Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Method to Distinguish Machine Translation from Human Translation

This paper introduces a machine learning approach to distinguish machine translation texts from human texts in the sentence level automatically. In stead of traditional methods, we extract some linguistic features only from the target language side to train the prediction model and these features are independent of the source language. Our prediction model presents an indicator to measure how much a sentence generated by a machine translation system looks like a real human translation. Furthermore, the indicator can directly and effectively enhance statistical machine translation systems, which can be proved as BLEU score improvements.
Chronic Contractile Dysfunction without Hypertrophy Does Not Provoke a Compensatory Transcriptional Response in Mouse Hearts

Diseased myocardium from humans and experimental animal models shows heightened expression and activity of a specific subtype of phospholipase C (PLC), the splice variant PLCβ1b. Previous studies from our group showed that increasing PLCβ1b expression in adult mouse hearts by viral transduction was sufficient to cause sustained contractile dysfunction of rapid onset, which was maintained indefinitely in the absence of other pathological changes in the myocardium. We hypothesized that impaired contractility alone would be sufficient to induce a compensatory transcriptional response. Unbiased, comprehensive mRNA-sequencing was performed on 6 biological replicates of rAAV6-treated blank, PLCβ1b and PLCβ1a (closely related but inactive splice variant) hearts 8 weeks after injection, when reduced contractility was manifest in PLCβ1b hearts without evidence of induced hypertrophy. Expression of PLCβ1b resulted in expression changes in only 9 genes at FDR<0.1 when compared with control and these genes appeared unrelated to contractility. Importantly, PLCβ1a caused similar mild expression changes to PLCβ1b, despite a complete lack of effect of this isoform on cardiac contractility. We conclude that contractile depression caused by PLCβ1b activation is largely independent of changes in the transcriptome, and thus that lowered contractility is not sufficient in itself to provoke measurable transcriptomic alterations. In addition, our data stress the importance of a stringent control group to filter out transcriptional changes unrelated to cardiac function.
Fecal prevalence and diversity of Salmonella species in lactating dairy cattle in four states.

Salmonella is one of the most serious foodborne pathogenic bacteria in the United States, causing an estimated 1.3 million human illnesses each year. Dairy cows can be reservoirs of foodborne pathogenic bacteria, including Salmonella spp. ; it is estimated that from 27 to 31% of dairy herds across the United States are colonized by Salmonella. The present study was designed to examine the occurrence of Salmonella spp. on dairies and to examine the serotypic diversity of Salmonella isolates on sampled dairies from across the United States. Fecal samples (n = 60 per dairy) were collected from 4 dairies in each of 4 states for a total of 960 fecal samples representing a total population of 13,200 dairy cattle. In the present study, 93 of 960 samples (9.96%) collected were culture-positive for Salmonella enterica. At least one Salmonella fecal-shedding cow was found in 9 of the 16 herds (56%) and the within-herd prevalence varied in our study from 0% in 7 herds to a maximum of 37% in 2 herds, with a mean prevalence among Salmonella-positive herds of 17%. Seventeen different serotypes were isolated, representing 7 different Salmonella serogroups. There were 2 or more different serogroups and serotypes present on 7 of the 9 Salmonella-positive farms. Serotypes Montevideo and Muenster were the most frequent and widespread. From our data, it appears that subclinical colonization with Salmonella enterica is relatively common on dairy farms and is represented by diverse serotypes on US dairy farms.
Duplicated hash routing: arobust algorithm for a distributed WWW cache System

Hash routing is an algorithm for a distributed WWW caching system that achieves a high hit rate by preventing overlaps of objects between caches. However, one of the drawbacks of hash routing is its lack of robustness against failure. Because WWW becomes a vital service on the Internet, the capabilities of fault tolerance of systems that provide the WWW service come to be important. In this paper, we propose a duplicated hash routing algorithm, an extension of hash routing. Our algorithm introduces minimum redundancy to keep system performance when some caching nodes are crashed. In addition, we optionally allow each node to cache objects requested by its local clients (local caching), which may waste cache capacity of the system but it can cut down the network traffic between caching nodes. We evaluate various aspects of the system performance such as hit rates, error rates and network traffic by simulations and compare them with those of other algorithms. The results show that our algorithm achieves both high fault tolerance and high performance with low system overhead. keywords: fault tolerance, distributed WWW caching systems, hash routing, hit rates, network traffic, cache redun-
Number and Function of Endothelial Progenitor Cells as a Marker of Severity for Diabetic Vasculopathy

Objective—Peripheral arterial disease (PAD) is a threatening complication of diabetes. As endothelial progenitor cells (EPCs) are involved in neovasculogenesis and maintenance of vascular homeostasis, their impairment may have a role in the pathogenesis of diabetic vasculopathy. This study aimed to establish whether number and function of EPCs correlate with PAD severity in type 2 diabetic patients. Methods and Results—EPCs were defined by the expression of CD34, CD133 and KDR, and quantified by flow cytometry in 127 diabetic patients with and without PAD. PAD severity has been assessed as carotid atherosclerosis and clinical stage of leg atherosclerosis obliterans. Diabetic patients with PAD displayed a significant 53% reduction in circulating EPCs versus non-PAD patients, and EPC levels were negatively correlated with the degree of carotid stenosis and the stage of leg claudication. Moreover, the clonogenic and adhesion capacity of cultured EPCs were significantly lower in diabetic patients with PAD versus patients without. Conclusions—This study demonstrates that EPC decrease is related to PAD severity and that EPC function is altered in diabetic subjects with PAD, strengthening the pathogenetic role of EPC dysregulation in diabetic vasculopathy. EPC count may be considered a novel biological marker of peripheral atherosclerosis in diabetes.
Hyper Natural Deduction

We introduce a Hyper Natural Deduction system as an extension of Gentzen's Natural Deduction system. A Hyper Natural Deduction consists of a finite set of derivations which may use, beside typical Natural Deduction rules, additional rules providing means for communication between derivations. We show that our Hyper Natural Deduction system is sound and complete for infinite-valued propositional Gödel Logic, by giving translations to and from Avron's Hyper sequent Calculus. We also provide conversions for normalisation and prove the existence of normal forms for our Hyper Natural Deduction system.
Beyond Mahalanobis metric: Cayley-Klein metric learning

Cayley-Klein metric is a kind of non-Euclidean metric suitable for projective space. In this paper, we introduce it into the computer vision community as a powerful metric and an alternative to the widely studied Mahalanobis metric. We show that besides its good characteristic in non-Euclidean space, it is a generalization of Mahalanobis metric in some specific cases. Furthermore, as many Mahalanobis metric learning, we give two kinds of Cayley-Klein metric learning methods: MMC Cayley-Klein metric learning and LMNN Cayley-Klein metric learning. Experiments have shown the superiority of Cayley-Klein metric over Mahalanobis ones and the effectiveness of our Cayley-Klein metric learning methods.
Visual Attributes in Videos

Attributes are descriptive characteristics with crosscategory applicability. They can be used to provide additional information that is not found in simple object classification, giving the computer a complete picture of the item and the state in which it appears. While attribute prediction has been studied extensively in still images, this is not the case in videos. However, there exist visual clues in a video that are not present in a still frame. In this paper, we discover and annotate visual attributes for the YoutubeBoundingBoxes dataset. This is the largest video attribute dataset to date. Using the Youtube-Attributes dataset, a model can be trained for scalable multi-label classification. We have collected a dataset of 20,000 videos paired with over 85 attribute annotations describing 10 different classes of objects. We demonstrate that our dataset can be used to produce attribute classifiers that are both accurate and highly scalable.
Translation elongation factor-1 alpha interacts with the 3' stem-loop region of West Nile virus genomic RNA

The conserved 3'-terminal stem-loop (3' SL) of the West Nile virus (WNV) genomic RNA was previously used to probe for cellular proteins that may be involved in flavivirus replication and three cellular proteins were detected that specifically interact with the WNV 3' SL RNA (J. L. Blackwell and M. A. Brinton, J. Virol. 69:5650-5658, 1995). In this study, one of these cellular proteins was purified to apparent homogeneity by ammonium sulfate precipitation and liquid chromatography. Amino acid sequence Western blotting, and supershift analyses identified the cellular protein as translation elongation factor-1 alpha (EF-1 alpha). Competition gel mobility shift assays demonstrated that the interaction between EF-1 alpha and WNV 3' SL RNA was specific. Dephosphorylation of EF-1 alpha by calf intestinal alkaline phosphatase inhibited its binding to WNV 3' SL RNA. The apparent equilibrium dissociation constant for the interaction between EF-1 alpha and WNV 3' SL RNA was calculated to be 1.1 x 10(-9) M. Calculation of the stoichiometry of the interaction indicated that one molecule of EF-1 alpha binds to each molecule of WNV 3' SL RNA. Using RNase footprinting and nitrocellulose filter binding assays, we detected a high-activity binding site on the main stem of the WNV 3' SL RNA. Interaction with EF-1 alpha at the high-activity binding site was sequence specific, since nucleotide substitution in this region reduced the binding activity of the WNV 3' SL RNA for EF-1 alpha by approximately 60%. Two low-activity binding sites were also detected, and each accounted for approximately 15 to 20% of the binding activity. Intracellular association between the host protein and the viral RNA was suggested by coimmunoprecipitation of WNV genomic RNA and EF-1 alpha, using an anti-EF-1 alpha antibody.
Contributions of Pneumolysin, Pneumococcal Surface Protein A (PspA), and PspC to Pathogenicity of Streptococcus pneumoniae D39 in a Mouse Model

ABSTRACT Successful colonization of the upper respiratory tract by Streptococcus pneumoniae is an essential first step in the pathogenesis of pneumococcal disease. However, the bacterial and host factors that provoke the progression from asymptomatic colonization to invasive disease are yet to be fully defined. In this study, we investigated the effects of single and combined mutations in genes encoding pneumolysin (Ply), pneumococcal surface protein A (PspA), and pneumococcal surface protein C (PspC, also known as choline-binding protein A) on the pathogenicity of Streptococcus pneumoniae serotype 2 (D39) in mice. Following intranasal challenge with D39, stable colonization of the nasopharynx was maintained over a 7-day period at a level of approximately 105 bacteria per mouse. The abilities of the mutant deficient in PspA to colonize the nasopharynx and to cause lung infection and bacteremia were significantly reduced. Likewise, the PspC mutant and, to a lesser extent, the Ply mutant also had reduced abilities to colonize the nasopharynx. As expected, the double mutants colonized less well than the parent to various degrees and had difficulty translocating to the lungs and blood. A significant additive attenuation was observed for the double and triple mutants in pneumonia and systemic disease models. Surprisingly, the colonization profile of the derivative lacking all three proteins was similar to that of the wild type, indicating virulence gene compensation. These findings further demonstrate that the mechanism of pneumococcal pathogenesis is highly complex and multifactorial but ascribes a role for each of these virulence proteins, alone or in combination, in the process.
When smartphones become the enemy: unveiling mobile apps anomalies through clustering techniques

The ever-increasing number of mobile devices connected to cellular networks is heavily modifying the traffic observed in these networks. The traffic volumes and patterns generated by smartphones pose novel challenges to cellular network operators. One of these challenges relates to the automatic detection and diagnosis of unforeseen network traffic anomalies caused by specific devices and apps. Synchronized apps generating flashcrowds, device-specific traffic misbehaviors impacting network performance and end-users Quality of Experience (QoE), and other similar anomalies need to be rapidly detected and diagnosed. In this paper we characterize a new type of anomalies impacting cellular networks, caused by the multiple, constantly-connected apps running in smartphones and other end-user devices. We additionally devise a novel detection and classification technique based on semi-supervised Machine Learning (ML) algorithms to automatically detect and diagnose anomalies of this class with minimal training, and compare its performance to that achieved by other well-known supervised learning classifiers. The proposed solution is evaluated using synthetically generated data from an operational cellular ISP, drawn from real traffic statistics to resemble both the real cellular network traffic and the characterized type of anomalies.
A strategy for converging dynamic action policies

In this paper we propose a novel strategy for converging dynamic policies generated by adaptive agents, which receive and accumulate rewards for their actions. The goal of the proposed strategy is to speed up the convergence of such agents to a good policy in dynamic environments. Since it is difficult to have the good value for a state due to the continuous changing in the environment, previous policies are kept in memory for reuse in future policies, avoiding delays or unexpected speedups in the agent's learning. Experimental results on dynamic environments with different policies have shown that the proposed strategy is able to speed up the convergence of the agent while achieving good action policies.
The determinants of employee participation in occupational health and safety management

This article focuses on employee direct participation in occupational health and safety (OHS) management. The article explains what determines employee opportunities to participate in OHS management. The explanatory framework focuses on safety culture and safety management at workplaces. The framework is empirically tested using Estonian cross-sectional, multilevel data of organizations and their employees. The analysis indicates that differences in employee participation in OHS management in the Estonian case could be explained by differences in OHS management practices rather than differences in safety culture. This indicates that throughout the institutional change and shift to the European model of employment relations system, change in management practices has preceded changes in safety culture which according to theoretical argument is supposed to follow culture change.
Citizen Confidence in State Governmental Institutions

How do people evaluate government institutions at the state level? Previous studies have focused on evaluations of politicians rather than institutions and on the national level rather than the subnational level. Yet investigating how people view state governments speaks not only to the sources of approval of state institutions, but also to how structural differences in state environments affect levels of citizen satisfaction. In this paper, we consider three questions – whether people hold distinct evaluations of state institutions, whether these evaluations differ from levels of confidence in national institutions, and what contextual factors explain variations in confidence in state legislatures, offices of the governor, and state courts. Specifically, we explore the effects of process evaluations, the nature of representation in the states, and economic and policy outcomes to explain levels of citizen confidence. Using data from a national survey by the National Center for State Courts supplemented by contextual measures, we find that people hold evaluations of state institutions distinct from both assessments of the national government and other state branches of government. In explaining why levels of institutional support vary across the states, we find that both process evaluations and policy outcomes have particular importance to explain the confidence that citizens hold in state institutions.
On the Influence of Committed Minorities on Social Consensus

Human behavior is profoundly affected by the influenceability of individuals and the social networks that link them together. In the sociological context, spread of ideas, ideologies and innovations is often studied to understand how individuals adopt new states in behavior, opinion, ideology or consumption through the influence of their neighbors. In this paper, we study the evolution of opinions and the dynamics of its spread. We use the binary agreement model starting from an initial state where all individuals adopt a given opinion B, except for a fraction p<1 of the total number of individuals who are committed to opinion A. In a generalization of this model, we consider also the initial state in which some of the individuals holding opinion B are also committed to it. Committed individuals are defined as those who are immune to influence but they can influence others to alter their opinion through the usual prescribed rules for opinion change. The question that we specifically ask is: how does the consensus time vary with the size of the committed fraction? More generally, our work addresses the conditions under which an inflexible set of minority opinion holders can win over the rest of the population. We show that the prevailing majority opinion in a population can be rapidly reversed by a small fraction p of randomly distributed committed agents who are immune to influence. Specifically, we show that when the committed fraction grows beyond a critical value pc=9.79%, there is a dramatic decrease in the time, Tc, taken for the entire population to adopt the committed opinion. Below this value, the consensus time is proportional to the exponential function of the network size, while above this value this time is proportional to the logarithm of the network size. This has enormous impact on stability/instability of the society opinions. We also discuss conditions under which the committed minority can rapidly reverse the influenceable majority even if the latter is supported by a small fraction of individuals committed to their opinion. The results are relevant in understanding and influencing the social perceptions of international missions operating in various countries and attitudes to the mission goals.
Tumor necrosis factor-alpha serum level in assessment of disease activity in inflammatory bowel diseases.

AIM To investigate an influence of the concentration of proinflammatory cytokines tumor necrosis factor-alpha (TNF-α) in serum on the activity of inflammatory bowel disease (IBD). METHODS The IBD patients of both genders (n=60) were divided in two equal groups, ulcerative colitis (UC) and Crohn's disease (CD). Based on the result of activity index each group was subdivided in two subgroups: active and inactive phase of the disease. Age and gender matched apparently healthy individuals (n=30) involved in the control group. Serum TNF-α concentration was determined by enzyme linked immune-adsorbent assay (ELISA). RESULTS The significant difference (Mann-Whitney Test) in serum TNF-α level was found between healthy controls 28.86 pg/ml (28.74 - 29.19 pg/ml) and CD patients (29.47 pg/ml (29.1 - 29.77 pg/ml) (p less than 0.05) and UC patients 29.34 pg/ml (29.14 - 29.71 pg/ ml) (p less than 0.05) respectively. Serum TNF-α level in patients with CD was higher compared to serum TNF-α level in patients with UC, but the difference was not significant (p more than 0,05). There were no significant difference in serum TNF-α concentrations either in CD or UC patients related to the phase of disease activity: active CD 29.53 pg/ml (29.20 - 29.90 pg/ml) vs inactive CD 29.26 pg/ml (29.15 - 29.53 pg/ml); active UC 29.53 pg/ml (29.32 - 29.85 pg/ ml) vs inactive UC 29.26 pg/ml (29.10 - 29.63 pg/ml). CONCLUSIONS Since there were no differences in serum TNF-α concentrations related to the disease activity we consider that TNF-α is not an adequate serum biomarker for an assessment of the disease activity in patients with IBD.
Penalized Covariance Matrix Estimation Using a Matrix-Logarithm Transformation

For statistical inferences that involve covariance matrices, it is desirable to obtain an accurate covariance matrix estimate with a well-structured eigen-system. We propose to estimate the covariance matrix through its matrix logarithm based on an approximate log-likelihood function. We develop a generalization of the Leonard and Hsu log-likelihood approximation that no longer requires a nonsingular sample covariance matrix. The matrix log-transformation provides the ability to impose a convex penalty on the transformed likelihood such that the largest and smallest eigenvalues of the covariance matrix estimate can be regularized simultaneously. The proposed method transforms the problem of estimating the covariance matrix into the problem of estimating a symmetric matrix, which can be solved efficiently by an iterative quadratic programming algorithm. The merits of the proposed method are illustrated by a simulation study and two real applications in classification and portfolio optimization. Supplementary materials for this article are available online.
Distinct RNA motifs are important for coactivation of steroid hormone receptors by steroid receptor RNA activator (SRA)

Steroid receptor RNA activator (SRA) is an RNA transcript that functions as a eukaryotic transcriptional coactivator for steroid hormone receptors. We report here the isolation and functional characterization of distinct RNA substructures within the SRA molecule that constitute its coactivation function. We used comparative sequence analysis and free energy calculations to systematically study SRA RNA subdomains for identification of structured regions and base pairings, and we used site-directed mutagenesis to assess their functional consequences. Together with genetic deletion analysis, this approach identified six RNA motifs in SRA important for coactivation. Because all nucleotide changes in the mutants that disrupted SRA function were silent mutations presumed not to alter deduced encoded amino acid sequence, our analysis provides strong evidence that SRA-mediated coactivation is executed by distinct RNA motifs and not by an encoded protein.