Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection Pressure Required for Long-Term Persistence of bla CMY-2-Positive IncA/C Plasmids

ABSTRACT Multidrug resistance bla CMY-2 plasmids that confer resistance to expanded-spectrum cephalosporins have been found in multiple bacterial species collected from different hosts worldwide. The widespread distribution of bla CMY-2 plasmids may be driven by antibiotic use that selects for the dissemination and persistence of these plasmids. Alternatively, these plasmids may persist and spread in bacterial populations in the absence of selection pressure if a balance exists among conjugative transfer, segregation loss during cell division, and fitness cost to the host. We conducted a series of experiments (both in vivo and in vitro) to study these mechanisms for three bla CMY-2 plasmids, peH4H, pAR060302, and pAM04528. Results of filter mating experiments showed that the conjugation efficiency of bla CMY-2 plasmids is variable, from <10−7 for pAM04528 and peH4H to ∼10−3 for pAR060302. Neither peH4H nor pAM04528 was transferred from Escherichia coli strain DH10B, but peH4H was apparently mobilized by the coresident trimethoprim resistance-encoding plasmid pTmpR. Competition studies showed that carriage of bla CMY-2 plasmids imposed a measurable fitness cost on the host bacteria both in vitro (0.095 to 0.25) and in vivo (dairy calf model). Long-term passage experiments in the absence of antibiotics demonstrated that plasmids with limited antibiotic resistance phenotypes arose, but eventually drug-sensitive, plasmid-free clones dominated the populations. Given that plasmid decay or loss is inevitable, we infer that some level of selection is required for the long-term persistence of bla CMY-2 plasmids in bacterial populations.
How Does Imperfect Competition in the Labor Market Affect Unemployment Policies?

We consider a continuum of workers ranked according to their abilities to acquire education and two firms with different technologies that imperfectly compete in wages to attract these workers. Once employed, each worker bears an education cost proportional to his/her initial ability, this cost being higher in the high-technology firm. At the Nash equilibrium, we show that the unemployed workers are those with the lowest initial abilities. We then study different policies that subsidy either the education cost or wages and compare them. We found that the first best allocation can only be implemented by selective policies. We then analyze second best non-selective policies that do not discriminate between workers and firms and show that, in terms of welfare, subsidizing education costs or wages is strictly equivalent.
Importance of Heart Rate Variability in Patients with Atrial Fibrillation

The autonomic nervous system plays an important role in the development of atrial fibrillation (AF). One of the most promising markers of autonomic activity is heart rate variability (HRV). Aim: The aim of the study was to evaluate HRV in patients with nonrheumatic atrial fibrillation. Patients and Methods: The study population consisted of 222 patients: group I 75 pts with previous paroxysmal or persistent non rheumatic AF without antiarrhythmics drugs; group II 71 pts with non-AF arrhythmias; group III 76 healthy controls. A 24-hour measurement of HRV was carried out with Holter monitoring. The mean of the following time domain parameters were analyzed: R-R, SDNN (standard deviation of all consecutive sizes (NN) during the total ECG record), SDANN (standard deviation of all consecutive intervals, which are obtained by dividing the entire record in five-minute segments and calculating their means), SDN Ninx (the mean standard deviation of NN intervals in successive 5-minute periods) , pNN50 (the proportion between adjacent NN intervals differing by more than 50 ms the percentage of the estimated total number of NN intervals), and rMSSD (square root of successive NN interval differences). Continuous variables are presented as mean ± standard deviation. Student’s t-test, Pearson Chi-square, and Mann-Whitney tests were applied where appropriate. P-value of < 0.05 was considered significant. The HRV prognostic value for AF was evaluated by using ROC curves. Results: According to our data, HRV was significantly lower in the group I compared with the groups II and III (p< 0.05): low HRV (rMSSD <30 ms) had46 patients (pts) (61.3%) in the group I, 21 pts (29.6%) in the group II and2 pts (2.6%) in the group III. Moderate HRV (30 =< rMSSD < 45 ms) had 27 pts (36.0%) in the group I, 25 pts (35.2%) in the group II and 42 pts (55.2%) in the group III. High HRV (rMSSD >= 45 ms) had 2 pts (2.6%) in the group I, 25 pts (35.2 %) in the group II, and 32 pts (42.2 %) in the group III. The ROC analysis of HRV role in AF prediction showed high AUC of 0.886 with sensitivity 90% and specificity 87% (cut-off 31.26 ms, p<0.001). HRV was significantly higher in patients with beta-blockers than without (p<0.005). Average heart rate was lower in the group I when beta-blockers were used (p<0,05): the group I 59.8±5.7 bpm, the group II 74.4±10.5 bpm, and the group III – 72.7±6.2 bpm. Conclusion: Patients with atrial fibrillation had a reduced HRV indicating a decreased vagal input in the heart rate regulation. HRV may be used as an important marker for the development of recurrent AF and may be used to optimize treatment. Beta–blockers had an effect on HR reduction and HRV increase in our study patients.
Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas

Employing robot teams at disaster areas requires usage of autonomous navigation methods. Moreover, autonomous navigation requires autonomous victim detection. Human skin color based victim detection methods may not be robust due to the variations in lightening conditions at disaster areas. Histograms of Oriented Gradients (HoG) were presented as an alternative way of human detection. In literature, HoG based methods proved their efficiency on the datasets including upright humans. But, the victims have very large variation of poses at a disaster area. In this work, the efficiency of HoG based methods was investigated on a dataset including very different poses and lightening conditions. We have reached 95% success on automatic victim detection problem in real time simulation environment.
Gemfibrozil for the secondary prevention of coronary heart disease in men with low levels of high-density lipoprotein cholesterol. Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial Study Group.

BACKGROUND Although it is generally accepted that lowering elevated serum levels of low-density lipoprotein (LDL) cholesterol in patients with coronary heart disease is beneficial, there are few data to guide decisions about therapy for patients whose primary lipid abnormality is a low level of high-density lipoprotein (HDL) cholesterol. METHODS We conducted a double-blind trial comparing gemfibrozil (1200 mg per day) with placebo in 2531 men with coronary heart disease, an HDL cholesterol level of 40 mg per deciliter (1.0 mmol per liter) or less, and an LDL cholesterol level of 140 mg per deciliter (3.6 mmol per liter) or less. The primary study outcome was nonfatal myocardial infarction or death from coronary causes. RESULTS The median follow-up was 5.1 years. At one year, the mean HDL cholesterol level was 6 percent higher, the mean triglyceride level was 31 percent lower, and the mean total cholesterol level was 4 percent lower in the gemfibrozil group than in the placebo group. LDL cholesterol levels did not differ significantly between the groups. A primary event occurred in 275 of the 1267 patients assigned to placebo (21.7 percent) and in 219 of the 1264 patients assigned to gemfibrozil (17.3 percent). The overall reduction in the risk of an event was 4.4 percentage points, and the reduction in relative risk was 22 percent (95 percent confidence interval, 7 to 35 percent; P=0.006). We observed a 24 percent reduction in the combined outcome of death from coronary heart disease, nonfatal myocardial infarction, and stroke (P< 0.001). There were no significant differences in the rates of coronary revascularization, hospitalization for unstable angina, death from any cause, and cancer. CONCLUSIONS Gemfibrozil therapy resulted in a significant reduction in the risk of major cardiovascular events in patients with coronary disease whose primary lipid abnormality was a low HDL cholesterol level. The findings suggest that the rate of coronary events is reduced by raising HDL cholesterol levels and lowering levels of triglycerides without lowering LDL cholesterol levels.
Big Data and Little Learners

Recent advances in the data sciences, particularly within the area of language technology, have been impressive and nonincremental. For example, within the domain of language translation, the application of deep Long Short Term Memory (LSTM) neural networks to large bodies of text have resulted in a 60% reduction in translation errors from traditional methods, significantly closing the gap between machine and human performance (Wu et al., 2016). Similarly impressive advances have been observed in, e.g., speech recognition (Hinton et al., 2012), syntactic parsing (Dyer et al., 2015) and automatic content extraction (Berant et al., 2015). Clearly, excitement is justified as a new era of linguistic technology is emerging. But should this excitement lead to a fundamental rethinking of our theories of child language and cognition? Doesn’t the “poverty of the stimulus” still pose a problem for human language learners? What role do hierarchical linguistic formalisms play within statistical theories of language learning and use? This symposium brings together leading figures in cognitive science who offer different informed perspectives on these matters.
A Multi-Heuristic framework for Humanoid Planning

Humanoid robots have been the subject of active research for several years, with the aim of developing systems that can potentially replace humans in performing dangerous tasks with similar agility and versatility. Motion planning for humanoid robots is a particularly challenging problem because of the high-dimensionality of the planning space, kinematic constraints and stability. The general approach to planning for humanoid mobility in complex environments, such as industries, with potentially multiple state-space abstractions, for e.g. bipedal, ladder, crawling etc., is to plan separately for each of these representations and combine the solution. A recently developed technique of planning with adaptive dimensionality can be used to develop a single planner to handle such multiple state-space abstractions. To this end, we develop a Multi-heuristic framework, as a generalization of MHA*, that can simultaneously plan across multiple state-space representations to produce a single solution. We test our planner in challenging environments containing several abstractions such as staircases, ladders, etc. A heuristic based planner for high-dimensional state-space planning has a potential drawback of the user having to define good heuristic functions that guide the search. This can become a very tedious task for a system as complex as the humanoid. In this thesis, we address the issue of automatically deriving heuristic functions by learning macro-actions from a database of previous plans. By extracting spatio-temporal bases of repeatedly seen motions in prior plans, we generate new macro-actions as a planning pre-computation, subject to various constraints. We also show how these macro-actions can be used as heuristics and provide preliminary results on full-body planning for humanoids.
Detection of bloodstream infection in neonatal foals with suspected sepsis using real-time PCR

BACTERIAL infection is a major cause of morbidity and mortality in neonatal foals ([Koterba and others 1984][1], [Wilson and Madigan 1989][2], [Paradis 1994][3], [Raisis and others 1996][4], [Marsh and Palmer 2001][5], [Corley and others 2007][6]). Early recognition of sepsis in neonatal foals is
Progression of Renal Disease Measured by the Reciprocal of Serum Creatinine

Background. This study aims to quantify the decrease in the reciprocal of serum creatinine during the two years prior to the start of dialysis and to identify the factors associated with the progression of renal disease. Methods. The study included 76 patients who were monitored for an average of 26.27 months. During the study 52 patients began dialysis treatments. The clinical and analytical variables were analyzed using the t-student test, ANOVA or the Kruskall-Wallis test. A multiple regression analysis was carried out with the decrease in the reciprocal of serum creatinine as the dependent variable. Results. There was a decrease between the mean value during the two years prior to dialysis, the mean value during the previous year and the value at the start of dialysis treatments of 0.29, 0.21 and 0.17 respectively. The nephropathy having the sharpest drop in the reciprocal of serum creatinine, expressed as a percentage of the initial value, was found to be nephroangiosclerosis (46.29%). A greater decline in the reciprocal of serum creatinine was exhibited by low albumin values and adjusted by means of different variables with LDL levels (p = 0.141). For different levels of haemoglobin, proteinuria and cholesterol, the drop in the reciprocal of serum creatinine was greater in patients having a diastolic blood pressure reading of 90 or greater. Conclusions. The decrease in the reciprocal of serum creatinine was associated with different variables in patients who required dialysis.
A Model-based Approach for Achieving Available Automation Systems

Abstract Nowadays automation systems are required to be flexible in order to cope with the ever changing requirements of the applications in terms of complexity, extensibility or dynamism. The use of reconfiguration techniques helps to meet these demanding requirements, but at the same time increase the complexity of the systems. This paper presents a supervisor architecture that allows maintaining the availability of a control system in spite of failures such as a node crash, by means of reconfigurations at the control level. To do this, the advantages that Model-Driven Development technology provides have been explored.
Effect of Tilt and Recline on Ischial and Coccygeal Interface Pressures in People with Spinal Cord Injury

Objective Clinicians commonly recommend that power wheelchair users with spinal cord injury perform wheelchair tilt and recline maneuvers to redistribute seating loads away from the ischial tuberosities. However, ischial pressure reduction may be accompanied by coccygeal pressure increases. Although the coccyx is among the most common sites of pressure ulcers, few studies have reported coccygeal interface pressure. The purpose of this study was to investigate both ischial and coccygeal interface pressures in response to changes in wheelchair tilt and recline angles. Design Thirteen power wheelchair users were recruited into this study. Six combinations of wheelchair tilt (15, 25, and 35 degrees) and recline (10 and 30 degrees, corresponding to traditional recline conventions of 100 and 120 degrees, respectively) angles were tested in random order. Each combination was tested with 5 mins of upright sitting, 5 mins of tilt and recline, as well as 5 mins of maximal pressure relief recovery. Peak pressure indices were calculated at the ischial and coccygeal sites. Results Ischial pressures monotonically decreased in response to increasing combinations of tilt and recline. Increments of 15 degrees of tilt did not produce significant differences under either recline angle, whereas increments of 25 degrees of tilt produced significant differences under both recline angles. Coccygeal pressures increased in response to the four smallest (of six) combinations of tilt and recline, whereas they decreased in response to the largest two combinations. Conclusions Ischial pressures seemed to be redistributed to the coccyx in response to the four smallest angle combinations and redistributed to the back support in response to the two largest angle combinations. Future work should confirm this pressure redistribution to the back support and determine the back support locations of redistribution.
Improving deep convolutional neural networks with mixed maxout units

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that “non-maximal features are unable to deliver” and “feature mapping subspace pooling is insufficient,” we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.
Least-Squares Galerkin Methods for Parabolic Problems III: Semidiscrete Case for Semilinear Problems

This is the third part of a series of papers on least-squares Galerkin methods for parabolic initial-boundary value problems. These methods are based on the minimization of a least-squares functional for an equivalent first-order system over space and time with respect to suitable discrete spaces. This paper presents the derivation and analysis of one-step methods for semidiscretization in time from least-squares principles for semilinear parabolic problems. One of the most important features of the least-squares methodology is a built-in a posteriori estimate for the approximation error. For the presentation in this paper, we focus our attention on the specific combination of piecewise linear, not necessarily continuous, functions in time with continuous piecewise linear for the flux and scalar variables, respectively. For the resulting method, a convergence result is shown for the scalar variable.
Switching signal reduction of load aggregator with optimal dispatch of electric vehicle performing V2G regulation service

Environmental concerns over the production of greenhouse gas have led to the development of environmental friendly transportation such as electric vehicle (EV). As the number of EVs is increasing day by day, it will have a great impact on grid operation and electricity market. Significant amount of EV charging during peak hour will cause branch congestions and low voltage. Moreover, high penetration of EVs plays an important role in altering electricity price. Thus, optimal scheduling of EVs charging is inevitable from the perspective of both system reliability and market. Load aggregators can combine the capacities of many EVs to participate in wholesale energy market. In this paper, optimal dispatch algorithm of EVs is developed and tested on a system consisting 1000 EVs. The advantage of the algorithm is that it requires less number of communication signals and less expensive infrastructure. EVs are turned on and turned off in binary fashion based on priority to follow the regulation signal.
Towards a low-cost production of monodispersed microbubbles using PCB-MEMS technology

This paper describes the fabrication process of a three-dimensional flow focusing device using the PCB-MEMS technology to produce microbubbles with a controlled diameter. The proposed process involves the typical photolithographic steps used in Printed Circuit Board (PCB) fabrication and a low temperature bonding technique based on tin/lead alloy. The fabricated device can be considered as low-cost nebulizer thank to the materials and bonding technique used in the fabrication process. The experimental results provide a diameter of the produced microbubbles of 230 and 250 μm. These results fit the microbubble flow focusing theory that predicts 226.71 and 245.66 μm.
Enhancing Bag-of-Words Models with Semantics-Preserving Metric Learning

The authors present an online semantics preserving, metric learning technique for improving the bag-of-words model and addressing the semantic-gap issue. This article investigates the challenge of reducing the semantic gap for building BoW models for image representation; propose a novel OSPML algorithm for enhancing BoW by minimizing the semantic loss, which is efficient and scalable for enhancing BoW models for large-scale applications; apply the proposed technique for large-scale image annotation and object recognition; and compare it to the state of the art.
SAR satellite image interpretation based on the multilayer level set approach

Synthetic aperture radar, SAR, is a remote sensing way to explore the ground truth in day and night. How to interpreter the given SAR images provides an import clue to study the characteristics of the imaged areas. However, the image interpretations for SAR images are difficult because of the effects of speckle signals shown in the images. In order to solve the problem, several algorithms have been proposed. The processed results show the proposed algorithms still have their own limits on reducing the effect of speckle signals. In this paper, the multilayer level set approach is employed to have the SAR images be grouped into several sub-regions such that the segmented regions are homogeneous. Based on this minimization of the energy, the multilayer level set method implicitly presents the regional boundaries as several nested level lines. By increasing iterations and preselected level values, these lines evolve close to the level boundaries based on the energy minimization. This method provides numerical stability and quick convergence. In order to implement the multilayer level set approach, several level values need to be established firstly. Those level values are determined with calculating the average values from the classified groups with applying K-means method. Based on the four-color theory, the multilayer level set method is able to generate an optimal piecewise continuous approximation for the SAR image such that each approximation sub-region is homogeneous. From the processed results, the multilayer level set approach can efficiently reduce the effect of the speckle signals, and quickly segment SAR images for further image interpretation.
Attributes of Hispanic Gifted Bilingual Students as Perceived by Bilingual Educators in Texas

The purpose of this study was to report the perceptions of observable characteristics that bilingual classroom teachers have regarding bilingual Hispanic students who are either currently identified as gifted or whom they perceive as potentially gifted but who did not meet district identification criteria. The research questions, exploratory in nature, were: (1) Given a group of certified bilingual educators, to what extent would they perceive various Hispanic sociological or linguistic characteristics or attributes as well as diverse gifted characteristics, as being exhibited among identified or potentially gifted, bilingual, Hispanic students? and (2) How would the same group of bilingually certified educators perceive similarities among the characteristics that they determined as observable among identified or potentially gifted, bilingual, Hispanic students? A sample of 61 elementary (K-4) bilingual teachers in Texas volunteered to complete a ninety-item questionnaire designed by the researchers and based on an extensive review of the literature. A descriptive analysis of those items was conducted through the computation of each item mean and standard deviation, and a cluster analysis was employed that grouped the data into eleven clusters.