Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of the housing crisis on self-reported health in Europe: multilevel longitudinal modelling of 27 EU countries

Background: Many EU nations experienced a significant housing crisis during the Great Recession of 2008–10. We evaluated the consequences of housing payment problems for people’s self-reported overall health. Methods: We used longitudinal data from the EU Statistics on Income and Living Conditions survey covering 27 countries from 2008 to 2010 to follow a baseline sample of persons who did not have housing debt and who were employed (45 457 persons, 136 371 person–years). Multivariate linear regression and multilevel models were used to evaluate the impact of transitions into housing arrears on self-reported health, correcting for the presence of chronic illness, health limitations, and other potential socio-demographic confounders. Results: Persons who transitioned into housing arrears experienced a significant deterioration in self-reported overall health by − 0.03 U (95% CI − 0.01 to − 0.04), even after correcting for chronic illness, disposable income and employment status, and individual fixed effects. This association was independent and similar in magnitude to that for job loss (−0.02, 95% CI: −0.01 to − 0.04). We also found that the impact of housing arrears was significantly worse among renters, corresponding to a mean 0.11 unit additional drop in health as compared with owner-occupiers. These adverse associations were only evident in persons below the 75th percentile of disposable income. Discussion: Our analysis demonstrates that persons who suffer housing arrears experience increased risk of worsening self-reported health, especially among those who rent. Future research is needed to understand the role of alternative housing support systems and available strategies for preventing the health consequences of housing insecurity.
Sex, germs, and health: pathogen-avoidance motives and health-protective behaviour

Objective: Recent work suggests that the psychology of pathogen-avoidance has wide-reaching effects on how people interact with the world. These processes – part of what has been referred to as the behavioural immune system – are, in a way, our ‘evolved’ health psychology. However, scholars have scarcely investigated how the behavioural immune system relates to health-protective behaviours. The current research attempts to fill this gap. Design: Across two cross-sectional studies (N = 386 and 470, respectively), we examined the relationship between pathogen-avoidance motives and health-protective behaviour. Outcome Measures: The studies used self-reported measures of attitude and intention as indicators of health-protective behaviour. Results: Data collected in Study 1 revealed that pathogen-avoidance motivation related to participants’ attitude and intention towards sexually transmitted infections screening. High levels of pathogen-avoidance motivation were also related to having had fewer sexual partners, which partially mediated the effect of pathogen-avoidance variables on testing motivation. Study 2 extended these findings by showing moderate associations between pathogen-avoidance motivation and a broad range of health-protective behaviours, including but not limited to pathogen-related health concerns. Conclusion: We argue that understanding and targeting pathogen-avoidance psychology can add novel and important understanding of health-protective behaviour.
Draft genome of the Peruvian scallop Argopecten purpuratus

Abstract Background The Peruvian scallop, Argopecten purpuratus, is mainly cultured in southern Chile and Peru was introduced into China in the last century. Unlike other Argopecten scallops, the Peruvian scallop normally has a long life span of up to 7 to 10 years. Therefore, researchers have been using it to develop hybrid vigor. Here, we performed whole genome sequencing, assembly, and gene annotation of the Peruvian scallop, with an important aim to develop genomic resources for genetic breeding in scallops. Findings A total of 463.19-Gb raw DNA reads were sequenced. A draft genome assembly of 724.78 Mb was generated (accounting for 81.87% of the estimated genome size of 885.29 Mb), with a contig N50 size of 80.11 kb and a scaffold N50 size of 1.02 Mb. Repeat sequences were calculated to reach 33.74% of the whole genome, and 26,256 protein-coding genes and 3,057 noncoding RNAs were predicted from the assembly. Conclusions We generated a high-quality draft genome assembly of the Peruvian scallop, which will provide a solid resource for further genetic breeding and for the analysis of the evolutionary history of this economically important scallop.
Support Vector Machine Based Robotic Traversability Prediction with Vision Features

Abstract This paper presents a novel method on building relationship between the vision features of the terrain images and the terrain traversability which manifests the difficulty of field robot traveling across one terrain. Vision features of the image are extracted based on color and texture. The travesability is labeled with the relative vibration. The support vector machine regression method is adopted to build up the inner relationship between them. In order to avoid the over-learning during training, k-fold method is used and average mean square error is defined as the target minimized to get the optimal parameters based on parameter space grid method. For the traveling smoothness of field robot, the original traversability prediction is transformed to computed traversability prediction based on different initial sub-regions. The optimal path is given by minimizing the sum of computed traversability prediction of all sub-regions in each path. Three experiments are discussed to demonstrate the effec.
Modeling surface appearance from a single photograph using self-augmented convolutional neural networks

We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.
The Race To Find Antivirals for Zika Virus

ABSTRACT Zika virus (ZIKV), a flavivirus transmitted by mosquitoes, was an almost neglected pathogen until its introduction in the Americas in 2015 and its subsequent explosive spread throughout the continent, where it has infected millions of people. The virus has caused social and sanitary alarm, mainly due to its association with severe neurological disorders (Guillain-Barré syndrome and microcephaly in fetuses and newborns). Nowadays, no specific antiviral therapy against ZIKV is available. However, during the past months, a great effort has been made to search for antiviral candidates using different approaches and methodologies, ranging from testing specific compounds with known antiviral activity to the screening of libraries with hundreds of bioactive molecules. The identified antiviral candidates include drugs targeting viral components as well as cellular ones. Here, an updated review of what has been done in this line is presented.
Evaluation of Knowledge Management Tools by Using An Interval Type-2 Fuzzy TOPSIS Method

AbstractKnowledge management (KM) systems can provide businesses a wide range of advantages and efficiency improvements. Increasing competition forces companies to seek new ways to streamline their processes and manage their information and knowledge better, leading to increased demand for KM solutions. Considering various needs of organizations and diverse features of available KM alternatives, choosing the most suitable KM tool is an important decision for businesses. The contribution of this paper to the KM literature is a KM evaluation framework for decision makers to compare available KM products of different vendors by first identifying relevant evaluation criteria and then proposing a group decision making framework using the Interval Type-2 TOPSIS technique. This method has more flexibility in handling uncertainties compared to the Type-1 fuzzy sets and enables decision makers to effectively analyze, compare and select the most appropriate KM tools. The framework is also used in a case study for t.
Maximum Principles for Forward-Backward Stochastic Control Systems with Correlated State and Observation Noises

In this paper, we study a partial information optimal control problem derived by forward-backward stochastic systems with correlated noises between the system and the observation. Utilizing a direct method, an approximation method, and a Malliavin derivative method, we establish three versions of maximum principle (i.e., necessary condition) for optimal control. To show their applications, we work out two illustrative examples within the frameworks of linear-quadratic control and recursive utility and then solve them via the maximum principles and stochastic filtering.
Nitrated fatty acids suppress angiotensin II-mediated fibrotic remodelling and atrial fibrillation.

AIM Atrial fibrosis, one of the most striking features in the pathology of atrial fibrillation (AF), is promoted by local and systemic inflammation. Electrophilic fatty acid nitroalkenes, endogenously generated by both metabolic and inflammatory reactions, are anti-inflammatory mediators that in synthetic form may be useful as drug candidates. Herein we investigate whether an exemplary nitro-fatty acid can limit atrial fibrosis and AF. METHODS AND RESULTS Wild-type C57BL6/J mice were treated for 2 weeks with angiotensin II (AngII) and vehicle or nitro-oleic acid (10-nitro-octadec-9-enoic acid, OA-NO2, 6 mg/kg body weight) via subcutaneous osmotic minipumps. OA-NO2 significantly inhibited atrial fibrosis and depressed vulnerability for AF during right atrial electrophysiological stimulation to levels observed for AngII-naive animals. Left atrial epicardial mapping studies demonstrated preservation of conduction homogeneity by OA-NO2. The protection from fibrotic remodelling was mediated by suppression of Smad2-dependent myofibroblast transdifferentiation and inhibition of Nox2-dependent atrial superoxide formation. CONCLUSION OA-NO2 potently inhibits atrial fibrosis and subsequent AF. Nitro-fatty acids and possibly other lipid electrophiles thus emerge as potential therapeutic agents for AF, either by increasing endogenous levels through dietary modulation or by administration as synthetic drugs.
Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent “conceptual worlds” that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering “approximation” methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds.
Feature extraction using convolutional neural network for classifying breast density in mammographic images

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is still required for evaluating the results.
TLR2 Signaling is Required for the Innate, but Not Adaptive Response to LVS clpB

Toll-like receptor 2 (TLR2) is the best-characterized pattern-recognition receptor for the highly pathogenic intracellular bacterium, Francisella tularensis. We previously identified a mutant in the live vaccine strain (LVS) of Francisella, LVS clpB, which is attenuated, but induces a protective immune response. We sought to determine whether TLR2 signaling was required during the immune response to LVS clpB. TLR2 knock-out (TLR2 KO) mice previously infected with LVS clpB are completely protected during a lethal challenge with LVS. Furthermore, the kinetics and magnitude of the primary T-cell response in B6 and TLR2 KO mice are similar indicating that TLR2 signaling is dispensable for the adaptive immune response to LVS clpB. TLR2 signaling was important, however, for the innate immune response to LVS clpB. We identified three classes of cytokines/chemokines that differ in their dependence on TLR2 signaling for production on day 3 post-inoculation in the bronchoalveolar lavage fluid. IL-1α, IL-1β, IL-2, IL-17, MIP-1α, and TNF-α production depended on TLR2 signaling, while GM-CSF, IFN-γ, and VEGF production were completely independent of TLR2 signaling. IL-6, IL-12, IP-10, KC, and MIG production were partially dependent on TLR2 signaling. Together our data indicate that the innate immune response to LVS clpB requires TLR2 signaling for the maximal innate response, whereas TLR2 is not required for the adaptive immune response.
A weighted U statistic for association analyses considering genetic heterogeneity

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity‐weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high‐dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome‐wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome‐wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd.
HOS1 acts as a key modulator of hypocotyl photomorphogenesis

ABSTRACT Plants recognize light as an environmental signal to determine the proper timing of growth and development. In Arabidopsis seedlings, hypocotyl growth is promoted in the dark but suppressed in the light. It is known that the red/far-red light-sensing receptor phytochrome B (phyB) suppresses the function of PHYTOCHROME INTERACTING FACTOR (PIF) transcription factors, which act as photomorphogenic repressors. However, molecular mechanisms underlying the phyB-mediated inhibition of PIF functioning remain unclear. We recently demonstrated that HIGH EXPRESSION OF OSMOTICALLY RESPONSIVE GENES 1 (HOS1) facilitates the phyB-mediated suppression of PIF4 during the light period to achieve hypocotyl photomorphogenesis. HOS1 inhibits the transcriptional activation activity of PIF4 by forming protein complexes. Notably, phyB-mediated light signals induce HOS1 activity, thus promoting hypocotyl photomorphogenesis. While HOS1 is known to act as an E3 ubiquitin ligase or a chromatin remodeling factor, our data illustrate a novel role of HOS1: it acts as a component of phyB-mediated light signaling in hypocotyl photomorphogenesis.
Application of Artificial Capital Market in Task Allocation in Multi-robot Foraging

AbstractBecause of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.
Indoor shadow detection for video segmentation

To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then, a Canny edge map is generated. After that, the shadow region is detected and removed through multi-frame integration, edge matching and region growing. Finally, a post-processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for removing moving cast shadows in indoor environments for better video segmentation. The experimental results demonstrate the good performance of our algorithm.
Mycobacterium tuberculosis Proteome Response to Antituberculosis Compounds Reveals Metabolic “Escape” Pathways That Prolong Bacterial Survival

ABSTRACT Tuberculosis (TB) continues to be one of the most common bacterial infectious diseases and is the leading cause of death in many parts of the world. A major limitation of TB therapy is slow killing of the infecting organism, increasing the risk for the development of a tolerance phenotype and drug resistance. Studies indicate that Mycobacterium tuberculosis takes several days to be killed upon treatment with lethal concentrations of antibiotics both in vitro and in vivo. To investigate how metabolic remodeling can enable transient bacterial survival during exposure to bactericidal concentrations of compounds, M. tuberculosis strain H37Rv was exposed to twice the MIC of isoniazid, rifampin, moxifloxacin, mefloquine, or bedaquiline for 24 h, 48 h, 4 days, and 6 days, and the bacterial proteomic response was analyzed using quantitative shotgun mass spectrometry. Numerous sets of de novo bacterial proteins were identified over the 6-day treatment. Network analysis and comparisons between the drug treatment groups revealed several shared sets of predominant proteins and enzymes simultaneously belonging to a number of diverse pathways. Overexpression of some of these proteins in the nonpathogenic Mycobacterium smegmatis extended bacterial survival upon exposure to bactericidal concentrations of antimicrobials, and inactivation of some proteins in M. tuberculosis prevented the pathogen from escaping the fast killing in vitro and in macrophages, as well. Our biology-driven approach identified promising bacterial metabolic pathways and enzymes that might be targeted by novel drugs to reduce the length of tuberculosis therapy.
RNA activation‐independent DNA targeting of the Type III CRISPR‐Cas system by a Csm complex

The CRISPR‐Cas system is an adaptive and heritable immune response that destroys invading foreign nucleic acids. The effector complex of the Type III CRISPR‐Cas system targets RNA and DNA in a transcription‐coupled manner, but the exact mechanism of DNA targeting by this complex remains elusive. In this study, an effector Csm holocomplex derived from Thermococcus onnurineus is reconstituted with a minimalistic combination of Csm1121334151, and shows RNA targeting and RNA‐activated single‐stranded DNA (ssDNA) targeting activities. Unexpectedly, in the absence of an RNA transcript, it cleaves ssDNA containing a sequence complementary to the bound crRNA guide region in a manner dependent on the HD domain of the Csm1 subunit. This nuclease activity is blocked by a repeat tag found in the host CRISPR loci. The specific cleavage of ssDNA without a target RNA suggests a novel ssDNA targeting mechanism of the Type III system, which could facilitate the efficient and complete degradation of foreign nucleic acids.