Miracle-dz/UniFAR-FTUs huggingface dataset:
Rem2 stabilizes intrinsic excitability and spontaneous firing in visual circuits

Sensory experience plays an important role in shaping neural circuitry by affecting the synaptic connectivity and intrinsic properties of individual neurons. Identifying the molecular players responsible for converting external stimuli into altered neuronal output remains a crucial step in understanding experience-dependent plasticity and circuit function. Here, we investigate the role of the activity-regulated, non-canonical Ras-like GTPase Rem2 in visual circuit plasticity. We demonstrate that Rem2-/- mice fail to exhibit normal ocular dominance plasticity during the critical period. At the cellular level, our data establish a cell-autonomous role for Rem2 in regulating intrinsic excitability of layer 2/3 pyramidal neurons, prior to changes in synaptic function. Consistent with these findings, both in vitro and in vivo recordings reveal increased spontaneous firing rates in the absence of Rem2. Taken together, our data demonstrate that Rem2 is a key molecule that regulates neuronal excitability and circuit function in the context of changing sensory experience.
Longest-commonest Match

Finding two-word collocations is a well-studied task within natural language processing. The result of this task for a given headword is usually a list of collocations sorted by a salience score. In corpus manager Sketch Engine, these pairs are extracted from data using a word sketch grammar relation rules and log-dice statistics resulting in a sorted list of triples 'head- word, grammar-relation, collocate'. The longest–commonest match is a straightforward ex- tension of these two-word collocations into multiword expressions. The resulting expressions are also very useful for representing the most common realisation of the collocational pair and to facilitate the interpretation of the raw triplet because sometimes, for such a triple, it is not clear from what texts it comes. We present here an algorithm behind the longest–commonest match together with a simple evaluation. The longest–commonest match is already imple- mented in Sketch Engine.
Combined Therapy with Extracorporeal Shock Wave and Adipose-Derived Mesenchymal Stem Cells Remarkably Improved Acute Ischemia-Reperfusion Injury of Quadriceps Muscle

Extracorporeal shock wave (ECSW) and adipose-derived mesenchymal stem cells (ADMSCs) have been recognized to have capacities of anti-inflammation and angiogenesis. We tested the hypothesis that ECSW and ADMSC therapy could attenuate ischemia-reperfusion- (IR-) induced thigh injury (femoral artery tightened for 6 h then the tightness was relieved) in rats. Adult male SD rats (n = 30) were divided into group 1 (sham-control), group 2 (IR), group 3 (IR + ECSW/120 impulses at 0.12 mJ/mm2 given at 3 h/24 h/72 h after IR), group 4 (allogenic ADMSC/1.2 × 106 cell intramuscular and 1.2 × 106 cell intravenous injections 3 h after IR procedure), and group 5 (ECSW + ADMSC). At day 7 after the IR procedure, the left quadriceps muscle was harvested for studies. At 18 h after the IR procedure, serum myoglobin/creatine phosphokinase (CPK) levels were highest in group 2, lowest in group 1, and with intermediate values significantly progressively reduced in groups 3 to 5 (all p < 0.0001). By day 5 after IR, the mechanical paw-withdrawal threshold displayed an opposite pattern of CPK (all p < 0.0001). The protein expressions of inflammatory, oxidative-stress, apoptotic, fibrotic, DNA-damaged, and mitochondrial-damaged biomarkers and cellular expressions of inflammatory and DNA-damaged biomarkers exhibited an identical pattern of CPK among the five groups (all p < 0.0001). The microscopic findings of endothelial-cell biomarkers and number of arterioles expressed an opposite pattern of CPK, and the angiogenesis marker was significantly progressively increased from groups 1 to 5, whereas the histopathology showed that muscle-damaged/fibrosis/collagen-deposition areas exhibited an identical pattern of CPK among the five groups (all p < 0.0001). In conclusion, ECSW-ADMSC therapy is superior to either one applied individually for protecting against IR-induced thigh injury.
Longitudinal Associations of Cigarette Prices With Smoking Cessation: The Coronary Artery Risk Development in Young Adults Study.

INTRODUCTION Few studies have used longitudinal cohort data to examine associations of cigarette prices with smoking cessation or whether price sensitivity varies by income or education. This study examines these associations in a multicenter US cohort and explores whether associations vary by education and income. METHODS Longitudinal data from baseline daily cigarette smokers aged 18-30 years in the Coronary Artery Risk Development in Young Adults study were linked to inflation-adjusted cigarette carton prices from the Council for Community and Economic Research Cost of Living Index based on residential address at baseline and in years 7, 10, and 15 (1985-2001). Multivariable Cox models estimated hazard ratios (HRs) of first (any) smoking cessation and sustained smoking cessation (no relapse) associated with each $1 increase in time-dependent cigarette price over 15 years of follow-up. Models were adjusted for sociodemographic, health-related, and policy covariates. We assessed effect modification by education and household income. RESULTS Among 1489 participants, a $1.00 higher cigarette carton price was associated with a 16% higher likelihood of first smoking cessation (HR = 1.16, 95% CI = 1.11 to 1.21) and an 8% higher likelihood of sustained smoking cessation (HR = 1.08, 95% CI = 1.02 to 1.14). Associations were strongest among participants with lower income for first cessation, and among those with higher income for sustained cessation. Associations were strongest for participants with less than a high school degree for both outcomes. CONCLUSIONS Results suggest higher cigarette prices promote smoking cessation among young to middle-aged adults, and that price sensitivity may differ by socioeconomic status. IMPLICATIONS Few studies have examined longitudinal associations of cigarette prices with smoking cessation, and findings are mixed on whether price sensitivity varies by education or income. In a cohort of US adult daily smokers, cigarette prices were associated with greater likelihood of both a first cessation and sustained cessation. Price associations with first cessation were stronger among low-income smokers, but associations with sustained cessation were stronger among high-income smokers. Results suggest that although higher cigarette prices may promote short-term smoking cessation among smokers at all income levels, additional supports may be needed to facilitate sustained smoking cessation among low-income smokers.
Improving DISCERN with Deep Learning

IHMC designed and implemented two variants of an event-detection system, one that used manually created rules ( DISCERN-R ) and another one that used rules learned using multiple deep neural networks ( DISCERN-D ). The former uses very rich linguistic resources (Verb-Net, CatVar, Semantic Role Labeling, NER, POS tagging, dependency parsing, and coreference resolution) and the latter supplants these features with learned embedding vectors. These systems were applied to two tasks in the NIST TAC KBP 2016 Event Track: Event Nugget Detection and Coreference (EN) and Event Argument Extraction and Linking (EAL) for English language. Additionally, the neural network system was used for these two tasks in Spanish.
Magnetorelaxometry in the Presence of a DC Bias Field of Ferromagnetic Nanoparticles Bearing a Viscoelastic Corona

With allowance for orientational Brownian motion, the magnetorelaxometry (MRX) signal, i.e., the decay of magnetization generated by an ensemble of ferromagnet nanoparticles, each of which bears a macromolecular corona (a loose layer of polymer gel) is studied. The rheology of corona is modelled by the Jeffreys scheme. The latter, although comprising only three phenomenological parameters, enables one to describe a wide spectrum of viscoelastic media: from linearly viscous liquids to weakly-fluent gels. The “transverse” configuration of MRX is considered where the system is subjected to a DC (constant bias) field, whereas the probing field is applied perpendicularly to the bias one. The analysis shows that the rate of magnetization decay strongly depends on the state of corona and slows down with enhancement of the corona elasticity. In addition, for the case of “transverse” MRX, we consider the integral time, i.e., the characteristic that is applicable to relaxation processes with an arbitrary number of decay modes. Expressions for the dependence of the integral time on the corona elasticity parameter and temperature are derived.
Deep Learning for Entity Matching: A Design Space Exploration

Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, and Hybrid. Next, we investigate the types of EM problems for which DL can be helpful. We consider three such problem types, which match structured data instances, textual instances, and dirty instances, respectively. We empirically compare the above four DL solutions with Magellan, a state-of-the-art learning-based EM solution. The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM. For practitioners, this suggests that they should seriously consider using DL for textual and dirty EM problems. Finally, we analyze DL's performance and discuss future research directions.
A Feasibility Study on Automated Protein Aggregate Characterization Utilizing a Hybrid Classification Model

The characterization of cytoplasmic protein aggregates based on time-lapse fluorescence microscopy imaging data is important for research in neuro-degenerative diseases such as Parkinson. As the manual assessment is time-consuming and subject to significant variability, incentive for the development of an objective automated system is provided. We propose and evaluate a pipeline consisting of cell-segmentation, tracking and classification of neurological cells. Focus is specifically on the novel and challenging classification task which is covered by relying on feature extraction followed by a hybrid classification approach incorporating a support vector machine focusing on mainly stationary information and a hidden Markov model to incorporate temporal context. Several image representations are experimentally evaluated to identify cell properties that are important for discrimination. Relying on the proposed approach, classification accuracies up to 80 % are reached. By extensively analyzing the outcomes, we discuss about strengths and weaknesses of our method as a quantitative assessment tool.
SAR and temperature rise distributions in a human head due to a multi-frequency antenna source

Temperature rise and specific absorption rate (SAR) distribution in a human head due to an antenna are evaluated at multiple frequencies in a single simulation. An algorithm is developed by integrating the Debye model of head tissues parameters into the finite-difference time-domain (FDTD) method by using the auxiliary differential equation (ADE) approach along with the use of bioheat equation for SAR and temperature computations. The tissue EM parameters are used from the available three-term Debye coefficients from 500 MHz to 20 GHz.
Training Prescription Guided by Heart Rate Variability in Cycling.

PURPOSE Road cycling is a sport with extreme physiological demands. Therefore, there is a need to find new strategies to improve performance. Heart rate variability (HRV) has been suggested as an effective alternative for prescribing training load against predefined training programs. The purpose of this study is to examine the effect of training prescription based on HRV in road cycling performance. METHODS Seventeen well-trained cyclists participated in this study. After an initial evaluation week (EW), cyclists performed 4 baseline weeks (BW) of standardized training to establish their resting HRV. Then, cyclists were divided into two groups, a HRV-guided group (HRV-G) and a traditional periodization group (TRAD) and they carried out 8 training weeks (TW). Cyclists performed two EW, after and before TW. During the EW, cyclists performed: (1) a graded exercise test to assess VO2max, peak power output (PPO) and ventilatory thresholds with their corresponding power output (VT1, VT2, WVT1, and WVT2, respectively) and (2) a 40-min simulated time-trial. RESULTS HRV-G improved PPO (5.1 ± 4.5 %; p = 0.024), WVT2 (13.9 ± 8.8 %; p = 0.004) and 40TT (7.3 ± 4.5 %; p = 0.005). VO2max and WVT1 remained similar. TRAD did not improve significantly after TW. There were no differences between groups. However, magnitude-based inference analysis showed likely beneficial and possibly beneficial effects for HRV-G instead of TRAD in 40TT and PPO, respectively. CONCLUSIONS Daily training prescription based on HRV could result in a better performance enhancement than a traditional periodization in well-trained cyclists.
Prevalence, Predictive Factors, and Characteristics of Osteoporosis in Hyperthyroid Patients

Objective The osteoporosis in thyroid disorder has the lowest report especially in sub-Saharan Africa. This study aims to determine the prevalence, predictive factors, and characteristics of osteoporosis in hyperthyroid patients. Method Forty (40) hyperthyroid patients and healthy controls ages 21–50 years were recruited in this study. Questionnaires were administered to capture bio- and clinical data. Biochemical tests included blood, thyroid functions, intact parathyroid hormone, corrected calcium, and 25-hydroxyvitamin D tests. Bone mineral density (BMD) was also evaluated. Data were analyzed using the SPSS 21. A p value < 0.05 was regarded as significant. Results Osteoporosis was observed in 18 (45%) of study subjects, 13 (72.2%) females and 5 (27.8%) males, respectively. The BMD of the hyperthyroid patients had a negative correlation with free triiodothyronine, FT3 (r = −0.49, p = 0.005), FT4 (r = −0.33, p = 0.009), corrected calcium (r = −0.31, p = 0.039), alkaline phosphatase (r = −0.53, p < 0.001), and osteocalcin (r = −0.61, p < 0.001). Conversely, a positive association with thyroid-stimulating hormone (TSH) (r = 0.54, p < 0.001) was observed. Multiple regression showed osteocalcin (p < 0.001) and TSH (p = 0.015) as independent predictors of osteoporosis. Conclusion Thyrotoxicosis is a risk factor for osteoporosis occurrence, and we recommend routine screening for this bone disease in persons over 20 years old with this disorder.
An Integrative Social Network and Review Content Based Recommender System

Traditional collaborative filtering (CF) recommender systems (RS) suffer the problems of poor rating accuracy, cold start and data sparsity. Friendship information is not used in current recommender systems, but such information may be useful for improving rating accuracy. Besides, traditional recommender systems only use rating scores to predict users’ future buying decisions. Review comments are usually ignored, and thus it will decrease the recommendation accuracy. In this paper, a novel algorithm which integrates the social network (i.e. friendship information) and latent semantic analysis (i.e. review text) is proposed and implemented in our recommender system, aiming to improve rating accuracy. To test the performance of our recommender system, we crawled the restaurant data in the greater New York area from the website of Yelp. Our experimental results have shown that our proposed algorithm performs 18.6% higher accuracy than the traditional CF algorithm, and 1.2% higher accuracy than friend-based algorithm. We have also shown that the use of distant friendship information in the recommender systems could dramatically increase coverage rate. 
Energy Modeling of IoT Mobile Terminals on WiFi Environmental Impacts †

With the popularity of various IoT mobile terminals such as mobile phones and sensors, the energy problems of IoT mobile terminals have attracted increasingly more attention. In this paper, we explore the impacts of some important factors of WiFi environments on the energy consumption of mobile phones, which are typical IoT end devices. The factors involve the WiFi signal strength under good signal conditions, the type and the amount of protocol packets that are initiated by WiFi APs (Access Points) to maintain basic network communication with the phones. Controlled experiments are conducted to quantitatively study the phone energy impacts by the above WiFi environmental factors. To describe such impacts, we construct a time-based signal strength-aware energy model and packet type/amount-aware energy models. The models constructed in the paper corroborate the following user experience on phone energy consumption: (1) a phone’s energy is drawn faster under higher WiFi signal strengths than under lower ones even in normal signal conditions; (2) phones consume energy faster in a public WiFi network than in a private one even in the basic phone state. The energy modeling methods proposed in the paper enable ordinary developers to analyze phone energy draw conveniently by utilizing inexpensive power meters as measurement tools. The modeling methods are general and are able to be used for phones of any type and any platform.
Spatial classification with fuzzy lattice reasoning

This work extends the Fuzzy Lattice Reasoning (FLR) Classifier to manage spatial attributes, and spatial relationships. Specifically, we concentrate on spatial entities, as countries, cities, or states. Lattice Theory requires the elements of a Lattice to be partially ordered. To match such requirement, spatial entities are represented as a graph, whose number of nodes is equal to the amount of unique values of the spatial attribute elements. Then, the graph nodes are linearly arranged to formulate a partially ordered set; and thus be included in the Fuzzy Lattice classifier. The overall problem of incorporating spatial attributes in FLR was deduced to a Minimum Linear Arrangement problem. A corresponding open-source implementation in R has been made available on CRAN repository. The proposed method was evaluated using an open spatial dataset from the National Ambient Air Quality Standards (NAAQS). We investigated whether the addition of the spatial attribute contributed to any improvements in classification accuracy; and how linear arrangement alternatives may affect it. Experimental results showed that classification accuracy is above 85% in all cases, and the use of spatial attributes resulted to an increased accuracy of 92%. Alternative linear arrangements did not contribute significantly in improving classification accuracy in this case study.
Fine-Grained Data Committing for Persistent Memory

Non-Volatile Memory (NVM) is better than traditional DRAM with respect to energy efficiency and larger capacity, so NVM has begun to be used as main memory. NVM provides data persistence that data written into NVM will not be lost during unexpected system failure occurs. Data persistence is mandatory for programs such as file system and database. However, traditional memory protocol cannot provide an mechanism for programs to guarantee data persistence because the write instructions do not ensure that data would be eventually written into the memory media. Furthermore, extra global operations such as PCOMMIT for data committing could incur significant performance loss, especially for multi-task programs. To address this issue, we propose a hardware-software coordinated mechanism to achieve low-overhead data committing. Write queues in memory controller are divided into multiple sub-queues for monitoring write commands for different address ranges. Programs can query write queues to check the execution status of previous written commands through a series of OS-managed library APIs. Fine-grained data committing can reduce the interferences among processes effectively. Extensive evaluations show that per-task data committing brings an average 1.78x performance improvement than original global committing mechanism and accelerates the data committing by 2.07 times.
Analysing the driving load on electric vehicles using unsupervised segmentation models as enabler to determine the time of battery replacement and assess driving mileage

The aim of this contribution is to study the driving load on electric vehicles through data mining methods. The battery pack is currently the major cost driver for these vehicles. Thus, battery durability is an important lever to economically establish electric mobility in the mass market. A segmentation modelling approach, based on unsupervised clustering algorithms, evaluates battery management system field data from BMW i3 vehicles such as state of charge, temperature and current in terms of their historical distribution. Clustering algorithms, due to their frequent use in the field of machine learning and information retrieval, are able to analyse big quantities of vehicle data with the aim to group vehicles with similar driving behaviour. The cluster analysis is further validated using the method of calculating respective silhouette coefficients to assess clustering performance and the influence of input parameters. The analysis of histograms concludes with the definition of the most common types of drivers worldwide. The vehicle clusters can further be correlated with battery ageing in order to find suitable 2nd life applications as part of stationary energy storage systems.
Observer-Based Fuzzy Adaptive Event-Triggered Control Codesign for a Class of Uncertain Nonlinear Systems

This paper is concerned with the simultaneous design of a fuzzy-based output feedback control law and an adaptive event-triggering condition for a class of single-input and single-output uncertain nonlinear systems in the presence of a communication channel between the plant and the controller. To this aim, a Lyapunov-based technique is adopted in an impulsive dynamic system framework to ensure local stability and tracking performance. The proposed Lyapunov formulation yields an event-triggered algorithm to update the control input and fuzzy weights based on conditions involving the closed-loop state, which are aperiodically updated only at the event-sampled instants. Furthermore, a positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. Different from the existing traditional time-triggered control schemes, the developed method samples the state and updates the controller and fuzzy weights only when it is necessary. Clearly, this can largely decrease the communication burden of the communication network. Finally, the effectiveness of the proposed method is illustrated using simulation examples.