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opencc-zeroMar 2022View details →

Predictability of thermal fluctuations influences functional traits of a cosmopolitan marine diatom

Evolutionary theory predicts that organismal plasticity should evolve in environments that fluctuate regularly. However, in environments that fluctuate less predictably, plasticity may be constrained because environmental cues become less reliable for expressing the optimum phenotype. Here we examine how the predictability of +5 °C temperature fluctuations impacts the phenotype of the marine diatom Thalassiosira pseudonana. Thermal regimes were informed by temperatures experienced by microbes in an ocean simulation, and featured regular or irregular temporal sequences of fluctuations that induced mild physiological stress. Physiological traits (growth, cell size, complexity, pigmentation) were quantified at the individual cell level using flow cytometry. Changes in cellular complexity emerged as the first impact of predictability after only 8-11 days, followed by deleterious impacts on growth on days 13-16. Specifically, cells with a history of irregular fluctuation exposure exhibited a 50% reduction in growth compared with the stable reference environment, while growth was 3-18 times higher when fluctuations were regular. We observed no evidence of heat hardening (increasingly positive growth) with recurrent fluctuations. This study demonstrates that unpredictable temperature fluctuations impact this cosmopolitan diatom under ecologically-relevant time frames, suggesting shifts in environmental stochasticity under a changing climate could have widespread consequences amongst ocean primary producers.

opencc-by-4.0Apr 2022View details →

Simultaneous Mnemonic and Predictive Representations in the Auditory Cortex

Recent studies have shown that stimulus history can be decoded via the use of broadband sensory impulses to reactivate mnemonic representations. It has also been shown that predictive mechanisms in the auditory system demonstrate similar tonotopic organization of neural activity as that elicited by the perceived stimuli. However, it remains unclear if the mnemonic and predictive information can be decoded from cortical activity simultaneously and from overlapping neural populations. Here, we recorded neural activity using electrocorticography (ECoG) in the auditory cortex of anesthetized rats while exposed to repeated stimulus sequences, where events within the sequence were occasionally replaced with a broadband noise burst or omitted entirely. We show that both stimulus history and predicted stimuli can be decoded from neural responses to broadband impulse at overlapping latencies but linked to largely independent neural populations. We also demonstrate that predictive representations are learned over the course of stimulation at two distinct time scales, reflected in two dissociable time windows of neural activity. These results establish a valuable tool for investigating the neural mechanisms of passive sequence learning, memory encoding, and prediction mechanisms within a single paradigm, and provide novel evidence for learning predictive representations even under anaesthesia.

opencc-zeroDec 2021View details →

Combining climatic and genomic data improves range-wide tree height growth prediction in a forest tree

Population response functions based on climatic and phenotypic data from common gardens have long been the gold standard for predicting quantitative trait variation in new environments. However, prediction accuracy might be enhanced by incorporating genomic information that captures the neutral and adaptive processes behind intra-population genetic variation. We used five clonal common gardens containing 34 provenances (523 genotypes) of maritime pine (Pinus pinaster Aiton) to determine whether models combining climatic and genomic data capture the underlying drivers of height-growth variation, and thus improve predictions at large geographical scales. The plastic component explained most of the height-growth variation, probably resulting from population responses to multiple environmental factors. The genetic component stemmed mainly from climate adaptation, and the distinct demographic and selective histories of the different maritime pine gene pools. Models combining climate-of-origin and gene pool of the provenances, and positive-effect height-associated alleles (PEAs) captured most of the genetic component of height-growth and better predicted new provenances compared to the climate-based population response functions. Regionally-selected PEAs were better predictors than globally-selected PEAs, showing high predictive ability in some environments, even when included alone in the models. These results are therefore promising for the future use of genome-based prediction of quantitative traits.

opencc-zeroDec 2021View details →

Fitness effects of mutations: An assessment of PROVEAN predictions using mutation accumulation data

Predicting fitness in natural populations is a major challenge in biology. It may be possible to leverage fast-accumulating genomic datasets to infer the fitness effects of mutant alleles, allowing evolutionary questions to be addressed in any organism. In this paper, we investigate the utility of one such tool, called PROVEAN. This program compares a query sequence with existing data to provide an alignment-based score for any protein variant, with scores categorized as neutral or deleterious based on a preset threshold. PROVEAN has been used widely in evolutionary studies, e.g., to estimate mutation load in natural populations, but has not been formally tested as a predictor of aggregate mutational effects on fitness. Using three large, published datasets on the genome sequences of laboratory mutation accumulation lines, we assessed how well PROVEAN predicted the actual fitness patterns observed, relative to other metrics. In most cases, we find that a simple count of the total number of mutant proteins is a better predictor of fitness than the number of variants scored as deleterious by PROVEAN. We also find that the sum of all mutant protein scores explains variation in fitness better than the number of mutant proteins in one of the datasets. We discuss the implications of these results for studies of populations in the wild.

opencc-by-4.0Mar 2021View details →

Raw data and metadata of SiO2 NP physicochemical characterisation, in vitro investigations and in silico predictions on protein corona formation

Raw data and metadata of SiO2 NP physicochemical characterisation, in vitro investigations and in silico predictions on protein corona formation. Data repository for Hasenkopf I. et al., 2022. Please note that “pristine” NPs in metadata are referred to as “bare” NPs in the paper. 1. NP phys-chem raw data (xlsx-formatted) + corresponding metadata (xlsx-formatted) 2. NP-protein binding raw data (xlsx-formatted) + corresponding metadata (xlsx-/pdf-formatted, ImageLab-formatted analyses, pdf-formatted ImageLab reports) 3. huRBL mediator release raw data (xlsx-formatted) + corresponding metadata (pdf-formatted) 4. in silico modelling raw data (xlsx-formatted) + corresponding metadata (config-/map-formatted)

Data from: How well do embryo development rate models derived from laboratory data predict embryo development in sea turtle nests?

Development rate of ectothermic animals varies with temperature. Here we use data derived from laboratory constant temperature incubation experiments to formulate development rate models that can be used to model embryonic development rate in sea turtle nests. We then use a novel method for detecting the time of hatching to measure the in situ incubation period of sea turtle clutches to test the accuracy of our models in predicting the incubation period from nest temperature traces. We found that all our models overestimated the incubation period. We hypothesize three possible explanations which are not mutually exclusive for the mismatch between our modeling and empirically measured in situ incubation period: (1) a difference in the way the incubation period is calculated in laboratory data and in our field nests, (2) inaccuracies in the assumptions made by our models at high incubation temperatures where there is no empirical laboratory data, and (3) a tendency for development rate in laboratory experiments to be progressively slower as temperature decreases compared with in situ incubation.

opencc-by-4.0Apr 2022View details →

Presentation scripts of "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues"

These are the presentation scripts accompanying the publication  "Predictability awareness rather than mere predictability enhances the perceptual benefits for targets in auditory rhythms over targets following temporal cues".  Check the readMe for an instruction.

opencc-by-4.0Mar 2022View details →

Missing eddy feedback may explain weak signal to noise ratios in climate predictions

Python scripts and data files required to reproduce all figures in the publication entitled "Missing eddy feedback may explain weak signal to noise ratios in climate predictions".  Please reference the publication if using this code or these data.

opencc-by-4.0Nov 2021View details →

Gut metabolites predict Clostridioides difficile recurrence

This repository contains: 1. Data used for analyses from: A) 16S rRNA gene amplicon analysis after paired-end Fastq files were truncated, filtered, denoised, and merged ["1-ASV_Counts.xlsx"] B) Untargeted metabolomics as provided by Metabolon ["2-UntargetedMetabolomics.xlsx"] C) Precision SCFA analysis ["3-PrecisionSCFA.xlsx"] D) Clincal demographic data of the study cohort ["Clinical Demographics.csv"]   2. Detailed results of univariate analyses in sub-folder "Univariate analysis results" for clinical data, untargeted metabolomics data, SCFAs, and ASVs   3. Detailed results of predictive analysis in subfolder "Predictive Analyses results", including per-fold performance results ("9-PredictiveResults.xlsx") and analysis of predictive features for ASVs ("10-ASVsPredictiveAnalysis.xlsx"), untargeted metabolites ("11-MetabsPredictiveAnalysis.xlsx"), targeted SCFAs ("12-SCFAPredictive Analysis.xlsx"), clinical data ("13-DemoPredictiveAnalysis.xlsx") and all data ("14-AllDataPredictiveAnalysis.xlsx") 4. Data for generating all figures and analyses in the publication ("GenFigure&AnalysesData")

opencc-by-4.0Aug 2021View details →

Predicting cancer prognosis and drug response from the tumor microbiome

Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression data, can predict cancer prognosis and drug response to some extent – microbial abundances are significantly less predictive of prognosis than gene expression, although remarkably, similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone.

Predictive scoring for risk of complications in pediatric dengue infection

Background: Dengue infection has been a worrisome cause of mortality and morbidity in children. Though numerous scoring systems have been developed, they are in the adult population or are too complicated for use in children. Pediatric dengue infection has a wide spectrum from a mild illness to severe complications and an unpredictable course. Hence the need for a predictive scoring system where the possibility of complications can be identified which can contribute to reduction in mortality and morbidity of dengue by prompt referrals and anticipatory management. Methodology: Prospective case cohort study of children with confirmed dengue. Results: 303 children were included and divided into two groups – the dengue fever group and the complicated dengue group based on the WHO clinical classification. The clinical and laboratory parameters were analysed individually, cut offs identified by ROC curves and compared for significance between the two groups. The parameters that emerged were hypotension, PCV ≥ 42%, platelet count ≤ 75000 cells/cumm, WBC ≥ 7000 cells/cumm, and ALT ≥ 70U/L. Using the adjusted odd's Ratio, and coefficient, individual predictive scores were tabulated ranging from 0 to 3, with a total score of 0 to 7. A cut-off score of 2 was then identified based upon the sensitivity(84.13%) and specificity(72.50%) as the ideal score to predict complicated dengue. Internal validation of the score was done where the area under the curve for predicting complicated dengue was 0.86(95% CI 0.8-0.92) with a P value of <0.001. Conclusion: Our dengue predictive scoring system has been developed using five indicators, with a score of 2 and above out of 7, suggesting increased risk of developing complications. This has been validated internally and can be used to predict complicated dengue among children.

Craniodental traits predict feeding performance and dietary hardness in a community of Neotropical free-tailed bats (Chiroptera: Molossidae)

Form-function studies have established a strong link between dental morphology and the mechanical properties of food items, with animals evolving tooth shapes theoretically ideal for their diets. However, information on how teeth perform under natural conditions is rare, which limits our understanding of how dental morphology influences dietary ecology and niche partitioning within animal communities. Free-tailed bats (Chiroptera: Molossidae) are a diverse clade of aerial insectivorous mammals that exhibit an outstanding variation in size and craniodental traits, which have been directly related to ecological segregation among sympatric species. We investigate the mechanisms that allow functional dietary specialization and trophic segregation among sympatric free-tailed bat species inhabiting a Neotropical forest. To do so, we coupled data on 3D dental topographic metrics, head and skull dimensions, field-collected feeding performance and dietary hardness measurements. We found that evolved differences in molar topography and skull size vary in tandem with the mechanical demands of prey items naturally consumed by sympatric molossid species. This may be explained by feeding performance capabilities resulting from both molar shape and the overall size of the feeding apparatus, which seem to allow efficient processing of prey items with specific mechanical properties. For instance, smaller bats with higher molar topographic values (sharper, more complex molars) and more gracile heads mainly feed on softer insects, whereas bigger bats with lower molar topographic values (blunter, less complex molars) and more robust heads mostly feed on tougher insects. Species with a broader range of sizes, craniodental morphologies, and insect hardness are also present in the community. Our results illustrate how the morphology and size of feeding structures, and how they perform, may facilitate trophic segregation among sympatric insectivorous bats. Similar mechanisms may help structure other communities of insectivorous mammals, therefore the approaches presented here could be used to generate a better understanding of the ecomorphological traits and processes that underlie their diversity.

Intraspecific variation in plant economic traits predicts trembling aspen resistance to a generalist insect herbivore

Patterns of trait expression within some plant species have recently been shown to follow patterns described by the leaf economics spectrum paradigm. Resistance to herbivores is also expected to covary with leaf economics traits. We selected multiple mature Populus tremuloides genotypes from a common garden to assess whether aspen leaf economics patterns follow those observed among species globally. We also evaluated leaf economics strategies in the context of insect resistance by conducting bioassays to determine the effects of plant traits on preference and performance of Lymantria dispar. We found that: 1) intraspecific trait patterns of P. tremuloides parallel those exhibited by the interspecific leaf economics spectrum, 2) herbivores preferred leaves from genotypes with resource-acquisitive strategies, and 3) herbivores also performed best on genotypes with resource-acquisitive strategies. We conclude that a leaf economics spectrum that incorporates defense traits is a useful tool for explaining intraspecific patterns of variation in plant strategies, including resistance to herbivores.

Publication release: How well do species distribution models predict occurrences in exotic ranges?

<div class="record-description"> Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examined the spatial transferability of SDMs for 647 non-indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. We performed a large-scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability. Here, we provide the code and data for publication in Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How well do species distribution models predict occurrences in exotic ranges?". Provided are the files and scripts necessary to fit and validate the SDMs using distirbutional data from their native and exotic ranges, respectively, formulated as generalized additive models (GAMs) or MaxEnt models. Additionally, provided is a script to validate the SDMs on their native fitting range using 10-fold cross-validation, and to fit the transferability model, as a linear mixed model (LMM), with a provided cleaned data.frame. The dataset provided includes a full species list with GBIF occurrence records, target-group background (TGB) records to use with model fitting and validation, as well as environmental data associated with the sightings. </div>

opencc-zeroMay 2022View details →

Diversity and predicted function of gut microbes from two species of viviparid snails

Animal gut bacteria are involved in numerous critical functions. In snails, gut bacteria play crucial roles in organic material digestion and nutrient production, and have been implicated in aspects of reproduction. Snail gut microbes are known to differ between species, and between anatomical compartments of the digestive tract; dietary changes are also known to alter snail gut flora. In an effort to better understand their diversity and function, we studied the gut microbial communities from two viviparid snails, Campeloma decisum and Cipangopaludina japonica. We were interested in whether significant differences in bacterial community composition existed between the two species, and whether differences in microbial diversity corresponded to differences in community function. Using next-generation sequencing of the bacterial 16S V4 region, we found no significant differences in alpha and beta diversity between Ca. decisum and Ci. japonica. Firmicutes and Proteobacteria were the most abundant bacterial phyla in both species, while Bacteroidetes had a higher mean abundance in Ci. japonica. Nine taxonomic groups were significantly different between the snail species. Pseudomonads and Enterobacteriaceae were notably more abundant in Ca. decisum, while Proteobacteria and Chitinophagaceae were more abundant in Ci. japonica. Peptidoglycan synthesis, pyruvate fermentation, and aerobic respiration by cytochrome c were the three most abundant microbial pathways represented in the viviparid gut. Fourteen functional pathways differed significantly between Ca. decisum and Ci. japonica, potentially correlated with differences in bacterial community composition and snail life history. Our data fill in data gaps regarding gut microbes in Viviparidae, and highlight future research paths examining the prevalence of Firmicutes and unidentified diversity in both snail species.

opencc-zeroMay 2022View details →

Data from: a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing

This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University.

opencc-by-4.0May 2022View details →

Towards Just-In-Time Feature Request Approval Prediction

This is the dataset of our publication "Towards Just-In-Time Feature Request Approval Prediction", which consists of feature requests collected from Sourceforge.net.

opencc-by-4.0May 2022View details →

Hydractinia strain 236-21 genome assembly and Alr domain predictions

This dataset is related to the preprint "A family of unusual A family of unusual immunoglobulin superfamily genes in an invertebrate histocompatibility complex" (<a href="https://www.biorxiv.org/content/10.1101/2022.03.04.482883v2">https://www.biorxiv.org/content/10.1101/2022.03.04.482883v2</a>). Preprint Abstract: Most colonial marine invertebrates are capable of allorecognition, the ability to distinguish between themselves and conspecifics. One long-standing question is whether invertebrate allorecognition genes are homologous to vertebrate histocompatibility genes. In the cnidarian Hydractinia symbiolongicarpus, allorecognition is controlled by at least two genes, Allorecognition 1 (Alr1) and Allorecognition 2 (Alr2), which encode highly polymorphic cell surface proteins that serve as markers of self. Here, we show that Alr1 and Alr2 are part of a family of 41 Alr genes, all of which reside a single genomic interval called the Allorecognition Complex (ARC). Using sensitive homology searches and highly accurate structural predictions, we demonstrate that the Alr proteins are members of the immunoglobulin superfamily (IgSF) with V-set and I-set Ig domains unlike any previously identified in animals. Specifically, their primary amino acid sequences lack many of the motifs considered diagnostic for V-set and I-set domains, yet they adopt secondary and tertiary structures nearly identical to canonical Ig domains. Thus, the V-set domain, which played a central role in the evolution of vertebrate adaptive immunity, was present in the last common ancestor of cnidarians and bilaterians. Unexpectedly, several Alr proteins also have immunoreceptor tyrosine-based activation motifs (ITAMs) and immunoreceptor tyrosine-based inhibitory motifs (ITIMs) in their cytoplasmic tails, suggesting they could participate in pathways homologous to those that regulate immunity in humans and flies. This work expands our definition of the IgSF with the addition of a family of unusual members, several of which play a role in invertebrate histocompatibility. This dataset contains: <ol> <li>Hsym-236-21-genome-assembly.fa.gz: A gzip-compressed FASTA-formatted file of the genome assembly generated in the paper. </li> <li>Alr-domain-structure-predictions.zip: a zip-compressed file with structural predictions produced with Colabfold for all domains of the Alr proteins described in that manuscript.</li> </ol>

opencc-zeroMay 2022View details →

Dead Again: Predictions of repeat tree die-off under hotter droughts confirm mortality thresholds for a dryland conifer species

Tree die-off, driven by extreme drought and exacerbated by a warming climate, is occurring rapidly across every wooded continent - threatening carbon sinks and other ecosystem services provided by forests and woodlands. Forecasting the spatial patterns of tree die-off in response to drought is a priority for the management and conservation of forested ecosystems under projected future hotter and drier climates. Several drought-related metrics have recently been proposed to predict the mortality threshold (i.e., tipping point) for Pinus edulis, a model tree species in many studies of drought-induced tree die-off. To improve future capacity to forecast tree mortality, we used a severe drought in 2018 across the southwestern United States as a natural experiment. We compared the ability of published mortality thresholds derived from four drought metrics (the Forest Drought Severity Index, the Standardized Precipitation Evapotranspiration Index, and raw values of precipitation and vapor pressure deficit) to predict areas of P. edulis die-off following extreme drought. Using aerial detection surveys of tree mortality in combination with gridded climate data, we calculated the agreement between these four proposed thresholds and the presence and absence of regional-scale tree die-off. Overall, such thresholds tended to over predict the spatial extent of tree die-off across the landscape, yet some retain moderate skill in discriminating between areas that experienced and did not experience tree die-off. Area under the curve (AUC) of these thresholds ranged from 0.51 to 0.71, sensitivity (true-positive rate) ranged from 0.54 to 0.86, and specificity (true-negative rates) ranged from 0.16 to 0.73. We highlight that empirically derived climate thresholds may be a useful forecasting tool to identify vulnerable areas to drought induced die-off, allowing for targeted responses to future droughts and improved management of at-risk areas.