Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and validation of the Polish version of Colquitt’s Organizational Justice Measure

OBJECTIVES Organizational justice is an important predictor of employees’ well-being and job performance. Colquitt’s Organizational Justice Measure (OJM) was designed to assess four aspects of justice – distributive, procedural, interpersonal and informational. The lack of a Polish version of the tool, however, has precluded its application in Poland. The objective of this study was to test the psychometric properties of the OJM in a Polish sample. MATERIAL AND METHODS The validating study was conducted on 2 participant samples (N = 209 and N = 659), employed in public and private companies. Both the exploratory and confirmatory factor analyses (EFA, CFA) as well as the estimation of internal consistency with Cronbach’s α method were conducted. Predictive validity was assessed by correlating organizational justice with job-related factors and outcomes, including job resources and counterproductive work behavior. RESULTS The EFA and CFA supported a 4-dimension model of the OJM Polish version. This model indicated a better fit to data than the alternatively tested 1-factor, 2-factor and 3-factor models. The internal consistency of the scales was satisfactory, ranging 0.81–0.93 for various subscales. As expected, the overall organizational justice and the four subscales correlated positively with job resources and negatively with counterproductive work behavior. CONCLUSIONS The Polish version of OJM has satisfactory psychometric properties and may be useful in assessing organizational justice in a Polish setting. Int J Occup Med Environ Health 2018;31(4):415–427
Impact of combining acerola by-product with a probiotic strain on a gut microbiome model

Abstract In this study, we first investigated the survival of three probiotic strains, individually and combined with acerola by-product during simulated gastrointestinal conditions. Next, we investigated the effects of acerola by-product combined with Bifidobacterium longum BB-46 on a gut microbiota model (SHIME®). Chemical composition, total phenolic compounds, antioxidant activity of the acerola by-product and microbial counts, denaturing gradient gel electrophoresis (DGGE), ammonium ions () and short-chain fatty acids (SCFAs) analysis of the SHIME® samples were performed. Acerola by-product revealed high protein and fibre, reduced lipid contents, and showed to be an excellent source of total phenolic compounds with high in vitro antioxidant activity. A decreased amount of in the ascending colon and an increase (p < .05) in SCFAs were observed in the three regions of colon during treatment with BB-46 and acerola by-product. BB-46 combined with acerola by-product showed positive effects on the gut microbiota metabolism in SHIME® model. Graphical Abstract
Application of borehole radar for dam leakage detection

A borehole radar investigation was performed at Sanzuodian reservoir, Chifeng, China to assess the existence of the deep rock-filled dam leakage. The key methodology used includes both single-hole reflection profile and cross-hole radar tomography, which allowed identification of the hydraulic connections between the upstream side and the downstream side in deep foundation. The leakage path is characterized by direct wave loss due to high attenuation in single-hole reflection profile, and horizontal banded low-velocity zone in cross-hole velocity tomography. Meanwhile, the core wall thickness change point, connection point of asphalt/concrete wall, and the water saturated interface can be identified by single-hole reflection clearly. The interpreted leakage path is proven by water flow measurement. Borehole radar is a high-resolution tool fitted for deep detection.
Chinese Herbal Medicine for Myasthenia Gravis: A Systematic Review and Meta-Analysis

Myasthenia gravis (MG) is an acquired autoimmune disease with the disorder of the neuromuscular junction transmission caused by autoantibodies. Currently, various Chinese herbal medicines (CHMs) are widely used for MG. This meta-analysis was conducted to assess the effectiveness and safety of CHMs for MG and its possible mechanisms. Fourteen studies with 1039 individuals were identified by searching seven databases from inception to March 2017. The methodological quality was assessed by using 7-item criteria from the Cochrane’s Collaboration tool, and which assessed ≥4 “yes” in the domains were selected for detailed assessment and meta-analysis. All the data were analyzed using Rev-Man 5.3 software. Meta-analysis showed a significant effect of CHM as adjuvant therapy for improving the effectiveness compared with WCM alone or placebo in treating MG (p < 0.01). Moreover, there were fewer adverse effects and relapse rate in total when compared with the control group. The possible mechanisms of CHM for MG are associated with immunoregulation by reconstituting the functional ability of Tregs. In conclusion, despite the apparent positive results, the present evidence supports, to an extent, that CHM can be used for MG patients because of the methodological flaws and CHM heterogeneity. Further rigorous RCT for MG is needed.
Electrical analysis of InGaAs-based planar and tri-gate nMOSFET with S/D resistance dependencies at different drain biases

This paper studies the electrical analysis of InGaAs-based planar and tri-gate nMOSFET and the influence of the source/drain (S/D) resistance, Rsd on the current-voltage (${\text{I}}_{\text{d}}-{\text{V}}_{\text{g}}$) relation at different drain biases (${\text{V}}_{\text{ds}}$). It is found that the tri-gate nMOSFET simulated at high Vds has shown better performance compared to planar nMOSFET simulated at low Vds. As the Rsd is reduced, the drain current of both planar and tri-gate devices increases. The on-current to off-current (${\text{I}}_{\text{on}})/{\text{I}}_{\text{off}}$) ratio of the devices also increases as the Rsd reduced. Tri-gate nMOSFET shows significant improvement as the Ion/Ioff ratio is $10^{3}$ higher than the planar nMOSFET device.
Coverage Optimization for Circular Sector Sensor Networks: A Joint Sensing Strength Approach

This paper considers the coverage optimization of a circular sector sensor network in a region. We first present a new comprehensive performance index called the joint sensing strength, originating from the concept of the joint probability. This index takes all sensors’ cover strength into account, hence, is a good reflection of information fusion among the network, and can be applied to quantify the fused sensing coverage strength on a specified point in the region with the circular sector sensor network. A total coverage performance function based on the joint sensing strength can then be constructed for the specified region and optimized with the gradient descent algorithm. A simulation example is provided to illustrate the effectiveness of the proposed method.
Associations Between Gait-Related Falls and Gait Adaptations When Stepping Onto a Curb: A Prospective Falls Study.

Objectives: To examine gait regulation during the approach to stepping onto a curb for older adults who did or did not report gait-related falls over a 12-month follow-up. Methods: A total of 98 participants aged 60 years and older were analyzed. Primary outcomes were step length adaptations (lengthening or shortening) during a curb approach and the occurrence of a gait-related fall during a 12-month follow-up. Results: Linear mixed-effects modeling indicated stronger adaptations toward the end of the approach. Participants who reported experiencing a gait-related fall showed a stronger relationship between the adjustment required and adjustment produced, indicating different gait adaptations during the step leading onto the curb. Discussion: The link between prospective gait-related falls and gait adaptations indicated that older adults with reduced capabilities require stronger adaptations to complete tasks reminiscent of everyday life. This finding may provide insight into the mechanisms of falls in older adults and should inform new fall prevention interventions.
A Real-Time System for Recognition of American Sign Language by using Deep Learning

Deaf people use sign languages to communicate with other people in the community. Although the sign language is known to hearing-impaired people due to its widespread use among them, it is not known much by other people. In this article, we have developed a real-time sign language recognition system for people who do not know sign language to communicate easily with hearing-impaired people. The sign language used in this paper is American sign language. In this study, the convolutional neural network was trained by using dataset collected in 2011 by Massey University, Institute of Information and Mathematical Sciences, and 100% test accuracy was obtained. After network training is completed, the network model and network weights are recorded for the real-time system. In the real-time system, the skin color is determined for a certain frame for hand use, and the hand gesture is determined using the convex hull algorithm, and the hand gesture is defined in real-time using the registered neural network model and network weights. The accuracy of the real-time system is 98.05%.
An Unsupervised Approach of Paraphrase Discovery from Large Crime Corpus

Massive crime reports often comprises valuable structured information regarding crime pattern but manual processing of these massive dataset is quite strenuous and error-prone. These huge dataset can be best exploited by identifying relational clusters of named entities from the crime reports. But often the clusters contain phrases not defining the same relation as the relational characterisation of the whole cluster. Therefore, paraphrasing is performed to filter out those phrases not defining the same relation. Paraphrases are mostly the phrases that reflect the same context in different articulations. Discovering paraphrases from a large corpus is a demanding task for various applications of natural language processing and researchers have been working on it since long time. But none have taken an attempt to perform the paraphrasing task on crime data. In order to deal with the perplexity of the phrases, the present work proposes an unsupervised approach for recognising synonymous phrases or paraphrases from an untagged crime corpus. This work mainly emphasises on the sentences that comprises two entities and each entity pair from different domain is represented as shallow parse tree. The head word from each parsing tree depicts the actual meaning of the phrase and all the phrases with the same headword have been accumulated for each domain of entity pairs. However, many phrases exist that reflects the same meaning without sharing the same headword. So, the objective is to cluster these phrases defining the same meaning by using an agglomerative hierarchical clustering technique. The method presented in this work is an unsupervised approach and it does not need any kind of training samples to work with.
Zero-Shot Cross-Lingual Neural Headline Generation

Neural headline generation (NHG) has been proven to be effective in generating a fully abstractive headline recently. Existing NHG systems are only capable of producing headline of the same language as the original document. Cross lingual headline generation is an important task since it provides an efficient way to understand the key point of a document in a different language. Due to the lack of those parallel corpora of direct source language articles and target language headlines, we propose to deal with the cross-lingual neural headline generation (CNHG) under the zero-shot scenario. A trivial solution is to translate and summarize the source document in a pipeline way. However, a pipeline solution will lead to error propagation in the translation and summarization phases. This challenge motivates us to build a direct source-to-target CNHG model based on existing parallel corpora of translation and monolingual headline generation. Specifically, we let a parameterized CNHG model (student model) mimic the output of a pretrained translation or headline generation model (teacher model). To the best of our knowledge, this is the first effort to address CNHG problem. Besides, we construct English–Chinese headline generation evaluation datasets by manual translation. Experimental results on English-to-Chinese cross-lingual headline generation demonstrate that our proposed method significantly outperforms the baseline models.
The ASA Physical Status Classification: What Is the Evidence for Recommending Its Use in Veterinary Anesthesia?—A Systematic Review

Background: The effectiveness of the American Society of Anesthesiologists (ASA) Physical Status (PS) classification to identify the animals at a greater risk of anesthesia-related death and complications is controversial. In this systematic review, we aimed to analyze studies associating the ASA PS scores with the outcome of anesthesia and to verify whether there was any evidence for recommending the use of the ASA PS in veterinary patients. Methods: Research articles found through a systematic literature search were assessed for eligibility, and data were extracted and analyzed using random-effects analysis. Results: A total of 15 observational prospective and retrospective studies including 258,298 dogs, cats, rabbits, and pigs were included. The analysis found consistency between the studies showing that dogs, cats and rabbits with an ASA-PS ≥III had 3.26 times (95% CI = 3.04–3.49), 4.83 times (95% CI = 3.10–7.53), and 11.31 times (95% CI = 2.70–47.39), respectively, the risk of anesthesia-related death within 24 h (dogs) and 72 h (cats and rabbits) after anesthesia compared with those with an ASA PS <III. In addition, the analysis showed that dogs and cats with ASA PS ≥III had 2.34 times the risk of developing severe hypothermia during anesthesia (95% CI = 1.82–3.01). Conclusions: The simple and practical ASA PS was shown to be a valuable prognostic tool and can be recommended to identify an increased risk of anesthetic mortality until 24–72 h after anesthesia, and a greater risk of development severe intraoperative hypothermia.
Time to Separate Persistent From Recurrent Differentiated Thyroid Cancer: Different Conditions With Different Outcomes

Context Differentiated thyroid cancer (DTC) has an excellent prognosis, but up to 20% of patients with DTC have disease events after initial treatment, indistinctly defined as persistent/recurrent disease. Objective To evaluate the prevalence and outcome of "recurrent" disease (relapse after being 12 months disease-free) compared with "persistent" disease (present ab initio since diagnosis). Design Retrospective analysis of persistent/recurrent disease in patients with DTC (1990 to 2016) with 6.5 years of mean follow-up. Setting Tertiary referral center for thyroid cancer. Patients In total, 4292 patients all underwent surgery ± 131I treatment of DTC. Main Outcome Measures DTC cure of disease persistence or recurrence. Results A total of 639 of 4292 (14.9%) patients had disease events after initial treatment, most (498/639, 78%) with persistent disease and 141 (22%) with recurrent disease. Relative to patients with recurrent disease, patients with persistent disease were significantly older (mean age 46.9 vs 45.7 years) and with a lower female to male ratio (1.9/1 vs 4.8/1). Moreover, in this group, structured disease was more frequent (65.7% vs 41.1%), and more important, distant metastases were significantly more frequent (38.4% vs 17.0%). At multivariate analysis, male sex (OR = 1.7), age (OR = 1.02), follicular histotype (OR = 1.5), T status (T3; OR = 3), and N status (N1b; OR = 7.7) were independently associated with persistent disease. Only the N status was associated with recurrent disease (N1b; OR = 2.5). Conclusions In patients with DTC not cured after initial treatment, persistent disease is more common and has a worse outcome than recurrent disease. Postoperative status evaluated during first-year follow-up may have important clinical implications for planning tailored treatment strategies and long-term follow-up procedures.
The Pulmonary Extracellular Matrix Is a Bactericidal Barrier Against Haemophilus influenzae in Chronic Obstructive Pulmonary Disease (COPD): Implications for an in vivo Innate Host Defense Function of Collagen VI

Non-typeable Haemophilus influenzae (NTHi) is a Gram-negative human commensal commonly residing in the nasopharynx of preschool children. It occasionally causes upper respiratory tract infection such as acute otitis media, but can also spread to the lower respiratory tract causing bronchitis and pneumonia. There is increasing recognition that NTHi has an important role in chronic lower respiratory tract inflammation, particularly in persistent infection in patients suffering from chronic obstructive pulmonary disease (COPD). Here, we set out to assess the innate protective effects of collagen VI, a ubiquitous extracellular matrix component, against NTHi infection in vivo. In vitro, collagen VI rapidly kills bacteria through pore formation and membrane rupture, followed by exudation of intracellular content. This effect is mediated by specific binding of the von Willebrand A (VWA) domains of collagen VI to the NTHi surface adhesins protein E (PE) and Haemophilus autotransporter protein (Hap). Similar observations were made in vivo specimens from murine airways and COPD patient biopsies. NTHi bacteria adhered to collagen fibrils in the airway mucosa and were rapidly killed by membrane destabilization. The significance in host-pathogen interplay of one of these molecules, PE, was highlighted by the observation that it confers partial protection from bacterial killing. Bacteria lacking PE were more prone to antimicrobial activity than NTHi expressing PE. Altogether the data shed new light on the carefully orchestrated molecular events of the host-pathogen interplay in COPD and emphasize the importance of the extracellular matrix as a novel branch of innate host defense.
Hair Manganese and Hyperactive Behaviors: Pilot Study of School-Age Children Exposed through Tap Water

Background Neurotoxic effects are known to occur with inhalation of manganese particulates, but very few data are available on exposure to Mn in water. We undertook a pilot study in a community in Québec (Canada) where naturally occurring high Mn levels were present in the public water system. Our objective was to test the hypothesis that greater exposure to Mn via drinking water would be reflected in higher Mn content in hair which, in turn, would be associated with increased level of hyperactive behaviors. Methods Forty-six children participated in the study, 24 boys and 22 girls, 6–15 years of age (median, 11 years). Their homes received water from one of two wells (W) with different Mn concentrations: W1: mean 610 μg/L; W2: mean 160 μg/L. The Revised Conners’ Rating Scale for parents (CPRS-R) and for teachers (CTRS-R) were administered, providing T-scores on the following subscales: Oppositional, Hyperactivity, Cognitive Problems/Inattention, and ADHD Index. Results Children whose houses were supplied by W1 had higher hair Mn (MnH) than those supplied by W2 (mean 6.2 ± 4.7 μg/g vs. 3.3 ± 3.0 μg/g, p = 0.025). MnH was significantly associated with T-scores on the CTRS-R Oppositional (p = 0.020) and Hyperactivity (p = 0.002) subscales, after adjustment for age, sex, and income. All children with Oppositional and Hyperactivity T-scores ≥ 65 had MnH > 3.0 μg/g. Conclusions The findings of this pilot study are sufficiently compelling to warrant more extensive investigations into the risks of Mn exposure in drinking water.
Decentralized Detection With Robust Information Privacy Protection

We consider a decentralized detection network whose aim is to infer a public hypothesis of interest. However, the raw sensor observations also allow the fusion center to infer private hypotheses that we wish to protect. We consider the case where there are an uncountable number of private hypotheses belonging to an uncertainty set, and develop local privacy mappings at every sensor so that the sanitized sensor information minimizes the Bayes error of detecting the public hypothesis at the fusion center while achieving information privacy for all private hypotheses. We introduce the concept of a most favorable hypothesis (MFH) and show how to find an MFH in the set of private hypotheses. By protecting the information privacy of the MFH, information privacy for every other private hypothesis is also achieved. We provide an iterative algorithm to find the optimal local privacy mappings, and derive some theoretical properties of these privacy mappings. The simulation results demonstrate that our proposed approach allows the fusion center to infer the public hypothesis with low error while protecting information privacy of all the private hypotheses.
Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.
Efficacy and safety of cardioversion with continuous landiolol infusion for atrial tachyarrhythmia in an inflammatory state caused by volvulus in a child with TARP syndrome and postoperative tetralogy of Fallot

A 2‐year‐old boy was diagnosed with TARP syndrome and underwent surgery for tetralogy of Fallot. He developed fever and had an acute abdomen. After 12 hours, atrial tachyarrhythmia (300 beats/min [bpm]) occurred. After nine administration of adenosine and two cardioversions, it relapsed promptly. Landiolol (10 μg/kg/min) was administered until the heart rate decreased to 270 bpm, and cardioversion was performed until sinus rhythm was normal. Exploratory laparotomy revealed small bowel volvulus. Systemic inflammation causing an acute abdomen may be associated with atrial tachyarrhythmia in postoperative tetralogy of Fallot. We speculated that landiolol lowered the defibrillation threshold of the atrium.
Clinical utility of red blood cell distribution width in inflammatory and non‐inflammatory joint diseases

Current studies demonstrate red blood cell distribution width (RDW) as a possible surrogate biomarker of inflammation. The objectives of the present study were to examine RDW in patients with osteoarthritis (OA), fibromyalgia (FM), rheumatoid arthritis (RA) and spondyloarthritis (SpA) and to evaluate its clinical importance.