Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of occupational e-mental health interventions: a systematic review and meta-analysis of randomized controlled trials.

Objective This systematic review and meta-analysis investigated the effectiveness of occupational e-mental health interventions aimed at stress, depression, anxiety, burnout, insomnia, mindfulness, well-being, and alcohol misuse and their potential treatment moderators. Methods We systematically reviewed randomized control trials published in English using three electronic databases (MEDLINE, PsycINFO, CINAHL) and three register trials. A pooled effect size for each mental health area was calculated using random-effects modelling. For each meta-analysis, we conducted an analysis of potential moderators (ie, type of recruitment, age, gender, initial psychological symptoms, guidance, therapy type, and study quality). Results In total, 51 studies were included in the systematic review, and 36 studies were included in the meta-analyses. We noted moderate treatment effects on stress (Hedges'g=0.54), insomnia (g=0.70), and burnout (g=0.51) and small treatment effects on depression (g=0.30), anxiety (g=0.34), well-being (g=0.35), and mindfulness (g=0.42). The pooled effect on alcohol intake was small and nonsignificant. Conclusions Our results suggest that occupational e-mental health interventions are associated with significant health improvements. However, more research is required to understand which factors contribute to the variation in effectiveness of particular interventions depending on the mental health area and characteristics of participants and interventions.
Forest management optimization across spatial scales to reconcile economic and conservation objectives

Conflicts between biodiversity conservation and resource production can be mitigated by multi-objective management planning. Optimizing management for multiple objectives over larger land areas likely entails trading off the practicability of the process against the goodness of the solution. It is therefore worthwhile to resolve how large areas are required as management planning regions to reconcile conflicting objectives as effectively as possible. We aimed to reveal how the extent of forestry planning regions impacts the potential to mitigate a forestry-conservation conflict in Finland, represented as a trade-off between harvest income and deadwood availability. We used forecasted data from a forest simulator, a hierarchy of forestry planning regions, and an optimization model to explore the production possibility frontier between harvest income and deadwood. We compared the overall outcomes when management was optimized within the different-sized planning regions in terms of the two objectives, the spatial variation of deadwood, and the optimal combinations of management regimes. Increasing the size of the planning regions did produce higher simultaneous levels of the two objectives, but the differences were most often of the magnitude of only a few percentages. The differences among the scales were minor also in terms of the spatial variation in deadwood availability and in the optimal management combinations. The conflict between timber harvesting and deadwood availability is only marginally easier to mitigate at large spatial scales than at small forest ownership scales. However, regardless of the spatial scale of planning, the achievable solutions may not be good enough to safeguard deadwood-dependent biodiversity without active deadwood creation.
Precision Interface Engineering of an Atomic Layer in Bulk Bi2Te3 Alloys for High Thermoelectric Performance.

Grafting nanotechnology on thermoelectric materials leads to significant advances in their performance. Creation of structural defects including nano-inclusion and interfaces via nanostructuring achieves higher thermoelectric efficiencies. However, it is still challenging to optimize the nanostructure via conventional fabrication techniques. The thermal instability of nanostructures remains an issue in the reproducibility of fabrication processes and long-term stability during operation. This work presents a versatile strategy to create numerous interfaces in a thermoelectric material via an atomic-layer deposition (ALD) technique. An extremely thin ZnO layer was conformally formed via ALD over the Bi0.4Sb1. 6Te3 powders, and numerous heterogeneous interfaces were generated from the formation of Bi0.4Sb1. 6Te3-ZnO core-shell structures even after high-temperature sintering. The incorporation of ALD-grown ZnO into the Bi0.4Sb1. 6Te3 matrix blocks phonon propagation and also provides tunability in electronic carrier density via impurity doping at the heterogeneous grain boundaries. The exquisite control in the ALD cycles provides a high thermoelectric performance of zT = 1.50 ± 0.15 (at 329-360 K). Specifically, ALD is an industry compatible technique that allows uniform and conformal coating over large quantities of powders. The study is promising in terms of the mass production of nanostructured thermoelectric materials with considerable improvements in performance via an industry compatible and reproducible route.
Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn’s disease, produces an inflammatory polysaccharide

Significance The bacteria that live within the human gut play crucial roles in regulating our primary metabolism, protecting us from pathogens, and developing our immune system. Imbalances in bacterial community structure have been implicated in many diseases, such as Crohn’s disease, an inflammatory bowel disease. We found and characterized an inflammatory polysaccharide produced by the gut bacterium Ruminococcus gnavus, populations of which bloom during flares of symptoms in patients with Crohn’s disease. This molecule induces the production of inflammatory cytokines like TNFα by dendritic cells and may contribute to the association between R. gnavus and Crohn’s disease. This work establishes a plausible molecular mechanism that may explain the association between a member of the gut microbiome and an inflammatory disease. A substantial and increasing number of human diseases are associated with changes in the gut microbiota, and discovering the molecules and mechanisms underlying these associations represents a major research goal. Multiple studies associate Ruminococcus gnavus, a prevalent gut microbe, with Crohn’s disease, a major type of inflammatory bowel disease. We have found that R. gnavus synthesizes and secretes a complex glucorhamnan polysaccharide with a rhamnose backbone and glucose sidechains. Chemical and spectroscopic studies indicated that the glucorhamnan was largely a repeating unit of five sugars with a linear backbone formed from three rhamnose units and a short sidechain composed of two glucose units. The rhamnose backbone is made from 1,2- and 1,3-linked rhamnose units, and the sidechain has a terminal glucose linked to a 1,6-glucose. This glucorhamnan potently induces inflammatory cytokine (TNFα) secretion by dendritic cells, and TNFα secretion is dependent on toll-like receptor 4 (TLR4). We also identify a putative biosynthetic gene cluster for this molecule, which has the four biosynthetic genes needed to convert glucose to rhamnose and the five glycosyl transferases needed to build the repeating pentasaccharide unit of the inflammatory glucorhamnan.
Comparison of Proliferation and Differentiation of Human Osteoblast-like Cells Harvested During Implant Osteotomy Preparation Using Two Different Drilling Protocols.

PURPOSE Autogenous bone grafts are considered a "gold standard. " The success of autografts mainly depends on their ability to promote an osteogenic response. The aim of this study was to collect autogenous bone during implant osteotomy preparation using two different drilling protocols and to evaluate and compare the proliferation and differentiation ability of the collected bone particles. MATERIALS AND METHODS Autogenous bone particles were harvested from 20 patients during implant osteotomy preparation using two different drilling protocols: (1) standard drilling protocol with saline irrigation (according to the manufacturer's recommendation) and (2) low-speed drilling protocol without saline irrigation (speed < 200 rpm). Bone samples collected were cultured in growth medium, and after 2 to 3 weeks, cells that grew out from bone grafts were cultured in the normal medium as well as in osteogenic medium for days 0, 4, 7, and 20. Scanning electron microscopy, alizarin red/toluidine blue staining, DNA, ALP, and calcium content measurements were performed. Repeated measures analysis of variance (ANOVA) with Bonferroni's test was employed to analyze the data of this study. RESULTS The total DNA content was significantly higher for the low-speed drilling samples compared with the standard drilling on day 4 (P < .05), day 7 (P < .01), and day 20 (P < .001) in the normal medium and on day 7 (P < .01) and day 20 (P < .01) in the osteogenic medium. Besides, calcium measurements and mineralized matrix formation observed with alizarin red/toluidine blue staining were significantly higher for the low-speed drilling group compared with the standard drilling group. CONCLUSION Osteogenic efficacy (differentiation and proliferation) of autogenous bone particles collected using low-speed drilling was superior compared with standard drilling samples.
TYR Gene in Llamas: Polymorphisms and Expression Study in Different Color Phenotypes

Tyrosinase, encoded by TYR gene, is an enzyme that plays a major role in mammalian pigmentation. It catalyzes the oxidation of L-dihydroxy-phenylalanine (DOPA) to DOPA quinone, a precursor of both types of melanin: eumelanin and pheomelanin. TYR is commonly known as the albino locus since mutations in this gene result in albinism in several species. However, many other TYR mutations have been found to cause diluted phenotypes, like the Himalayan or chinchilla phenotypes in mice. The llama (Lama glama) presents a wide variety of coat colors ranging from non-diluted phenotypes (eumelanic and pheomelanic), through different degrees of dilution, to white. To investigate the possible contribution of TYR gene to coat color variation in llamas, we sequenced TYR exons and their flanking regions and genotyped animals with diluted, non-diluted, and white coat, including three blue-eyed white individuals. Moreover, we analyzed mRNA expression levels in skin biopsies by qPCR. TYR coding region presented nine SNPs, of which three were non-synonymous, c.428A > G, c.859G > T, and c.1490G > T. We also identified seven polymorphisms in non-coding regions, including two microsatellites, an homopolymeric repeat, and five SNPs: one in the promoter region (c.1-26C > T), two in the 3′-UTR, and two flanking the exons. Although no complete association was found between coat color and SNPs, c.1-26C > T was partially associated to diluted phenotypes. Additionally, the frequency of the G allele from c.428A > G was significantly higher in white compared to non-diluted. Results from qPCR showed that expression levels of TYR in white llamas were significantly lower (p < 0.05) than those in diluted and non-diluted phenotypes. Screening for variation in regulatory regions of TYR did not reveal polymorphisms that explain such differences. However, data from this study showed that TYR expression levels play a role in llama pigmentation.
Rethinking Generative Coverage: A Pointwise Guaranteed Approach

All generative models have to combat missing modes. The conventional wisdom is by reducing a statistical distance (such as f-divergence) between the generated distribution and the provided data distribution through training. We defy this wisdom. We show that even a small statistical distance does not imply a plausible mode coverage, because this distance measures a global similarity between two distributions, but not their similarity in local regions--which is needed to ensure a complete mode coverage. From a starkly different perspective, we view the battle against missing modes as a two-player game, between a player choosing a data point and an adversary choosing a generator aiming to cover that data point. Enlightened by von Neumann's minimax theorem, we see that if a generative model can approximate a data distribution moderately well under a global statistical distance measure, then we should be able to find a mixture of generators which collectively covers every data point and thus every mode with a lower-bounded probability density. A constructive realization of this minimax duality--that is, our proposed algorithm of finding the mixture of generators--is connected to a multiplicative weights update rule. We prove the pointwise coverage guarantee of our algorithm, and our experiments on real and synthetic data confirm better mode coverage over recent approaches that also use a mixture of generators but focus on global statistical distances.
Modeling the Past and Future Contexts for Session-based Recommendation

Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, previous sequential recommendation models resort either to data augmentation techniques or a left-to-right style autoregressive training approach. While effective, an obvious drawback is that future user behaviors are always missing during model training. In this paper, we argue that users' future action signals can be exploited to boost the recommendation quality. We present GRec, a simple Gap-filling based encoder-decoder Recommendation framework to generative modelling using both past and future contexts. GfedRec encodes a partially-complete item sequence with blank masks, and autoregressively reconstructs the missing item distributions. In contrast with the typical encoder-decoder paradigm used in the computer vision and NLP domains, GfedRec does not have the data leakage problem when jointly training the encoder and decoder conditioned on the same user action sequence. Experiments on real-word datasets with short-, medium- and long-range user sessions show that GRec largely exceeds the performance of its left-to-right counterparts. Empirical evidence confirms that training sequential recommendation models with future contexts is a promising way to offer better recommendation accuracy.
Adaptive Neural Signal Detection for Massive MIMO

Traditional symbol detection algorithms either perform poorly or are impractical to implement for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning-based approaches have achieved promising results on simple channel models (e.g., i.i.d. Gaussian channel coefficients), but as we show, their performance degrades on real-world channels with spatial correlation. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet ’s design builds on the theory of iterative soft-thresholding algorithms, and uses a novel training algorithm that leverages temporal and spectral correlation in real channels to accelerate training. These innovations make it practical to train MMNet online for every realization of the channel. On i.i. d. Gaussian channels, MMNet requires two orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatially-correlated channels, it achieves the same error rate as the next-best learning scheme (OAMPNet) at 2.5dB lower signal-to-noise ratio (SNR), and with at least $10\times $ less computational complexity. MMNet is also 4–8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.
Micronutrient Requirements and Sharing Capabilities of the Human Gut Microbiome

The human gut microbiome harbors a diverse array of metabolic pathways contributing to its development and homeostasis via a complex web of diet-dependent metabolic interactions within the microbial community and host. Genomics-based reconstruction and predictive modeling of these interactions would provide a framework for diagnostics and treatment of dysbiosis-related syndromes via rational selection of therapeutic prebiotics and dietary nutrients. Of particular interest are micronutrients, such as B-group vitamins, precursors of indispensable metabolic cofactors, that are produced de novo by some gut bacteria (prototrophs) but must be provided exogenously in the diet for many other bacterial species (auxotrophs) as well as for the mammalian host. Cross-feeding of B vitamins between prototrophic and auxotrophic species is expected to strongly contribute to the homeostasis of microbial communities in the distal gut given the efficient absorption of dietary vitamins in the upper gastrointestinal tract. To confidently estimate the balance of microbiome micronutrient biosynthetic capabilities and requirements using available genomic data, we have performed a subsystems-based reconstruction of biogenesis, salvage and uptake for eight B vitamins (B1, B2, B3, B5, B6, B7, B9, and B12) and queuosine (essential factor in tRNA modification) over a reference set of 2,228 bacterial genomes representing 690 cultured species of the human gastrointestinal microbiota. This allowed us to classify the studied organisms with respect to their pathway variants and infer their prototrophic vs. auxotrophic phenotypes. In addition to canonical vitamin pathways, several conserved partial pathways were identified pointing to alternative routes of syntrophic metabolism and expanding a microbial vitamin “menu” by several pathway intermediates (vitamers) such as thiazole, quinolinate, dethiobiotin, pantoate. A cross-species comparison was applied to assess the extent of conservation of vitamin phenotypes at distinct taxonomic levels (from strains to families). The obtained reference collection combined with 16S rRNA gene-based phylogenetic profiles was used to deduce phenotype profiles of the human gut microbiota across in two large cohorts. This analysis provided the first estimate of B-vitamin requirements, production and sharing capabilities in the human gut microbiome establishing predictive phenotype profiling as a new approach to classification of microbiome samples. Future expansion of our reference genomic collection of metabolic phenotypes will allow further improvement in coverage and accuracy of predictive phenotype profiling of the human microbiome.
A Massive Magnetic Helium Atmosphere White Dwarf Binary in a Young Star Cluster

We have searched the Gaia DR2 catalog for previously unknown hot white dwarfs in the direction of young open star clusters. The aim of this experiment was to try and extend the initial–final mass relation (IFMR) to somewhat higher masses, potentially challenging the Chandrasekhar limit currently thought to be around 1.38 M⊙. We discovered a particularly interesting white dwarf in the direction of the young ∼150 Myr old cluster Messier 47 (NGC 2422). All Gaia indicators (proper motion, parallax, location in the Gaia color–magnitude diagram) suggest that it is a cluster member. Its spectrum, obtained from Gemini-South, yields a number of anomalies: it is a DB (helium-rich atmosphere) white dwarf, it has a large magnetic field (2.5 MG), is of high mass (∼1.06 M⊙), and its colors are very peculiar—particularly the redder ones (r, i, z and y), which suggests that it may have a late-type companion. This may be the only magnetized, detached binary white dwarf with a non-degenerate companion of any spectral type known in or out of a star cluster. If the white dwarf is a cluster member, as all indicators suggest, its progenitor had a mass just over 6 M⊙. It may, however, be telling an even more interesting story than the one related to the IFMR, one about the origin of stellar magnetic fields, SNe I, and gravitational waves from low-mass stellar systems.
Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models

The neurons of the olivocerebellar circuit exhibit complex electroresponsive dynamics, which are thought to play a fundamental role for network entraining, plasticity induction, signal processing, and noise filtering. In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input–output relationships. In this way, E-GLIF was tuned toward the unique input–output properties of Golgi cells, granule cells, Purkinje cells, molecular layer interneurons, deep cerebellar nuclei cells, and inferior olivary cells. E-GLIF proved able to simulate the complex cell-specific electroresponsive dynamics of the main olivocerebellar neurons including pacemaking, adaptation, bursting, post-inhibitory rebound excitation, subthreshold oscillations, resonance, and phase reset. The integration of these E-GLIF point-neuron models into olivocerebellar Spiking Neural Networks will allow to evaluate the impact of complex electroresponsive dynamics at the higher scales, up to motor behavior, in closed-loop simulations of sensorimotor tasks.
Acidity Drop and Coloration in Clementine: Implications for Fruit Quality and Harvesting Practices

The commercial quality of fruit is the result of a combination of internal (acidity, sugars, juice, etc.) and external characteristics (shape, size, color, visual defects, etc.). On citrus, the internal maturity of fruit is often reached prior and independently to their external maturity, inducing the use of degreening practices to artificially color fruit. However, for some sectors where degreening is not authorized, such as organic farming or up-market, it is important to understand the co-occurrence between fruit coloration and internal ripening, and its impact on fruit quality and harvesting management. Our study was based on a monitoring of the color and acidity of Protected Geographical Indication “Clémentine de Corse” orchards of producers in 2013 and 2014. Our results show that: (i) the dynamics of acidity drop during maturation are similar from one plot to another but staggered in time; (ii) fruit coloring occurs at different times during acidity drop; (iii) the synchronization between the coloring process and acidity drop determines both the quality of harvested fruit and the period during which orchards are harvestable, which we called the “harvestability window. ” This study sheds new light on the quality of citrus harvested without fruit degreening and leads to propose actions to anticipate internal maturity evolution according to the coloring and spreading of the harvest period. The fruit acidity model obtained in this study will be extended to a practical application tool to predict fruit acidity and quality for a better-controlled harvest management.
SCGDet: Malware Detection using Semantic Features Based on Reachability Relation

Recently, with the booming development of software industry, more and more malware variants are designed to perform malicious behaviors. The evolution of malware makes it difficult to detect using traditional signature-based methods. Moreover, malware detection has important effect on system security. In this paper, we present SCGDet, which is a novel malware detection method based on system call graph model (SCGM). We first develop a system call pruning method, which can exclude system calls that have little impact on malware detection. Then we propose the SCGM, which can capture the semantic features of run-time program by grouping the system calls based on the reachability relation. We aim to obtain the generic representation of malicious behaviors with similar system call patterns. We evaluate the performance of SCGDet using different machine learning algorithms on the dataset including 854 malware samples and 740 benign samples. Compared with the traditional n-gram method, the SCGDet has the smaller feature space, the higher detection accuracy and the lower false positives. Experimental results show that SCGDet can reduce the average FPR of 14.75% and improve the average Accuracy of 8.887%, and can obtain a TPR of 97.44%, an FPR of 1.96% and an Accuracy of 97.78% in the best case.