Miracle-dz/UniFAR-FTUs huggingface dataset:
Climatic and Resource Determinants of Forest Elephant Movements

As a keystone megafaunal species, African forest elephants (Loxodonta cyclotis) influence the structure and composition of tropical forests. Determining the links between food resources, environmental conditions and elephant movement behavior is crucial to understanding their habitat requirements and their effects on the ecosystem, particularly in the face of poaching and global change. We investigate whether fruit abundance or climate most strongly influence forest elephant movement behavior at the landscape scale in Gabon. Trained teams of “elephant trackers” performed daily fruit availability and dietary composition surveys over a year within two relatively pristine and intact protected areas. With data from 100 in-depth field follows of 28 satellite-collared elephants and remotely sensed environmental layers, we use linear mixed-effects models to assess the effects of sites, seasons, focal elephant identification, elephant diet, and fruit availability on elephant movement behavior at monthly and 3-day time scales. At the month-level, rainfall, and to a lesser extent fruit availability, most strongly predicted the proportion of time elephants spent in long, directionally persistent movements. Thus, even elephants in moist tropical rainforests show seasonal behavioral phenotypes linked to rainfall. At the follow-level (2–4 day intervals), relative support for both rainfall and fruit availability decreased markedly, suggesting that at finer spatial scales forest elephants make foraging decisions largely based on other factors not directly assessed here. Focal elephant identity explained the majority of the variance in the data, and there was strong support for interindividual variation in behavioral responses to rainfall. Taken together, this highlights the importance of approaches which follow individuals through space and time. The links between climate, resource availability and movement behavior provide important insights into the behavioral ecology of forest elephants that can contribute to understanding their role as seed dispersers, improving management of populations, and informing development of solutions to human-elephant conflict.
Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine

A network analysis including relative abundances of all ruminal microbial genera (archaea, bacteria, fungi, and protists) and their genes was performed to improve our understanding of how the interactions within the ruminal microbiome affects methane emissions (CH4). Metagenomics and CH4 data were available from 63 bovines of a two-breed rotational cross, offered two basal diets. Co-abundance network analysis revealed 10 clusters of functional niches. The most abundant hydrogenotrophic Methanobacteriales with key microbial genes involved in methanogenesis occupied a different functional niche (i.e., “methanogenesis” cluster) than methylotrophic Methanomassiliicoccales (Candidatus Methanomethylophylus) and acetogens (Blautia). Fungi and protists clustered together and other plant fiber degraders like Fibrobacter occupied a seperate cluster. A Partial Least Squares analysis approach to predict CH4 variation in each cluster showed the methanogenesis cluster had the best prediction ability (57.3%). However, the most important explanatory variables in this cluster were genes involved in complex carbohydrate degradation, metabolism of sugars and amino acids and Candidatus Azobacteroides carrying nitrogen fixation genes, but not methanogenic archaea and their genes. The cluster containing Fibrobacter, isolated from other microorganisms, was positively associated with CH4 and explained 49.8% of its variability, showing fermentative advantages compared to other bacteria and fungi in providing substrates (e.g., formate) for methanogenesis. In other clusters, genes with enhancing effect on CH4 were related to lactate and butyrate (Butyrivibrio and Pseudobutyrivibrio) production and simple amino acids metabolism. In comparison, ruminal genes negatively related to CH4 were involved in carbohydrate degradation via lactate and succinate and synthesis of more complex amino acids by γ-Proteobacteria. When analyzing low- and high-methane emitters data in separate networks, competition between methanogens in the methanogenesis cluster was uncovered by a broader diversity of methanogens involved in the three methanogenesis pathways and larger interactions within and between communities in low compared to high emitters. Generally, our results suggest that differences in CH4 are mainly explained by other microbial communities and their activities rather than being only methanogens-driven. Our study provides insight into the interactions of the rumen microbial communities and their genes by uncovering functional niches affecting CH4, which will benefit the development of efficient CH4 mitigation strategies.
Metabolic Checkpoints in Rheumatoid Arthritis

Several studies have highlighted the interplay between metabolism, immunity and inflammation. Both tissue resident and infiltrating immune cells play a major role in the inflammatory process of rheumatoid arthritis (RA) via the production of cytokines, adipo-cytokines and metabolic intermediates. These functions are metabolically demanding and require the most efficient use of bioenergetic pathways. The synovial membrane is the primary site of inflammation in RA and exhibits distinctive histological patterns characterized by different metabolism, prognosis and response to treatment. In the RA synovium, the high energy demand by stromal and infiltrating immune cells, causes the accumulation of metabolites, and adipo-cytokines, which carry out signaling functions, as well as activating transcription factors which act as metabolic sensors. These events drive immune and joint-resident cells to acquire pro-inflammatory effector functions which in turn perpetuate chronic inflammation. Whether metabolic changes are a consequence of the disease or one of the causes of RA pathogenesis is still under investigation. This review covers our current knowledge of cell metabolism in RA. Understanding the intricate interactions between metabolic pathways and the inflammatory and immune responses will provide more awareness of the mechanisms underlying RA pathogenesis and will identify novel therapeutic options to treat this disease.
Exceptionization

Java virtual machine (JVM) has recently evolved into a general-purpose language runtime environment to execute popular programming languages such as JavaScript, Ruby, Python, and Scala. These languages have complex non-Java features, including dynamic typing and first-class function, so additional language runtimes (engines) are provided on top of the JVM to support them with bytecode extensions. Although there are high-performance JVMs with powerful just-in-time (JIT) compilers, running these languages efficiently on the JVM is still a challenge. This article introduces a simple and novel technique for the JVM JIT compiler called exceptionization to improve the performance of JVM-based language runtimes. We observed that the JVM executing some non-Java languages encounters at least 2 times more branch bytecodes than Java, most of which are highly biased to take only one target. Exceptionization treats such a highly biased branch as some implicit exception-throwing instruction. This allows the JVM JIT compiler to prune the infrequent target of the branch from the frequent control flow, thus compiling the frequent control flow more aggressively with better optimization. If a pruned path were taken, then it would run like a Java exception handler, that is, a catch block. We also devised de-exceptionization, a mechanism to cope with the case when a pruned path is executed more often than expected. Since exceptionization is a generic JVM optimization, independent of any specific language runtime, it would be generally applicable to other language runtimes on the JVM. Our experimental result shows that exceptionization accelerates the performance of several non-Java languages. For example, JavaScript-on-JVM runs faster by as much as 60% and by 6% on average, when experimented with the Octane benchmark suite on Oracle’s latest Nashorn JavaScript engine and HotSpot 1.9 JVM. Furthermore, the performance of Ruby-on-JVM shows an improvement by as much as 60% and by 6% on average, while Python-on-JVM improves by as much as 6% and by 2% on average. We found that exceptionization is more effective to apply to the branch bytecode of the language runtime itself than the bytecode corresponding to the application code or the bytecode of the Java class libraries. This implies that the performance benefit of exceptionization comes from better JIT compilation of the language runtime of non-Java languages.
Deep learning discerns cancer mutation exclusivity

The exclusivity of a vast majority of cancer mutations remains poorly understood, despite the availability of large amounts of whole genome and exome sequencing data. In clinical settings, this markedly hinders the identification of the previously uncharacterized deleterious mutations due to the unavailability of matched normal samples. We employed state of the art deep learning algorithms for cross-exome learning of mutational embeddings and demonstrated their utility in sequence based detection of cancer-specific Single Nucleotide Variants (SNVs).
Turning old foes into new allies – harnessing drainage canals for biodiversity conservation in desiccated novel ecosystems

Drainage canals are ubiquitous components of agricultural landscapes worldwide. Although canals have greatly contributed to biodiversity loss by desiccating wetlands, they have recently attracted conservation attention due to their potential to function as refugia for native wetland-dependent species in intensively managed landscapes. However, their conservation role in complex landscapes comprising a mosaic of agricultural and desiccated semi-natural habitats, on which canals still pose a heavy burden, is unknown. Improved understanding of drainage canals and related biodiversity in these landscapes could help unlock their potential and support synergistic land management for nature conservation and water management. We applied a multitaxon approach, including plants, butterflies, true bugs, spiders and birds, to (1) assess the conservation value of drainage canals in a heavily drained European lowland region, (2) to test landscape-level and local canal parameters for aiding prioritization among canal types, and (3) to propose a reconciliation-based management framework that suits the interest of all stakeholders. We found that drainage canals concentrate more species across most taxa than adjacent semi-natural habitats, owing to the micro-environmental heterogeneity and the comparatively low management intensity in the canals. The species-concentrating capacity is particularly high in canals that traverse semi-natural habitats, although agricultural canals also support remarkable species diversity. However, agricultural canals are important dispersal corridors for invasive plants, which may negatively affect native species. Canal size has little effect on biodiversity but habitat stress is an important determinant. The higher the stress (due to sandiness and salinity), the higher is the added value of canals to landscape-wide biodiversity. Synthesis and applications We provide evidence that drainage canals can harbour surprisingly high levels of biodiversity and should therefore be recognized as important novel ecosystems with high conservation value, even within semi-natural habitats. Canals have previously been considered detrimental to nature conservation due to their association with loss of wetlands. However, by reducing water loss with reversible obstructions, controlling invasive species and applying specific conservation measures, they may be turned into conservation allies without compromising long-term interests of water management and agricultural land use.
Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19

COVID-19 is prevalent in the elderly. Old individuals are more likely to develop pneumonia and respiratory failure due to alveolar damage, suggesting that lung senescence may increase the susceptibility to SARS-CoV-2 infection and replication. Considering that human coronavirus (HCoVs; SARS-CoV-2 and SARS-CoV) require host cellular factors for infection and replication, we analyzed Genotype-Tissue Expression (GTEx) data to test whether lung aging is associated with transcriptional changes in human protein-coding genes that potentially interact with these viruses. We found decreased expression of the gene tribbles homolog 3 (TRIB3) during aging in male individuals, and its protein was predicted to interact with HCoVs nucleocapsid protein and RNA-dependent RNA polymerase. Using publicly available lung single-cell data, we found TRIB3 expressed mainly in alveolar epithelial cells that express SARS-CoV-2 receptor ACE2. Functional enrichment analysis of age-related genes, in common with SARS-CoV-induced perturbations, revealed genes associated with the mitotic cell cycle and surfactant metabolism. Given that TRIB3 was previously reported to decrease virus infection and replication, the decreased expression of TRIB3 in aged lungs may help explain why older male patients are related to more severe cases of the COVID-19. Thus, drugs that stimulate TRIB3 expression should be evaluated as a potential therapy for the disease.
DeepRC: Immune repertoire classiﬁcation with attention-based deep massive multiple instance learning

High-throughput immunosequencing allows re-constructing the immune repertoire of an individual, which is an exceptional opportunity for new immunotherapies, immunodiagnostics, and vaccine design. Such immune repertoires are shaped by past and current immune events, for example infection and disease, and thus record an individual’s state of health. Consequently, immune repertoire sequencing data may enable the prediction of health and disease using machine learning. However, ﬁnding the connections between an individual’s repertoire and the individual’s disease class, with potentially hundreds of thousands to millions of short sequences per individual, poses a difﬁcult and unique challenge for machine learning methods. In this work, we present our method DeepRC that combines a Deep Learning architecture with attention-based multiple instance learning. To validate that DeepRC accurately predicts an individual’s disease class based on its immune repertoire and determines the associated class-speciﬁc sequence motifs, we applied DeepRC in four large-scale experiments encompassing ground-truth simulated as well as real-world virus infection data. We demonstrate that DeepRC out-performs all tested methods with respect to predictive performance and enables the extraction of those sequence motifs that are connected to a given disease class.
Resistance, resilience, and functional redundancy of freshwater microbial communities facing multiple agricultural stressors in a mesocosm experiment

Freshwater biodiversity is threatened by fertilizers and pesticides from agricultural sources. Microbial communities can be resistant (i.e., community composition stays largely the same) or resilient (i.e., composition changes but then returns to its initial state) to these contaminants. Even after changes in composition, communities may continue to support ecosystem processes due to functional redundancy, in which different taxa carry out the same process, such that the process is maintained even after some taxa are lost. To test the extent of resistance, resilience, and functional redundancy in aquatic bacterial communities (bacterioplankton) faced with agricultural stressors, we exposed freshwater mesocosms to two commonly used pesticides: the herbicide Roundup (glyphosate) and the neonicotinoid insecticide imidacloprid, alone or in combination, and in high or low nutrient backgrounds. Over the 42-day experiment, we tracked bacterial density with flow cytometry, functional composition with Biolog EcoPlates, and taxonomic diversity with culture-independent 16S rRNA gene amplicon sequencing. We show that only glyphosate, but not imidacloprid or nutrients, measurably changed community structure. Despite this change, metabolic capabilities were maintained, suggesting functional redundancy. We further show that communities are resilient at broad, but not fine phylogenetic levels: the precise amplicon sequence variants (ASVs) driven below the limit of detection by glyphosate stress do not return, but tend to be replaced by relatives within the same genus. Together, our results show that bacterioplankton are broadly resistant, resilient, and redundant faced with severe agricultural stressors on weekly time scales, but that high doses of glyphosate can have longer-lasting effects on fine-scale diversity.
Dynamics of the cell fate specifications during female gametophyte development in Arabidopsis

The female gametophytes of angiosperms contain cells with distinct functions, such as those that enable reproduction via pollen tube attraction and fertilization. Although the female gametophyte undergoes unique developmental processes, such as several rounds of nuclear division without cell plate formation, and the final cellularization, it remains unknown when and how the cell fate is determined during their development. Here, we visualized the living dynamics of female gametophyte development and performed transcriptome analysis of its individual cell types, to assess the cell fate specifications in Arabidopsis thaliana. We recorded time lapses of the nuclear dynamics and cell plate formation from the one-nucleate stage to the seven-cell stage after cellularization, using the in vitro ovule culture system. The movies showed that the nuclear division occurred along the micropylar–chalazal axis. During cellularization, the polar nuclei migrated while associating with forming edge of the cell plate. Then, each polar nucleus migrated to fuse linearly towards each other. We also tracked the gene expression dynamics and identified that the expression of the MYB98pro∷GFP, a synergid-specific marker, was initiated before cellularization, and then restricted to the synergid cells after cellularization. This indicated that cell fates are determined immediately after cellularization. Transcriptome analysis of the female gametophyte cells of the wild type and myb98 mutant, revealed that the myb98 synergid cells had the egg cell-like gene expression profile. Although in the myb98, the egg cell-specific gene expressions were properly initiated only in the egg cells after cellularization, but subsequently expressed ectopically in one of the two synergid cells. These results, together with the various initiation timings of the egg cell-specific genes suggest the complex regulation of the individual gametophyte cells, such as cellularization-triggered fate initiation, MYB98-dependent fate maintenance, cell morphogenesis, and organelle positioning. Our system of live-cell imaging and cell-type-specific gene expression analysis provides insights into the dynamics and mechanisms of cell fate specifications in the development of female gametophytes in plants.
Hierarchical fear: parental behaviour and corticosterone release mediate nestling growth in response to predation risk

Nestling development, a critical life-stage for altricial songbirds, is highly vulnerable to predation, particularly for open-cup nesting species. Since nest predation risk increases cumulatively with time, rapid growth may be an adaptive response that promotes early fledging. However, greater predation risk can reduce parental provisioning rate as a risk aversion strategy and subsequently constrain nestling growth, or directly elicit a physiological response in nestlings with adaptive or detrimental effects on development rate. Despite extensive theory, evidence for the relative strength of these effects on nestling development in response to prevailing predation risk and the underlying mechanisms remain unclear. For an alpine population of horned lark (Eremophila alpestris), we elevated perceived predation risk (decoys/playback) during the nestling stage to assess the influence of predator cues and parental care on nestling wing growth and the glucocorticoid hormone corticosterone. We used piecewise path analysis to test a hypothesized causal response structure composed of direct and indirect pathways. Nestlings under greater perceived predation risk reduced corticosterone and increased wing growth, resulting in an earlier age at fledge. This represented both a direct response that was predator-specific, and an indirect response dependent on parental provisioning rate. Parents that reduced provisioning rate most severely in response to predator cues had smaller nestlings with greater corticosterone. Model comparisons indicated the strongest support for a directed, causal influence of corticosterone on nestling wing growth, highlighting corticosterone as a potential physiological mediator of the nestling growth response to predation risk. Finally, cold temperatures prior to the experiment constrained wing growth closer to fledge, illustrating the importance of considering the combined influence of weather and predation risk across developmental stages. We present the first study to separate the direct and indirect effects of predation risk on nestling development in a causal, hierarchical framework that incorporates corticosterone as an underlying mechanism and provides experimental evidence for an adaptive developmental response to predation risk in ground-nesting songbirds.
Temporal patterns in electrical nerve stimulation: Burst gap code shapes tactile frequency perception

We have previously described a novel temporal encoding mechanism in the somatosensory system, where mechanical pulses grouped into periodic bursts create a perceived tactile frequency based on the duration of the silent gap between bursts, rather than the mean rate or the periodicity. This coding strategy may offer new opportunities for transmitting information to the brain using various sensory neural prostheses and haptic interfaces. However, it was not known whether the same coding mechanisms apply when using electrical stimulation, which recruits a different spectrum of afferents. Here, we demonstrate that the predictions of the burst gap coding model for frequency perception apply to burst stimuli delivered with electrical pulses, re-emphasising the importance of the temporal structure of spike patterns in neural processing and perception of tactile stimuli. Reciprocally, the electrical stimulation data confirm that the results observed with mechanical stimulation do indeed depend on neural processing mechanisms in the central nervous system, and are not due to skin mechanical factors and resulting patterns of afferent activation.