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opencc-zeroOct 2022View details →

Incorporation of soil-derived covariates in progeny testing and line selection to enhance genomic prediction accuracy in soybean breeding

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a highly complex and quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture and its interaction with molecular markers through covariance structures to enhance predictive ability. A total of 797 advanced soybean breeding lines derived from 367 unique bi-parental populations were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip and tested for yield for five years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including a default GBLUP model (M1), a reaction norm model (M2) accounting for the interaction between molecular markers and the environment (GE), an expansion of M2 including soil type (S), and the interaction between soil type and molecular markers (GS) (M3), and an alternative version of M3 without the GE term. Four cross-validation scenarios simulating progeny testing and line selection were implemented (CV2, CV1, CV0, and CV00). Across environments, the addition of GS in M3 decreased the amount of variability captured by both the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the GS term in M3 reduced the variability captured by the residual term by roughly 60% and 30% when compared to M1 and M2, respectively. M3 outperformed all models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). The addition of soil texture seems to structure the environment term revealing its components that could enhance or hinder the predictability of a model. The availability of soil texture before the growing season may maximize the functionality of covariance structures, particularly in scenarios with untested genotypes in untested environments. Genomic selection can optimize the efficiency of a soybean breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

opencc-by-4.0Oct 2022View details →

Dataset used for paper "Issues-Driven Features for Software Fault Prediction"

<div>Dataset used for paper "Issues-Driven Features for Software Fault Prediction".</div> <div> </div> <div> </div> <div> </div> <div>The dataset contains 86 projects from the open source organizations Apache and Spring were written in Java that managed their source code using the Git version control system and an issue tracking system (JIRA or BUGZILLA). </div> <div> </div> <div>For each project, we extracted data for software fault prediction (SFL) task as follows:</div> <div> </div> <div>First, we filtered out projects without reported resolved bugs or less than 5 released versions. Then we iterated the resolved bugs and mapped them to the commits that fixed them. Next, for each version, we labeled the faulty files in the version. A faulty file is a file that was modified in a commit in the version that resolved a bug.  The methodology for labeling the files is a variant of the very known approach implemented in the {\it SZZ} algorithm, which accounts for its vulnerabilities.</div> <div>Finally, for each project, we filtered out versions with faulty files' ratios lower than 5\% and higher than 30\%. The remaining set includes a good representation of bugs and reduces the class imbalance produced by the low number of defects. In addition, filtering versions with a low number of bugs helps to prune outliers, such as a version created to fix specific issues.</div> <div> </div> <div>Extracted using Beirut repository mining tool:</div> https://github.com/beirut-repository-mining/repository_mining

Personality predicts mode of attack in a generalist ground spider predator

Personality traits, such as boldness and/or aggressiveness, have long been accepted to have a profound influence on many aspects of the lives of animals, including foraging. However, little is known about how personality traits shape the use of a particular attack strategy. Ground spiders use either venom or silk attack to immobilise prey. In this study, we tested the hypothesis that behavioural differences among individuals (namely boldness, measured as the time spent exploring a novel environment; and aggressiveness, measured as the number of killed but not consumed prey) drive the use of a particular attack strategy. We used a generalist ground spider, Drassodes lapidosus, and recorded the mode of attack on two types of prey, dangerous and safe. Moreover, we measured the size of the venom gland to test the relationship between the size of venom volume and the personality, as well as the mode of attack. Drassodes individuals showed consistent behavioural differences in the way they attacked prey. Venom attack was significantly related to increased aggressiveness when attacking spider (dangerous) prey and to increased boldness when attacking cricket (safe) prey. Silk attack was more frequently used by shy (for cricket prey) and docile (for spider prey). The volume of venom was not related to the attack strategy. We conclude that personality traits are important drivers of prey capture behaviour in generalist ground spiders.

Training data and prediction results for predicting water diffusion in various polymers using MD-GAN

Training data and prediction results for predicting water diffusion in various polymers using MD-GAN. MD trajectories used as input, MSDs calculated from MD, predicted MSDs, and diffusion coefficient values. MD trajectories were obtained from the following paper. Kojima, H.; Handa, K.; Yamada, K.; Matubayasi, N. Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions. The Journal of Physical Chemistry B 2021, 125, 9357-9371.

On the predictability of the extreme drought in East Africa during the short rains season

The SINTEX-F2 re-forecast data for our manuscript "On the predictability of the extreme drought in East Africa during the short rains season" in Geophysical Research Letters.

opencc-by-4.0Oct 2022View details →

C.Origami: Cell type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

This data repository includes dataset generated from IMR-90, GM12878 cell lines and C.Origami pre-trained model weights. GitHub page: https://github.com/tanjimin/C.Origami

Structure prediction from SARS-CoV-2 accessory proteins ORF-6

Structure prediction made with Collabfold for SARS-CoV-2 accessory protein ORF-6. The archive contains both the structure and

Structure prediction from SARS-CoV-2 accessory proteins ORF-7B

Structure prediction made with Collabfold for SARS-CoV-2 accessory protein ORF-7B. The archive contains both the structure and the logs from the prediction.

Shape coordinates and centroid size for adults and ontogenetic series analyzed in predictable complexity of evolutionary allometry

Allometry has been a paradigm of constraints, including intrinsic constraints on the evolvability of allometry, as a source of developmental and genetic constraints on the evolution of form, and of functional constraints, maintaining functional equivalence as body size evolves. Yet, allometry may be the simplest case of varied constraints, and of morphological integration, even though allometry itself is not simple. Evolutionary allometry may be especially complex because it depends not only on the developmental origins of allometry and determinants of allometric variation but also on the evolutionary dynamics of size and shape. It should also depend on the ecological opportunity for size-dependent ecomorphological specialization. We predict that lineages that converge in those would exhibit similar evolutionary allometries but otherwise, evolutionary allometries would be heterogeneous. Countering this expectation are familiar craniofacial evolutionary allometries, often ascribed to developmental bias. To test both those hypotheses, we compare evolutionary allometries of mandibles across lineages of squirrels and evolutionary to growth allometries. As expected, lineages that converge on size-dependent specializations exhibit similar evolutionary allometries, but otherwise, their allometries are no more similar than expected by chance. Growth allometries of squirrels (and a cricetid rodent) slightly resemble the evolutionary allometry of one lineage, but growth allometries of species from other lineages are orthogonal to their own lineages' evolutionary allometry. We would expect that craniofacial allometries that are not brain-driven would, like mandibular evolutionary allometries, be predictable only from size-dependent ecological specializations.

Data from: Niche width predicts extinction from climate change and vulnerability of tropical species

Climate change may be a major threat to global biodiversity, especially to tropical species. Yet, why tropical species are more vulnerable to climate change remains unclear. Tropical species are thought to have narrower physiological tolerances to temperature, and they have already experienced a higher estimated frequency of climate-related local extinctions. These two patterns suggest that tropical species are more vulnerable to climate change because they have narrower thermal niche widths. However, no studies have tested whether species with narrower climatic niche widths for temperature have experienced more local extinctions, and if these narrower niche widths can explain the higher frequency of tropical local extinctions. Here, we test these ideas using resurvey data from 538 plant and animal species from 10 studies. We found that mean niche widths among species and the extent of climate change (increase in maximum annual temperatures) together explained most variation (>75%) in the frequency of local extinction among studies. Surprisingly, neither latitude nor occurrence in the tropics alone significantly predicted local extinction among studies, but latitude and niche widths were strongly inversely related. Niche width also significantly predicted local extinction among species, as well as among and (sometimes) within studies. Overall, niche width may offer a relatively simple and accessible predictor of the vulnerability of populations to climate change. Intriguingly, niche width has the best predictive power to explain extinction from global warming when it incorporates coldest yearly temperatures.

Demographic changes and life-history strategies predict the genetic diversity in crabs

Uncovering what predicts genetic diversity (GD) within species can help us access the status of populations and their evolutionary potential. Traits related to effective population size show a proportional association to GD, but evidence supports life-history strategies and habitat as the drivers of GD variation. Instead of investigating highly divergent taxa, focusing on one group could help to elucidate the factors influencing the GD. Additionally, most empirical data is based on vertebrate taxa; therefore, we might be missing novel patterns of GD found in neglected invertebrate groups. Here, we investigated the predictors of the GD in crabs (Brachyura) by compiling the most comprehensive cytochrome c oxidase subunit I (COI) available. Eight predictor variables were analyzed across 150 species (16,992 sequences) using linear models (multiple linear regression) and comparative methods (PGLS). Our results indicate that population size fluctuation represents the most critical trait predicting GD, with species that have undergone bottlenecks followed by population expansion showing lower GD. Egg size, pelagic larval duration, and habitat might play a role probably because of their association with how species respond to disturbances. Ultimately, K-strategists that have undergone bottlenecks are the species showing lower GD. Some variables do not show an association with GD as expected, most likely due to the taxon-specific role of some predictors, which should be considered in further investigations and generalizations. This work highlights the complexity underlying the predictors of GD and adds results from a marine invertebrate group to the current understanding of this topic.

Potential distribution prediction of Amaranthus palmeri S. Watson in China under current and future climate scenarios

The vicious invasive alien plant <a name="_Hlk99445053"></a>Amaranthus palmeri poses a serious threat to ecological security and food security due to its strong adaptability, competitiveness, and herbicide resistance. Predicting its potential habitats under current and future climate change is critical for monitoring and early warning. In this study, we used two sets of climate data, namely, WorldClim1.4 and RCPs (the historical climate data of WorldClim version 1.4 and future climate data of RCPs), WorldClim2.1 and SSPs (the historical climate data of WorldClim version 2.1 and future climate data of SSPs), to analyze the dominant environmental variables affecting the habitat suitability and predict the potential distribution of A. palmeri to climate change in China based on the MaxEnt model. The results show that (i) Temperature has a greater impact on the distribution of A. palmeri. The relative contributions of temperature-related variables count to 70 % or more, and the annual mean temperature (bio1) reached more than 40 %. (ii) At present, the potentially suitable area is widely distributed in the central-east and parts of southwest China, and the high suitable area is focused on the North China Plain. The potential suitable area predicted by WorldClim1.4 and WorldClim2.1 both accounts for about 31% of China's total land area. (iii) Future climate change will expand the suitable habitats to high latitudes and altitudes. The overall suitable area maximum increased to 44.93% under SSPs and 38.91% under RCPs. We conclude that climate change would increase the risk of A. palmeri expanding to high latitudes and altitudes, the results have practical implications for the effective long-term management in response to the global warming of A. palmeri.

Supplementary Materials for 'Spatiotemporal Prediction of Air Quality Using Machine Learning Techniques'

This package includes supplementary materials used to implement air quality prediction in the city of Madrid. It consists of two main subdirectories: Data and Code. The Data directory contains Raw-Data (air quality, meteorological and traffic data from the period of January-June 2019 and January-June 2020, and the location of air quality and meteorological monitoring stations and traffic measurement points of the city of Madrid) and Processed-Data (the output after raws data has gone through the workflow to meet the requirements corresponding to the implementation of the proposed forecasting approaches). The Code directory contains Process Raw Data, Chapter4-ConvLSTM, Chapter5-BiConvLSTM, and Chapter6-A3T_GCN, which provides the procedure for constructing and implementing the proposed approaches.

Assessing and predicting the quality of peer reviews: a text mining approach

Dataset

Chaos and Predictability HW

ECMWF reforecast 500hPa geopotential height (Jan 2001)

opencc-by-4.0Sep 2022View details →

Fatty acids homeostasis during fasting predicts protection from chemotherapy toxicity

Fasting exerts beneficial effects in mice and humans, including protection from chemotherapy toxicity. To explore the involved mechanisms, we collect blood from humans and mice before and after 36 or 24 hours of fasting, respectively, and measure lipid composition of erythrocyte membranes, circulating micro RNAs (miRNAs), and RNA expression at peripheral blood mononuclear cells (PBMCs). Fasting coordinately affects the proportion of polyunsaturated versus saturated and monounsaturated fatty acids at the erythrocyte membrane; and reduces the expression of insulin signaling-related genes in PBMCs. When fasted for 24 hours before and 24 hours after administration of oxaliplatin or doxorubicin, mice show a strong protection from toxicity in several tissues. Erythrocyte membrane lipids and PBMC gene expression define two separate groups of individuals that accurately predict a differential protection from chemotherapy toxicity, with important clinical implications. Our results reveal a mechanism of fasting associated with lipid homeostasis, and provide biomarkers of fasting to predict fasting-mediated protection from chemotherapy toxicity.

opencc-by-4.0Dec 2021View details →

Digital rock dataset for use in machine learning research of permeability prediction

Datasets and codes for the paper "Hierarchical homogenization with deep-learning-based surrogate model for rapid estimation of effective permeability from digital rocks"

opencc-by-4.0Dec 2022View details →

Supplementary to "Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning - a retrospective multicentric study"

High-resolution images of heatmaps. Top tiles and GradCam of figure 5

opencc-zeroJan 2023View details →

Data from: Predicting multi-predator risk to elk (Cervus canadensis) in summer using predator scats

1. There is growing evidence that prey perceive the risk of predation and alter their behaviour in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk across a landscape quantify predator space use to estimate potential predator-prey encounters, yet this approach does not account for successful predator attack resulting in prey mortality. An exception is a prey kill site that reflects an encounter resulting in mortality, but obtaining information on kill sites is expensive and requires time to accumulate adequate sample sizes. 2. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk (Cervus canadensis) from multiple predators in the Rocky Mountains of Alberta, Canada. We surveyed over 1300km to detect scats of bears (Ursus arctos/U. americanus), cougars (Puma concolor), coyotes (Canis latrans), and wolves (C. lupus). To derive spatial predation risk, we combined predictions of scat-based resource selection functions (RSFs) weighted by predator abundance with predictions that a predator-specific scat in a location contained elk. We evaluated the scat-based predictions of predation risk by correlating them to predictions based on elk kill sites. We also compared scat-based predation risk on summer ranges of elk following three migratory tactics for consistency with telemetry-based metrics of predation risk and cause-specific mortality of elk. 3. We found a strong correlation between the scat-based approach presented here and predation risk predicted by kill sites and (r = 0.98, P < 0.001). Elk migrating east of the Ya Ha Tinda winter range were exposed to the highest predation risk from cougars, resident elk summering on the Ya Ha Tinda winter range were exposed to the highest predation risk from wolves and coyotes, and elk migrating west to summer in Banff National Park were exposed to highest risk of encountering bears, but it was less likely to find elk in bear scats than in other areas. These patterns were consistent with previous estimates of spatial risk based on telemetry of collared predators and recent cause-specific mortality patterns in elk. 4. A scat-based approach can provide a cost-efficient alternative to kill sites of quantifying broad-scale, spatial patterns in risk of predation for prey particularly in multiple predator species systems.