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opencc-zeroAug 2021View details →

Supplementary information from: Species interactions have predictable impacts on diversification

A fundamental goal of ecology is to reveal generalities in the myriad types of interactions among species, such as competition, mutualism, and predation. Another goal is to explain the enormous differences in species richness among groups of organisms. Here, we show how these two goals are intertwined: we find that different types of species interactions have predictable impacts on rates of species diversification, which underlie richness patterns. Based on a systematic review, we show that interactions with positive fitness effects for individuals of a clade (e.g. insect pollination for plants) generally increase that clade's diversification rates. Conversely, we find that interactions with negative fitness effects (e.g. predation for prey, competition) generally decrease diversification rates. The sampled clades incorporate all animals and land plants, encompassing ~90% of all described species across life. Overall, we show that different types of local-scale species interactions can predictably impact large-scale patterns of diversification and richness.

opencc-by-4.0Jan 2021View details →

Pragmatic prediction in the processing of referring expressions containing scalar quantifiers

Data and analysis scripts pertaining to a study by Macuch Silva & Franke (submitted).

Data for "Ab initio quantum-mechanical predictions of semiconducting photocathode materials"

Input and output files of the calculations presented in the publication "Ab initio quantum-mechanical predictions of semiconducting photocathode materials". The zip-archives contain the data relevant for subsections 3.1 Electronic structure, 3.2 Optical Spectroscopy and 3.3. Core-level Spectroscopy. The aiida-archives (suffix ".aiida") contain the calculation and provenance details for the high-throughput workflow described in subsection 3.4 High-throughput material screening.

Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.

Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers Manuscript Title: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy. Authors: Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+ Affiliations: 1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy 2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden 3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden 4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden 5 Genevia technologies OY, Tampere, Finland # these authors equally contributed + Corresponding author Abstract of submitted Manuscript: The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy. Funding: This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.

Data and codes for predictability experiments

It consists of two parts.  One part is the initial conditions and code of 5VCM, and the other part is the experimental processing data and drawing code of 5VCM, FOAM, and CM2.1.

opencc-zeroAug 2021View details →

Data from: Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize

Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for prediction within populations. However, it usually cannot capture the complex effects due to combination of alleles in haplotypes. Therefore, accuracy across populations has usually been low. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE, RNA expression of genes by haplotype), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism, so it would be more transferrable across different tissues and populations. We showed that HARE estimates captured one-third of the variation in gene expression and were more transferable across diverse tissues than the measured transcript expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels – a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel) – for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues whereas accuracy using HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations.

Figure 2 in Arboreality predicts Batrachochytrium dendrobatidis infection level in tropical direct-developing frogs

Figure 2. Agar models in the shape of Eleutherodactylus coqui with thermal couplers inserted. Models were placed on the forest floor (a) and in arboreal vegetation (b) to determine operative body temperatures that hosts encounter in these microhabitats.

Figure 4 in Arboreality predicts Batrachochytrium dendrobatidis infection level in tropical direct-developing frogs

Figure 4. Proportion of the infected adults (a) and the infected juveniles (b) using forest-floor (brown) or arboreal (green) microhabitats that presented different levels of Batrachochytrium dendrobatidis infection intensities (given as Batrachochytrium dendrobatidis zoospore genomic equivalents). Above each bar we indicate the number of infected individuals in each category. For example there were only five infected juveniles found in arboreal substrate, and 20% of these had low Batrachochytrium dendrobatidis infection loads.

Figure 1 in Arboreality predicts Batrachochytrium dendrobatidis infection level in tropical direct-developing frogs

Figure 1. Microhabitats considered in this study: forest floor and curled palm fronds in: (a) the floor, (b) bromeliads on vegetation above ground, and (c) foliage, tree trunks or branches ≥50 cm to 2.5 m above ground.

Figure 3 in Arboreality predicts Batrachochytrium dendrobatidis infection level in tropical direct-developing frogs

Figure 3. Variation on Batrachochytrium dendrobatidis infection intensity (zoospore genomic equivalents) among microhabitats used by all individuals (a), adults only (b), males only (c) and juveniles only (d), of Eleutherodactylus coqui. Prevalence of infection in each microhabitat is also observable by comparing percentage of colour versus white on each bar.

Figure 5 in Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

Figure 5. Limiting factor analysis for Geraeocormobius sylvarum, applied to current climate (above left), −6k (above right), −21k CCSM (below left) and −21k MIROC (below right). Most relevant variables recognized as limiting: bc18, precipitation of warmest quarter; bc7, temperature annual range; bc9, mean temperature of driest quarter; bc8, mean temperature of wettest quarter; bc14, precipitation of driest month and bc3, isothermality. Small points: presence records.

Figure 4 in Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

Figure 4. Marginal response curves for the most relevant variables used to model the distribution of Geraeocormobius sylvarum. Curves depict average values (black curve) and ± SD (grey curves) of the 50-replicates run; vertical dotted lines represent the lowest and highest values in the record set, i.e. they delimit the empiric range of the variable. Above: bc18, precipitation of warmest quarter (critical values: 390–560 mm) and bc7, temperature annual range (critical values: 21°C–24.3°C); bottom: bc14, precipitation of driest month (critical value:>42 mm) and bc9, mean temperature of driest quarter (critical values: 14.3°C–18.9°C, peak at 17.3°C). The y-axis displays the logistic probability of presence; horizontal line indicates the 0.50 value.

Figure 3 in Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

Figure 3. Distribution models of Geraeocormobius sylvarum for current climate (above left), −6k (above right), −21k CCSM (below left) and −21k MIROC (below right). Maps display the binary prediction (grey), with the stable area highlighted in colour. Small points: presence records. AUC for the 50-replicates run: 0.9839–0.9865; average 0.9854.

Figure 1 in Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

Figure 1. Adult male of Geraeocormobius sylvarum, from Tucumán Province, Argentina. Photo: Gonzalo D. Rubio.

Figure 2 in Palaeoclimatic distribution models predict Pleistocene refuges for the Neotropical harvestman Geraeocormobius sylvarum (Arachnida: Opiliones: Gonyleptidae)

Figure 2. Records of Geraeocormobius sylvarum, displayed along with the relevant ecoregions in subtropical South America. APAF, Alto Paraná Atlantic forests; AMF, Araucaria moist forests; HCh, Humid Chaco; DCh, Dry Chaco; Y, Southern Andean Yungas; MS, Southern Cone Mesopotamian savanna; FS, Paraná flooded savanna; SM, Serra do Mar coastal forests (nomenclature after Olson et al. 2001).

Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]

The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited. The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization  For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.

opencc-zeroAug 2021View details →

CarcSeq measurement of lung cancer driver mutations predicts mouse strain- and sex-related incidence of spontaneous lung neoplasia

The data contained herein are the fastq files collected for individual mouse lung DNA samples. Ten samples each of male and female, B6C3F1 and CD-1 mouse lung DNA were analyzed using the CarcSeq method for error-corrected next-generation sequencing as previously described [Harris, KL, Walia, V, Gong, B, et al. Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq. Environ Mol Mutagen. 2020; 61: 872– 889. <a href="https://doi.org/10.1002/em.22409">https://doi.org/10.1002/em.22409</a> and Karen L McKim, Meagan B Myers, Kelly L Harris, Binsheng Gong, Joshua Xu, Barbara L Parsons, CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia, Toxicological Sciences, 2021;, kfab040, <a href="https://doi.org/10.1093/toxsci/kfab040">https://doi.org/10.1093/toxsci/kfab040</a>]. A readme text file provides the key to which sample numbers correspond with which mouse strain and sex. All the fastq files within a given folder were used to construct single strand consensus sequences, which were then subject to downstream analyses as reported in the associated publication.

opencc-zeroSep 2022View details →

Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat

Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration. --

Deep learning and surge predictions in coastal areas dataset

To improve coastal adaptation and management, it is critical to better understand and predict the characteristics of sea levels. Here, we present a dataset of four deep learning methods to predict the surge component of sea-level variability based on local atmospheric conditions. We use an Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory layer (LSTM) and a combination of the latter two (ConvLSTM), to construct ensembles of Neural Network (NN) models at 736 tide stations globally. The NN models show similar patterns of performance, with much higher skill in the mid-latitudes. Using our global model settings, the LSTM generally outperforms the other NN models. While we focus on minimising mean absolute error for the full time series, the NN models presented here could be adapted for use in forecasting extreme sea levels or emergency response.

Observed and predicted organic matter data

Punctual soil organic matter data observed in the field and predicted with prediction models fitted by Partial Least Squares Multiple Linear Regression - PLS.