Relying on Incomplete Information Can Lead to the Wrong Conclusions. Comment on van Hassel, F.; Bovenkerk, B. How Should We Help Wild Animals Cope with Climate Change? The Case of the Iberian Lynx. 2023, , 453.

In their recent paper, Van Hassel and Bovenkerk [...

Effect of natural abiotic soil vibrations, rainfall and wind on anuran calling behavior: a test with captive-bred midwife toads (Alytes obstetricans).

Anurans are known to detect vibrations, but few studies explore relationships between vibrations and resultant behaviors. We studied the reaction of calling captive-bred male midwife toads (Alytes obstetricans) to the randomized playback of a vibrational crescendo stimulus train. We considered two sources of natural abiotic vibrational stimuli: rainfall and wind.

Host-microbiome protein-protein interactions capture disease-relevant pathways.

Background: Host-microbe interactions are crucial for normal physiological and immune system development and are implicated in a variety of diseases, including inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D). Despite large-scale case-control studies aimed at identifying microbial taxa or genes involved in pathogeneses, the mechanisms linking them to disease have thus far remained elusive.

Results: To identify potential pathways through which human-associated bacteria impact host health, we leverage publicly-available interspecies protein-protein interaction (PPI) data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors.

Functions predict horizontal gene transfer and the emergence of antibiotic resistance.

Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer (HGT), although their relative contributions are unclear. Here, we apply machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network [area under the receiver operating characteristic curve (AUROC) = 0.

Cyber-Physical System for Environmental Monitoring Based on Deep Learning.

Cyber-physical systems (CPS) constitute a promising paradigm that could fit various applications. Monitoring based on the Internet of Things (IoT) has become a research area with new challenges in which to extract valuable information. This paper proposes a deep learning classification sound system for execution over CPS.

Variation in upper thermal tolerance among 19 species from temperate wetlands.

Communities usually possess a multitude of interconnected trophic interactions within food webs. Their regulation generally depends on a balance between bottom-up and top-down effects. However, if sensitivity to temperature varies among species, rising temperatures may change trophic interactions via direct and indirect effects.

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic resonance imaging is a major component of the project. Full brain images are obtained at every 6-month visit.

Niche models at inter- and intraspecific levels reveal hierarchical niche differentiation in midwife toads.

Variation and population structure play key roles in the speciation process, but adaptive intraspecific genetic variation is commonly ignored when forecasting species niches. Amphibians serve as excellent models for testing how climate and local adaptations shape species distributions due to physiological and dispersal constraints and long generational times. In this study, we analysed the climatic factors driving the evolution of the genus Alytes at inter- and intraspecific levels that may limit realized niches.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations.

Interactome INSIDER: a structural interactome browser for genomic studies.

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations.

GeMSTONE: orchestrated prioritization of human germline mutations in the cloud.

Integrative analysis of whole-genome/exome-sequencing data has been challenging, especially for the non-programming research community, as it requires simultaneously managing a large number of computational tools. Even computational biologists find it unexpectedly difficult to reproduce results from others or optimize their strategies in an end-to-end workflow. We introduce Germline Mutation Scoring Tool fOr Next-generation sEquencing data (GeMSTONE), a cloud-based variant prioritization tool with high-level customization and a comprehensive collection of bioinformatics tools and data libraries (http://gemstone.

Synthetic rainfall vibrations evoke toad emergence.

Toads occupy underground refugia during periods of daily or seasonal inactivity, emerging only during rainfall [1]. We test the hypothesis that rainfall-induced vibrations in soil are the cues that trigger the emergence of toads from underground. Using playback experiments in the absence of natural rainfall in native habitats, we observed that two Iberian toad species (Pelobates cultripes and Bufo calamita) emerged significantly earlier than controls when exposed to low-frequency soil vibrations that closely mimic those of rainfall.

mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.

A new algorithm and Web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface.

Subsymmetries predict auditory and visual pattern complexity.

A mathematical measure of pattern complexity based on subsymmetries possessed by the pattern, previously shown to correlate highly with empirically derived measures of cognitive complexity in the visual domain, is found to also correlate significantly with empirically derived complexity measures of perception and production of auditory temporal and musical rhythmic patterns. Not only does the subsymmetry measure correlate highly with the difficulty of reproducing the rhythms by tapping after listening to them, but also the empirical measures exhibit similar behavior, for both the visual and auditory patterns, as a function of the relative number of subsymmetries present in the patterns.

Calling behaviour under climate change: geographical and seasonal variation of calling temperatures in ectotherms.

Calling behaviour is strongly temperature-dependent and critical for sexual selection and reproduction in a variety of ectothermic taxa, including anuran amphibians, which are the most globally threatened vertebrates. However, few studies have explored how species respond to distinct thermal environments at time of displaying calling behaviour, and thus it is still unknown whether ongoing climate change might compromise the performance of calling activity in ectotherms. Here, we used new audio-trapping techniques (automated sound recording and detection systems) between 2006 and 2009 to examine annual calling temperatures of five temperate anurans and their patterns of geographical and seasonal variation at the thermal extremes of species ranges, providing insights into the thermal breadths of calling activity of species, and the mechanisms that enable ectotherms to adjust to changing thermal environments.