Miracle-dz/UniFAR-FTUs huggingface dataset:
Pan-β-Lactam Resistance Development in Pseudomonas aeruginosa Clinical Strains: Molecular Mechanisms, Penicillin-Binding Protein Profiles, and Binding Affinities

ABSTRACT We investigated the mechanisms leading to Pseudomonas aeruginosa pan-β-lactam resistance (PBLR) development during the treatment of nosocomial infections, with a particular focus on the modification of penicillin-binding protein (PBP) profiles and imipenem, ceftazidime, and ceftolozane (former CXA-101) PBP binding affinities. For this purpose, six clonally related pairs of sequential susceptible-PBLR isolates were studied. The presence of oprD, ampD, and dacB mutations was explored by PCR followed by sequencing and the expression of ampC and efflux pump genes by real-time reverse transcription-PCR. The fluorescent penicillin Bocillin FL was used to determine PBP profiles in membrane preparations from all pairs, and 50% inhibitory concentrations (IC50s) of ceftolozane, ceftazidime, and imipenem were analyzed in 3 of them. Although a certain increase was noted (0 to 5 2-fold dilutions), the MICs of ceftolozane were ≤4 μg/ml in all PBLR isolates. All 6 PBLR isolates lacked OprD and overexpressed ampC and one or several efflux pumps, particularly mexB and/or mexY. Additionally, 5 of them showed modified PBP profiles, including a modified pattern (n = 1) or diminished expression (n = 1) of PBP1a and a lack of PBP4 expression (n = 4), which correlated with AmpC overexpression driven by dacB mutation. Analysis of the essential PBP IC50s revealed significant variation of PBP1a/b binding affinities, both within each susceptible-PBLR pair and across the different pairs. Moreover, despite the absence of significant differences in gene expression or sequence, a clear tendency toward increased PBP2 (imipenem) and PBP3 (ceftazidime, ceftolozane, imipenem) IC50s was noted in PBLR isolates. Thus, our results suggest that in addition to AmpC, efflux pumps, and OprD, the modification of PBP patterns appears to play a role in the in vivo emergence of PBLR strains, which still conserve certain susceptibility to the new antipseudomonal cephalosporin ceftolozane.
You’ll Never Tweet Alone: Building Sports Match Timelines from Microblog Posts

In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets collected during the EURO 2016 Championship and evaluate the output against live summaries produced by sports channels.
What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and Geographic Information System

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: how can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general, and health researchers in specific, to better understand health problems from large conversational datasets.
MIMAC : A Rate Adaptive MAC Protocol for MIMO-based Wireless Networks

This paper presents a rate adaptive medium access control (MAC) protocol for wireless networks with MIMO links. It is envisioned that the next generation high-throughput wireless LAN standard (IEEE 802.11n), which is currently under development, would use MIMO technology to achieve high data rates. An important design consideration is maintaining backward compatibility with the IEEE 802.11a/g standards. We adopt a joint MAC and physical layer strategy for channel access, based on the instantaneous channel conditions at the receiver. Our contributions include a transmit antenna and data rate selection scheme based on the optimal tradeoff between spatial multiplexing and diversity. The goal is to maximize the achievable data rate, given a MIMO channel instance and a target bit error rate. We also provide a feedback mechanism for the transmitter to obtain the rate selection settings from the receiver. Moreover, we maintain compatibility with legacy 802.11a/g devices and our protocol supports communication between devices with different number of antennas. The overall contribution is a MIMO physical layer aware, rate adaptive MAC protocol, which is compatible with 802.11a/g and can also be readily integrated with the 802.11n proposals. Categories & Subject Descriptors: C.2. 1 [Computer-Communication Networks]: Network Architecture and Design – wireless communication General Terms: Algorithms, Performance, Design.
FastCollect: Offloading Generational Garbage Collection to integrated GPUs

Generational Mark-Sweep Garbage Collection is a widely used garbage collection technique. However, the garbage collector has poor execution efficiency for large programs. Aggressive collection causes execution pauses in the program, while reducing the collection frequency leads to memory wastage. In this work, we develop FastCollect, a parallel version of the generational mark-sweep garbage collector running on a graphics processing unit (GPU). At the core of our parallel implementation lies a parallel Depth First Search using a space-efficient concurrent stack, which we develop for the young and the mature garbage collection. To further improve performance, (i) we reduce thread-divergence and improve load-balancing by devising a distributed work-stealing approach, (ii) we optimize our garbage collection algorithm to reduce the number of atomic instructions, (iii) we exploit the memory hierarchy to design a hybrid stack, and (iv) we extract multiple adjacent objects simultaneously by exploiting vectorized memory accesses. We implemented FastCollect in Java Hotspot VM and evaluated our results by executing DaCapo benchmarks. FastCollect is 4-5× faster than the Parallel Hotspot garbage collector and 42% faster than a previous GPU implementation. In addition, while the existing GPU version requires memory linear in the number of compute units, FastCollect's memory requirement is fixed and low. FastCollect not only improves execution time of garbage collection, but also relieves the CPU for improved user interaction.
Resampling Markov Chain Monte Carlo Algorithms: Basic Analysis and Empirical Comparisons

Sampling from complex distributions is an important but challenging topic in scientific and statistical computation. We synthesize three ideas, tempering, resampling, and Markov moving, and propose a general framework of resampling Markov chain Monte Carlo (MCMC). This framework not only accommodates various existing algorithms, including resample-move, importance resampling MCMC, and equi-energy sampling, but also leads to a generalized resample-move algorithm. We provide some basic analysis of these algorithms within the general framework, and present three simulation studies to compare these algorithms together with parallel tempering in the difficult situation where new modes emerge in the tails of previous tempering distributions. Our analysis and empirical results suggest that generalized resample-move tends to perform the best among all the algorithms studied when the Markov kernels lead to fast mixing or even locally so toward restricted distributions, whereas parallel tempering tends to perform the best when the Markov kernels lead to slow mixing, without even converging fast to restricted distributions. Moreover, importance resampling MCMC and equi-energy sampling perform similarly to each other, often worse than independence Metropolis resampling MCMC. Therefore, different algorithms seem to have advantages in different settings.
Probabilistic Lexical Generalization for French Dependency Parsing

This paper investigates the impact on French dependency parsing of lexical generalization methods beyond lemmatization and morphological analysis. A distributional thesaurus is created from a large text corpus and used for distributional clustering and WordNet automatic sense ranking. The standard approach for lexical generalization in parsing is to map a word to a single generalized class, either replacing the word with the class or adding a new feature for the class. We use a richer framework that allows for probabilistic generalization, with a word represented as a probability distribution over a space of generalized classes: lemmas, clusters, or synsets. Probabilistic lexical information is introduced into parser feature vectors by modifying the weights of lexical features. We obtain improvements in parsing accuracy with some lexical generalization configurations in experiments run on the French Treebank and two out-of-domain treebanks, with slightly better performance for the probabilistic lexical generalization approach compared to the standard single-mapping approach.
A Penalized Autoencoder Approach for Nonlinear Independent Component Analysis

We propose Independent Component Autoencoder (ICAE), a deep neural network-based framework for nonlinear Independent Component Analysis (ICA). The proposed method consists of a penalized autoencoder and a training objective that is to minimize a combination of the reconstruction loss and an ICA contrast. Unlike many previous ICA methods that are usually tailored to separate specific mixture, our method can recover sources from various mixtures, without prior knowledge on the nature of that mixture.
Atomistic characterization of three-dimensional lattice trapping barriers to brittle fracture

We present a detailed account of an atomistic study of three-dimensional lattice trapping barriers to brittle fracture in Si. By means of a prototypical interatomic potential model, we map out the molecular details of the evolution of atomically sharp cracks in the (111) cleavage plane with straight crack fronts along the and directions, respectively. The thermally activated processes of bond rupturing along the crack front are quantitatively characterized using a reaction pathway sampling scheme. The calculated minimum energy paths reveal a mechanism of kink-pair formation and migration in facilitating the crack front advancement. We show that the physical origin of directional anisotropy in cleavage crack propagation can be attributed to a difference in the kink-pair formation energy for different crack orientations. The effects of interatomic potentials are delineated by comparing the Stillinger–Weber model with an environment-dependent model.
Plasma 1-deoxysphingolipids are predictive biomarkers for type 2 diabetes mellitus

Objective Serine palmitoyltransferase (SPT) catalyzes the condensation of serine and palmitoyl coenzyme A, the first step in the de novo sphingolipid synthesis. Apart from these canonical substrates, SPT can also metabolize alanine and other acyl coenzyme As. This forms a spectrum of atypical sphingoid bases which are altered in the context of the metabolic syndrome (MetS) and type 2 diabetes mellitus (T2DM). We investigated whether atypical sphingolipids can be used as prospective markers to predict the incidence of T2DM. Research design and methods Using liquid chromatography/mass spectrometry, we analyzed the sphingoid base profile in a prospective cohort with 339 individuals. All individuals were followed up for a period of 8 years. Results Confirming earlier results, we found 1-deoxysphingolipids (1-deoxySLs) to be significantly elevated in patients with MetS, impaired fasting glucose, and T2DM. Patients who developed T2DM during the follow-up period (n=32) showed significantly higher 1-deoxySL levels at baseline compared with those who did not develop T2DM until the end of the study (n=70). 1-Deoxysphingosine levels were independent predictors for T2DM even after adjusting for glycated hemoglobin (standardized adjusted OR=2.1, CI 95% (1.19 to 3.71); p=0.010), MetS (standardized adjusted OR=1.97, CI 95% (1.13 to 3.43); p=0.017), and other risk factors such as age, sex, BMI, and lipid-lowering drugs. Similar results were observed for the 1-deoxysphinganine levels. Conclusions Our results support a novel role for 1-deoxySL as predictive biomarkers for the development of T2DM in risk patients and warrants further larger prospective trials in lower risk cohorts.
First-Pass Contrast-Enhanced Myocardial Perfusion MRI Using a Maximum Up-Slope Parametric Map

Magnetic resonance first-pass perfusion imaging offers a noninvasive method for the rapid, accurate, and reproducible assessment of cardiac function without ionizing radiation. Quantitative or semiquantitative analysis of changes in signal intensity (SI) over the whole image sequence yields a more efficient analysis than direct visual inspection. In this paper, a method to generate maximum up-slope myocardial perfusion maps is presented. The maximum up-slope is defined by comparison of the SI variations using frame-to-frame analysis. A map of first-pass transit of the contrast agent is constructed pixel by pixel using a linear curve fitting model. The proposed method was evaluated using data from eight subjects. The data from the parametric maps agreed well with those obtained from traditional, manually derived region-of-interest methods as shown through ANOVA. The straightforward implementation and increase in image analysis efficiency resulting from this method suggests that it may be useful for clinical practice
Comparative Study of Data Mining Algorithms in Medical Data

Dermatological diseases are the most crucial disease for both men and women. Skin diseases are commonly misdiagnosed with each other. One of the most common forms of medical malpractices globally is an error in diagnosis. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, Decision Tree, Naive bayes and Artificial Neural Network to massive volume of healthcare data. Because of the huge amount of data, they are not “mined” to discover hidden information. This is an extension of naive bayes which discover hidden patterns and relationships often go unexploited. Diagnosis of Dermatological Diseases can answer complex “what if” queries which traditional decision support systems cannot. Using generic symptoms such as age, sex, fever, whether, eye symptoms, it can predict the likelihood of patients are used. Aim of the study is to propose a model for early detection and correct diagnosis of the disease which will help the doctor to predict the disease So diagnosis of a single patient can differ significantly if he was examined by different physicians. So we need a standard format for predicting such a disease. Today, automated medical analysis help doctors to diagnose and predict diseases, at a very fast pace. Medical dataset used for this work contain 230 instances with 21 attributes. In this paper we have experimented on data gathered from the southern part of Kerala, India. Weka is used to calculate the accuracy of the dataset we are collected. This paper evaluates three classification algorithms such as Naive Bayes classifier, J48 and Support Vector Machine for the evaluation of some skin patient
Generalizing to new subjects in brain-computer interfacing

This paper evaluates an algorithm based on support vector machines to analyze EEG data from the P300 speller brain-computer interface paradigm. We evaluated the performance of this technique on own experimental data from 8 subjects and achieved high transfer rates of up to 97.57 bits/min (mean 47.26 bits/min) within subjects. We then investigated how well the classifier generalizes when it is trained on data from a set of several subjects and then applied on data from a new subject to use this BCI in a pretrained fashion. Transfer rates up to 61.04 bits/min were achieved (mean 17.64 bits/min) for this situation indicating an encouraging generalization performance.
Decentralized $H_{\infty }$ Filter Design for Discrete-Time Interconnected Fuzzy Systems

This paper describes a decentralized H infin filter design for discrete-time interconnected fuzzy systems based on piecewise-quadratic Lyapunov functions. The systems consist of J discrete-time interconnected Takagi-Sugeno (T-S) fuzzy subsystems, and a decentralized H infin filter is designed for each subsystem. It is shown that the stability of the overall filtering-error system with H infin performance can be established if a piecewise-quadratic Lyapunov function can be constructed. Moreover, the parameters of filters can be obtained by solving a set of linear matrix inequalities that are numerically feasible. Two simulation examples are given to show the effectiveness of the proposed approach.
Direct Digital Manufacturing of Nanocomposites

Additive manufacturing has surged in popularity as a route to designing and preparing functional parts. Depending on the parts function, certain attributes such as high mechanical performances may be desired. We develop a route for improving the mechanical properties of polymer devices, fabricated through additive manufacturing by combining electrospinning and stereo-lithography into one automated process. This process utilises the impressive mechanical properties of carbon nanotubes by encapsulating and aligning them in electrospun fibres. Composite fibres will be incorporated into polymer resins prepared with stereo-lithography, thereby providing resins that benefit from the composite fibres properties, enhancing their overall mechanical properties.
Exploiting instruction level parallelism in geometry processing for three dimensional graphics applications

Three dimensional (3D) graphics applications have become very important workloads running on today's computer systems. A cost-effective graphics solution is to perform geometry processing of 3D graphics on the host CPU and have specialized hardware handle the rendering task. In this paper, we analyze microarchitecture and SIMD instruction set enhancements to a RISC superscalar processor for exploiting instruction level parallelism (ILP) in geometry processing for 3D computer graphics. Our results show that 3D geometry processing has inherent parallelism. When ignoring cycle time effects, an 8-issue processor can achieve up to 60% performance improvement over a 4-issue. However, certain application attributes can hinder the exploitation of ILP on a superscalar processor. Adding SIMD operations improves performance from 8% to 28% on a 4-issue processor that can issue at most 2 floating-point operations. If processor cycle time scales with the number of ports to the register file, doubling only the floating-point issue width of a 4-issue processor with SIMD instructions gives the best performance among the architecture configurations that we examine (the most aggressive configuration is an 8-issue processor with SIMD instructions).
Differential Killing of Salmonella enterica Serovar Typhi by Antibodies Targeting Vi and Lipopolysaccharide O:9 Antigen

Salmonella enterica serovar Typhi expresses a capsule of Vi polysaccharide, while most Salmonella serovars, including S. Enteritidis and S. Typhimurium, do not. Both S. Typhi and S. Enteritidis express the lipopolysaccharide O:9 antigen, yet there is little evidence of cross-protection from anti-O:9 antibodies. Vaccines based on Vi polysaccharide have efficacy against typhoid fever, indicating that antibodies against Vi confer protection. Here we investigate the role of Vi capsule and antibodies against Vi and O:9 in antibody-dependent complement- and phagocyte-mediated killing of Salmonella. Using isogenic Vi-expressing and non-Vi-expressing derivatives of S. Typhi and S. Typhimurium, we show that S. Typhi is inherently more sensitive to serum and blood than S. Typhimurium. Vi expression confers increased resistance to both complement- and phagocyte-mediated modalities of antibody-dependent killing in human blood. The Vi capsule is associated with reduced C3 and C5b-9 deposition, and decreased overall antibody binding to S. Typhi. However, purified human anti-Vi antibodies in the presence of complement are able to kill Vi-expressing Salmonella, while killing by anti-O:9 antibodies is inversely related to Vi expression. Human serum depleted of antibodies to antigens other than Vi retains the ability to kill Vi-expressing bacteria. Our findings support a protective role for Vi capsule in preventing complement and phagocyte killing of Salmonella that can be overcome by specific anti-Vi antibodies, but only to a limited extent by anti-O:9 antibodies.
A joint longitudinal and survival model with health care usage for insured elderly

We study longevity and usage of medical resources of a sample of individuals aged 65 years or more who are covered by a private insurance policy. A longitudinal analysis is presented, where the yearly cumulative number of medical coverage requests by each subject characterizes insurance intensity of care until death. We confirm that there is a significant correlation between the longitudinal data on usage level and the survival time processes. We obtain dynamic estimations of event probabilities and we exploit the potential of joint models for personalized survival curve adjustment.
Multiplicative versus additive noise in multistate neural networks

The effects of a variable amount of random dilution of the synaptic couplings in Q-Ising multi-state neural networks with Hebbian learning are examined. A fraction of the couplings is explicitly allowed to be anti-Hebbian. Random dilution represents the dying or pruning of synapses and, hence, a static disruption of the learning process which can be considered as a form of multiplicative noise in the learning rule. Both parallel and sequential updating of the neurons can be treated. Symmetric dilution in the statics of the network is studied using the mean-field theory approach of statistical mechanics. General dilution, including asymmetric pruning of the couplings, is examined using the generating functional (path integral) approach of disordered systems. It is shown that random dilution acts as additive gaussian noise in the Hebbian learning rule with a mean zero and a variance depending on the connectivity of the network and on the symmetry. Furthermore, a scaling factor appears that essentially measures the average amount of anti-Hebbian couplings.