Miracle-dz/UniFAR-FTUs huggingface dataset:
Death and gastrointestinal bleeding complicate encephalomyelitis in mice with delayed appearance of CNS IgM after intranasal alphavirus infection.

Central nervous system (CNS) infection of C57BL/6 mice with the TE strain of Sindbis virus (SINV) provides a valuable animal model for studying the pathogenesis of alphavirus encephalomyelitis. While SINV TE inoculated intracranially causes little mortality, 20-30 % of mice inoculated intranasally (IN) died 8 to 11 days after infection, the period during which immune cells typically infiltrate the brain and clear infectious virus. To examine the mechanism behind the mortality, mice infected IN with SINV TE were monitored for evidence of neurological disease, and those with signs of severe disease (moribund) were sacrificed and tissues collected. Mice showing the usual mild signs of encephalomyelitis were concurrently sacrificed to serve as time-matched controls (sick). Sixty-eight per cent of the moribund mice, but none of the sick mice, showed upper gastrointestinal bleeding due to gastric ulceration. Clinical disease and gastrointestinal pathology could not be attributed to direct viral infection of tissues outside of the CNS, and brain pathology and inflammation were comparable in sick and moribund mice. However, more SINV antigen was present in the brains of moribund mice, and clearance of infectious virus from the CNS was delayed compared to sick mice. Lower levels of SINV-specific IgM and fewer B220+ B cells were present in the brains of moribund mice compared to sick mice, despite similar levels of antiviral IgM and IgG in serum. These findings highlight the importance of the local antibody response in determining the outcome of viral encephalomyelitis and offer a model system for understanding individual variation in this response.
Adaptive Finite-Time Controller Design for T–S Fuzzy Systems

This paper studies the adaptive finite-time stabilization problem for a class of nonlinear systems described by Takagi–Sugeno (T–S) fuzzy dynamic models with parametric uncertainties. A novel adaptive state feedback control scheme for the T–S fuzzy systems is proposed, and the scheme is developed based on finite-time Lyapunov theorem and adaptive backstepping-like method. Augmented dynamics are introduced in the design of finite-time stabilization controllers to construct suitable finite-time Lyapunov functions. It is shown that finite-time convergence of the closed-loop adaptive control system can be achieved, and the potential controller singularity problem caused by the augmented dynamics can be avoided. In addition, constructive procedures to obtain such an adaptive finite-time controller are given. Convergence time as a transient performance specification is also taken into account, and a finite upper bound on the convergence time is estimated. Finally, two numerical examples are provided to illustrate the effectiveness and practicality of the proposed adaptive control approach.
Analysing animal social network dynamics: the potential of stochastic actor‐oriented models

Summary Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.
Vision-based deep reinforcement learning to control a manipulator

In this paper, we apply Reinforcement Learning (RL) to control a manipulator using camera images. Basically, RL algorithm helps the agent to choose actions under some specific policies, which maximize rewards accumulated from a sequential result of the actions. In order to control a manipulator, the agent generally considers joint angles or torques of the manipulator as the states. Deep learning has enabled to deal with a very high dimensional state space so that raw images can be considered as states. However, using raw images as state spaces can require extensive computational resources because of their high dimension, so there has been much research using additory algorithms such as Convolutional Neural Network (CNN) or autoencoder to extract features from images. While those algorithms reduce the computational load, it is still a quite complicated process. In this paper we consider a task for the end-effector to reach a random target and propose a new approach using a vision-based direction vector, which has low dimension and can be simply implemented. We calculate the direction vectors via the camera and exploit them as the states of the policy. Then, based on the direction vectors, the manipulator can learn how to control each joint. For the simulation, we demonstrate a multiple degree of freedom(dof) manipulator of a commercial robot, and the simulation results show that the manipulator successfully tracks the target.
Cross Linguistic Comparison of Mandarin and English EMA Articulatory Data

This paper aims at effectively identifying common English mispronunciations by Mandarin speakers and incorporating this knowledge into computer assisted language learning (CALL) to improve the learner’s accented English. For this purpose, English and Mandarin multi-channel EMA articulatory datasets collected from native English and native Mandarin speakers respectively have been used to uncover cross-linguistic distinctions. The Procrustes based speaker normalization is used to eliminate the variability which comes from speaker-specific vocal-tract anatomies and other individual biomechanical properties. Then the English phonemes missing from Mandarin and their Mandarin confusing equivalents are identified using phonological knowledge. These English and Mandarin phoneme pairs may be hard to distinguish in acoustics, but by extracting useful information from the changing on tongue positions and shapes of the lips while speaking can be good cross linguistic phoneme level comparison metrics both empirical and quantified. With this method, the same analysis can be done between languages, or different accents within the same language in the future.
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds

Clustering is one of the fundamental topics in data mining and pattern recognition. As a prospective clustering method, the subspace clustering has made considerable progress in recent researches, e.g., sparse subspace clustering (SSC) and low rank representation (LRR). However, most existing subspace clustering algorithms are designed for vectorial data from linear spaces, thus not suitable for high-dimensional data with intrinsic non-linear manifold structure. For high-dimensional or manifold data, few research pays attention to clustering problems. The purpose of clustering on manifolds tends to cluster manifold-valued data into several groups according to the mainfold-based similarity metric. This article proposes an extended LRR model for manifold-valued Grassmann data that incorporates prior knowledge by minimizing partial sum of singular values instead of the nuclear norm, namely Partial Sum minimization of Singular Values Representation (GPSSVR). The new model not only enforces the global structure of data in low rank, but also retains important information by minimizing only smaller singular values. To further maintain the local structures among Grassmann points, we also integrate the Laplacian penalty with GPSSVR. The proposed model and algorithms are assessed on a public human face dataset, some widely used human action video datasets and a real scenery dataset. The experimental results show that the proposed methods obviously outperform other state-of-the-art methods.
RFLUSH: Rethink the Flush

A FLUSH command has been used for decades to enforce persistence and ordering of updates in a storage device. The command forces all the data in the volatile buffer of the storage device to non-volatile media to achieve persistency. This lump-sum approach to flushing has two performance consequences. First, it slows down nonvolatile materialization of the writes that actually need to be made durable. Second, it deprives the writes that do not need to be made durable of an opportunity for absorbing future writes and coalescing. We attempt to characterize the problems of this semantic gap of flushing in storage devices and propose RFLUSH that allows a fine-grained control over nonvolatile materialization. The RFLUSH command delivers a range of logical block addresses (LBAs) that need to be flushed and thus enables the storage device to force only a subset of data in its buffer. We implemented this fine-grained flush command in a storage device using an open-source flash development platform and modified the F2FS file system to make use of the command in processing fsync requests as a case study. Performance evaluation using the prototype shows that the inclusion of RFLUSH improves the throughput by up to 6.5x; reduces the write traffic by up to 43%; and eliminates the long tail in the response time.
Optimal Filtering for Systems With Multiple Packet Dropouts

This paper is concerned with the optimal filtering problem for discrete-time stochastic linear systems with multiple packet dropouts, where the number of consecutive packet dropouts is limited by a known upper bound. Without resorting to state augmentation, the system is converted to one with measurement delays and a moving average (MV) colored measurement noise. An unbiased optimal filter is developed in the linear least-mean-square sense. Its solution depends on the recursion of a Riccati equation and a Lyapunov equation. A numerical example shows the effectiveness of the proposed filter.
Distributed Event-Based Set-Membership Filtering for a Class of Nonlinear Systems With Sensor Saturations Over Sensor Networks

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.
Un Enfoque Inteligente para Derivar Use Case Maps a partir de Requerimientos de Software

Resumen. Los requerimientos de software descritos en lenguaje natural son la base para realizar la creativa pero exhaustiva tarea de diseñar una arquitectura de software. No obstante, suele existir una amplia brecha conceptual entre los requerimientos y la arquitectura de software que describe su funcionalidad; más aún, poco se ha explorado en este campo para reducir esta brecha. En este trabajo, proponemos un enfoque inteligente para derivar una descripción arquitectónica de un sistema, en particular utilizando la notación Use Case Maps (UCMs) a partir de requerimientos redactados en lenguaje natural con el objetivo de facilitar la transición entre la especificación de requerimientos y el diseño arquitectónico. El enfoque combina algoritmos de clasificación de texto, procesamiento de lenguaje natural y clusterización de texto para generar UCMs de la arquitectura de software prevista. La experimentación llevada a cabo con varios casos de estudio muestra resultados prometedores en cuanto al potencial del enfoque para asistir a los arquitectos de software durante las primeras etapas del proceso de desarrollo de software.
The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster‐specific random effects which allow one to partition the total individual variance into between‐cluster variation and between‐individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time‐to‐event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., ‘frailty’) Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Transmit signal design for MIMO wiretap channels with statistical CSI and arbitrary inputs

We propose transmit optimization techniques for multi-input multi-output (MIMO) wiretap channels with statistical channel state information (CSI) at the transmitter. We consider doubly correlated channels towards the legitimate receiver and the eavesdropper. The aim is to maximize the secrecy rates using the knowledge of the channel correlation matrices. We develop gradient-descent based optimization algorithms for obtaining the optimal transmit signals for both Gaussian and finite-alphabet inputs. Furthermore, we introduce a joint precoder and artificial noise (AN) design scheme. We demonstrate the efficacy of the proposed schemes via numerical examples.
Comparing empathy perceived by interlocutors in multiparty conversation and external observers

This paper investigates the basic characteristics of perceived empathy in Breithaupt's three-person model to consider a way of realizing its automatic prediction or empathy reading machines. More specifically, we report the extent to which interlocutors differ from external observers in perceiving the empathy aroused during group conversation. We also evaluate the accuracies of various frequently used models, including majority voting and multiple regression, in predicting an interlocutor's ratings from those of other interlocutors and/or those of the observers. Defining empathy as the emotional congruence between pairs of interlocutors, we studied a four-person conversation, in which previously unacquainted people held a decision-making discussion. We used a 5-point Likert scale when collecting self-reports of empathy from the interlocutors, and reports from a total of forty external observers (ten for each interlocutor) who adopted a target interlocutor's perspective. We obtained three indications. First, when no empathy ratings are available from the target interlocutor for model training, it is beneficial to ask observers to take the target interlocutor's perspective. Second, when target interlocutors' self-reports are available, it is advantageous to instruct observers not to take the target interlocutor's perspective. Third, in both scenarios, it is useful to ask interlocutors to rate the pairs excluding themselves. These findings provide some insights into good rating procedure as regards studying perceived empathy.
VHL missense mutations in the p53 binding domain show different effects on p53 signaling and HIFα degradation in clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) formation is connected to functional loss of the von Hippel-Lindau (VHL) gene. Recent data identified its gene product, pVHL, as a multifunctional adaptor protein which interacts with HIFα subunits but also with the tumor suppressor p53. p53 is hardly expressed and rarely mutated in most ccRCC. We showed that low and absent p53 expression correlated with the severity of VHL mutations in 262 analyzed ccRCC tissues. In contrast to nonsense and frameshift mutations which abrogate virtually all pVHL functions, missense mutations may rather influence one or few functions. Therefore, we focused on four VHL missense mutations, which affect the overlapping pVHL binding sites of p53 and Elongin C, by investigating their impact on HIFα degradation, p53 expression and signaling, as well as on cellular behavior using ccRCC cell lines and tissues. TP53 mRNA and its effector targets p21, Bax and Noxa, were altered both in engineered cell lines and in tumor tissues which carried the same missense mutations. Two of these mutations were not able to degrade HIFα whereas the remaining two mutations led to HIFα downregulation, suggesting the latter are p53 binding site-specific. The selected VHL missense mutations further enhanced tumor cell survival, but had no effects on cell proliferation. Whereas Sunitinib was able to efficiently reduce cell proliferation, Camptothecin was additionally able to increase apoptotic activity of the tumor cells. It is concluded that systematic characterization of the VHL mutation status may help optimizing targeted therapy for patients with metastatic ccRCC.
ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads

ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to 20GB files, 5M web pages and 500M tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes 1/3rd the energy, and has an Energy Delay Product (EDP) that is 50-71% lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.
Pancreatic Injury in Blunt Abdominal Trauma

Introduction: Pancreatic injuries are not common after blunt and penetrating trauma, but can be challenging to diagnose and manage. Case report: Twenty-three year old man, injured during a fall from a motorcycle two days earlier, was admitted to Department of Surgery, University Clinical Centre Tuzla because of suspicion of pancreatic trauma. Immediately after hospitalization, patient underwent laboratory and radiological tests that revealed the existence of pancreatic trauma, so we opted for urgent surgical treatment. Surgery and early postoperative course were normal and the patient was discharged on the ninth postoperative day. Conclusion: Proper diagnosis and well-selected surgical treatment significantly increases the chances for recovery of these patients.
Acute ischemic stroke management review for the hospitalist

The treatment of acute ischemic stroke is dependent on timely recognition. After ensuring airway, respiratory and circulatory stability, NIHSS should be performed and urgent CT scan obtained. If no exclusions exist, recombinant tissue plasminogen activator (rtPA) should be administered as soon as possible. Select patients may be eligible for mechanical thrombectomy. Stroke patients should be admitted to a dedicated stroke service to determine stroke mechanism, manage risk factors, and initiate preventive therapies.