Miracle-dz/UniFAR-FTUs huggingface dataset:
A double blind randomized clinical trial to investigate the effect of vitamin D supplementation on metabolic and hepato-renal markers in type 2 diabetes and obesity

Background: According to the recent studies, vitamin D deficiency has been correlated with progress in type 2 Diabetes and Metabolic Syndrome. The aim of this study was to assess the effect of vitamin D supplementation on glucose and lipid profiles, blood pressure, and biomarkers of liver and kidney in type 2 diabetic patients. Methods: In this Double blinded randomized clinical trial, 90 patients with type 2 diabetes and serum 25-Hydroxy vitamin D levels of less than 30 ng/ml recruited from "Besat Diabetes Clinic" in Rasht, North of Iran. The subjects took 50000 IU vitamin D supplements or placebo for 8 weeks. We assessed the levels of serum 25 (OH) vitamin D, glucose and lipid profiles, oxidative and inflammatory indices, liver and kidney biomarkers, blood pressure, and sun exposure time, physical activity before and after intervention, and compared them between cases and controls. Results: Vitamin D supplementation significantly increased serum vitamin D level, Superoxide Dismutase (SOD) activity, and significantly decreased serum HbA1C (Glycosylated Hemoglobin) level (p<0.001). High Density Lipoprotein (HDL) Cholesterol increased significantly (p=0.016), and Erythrocyte Sedimentation Rate (ESR) significantly decreased (p=0.039) after the intervention. Conclusion: Our results represented that weekly supplementation with 50000 IU vitamin D for 8 weeks may be effective by improving HbA1C and lipid profile in type 2 diabetes mellitus.
Applying Non-Nested Generalized Exemplars Classification for Cyber-Power Event and Intrusion Detection

Non-nested generalized exemplars (NNGEs) is a state of the art data mining algorithm which uses distance between a new example and a set of exemplars for classification. The state extraction method (STEM) preprocesses power system wide area measurement system data to reduce data size while maintaining critical patterns. Together NNGE+STEM make an effective event and intrusion detection system which can effectively classify power system events and cyber-attacks in real time. This paper documents the results of two experiments in which NNGE+STEM was used to classify cyber power contingency, control action, and cyber-attack events. Experimental results show that NNGE+STEM achieved greater than 94 and 97% accuracy for multiclass and binary class classification. Additionally, the NNGE+STEM false positive rate was below 0.5%, the average classification time was 0.2 ms, and the classifier had low memory requirements.
Learning to Act in Continuous Dec-POMDPs

We address a long-standing open problem of reinforcement learning in continuous decentralized partially observable Markov decision processes. Previous attempts focused on different forms of generalized policy iteration, which at best led to local optima. In this paper, we restrict attention to plans, which are simpler to store and update than policies. We derive, under mild conditions, the first optimal cooperative multi-agent reinforcement learning algorithm. To achieve significant scalability gains, we replace the greedy maximization by mixed-integer linear programming. Experiments show our approach can learn to act optimally in many finite domains from the literature.
Moisture‐mediated responsiveness of treeline shifts to global warming in the Himalayas

Among forest ecosystems, the alpine treeline ecotone can be considered to be a simplified model to study global ecology and climate change. Alpine treelines are expected to shift upwards in response to global warming given that tree recruitment and growth are assumed to be mainly limited by low temperatures. However, little is known whether precipitation and temperature interact to drive long‐term Himalayan treeline dynamics. Tree growth is affected by spring rainfall in the central Himalayan treelines, being good locations for testing if, in addition to temperature, precipitation mediates treeline dynamics. To test this hypothesis, we reconstructed spatiotemporal variations in treeline dynamics in 20 plots located at six alpine treeline sites, dominated by two tree species (birch, fir), and situated along an east–west precipitation gradient in the central Himalayas. Our reconstructions evidenced that treelines shifted upward in response to recent climate warming, but their shift rates were primarily mediated by spring precipitation. The rate of upward shift was higher in the wettest eastern Himalayas, suggesting that its ascent rate was facilitated by spring precipitation. The drying tendency in association with the recent warming trends observed in the central Himalayas, however, will likely hinder an upslope advancement of alpine treelines and promote downward treeline shifts if moisture availability crosses a critical minimum threshold. Our study highlights the complexity of plant responses to climate and the need to consider multiple climate factors when analyzing treeline dynamics.
Most Valuable Player Algorithm for Circular Antenna Arrays Optimization to Maximum Sidelobe Levels Reduction

Optimally designed arrays are used to obtain high gain and directivity. Based on the desired application, different radiation patterns can be generated. These patterns have generally a main beam and some sidelobes. These last ones are undesirable because they can cause electromagnetic interference and compatibility issues. Therefore, sidelobe levels (SLLs) reduction is one of the most important factors for the design of antenna arrays and arrays must be designed in a way to reduce SLLs. In this paper, the optimal design of circular antenna arrays (CAA) for maximum SLLs reduction is investigated using a newly developed metaheuristic that is the most valuable player algorithm. This algorithm is inspired from sport and it has been proven to be an excellent optimization algorithm for mathematical benchmarks. Three cases with different number of arrays are investigated. For comparison purposes, ten other well-known optimization algorithms are used. The design results obtained show the superiority of the proposed algorithm for the optimal design of CAA and for sidelobe reduction over many other algorithms.
Association between obesity and sickness in the past two weeks among middle-aged and elderly women: A cross-sectional study in Southern China

Objectives Sickness situation in the past two weeks, an indicator of health service needs, is an increasing major health concern. However, data on the relationship between obesity and two-week morbidity in the female population, particularly in middle-aged and elderly women, is sparse. The present study aimed to examine the association between obesity and two-week morbidity among middle-aged and elderly women in Southern China, and to explore the independent contributions of socio-demographic variables, health-related factors, and obesity to two-week morbidity. Methods In total, 2364 middle-aged and elderly women were included in this cross-sectional, community-based survey. Obesity was assessed using body mass index (BMI). The outcome variable was sickness situation over the past two weeks (two-week morbidity). Clustered logistic regression was applied to analyze the independent contribution of obesity to two-week morbidity. Results Approximately 14.6% of participants experienced sickness in the past two weeks. Obesity (odds ratio [OR] = 1.47, 95% confidence interval [CI] = 1.02–2.12) was significantly associated with two-week morbidity and its independent contribution accounted for 3.7%, lower than that of socio-demographic variables (73.7%) and health-related factors(22.6%). Conclusions Some degree of correlation was observed between obesity and two-week morbidity among middle-aged and elderly women in Southern China, which can be used as a reference for health-related decision-making.
Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding

As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation. According to previous studies, device level consumption could lead to significant freshwater conservation. Existing water disaggregation methods focus on learning the signatures for appliances; however, they are lack of the mechanism to accurately discriminate parallel appliances' consumption. In this paper, we propose a Bayesian Discriminative Sparse Coding model using Laplace Prior (BDSC-LP) to extensively enhance the disaggregation performance. To derive discriminative basis functions, shape features are presented to describe the low-sampling-rate water consumption patterns. A Gibbs sampling based inference method is designed to extend the discriminative capability of the disaggregation dictionaries. Extensive experiments were performed to validate the effectiveness of the proposed model using both real-world and synthetic datasets.
Simple Strategies to Build Random Compressive Sensing Matrices in Step-Frequency Radars

By applying compressive sensing, we can obtain radar range profile using only part of the frequency bins in step-frequency radars. While the probability of success in obtaining the range profile depends on which frequency bins are used, it seems that there is no general and simple method to choose the set of frequency bins for better probability of success. Although random selection is said to be good enough, it would be better if we select those in some strategic ways. This letter deals with frequency selection strategies when deterministic ways are not applicable and the super-resolution range profile is necessary. The proposed strategies are tested with simulations and real measurement data set. The results show that the proposed method is better in achieving the probability of exact recovery than casual random selections.
Kidney Transplant Outcomes in Recipients With Cognitive Impairment: A National Registry and Prospective Cohort Study.

BACKGROUND Cognitive impairment is common in patients with end-stage renal disease and is associated with poor outcomes on dialysis. We hypothesized that cognitive impairment might be associated with an increased risk of all-cause graft loss (ACGL) in kidney transplant (KT) recipients. METHODS Using the Modified Mini-Mental State (3MS) examination, we measured global cognitive function at KT hospital admission in a prospective, 2-center cohort of 864 KT candidates (August 2009 to July 2016). We estimated the association between pre-KT cognitive impairment and ACGL using Cox regression, adjusting for recipient, donor, and transplant factors. RESULTS In living donor KT (LDKT) recipients, the prevalence was 3.3% for mild impairment (60 ≤ 3MS < 80) and 3.3% for severe impairment (3MS < 60). In deceased donor KT (DDKT) recipients, the prevalence was 9.8% for mild impairment and 2.6% for severe impairment. The LDKT recipients with cognitive impairment had substantially higher ACGL risk than unimpaired recipients (5-year ACGL: 45.5% vs 10.6%; P < 0.01; adjusted hazard ratio [aHR] any impairment, 5.40 (95% confidence interval [CI], 1.78-16.34; P < 0.01); aHR severe impairment, 5.57 (95% CI, 1.29-24.00; P = 0.02). Similarly, DDKT recipients with severe impairment had higher ACGL risk than recipients without severe impairment (5-year ACGL, 53.0% vs 24.2%; P = 0.04); aHR severe impairment, 2.92 (95% CI, 1.13-7.50; P = 0.03). CONCLUSIONS Given the elevated risk of ACGL among KT recipients with cognitive impairment observed in this 2-center cohort, research efforts should explore the mechanisms of graft loss and mortality associated with cognitive impairment and identify potential interventions to improve posttransplant survival.
Design and Implementation of Computer Network Vulnerability Assessment System

With the rapid development of computer technology and network technology, the network speed is increasing fast, the expansion of computer network and data transmission medium can easily lead to network outages, an accurate assessment for the vulnerability of network should be made to ensure the safety and stability of the network. The design method of evaluation system for fragile control with dynamic connection structure of network topology design and bus system based on computer network, the dynamic connection structure of network node routing repair, computer network system and I/O service operations can be upgraded and dynamic connection patch mode. Network applications using the B/S structure of the operating system vulnerability assessment, the system bus control method to configure network reset serial receiver and transmitter configuration, send and receive control register and check the remote network system available on the service And configure, operate directly on the database application using cross compiler, network operating system architecture editor, compiler, connection and debugging, realize the network vulnerability assessment system. Test results show that this method of computer network vulnerability assessment, it can effectively repair the network routing and interrupt node, improve the connectivity, the safety and stability of the network.
Eye movement control during visual pursuit in Parkinson’s disease

Background Prior studies of oculomotor function in Parkinson’s disease (PD) have either focused on saccades without considering smooth pursuit, or tested smooth pursuit while excluding saccades. The present study investigated the control of saccadic eye movements during pursuit tasksand assessed the quality of binocular coordinationas potential sensitive markers of PD. Methods Observers fixated on a central cross while a target moved toward it. Once the target reached the fixation cross, observers began to pursue the moving target. To further investigate binocular coordination, the moving target was presented on both eyes (binocular condition), or on one eye only (dichoptic condition). Results The PD group made more saccades than age-matched normal control adults (NC) both during fixation and pursuit. The difference between left and right gaze positions increased over time during the pursuit period for PD but not for NC. The findings were not related to age, as NC and young-adult control group (YC) performed similarly on most of the eye movement measures, and were not correlated with classical measures of PD severity (e.g., Unified Parkinson’s Disease Rating Scale (UPDRS) score). Discussion Our results suggest that PD may be associated with impairment not only in saccade inhibition, but also in binocular coordination during pursuit, and these aspects of dysfunction may be useful in PD diagnosis or tracking of disease course.
Semantic and Spatial Co-Occurrence Analysis on Object Pairs for Urban Scene Classification

Urban scene classifications with very-high-resolution (VHR) satellite images play an important role in urban functional-zone investigation and landscape analysis. Most scene classification techniques utilize visual features which however are weak in identifying scenes, as scenes are usually composed of diverse objects with variant visual cues. To resolve this issue, we use spatial object relations to characterize urban scenes and recognize their categories. Accordingly, two questions are considered in this study: how to measure spatial object relations and how to use these relations to classify urban scenes? First, we propose a novel scene feature, semantic and spatial co-occurrence probability (SSCP), to measure spatial relations between objects with considering their directions, distances, and semantics, which can hopefully resolve the three key issues in measuring spatial object relations, i.e., anisotropy, scale- and semantic-dependencies. Then, a semilatent Dirichlet allocation is employed to classify scenes based on the proposed SSCP. In experiments, our method is first verified based on the UC Merced data set, and then used to generate an urban functional-zone map for Beijing, China. Both experiments indicate high accuracies and state-of-the-art performances of our method. In addition, we give a deep insight into SSCP's characteristics including convergence, high efficiency, and invariance to affine transformation, which can make our method more applicable.
Towards Flexible Patch Antennas: Characterization of Introduced Gaps

A quantitative study of the effect of introducing gaps of various widths in a rectangular microstrip patch antenna is presented. Simulations were performed using Keysight Advanced Design System®, and the simulations were confirmed by the fabrication and measurement of three antennas. Gaps with widths of $\boldsymbol{\lambda}_{\boldsymbol{eff}}/31$ and smaller have a negligible effect on the return loss and radiation patterns of the patch antennas that were analyzed.
MMSE Detection for 1-Bit Quantized Massive MIMO with Imperfect Channel Estimation

In this paper, we study the uplink achievable rate by a massive multiple-input multiple-output (MIMO) system in which the base station is equipped with a large number of 1-bit analog-to-digital converters (ADCs). We propose a linear minimum mean-squared error (MMSE)-based detector that accounts for the non-linearity effects of the 1-bit quantization as well as for channel estimation error. An analytical framework that derives the achievable rate of the MMSE-based detector in a massive MIMO configuration under the assumption of 1-bit quantized ADCs and channel estimation error is presented. We compare the achievable rates of a massive MIMO system using a 1-bit ADC and a linear detector against a conventional MIMO system with higher-order modulation and near maximum likelihood (ML) detection. We show that in the low signal-to-noise ratio (SNR) regime with channel estimation error, the quantized massive MIMO system can outperform the conventional large MIMO system; however for high SNR, the conventional MIMO system with a near ML detector can outperform the quantized massive MIMO system.
Weighted total variation based convex clustering

Data clustering is a fundamental problem with a wide range of applications. Standard methods, eg the $k$-means method, usually require solving a non-convex optimization problem. Recently, total variation based convex relaxation to the $k$-means model has emerged as an attractive alternative for data clustering. However, the existing results on its exact clustering property, ie, the condition imposed on data so that the method can provably give correct identification of all cluster memberships, is only applicable to very specific data and is also much more restrictive than that of some other methods. This paper aims at the revisit of total variation based convex clustering, by proposing a weighted sum-of-$\ell_1$-norm relating convex model. Its exact clustering property established in this paper, in both deterministic and probabilistic context, is applicable to general data and is much sharper than the existing results. These results provided good insights to advance the research on convex clustering. Moreover, the experiments also demonstrated that the proposed convex model has better empirical performance when be compared to standard clustering methods, and thus it can see its potential in practice.
Temporal Information Extraction by Predicting Relative Time-lines

The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.
Enhancing multilabel classification for food truck recommendation

Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.
A systematic approach for analysis and characterization of mispairing in bispecific antibodies with asymmetric architecture

ABSTRACT Immunoglobulin G–like bispecific antibodies with asymmetric architecture are among the most widely used bispecific antibody formats for diagnostic and therapeutic applications. The primary technical challenge for this format is how to achieve correctly paired assembly of four unique polypeptide chains. Advances in protein engineering and process development are being used to overcome these challenges and are driving a corresponding demand for sensitive analytical tools to monitor and control mispaired species. Here, we report a systematic approach for analysis and characterization of mispairing in asymmetric bispecific antibodies. This approach consists of three orthogonal components, the first of which is a liquid chromatography (LC)-mass spectrometry (MS)–based method to measure the mass of intact antibodies. This method is used for fast analysis of mispairing and requires minimal method development, which makes it an ideal choice for early-stage development. The second component is a hydrophobic interaction chromatography (HIC)–based mispairing method that is suitable for lot release testing. The HIC method is robust and quality control friendly, and offers great linearity, precision, and accuracy. The third component is a two-dimensional LC-MS method for on-line chromatographic peak identification, which not only expedites this task but also reduces the risk of undesirable modifications during conventional fraction collection. These three methods dovetail to form the foundation of a complementary toolbox for analysis and characterization of mispairing in asymmetric bispecific antibodies and provide guidance and support for process development throughout the drug development life cycle.