antimicrobial resistant (AMR) genes in bacteria are often carried on plasmids. Since these plasmids can spread the AMR genes between bacteria, it is important to know if the genes are located on highly transferable plasmids or in the more stable chromosomes. Whole genome sequence (WGS) analysis makes it easy to determine if a strain contains a resistance gene, however, it is not easy to determine if the gene is located on the chromosome or on a plasmid as genome sequence assembly generally results in 50-300 DNA fragments (contigs). With our newly developed prediction tool, we analyze the composition of these contigs to predict their likely source, plasmid or chromosomal. This information can be used to determine if a resistant gene is chromosomally located or on a plasmid. The tool is optimized for 19 different bacterial species, including Campylobacter, E. coli, and Salmonella, and can also be used for metagenomic assemblies.</p> <p>Methods: The tool identifies the number of chromosomal marker genes, plasmid replication genes and plasmid typing genes using CheckM and DIAMOND Blast, and determines pentamer frequencies and contig sizes per contig. A prediction model was trained using Random Forest on an extensive set of plasmids and chromosomes from 19 different bacterial species and validated on separate test sets of known chromosomal and plasmid contigs of the different bacteria. Results: Prediction of plasmid contigs was nearly perfect when calculated based on number of correctly predicted bases, with up to 99% specificity and 99% sensitivity. Prediction of small contigs remains difficult, since these contigs consists primarily of repeated sequences present in both plasmid and chromosome, e.g. transposases.</p> <p>Conclusion: The newly developed tool is able to determine if contigs are chromosomal or plasmid with a very high specificity and sensitivity (up to 99%) and can be very useful to analyze WGS data of bacterial genomes and their antimicrobial resistance genes.</p> <p>Plasmid databases can be downloaded from: http://klif.uu.nl/download/plasmid_db/</p> <p>Data used for training can be downloaded here: http://klif.uu.nl/download/plasmid_db/trainingsets2/</p>
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs
<p><strong>Background: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. Machine learning (ML) methods in general, and Deep Learning (DL) methods in particular, are a potential way forward because they are able to cope with the nonlinearity of natural systems. However, there are several barriers that exist, including the absence of ML-ready data. We propose to develop a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. A critical part of this framework are data transformation methods that map the heterogeneous input data into formats that are consumable by the ML techniques. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. Our long term goal is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches. This pilot project on predicting emergent properties of complex systems and multidimensional interactions is funded by the NSF (Award # 1939945, 1940059, 1940062, 1940330). </strong></p> <p> </p> <p><strong>Results: We have established shared project governance, communication channels, project timeline, and data and computing environment across four universities. We have successfully reached out to three other projects for broader collaboration.</strong></p>
Correlated and geographically predictable Neanderthal and Denisovan legacies are difficult to reconcile with a simple model based on inter-breeding
<p>Although the presence of archaic hominin legacies in humans is taken for granted, little attention has been given as to how the data fit with how humans colonised the world. Here I show that Neanderthal and Denisovan legacies are strongly correlated and that, like heterozygosity, distance from Africa predicts legacy size. Simulations confirm that, once created, legacy size is extremely stable: it may reduce through admixture with lower legacy populations but cannot increase detectably through neutral drift. Consequently, populations carrying the highest legacies must also be those whose ancestors inter-bred most with archaics. However, the populations with the highest legacies are globally scattered and are unified, not by having origins within the known Neanderthal range, but instead by living in locations that lie furthest from Africa. Furthermore, the Simons Genome Diversity Project data reveal two very distinct correlations between Neanderthal and Denisovan legacies, one that starts in North Africa and increases west to east across Eurasia and into some parts of Oceania, and a second, much steeper trend that starts in Africa, peaking with the San and Ju/'hoansi and which, if extrapolated, predicts the large inferred legacies of both archaics found in Oceania / Australia. These trends are difficult to reconcile with classical models of how introgression occurred but may fit a speculative model in which the loss of diversity that occurred when humans moved further from Africa created a gradient in heterozygosity that in turn progressively reduced mutation rate such that populations furthest from Africa have diverged less from our common ancestor and hence from the archaics. The two distinct trends could be interpreted in terms of two 'out of Africa' events, an early one ending in Oceania and Australia and a later one that colonised Eurasia and the Americas.</p>
Predicted Secreted Proteins for the Citrus Stem-End Rot Fungal Pathogen Lasiodiplodia theobromae CITRA15
<p>Lasiodiplodia theobromae (L. theobromae) belongs to a diverse genus of fungal pathogens associated with perennial tropical fruit plants worldwide. In citrus, L. theobromae causes stem-end rot (SER), a damaging postharvest disease that is aggravated when trees are also infected with the citrus greening bacterium, a Candidatus Liberibacter asiaticus. Due to the latent infection of L. theobromae during the preharvest stage, it is also difficult to control by chemical and physical treatment. We sequenced and assembled the first genome of isolate CITRA15 of L. theobromae obtained from citrus, providing a resource for future research on postharvest and preharvest disease management of citrus and other fruit crops and diagnostics. Here we want to share a set of i) 10,496 predicted coding gene sequences (cds) and ii) 10,496 predicted proteins and ii) 1,016 predicted secreted proteins using SignalP v2.0 and TMHMM v2.0.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.