Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure of Clinical MRSA Heterogeneous Strains to β-Lactams Redirects Metabolism to Optimize Energy Production through the TCA Cycle

Methicillin-resistant Staphylococcus aureus (MRSA) has emerged as one of the most important pathogens both in health care and community-onset infections. The prerequisite for methicillin resistance is mecA, which encodes a β-lactam-insensitive penicillin binding protein PBP2a. A characteristic of MRSA strains from hospital and community associated infections is their heterogeneous expression of resistance to β-lactam (HeR) in which only a small portion (≤0.1%) of the population expresses resistance to oxacillin (OXA) ≥10 µg/ml, while in other isolates, most of the population expresses resistance to a high level (homotypic resistance, HoR). The mechanism associated with heterogeneous expression requires both increase expression of mecA and a mutational event that involved the triggering of a β-lactam-mediated SOS response and related lexA and recA genes. In the present study we investigated the cellular physiology of HeR-MRSA strains during the process of β-lactam-mediated HeR/HoR selection at sub-inhibitory concentrations by using a combinatorial approach of microarray analyses and global biochemical profiling employing gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) to investigate changes in metabolic pathways and the metabolome associated with β-lactam-mediated HeR/HoR selection in clinically relevant heterogeneous MRSA. We found unique features present in the oxacillin-selected SA13011-HoR derivative when compared to the corresponding SA13011-HeR parental strain that included significant increases in tricarboxyl citric acid (TCA) cycle intermediates and a concomitant decrease in fermentative pathways. Inactivation of the TCA cycle enzyme cis-aconitase gene in the SA13011-HeR strain abolished β-lactam-mediated HeR/HoR selection demonstrating the significance of altered TCA cycle activity during the HeR/HoR selection. These results provide evidence of both the metabolic cost and the adaptation that HeR-MRSA clinical strains undergo when exposed to β-lactam pressure, indicating that the energy production is redirected to supply the cell wall synthesis/metabolism, which in turn contributes to the survival response in the presence of β-lactam antibiotics.
Distributed Software Decision Support Systems for Heterogeneous Coordination in Chemical and Biological Response

Abstract : During detection and response to a Chem-Bio incident, heterogeneous groups, who may not ordinarily interact, must form a team. A Sensible Agent is a type of software agent (a Distributed Artificial Intelligence component that uses sensing, reasoning, and acting capabilities to achieve a set of goals) with additional coordination capabilities. For the Chem-Bio terrorism domain, Sensible Agents can offer the following decision-support capabilities: (1) belief revision based on models, certainty and the trustworthiness of incoming information sources, (2) situation-based recommendations on the composition of decision-making groups, the relative strength of members of the group and over whom the group has authority, and (3) planning and resource allocation. This paper describes possible benefits of applying a Sensible Agent system to provide Chem-Bio detectors and responders with in situ decision support for task and resource management.
Haptic object recognition for multi-fingered robot hands

In this paper, we present an approach for haptic object recognition and its evaluation on multi-fingered robot hands. The recognition approach is based on extracting key features of tactile and kinesthetic data from multiple palpations using a clustering algorithm. A multi-sensory object representation is built by fusion of tactile and kinesthetic features. We evaluated our approach on three robot hands and compared the recognition performance using object sets consisting of daily household objects. Experimental results using the five-fingered hand of the humanoid robot ARMAR, the three-fingered Schunk Dexterous Hand 2 and a parallel Gripper are performed. The results show that the proposed approach generalizes to different robot hands.
Component-based Orchestration of Dynamic IaaS Applications

Orchestration tools for Infrastructure-as-a-Service (IaaS) environments facilitate the deployment and management of cloud-based applications by automating the instantiation of virtual resources and configuration of software packages. While contemporary tools support the deployment of predefined applications, they lack internal support for taking care of changes in application designs. Dealing with this application dynamism in a timely manner requires new development and management techniques. This paper proposes a component-based approach that captures the entire design and runtime management cycle of IaaS applications. First, a component model is introduced that represents virtual computational resources as components and externalises control over their application logic. This model is then applied in a component framework that enables the rapid design of verified applications, and features additional support for the pre-provisioning of generic infrastructure and late configuration of application components. To showcase the capabilities of this approach, it is applied to an elastic media distribution use case.
Bladder cancer specific pathway interaction networks

With the development of high-throughput technologies, monitoring biological systems comprehensively has became feasible and affordable. However, the transition from highthroughput data to the underlying biology of various phenotypes remains challenging. Pathway analysis identifies biological processes that are associated with a particular phenotype, which provides insights into the underlying biological mechanisms. Therefore, pathway analysis has became a popular tool for analyzing high-throughput data. Most existing pathway analysis methods are based on a simple assumption that pathways act in isolation whereas they cooperate with each other in a complex manner. In this study, we focus on pathway interactions that are associated with bladder cancer risk. We identify disease-specific pathway-pathway interactions based on SNP-SNP interactions and gene-gene coexpression relationships. By analyzing the structure of pathway interaction networks, we highlight the “central” pathways that should be further studied.
Neurologic Deficits Including Auditory Loss and Recovery of Function in Horses with Temporohyoid Osteoarthropathy

Background Auditory loss is a common deficit in horses with temporohyoid osteoarthropathy (THO), however, recovery of function is unknown. Hypothesis/Objectives To investigate neurologic function with emphasis in audition in horses with THO after treatment. To describe anatomical alterations of the petrous temporal bone that might result in auditory loss. Animals Twenty‐four horses with a clinical diagnosis of THO. Methods Prospective study. A brainstem auditory evoked response (BAER) study was done as part of the criteria for inclusion in horses with a clinical diagnosis of THO from the years of 2005 to 2014. Physical and neurologic status and BAER findings were recorded. Brainstem auditory evoked response variables were compared by using Wilcoxon sign test. Fisher's exact test was also used. Significance was set at P < 0.05. Results The most common signs included auditory loss (100% of horses), vestibular and facial nerve dysfunction (83%), and exposure ulcerative keratitis (71%). Concurrent left laryngeal hemiparesis was observed in 61% of horses through endoscopy. Auditory dysfunction was bilateral in 50% of the cases (complete and partial), and unilateral affecting more commonly the right ear (R = 8, L = 4). Short‐ and long‐term follow‐up revealed persistent auditory loss in all horses based on abnormal response to sound, and further confirmed through a BAER in 8 horses. Conclusions and Clinical Importance Auditory dysfunction appears to be a permanent neurologic deficit in horses diagnosed with THO despite overall neurologic improvement.
Multi-locus phylogenetic analysis of neotropical figs does not support co-speciation with the pollinators : The importance of systematic scale in fig/wasp cophylogenetic studies

For 18 species of Panamanian Ficus, representing both basal (Pharmacosycea; 4 spp.) and derived (Urostigma, Americana; 14 spp.) sections, we sampled multiple individuals per species and analyzed sequence data from multiple (3) genetic markers (tpi, g3pdh, ITS). In contrast to previous phylogenetic studies of figs, this sampling design allowed us to evaluate the degree to which different alleles within loci, and different loci within individual species suggest consistent phylogenetic relationships among both distantly and closely related figs. We found multiple instances within both tpi and g3pdh genes in which different haplotypes were not monophyletic by species. Haplotype and reconciliation analyses suggested that genetic exchange among closely related figs is necessary to fully explain the patterns. In contrast, analyses of multiple loci from multiple individuals in the wasps are monophyletic by species, producing a well resolved species phylogeny. Although combining fig genetic data sets produced a resolved and robust fig topology, no fig phylogenies based on either combined or individual gene trees showed any significant correspondence with the wasp tree. Recent studies have shown that many of the fig species considered here have multiple pollinators and several share genetically indistinguishable pollinators. Together with these genetic results, it appears that a strict co-speciation model does not adequately describe the general evolutionary dynamics of the fig/wasp mutualism, particularly for sympatric, closely related species (within section). Importantly, these results emphasize the need to consider multiple genes, multiple individuals, and systematic scale in order to conduct and interpret robust co-phylogenetic studies of figs and their pollinating wasps.
Inverse Tone Mapping Based upon Retina Response

The development of high dynamic range (HDR) display arouses the research of inverse tone mapping methods, which expand dynamic range of the low dynamic range (LDR) image to match that of HDR monitor. This paper proposed a novel physiological approach, which could avoid artifacts occurred in most existing algorithms. Inspired by the property of the human visual system (HVS), this dynamic range expansion scheme performs with a low computational complexity and a limited number of parameters and obtains high-quality HDR results. Comparisons with three recent algorithms in the literature also show that the proposed method reveals more important image details and produces less contrast loss and distortion.
Gesture recognition toolkit using a Kinect sensor

Computational modeling of human behavior has become a very important field of computer vision. Gesture recognition allows people to interact with machines in a natural way without the use of dedicated I/O devices. This paper presents a simple system that can recognize dynamic and static gestures using the depth map and the higher level output (skeleton and facial features) provided by a Kinect sensor. Two approaches are chosen for the recognition task: the Dynamic Time Warping Algorithm is used to recognize dynamic gestures, while a Bayesian classifier is used for the static gestures/postures. In contrast with some specialized methods presented in the literature, the current approach is very generic and can be used with minimal modification for recognizing a large variety of gestures. As a result, it can be deployed in a multitude of fields from security (monitoring rooms and sending alarm signals), medicine (helping people with physical disabilities) to education and so on. The tests results show that the system is accurate, easy to use and highly customizable.
Language comprehension, emotion, and sociality: Aren’t we missing something?

The handbook chapter "Language comprehension, emotion and sociality" presents a theory of language processing that goes beyond the usual focus on constructing representations of what is said and meant, and that explicitly models how such construction processes mesh with emotion. The chapter starts by asking why research on the interface between language and emotion is relatively marginal in psycholinguistics, and subsequently reviews current ideas on the nature and function of emotion (covering short-lived emotions, evaluations, and mood). Next, it presents the Affective Language Comprehension or ALC model, a wide-scope processing model that combines insights from the psycholinguistics of word and sentence processing, the pragmatic analysis of communication, and emotion science. The model accommodates verbal and nonverbal (e.g., emoji) signing, and provides a principled take on word valence. By examining how linguistic and other signs actually move people, it also adds to our understanding of the relation between language and human sociality.
A Language for Multi-threaded Active Objects

The active object programming model is particularly adapted to easily program distributed objects: it separates objects into several activities, each manipulated by a single thread, preventing data races. However, this programming model has its limitations in terms of expressiveness -- risk of deadlocks -- and of efficiency on multicore machines. To overcome these limitations, this paper presents an extension of the active object model, called multi-active objects, that allows each activity to be multithreaded. The new model is implemented as a Java library and relies on method annotations to decide which requests can be run in parallel. It provides implicit parallelism, sparing the programmer from low-level concurrency mechanisms. We define the operational semantics of the multi-active objects and study the basic properties of this model. The benefits of our proposal are highlighted using two different applications: the NAS Parallel Benchmarks and a peer-to-peer overlay. This report presents the multi-active object programming model, its implementation in Java, its formal semantics and properties, and some experiments showing its efficiency.
Rethinking statistical analysis methods for CHI

CHI researchers typically use a significance testing approach to statistical analysis when testing hypotheses during usability evaluations. However, the appropriateness of this approach is under increasing criticism, with statisticians, economists, and psychologists arguing against the use of routine interpretation of results using "canned" p values. Three problems with current practice - the fallacy of the transposed conditional, a neglect of power, and the reluctance to interpret the size of effects - can lead us to build weak theories based on vaguely specified hypothesis, resulting in empirical studies which produce results that are of limited practical or scientific use. Using publicly available data presented at CHI 2010 [19] as an example we address each of the three concerns and promote consideration of the magnitude and actual importance of effects, as opposed to statistical significance, as the new criteria for evaluating CHI research.
From use cases to users perspectives A process towards the definition of a tool for IoT urban service design

The paper presents some early results of a new design investigation and research that is nowadays in progress, into the specific domain of the design and implementation of Internet of Things (IoT) services in urban areas. The generation of new added values during the usage of and the active involvement into urban services, perceived by the users themselves, both in terms of personal satisfaction or community goals, can be powerful drivers for more extensive usage and further enhancement of existing services, or for the implementation of new ones. For this reason, we investigated possible evolutions of some classic design tools, that could be able to better focus the attention on the motivational aspects of future IoT services’ success and popularity, and in the context of our research gave meaningful insights about the possible impact of their usage in the future everyday life of citizens. Design tool, internet of things, scenario design, storytelling
More transparency in testing results: Towards an open collective knowledge base

We are currently witnessing an explosion of advances in database technology, that cover all phases of database application design: non-functional requirements, conceptual modeling, logical modeling, deployment, physical design and exploitation. Researchers and engineers cooperate to integrate these advances in the database design. Their proposed solutions have to be confronted with similar studies whose results have been either published in scientific papers or on specific websites such as that of TPC (the Transaction Processing Council). Recently, several researchers have highlighted difficulties in reproducing the results of existing studies. As a consequence, certain research communities require that the environment and the results of the testing activities have to be published in order to facilitate their reproduction, to publish their simulator environments, in order to allow evaluators to perform by their own the experiments. This is quite similar to the Volkswagen fake pollution controls scandal. In this paper, we firstly, advocate the transparency of testing in the database field. Secondly, we propose the use of a repository dedicated to store testing environment and results. The environment includes used data sets, deployment platform, non-functional requirements, used algorithms, hypotheses, etc. and the results completed with their measurement units.
Speaker identification with distant microphone speech

The field of speaker identification has recently seen significant advancement, but improvements have tended to be benchmarked on near-field speech, ignoring the more realistic setting of far-field-instrumented speakers. In this work we present several findings on far-field speech from the MIXER5 Corpus, in the areas of feature extraction, speaker modeling, and multichannel score combination. First, we observe that minimum-variance distortionless response (MVDR) features outperform Mel-frequency cepstral coefficient (MFCC) features, and that fundamental frequency variation (FFV) features offer complimentary information to both MFCC and MVDR features. Second, we present evidence that factor analysis significantly improves system performance, compared to the more traditional GMM/UBM strategy. Third, we find that frame-based score competition significantly improves performance under mismatched conditions with multiple channels available.
Analysis of Four A-F Supergiants in M31 from Keck HIRES Spectroscopy

The first stellar abundances in M31 are presented, based on Keck I HIRES spectroscopy and model atmospheres analyses of three A-F supergiants, 41-2368, 41-3712, and A-207. We also present the preliminary analysis of a fourth star, 41-3654. We find that the stellar oxygen abundances are in good agreement with those determined from nebular studies, even though the stars do not show a clear radial gradient in oxygen. The uncertainties in the stellar abundances are smaller than the range in the nebular results, making these stars ideal objects for further studies of the distribution of oxygen in M31. We show that the stars can be used to study the abundance distributions of other elements as well, including iron-group and heavier elements. The A-F supergiants also provide direct information on the metallicity and reddening of nearby Cepheid stars. We have examined the metallicity and reddening assumptions used for Cepheids within 1' of our targets and noted the differences from values used in the literature.
Label Efficient Learning by Exploiting Multi-Class Output Codes

We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes. Rather than studying the behavior of these techniques for supervised learning, we establish a connection between the success of these methods and the existence of label-efficient learning procedures. We show that in both the realizable and agnostic cases, if output codes are successful at learning from labeled data, they implicitly assume structure on how the classes are related. By making that structure explicit, we design learning algorithms to recover the classes with low label complexity. We provide results for the commonly studied cases of one-vs-all learning and when the codewords of the classes are well separated. We additionally consider the more challenging case where the codewords are not well separated, but satisfy a boundary features condition that captures the natural intuition that every bit of the codewords should be significant.
Traffic-Related Air Toxics and Term Low Birth Weight in Los Angeles County, California

Background: Numerous studies have linked criteria air pollutants with adverse birth outcomes, but there is less information on the importance of specific emission sources, such as traffic, and air toxics. Objectives: We used three exposure data sources to examine odds of term low birth weight (LBW) in Los Angeles, California, women when exposed to high levels of traffic-related air pollutants during pregnancy. Methods: We identified term births during 1 June 2004 to 30 March 2006 to women residing within 5 miles of a South Coast Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. Pregnancy period average exposures were estimated for air toxics, including polycyclic aromatic hydrocarbons (PAHs), source-specific particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) based on a chemical mass balance model, criteria air pollutants from government monitoring data, and land use regression (LUR) model estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of term LBW (< 2,500 g) were examined using logistic regression. Results: Odds of term LBW increased approximately 5% per interquartile range increase in entire pregnancy exposures to several correlated traffic pollutants: LUR measures of NO, NO2, and NOx, elemental carbon, and PM2.5 from diesel and gasoline combustion and paved road dust (geological PM2.5). Conclusions: These analyses provide additional evidence of the potential impact of traffic-related air pollution on fetal growth. Particles from traffic sources should be a focus of future studies.