Miracle-dz/UniFAR-FTUs huggingface dataset:
Nosocomial Outbreak of a Novel Extended-Spectrum β-Lactamase Salmonella enterica Serotype Isangi Among Surgical Patients

OBJECTIVE Nosocomial outbreaks caused by Salmonella are rare. We describe the investigation and control of a cluster of novel extended-spectrum β-lactamase (ESBL) Salmonella enterica serotype Isangi in a hospital in southeastern Michigan. METHODS An epidemiologic investigation, including case-control study, assessment of infection control practices and environmental cultures, was performed to identify modes of transmission. Healthcare workers (HCWs) exposed to case patients were screened. Strain relatedness was determined using pulsed-field gel electrophoresis (PFGE); ESBL confirmation was conducted using real-time PCR. Control measures were implemented to prevent further transmission. RESULTS Between September 2 and October 22, 2015, 19 surgical patients, including 10 organ transplant recipients and 1 HCW, had positive S. Isangi cultures. Of these case patients and HCW, 13 had gastroenteritis, 2 had bacteremia, 1 had surgical-site infection, and 4 were asymptomatic. Pulsed-field gel electrophoresis (PFGE) showed 89.5% similarity among the isolates in these cases. Isolates with resistant-phenotypes possessed plasmid-mediated CTX-M15 ESBL. A total of 19 case patients were compared with 57 control participants. Case patients had significantly higher odds of exposure to an intraoperative transesophageal (TEE) probe (adjusted odds ratio 9.0; 95% confidence interval, 1.12–72.60; P=.02). Possible cross-transmission occurred in the HCW and 2 patients. Cultures of TEE probes and the environment were negative. The outbreak ended after removal of TEE probes, modification of reprocessing procedures, implementation of strict infection control practices, and enhanced environmental cleaning. CONCLUSIONS We report the first nosocomial ESBL S. Isangi outbreak in the United States. Multiple control measures were necessary to interrupt transmission of this gastrointestinal pathogen. Exposure to possibly contaminated TEE probes was associated with transmission. Periodic monitoring of reprocessing procedures of TEE probes may be required to ensure optimal disinfection. Infect Control Hosp Epidemiol 2016;37:954–961
Sacral Variability in Tailless Species: Homo sapiens and Ochotona princeps

Homo sapiens is variable in number of sacral vertebrae, and this variability can lead to obstetrical complication. This study uses the comparative method to test the hypothesis that sacral variability in H. sapiens is associated with absence of a tail. Three species of lagomorphs are studied: Ochotona princeps (N = 271), which is tailless, and Lepus californicus (N = 212) and Sylvilagus audubonii (N = 206), which have tails. Results show that O. princeps has (1) higher diversity index for number of sacral vertebrae (0.49) compared to L. californicus (0.25) and S. audubonii (0.26) and (2) significantly higher percentage of individuals with the species‐specific nonmodal number of sacral vertebrae (43.9%) compared to L. californicus (14.2%) and S. audubonii (15.5%). Comparison of H. sapiens (N = 1,030; individuals of age 20–39 years) with O. princeps shows similarities between the species in diversity index (also 0.49 in H. sapiens) and percentage of individuals with nonmodal number of sacral vertebrae (37.3% in H. sapiens). Homeotic transformation best explains the results. H. sapiens and O. princeps show propensity for caudal shift at the sacral–caudal border (i.e., homeotic transformation of the first caudal vertebra to a sacral vertebra). Caudal and cranial shift among presacral vertebrae increases or decreases this propensity, respectively. Increase in number of sacral vertebrae in H. sapiens by homeotic transformation reduces pelvic outlet capacity and can be obstetrically hazardous. Anat Rec, 300:798–809, 2017. © 2017 Wiley Periodicals, Inc.
Nutritional Profile and Dietary Patterns of Lebanese Non-Alcoholic Fatty Liver Disease Patients: A Case-Control Study

Nonalcoholic fatty liver disease (NAFLD) is considered the most common liver disease in the world. Dietary habits have a significant impact on the biological and physical profile of patients and increase the risk of NAFLD. The overall pattern of diet intake is more associated with health outcomes than nutrients. The aim of this study was to evaluate the nutritional profile and the dietary patterns of Lebanese NAFLD patients and compare it with controls. During this study; 112 NAFLD Lebanese adult patients (55 men and 57 women); and 110 controls (44 men and 66 women) were recruited. Dietary intake was evaluated by two 24-h recalls and a semi-quantitative 90-item food frequency questionnaire. Dietary patterns were determined by factor analysis. Results from the study demonstrated that 40% of cases belonged to the high fruit group as compared to 30% following a high meat; fast food dietary pattern. Both groups increased the odds of NAFLD by four-fold (p < 0.05). The traditional diet decreases the odds by 33% after adjustment with the covariables. The high fruit diet group was, as with the high meat, fast food dietary pattern, the main potential risk factor for NAFLD in Lebanese patients.
Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

In this paper, crowdsourcing-based industrial sensing intelligence (CISI) is proposed as a collaborative approach for large-scale monitoring in modern industrial plants, targeting at improved productivity and increased workplace safety. The proposed approach focuses on middleware, which considers both application and industry-grade requirements. Through embedding crowdsourcing knowledge at different levels and supporting QoS services, systems based on CISI can perform effective work assignment and flexible configuration of wireless sensor networks (WSNs). This paper presents a middleware that addresses these characteristics, which is an extension of GSN, our earlier work on middleware for rapid deployment and integration of heterogeneous sensor networks. Wireless sensor devices and wearable equipment are employed as modeling tools for the middleware implementation.
Email Classification into Relevant Category Using Neural Networks

In the real world, many online shopping websites or service provider have single email-id where customers can send their query, concern etc. At the back-end service provider receive million of emails every week, how they can identify which email is belonged of a particular department? This paper presents an artificial neural network (ANN) model that is used to solve this problem and experiments are carried out on user personal Gmail emails datasets. This problem can be generalised as typical Text Classification or Categorization.
OUS: Optimal user selection in MU-MIMO WLAN

In multiuser MIMO (MU-MIMO) networks, different users can transmit packets to the AP concurrently, thereby network capacity could be significantly improved. Previous works, using the conventional user selection schemes, have shown the promise of MU-MIMO in practical WLANs. However, we find that, to harness the full potential of MU-MIMO, an optimal user selection scheme is required. In this paper, we formulate the user selection problem in MU-MIMO as a discrete constrained optimization problem, and by decomposing and solving this problem, we design an optimal selection scheme called OUS. OUS efficiently allocates network resources so as to improve both the overall throughput of a network and fairness among users. We conduct extensive simulations to evaluate performance of OUS, and the simulation results show that compared with previous works, OUS improves the overall throughput by 40% and improves the fairness by 30% in most cases.
Evaluating Compositionality in Sentence Embeddings

An important frontier in the quest for human-like AI is compositional semantics: how do we design systems that understand an infinite number of expressions built from a finite vocabulary? Recent research has attempted to solve this problem by using deep neural networks to learn vector space embeddings of sentences, which then serve as input to supervised learning problems like paraphrase detection and sentiment analysis. Here we focus on 'natural language inference' (NLI) as a critical test of a system's capacity for semantic compositionality. In the NLI task, sentence pairs are assigned one of three categories: entailment, contradiction, or neutral. We present a new set of NLI sentence pairs that cannot be solved using only word-level knowledge and instead require some degree of compositionality. We use state of the art sentence embeddings trained on NLI (InferSent, Conneau et al. (2017)), and find that performance on our new dataset is poor, indicating that the representations learned by this model fail to capture the needed compositionality. We analyze some of the decision rules learned by InferSent and find that they are largely driven by simple heuristics at the word level that are ecologically valid in the SNLI dataset on which InferSent is trained. Further, we find that augmenting the training dataset with our new dataset improves performance on a held-out test set without loss of performance on the SNLI test set. This highlights the importance of structured datasets in better understanding, as well as improving the performance of, AI systems.
Brainstem network dynamics underlying the encoding of bladder information

Urodynamic status must interact with arousal and attentional processes so that voiding occurs under appropriate conditions. To elucidate the central encoding of this visceral demand, multisite recordings were made within a putative pontine-cortical micturition circuit from the pontine micturition center (PMC), locus coeruleus (LC) and medial prefrontal cortex (mPFC) during cystometry in unanesthetized rats. PMC neurons had homogeneous firing patterns, characterized by tonic activity and phasic bursts that were temporally associated with distinct phases of the micturition cycle. LC and cortical activation became synchronized 20-30 s prior to micturition. During this pre-micturition interval, a theta oscillation developed in the LC, the mPFC desynchronized and LC-mPFC coherence increased in the theta frequency range. The temporal offset between the shift in LC-mPFC network activity and micturition may allow time to disengage from ongoing behaviors unrelated to micturition and initiate specific voiding behaviors so that micturition occurs in environmentally and socially appropriate conditions.
Inhibition of β-oxidation is not a valid therapeutic tool for reducing oxidative stress in conditions of neurodegeneration

According to recent reports, systemic treatment of rats with methylpalmoxirate (carnitine palmitoyltransferase-1 inhibitor) decreased peroxidation of polyunsaturated fatty acids in brain tissue. This was taken as evidence of mitochondrial β-oxidation in brain, thereby contradicting long-standing paradigms of cerebral metabolism, which claim that β-oxidation of activated fatty acids has minor importance for brain energy homeostasis. We addressed this controversy. Our experiments are the first direct experimental analysis of this question. We fueled isolated brain mitochondria or rat brain astrocytes with octanoic acid, but octanoic acid does not enhance formation of reactive oxygen species, neither in isolated brain mitochondria nor in astrocytes, even at limited hydrogen delivery to mitochondria. Thus, octanoic acid or l-octanoylcarnitine does not stimulate H2O2 release from brain mitochondria fueled with malate, in contrast to liver mitochondria (2.25-fold rise). This does obviously not support the possible occurrence of β-oxidation of the fatty acid octanoate in the brain. We conclude that a proposed inhibition of β-oxidation does not seem to be a helpful strategy for therapies aiming at lowering oxidative stress in cerebral tissue. This question is important, since oxidative stress is the cause of neurodegeneration in numerous neurodegenerative or inflammatory disease situations.
Background subtraction using the factored 3-way restricted Boltzmann machines

In this paper, we proposed a method for reconstructing the 3D model based on continuous sensory input. The robot can draw on extremely large data from the real world using various sensors. However, the sensory inputs are usually too noisy and high-dimensional data. It is very difficult and time consuming for robot to process using such raw data when the robot tries to construct 3D model. Hence, there needs to be a method that can extract useful information from such sensory inputs. To address this problem our method utilizes the concept of Object Semantic Hierarchy (OSH). Different from the previous work that used this hierarchy framework, we extract the motion information using the Deep Belief Network technique instead of applying classical computer vision approaches. We have trained on two large sets of random dot images (10,000) which are translated and rotated, respectively, and have successfully extracted several bases that explain the translation and rotation motion. Based on this translation and rotation bases, background subtraction have become possible using Object Semantic Hierarchy.
Physical Activity, Body Mass Index, and Clustered Metabolic Risk in U.S. Adolescents: 2007-2012 Nhanes.

BACKGROUND While studies to date have shown that children and adolescents who meet the current physical activity (PA) recommendations and maintain a healthy body weight demonstrate significantly lower cardiometabolic risk, there are some studies that suggest that the relationship between PA and metabolic risk may be mediated by adiposity. The aim of the present study was to examine variation in clustered metabolic risk (cMetS) in adolescents classified as not overweight/active (NOA), not overweight/not active (NONA), overweight/active (OA), and overweight/not active (ONA). METHODS The sample included adolescent participants (n = 875; 12-17 years) of the 2007-2012 National Health and Nutrition Examination Survey (NHANES). The cMetS score included triglycerides, high-density lipoprotein cholesterol, fasting plasma glucose, and mean arterial pressure. Age- and sex-specific body mass index (BMI) percentiles were utilized; overweight was defined as BMI percentile ≥85th. Activity data included self-reported frequency of moderate-to-vigorous PA. Adolescents reporting ≥60 min/day of PA were considered "active. " General linear models, adjusted for age, sex, and race-ethnicity, were used. A 6-year fasting sample weight was applied to the analyses to ensure representativeness of the data. RESULTS The cMetS scores were significantly (P < 0.05) higher in OA and ONA adolescents compared to NOA (β = 1.08 and β = 1.57, respectively). In ONA males, cMetS was significantly (P < 0.01) higher compared to NOA males. In OA and ONA females, cMetS scores were significantly higher compared to the referent group (P < 0.01 for both). CONCLUSIONS The cMetS scores were increased in overweight adolescents compared to those who were not overweight, regardless of their reported activity level.
Revisiting constraint based geo location: improving accuracy through removal of outliers

IP based GeoLocation has become increasingly important for network administrators for a number of reasons. The first and foremost is to determine cyber attackers’ geographical location to prevent, contain and thwart further attacks. A secondary reason may be to target potential advertisements to users based on their geographical location. IP addresses are indicators of the users location at a course level such as country or city. For further refined estimate of user location within a city, for example, network measurements as simple as simple round trip time of pings to the user can give a more precise estimate of users location. Recently, several researchers have proposed constraint based optimization approaches to estimate a users location using a set of landmark hosts using multi-lateration. In this work, we show that the optimization of such schemes are sometimes plagued by outliers which causes location estimated to be deteriorated greatly. We provide anecdotal evidence for this and proposals to alleviate these problems for detection, masking or removal of these outlier measurements and show that location error improvement of several hundreds or thousands of km are possible.
Synthesis and Characterization of MgO/TiO2 Nanocomposites

The nanostructured material properties were showing surprisingly different from the bulk materials. Among these nanostructured materials nanocomposites are of great technological importance because of their small size and high surface to volume ratio. Due to these reason the nanocomposites were used in various potential applications such as optoelectronics, microelectronics, water purification, paints, biomedical field and different sensor fabrication. Present work concentrates on synthesis and characterization of MgO/TiO2 nanocomposites by Microwave-assisted method using ionic liquid as the initial precursor and different temperature effects were investigated on it. The obtained MgO/ TiO2 nanocomposites have been characterized by X-Ray Diffraction (XRD), particle size analyser (PSA), transmission electron microscopy (TEM), Fourier transform infrared spectrometer (FTIR), thermo gravimetric/differential thermal analyzer (TG/DTA) and Keithley electric meter for average crystallite size, average particle size, morphological studies, bond analysis, thermal stability and resistance respectively.
Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem

This research proposes a novel indicator-based hybrid evolutionary approach that combines approximate and exact algorithms. We apply it to a new bi-criteria formulation of the travelling thief problem, which is known to the Evolutionary Computation community as a benchmark multi-component optimisation problem that interconnects two classical NP-hard problems: the travelling salesman problem and the 0-1 knapsack problem. Our approach employs the exact dynamic programming algorithm for the underlying packing while travelling problem as a subroutine within a bi-objective evolutionary algorithm. This design takes advantage of the data extracted from Pareto fronts generated by the dynamic program to achieve better solutions. Furthermore, we develop a number of novel indicators and selection mechanisms to strengthen synergy of the two algorithmic components of our approach. The results of computational experiments show that the approach is capable to outperform the state-of-the-art results for the single-objective case of the problem.
Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments

This paper considers a wireless energy harvesting two-way relay (TWR) network where the relay has energy-harvesting abilities and the effects of practical hardware impairments are taken into consideration. In particular, power splitting (PS) receiver is adopted at relay to harvests the power it needs for relaying the information between the source nodes from the signals transmitted by the source nodes, and hardware impairments is assumed suffered by each node. We analyze the effect of hardware impairments on both decode-and-forward (DF) relaying and amplify-and-forward (AF) relaying networks. By utilizing the obtained new expressions of signal-to-noise-plus-distortion ratios, the exact analytical expressions of the achievable sum rate and ergodic capacities for both DF and AF relaying protocols are derived. Additionally, the optimal power splitting (OPS) ratio that maximizes the instantaneous achievable sum rate is formulated and solved for both protocols. The performances of DF and AF protocols are evaluated via numerical results, which also show the effects of various network parameters on the system performance and on the OPS ratio design.
IoT Service Clustering for Dynamic Service Matchmaking

As the adoption of service-oriented paradigms in the IoT (Internet of Things) environment, real-world devices will open their capabilities through service interfaces, which enable other functional entities to interact with them. In an IoT application, it is indispensable to find suitable services for satisfying users’ requirements or replacing the unavailable services. However, from the perspective of performance, it is inappropriate to find desired services from the service repository online directly. Instead, clustering services offline according to their similarity and matchmaking or discovering service online in limited clusters is necessary. This paper proposes a multidimensional model-based approach to measure the similarity between IoT services. Then, density-peaks-based clustering is employed to gather similar services together according to the result of similarity measurement. Based on the service clustering, the algorithms of dynamic service matchmaking, discovery, and replacement will be performed efficiently. Evaluating experiments are conducted to validate the performance of proposed approaches, and the results are promising.
Inaudible Voice Commands: The Long-Range Attack and Defense

Recent work has shown that inaudible signals (at ultrasound frequencies) can be designed in a way that they become audible to microphones. Designed well, this can empower an adversary to stand on the road and silently control Amazon Echo and Google Home-like devices in people’s homes. A voice command like “Alexa, open the garage door” can be a serious threat. While recent work has demonstrated feasibility, two issues remain open: (1) The attacks can only be launched from within 5 ft of Amazon Echo, and increasing this range makes the attack audible. (2) There is no clear so-lution against these ultrasound attacks, since they exploit a recently discovered loophole in hardware non-linearity. This paper is an attempt to close both these gaps. We begin by developing an attack that achieves 25 ft range, limited by the power of our ampliﬁer. We then develop a defense against this class of voice attacks that exploit non-linearity. Our core ideas emerge from a careful forensics on voice, i.e., ﬁnding indelible traces of non-linearity in recorded voice signals. Our system, LipRead , demonstrates the inaudible attack in various conditions, followed by defenses that only require software changes to the microphone.
A DoF-Based Link Layer Model for Multi-Hop MIMO Networks

The rapid advances of MIMO to date have mainly stayed at the physical layer. Such fruits have not fully benefited MIMO research at the network layer mainly due to the computational complexity associated with the matrix-based model that MIMO involves. Recently, there have been some efforts to simplify link layer model for MIMO so as to facilitate research at the upper layers. These models only require simple numeric computations on MIMO's degrees-of-freedom (DoFs) to characterize spatial multiplexing (SM) and interference cancellation (IC). Thus, these models are much simpler than the original matrix-based model from the communications world. However, achievable DoF regions of these DoF-based models are not analyzed. In this paper, we re-visit this important problem of MIMO modeling. Based on accounting of how DoFs are consumed for SM and IC, we develop a tractable link layer model for multi-hop MIMO networks. We show that under common assumptions of DoF-based models and additional assumption of no dependency cycle, this model includes all the feasible solutions by the matrix-based model under SM and IC for any network topology. This work offers an important building block for theoretical research on multi-hop MIMO networks.