Miracle-dz/UniFAR-FTUs huggingface dataset:
Insertion Sequence–Driven Diversification Creates a Globally Dispersed Emerging Multiresistant Subspecies of E. faecium

Enterococcus faecium, an ubiquous colonizer of humans and animals, has evolved in the last 15 years from an avirulent commensal to the third most frequently isolated nosocomial pathogen among intensive care unit patients in the United States. E. faecium combines multidrug resistance with the potential of horizontal resistance gene transfer to even more pathogenic bacteria. Little is known about the evolution and virulence of E. faecium, and genomic studies are hampered by the absence of a completely annotated genome sequence. To further unravel its evolution, we used a mixed whole-genome microarray and hybridized 97 E. faecium isolates from different backgrounds (hospital outbreaks (n = 18), documented infections (n = 34) and asymptomatic carriage of hospitalized patients (n = 15), and healthy persons (n = 15) and animals (n = 21)). Supported by Bayesian posterior probabilities (PP = 1.0), a specific clade containing all outbreak-associated strains and 63% of clinical isolates was identified. Sequencing of 146 of 437 clade-specific inserts revealed mobile elements (n = 74), including insertion sequence (IS) elements (n = 42), phage genes (n = 6) and plasmid sequences (n = 26), hypothetical (n = 58) and membrane proteins (n = 10), and antibiotic resistance (n = 9) and regulatory genes (n = 11), mainly located on two contigs of the unfinished E. faecium DO genome. Split decomposition analysis, varying guanine cytosine content, and aberrant codon adaptation indices all supported acquisition of these genes through horizontal gene transfer with IS16 as the predicted most prominent insert (98% sensitive, 100% specific). These findings suggest that acquisition of IS elements has facilitated niche adaptation of a distinct E. faecium subpopulation by increasing its genome plasticity. Increased genome plasticity was supported by higher diversity indices (ratio of average genetic similarities of pulsed-field gel electrophoresis and multi locus sequence typing) for clade-specific isolates. Interestingly, the previously described multi locus sequence typing–based clonal complex 17 largely overlapped with this clade. The present data imply that the global emergence of E. faecium, as observed since 1990, represents the evolution of a subspecies with a presumably better adaptation than other E. faecium isolates to the constraints of a hospital environment.
Injurious mechanical ventilation and end-organ epithelial cell apoptosis and organ dysfunction in an experimental model of acute respiratory distress syndrome.

CONTEXT Recent clinical trials have demonstrated a decrease in multiple organ dysfunction syndrome (MODS) and mortality in patients with acute respiratory distress syndrome (ARDS) treated with a protective ventilatory strategy. OBJECTIVE To examine the hypothesis that an injurious ventilatory strategy may lead to end-organ epithelial cell apoptosis and organ dysfunction. DESIGN AND SETTING In vivo animals: 24 rabbits with acid-aspiration lung injury were ventilated with injurious or noninjurious ventilatory strategies. In vitro: rabbit epithelial cells were exposed to plasma from in vivo rabbit studies. In vivo human: plasma samples from patients included in a previous randomized controlled trial examining a lung protective strategy were analyzed (lung protection group, n = 9 and controls, n = 11). MAIN OUTCOME MEASURES In vivo animals: biochemical markers of liver and renal dysfunction; apoptosis in end organs. In vitro: induction of apoptosis in LLC-RK1 renal tubular epithelial cells. In vivo human: correlation of plasma creatinine and soluble Fas ligand. RESULTS The injurious ventilatory strategy led to increased rates of epithelial cell apoptosis in the kidney (mean [SE]: injurious, 10.9% [0.88%]; noninjurious, 1.86% [0.17%]; P<.001) and small intestine villi (injurious, 6.7% [0.66%]; noninjurious, 0.97% [0.14%]; P<.001), and led to the elevation of biochemical markers indicating renal dysfunction in vivo. Induction of apoptosis was increased in LLC-RK1 cells incubated with plasma from rabbits ventilated with injurious ventilatory strategy at 4 hours (P =.03) and 8 hours (P =.002). The Fas:Ig, a fusion protein that blocks soluble Fas ligand, attenuated induction of apoptosis in vitro. There was a significant correlation between changes in soluble Fas ligand and changes in creatinine in patients with ARDS (R = 0.64, P =.002). CONCLUSIONS Mechanical ventilation can lead to epithelial cell apoptosis in the kidney and small intestine, accompanied by biochemical evidence of organ dysfunction. This may partially explain the high rate of MODS observed in patients with ARDS and the decrease in morbidity and mortality in patients treated with a lung protective strategy.
Order-Optimal Data Aggregation in Regular Wireless Sensor Networks

The predominate traffic patterns in a wireless sensor network are many-to-one and one-to-many communication. Hence, the performance of wireless sensor networks is characterized by the rate at which data can be disseminated from or aggregated to a data sink. In this paper, the problem of data aggregation at a single sink is considered. It is shown that a data aggregation rate of Θ(logn/n) per node is optimal and that this rate can be achieved in regular wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.
Bottom Extraction Factoring error handling out of functional programs

Many utility functions in a functional programming languag e consist of just a few lines of code. They are written with the expe ctation that they will be inlined and heavily optimized. For m any of these functions, the size of the function being inlined is ominated by the size of code required to signal an error if the inp uts are inconsistent. In this paper we describe ottom extraction , a simple technique which mitigates this problem. Using a combina tio of type information and strictness information, we can easi ly identify expressions which are devoted to error handling (seman tic lly bottom). Such a bottom expression is enlarged to encompass a s much code as possible by a set of local rules. Bottom regions a re extracted and hoisted to the top level of the program. Bottom extraction greatly reduces code bloat in the presence of inlin ing by reducing the size of inlined code. In addition, it provides a imple and fast test to identify error handling code in the rest o f the compiler, enabling us to easily perform program transforma tions around expressions which are semantically bottom. Bottom e xtraction is a simple but important part of our compiler for pH, and markedly improves every aspect of the compiler’s behavior.
Formulating a Laplace domain approach for tuning motion profiles

In this paper, a unified tuning rule of the motion profiles for less vibration with flexible system is proposed. For the motion profiles including trapezoidal velocity profile, S and AS curve, the tuning rule is formulated using a Laplace-domain approach. By placing zeros of the motion profiles on poles of the system, which is so-called pole-zero cancellation, the vibration mode of the system can be eliminated, which results in obtaining shorter settling time. Through the Laplace-domain approach, conditions for reducing the residual vibration are derived systematically and the proposed approach can illustrate the characteristics of the motion profiles with ease. The effectiveness of the proposed tuning rule will be illustrated and verified by simulation with a XY stage with flexural structure.
Towards a dynamic top-N recommendation framework

Real world large-scale recommender systems are always dynamic: new users and items continuously enter the system, and the status of old ones (e.g., users' preference and items' popularity) evolve over time. In order to handle such dynamics, we propose a recommendation framework consisting of an online component and an offline component, where the newly arrived items are processed by the online component such that users are able to get suggestions for fresh information, and the influence of longstanding items is captured by the offline component. Based on individual users' rating behavior, recommendations from the two components are combined to provide top-N recommendation. We formulate recommendation problem as a ranking problem where learning to rank is applied to extend upon matrix factorization to optimize item rankings by minimizing a pairwise loss function. Furthermore, to better model interactions between users and items, Latent Dirichlet Allocation is incorporated to fuse rating information and textual information. Real data based experiments demonstrate that our approach outperforms the state-of-the-art models by at least 61.21% and 50.27% in terms of mean average precision (MAP) and normalized discounted cumulative gain (NDCG) respectively.
Multi-Step Look-Ahead Trajectory Planning in SLAM: Possibility and Necessity

In this paper, the possibility and necessity of multi-step trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step look-ahead technique.
Evaluation of a Short-Form of the Berg Card Sorting Test

The Psychology Experimental Building Language http://pebl.sourceforge. net/ Berg Card Sorting Test is an open-source neurobehavioral test. Participants (N = 207, ages 6 to 74) completed the Berg Card Sorting Test. Performance on the first 64 trials were isolated and compared to that on the full-length (128 trials) test. Strong correlations between the short and long forms (total errors: r = .87, perseverative response: r = .83, perseverative errors r = .77, categories completed r = .86) support the Berg Card Sorting Test-64 as an abbreviated alternative for the full-length executive function test.
Uncovering the Challenges in Collection, Sharing and Documentation: The Hidden Data of Social Media Research?

This paper offers insights into social media researchers’ everyday practices in dealing with constraints and challenges in the areas of data access, in terms of data collection and sharing, and data publication. We believe that such insights need to be taken into account when discussing methodology and epistemology of social media data to ensure that strategies employed to achieve validity are appropriate. A qualitative, ethnographic research approach (based on expert interviews, observation and a qualitative questionnaire) is used to capture the practices and discussions of a variety of social media researchers. The paper concludes that due to the current situation in data sharing and publication opportunities, both the actual research data and much of the technical knowledge in social media research remain hidden unless revealed by studying researchers’ everyday practices. Such studies reveal a considerable impact of external constraints on researchers’ attempts to achieve validity and better re-search quality.
Mitochondrial Toxicity of Cadmium Telluride Quantum Dot Nanoparticles in Mammalian Hepatocytes

There are an increasing number of studies indicating that mitochondria are relevant targets in nanomaterial-induced toxicity. However, the underlying mechanisms by which nanoparticles (NPs) interact with these organelles and affect their functions are unknown. The aim of this study was to investigate the effects of cadmium telluride quantum dot (CdTe-QD) NPs on mitochondria in human hepatocellular carcinoma HepG2 cells. CdTe-QD treatment resulted in the enlargement of mitochondria as examined with transmission electron microscopy and confocal microscopy. CdTe-QDs appeared to associate with the isolated mitochondria as detected by their inherent fluorescence. Further analyses revealed that CdTe-QD caused disruption of mitochondrial membrane potential, increased intracellular calcium levels, impaired cellular respiration, and decreased adenosine triphosphate synthesis. The effects of CdTe-QDs on mitochondrial oxidative phosphorylation were evidenced by changes in levels and activities of the enzymes of the electron transport chain. Elevation of peroxisome proliferator-activated receptor-γ coactivator levels after CdTe-QD treatment suggested the effects of CdTe-QDs on mitochondrial biogenesis. Our results also showed that the effects of CdTe-QDs were similar or greater to those of cadmium chloride at equivalent concentrations of cadmium, suggesting that the toxic effects of CdTe-QDs were not solely due to cadmium released from the NPs. Overall, the study demonstrated that CdTe-QDs induced multifarious toxicity by causing changes in mitochondrial morphology and structure, as well as impairing their function and stimulating their biogenesis.
Optimal design, analysis and additive manufacturing for two-level stochastic honeycomb structure

ABSTRACT Recent advances in the aviation, aerospace, and energy industries have created a huge demand for multifunctional structures with lightweight, load-carrying capacity, and heat resistance. In order to further explore the potential of honeycomb structure, a design method for hierarchically architected structures constructed by replacing side walls of pores with Voronoi lattices is put forward. A digital representation model of hierarchical honeycomb structures is first proposed. Then based on centroidal Voronoi tessellation (CVT) algorithm, the first level honeycomb structures are generated by decomposition of design domain as a set of Voronoi unit. At last, based on modified bidirectional evolutionary structural optimization and combined with the control function of the CVT algorithm, the internal pore size and distribution of cell walls are controlled to optimize the distribution of materials. The proposed approach is verified through three case studies, which provides an important basis for the wide application of additive manufacturing technology of the two-level stochastic honeycomb structure.
Survey on Model-based Manipulation Planning of Rigid Objects

Rigid bodies aren’t but a particular instance of the broader category of deformable objects, where for practical purposes and under the range of applied forces they can be considered as experimenting no change in shape or dimensions. The problem is viewed as an extension of the basic motion planning problem [12]. The manipulation task consists in changing their placement while avoiding collisions. This approach reflects the same methodological evolution than motion planning: from complete, exact cell-decomposition methods, in practice suited only for very simple instances of the problem, to sampling based algorithms, able to tackle with more degrees of freedom and more realistic settings. This framework should constitute a formal paradigm for further work involving more complex manipulation, like handling deformable objects. As such objects may also be just transferred from one point to the other, the knowledge of how to tackle this problem with the simpler rigid objects constitutes an unavoidable starting point.
Identifying and classifying semantic relations between medical concepts in clinical data ( I 2 b 2 Challenge )

In this paper, we describe the three system runs that we submitted to the I2B2-10 Shared Task Challenges in Natural Language Processing and Clinical Data. We participated in the relation identification track of the competition. Our models use a combination of lexical representation, medical semantic information, and additional contextual knowledge in combination with SVM classification algorithms. The best results on the test set are obtained by a 9-class classification algorithm using all types of features as representation technique.
A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations1, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.
Circulating cortisol levels after exogenous cortisol administration are higher in women using hormonal contraceptives: data from two preliminary studies

Abstract Exogenous cortisol administration has been used to test the influence of glucocorticoids on a variety of outcomes, including memory and affect. Careful control of factors known to influence cortisol and other endogenous hormone levels is central to the success of this research. While the use of hormonal birth control (HBC) is known to exert many physiological effects, including decreasing the salivary cortisol response to stress, it is unknown how HBC influences circulating cortisol levels after exogenous cortisol administration. To determine those effects, we examined the role of HBC on participants’ cortisol levels after receiving synthetic cortisol (hydrocortisone) in two separate studies. In Study 1, 24 healthy women taking HBC and 26 healthy men were administered a 0.1 mg/kg body weight intravenous dose of hydrocortisone, and plasma cortisol levels were measured over 3 h. In Study 2, 61 participants (34 women; 16 were on HBC) received a 15 mg hydrocortisone pill, and salivary cortisol levels were measured over 6 h. Taken together, results from these studies suggest that HBC use is associated with a greater cortisol increase following cortisol administration. These data have important methodological implications: (1) when given a controlled dose of hydrocortisone, cortisol levels may increase more dramatically in women taking HBC versus women not on HBC or men; and (2) in studies manipulating cortisol levels, women on hormonal contraceptives should be investigated as a separate group.
Get more Clicks

Sponsored search has become increasingly important due to the rapid development of Web search engines and pay per click (PPC) is amongst one of the most important advertising models search engines currently use. One of the key questions in sponsored search is that: Given a query or a substituted keyword, which ads should search engines display to the users in order to maximize their revenue? In other words, given a keyword, how can we choose ads out of a candidate list that will have higher click-through rates (CTR)? Previous works have attempted to estimate the CTR of ads via a query-independent perspective. In this paper, instead of predicting the CTR of ads, we will propose a new ranking-based approach to select ads that would have higher click-through rates via a query-dependent perspective. We first analyze some manually constructed heuristic rules that could be used to distinguish good ads from bad ones and then show how we could combine these rules into our ranking-based approach to reach our aim. Experiments on real-world datasets have confirmed the effectiveness of our proposed approach.
An Efficient Scheme for Locating Nodes in the Internet Geo-LPM: Geographic Longest Prefix Matching

One of the major weakness of existing peer-to-peer networks is that their overlay structures do not match well with the physical underlying network topology. In this paper, we present a Geographical Longest Prefix Matching (Geo-LPM) scheme whereby nodes selforganise themselves into clusters such that nodes in the same cluster are close to each other in terms of network latency and membership. Geo-LPM combines IP prefixes and network metric measurements to quickly and easily locate nodes in the geographical space. It is simple, scalable, self-organising and distributed with no form of global knowledge required. Simulation results are presented.
Predictive Game Theory

Probability theory governs the outcome of a game; there is a distribution over mixed strat. 's, not a single "equilibrium". To predict a single mixed strategy must use our loss function (external to the game's players. Provides a quantification of any strategy's rationality. Prove rationality falls as cost of computation rises (for players who have not previously interacted). All extends to games with varying numbers of players.
Distributed learning automata-based algorithm for community detection in complex networks

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.
Realtime human motion control with a small number of inertial sensors

This paper introduces an approach to performance animation that employs a small number of motion sensors to create an easy-to-use system for an interactive control of a full-body human character. Our key idea is to construct a series of online local dynamic models from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori framework (MAP). We have demonstrated the effectiveness of our system by controlling a variety of human actions, such as boxing, golf swinging, and table tennis, in real time. Given an appropriate motion capture database, the results are comparable in quality to those obtained from a commercial motion capture system with a full set of motion sensors (e.g., XSens [2009]); however, our performance animation system is far less intrusive and expensive because it requires a small of motion sensors for full body control. We have also evaluated the performance of our system by leave-one-out-experiments and by comparing with two baseline algorithms.