Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Growth on Dust-Loaded Filtering Materials Used for the Protection of Respiratory Tract as a Factor Affecting Filtration Efficiency

This work aims at understanding the effects of various dust-loading conditions and the type of nonwovens used in the construction of FFRs on the safe use of those protective devices in situations of exposure to biological agents. The survival of microorganisms (Escherichia coli, Candida albicans, and Aspergillus niger) in dust-loaded polypropylene nonwovens (melt-blown, spun-bonded, and needle-punched) was experimentally determined using microbiological quantitative method (AATCC TM 100-2004). Scanning electron microscope was used to assess biofilm formation on dust-loaded filtering nonwovens. The impact of the growth of microorganisms on filtration efficiency of nonwovens was analysed based on the measurements of penetration of sodium chloride particles (size range 7–270 nm). Results showed that tested microorganisms were able to survive on dust-loaded polypropylene filtering nonwovens. The survival rate of microorganisms and penetration of nanoparticles and submicron particles depended on the type of microorganism, as well as the type and the amount of dust, which indicates that both of those factors should be considered for FFR use recommendations.
Identification and Quantification of Volatile Ramson-Derived Metabolites in Humans

Ramson (Allium ursinum) is known for its typical garlic-like aroma. Both ramson and garlic belong to the genus allium which is characterized by a high content of sulfurous compounds. However, in contrast to garlic, ramson is in general not associated with an unpleasant breath following consumption. While there is data available regarding the metabolism of volatile garlic constituents in the human body, the metabolism of ramson was not yet addressed. To elucidate if ramson has an impact on the body odor, this study aimed at identifying volatile ramson-derived metabolites in human milk and urine. Therefore, milk and urine samples were gathered before and after ramson consumption, and were analyzed sensorially by a trained human sensory panel as well as chemo-analytically applying gas chromatography-mass spectrometry/olfactometry (GC-MS/O). Sensory evaluation revealed a garlic-/cabbage like odor in milk samples obtained after ramson consumption, demonstrating that ramson consumption affected the milk aroma. Analyzes by means of GC-MS/O further confirmed excretion of three ramson-derived metabolites in milk and urine samples collected after ramson consumption, namely allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO) and allyl methyl sulfone (AMSO2). Of these metabolites only AMS had a garlic-/cabbage-like odor, while the other two were odorless. These metabolites were subsequently quantified using stable isotope dilution assays. Nine urine sets, each comprising eight urine samples, and nine milk sets, each comprising four samples, were analyzed. In case of the urine sets a time interval of about 24 h was monitored, in case of the milk sets a time interval of up to 9 h. Despite the fact that all samples contained the same metabolites there were relevant differences found between individual subjects, especially with regard to the temporal rate of metabolite excretion. Generally, the maxima of metabolite excretion were observed in milk sets within 3 h after ramson consumption. In urine the highest AMS and AMSO amounts were observed within 2 h whereas the maximum concentration of AMSO2 was reached about 2 to 4 h after ramson ingestion. This study suggests that ramson constituents are heavily metabolized in the human body.
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations

Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.
A Neural Temporal Model for Human Motion Prediction

We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation. Key aspects of our proposed system include: 1) a novel, two-level processing architecture that aids in generating planned trajectories, 2) a simple set of easily computable features that integrate derivative information, and 3) a novel multi-objective loss function that helps the model to slowly progress from simple next-step prediction to the harder task of multi-step, closed-loop prediction. Our results demonstrate that these innovations improve the modeling of long-term motion trajectories. Finally, we propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to evaluate the long-term predictive ability of motion synthesis models, complementing the popular mean-squared error (MSE) measure of Euler joint angles over time. We conduct a user study to determine if the proposed NPSS correlates with human evaluation of long-term motion more strongly than MSE and find that it indeed does. We release code and additional results (visualizations) for this paper at: https://github.com/cr7anand/neural_temporal_models
Self-stigma of antidepressant users through secondary analysis of PhotoVoice data

Introduction: Aspects of self-stigma and medication-related stigma among individuals with depressive disorders remain largely unexplored. The primary objective of this study is to highlight and characterize self-stigma and medication-related stigma experiences of antidepressant users. Methods: This is a secondary analysis of data obtained from PhotoVoice studies examining psychotropic medication experiences. Transcripts of reflections from 12 individuals self-reporting a depressive disorder diagnosis and receipt of a prescription for an antidepressant were included. A directed content analysis approach based on expansion of the Self-Stigma of Depression Scale and an iterative process of identification of medication-stigma and stigma-resistance were used. Total mentions of self-stigma, stigma resistance, medication stigma, and underlying themes were tallied and evaluated. Results: Self-stigma was mentioned a total of 100 times with at least 2 mentions per participant. Self-blame was the most prominent construct of self-stigma and was mentioned nearly twice as often as any other self-stigma construct. Most participants also made mentions of self-stigma resistance. Half of the individual participants mentioned stigma resistance more times than they mentioned self-stigma, which suggests some surmounting of self-stigma. Medication-related stigma was also prominent, denoting negativity about the presence of medications in one's life. Discussion: Self-stigma related to self-blame may be problematic for antidepressant users. Identification and measurement of stigma resistance, especially in peer interactions, may represent a promising concept in overcoming self-stigma. Future work should explore emphasizing self-blame aspects when designing interventions to reduce self-stigma among individuals with depressive disorders and explore development of tools to measure stigma resistance.
Isolation of CarE genes from the Chinese white pine beetle Dendroctonus armandi (Curculionidae: Scolytinae) and their response to host chemical defense.

BACKGROUND Bark beetles rely on detoxifying enzymes to resist the defensive terpenoids of their host trees. Research on carboxylesterases (CarEs) has focused on their multiple functions in the metabolic detoxification of pesticides and plant allelochemicals, drug resistance, and juvenile hormone and pheromone degradation. RESULT We identified eight new CarE genes in the Chinese white pine beetle (Dendroctonus armandi) and carried out bioinformatics analysis on the deduced full-length amino acid sequences. Differential transcript levels of CarE genes were observed between sexes; within these levels, significant differences were found among the different development stages, and between insects fed on the phloem of Pinus armandi and exposed to five stimuli [(-)-α-pinene, (-)-β-pinene, (+)-3-carene, limonene and turpentine] at 8 and 24 h. CONCLUSION Transcription levels of CarE genes suggest some relationship with the detoxification of terpenoids released by host trees. The functions of bark beetle esterase are mainly in hydrolyzing the host chemical defense and degrading odorant molecules during host selection and colonization. © 2018 Society of Chemical Industry.
Herpes Simplex Virus Type 1 Engages Toll Like Receptor 2 to Recruit Macrophages During Infection of Enteric Neurons

Herpes simplex virus type 1 (HSV-1) is a widespread neurotropic pathogen responsible for a range of clinical manifestations. Inflammatory cell infiltrate is a common feature of HSV-1 infections and has been implicated in neurodegeneration. Therefore, viral recognition by innate immune receptors (i.e., TLR2) and the subsequent inflammatory response are now deemed key players in HSV-1 pathogenesis. In this study we infected with HSV-1 the enteric nervous system (ENS) of wild-type (WT) and TLR2 knock-out (TLR2ko) mice to investigate whether and how TLR2 participates in HSV-1 induced neuromuscular dysfunction. Our findings demonstrated viral specific transcripts suggestive of abortive replication in the ENS of both WT and TLR2ko mice. Moreover, HSV-1 triggered TLR2-MyD88 depend signaling in myenteric neurons and induced structural and functional alterations of the ENS. Gastrointestinal dysmotility was, however, less pronounced in TLR2ko as compared with WT mice. Interesting, HSV-1 caused up-regulation of monocyte chemoattractant protein-1 (CCL2) and recruitment of CD11b+ macrophages in the myenteric ganglia of WT but not TLR2ko mice. At the opposite, the myenteric plexuses of TLR2ko mice were surrounded by a dense infiltration of HSV-1 reactive CD3+CD8+INFγ+ lymphocytes. Indeed, depletion CD3+CD8+ cells by means of administration of anti-CD8 monoclonal antibody reduced neuromuscular dysfunction in TLR2ko mice infected with HSV-1. During HSV-1 infection, the engagement of TLR2 mediates production of CCL2 in infected neurons and coordinates macrophage recruitment. Bearing in mind these observations, blockage of TLR2 signaling could provide novel therapeutic strategies to support protective and specific T-cell responses and to improve neuromuscular dysfunction in pathogen-mediated alterations of the ENS.
Role of Ttca of Citrobacter Werkmanii in Bacterial Growth, Biocides Resistance, Biofilm Formation and Swimming Motility

To screen, identify and study the genes involved in isothiazolone resistance and biofilm formation in Citrobacter werkmanii strain BF-6. A Tn5 transposon library of approximately 900 mutants of C. werkmanii strain BF-6 was generated and screened to isolate 1,2-benzisothiazolin-3-one (BIT) resistant strains. In addition, the tRNA 2-thiocytidine (32) synthetase gene (ttcA) was deleted through homologous recombination and the resulting phenotypic changes of the ΔttcA mutant were studied. A total of 3 genes were successfully identified, among which ΔttcA mutant exhibited a reduction in growth rate and swimming motility. On the other hand, an increase in biofilms formation in ΔttcA were observed but not with a significant resistance enhancement to BIT. This work, for the first time, highlights the role of ttcA gene of C. werkmanii strain BF-6 in BIT resistance and biofilm formation.
Gut Bacterial Communities in Geographically Distant Populations of Farmed Sea Bream (Sparus aurata) and Sea Bass (Dicentrarchus labrax)

This study investigated the profile of the autochthonous gut bacterial communities in adult individuals of Sparus aurata and Dicentrarchus labrax reared in sea cages in five distantly located aquaculture farms in Greece and determine the impact of geographic location on them in order to detect the core gut microbiota of these commercially important fish species. Data analyses resulted in no significant geographic impact in the gut microbial communities within the two host species, while strong similarities between them were also present. Our survey revealed the existence of a core gut microbiota within and between the two host species independent of diet and geographic location consisting of the Delftia, Pseudomonas, Pelomonas, Propionibacterium, and Atopostipes genera.
The Causal Effect of Education on Tooth Loss: Evidence From United Kingdom Schooling Reforms

Associations between education and oral health have frequently been reported, but until now there has been no causal evidence. Exploiting exogenous variation in the duration of schooling due to 1947 and 1972 reforms in mandatory schooling in the United Kingdom, we examined the causal relationship between education and tooth loss in older age. We conducted a cross-sectional study using data from waves 3 (2006-2007), 5 (2010-2011), and 7 (2014-2015) of the English Longitudinal Study of Ageing. We used a 2-stage least squares instrumental variable approach and included 5,667 respondents (average age = 67.8 years; 44.4% were men) in the analyses, of whom 819 (14.5%) had no teeth. The schooling reforms increased the duration of education by an average of 0.624 years (95% confidence interval: 0.412, 0.835). For respondents born within ±6 years of the pivotal cohorts, a 1-year increment of education causally reduced the probability of edentulism by 9.1 (95% confidence interval: 1.5, 16.8) percentage points. The effects were stronger for the 1947 reform than for the 1972 reform. Results were robust to broadening of the cohort bandwidth and functional form of the cohort trend. The findings suggest that investment in education produces improved oral health later in life.
Altered whole-brain gray matter volume in primary angle closure glaucoma patients: a voxel-based morphometry study

The study aimed to compare the whole-brain gray matter volume (GMV) and white matter volume (WMV) difference between primary angle closure glaucoma (PACG) patients and health controls (HCs) using a voxel-based morphometry method. A total of 27 patients with PACG (17 males and 10 females) and 27 HCs (17 males and 10 females), closely matched for age and education, were enrolled in the study. All subjects underwent magnetic resonance imaging (MRI) scans. The MRI data were processed using SPM8 software in voxel-based morphometry 8 toolbox. The relationship between the mean GMV values of brain regions and the clinical features including psychological testing and mean retinal nerve fiber layer (RNFL) thickness in PACG groups were analyzed by using Pearson correlation. Compared with HCs, PACG patients showed significantly decreased GMV values in the left cerebellum posterior lobe (CPL), right extra-nuclear, and right superior temporal gyrus. In contrast, PACG patients showed significantly increased GMV values in the left CPL, right CPL, right superior temporal gyrus, right thalamus and right insula (P<0.01). Moreover, in the PACG group, the left mean RNFL showed a positive correlation with the mean GMV values of the left CPL (r=0.719; P<0.001) and the right mean RNFL showed a positive correlation with the mean GMV values of the left CPL (r=0.721; P<0.001). The Hamilton depression score showed a positive correlation with the mean GMV values of right insula (r=0.897; P<0.001). Our results demonstrated that PACG patients showed altered brain structure in various regions related to visuomotor function, thalamocortical pathway, and emotion function, which might provide a useful informations to understanding the anatomy neural mechanisms of deficit in vision loss and depression in PACG.
QVM: an efficient runtime for detecting defects in deployed systems

Coping with software defects that occur in the post-deployment stage is a challenging problem: bugs may occur only when the system uses a specific configuration and only under certain usage scenarios. Nevertheless, halting production systems until the bug is tracked and fixed is often impossible. Thus, developers have to try to reproduce the bug in laboratory conditions. Often the reproduction of the bug consists of the lion share of the debugging effort. In this paper we suggest an approach to address the aforementioned problem by using a specialized runtime environment (QVM, for Quality Virtual Machine). QVM efficiently detects defects by continuously monitoring the execution of the application in a production setting. QVM enables the efficient checking of violations of user-specified correctness properties, e.g., typestate safety properties, Java assertions, and heap properties pertaining to ownership. QVM is markedly different from existing techniques for continuous monitoring by using a novel overhead manager which enforces a user-specified overhead budget for quality checks. Existing tools for error detection in the field usually disrupt the operation of the deployed system. QVM, on the other hand, provides a balanced trade off between the cost of the monitoring process and the maintenance of sufficient accuracy for detecting defects. Specifically, the overhead cost of using QVM instead of a standard JVM, is low enough to be acceptable in production environments. We implemented QVM on top of IBM's J9 Java Virtual Machine and used it to detect and fix various errors in real-world applications.
A Simplicial Complex Model for Dynamic Epistemic Logic to study Distributed Task Computability

The usual epistemic model S5n for a multi-agent system is based on a Kripke frame, which is a graph whose edges are labeled with agents that do not distinguish between two states. We propose to uncover the higher dimensional information implicit in this structure, by considering a dual, simplicial complex model. We use dynamic epistemic logic (DEL) to study how an epistemic simplicial complex model changes after a set of agents communicate with each other. We concentrate on an action model that represents the so called immediate snapshot communication patterns of asynchronous agents, because it is central to distributed computability (but our setting works for other communication patterns). There are topological invariants preserved from the initial epistemic complex to the one after the action model is applied, which determine the knowledge that the agents gain after communication. Finally, we describe how a distributed task specification can be modeled as a DEL action model, and show that the topological invariants determine whether the task is solvable. We thus provide a bridge between DEL and the topological theory of distributed computability, which studies task solvability in a shared memory or message passing architecture.
A note on the edge partition of graphs containing either a light edge or an alternating 2-cycle

Let $\mathcal{G}_{\alpha}$ be a hereditary graph class (i.e, every subgraph of $G_{\alpha}\in \mathcal{G}_{\alpha}$ belongs to $\mathcal{G}_{\alpha}$) such that every graph $G_{\alpha}$ in $\mathcal{G}_{\alpha}$ has minimum degree at most 1, or contains either an edge $uv$ such that $d_{G_{\alpha}}(u)+d_{G_{\alpha}}(v)\leq \alpha$ or a 2-alternating cycle. It is proved that every graph in $\mathcal{G}_{\alpha}$ ($\alpha\geq 5$) with maximum degree $\Delta$ can be edge-partitioned into two forests $F_1$, $F_2$ and a subgraph $H$ such that $\Delta(F_i)\leq \max\{2,\lceil\frac{\Delta-\alpha+6}{2}\rceil\}$ for $i=1,2$ and $\Delta(H)\leq \alpha-5$.
A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets

Motivation: P values derived from the null hypothesis significance testing framework are strongly affected by sample size, and are known to be irreproducible in underpowered studies, yet no suitable replacement has been proposed. Results: Here we present implementations of non-parametric standardized median effect size estimates, dNEF, for high-throughput sequencing datasets. Case studies are shown for transcriptome and tag-sequencing datasets. The dNEF measure is shown to be more reproducible and robust than P values and requires sample sizes as small as 3 to reproducibly identify differentially abundant features. Availability: Source code and binaries freely available at: this https URL , omicplotR, and this https URL .
A Motivational Model of BCI-Controlled Heuristic Search

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.