Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene expression plasticity, genetic variation and fatty acid remodelling in divergent populations of a tropical bivalve species

Ocean warming is a particularly challenging threat for tropical marine bivalves’ species because many live already near their upper thermal limits. The thermal sensitivity of organisms is a strong contributor to the biogeographic boundaries of populations and species. The potential of thermal plastic response (range of thermal breadth) is typically reduced for marine populations living in stable thermal environments, e.g., in extreme latitudes organisms as traduced in the climatic variability hypothesis. However, regional-scale heterogeneity among tropical environments, such as archipelagos in French Polynesia, might also serve in modulating this plastic potential. The questions remain now, how tropical organisms are able to cope with abnormally elevated temperature on long-term (several weeks) and how environmental-variability might drive the potential of resilience? To answer these questions, we benefit from two ecologically divergent populations of a marine tropical mollusc species, Pinctada margaritifera, that usually experience either large diurnal variations (tide-pools, Marquesas archipelago) or lower temperature with stable to moderate variations (Gambier archipelago). Individuals were maintained in common garden experiment at several controlled temperature conditions (23°C, 28°C, 32°C and 34°C) over a 48 days period. We explored genetic divergence as well as thermal plastic responses by combining lipidomic and transcriptomic approaches. We show that P. margaritifera have capacities to adjust to long-term elevated temperatures that was thus far largely underestimated. Furthermore, we identified genetic variation between populations that overlapped with genes expression variations, including genes involved in the respiration machinery, a central process delimiting critical temperatures in marine invertebrates. This study is the first of a series looking at the global adaptation and acclimation mechanisms in response to climate change in Pinctada species.
Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5’ and 3’ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.
A bacteriophage cocktail significantly reduces Listeria monocytogenes without deleterious impact on the commensal gut microbiota under simulated gastro-intestinal conditions

In this study, we examined the effect of a bacteriophage cocktail (tentatively designated FOP, for Foodborne Outbreak Pill) on the levels of Listeria monocytogenes in simulated small intestine, large intestine, and Caco-2 model systems. We found that FOP survival during simulated passage of the upper gastrointestinal was dependent on stomach pH, and that FOP robustly inhibited L. monocytogenes levels with effectiveness comparable to antibiotic treatment (ampicillin) under simulated ilium and colon conditions. FOP did not inhibit the commensal bacteria, whereas ampicillin treatment led to dysbiosis-like conditions. FOP was also more effective than antibiotic in protecting Caco-2 cells from adhesion and invasion by L. monocytogenes, while not triggering an inflammatory response. Our data suggest that FOP may provide a robust protection against L. monocytogenes should the bacterium enter the human gastrointestinal tract (e.g., by consumption of contaminated food), without deleterious impact on the commensal bacteria.
Westward branching of LIW induced by Algerian anticyclonic eddies close to the Sardinian slope

The interaction between anticyclonic Algerian eddies and Levantine Intermediate Water (LIW) flow is investigated along the western Sardinian slope by analysing in situ data and model results. In March–April 2001, LIW flowed northward inshore the isobath 2000 m. At about 30 km far offshore, LIW was more distributed in patches than in a vein, and there this variability was associated with a distant anticylonic eddy. In May 2002, the situation was very different: LIW was also patchy inshore from the 2000 m isobath. On horizontal sections, LIW was observed in a relative wide latitudinal band (∼50 km) centred on ∼40° N, in the southern part of an anticyclonic eddy located over the slope. This study shows that mesoscale structures can disturb the LIW flows along the Sardinian slope by diverting a part of LIW flow westward.
The seasonal dynamics of bud dormancy in grapevine suggest a regulated checkpoint prior to acclimation

Grapevine (Vitis vinifera L.) displays wide plasticity to climate and seasonality, ranging from strongly deciduous to evergreen. Understanding the physiology of decisions to grow or quiesce is critical for improved crop management, prediction, and the adaptability of production to alternative climate scenarios. The perenniating bud (N+2) is a major economic unit and focus of study. Here we investigated the physiology and transcriptome of cv. Merlot buds grown in a temperate maritime climate from summer to spring in two consecutive years. The changes in bud respiration, hydration and internal tissue oxygen data were consistent with the transcriptome data. ABA-responsive gene processes prevailed upon the transition to a deep metabolic and cellular quiescence in the bud during autumn. Light, together with hypoxia and redox signalling presided over the resumption of nuclear and cellular growth in the transition to spring. Comparisons with transcriptome data from bud burst studies revealed a number of regulatory candidates for the orderly resumption of growth in spring, including components that may integrate light and temperature signalling. Importantly however, the bud burst forcing data, which is widely used as a measure of bud dormancy, were not consistent with the physiological and transcription data. We hypothesise the existence of a physiological checkpoint following bud set in summer, which if not met results in extreme quiescence. Collectively this is the most integrated developmental dataset of the latent bud of cultivated grapevine, and establishes a platform for systems approaches to study seasonal plasticity. One sentence summary Physiology and transcriptome data provide strong evidence of a regulatory checkpoint prior to acclimation and dormancy in latent grapevine buds.
Interspecies activation correlations reveal functional correspondences between marmoset and human brain areas

Significance The common marmoset has enormous promise as a nonhuman primate model. While resting-state functional MRI (fMRI) has provided evidence for a similar organization of marmoset and human cortices, the technique cannot be used to map the functional correspondences of brain regions between species. Here, we used movie-driven fMRI in marmosets and humans to identify whole-brain functional correspondences between the two primate species. In particular, we describe functional correlates for the well-known human face, body, and scene patches in marmosets. We find that these networks have a similar organization in both species, suggesting a largely conserved organization of higher-order visual areas between marmoset monkeys and humans. The common marmoset has enormous promise as a nonhuman primate model of human brain functions. While resting-state functional MRI (fMRI) has provided evidence for a similar organization of marmoset and human cortices, the technique cannot be used to map the functional correspondences of brain regions between species. This limitation can be overcome by movie-driven fMRI (md-fMRI), which has become a popular tool for noninvasively mapping the neural patterns generated by rich and naturalistic stimulation. Here, we used md-fMRI in marmosets and humans to identify whole-brain functional correspondences between the two primate species. In particular, we describe functional correlates for the well-known human face, body, and scene patches in marmosets. We find that these networks have a similar organization in both species, suggesting a largely conserved organization of higher-order visual areas between New World marmoset monkeys and humans. However, while face patches in humans and marmosets were activated by marmoset faces, only human face patches responded to the faces of other animals. Together, the results demonstrate that higher-order visual processing might be a conserved feature between humans and New World marmoset monkeys but that small, potentially important functional differences exist.
Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

The purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.
Motor planning under uncertainty

Actions often require the selection of a specific goal amongst a range of possibilities, like when a softball player must precisely position her glove to field a fast-approaching ground ball. Previous studies have suggested that during goal uncertainty, the brain prepares for all potential goals in parallel and averages the corresponding motor plans to command an intermediate movement that is progressively refined as additional information becomes available. Although intermediate movements are widely observed, they could instead reflect a neural decision about the single best action choice at each point in time given the remaining uncertainty. Here we systematically dissociate these possibilities using novel experimental manipulations, and find that when confronted with uncertainty, humans generate a single motor plan that optimizes task performance, rather than averaging potential motor plans. In addition to accurate predictions of population-averaged changes in motor output, a novel computational model based on this performance-optimization theory accounted for a remarkable 80-90% of the variance for individual differences between participants. Our findings resolve a long-standing question about how the brain selects an action to execute during goal uncertainty, providing fundamental insight into motor planning in the nervous system.
The structure of the human cell cycle

The human cell cycle is conventionally depicted as a five-phase model consisting of four proliferative phases (G1, S, G2, M) and a single state of arrest (G0). However, recent studies show that individual cells can take different paths through the cell cycle and exit into distinct arrest states, thus necessitating an update to the canonical model. We combined time lapse microscopy, highly multiplexed single cell imaging and manifold learning to determine the underlying “structure” of the human cell cycle under multiple growth and arrest conditions. By visualizing the cell cycle as a complete biological process, we identified multiple points of divergence from the proliferative cell cycle into distinct states of arrest, revealing multiple mechanisms of cell cycle exit and re-entry and the molecular routes to senescence, endoreduplication and polyploidy. These findings enable the visualization and comparison of alternative cell cycles in development and disease. One-sentence summary A systems-level view of single-cell states reveals the underlying architecture of the human cell cycle
Effects of the social environment on movement-integrated habitat selection

Movement links the distribution of habitats with the social environment of animals using those habitats; yet integrating movement, habitat selection, and socioecology remains an opportunity for further study. Here, our objective was to disentangle the roles of habitat selection and social association as drivers of collective movement in a gregarious ungulate. To accomplish this objective, we (1) assessed whether socially familiar individuals form discrete social communities and whether social communities have high spatial, but not necessarily temporal, overlap; and (2) we modelled the relationship between collective movement and selection of foraging habitats using socially informed integrated step selection analysis. We used social network analysis to assign individuals to social communities and determine short and long-term social preference among individuals. Using integrated step selection functions (iSSF), we then modelled the effect of social processes, i.e., nearest neighbour distance and social preference, and movement behaviour on patterns of habitat selection. Based on assignment of individuals to social communities and home range overlap analyses, individuals assorted into discrete social communities, and these communities had high spatial overlap. By unifying social network analysis with iSSF, we identified movement-dependent social association, where individuals foraged with more familiar individuals, but moved collectively with any between foraging patches. Our study demonstrates that social behaviour and space use are inter-related based on spatial overlap of social communities and movement-dependent habitat selection. Movement, habitat selection, and social behaviour are linked in theory. Here, we put these concepts into practice to demonstrate that movement is the glue connecting individual habitat selection to the social environment.
MSA Transformer

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins. Protein language models studied to date have been trained to perform inference from individual sequences. The longstanding approach in computational biology has been to make inferences from a family of evo lutionarily related sequences by fitting a model to each family independently. In this work we combine the two paradigms. We introduce a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment. The model interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families. The performance of the model surpasses current state-of-the-art unsupervised structure learning methods by a wide margin, with far greater parameter efficiency than prior state-of-the-art protein language models.
Using a Human Circulation Mathematical Model to Simulate the Effects of Hemodialysis and Therapeutic Hypothermia

Background The human blood circulation is an intricate process regulated by multiple biophysical factors. Our patients often suffer from renal disease and atrial fibrillation, and are given treatments such as therapeutic hypothermia, exercise, and hemodialysis. In this work, a hemodynamic mathematical model of human circulation coupled to a representative dialysis machine is developed and used to explore causal mechanisms of our recent clinical observations. Methods An ordinary differential equation model consisting of human whole body circulation, baroreflex control, and a hemodialysis machine was implemented. Experimentally informed parameter alterations were used to implement hemodialysis and therapeutic hypothermia. By means of parameter perturbation, four model populations encompassing baseline, dialysed, hypothermia treated, and simultaneous dialysed with hypothermia were generated. In model populations, multiple conditions including atrial fibrillation, exercise, and renal failure were simulated. The effects of all conditions on clinically relevant non-invasive measurables such as heart rate and blood pressure were quantified. A parameter sensitivity analysis was implemented to rank model output influencing parameters in the presented model. Results Results were interpreted as alterations of the respective populations mean values and standard deviations of the clinical measurables, both in relation to the baseline population. A clinical measurable’s smaller standard deviation (in comparison to baseline population) was interpreted as a stronger association between a given clinical measure and the corresponding underlying process, which may permit the use of deducing one by observation of the other. The modelled dialysis was observed to increase systolic blood pressure, vessel shear, and heart rate. Therapeutic hypothermia was observed to reduce blood pressure as well as the intra-population standard deviation (heterogeneity) of blood flow in the large (aorta) and small (kidney) vasculature. Therapeutic hypothermia reduced shear in vessels, suggesting a potential benefit with respect to endothelial dysfunction and maintenance of microcirculatory blood flow. The action of therapeutic hypothermia under conditions such as atrial fibrillation, exercise, and renal failure was to reduce total blood flow, which was applicable in all simulated populations. Therapeutic hypothermia did not affect the dialysis function, but exercise improved the efficacy of dialysis by facilitating water removal. Conclusions This study illuminates some mechanisms of action for therapeutic hypothermia. It also suggests clinical measurables that may be used as surrogates to diagnose underlying diseases such as atrial fibrillation.
A linguistic representation in the visual system underlies successful lipreading

There is considerable debate over how visual speech is processed in the absence of sound and whether neural activity supporting lipreading occurs in visual brain areas. Surprisingly, much of this ambiguity stems from a lack of behaviorally grounded neurophysiological findings. To address this, we conducted an experiment in which human observers rehearsed audiovisual speech for the purpose of lipreading silent versions during testing. Using a combination of computational modeling, electroencephalography, and simultaneously recorded behavior, we show that the visual system produces its own specialized representation of speech that is 1) well-described by categorical linguistic units (“visemes”) 2) dissociable from lip movements, and 3) predictive of lipreading ability. These findings contradict a long-held view that visual speech processing co-opts auditory cortex after early visual processing stages. Consistent with hierarchical accounts of visual and audiovisual speech perception, our findings show that visual cortex performs at least a basic level of linguistic processing.
Physical and Chemical Changes in Anchovy (Engraulis anchoita) Flesh During Marination

ABSTRACT In this work the physical and chemical changes promoted by the marinating process in anchovy fillet during brining and marinating stages were studied. During the marinating stage a diffusion of acetic acid and sodium chloride takes place into the tissue of fish flesh until a balanced concentration is reached. Most changes in the components of the fillet occurred in the first 24 hours being called by the authors “variable grade period” in contrast with “constant grade period” that occurred at the end of the marinating process. The sensorial score of full marinating was reached at nine days, when the structural changes had concluded.