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Abstract In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

Abstract Asynchronous migration of insect herbivores and their host plants towards higher elevations following climate warming is expected to generate novel plant–insect interactions. While the disassociation of specialised interactions can challenge species' persistence, consequences for specialised low‐elevation insect herbivores encountering novel high‐elevation plants under climate change remain largely unknown. To explore the ability of two low‐elevation Lepidoptera species, Melitaea celadussa and Zygaena filipendulae, to undergo shifts from low‐ to high‐elevation host plants, we combined a translocation experiment performed at two elevations in the Swiss Alps with experiments conducted under controlled conditions. Specifically, we exposed M. celadussa and Z. filipendulae to current low‐ and congeneric high‐elevation host plants, to test how shifts in host plant use impact oviposition probability, number of eggs clutches laid, caterpillar feeding preference and growth, pupation rate and wing size. While our study shows that both M. celadussa and Z. filipendulae can oviposit and feed on novel high‐elevation host plants, we reveal strong preferences towards ovipositing and feeding on current low‐elevation host plants. In addition, shifts from current low‐ to novel high‐elevation host plants reduced pupation rates as well as wing size for M. celadussa, while caterpillar growth was unaffected by host plant identity for both species. Our study suggests that populations of M. celadussa and Z. filipendulae have the ability to undergo host plant shifts under climate change. However, these shifts may impact the ability of populations to respond to rapid climate change by altering developmental processes and morphology. Our study highlights the importance of considering altered biotic interactions when predicting consequences for natural populations facing novel abiotic and biotic environments.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Abstract The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist’s expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT’s superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases.

Abstract Background RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts. Results Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions. Conclusion We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com.

Model output statistics (MOS) approaches relying on machine learning algorithms were applied to downscale regional air quality forecasts produced by CAMS (Copernicus Atmosphere Monitoring Service) at hundreds of monitoring sites across Europe. Besides the CAMS forecast, the predictors in the MOS typically include meteorological variables but also ancillary data. We explored first a “local” approach where specific models are trained at each site. An alternative “global” approach where a single model is trained with data from the whole geographical domain was also investigated. In both cases, local predictors are used for a given station in predictive mode. Because of its global nature, the latter approach can capture a variety of meteorological situations within a very short training period and is thereby more suited to cope with operational constraints in relation to the training of the MOS (frequent upgrades of the modelling system, addition of new monitoring sites). Both approaches have been implemented using a variety of machine learning algorithms: random forest, gradient boosting, and standard and regularized multi-linear models. The quality of the MOS predictions is evaluated in this work for four key pollutants, namely particulate matter (PM10 and PM2.5), ozone (O3) and nitrogen dioxide (NO2), according to scores based on the predictive errors and on the detection of pollution peaks (exceedances of the regulatory thresholds). Both the local and the global approaches significantly improve the performances of the raw ensemble forecast. The most important result of this study is that the global approach competes with and can even outperform the local approach in some cases. This global approach gives the best RMSE scores when relying on a random forest model for the prediction of daily mean, daily max and hourly concentrations. By contrast, it is the gradient boosting model which is better suited for the detection of exceedances of the European Union regulated threshold values for O3 and PM10.

Abstract Background The high-density oligonucleotide microarray (GeneChip) is an important tool for molecular biological research aiming at large-scale detection of small nucleotide polymorphisms in DNA and genome-wide analysis of mRNA concentrations. Local array data management solutions are instrumental for efficient processing of the results and for subsequent uploading of data and annotations to a global certified data repository at the EBI (ArrayExpress) or the NCBI (GeneOmnibus). Description To facilitate and accelerate annotation of high-throughput expression profiling experiments, the Microarray Information Management and Annotation System (MIMAS) was developed. The system is fully compliant with the Minimal Information About a Microarray Experiment (MIAME) convention. MIMAS provides life scientists with a highly flexible and focused GeneChip data storage and annotation platform essential for subsequent analysis and interpretation of experimental results with clustering and mining tools. The system software can be downloaded for academic use upon request. Conclusion MIMAS implements a novel concept for nation-wide GeneChip data management whereby a network of facilities is centered on one data node directly connected to the European certified public microarray data repository located at the EBI. The solution proposed may serve as a prototype approach to array data management between research institutes organized in a consortium.

Revascularisation of renal arterial stenosis in acute settings, such as uncontrolled arterial hypertension, flash pulmonary oedema and/or acute renal failure, has shown controversial results in observational and prospective studies. Current guidelines do not recommend revascularisation in the occurrence of renal failure as revascularisation and best medical treatment have shown similar long-term outcomes on renal function. We describe a case of acute degradation of the renal function (with oligo-anuria and a peak creatinine of 462 µmol/L) after the re-introduction of an angiotensin-II receptor blocker (irbesartan) in a 66-year-old Caucasian diabetic male patient with bilateral renal stenosis and a right-sided single-functioning kidney, with a rapid improvement of the renal function which normalized 5 days after percutaneous angioplasty and stenting of the right renal artery.

Abstract European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split‐sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert‐based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat‐type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient‐poor to nutrient‐rich grasslands, which is highly important for biodiversity conservation.

Abstract Background Somatic embryogenesis (SE) is one of the most promising processes for large-scale dissemination of elite varieties. However, for many plant species, optimizing SE protocols still relies on a trial and error approach. We report the first global scale transcriptome profiling performed at all developmental stages of SE in coffee to unravel the mechanisms that regulate cell fate and totipotency. Results RNA-seq of 48 samples (12 developmental stages × 4 biological replicates) generated 90 million high quality reads per sample, approximately 74% of which were uniquely mapped to the Arabica genome. First, the statistical analysis of transcript data clearly grouped SE developmental stages into seven important phases (Leaf, Dedifferentiation, Primary callus, Embryogenic callus, Embryogenic cell clusters, Redifferentiation and Embryo) enabling the identification of six key developmental phase switches, which are strategic for the overall biological efficiency of embryo regeneration. Differential gene expression and functional analysis showed that genes encoding transcription factors, stress-related genes, metabolism-related genes and hormone signaling-related genes were significantly enriched. Second, the standard environmental drivers used to control SE, i.e. light, growth regulators and cell density, were clearly perceived at the molecular level at different developmental stages. Third, expression profiles of auxin-related genes, transcription factor-related genes and secondary metabolism-related genes were analyzed during SE. Gene co-expression networks were also inferred. Auxin-related genes were upregulated during dedifferentiation and redifferentiation while transcription factor-related genes were switched on from the embryogenic callus and onward. Secondary metabolism-related genes were switched off during dedifferentiation and switched back on at the onset of redifferentiation. Secondary metabolites and endogenous IAA content were tightly linked with their respective gene expression. Lastly, comparing Arabica embryogenic and non-embryogenic cell transcriptomes enabled the identification of biological processes involved in the acquisition of embryogenic capacity. Conclusions The present analysis showed that transcript fingerprints are discriminating signatures of cell fate and are under the direct influence of environmental drivers. A total of 23 molecular candidates were successfully identified overall the 12 developmental stages and can be tested in many plant species to optimize SE protocols in a rational way.

Abstract To describe the relationship between the use of laboratory tests and changes in laboratory parameters in ICU patients is necessary to help optimize routine laboratory testing. A retrospective, descriptive study was conducted on the large eICU-Collaborative Research Database. The relationship between the use of routine laboratory tests (chemistry and blood counts) and changes in ten common laboratory parameters was studied. Factors associated with laboratory tests were identified in a multivariate regression analysis using generalized estimating equation Poisson models. The study included 138,734 patient stays, with an ICU mortality of 8.97%. For all parameters, the proportion of patients with at least one test decreased from day 0 to day 1 and then gradually increased until the end of the ICU stay. Paradoxically, the results of almost all tests moved toward normal values, and the daily variation in the results of almost all tests decreased over time. The presence of an arterial catheter or teaching hospitals were independently associated with an increase in the number of laboratory tests performed. The paradox of routine laboratory testing should be further explored by assessing the factors that drive the decision to perform routine laboratory testing in ICU and the impact of such testing on patient.

Interest in Metschnikowia (M.) pulcherrima is growing in the world of winemaking. M. pulcherrima is used both to protect musts from microbial spoilage and to modulate the aromatic profile of wines. Here, we describe the isolation, characterization, and use of an autochthonous strain of M. pulcherrima in the vinification of Chasselas musts from the 2022 vintage. M. pulcherrima was used in co-fermentation with Saccharomyces cerevisiae at both laboratory and experimental cellar scales. Our results showed that M. pulcherrima does not ferment sugars but has high metabolic activity, as detected by flow cytometry. Furthermore, sensory analysis showed that M. pulcherrima contributed slightly to the aromatic profile when compared to the control vinifications. The overall results suggest that our bioprospecting strategy can guide the selection of microorganisms that can be effectively used in the winemaking process.

Due to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description of their spatial properties. One such network is the extracellular matrix (ECM), a biological scaffold for which changes in its spatial organization significantly impact tissue functions in health and disease. Quantifying variations in the fibrillar architecture of major ECM proteins should considerably advance our understanding of the link between tissue structure and function. Inspired by the analysis of functional magnetic resonance imaging (fMRI) images, we propose a novel statistical analysis approach embedded into a machine learning paradigm, to measure and detect local variations of meaningful ECM parameters. We show that parametric maps representing fiber length and pore directionality can be analyzed within the proposed framework to differentiate among various tissue states. The parametric maps are derived from graph-based representations that reflect the network architecture of fibronectin (FN) fibers in a normal, or disease-mimicking in vitro setting. Such tools can potentially lead to a better characterization of dynamic matrix networks within fibrotic tumor microenvironments and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention.

This article proposes a first approach of the science-fiction genre through the lens of thought experiment. Thought experiment opens a path to a new definition of the genre that would enable to avoid the main pitfalls raised by John Rieder. Therefore it provides an opportunity to take simultaneously into account the historical context of the generic objects, their reception and their use. A first definition of thought experiment will enable us to define the fictional nature and will as well raise some functional features relating to this kind of experiment. Then we will try to integrate the thought experiment as the characteristic feature of the promise of SF genre, as defined by François Jost. Finally, we will draft an analysis of several objects founded on five proposals and confirmed by a semantic / syntactic approach, inspired by Rick Altman’s works.

The fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), is a polyphagous pest highly damaging to maize and other food crops in Africa, particularly in Côte d’Ivoire. Chemical pesticides not only have often proved to be unsuccessful, but cause adverse effects on the environment and human health; therefore, entomopathogenic fungi could represent an alternative biocontrol solution. Against this background, fungi were isolated from soil samples collected in maize fields in three regions of Côte d’Ivoire, by the methods of soil dilution and baiting with Galleria mellonella. The resulting 86 fungal isolates were phenotypically and genetically identified. The pathogenicity of seven isolates of Metarhizium spp., three isolates of Beauveria bassiana and two isolates of Trichoderma sp. was evaluated on fifth instar larvae (L5) of G. mellonella. Larval mortality rates and the median lethal time (LT50) were determined seven days after inoculation for each of these selected isolates. The median lethal concentration (LC50) was determined for a selection of isolates. Beauveria bassiana isolate A214b was the most effective, causing 100% mortality, with an LT50 of 2.64 days and an LC50 of 1.12 × 104 conidia mL−1. Two other promising isolates, A211 and A214a, belonging to B. bassiana, caused 100% mortality with LT50 values of 3.44 and 4.04 days, respectively. Mortality caused by Metarhizium isolates varied from 65.38% to 100%, with Metarhizium anisopliae isolate T331 causing 100% mortality with an LT50 of 3.08 days at an LC50 of 3.33 × 104 conidia mL−1. Trichoderma sp. isolates were the least pathogenic ones. Beauveria bassiana and Metarhizium isolates showed to be virulent against the model Lepidopteran G. mellonella and will be tested on S. frugiperda.

An understanding of insect olfaction allows for more specific alternative methods of pest control. We evaluated the responses of the western flower thrips (WFT, Frankliniella occidentalis) in a Y-olfactometer to estimate gas-phase concentrations of the aggregation pheromone neryl (S)-2-methylbutanoate and known kairomones such as methyl isonicotinate, (S)-(-)-verbenone, and p-anisaldehyde. The gas-phase concentrations of these compounds were obtained from the release rates measured in dynamic headspace cells. The compounds were collected from the headspace using dried solid-phase extraction (SPE) cartridges and analyzed with a triple quadrupole GC-MS/MS. We observed that the aggregation pheromone significantly attracted WFT females at doses of 10 and 100 µg, whereas methyl isonicotinate and p-anisaldehyde significantly attracted WFT females at the highest dose. Verbenone did not produce any significant results. A completely different picture was obtained when the gas-phase concentrations were considered. The minimal gas-phase concentrations of the pheromone required to attract WFT females was 0.027 ng/mL, at least 100 times lower than that of the other two compounds. The relevance and implications of our results are discussed in light of the insect’s biology and pest management methods.

Sustained exposure to a young systemic environment rejuvenates aged organisms and promotes cellular function. However, due to the intrinsic complexity of tissues it remains challenging to pinpoint niche-independent effects of circulating factors on specific cell populations. Here, we describe a method for the encapsulation of human and mouse skeletal muscle progenitors in diffusible polyethersulfone hollow fiber capsules that can be used to profile systemic aging in vivo independent of heterogeneous short-range tissue interactions. We observed that circulating long-range signaling factors in the old systemic environment lead to an activation of Myc and E2F transcription factors, induce senescence, and suppress myogenic differentiation. Importantly, in vitro profiling using young and old serum in 2D culture does not capture all pathways deregulated in encapsulated cells in aged mice. Thus, in vivo transcriptomic profiling using cell encapsulation allows for the characterization of effector pathways of systemic aging with unparalleled accuracy.

MIMAC (MIcro-TPC MAtrix of Chambers) is a directional WIMP Dark Matter detector project. Direct dark matter experiments need a high level of electron/recoil discrimination to search for nuclear recoils produced by WIMP-nucleus elastic scattering. In this paper, we proposed an original method for electron event rejection based on a multivariate analysis applied to experimental data acquired using monochromatic neutron fields. This analysis shows that a 105 rejection power is reachable for electron/recoil discrimination. Moreover, the efficiency was estimated by a Monte-Carlo simulation showing that a 105 electron rejection power is reached with a 86.49 ± 0.17% nuclear recoil efficiency considering the full energy range and 94.67 ± 0.19% considering a 5 keV lower threshold.

Abstract Background Deletion or mutations of SHANK3 lead to Phelan–McDermid syndrome and monogenic forms of autism spectrum disorder (ASD). SHANK3 encodes its eponymous scaffolding protein at excitatory glutamatergic synapses. Altered morphology of dendrites and spines in the hippocampus, cerebellum, and striatum have been associated with behavioral impairments in Shank3-deficient animal models. Given the attentional deficit in these animals, our study explored whether deficiency of Shank3 in a rat model alters neuron morphology and synaptic ultrastructure in the medial prefrontal cortex (mPFC). Methods We assessed dendrite and spine morphology and spine density in mPFC layer III neurons in Shank3-homozygous knockout (Shank3-KO), heterozygous (Shank3-Het), and wild-type (WT) rats. We used electron microscopy to determine the density of asymmetric synapses in mPFC layer III excitatory neurons in these rats. We measured postsynaptic density (PSD) length, PSD area, and head diameter (HD) of spines at these synapses. Results Basal dendritic morphology was similar among the three genotypes. Spine density and morphology were comparable, but more thin and mushroom spines had larger head volumes in Shank3-Het compared to WT and Shank3-KO. All three groups had comparable synapse density and PSD length. Spine HD of total and non-perforated synapses in Shank3-Het rats, but not Shank3-KO rats, was significantly larger than in WT rats. The total and non-perforated PSD area was significantly larger in Shank3-Het rats compared to Shank3-KO rats. These findings represent preliminary evidence for synaptic ultrastructural alterations in the mPFC of rats that lack one copy of Shank3 and mimic the heterozygous loss of SHANK3 in Phelan–McDermid syndrome. Limitations The Shank3 deletion in the rat model we used does not affect all isoforms of the protein and would only model the effect of mutations resulting in loss of the N-terminus of the protein. Given the higher prevalence of ASD in males, the ultrastructural study focused only on synaptic structure in male Shank3-deficient rats. Conclusions We observed increased HD and PSD area in Shank3-Het rats. These observations suggest the occurrence of altered synaptic ultrastructure in this animal model, further pointing to a key role of defective expression of the Shank3 protein in ASD and Phelan–McDermid syndrome.

Abstract Livestock farmers rely on a high and stable grassland productivity for fodder production to sustain their livelihoods. Future drought events related to climate change, however, threaten grassland functionality in many regions across the globe. The introduction of sustainable grassland management could buffer these negative effects. According to the biodiversity–productivity hypothesis, productivity positively associates with local biodiversity. The biodiversity–insurance hypothesis states that higher biodiversity enhances the temporal stability of productivity. To date, these hypotheses have mostly been tested through experimental studies under restricted environmental conditions, hereby neglecting climatic variations at a landscape‐scale. Here, we provide a landscape‐scale assessment of the contribution of species richness, functional composition, temperature, and precipitation on grassland productivity. We found that the variation in grassland productivity during the growing season was best explained by functional trait composition. The community mean of plant preference for nutrients explained 24.8% of the variation in productivity and the community mean of specific leaf area explained 18.6%, while species richness explained only 2.4%. Temperature and precipitation explained an additional 22.1% of the variation in productivity. Our results indicate that functional trait composition is an important predictor of landscape‐scale grassland productivity.