Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Outcome of Isolated Cerebellar Stroke—A Prospective Observational Study

Background: The aim of this prospective study was to investigate clinical deficits of patients with isolated cerebellar stroke applying a dedicated clinical score, the modified International Cooperative Ataxia Rating Scale (MICARS) and identifying factors that influence recovery. Methods: Fifteen patients with acute isolated cerebellar stroke received a standard stroke MRI on the day of admission and were clinically assessed using the mRS, NIHSS and the modified International Cooperative Ataxia Rating Scale (MICARS) on day 1, 3, 7, 30, and 90. A generalized linear model for repeated measures was employed to analyze the effect of stroke lesion location, volume, days after stroke, patient age, and MICARS score at admission on the total MICARS score. Results: Median patient age was 54 years, lesion location in most cases was right (87%) and in the PICA territory (11/15). Median lesion volume was 3.2 ml. Median NIHSS was 1. The median MICARS decreased from on day 1 with 23–4 at day 90. The generalized linear model identified MICARS score at day 1, lesion location, days after admission and the interaction of the last two on the total MICARS score, whereas there was no significant effect of stroke volume or patient age. Conclusions: Isolated cerebellar stroke can present with low NIHSS while more specific scales like the MICARS indicate a severe deficit. Patient age at onset of stroke and lesion volume had no significant effect on recovery from cerebellar symptoms as opposed to severity of symptoms at admission and lesion location.
Understanding Deep Architectures by Visual Summaries

In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class. The approach is based on a sparse optimization step providing sharp image saliency masks that are clustered together by means of a semantic flow similarity measure. The summaries communicate clearly what a network has exploited of a particular image class, and this is proved through automatic image tagging and with a user study. Beyond the deep network understanding, summaries are also useful for many quantitative reasons: their number is correlated with ability of a network to classify (more summaries, better performances), and they can be used to improve the classification accuracy of a network through summary-driven specializations.
Multiclass Classification and Feature Selection Based on Least Squares Regression with Large Margin

Abstract Least squares regression (LSR) is a fundamental statistical analysis technique that has been widely applied to feature learning. However, limited by its simplicity, the local structure of data is easy to neglect, and many methods have considered using orthogonal constraint for preserving more local information. Another major drawback of LSR is that the loss function between soft regression results and hard target values cannot precisely reflect the classification ability; thus, the idea of the large margin constraint is put forward. As a consequence, we pay attention to the concepts of large margin and orthogonal constraint to propose a novel algorithm, orthogonal least squares regression with large margin (OLSLM), for multiclass classification in this letter. The core task of this algorithm is to learn regression targets from data and an orthogonal transformation matrix simultaneously such that the proposed model not only ensures every data point can be correctly classified with a large margin than conventional least squares regression, but also can preserve more local data structure information in the subspace. Our efficient optimization method for solving the large margin constraint and orthogonal constraint iteratively proved to be convergent in both theory and practice. We also apply the large margin constraint in the process of generating a sparse learning model for feature selection via joint ℓ2,1-norm minimization on both loss function and regularization terms. Experimental results validate that our method performs better than state-of-the-art methods on various real-world data sets.
An emission management framework for implementing a large railway system with interface complexity

This paper presents a framework for a life cycleapproach electromagnetic compatibility (EMC) management to control the overall emission of the modern railway system to external environment. With a large number of electrical equipment interfaces following integration of mechanical and electrical subsystems, cooperation across all disciplines during system implementation through key EMC activities is described at each project development phase to guarantee the success of a large-scale project EMC safety assurance. A case study of a large Mass Rapid Transit project is examined to illustrate how the framework may be applied. The validation of safe railway system emission is demonstrated through positive system integration test results under EN 50121-2 criteria. The framework flexibility to accommodate foreseeable impacts of future change is also discussed.
BitSurfing: Wireless Communications with Outsourced Symbol Generation

Nano-IoT enables a wide range of ground-breaking technologies, but face implementation challenges due to the extremity of the scale. Space restrictions pose severe power supply considerations, to the point where just a few packet transmissions are sufficient to deplete state-of-the-art supplies. In turn, this translates to difficulties in developing efficient protocols even for basic operations, such as addressing and routing. The present work proposes a new network adapter architecture that can address these challenges. The BitSurfing adapter does not generate packets and, hence, abolishes the need for the corresponding transmission circuitry and power consumption. Instead, it relies on an external symbol generator. The BitSurfing adapter reads incoming symbols, waiting for intended messages to appear in the symbol stream. A short (1-bit), low-energy pulse is then emitted to notify neighboring nodes. BitSurfing adapters are shown to exhibit perpetual (and even battery-less) operation, ability to operate without medium access control, while being completely transparent to applications. Moreover, their operation is event-driven, allowing for clock-less implementations. The new adapters are evaluated in a simulated multi-hop nano-IoT network and are shown to offer nearly-perfect packet delivery rates and practically no collisions, under any congestion level.
Comparisons of therapeutic efficacy and safety of ipilimumab plus GM-CSF versus ipilimumab alone in patients with cancer: a meta-analysis of outcomes

Background Recent clinical studies have shown that initial therapy with combined cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) blockade and granulocyte-macrophage colony-stimulating factor (GM-CSF)-based immunotherapies can enhance the antitumor efficacy of this approach. A key unanswered question is whether systemic GM-CSF enhances CTLA-4 blockade. Thus, the objective of this study was taking a meta-analysis of randomized controlled trials to compare the effect of ipilimumab plus GM-CSF versus ipilimumab alone on overall response, overall survival, and progression-free survival, as well as the risk of adverse events (AEs) in patients with cancer. Materials and methods Searches were made in electronic databases PubMed and Embase, and conference abstracts published by the American Society of Clinical Oncology from 2000 to 2017. Statistical analyses were carried out using either random-effects or fixed-effects models according to the heterogeneity of eligible studies. Results Six trials comprising of 445 patients were included in the meta-analysis. Combination group was superior to the ipilimumab alone in overall response rate, progression-free survival, and overall survival rate (combined relative risk [RR]=1.34, 95% CI: 1.24–1.45, P=0.09; combined hazard ratio [HR]=0.57, 95% CI: 0.32–1.02, P=0.06; combined HR=0.70, 95% CI: 0.60–0.82, P<0.001). Patients with combination therapies had a lower incidence of AEs including high-grade diarrhea (combined RR=0.27, 95% CI: 0.11–0.70, P=0.007), nausea (combined RR=0.25, 95% CI: 0.07–0.89, P=0.03), colitis (combined RR=0.34, 95% CI: 0.13–0.86, P=0.02), and fatigue (combined RR=0.91, 95% CI: 0.37–2.2.3, P=0.84) compared to the group having ipilimumab alone. Conclusion These data suggested that the combination of ipilimumab and GM-CSF was associated with a significant improvement in overall survival and lower high-grade toxicities, but there is no difference in overall response rate and progression-free survival among the cancer patients. Therefore, large-scale and well-designed studies are needed to summarize and analyze the data to draw a more convincing conclusion.
Mobility Assisted Content Transmission For Device-to-Device Communication Underlaying Cellular Networks

With the explosion growth of mobile data demands for proximal services, device-to-device communication is proposed as a vital technology for the next generation cellular network. With the extensive increase in personal mobile devices, content transmission of proximal devices underlaying cellular networks is gaining considerable attention. However, due to the mobility constraint of mobile devices, content transmission underlaying cellular networks greatly affects the overall transmission capacity of these proximal devices. In this paper, we investigate the problem of mobility assisted content transmission and resource allocation by exploiting the contact patterns regulated by these proximal devices’ mobility. We formulate the content transmission and resource allocation with the help of the statistic property of contact rates, and then utilize convex optimization to determine the successful content transmission and resource allocation scheme. We present the optimal resource allocated content transmission algorithm based on the pseudo-polynomial time algorithm using dynamic programming to solve the optimization problem. Real data traces are utilized in our proposed algorithm to find out the natural strategies of mobile homophily. Extensive simulations under the realistic human mobility factors are evaluated to demonstrate the efficiency of our proposed scheme.
How Humans Versus Bots React to Deceptive and Trusted News Sources: A Case Study of Active Users

Society‘s reliance on social media as a primary source of news has spawned a renewed focus on the spread of misinformation. In this work, we identify the differences in how social media accounts identified as bots react to news sources of varying credibility, regardless of the veracity of the content those sources have shared. We analyze bot and human responses annotated using a fine-grained model that labels responses as being an answer, appreciation, agreement, disagreement, an elaboration, humor, or a negative reaction. We present key findings of our analysis into the prevalence of bots, the variety and speed of bot and human reactions, and the disparity in authorship of reaction tweets between these two sub-populations. We observe that bots are responsible for 9–15 % of the reactions to sources of any given type but comprise only 7-10% of accounts responsible for reaction-tweets; trusted news sources have the highest proportion of humans who reacted; bots respond with significantly shorter delays than humans when posting answer-reactions in response to sources identified as propaganda. Finally, we report significantly different inequality levels in reaction rates for accounts identified as bots vs not.
Autonomous Vehicle Control Through the Dynamics and Controller Learning

Parameters tuning of the model and the controller is an essential problem for autonomous vehicles. Traditionally, parameter tuning work is accomplished by manual operation or grid search, which is a tedious and time-consuming work. Recently, thanks to the development of machine learning community, several automatic controller parameter tuning approaches emerged, which usually model the performance function as a Gaussian process, and complete the automatic tuning procedure via Bayesian optimization. However, the existing approaches rarely consider the time-varying feature of the system performance, which is practical in many scenarios, induced by the unmodeled interactions between the system and the environment. In this paper, we take both of the dynamic model uncertainty and the controller parameters uncertainty into account, and tune them to find a global optimal choice for minimizing the time-varying control costs, which is modeled by the time-varying Gaussian process. We provide a novel algorithm, named as time-varying controller optimization. We validate our approach on synthetic simulation and real experiment, respectively. Taking the cumulative regret as the performance metric, we find that our approach has a better performance compared with the stationary algorithm.
Health Effects and Sources of Prebiotic Dietary Fiber

Abstract Prebiotic dietary fibers act as carbon sources for primary and secondary fermentation pathways in the colon, and support digestive health in many ways. Fructooligosaccharides, inulin, and galactooligosaccharides are universally agreed-upon prebiotics. The objective of this paper is to summarize the 8 most prominent health benefits of prebiotic dietary fibers that are due to their fermentability by colonic microbiota, as well as summarize the 8 categories of prebiotic dietary fibers that support these health benefits. Although not all categories exhibit similar effects in human studies, all of these categories promote digestive health due to their fermentability. Scientific and regulatory definitions of prebiotics differ greatly, although health benefits of these compounds are uniformly agreed upon to be due to their fermentability by gut microbiota. Scientific evidence suggests that 8 categories of compounds all exhibit health benefits related to their metabolism by colonic taxa.
Genetic and Historical Colonization Analyses of an Endemic Savanna Tree, Qualea grandiflora, Reveal Ancient Connections Between Amazonian Savannas and Cerrado Core

The evolutionary processes underlying the high diversity and endemism in the Cerrado, the most extensive Neotropical savanna, remain unclear, including the factors promoting the presence and evolution of savanna enclaves in the Amazon forest. In this study, we investigated the effects of past climate changes on genetic diversity, dynamics of species range and the historical connections between the savanna enclaves and Cerrado core for Qualea grandiflora, a tree species widely distributed in the biome. Totally, 40 populations distributed in the Cerrado core and Amazon savannas were analyzed using chloroplast and nuclear DNA sequences. We used phylogeographic, coalescent and ecological niche modeling approaches. Genetic data revealed a phylogeographic structure shaped by Pleistocene climatic oscillations. An eastern-western split in the Cerrado core was observed. The central portion of the Cerrado core harbored most of the sampled diversity for cpDNA. Ecological niche models predicted the presence of a large historical refuge in this region and multiple small refuges in peripheral areas. Relaxed Random Walk (RRW) models indicated the ancestral population in the north-western border of the central portion of the Cerrado core and cyclical dynamics of colonization related to Pleistocene climatic oscillations. Central and western ancient connections between Cerrado core and Amazonian savannas were observed. No evidence of connections among the Amazonian savannas was detected. Our study highlights the importance of Pleistocene climatic oscillations for structuring the genetic diversity of Q. grandiflora and complex evolutionary history of ecotonal areas in the Cerrado. Our results do not support the recent replacement of a large area in the Amazon forest by savanna vegetation. The Amazonian savannas appear to be fragmented and isolated from each other, evolving independently a long ago.
Constraints and Priorities for Conducting Experimental Exposures of Marine Organisms to Microplastics

Marine plastic pollution is a major environmental issue. Given their ubiquitous nature and small dimensions, ingestion of microplastic (MP) and nanoplastic (NP) particles and their subsequent impact on marine life are a growing concern worldwide. Transfers along the trophic chain, including possible translocation, for which the hazards are less understood, are also a major preoccupation. Effects of MP ingestion have been studied on animals through laboratory exposure, showing impacts on feeding activity, reserve depletion and inflammatory responses, with consequences for fitness, notably reproduction. However, most experimental studies have used doses of manufactured virgin microspheres that may not be environmentally realistic. As for most ecotoxicological issues, the environmental relevance of laboratory exposure experiments has recently been debated. Here we review constraints and priorities for conducting experimental exposures of marine wildlife to microplastics based on the literature, feedback from peer reviewers and knowledge gained from our experience. Priorities are suggested taking into account the complexity of microplastics in terms of (i) aggregation status, surface properties and interactions with organic and inorganic materials, (ii) diversity of encountered particles types and concentrations, (iii) particle bioavailability and distribution in experimental tanks to achieve reproducibility and repeatability in estimating effects, and (iv) strict experimental procedures to verify the existence of genuine translocation. Relevant integrative approaches encompass a wide spectrum of methods from -omics to ecophysiological approaches, including modelling, are discussed to provide novel insights on the impacts of MP/NP on marine ecosystems from a long-term perspective. Knowledge obtained in this way would inform stakeholders in such a way as to help them mitigate impacts of the micro- and nano-plastic legacy.
Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles

State of Charge (SOC) is a key parameter for battery management and vehicle energy management. Recently used SOC estimation methods for lithium-ion battery for vehicles have problems of too simple a base model for the battery and large sampling noise in both the voltage and current signals. To improve the accuracy of SOC estimation and consider that the extended Kalman filter algorithm needs linear approximation of the system equation, the unscented Kalman filter (UKF) algorithm was used to reduce the influence of sampling noise, and an improved algorithm with better filtering effect and SOC estimation accuracy was proposed. Based on the SOC estimation and battery model, the peak power prediction method for the battery is proposed and used in the power distribution strategy for Series HEV. Considering the frequent changes in load current and sampling noise, an experiment was designed to verify the effectiveness and robustness of the algorithm. The experimental results show that the UKF algorithm and the improved UKF algorithm can achieve 6% and 1.5% estimation error. The power distribution strategy based on battery SOC estimation and peak power prediction is tested and validated.
Progressive muscle relaxation technique on anxiety and depression among persons affected by leprosy

Anxiety and depression have been found to be increasing among people with leprosy and it may lead to decreased social participation. The progressive muscle relaxation technique (PMRT) is widely used today in choice of treatment for reducing the anxiety and depression. This study aimed to assess the effectiveness of PMRT in reducing anxiety and depression among the hospitalized leprosy affected person in a tertiary care centre. This study is a case series of 50 leprosy affected people aged between 18–60 years who were admitted for leprosy complications in tertiary leprosy referral hospital. The Anxiety-Depression scale was developed and validated by the investigators and administered before intervention of PMRT and after 2 weeks. The follow-up assessment was done at 6 weeks after the initial intervention. The finding shows that a statistically significant difference was observed on anxiety domain before and after application of PMRT. The anxiety means score showed steady decline from 6.76 at pretest to 3.0 (t=25.068, P≤0.001) at post test and 1.12 (t=22.679, P≤0.001) at follow-up. In depression domain, a statistically significant difference was seen in before and after application of PMRT. The depression means score showed steady decline from 6.92 at pre test to 3.28 (t=16.082, P≤0.001) at post test and to 1.16 (t=18.918, P≤0.001) at follow-up. This study proved that the PMRT as a valid treatment option for hospitalized person with leprosy in minimizing the anxiety and depression related symptoms and to benefit the psychosocial wellbeing of leprosy affected patients.
Tests from traces: automated unit test extraction for R

Unit tests are labor-intensive to write and maintain. This paper looks into how well unit tests for a target software package can be extracted from the execution traces of client code. Our objective is to reduce the effort involved in creating test suites while minimizing the number and size of individual tests, and maximizing coverage. To evaluate the viability of our approach, we select a challenging target for automated test extraction, namely R, a programming language that is popular for data science applications. The challenges presented by R are its extreme dynamism, coerciveness, and lack of types. This combination decrease the efficacy of traditional test extraction techniques. We present Genthat, a tool developed over the last couple of years to non-invasively record execution traces of R programs and extract unit tests from those traces. We have carried out an evaluation on 1,545 packages comprising 1.7M lines of R code. The tests extracted by Genthat improved code coverage from the original rather low value of 267,496 lines to 700,918 lines. The running time of the generated tests is 1.9 times faster than the code they came from
Matrin 3-dependent neurotoxicity is modified by nucleic acid binding and nucleocytoplasmic localization

Abnormalities in nucleic acid processing are associated with the development of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Mutations in Matrin 3 (MATR3), a poorly understood DNA- and RNA-binding protein, cause familial ALS/FTD, and MATR3 pathology is a feature of sporadic disease, suggesting that MATR3 dysfunction is integrally linked to ALS pathogenesis. Using a primary neuron model to assess MATR3-mediated toxicity, we noted that neurons were bidirectionally vulnerable to MATR3 levels, with pathogenic MATR3 mutants displaying enhanced toxicity. MATR3’s zinc finger domains partially modulated toxicity, but elimination of its RNA recognition motifs had no effect on neuronal survival, instead facilitating its self-assembly into liquid-like droplets. In contrast to other RNA-binding proteins associated with ALS, cytoplasmic MATR3 redistribution mitigated neurodegeneration, suggesting that nuclear MATR3 mediates toxicity. Our findings offer a foundation for understanding MATR3-related neurodegeneration and how nucleic acid binding functions, localization, and pathogenic mutations drive sporadic and familial disease.