Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of distinct fibroblast lineages and immune cells in dermal repair following UV radiation-induced tissue damage

Solar ultraviolet radiation (UVR) is a major source of skin damage, resulting in inflammation, premature ageing and cancer. While several UVR-induced changes, including extracellular matrix reorganisation and epidermal DNA damage, have been documented, the role of different fibroblast lineages and their communication with immune cells has not been explored. We show that acute and chronic UVR exposure led to selective loss of fibroblasts from the upper dermis in human and mouse skin. Lineage tracing and in vivo live imaging revealed that repair following acute UVR is predominantly mediated by papillary fibroblast proliferation and migration. In contrast, chronic UVR exposure led to a permanent loss of papillary fibroblasts, with expansion of fibroblast membrane protrusions partially compensating for the reduction in cell number. Although UVR strongly activated Wnt-signalling in skin, stimulation of fibroblast proliferation by epidermal β-catenin stabilisation did not support papillary dermis repair. Acute UVR triggered an infiltrate of neutrophils and T cell subpopulations and increased pro-inflammatory prostaglandin signalling in skin. Depletion of CD4 and CD8 positive cells resulted in increased papillary fibroblast depletion, which correlated with an increase in DNA damage and reduction in fibroblast proliferation. Conversely, topical COX-2 inhibition prevented fibroblast depletion and neutrophil infiltration after UVR. We conclude that loss of papillary fibroblasts is primarily induced by a deregulated inflammatory response, with infiltrating T cells supporting fibroblast survival upon UVR-induced environmental stress. Graphical abstract
Point-estimating observer models for latent cause detection

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.
Optimized splitting of RNA sequencing data by species

Gene expression studies using chimeric xenograft transplants or co-culture systems have proven to be valuable to uncover cellular dynamics and interactions during development or in disease models. However, the mRNA sequence similarities among species presents a challenge for accurate transcript quantification. To identify optimal strategies for analyzing mixed-species RNA sequencing data, we evaluate both alignment-dependent and alignment-independent methods. Alignment of reads to a pooled reference index is effective, particularly if optimal alignments are used to classify sequencing reads by species, which are re-aligned with individual genomes, generating >97% accuracy across a range of species ratios. Alignment-independent methods, such as Convolutional Neural Networks, which extract the conserved patterns of sequences from two species, classify RNA sequencing reads with over 85% accuracy. Importantly, both methods perform well with different ratios of human and mouse reads. Our evaluation identifies valuable and effective strategies to dissect species composition of RNA sequencing data from mixed populations.
Probing Atmospheric Composition over Canada using Ground-based FTIR Spectroscopy

We describe the use of ground-based Fourier transform (FTIR) solar absorption spectroscopy to measure vertical columns of tropospheric and stratospheric trace gases. We focus on measurements made at the University of Toronto Atmospheric Observatory and the Polar Environment Atmospheric Research Laboratory in the high Arctic, and introduce the new Canadian FTIR Observing Network.
Oxaliplatin kills cells via liquid-liquid demixing of nucleoli

Platinum (Pt) compounds such as oxaliplatin are amongst the most commonly prescribed anti-cancer drugs. Despite their considerable clinical impact, the molecular basis of platinum cytotoxicity and cancer specificity remain unclear. Here, we show that oxaliplatin, a backbone for the treatment of colorectal cancer, causes liquid-liquid demixing of nucleoli at clinically-relevant concentrations by interfering with the interaction networks that organize nucleoli. This biophysical defect leads to cell cycle arrest, impaired rRNA processing and shutdown of PolI-mediated transcription, ultimately resulting in cell death. We propose that the mechanism of action of oxaliplatin provides a blueprint for the therapeutic targeting of the increasing number of cellular processes being linked to biomolecular condensates.
The long arm of childhood socioeconomic deprivation on mid‐ to later‐life cognitive trajectories: A cross‐cohort analysis

INTRODUCTION Earlier studies of the effects of childhood socioeconomic status (SES) on later life cognitive function consistently report a social gradient in later life cognitive function. Evidence for their effects on cognitive decline is, however, less clear. METHODS The sample consists of 5,324 participants in the Whitehall II Study, 8,572 in the Health and Retirement Study, and 1,413 in the Kame Project, who completed self-report questionnaires on their early-life experiences and underwent repeated cognitive assessments. We characterised cognitive trajectories using latent class mixed models, and explored associations between childhood SES and latent class membership using logistic regressions. RESULTS We identified distinct trajectories classes for all cognitive measures examined. Childhood socioeconomic deprivation was associated with an increased likelihood of being in a lower trajectory class. DISCUSSION Our findings support the notions that cognitive ageing is a heterogeneous process and early-life circumstances may have lasting effects on cognition across the life-course. Research in context Systematic review: We reviewed the literature on childhood socioeconomic status (SES) as a predictor for cognitive decline in mid- to later-life using PubMed. Studies generally reported lower childhood SES is associated with poorer baseline cognition, but not a faster rate of decline. These studies generally focused on the mean rate of decline in the population; no study to date has explored associations between childhood SES and different cognitive trajectories. Relevant studies have been appropriately cited. Interpretation: Our findings suggest that cognitive trajectories differ between individuals and across cognitive domains. Individuals of lower childhood SES were more likely to be in a lower cognitive trajectory class, which may or may not involve more rapid decline. Future directions: Future studies should include more cognitive outcomes and longer follow-ups, as well as investigate the impact of social mobility to further improve our understanding on how early-life circumstances influence cognitive decline.
Opposing dominance within a color gene block underpins hybrid plumage signal discordance

The divergence of plumage color genes is increasingly recognized as important for speciation in songbirds through its influence on social signaling. However, the behavioral mechanisms underlying the eco-evolutionary feedback that acts across species boundaries is poorly understood. The hybrid zone between Setophaga occidentalis (SOCC) and S. townsendi (STOW) in the Cascade Mountain range provides a natural observatory to test the interplay between genetics, plumage signals, and territoriality in maintaining nascent species boundaries. Recently, we found that selection within a gene block underpinning color variation (ASIP-RALY) has maintained a stable and narrow hybrid zone. Here we investigated the roles of cheek darkness and flank streaking as plumage signals during simulated territorial intrusion, two melanin-based traits underpinned by ASIP-RALY that reflect opposing dominance of SOCC and STOW alleles. We found that both plumage traits act as honest signals of body size in the territorial sex (i.e., males). The opposing dominance effects of ASIP-RALY resulted in plumage signal discordance for heterozygotes, which in turn was associated with reduced territorial performance, a fitness proxy quantified by vocal and physical responses to a decoy intruder. Taken together, this study highlights a behavioral mechanism underlying selection acting on a simple genetic architecture that shapes species boundary despite gene flow.
A Genome-wide CRISPR-Cas9 Screen Identifies Host Factors Essential for Optimal Plasmodium Liver Stage Development

Prior to initiating symptomatic malaria, Plasmodium parasites infect and develop within hepatocytes. We performed a forward genetic, genome-wide CRISPR-Cas9 screen to identify host regulators of Plasmodium liver infection. Single guide RNAs targeting genes involved in vesicle trafficking, cytoskeleton organization and lipid biogenesis altered Plasmodium liver development. We observed a redistribution of Golgi-derived vesicles and fragmented Golgi stacks with the parasitophorous vacuolar membrane (PVM). The host microtubule network and non-centrosomal microtubule organizing centers (ncMTOC) also re-localized following infection, closely associating with the parasite. Knocking out the centrosomal MTOC protein CENPJ exasperated the re-localization of MTOCs to the parasite and increased infection, suggesting that the parasite relies on ncMTOC assembly. Thus, we have uncovered a mechanism by which parasites sequester host material for survival and development. Our data provide a wealth of yet untested hypotheses about the elusive biology of the liver stage parasite and serves as a foundation for future investigation.
Synthesis of Self-Emulsifying Aqueous Epoxy Resin

2,2-dimethylol propionic acid was inducted to react with the secondary hydroxyl group of the bisphenol-A-type epoxy resin through toluene diisocyanate, which results in the induction of the hydrophilic carboxylic group; a self-emulsifying type aqueous epoxy resin was thus developed. The chemical structure of the resultant was analyzed by Fourier transform infrared spectroscopic(FT-IR). The effects of solvent, temperature, time, ratio of reactants on the reaction was discussed; and the optimum reaction condition was determined through orthogonal experiment design. Experiment results indicate that with acetone as solvent, at about 50 ℃ for 4.5 h, m(2, 2-dimethylol propionic acid)∶m(toluene diisocyanate)∶m(E-44)∶m(dibutyltin dilaurate)=12∶17∶100∶0.2, the aqueous epoxy resin with good water dispersibility and excellent curing performance was synthesized.
Exploration of Therapeutic Potential of Bacopa monnieri Using in silico Approach and Optimization of Culture Conditions for Bacoside Production

Bacopa monnieri plant extract has been used as a nerve tonic in ancient Indian folklore since ages and bacosides are the main key component behind neuro-potential of the herb. Accumulated β-amyloid protein causes the hyperphosphorylation of tau protein which in turn is the cause of tangled nerve projections in Alzheimer’s brain. However, the interaction of bacosides with amyloid protein has not been studied so far. Computational approach including docking analysis showed hydrogen bonding of bacoside molecule with important amino acid residues present in amyloid protein which proved the potential of bacoside A 3 in targeting Alzheimer’s. In addition to this, in vitro culture conditions have demonstrated their critical influence on secondary plant metabolism. Kinetic studies conducted in our experiment showed that cell suspension cultures of bacopa placed under shaking light conditions produce more bacoside content as compared to other conditions. Besides these properties, bacopa plants have also been known to improve the chemical characteristics of saline and clayey soils thus, enhancing the growth of agriculturally important crops. In this work, we attempted the docking of the bacoside A3 molecule to find the binding mechanism with the suitable target which will be helpful in understanding the molecular mechanism involved in the development of Alzheimer’s disease. Up-scaling of memory enhancer bacoside molecule has also been attempted under in vitro conditions that should deliver maximum benefits with minimal tissue loss during harvestation.
Electrocatalytic Oxidation of Hydrogen Peroxide at a Glassy Carbon Electrode Modified with Platinum-decorated Carbon Nanotubes

A chemical vapour deposit(CVD) method was used for decoration nano-platinum particles onto carbon nanotube surface. A glassy carbon electrode modified with platinum-decorated carbon nanotube(CNT-Pt/GCE) was prepared. The electrochemical oxidation of hydrogen peroxide(H 2O 2) was studied on this modified electrode. The rate constant k s of the electro-oxidation reaction was obtained from kinetic {equations}. The k s at CNT-Pt /GCE was 2.65 times of that at Pt electrode, which shows good catalytic {activities} for electro-oxidation of H 2O 2 in phosphate buffer medium. The mechanism of the electro-oxidation reactions was proposed. This modified electrode can be used to fabricate biosensors based on oxidase.
Reinforcement Learning for value-based Placement of Fog Services

Optimal service and resource management in Fog Computing is an active research area in academia. In fact, to fulfill the promise to enable a new generation of immersive, adaptive, and context-aware services, Fog Computing requires novel solutions capable of better exploiting the available computational and network resources at the edge. Resource management in Fog Computing could particularly benefit from self-* approaches capable of learning the best resource allocation strategies to adapt to the ever changing conditions. In this context, Reinforcement Learning (RL), a technique that allows to train software agents to learn which actions maximize a reward, represents a compelling solution to investigate. In this paper, we explore RL as an optimization method for the value-based management of Fog services over a pool of Fog nodes. More specifically, we propose FogReinForce, a solution based on Deep Q-Network (DQN) algorithm that learns to select the allocation for service components that maximizes the value-based utility provided by those services.
Guidelines for Annotating Fine-grained Emotion Trajectories in Customer Service Dialogues

Sofie Labat, Thomas Demeester and Veronique Hoste LT3 – Language and Translation Technology Team Department of Translation, Interpreting and Communication – Ghent University firstname.lastname@ugent.be All rights reserved. LT3, Faculty of Arts, Humanities and Law, Ghent University, Belgium. This research received funding from the Flemish Government under the Research Program Artificial Intelligence 174B09119.
SAXS, FESEM AND BET STUDIES OF MESOPOROUS CATALYST SBA-15CONTAINING ZINC PORPHYRIN FOR EPOXIDATION OF LIMONENE

SBA-15 nanoporous silica was prepared by cooperative self-assembly of tetraethylorthosilicate precursor in the presence of poly(ethylene glycol)–block–poly(propylene glycol)–block–poly(ethylene glycol) copolymer surfactant and functionalized with (3-aminopropyl) triethoxysilane (APTES) via sol-gel reaction to obtain NH 2 -SBA-15. The metalloporphyrin, [meso-tetrakis-(p-chlorophenyl)porphyrinato]Zn(II) (ZnTClPP) was synthesized from the reaction of meso-tetrakis-(p-chlorophenyl) porphyrin (H2TClPP) using zinc acetate dihydrate as a metal source and then immobilized on SBA-15 surface. The material, NH 2 -SBA-15-ZnP was characterized by SAXS, FESEM and BET studies and showed a similar pattern as SBA-15 indicating that the mesoporous hexagonal structure of SBA-15 was still retained. Then, the material was applied to catalyze the epoxidation of limonene, using H 2 O 2 / ammonium acetate at various temperatures and conditions. All the products formed from the epoxidation reaction were analyzed using GC-FID and GC-MS.
A Low-Rank Approximation for MDPs via Moment Coupling

Markov Decision Process Tayloring for Approximation Design Optimal control problems are difficult to solve for problems on large state spaces, calling for the development of approximate solution methods. In “A Low-rank Approximation for MDPs via Moment Coupling,” Zhang and Gurvich introduce a novel framework to approximate Markov decision processes (MDPs) that stands on two pillars: (i) state aggregation, as the algorithmic infrastructure, and (ii) central-limit-theorem-type approximations, as the mathematical underpinning. The theoretical guarantees are grounded in the approximation of the Bellman equation by a partial differential equation (PDE) where, in the spirit of the central limit theorem, the transition matrix of the controlled Markov chain is reduced to its local first and second moments. Instead of solving the PDE, the algorithm introduced in the paper constructs a “sister”' (controlled) Markov chain whose two local transition moments are approximately identical with those of the focal chain. Because of this moment matching, the original chain and its sister are coupled through the PDE, facilitating optimality guarantees. Embedded into standard soft aggregation, moment matching provides a disciplined mechanism to tune the aggregation and disaggregation probabilities.
Responses of Arctic black carbon and surface temperature to multi-region emission reductions: a Hemispheric Transport of Air Pollution Phase 2 (HTAP2) ensemble modeling study

Abstract. Black carbon (BC) emissions play an important role in regional climate change in the Arctic. It is necessary to pay attention to the impact of long-range transport from regions outside the Arctic as BC emissions from local sources in the Arctic were relatively small. The task force Hemispheric Transport of Air Pollution Phase 2 (HTAP2) set up a series of simulation scenarios to investigate the response of BC in a given region to different source regions. This study investigated the responses of Arctic BC concentrations and surface temperature to 20 % anthropogenic emission reductions from six regions in 2010 within the framework of HTAP2 based on ensemble modeling results. Emission reductions from East Asia (EAS) had the most (monthly contributions: 0.2–1.5 ng m −3 ) significant impact on the Arctic near-surface BC concentrations, while the monthly contributions from Europe (EUR), Middle East (MDE), North America (NAM), Russia–Belarus–Ukraine (RBU), and South Asia (SAS) were 0.2–1.0, 0.001–0.01, 0.1–0.3, 0.1–0.7, and 0.0–0.2 ng m −3 , respectively. The responses of the vertical profiles of the Arctic BC to the six regions were found to be different due to multiple transport pathways. Emission reductions from NAM, RBU, EUR, and EAS mainly influenced the BC concentrations in the low troposphere of the Arctic, while most of the BC in the upper troposphere of the Arctic derived from SAS. The response of the Arctic BC to emission reductions in six source regions became less significant with the increase in the latitude. The benefit of BC emission reductions in terms of slowing down surface warming in the Arctic was evaluated by using absolute regional temperature change potential (ARTP). Compared to the response of global temperature to BC emission reductions, the response of Arctic temperature was substantially more sensitive, highlighting the need for curbing global BC emissions.
Snafu: An Ultra-Low-Power, Energy-Minimal CGRA-Generation Framework and Architecture

Ultra-low-power (ULP) devices are becoming pervasive, enabling many emerging sensing applications. Energy-efficiency is paramount in these applications, as efficiency determines device lifetime in battery-powered deployments and performance in energy-harvesting deployments. Unfortunately, existing designs fall short because ASICs’ upfront costs are too high and prior ULP architectures are too inefficient or inflexible.We present Snafu, the first framework to flexibly generate ULP coarse-grain reconfigurable arrays (CGRAs). Snafu provides a standard interface for processing elements (PE), making it easy to integrate new types of PEs for new applications. Unlike prior high-performance, high-power CGRAs, Snafu is designed from the ground up to minimize energy consumption while maximizing flexibility. Snafu saves energy by configuring PEs and routers for a single operation to minimize switching activity; by minimizing buffering within the fabric; by implementing a statically routed, bufferless, multi-hop network; and by executing operations in-order to avoid expensive tag-token matching.We further present Snafu-Arch, a complete ULP system that integrates an instantiation of the Snafu fabric alongside a scalar RISC-V core and memory. We implement Snafu in RTL and evaluate it on an industrial sub-28 nm FinFET process across a suite of common sensing benchmarks. Snafu-Arch operates at <1 mW, orders-of-magnitude less power than most prior CGRAs. Snafu-Arch uses 41% less energy and runs 4.4× faster than the prior state-of-the-art general-purpose ULP architecture. Moreover, we conduct three comprehensive case-studies to quantify the cost of programmability in Snafu. We find that Snafu-Arch is close to ASIC designs built in the same technology, using just 2.6× more energy on average.
Coronavirus Pandemic Is venous thromboembolism a predictable marker in older patients with COVID-19 infection? A single-center observational study

Venous thromboembolism (VTE) represents an important clinical complication of patients with SARS-CoV-2 infection, and high plasma Ddimer levels could suggest a higher risk of hypercoagulability. We aimed to analyse if laboratory exams, risk assessment scores, comorbidity scores were useful in predicting the VTE in SARS-CoV-2 patients admitted in internal medicine (IM). We evaluated 49 older adults with suspected VTE analysing history and blood chemistry, besides we calculated the Padua Prediction Score, the modified early warning scoring (MEWS) and the modified Elixhauser index (mEI). All patients underwent venous color-doppler ultrasounds of the lower limbs. Out of the 49 patients enrolled (mean age 79.3±14 years), 10 (20.4%) had deep vein thrombosis (DVT), and they were more frequently female (80% vs 20%, p = 0.04). We could not find any association with the Padua Prediction Score, the MEWS, and the mEI. D-dimer plasma levels were also not associated with DVT. In elderly people hospitalized with SARS-CoV-2 infection hospitalized in IM, our data, although limited by the sample size, suggest that prediction and diagnosis of VTE is difficult, due to lack of precise biomarkers and scores.