Miracle-dz/UniFAR-FTUs huggingface dataset:
Species-Wide Transposable Element Repertoires Retrace the Evolutionary History of the Saccharomyces cerevisiae Host

Transposable elements (TE) are an important source of genetic variation with a dynamic and content that greatly differ in a wide range of species. The origin of the intraspecific content variation is not always clear and little is known about the precise nature of it. Here, we surveyed the species-wide content of the Ty LTR-retrotransposons in a broad collection of 1,011 Saccharomyces cerevisiae natural isolates to understand what can stand behind the variation of the repertoire, i.e. the type and number of Ty elements. We have compiled an exhaustive catalog of all TE variants present in the S. cerevisiae species by identifying a large set of new variants. The characterization of the TE content in each isolate clearly highlighted that each subpopulation exhibits a unique and specific repertoire, retracing the evolutionary history of the species. Most interestingly, we have shown that ancient interspecific hybridization events had a major impact in the birth of new variants and therefore in the shaping of the TE repertoires. We also investigated the transpositional activity of these elements in a large set of natural isolates, and we found a broad variability related to the level of ploidy as well as the genetic background. Overall, our results pointed out that the evolution of the Ty content is deeply impacted by clade-specific events such as introgressions and therefore follows the population structure. In addition, our study lays the foundation for future investigations to better understand the transpositional regulation and more broadly the TE-host interactions. Authors summary Mobile DNA elements are widely distributed in the genomes of many eukaryotes, but their contents greatly vary between species, populations and even individuals. In fact, little is known about the origin of this variation of transposable element (TE) content across individuals of the same species. Here, we surveyed the Ty LTR-retrotransposon content in a broad collection of 1,011 Saccharomyces cerevisiae yeast natural isolates. We have defined an exhaustive and precise catalog of the TE variants present in the S. cerevisiae species. We found that the TE content follows the evolutionary history of the species because each subpopulation has a unique and specific content. Interestingly, our results highlighted that ancient interspecific hybridization events led to the appearance of new TE variants and therefore had a strong impact on the variation of the TE repertoires in this species. We also investigated the transpositional activity of these elements and found a wide variability related to the genetic background diversity. Altogether, our results have led to a better understanding of the variability of TE content at a species level.
Rhythmic neuronal activities of the rat nucleus of the solitary tract are impaired by short-term high fat diet - implications for daily control of satiety by the brainstem

Temporal partitioning of daily food intake is crucial for survival and involves the integration of internal circadian states and external influences such as the light-dark cycle and the composition of diet. These intrinsic and extrinsic factors are interdependent with misalignment of circadian rhythms promoting body weight gain, while consumption of a calorie dense diet elevates the risk of obesity and blunts circadian rhythms. Since cardiovascular disease, metabolic disorders, and cancer are comorbid with obesity, understanding the relationships between brain activity and diet is of pivotal importance. Recently, we defined for the first time the circadian properties of the dorsal vagal complex of the brainstem, a structure implicated in the control of food intake and autonomic tone, but if and how 24 h rhythms in this area are influenced by diet remains unresolved. Here we focused on a key structure of this complex, the nucleus of the solitary tract, and using a range of approaches, we interrogated how its neuronal and cellular rhythms are affected by high-fat diet. We report that short term consumption of this diet increases food intake during the day and blunts daily rhythms in gene expression and neuronal discharge in the nucleus of the solitary tract. These alterations in this structure occur without prominent body weight gain, suggesting that high-fat diet acts initially to reduce activity in the nucleus of the solitary tract, thereby disinhibiting mechanisms that suppress daytime feeding. GRAPHICAL ABSTRACT
Dynamic signatures of the Eureka effect: an EEG study

The Eureka effect refers to the common experience of suddenly solving a problem. Here we study this effect in a pattern recognition paradigm that requires the segmentation of complex scenes and recognition of objects on the basis of Gestalt rules and prior knowledge. In the experiments both sensory evidence and prior knowledge were manipulated in order to obtain trials that do or do not converge towards a perceptual solution. Subjects had to detect objects in blurred scenes and signal recognition with manual responses. Neural dynamics were analyzed with high-density Electroencephalography (EEG) recordings. The results show significant changes of neural dynamics with respect to spectral distribution, coherence, phase locking, and fractal dimensionality. The Eureka effect was associated with increased coherence of oscillations in the alpha and theta band over widely distributed regions of the cortical mantle predominantly in the right hemisphere. This increase in coherence was associated with a decrease of beta band activity over parietal and central regions, and with a decrease of alpha power over frontal and occipital areas. In addition, there was a lateralized reduction of fractal dimensionality for activity recorded from the right hemisphere. These results suggest that the transition towards the solution of a perceptual task is mainly associated with a change of network dynamics in the right hemisphere that is characterized by enhanced coherence and reduced complexity. We propose that the Eureka effect requires cooperation of cortical regions involved in working memory, creative thinking, and the control of attention.
HNF4α isoforms regulate the circadian balance between carbohydrate and lipid metabolism in the liver

Hepatocyte Nuclear Factor 4α (HNF4α), a master regulator of hepatocyte differentiation, is regulated by two promoters (P1 and P2). P1-HNF4α is the major isoform in the adult liver while P2-HNF4α is thought to be expressed only in fetal liver and liver cancer. Here, we show that P2- HNF4α is expressed at ZT9 and ZT21 in the normal adult liver and orchestrates a distinct transcriptome and metabolome via unique chromatin and protein-protein interactions. We demonstrate that while P1-HNF4α drives gluconeogenesis, P2-HNF4α drives ketogenesis and is required for elevated levels of ketone bodies in females. Exon swap mice expressing only P2- HNF4α exhibit subtle differences in circadian gene regulation and disruption of the clock increases expression of P2-HNF4α. Taken together, we propose that the highly conserved two-promoter structure of the Hnfa gene is an evolutionarily conserved mechanism to maintain the balance between gluconeogenesis and ketogenesis in the liver in a circadian fashion.
Approximate Hidden Semi-Markov Models for Dynamic Connectivity Analysis in Resting-State fMRI

Motivated by a study on adolescent mental health, we conduct a dynamic connectivity analysis using resting-state functional magnetic resonance imaging (fMRI) data. A dynamic connectivity analysis investigates how the interactions between different regions of the brain, represented by the different dimensions of a multivariate time series, change over time. Hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) are common analytic approaches for conducting dynamic connectivity analyses. However, existing approaches for HSMMs are limited in their ability to incorporate covariate information. In this work, we approximate an HSMM using an HMM for modeling multivariate time series data. The approximate HSMM model allows one to explicitly model dwell-time distributions that are available to HSMMs, while maintaining the theoretical and methodological advances that are available to HMMs. We conducted a simulation study to show the performance of the approximate HSMM relative to other approaches. Finally, we used the approximate HSMM to conduct a dynamic connectivity analysis, where we showed how dwell-time distributions vary across the severity of non-suicidal self-injury (NSSI) in female adolescents, and how those with moderate or severe NSSI have greater state-switching frequency.
ISTDECO: In Situ Transcriptomics Decoding by Deconvolution

In Situ Transcriptomics (IST) is a set of image-based transcriptomics approaches that enables localisation of gene expression directly in tissue samples. IST techniques produce multiplexed image series in which fluorescent spots are either present or absent across imaging rounds and colour channels. A spot’s presence and absence form a type of barcoded pattern that labels a particular type of mRNA. Therefore, the expression of a gene can be determined by localising the fluorescent spots and decode the barcode that they form. Existing IST algorithms usually do this in two separate steps: spot localisation and barcode decoding. Although these algorithms are efficient, they are limited by strictly separating the localisation and decoding steps. This limitation becomes apparent in regions with low signal-to-noise ratio or high spot densities. We argue that an improved gene expression decoding can be obtained by combining these two steps into a single algorithm. This allows for an efficient decoding that is less sensitive to noise and optical crowding. We present IST Decoding by Deconvolution (ISTDECO), a principled decoding approach combining spectral and spatial deconvolution into a single algorithm. We evaluate ISTDECO on simulated data, as well as on two real IST datasets, and compare with state-of-the-art. ISTDECO achieves state-of-the-art performance despite high spot densities and low signal-to-noise ratios. It is easily implemented and runs efficiently using a GPU. ISTDECO implementation, datasets and demos are available online at: github.com/axanderssonuu/istdeco
Confirmation bias optimizes reward learning

Confirmation bias—the tendency to overweight information that matches prior beliefs or choices—has been shown to manifest even in simple reinforcement learning. In line with recent work, we find that participants learned significantly more from choice-confirming outcomes in a reward-learning task. What is less clear is whether asymmetric learning rates somehow benefit the learner. Here, we combine data from human participants and artificial agents to examine how confirmation-biased learning might improve performance by counteracting decisional and environmental noise. We evaluate one potential mechanism for such noise reduction: visual attention—a demonstrated driver of both value-based choice and predictive learning. Surprisingly, visual attention showed the opposite pattern to confirmation bias, as participants were most likely to fixate on “missed opportunities”, slightly dampening the effects of the confirmation bias we observed. Several million simulated experiments with artificial agents showed this bias to be a reward-maximizing strategy compared to several alternatives, but only if disconfirming feedback is not completely ignored—a condition that visual attention may help to enforce.
« Idol with feet of clay »: reliable predictions of forest ecosystem functioning require flawless climate forcings

Climate change affects various aspects of the functioning of ecosystem, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate the functioning, vitality and provision of ecosystem services of forests in a warmer world. These impact models require climate data as forcings, which are often produced by climate models comparing more or less well with observational climate data. The bias percentage of the climate forcings propagates throughout the modeling chain from the climate model to the impact model. In this study, we aimed to quantify these bias percentage, addressing three questions: (1) Do the impact model predictions vary when forcing it with different climate models, and how do the predictions under climate model vs. observational climate forcing differ? (2) Does the variability in the impact climate simulations caused by climate forcings fade out at large spatial scale? (3) How the fact of using simulated climatic data affects the process-based model predictions in the case of stressful events? To answer these questions, we present results obtained over the historical period (e.g. 1970-2010) with the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on French forests, studying European beech (Fagus sylvatica), temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) monospecific stands. We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used, whether debiased or not, and also on species and region considered. Overall, we observed an improvement of prediction after a monthly mean bias or monthly quantile mapping correction for three model considered, but not with the same success. Then we highlighted a large variability in the processes simulated by the impact model under different climate forcings when considering the plot (i.e. scale of a few hectares) scale. This variability fades out at larger scale (e.g. the scale of an ecological region, i.e. 100 km2), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings are more variable during driest years. These results highlight the necessity to quantify bias and uncertainties in climate forcings before predicting fluxes dynamics with process-based model.
The SWI/SNF chromatin remodeling assemblies BAF and PBAF differentially regulate cell cycle exit and cellular invasion in vivo

Chromatin remodelers such as the SWI/SNF complex coordinate metazoan development through broad regulation of chromatin accessibility and transcription, ensuring normal cell cycle control and cellular differentiation in a lineage-specific and temporally restricted manner. Mutations in genes encoding the structural subunits of chromatin, such as histone subunits, and chromatin regulating factors (CRFs) are associated with a variety of disease mechanisms including cancer metastasis, in which cancer co-opts cellular invasion programs functioning in healthy cells during development. Here we utilize Caenorhabditis elegans anchor cell (AC) invasion as an in vivo model to identify the suite of chromatin agents and CRFs that promote cellular invasiveness. We demonstrate that the SWI/SNF ATP-dependent chromatin remodeling complex is a critical regulator of AC invasion, with pleiotropic effects on both G0 cell cycle arrest and activation of invasive machinery. Using targeted protein degradation and enhanced RNA interference (RNAi) vectors, we show that SWI/SNF contributes to AC invasion in a dose-dependent fashion, with lower levels of activity in the AC corresponding to aberrant cell cycle entry and increased loss of invasion. Our data specifically implicate the SWI/SNF BAF assembly in the regulation of the G0 cell cycle arrest in the AC, whereas the SWI/SNF PBAF assembly promotes AC invasion via cell cycle-independent mechanisms, including attachment to the basement membrane (BM) and activation of the pro-invasive fos-1/FOS gene. Together these findings demonstrate that the SWI/SNF complex is necessary for two essential components of AC invasion: arresting cell cycle progression and remodeling the BM. The work here provides valuable single-cell mechanistic insight into how the SWI/SNF assemblies differentially contribute to cellular invasion and how SWI/SNF subunit-specific disruptions may contribute to tumorigeneses and cancer metastasis. SUMMARY STATEMENT Cellular invasion through the basement membrane by the C. elegans anchor cell requires both BAF and PBAF SWI/SNF assemblies to arrest the cell cycle and promote the expression of pro-invasive genes.
Studies on the release of ibuprofen from Al3+ ion cross‐linked homopolymeric and interpenetrating network hydrogel beads of carboxymethyl xanthan and sodium alginate

Homopolymeric and interpenetrating network (IPN) hydrogel beads of ibuprofen (IBP), a nonsteroidal anti-inflammatory drug, were prepared using carboxymethyl xanthan and sodium alginate as polymers and AlCl3 as a cross-linking agent. Formation of IPN structure was examined using FTIR analysis. The effect of composition of the hydrogel beads and the amount drug load on the release of IBP was studied in acidic and phosphate buffer (PB) solution of pH 6.8. In accordance with the swelling behavior of the beads, drug release from the IPN beads in acidic solution was found to be confined within those from homopolymeric beads. On the other hand, drug release from IPN beads in PB solution was found to be less than those from the homopolymeric beads although the swelling of the beads followed the same trend as in an acidic solution. An increase in the drug load further decreased the drug release from all the beads in both the dissolution media. FTIR, XRD, and DSC analyses demonstrated the absence of any interaction between the drug and the polymers. The results indicated that IPN hydrogel beads provided more sustained release of IBP than homopolymeric beads. © 2011 Wiley Periodicals, Inc. Adv Polym Techn 30:1–11, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/adv.20199
Detecting selection in low-coverage high-throughput sequencing data using principal component analysis

Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure. Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.
Modeling and Stress Analysis During Drying of a Deformable and Saturated Porous Medium

This article addresses how to express the behaviors that develop stresses within a porous media during convective drying processes. The work is focused on the coupling of the thermal (temperature distribution), hygroscopic (moisture, humidity), and mechanical (strains and stresses) aspects shown during the drying process of a saturated porous medium. Natural clay plate samples were used as a model material. Using two different mechanical behaviors (elastic and viscoelastic), the strain–stress equations were studied and discussed through the simulation results. Obtaining almost the same parameters of the main modeling variables (temperature, liquid pressure, and moisture content), a significant difference was observed between the results obtained for the stresses assuming the two behaviors, particularly depending on the viscoelastic parameters deduced from an experimental study. The simulation highlights a response of the medium supposed viscoelastic different to that of elastic case in intensity and response time.
Human iPSC-Derived Renal Cells Change Their Immunogenic Properties during Maturation: Implications for Regenerative Therapies

Therapeutic success of human induced pluripotent stem cell (hiPSC)-based therapies critically depends on immunological compatibility of the hiPSC-derived transplant. As grafted hiPSC-derived cells are often immature, we hypothesized that their immunologic properties may change due to post-grafting maturation. Subsequently, this will affect their interaction with the host immune system and may compromise graft tolerance. In the present study allogeneic and autologous cellular immunity of primary cells, therof reprogrammed hiPSC, hiPSC-derived progenitor and terminally differentiated cells was investigated in vitro by using renal cells as a model system. In contrast to allogeneic primary cells, hiPSC-derived early renal progenitors and mature renal epithelial cells were both tolerated not only by autologous but also by allogeneic T cells. These immune-privileged properties resulted from active immune-modulation and low immune visibility, which declined during the process of cell maturation. However, autologous and allogeneic natural killer (NK) cell responses were not suppressed by hiPSC-derived renal cells and efficiently changed NK cell activation status. These findings clearly show a dynamic stage-specific dependency of autologous and allogeneic T- and NK cell responses to the hiPSC-derived renal cell lineage with consequences for effective cell therapies. The study suggests that hiPSC-derived early progenitors may provide advantageous immune suppressive properties when applied in cell therapy. The data furthermore indicate a need to suppress NK cell activation in allogeneic as well as autologous settings.