Typical disease-associated microbiota changes are widely studied as potential diagnostic or therapeutic targets. Our aim was to analyze a hospitalized cohort including various gastroenterological pathologies in order to fine-map the gut microbiota dysbiosis. Bacterial (V3 V4) and fungal (ITS2) communities were determined in 121 hospitalized gastrointestinal patients from a single ward and compared to 162 healthy controls. Random Forest models implemented in this study indicated that the gut community structure is in most cases not sufficient to differentiate the subjects based on their underlying disease. Instead, hospitalized patients in our study formed three distinct disease non-related clusters (C1, C2, and C3), partially explained by antibiotic use. Majority of the subjects (cluster C1) closely resembled healthy controls, showing only mild signs of community disruption; most significantly decreased in this cluster were Faecalibacterium and Roseburia. The remaining two clusters (C2 and C3) were characterized by severe signs of dysbiosis; cluster C2 was associated with an increase in Enterobacteriaceae and cluster C3 by an increase in Enterococcus. According to the cluster affiliation, subjects also showed different degrees of inflammation, most prominent was the positive correlation between levels of C-reactive protein and the abundance of Enterococcus.

Beach safety regulation is based on faecal indicators in water, leaving out sand and fungi, whose presence in both matrices has often been reported. To study the abundance, diversity and possible fluctuations of mycobiota, fungi from sand and seawater were isolated from the Portorož beach (Slovenia) during a 1-year period. Sand analyses yielded 64 species of 43 genera, whereas seawater samples yielded 29 species of 18 genera. Environmental and taxonomical data of fungal communities were analysed using machine learning approaches. Changes in the air and water temperature, sunshine hours, humidity and precipitation, air pressure and wind speed appeared to affect mycobiota. The core genera Aphanoascus, Aspergillus, Fusarium, Bisifusarium, Penicillium, Talaromyces, and Rhizopus were found to compose a stable community within sand, although their presence and abundance fluctuated along with weather changes. Aspergillus spp. were the most abundant and thus tested against nine antimycotics using Sensititre Yeast One kit. Aspergillus niger and A. welwitschiae isolates were found to be resistant to amphotericin B. Additionally, four possible human pollution indicators were isolated during the bathing season, including Meyerozyma, which can be used in beach microbial regulation. Our findings provide the foundations for additional research on sand and seawater mycobiota and show the potential effect of global warming and extreme weather events on fungi in sand and sea.

The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its predefined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes. We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation.

Montane temperate forests in central and southern Europe host diverse small mammal assemblages, but the fluctuations in these assemblages in correlation with owl predators are still poorly explored. The key questions of our study were how coexisting owls responded to different prey fluctuations and whether any particular small mammal species governed predator–prey co‐dynamics. We conducted a long‐term study (2004–2020) in low‐elevation (300–1100 m above sea level) mixed Beech and Silver Fir forest in the northern Dinaric Alps (central Slovenia). Monitoring data on the main small mammal groups – mice Muridae, voles Cricetidae, dormice Gliridae and shrews Soricidae – and three owl species – the Ural OwlStrix uralensis, Tawny OwlStrix alucoand Boreal OwlAegolius funereus– were collected annually. To find relationships between prey and predator populations, we used two types of supervised machine learning approaches and addressed three predictive modelling tasks of multi‐target regression. The dominant species in the small mammal assemblage, the Yellow‐necked MouseApodemus flavicollis, had a key role in determining predator populations and their breeding performance. We noted higher sensitivity to small mammal fluctuations in boreal zone owl species (Boreal Owl and Ural Owl), which reach their southern distribution limit in the Dinaric Alps, whereas the temperate zone species (Tawny Owl) seemed to be less affected. In years of prey shortage, the Boreal Owl was found to presumably abandon its territories, the Ural Owl suppressed breeding and the Tawny Owl sustained breeding activity by shifting prey selection. Low‐elevation forests appeared to be suboptimal habitat for the competitive subordinate Boreal Owl, which may exploit occasional outbreaks of small mammal populations in these habitats even in the presence of larger competitors. Whether low‐elevation forests can play a role in maintaining threatened and cold‐adapted Boreal Owl populations in central and southern Europe in the face of recent ecosystem changes due to climate and environmental changes remains an open scientific question.

The thermal subsystem of the Mars Express (MEX) orbiter keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The residual power can then be allocated for scientific purposes. We present a machine learning-based pipeline for the prediction of MEX's thermal power consumption. We show that the proposed pipeline is superior in accuracy to the models currently used by MEX's operators. We also demonstrate that machine learning can provide the operators with insight about the orbiter's thermal behavior. Better understanding of the thermal subsystem and improved predictive accuracy of the thermal power consumption could help operators to improve science return and to prolong the operating life of MEX.