Miracle-dz/UniFAR-FTUs huggingface dataset:
Large DNA viruses in early diverging fungal genomes are relics of past and present infections

Giant viruses of the phylum Nucleocytoviricota have captured researchers’ attention due to their increasingly recognized impacts on eukaryotic genome evolution. Their origins are hypothesized to predate or coincide with the diversification of eukaryotes and their natural hosts span the eukaryotic tree of life. But surprisingly, giant viruses have not been found in the fungal kingdom, though recent metagenomic work suggests a putative association. Here we show that early-diverging fungal lineages maintain both “viral fossils” and host active infections by giant viruses, which form a monophyletic clade that we name Mycodnaviridae. Viral genomes described here span up to 350kb and encode over 300 genes, including many genes characteristic of giant viruses. Interestingly, we observed variation in infection status among the isolates including apparent active infection and transcriptionally-suppressed states, suggestive that viral activation may be constrained to certain life stages of the host. Our experimental findings add to the scant few natural virus-host systems available in culture for the study of giant viruses. These viruses have likely shaped the early evolution of these fungal lineages and should prove a useful model for their study.
PEMPS: A Phylogenetic Software Tool to Model the Evolution of Metabolic Pathways

Background Metabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype-phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function. Results A software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters. Conclusions The PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS.
Maternal Care Leads to the Evolution of Long, Slow Lives

Animals, and mammals in particular, vary widely in their “pace of life,” with some species living long lives and reproducing infrequently (slow life histories) and others living short lives and reproducing often (fast life histories). These species also vary in the importance of maternal care in offspring fitness: in some species, offspring are fully independent of their mothers following a brief period of nutritional input, while others display a long period of continued dependence on mothers well after nutritional dependence. Here we hypothesize that these two axes of variation are causally related to each other, such that extended dependence of offspring on maternal presence leads to the evolution of longer lives at the expense of reproduction. We use a combination of deterministic modeling and stochastic agent-based modeling to explore how empirically-observed links between maternal survival and offspring fitness are likely to shape the evolution of mortality and fertility. Each of our modelling approaches leads to the same conclusion: when maternal survival has strong impacts on the survival of offspring and grandoffspring, populations evolve longer lives with less frequent reproduction. Our results suggest the slow life histories of humans and other primates as well as other long-lived, highly social animals such as hyenas, whales, and elephants, are partially the result of the strong maternal care that these animals display. We have designed our models to be readily parameterized with demographic data that is routinely collected by long-term researchers, which will facilitate more thorough testing of our hypothesis. Significance Statement Humans and other primates live longer lives and reproduce less often than other mammals of similar body mass. What is the cause of these long lives? Here we add to existing hypotheses, including the Mother and Grandmother hypotheses, by arguing that these increased lifespans are partially explained by the intense maternal care that many primates express. Using a combination of deterministic and stochastic modeling approaches, informed by empirical data, we show that stronger connections between maternal survival and offspring fitness leads to selection for longer lives and slower reproduction. Our models suggest that the importance of the mother-offspring relationship, which defines much of human and non-human primate lives, lies at the core of the evolution of our long lives.
The CD4 T cell epigenetic JUNB+ state is associated with proliferation and exhaustion

Adoptive cell therapy (ACT) requires the in vitro expansion of T cells, a process where currently several variables are poorly controlled. As the state and quality of the cells affects the treatment outcome, the lack of insight is problematic. To get a better understanding of the production process and its degrees of freedom, we have generated a multiome CD4 T cell single-cell atlas. We find in particular a JUNB+ epigenetic state, orthogonal to traditional CD4 T cell subtype categorization. This new state is present but overlooked in previous transcriptomic CD4 T cell atlases. We characterize it to be highly proliferative, having condensed and actively remodeled chromatin, and correlating with exhaustion. JUNB+ subsets are also linked to memory formation, as well as circadian rhythm, connecting several important processes into one state. To dissect JUNB regulation, we also derived a gene regulatory network (GRN) and developed a new explainable machine learning package, Nando. We propose potential upstream drivers of JUNB, verified by other atlases and orthogonal data. We expect our results to be relevant for optimizing in vitro ACT conditions as well as modulation of gene expression through novel gene editing.
Neural representations of predicted events: Evidence from time-resolved EEG decoding

Through statistical learning, humans are able to extract temporal regularities, using the past to predict the future. Evidence suggests that learning relational structures makes it possible to anticipate the imminent future; yet, the neural dynamics of predicting the future and its time-course remain elusive. To examine whether future representations are denoted in a temporally discounted fashion, we used the high-temporal-resolution of electroencephalography (EEG). Observers were exposed to a fixed sequence of events at four unique spatial positions within the display. Using multivariate pattern analyses trained on independent pattern estimators, we were able to decode the spatial position of dots within full sequences, and within randomly intermixed partial sequences wherein only a single dot was presented. Crucially, within these partial sequences, subsequent spatial positions could be reliably decoded at their expected moment in time. These findings highlight the dynamic weight changes within the assumed spatial priority map and mark the first implementation of EEG to decode predicted, yet critically omitted events. Impact statement Utilizing high-temporal-resolution EEG, the dynamic weight changes of assumed spatial priority map were visualized by decoding the spatial position of expected, yet omitted, events at their expected moment in time.
Patterns of Arc mRNA expression in the rat brain following dual recall of fear- and reward-based socially acquired information

The ability to learn new information and behaviors is a vital component of survival in most animal species. This learning can occur via direct experience or through observation of another individual (i.e., social learning). While research focused on understanding the neural mechanisms of direct learning is prevalent, less work has aimed at understanding the brain circuitry mediating the acquisition and recall of socially acquired information. We aimed to further elucidate the mechanisms underlying recall of socially acquired information by having rats sequentially recall a socially transmitted food preference (STFP) and a fear association via fear conditioning by-proxy (FCbP). Brain tissue was processed for mRNA expression of the immediate early gene (IEG) Arc, which reliably expresses in the cell nucleus following transcription before migrating to the cytoplasm over the next 25 minutes. Given this timeframe, we were able to identify whether Arc transcription was triggered by STFP recall, FCbP recall, or following recall of both memories. Surprisingly – and contrary to past research examining expression of other IEGs following STFP or FCbP recall separately – we found no differences in any of the Arc expression measures across a number of prefrontal regions and the vCA3 of the hippocampus between controls, demonstrators, and observers, though we did detect an overall effect of sex in a number of regions. We theorize that these results may indicate that relatively little Arc-dependent neural restructuring is taking place in the prefrontal cortices following recall of a recently socially acquired information or directly acquired fear associations in these areas.
Production and Lifetime Performance Enhancement in FPGA Designs

—Due to increasing variability, significant performance loss in FPGA designs occurs, as maximum performance is estimated using vendor tools and pessimistic worst-case design corners. In this paper, a methodology, based on a novel local sensor, is proposed to safely recover available performance. The sensor’s functionality is user’s defined in a Hardware Description Language (HDL), according to the target circuit configuration in the FPGA tissue. Embedded sensors monitor circuit performance at production and during product lifetime. At production, sensors identify each device´s maximum performance, as limited by process variations. Performance improvement (system clock frequency increase) is achieved by reducing the pessimistic safety-margins defined by standard FPGA simulation tools. Conversely, power consumption reduction (with the same performance) can be obtained using lower core supply voltage, V DD . During product lifetime, performance degradation (due to aging effects) is flagged. Sensors monitor performance degradation in pre-identified critical paths, by using a predictive fault detection methodology and a user’s defined safety margin. By doing so, error occurrence can be avoided, as demanded in safety-critical applications. During circuit operation, local sensors are always ON, in order to monitor long-term performance variations caused by aging effects. Simulation, using standard FPGAs vendors’ tools, and physical results on 65nm and 45nm technology commercial FPGAs show that significant improvements in circuit performance can be achieved with the proposed methodology.
Analysis of BRCA1/2 variants of unknown signi cance in patients with breast cancer from a prospective KOHBRA study

Joo Heung Kim Yonsei University College of Medicine https://orcid.org/0000-0002-0417-8434 Sunggyun Park Keimyung University School of Medicine Hyung Seok Park (  imgenius@yuhs.ac ) Yonsei University College of Medicine https://orcid.org/0000-0001-5322-6036 Ji Soo Park Yonsei University College of Medicine Seung-Tae Lee Yonsei University College of Medicine Sung-Won Kim Daerim St. Mary's Hospital Jong Won Lee University of Ulsan College of Medicine Min Hyuk Lee Soonchunhyang University College of Medicine Sue K. Park Seoul National University College of Medicine Woo-Chul Noh korea cancer center hospital Doo Ho Choi Sungkyunkwan University School of Medicine Wonshik Han Seoul National University College of Medicine Sung Hoo Jung Chonbuk National University Medical School
Amygdala subdivisions exhibit aberrant whole-brain functional connectivity in relation to stress intolerance and psychotic symptoms in 22q11.2DS

The amygdala is a key region in emotional regulation, which is often impaired in psychosis. However, it is unclear if amygdala dysfunction directly contributes to psychosis, or whether it contributes to psychosis through symptoms of emotional dysregulation. We studied the functional connectivity of amygdala subdivisions in patients with 22q11.2DS, a known genetic model for psychosis susceptibility. We investigated how dysmaturation of each subdivisions connectivity contributes to positive psychotic symptoms and impaired tolerance to stress in deletion carriers. Longitudinally-repeated MRI scans from 105 patients with 22q11.2DS (64 at high-risk for psychosis and 37 with impaired tolerance to stress) and 120 healthy controls between the ages of 5 to 30 years were included. We calculated seed-based whole-brain functional connectivity for amygdalar subdivisions and employed a longitudinal multivariate approach to evaluate the developmental trajectory of functional connectivity across groups. Patients with 22q11.2DS presented a multivariate pattern of decreased basolateral amygdala (BLA)-frontal connectivity alongside increased BLA-hippocampal connectivity. Moreover, associations between developmental drops in centro-medial amygdala (CMA)-frontal connectivity to both impaired tolerance to stress and positive psychotic symptoms in deletion carriers were detected. SFA hyperconnectivity to the striatum was revealed as a specific pattern arising in patients who develop mild to moderate positive psychotic symptoms. Overall, CMA-frontal dysconnectivity was found as a mutual neurobiological substrate in both impaired tolerance to stress and psychosis, suggesting a role in prodromal dysregulation of emotions in psychosis. While BLA dysconnectivity was presented as an early finding in patients with 22q11.2DS, contributing to impaired tolerance to stress.
Evaluation of the COVID-19 pandemic' effects on elderly practitioners of physical activities

Introduction: The pandemic has changed the population's lifestyle. Objective: to evaluate the effects of the COVID-19 pandemic on elderly people attending the Social Service of Commerce (SESC). Materials and methods: Quantitative research, developed with elderly people aged 60 years or older. Data were collected through semi-structured interviews carried out remotely via telephone calls with 120 elderly people, collecting information related to sociodemographic data; emotional health; physical activity and health. Results: More than 50% of participants practicing social isolation and said that the pandemic influenced their emotional state. There was an association between physical activity and hours sitting per day. Conclusion: According to the results, the pandemic influenced the emotional aspect of the participants and those who practiced physical activity spent less time sitting.
Artiﬁcial intelligence and Fourier optics: Application of DeepLabV3+ in the recovery of a diffracting aperture in light propagation

The combination of Fourier Optics and Artiﬁcial Intelligence has driven signiﬁcant advances in image processing and modeling of optical systems, with the UNet architecture being the main protagonist. However, the DeepLabV3+ network has recently shown promising performance detecting diffracting apertures. In this study, we investigate the effectiveness of DeepLabV3+ in identifying diffracting apertures in light propagation models and compare its performance with that of UNet. The results reveal that DeepLabV3+ outperforms UNet in accuracy and robustness in identifying diffracting apertures, even in the presence of noise and aperture shape variations.
Investigating sentence severity with judicial open data - A case study on sentencing high-tech crime in the Dutch criminal justice system

Open data promotes transparency and accountability as everyone can analyse it. Law enforcement and the judiciary are increasingly making data available, to increase trust and confidence in the criminal justice system. Due to privacy legislation, judicial open data -- like court judgments -- in Europe is usually anonymised. Because this removes part of the information on for instance offenders, the question arises to what extent criminological research into sentencing can make use of anonymised open data. We answer this question based on a case study in which we use the open data of the Dutch criminal justice system that rechtspraak.nl/Uitspraken makes available. Over the period 2015-2020, we analysed sentencing in 25,366 court judgments and, in particular, investigated the relationship between sentence severity and the offender's use of advanced ICT -- as this is information that is readily available in open data. The most important results are, firstly, that offenders who use advanced ICT are sentenced to longer custodial sentences compared to other offenders. Second, our results show that the quality of sentencing research with open data is comparable to the quality of sentencing research with judicial databases, which are not anonymised.
Jurnal Kimia Sains dan Aplikasi

Silver nanoparticles have superior properties that attract much attention because they can increase their effectiveness and efficiency compared to their macroscopic size. Silver nanoparticles have several benefits, one of which is as an antibacterial agent. Synthesis of silver nanoparticles was carried out by reducing silver ions (Ag + ) to uncharged silver (Ag 0 ) using a reducing agent from Hibiscus sabdariffa Linn. petal extract. This research aims to synthesize silver nanoparticles by studying several parameters that influence their formation, such as the pH of Hibiscus sabdariffa Linn. petal extract, reaction time, silver nitrate concentration, and Hibiscus sabdariffa Linn. petal extract concentration. The silver nanoparticles formed were then tested for their antibacterial activity. The experimental results showed that the best synthesis was performed using Hibiscus sabdariffa Linn. petal extract with pH 10, reaction time of 45 minutes, silver nitrate concentration of 1×10 -4 M, and Hibiscus sabdariffa Linn. petal extract concentration of 0.004%. The synthesized silver nanoparticles, examined through a transmission electron microscope, exhibited a size distribution ranging from 2 to 26 nm with an average size of 12 ± 3 nm, which was stable with a storage time of 3 months. Silver nanoparticles showed antibacterial activity against Staphylococcus aureus . Hibiscus sabdariffa Linn. petal extract is a promising reducing agent for producing antibacterial silver nanoparticles, showcasing its potential in this application.
Running crabs, walking crinoids, grazing gastropods: behavioral diversity and evolutionary implications in the Cabeço da Ladeira lagerstätte (Middle Jurassic, Portugal)

Ichnology is a powerful tool for understanding the evolutionary paths of animal clades, through the paleobiology of behavior preserved in Lagerstätten such as the Cabeço da Ladeira (Portugal) site. Here, the peritidal carbonates of the Chão das Pias Formation (Middle Jurassic, upper Bajocian) record the development of microbial mats in a tidal flat. Episodically, the flat was expanded during periods of equinoctial spring tides that may have been responsible for the in situ killing of several members of echinoderm clades, and the fine preservation of their endoskeleton, in addition to the behavior of an arthropod-dominated endoand epifauna. Mat-preserved shallow tiers with a moderate ichnodiversity attributed to the Cruziana ichnofacies include trackways of crabs (Laterigradus lusitanica igen. nov., isp. nov.) and shrimps (Diplopodichnus isp.); trails of fishes (? Undichna), gastropods (Archaeonassa fossulata), isocrinid crinoids (Krinodromos bentou igen. nov., isp. nov.) and insects (Haplotichnus indianensis); and burrows of shrimps or lobsters (Thalassinoides suevicus) and other arthropods (Asterosoma ludwigae, Gyrochorte comosa), or polychaetes (Rhizocorallium commune). This is implied already above (igen. nov., etc.) and seems not needed. The crab trackways and gastropod bulldozing trails, extending up to 12.3 m, are among the longest invertebrate cursorial traces ever found in the fossil record. The mortichnial crawling trail of a crinoid is the first evidence of locomotion for this group in the fossil record, and a rare unambiguous example of this ethology. Extensive continuous trackways attributed to amphibious crabs record, with great detail, typical gaits of underwater punting and walking sideways on dry land. The earliest evidences of sidewalking behavior may also be evidence for the development of the crab form during their rise in the Jurassic.
Calculus of Variations Regularity results and large time behavior for integro-differential equations with coercive Hamiltonians

In this paper we obtain regularity results for elliptic integro-differential equations driven by the stronger effect of coercive gradient terms. This feature allows us to construct suitable strict supersolutions from which we conclude Hölder estimates for bounded subsolutions. In many interesting situations, this gives way to a priori estimates for subsolutions. We apply this regularity results to obtain the ergodic asymptotic behavior of the associated evolution problem in the case of superlinear equations. One of the surprising features in our proof is that it avoids the key ingredient which are usually necessary to use the strong maximum principle: linearization based on the Lipschitz regularity of the solution of the ergodic problem. The proof entirely relies on the Hölder regularity. Mathematics Subject Classification 35R09 · 35B51 · 35B65 · 35D40 · 35B10 · 35B40 Communicated by L. Ambrosio. G. Barles · E. Topp Laboratoire de Mathématiques et Physique Théorique (UMR CNRS 7350), Fédération Denis Poisson (FR CNRS 2964), Université François Rabelais Tours, Parc de Grandmont, 37200 Tours, France e-mail: Guy.Barles@lmpt.univ-tours.fr S. Koike Mathematical Institute, Tohoku University, Aoba, Sendai 980-8578, Japan e-mail: koike@math.tohoku. ac.jp O. Ley IRMAR, INSA de Rennes, 35708 Rennes, France e-mail: olivier.ley@insa-rennes.fr E. Topp (B) Departamento de Ingeniería Matemática (UMI 2807 CNRS), Universidad de Chile, Casilla 170, Correo 3, Santiago, Chile e-mail: etopp@dim.uchile. cl