Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep Bruxism-Tooth Grinding Prevalence, Characteristics and Familial Aggregation: A Large Cross-Sectional Survey and Polysomnographic Validation.

STUDY OBJECTIVES Sleep bruxism (SB) is characterized by tooth grinding and jaw clenching during sleep. Familial factors may contribute to the occurrence of SB. This study aims are: (1) revisit the prevalence and characteristics of SB in a large cross-sectional survey and assess familial aggregation of SB, (2) assess comorbidity such as insomnia and pain, (3) compare survey data in a subset of subjects diagnosed using polysomnography research criteria. METHODS A sample of 6,357 individuals from the general population in Quebec, Canada, undertook an online survey to assess the prevalence of SB, comorbidities, and familial aggregation. Data on familial aggregation were compared to 111 SB subjects diagnosed using polysomnography. RESULTS Regularly occurring SB was reported by 8.6% of the general population, decreases with age, without any gender difference. SB awareness is concomitant with complaints of difficulties maintaining sleep in 47.6% of the cases. A third of SB positive probands reported pain. A 2.5 risk ratio of having a first-degree family member with SB was found in SB positive probands. The risk of reporting SB in first-degree family ranges from 1.4 to 2.9 with increasing severity of reported SB. Polysomnographic data shows that 37% of SB subjects had at least one first-degree relative with reported SB with a relative risk ratio of 4.625. CONCLUSIONS Our results support the heritability of SB-tooth grinding and that sleep quality and pain are concomitant in a significant number of SB subjects.
Real-Time Optimal State Estimation Scheme With Delayed and Periodic Measurements

This paper proposes an efficient real-time optimal estimation scheme that uses accurate but delayed measurements obtained periodically from high-performance sensing devices. For real-time optimal estimation, we employ two Kalman filters: one to conventionally estimate the current state and the other to precompute for the future state estimation to be carried out, when a new, accurate but delayed measurement arrives. The precomputing Kalman filter does the necessary computation in advance, for the future state estimation, from the available measurements for distributing the computational burden over time, thereby obtaining an optimal estimate in real time. By optimally incorporating accurate but delayed measurements, the optimality is preserved at all times, without imposing a heavy computational burden in a short sampling time interval. It is demonstrated through experiments that the proposed scheme can significantly improve the estimation performance with the least detriment to the real-time computation and memory size, when delayed and periodic measurements are available.
Towards Faster Annotation Interfaces for Learning to Filter in Information Extraction and Search

This work explores the design of an annotation interface for a document filtering system based on supervised and semisupervised machine learning, focusing on usability improvements to the user interface to improve the efficiency of annotation without loss of precision, recall, and accuracy. Our objective is to create an automated pipeline for information extraction (IE) and exploratory search for which the learning filter serves as an intake mechanism. The purpose of this IE and search system is ultimately to help users create structured recipes for nanomaterial synthesis from scientific documents crawled from the web. A key part of each text corpus used to train our learning classifiers is a set of thousands of documents that are hand-labeled for relevance to nanomaterials search criteria of interest. This annotation process becomes expensive as the text corpus is expanded through focused web crawling over open-access documents and the addition of new publisher collections. To speed up annotation, we present a user interface that facilitates and optimizes the interactive steps of document presentation, inspection, and labeling. We aim towards transfer of these improvements to usability and response time for this annotator to other classification learning domains for text documents and beyond. Author
Z-drugs and risk for falls and fractures in older adults—a systematic review and meta-analysis

Objective zolpidem, zopiclone, eszopiclone and zaleplon, also known as 'Z-drugs', are commonly used as alternatives to benzodiazepines (BZDs) to treat insomnia. Z-drugs are often perceived as safer than BZDs. We conducted a systematic review and meta-analysis evaluating the association between Z-drugs and fracutres, falls and injuries. Methods a systematic review was performed using MEDLINE, EMBASE and ClinicalTials.gov. Pooled effect-sizes were calculated comparing Z-drugs users with non-users, using fixed and random-effect models with corresponding 95% confidence of intervals (CI). Results we identified 14 eligible studies reporting on the association between Z-drugs and outcomes of interest. Z-Drugs were associated with a statistically significant increased risk for fractures, with evidence of considerable heterogeneity (OR = 1.63; 95% CI: 1.42-1.87; I2 = 90%; n = 830,877). Likewise, there was a trend suggesting a 2-fold increase in the odds for falls, however, this result was not statistically significant and there was evidence of considerable heterogeneity (OR = 2.40; 95% CI: 0.92-6.27; I2 = 95%; n = 19,505). In an analysis assessing the risk for injuries following exposure to zolpidem we found a statistically significant increased risk of injuries, with no evidence of heterogeneity (OR = 2.05; CI 95%: 1.95-2.15; I2 = 0; n = 160,502). Results were similar in sensitivity analyses, including analyses restricted to studies of high-quality, studies with control groups suffering from insomnia, and with specific Z-drugs. Conclusion our results indicate that Z-drugs are associated with an increased risk for fractures, and suggest a possible increased risk for falls and injuries as well. However, studies included were observational and susceptible to confounding. Physicians should consider these potential risks before prescribing these medications in older adults.
Comparative analysis of ROS-based monocular SLAM methods for indoor navigation

This paper presents a comparison of four most recent ROS-based monocular SLAM-related methods: ORB-SLAM, REMODE, LSD-SLAM, and DPPTAM, and analyzes their feasibility for a mobile robot application in indoor environment. We tested these methods using video data that was recorded from a conventional wide-angle full HD webcam with a rolling shutter. The camera was mounted on a human-operated prototype of an unmanned ground vehicle, which followed a closed-loop trajectory. Both feature-based methods (ORB-SLAM, REMODE) and direct SLAMrelated algorithms (LSD-SLAM, DPPTAM) demonstrated reasonably good results in detection of volumetric objects, corners, obstacles and other local features. However, we met difficulties with recovering typical for offices homogeneously colored walls, since all of these methods created empty spaces in a reconstructed sparse 3D scene. This may cause collisions of an autonomously guided robot with unfeatured walls and thus limits applicability of maps, which are obtained by the considered monocular SLAM-related methods for indoor robot navigation.
Improved Electrochemical Performance of Modified Mesocarbon Microbeads for Lithium-Ion Batteries Studied using Solid-State Nuclear Magnetic Resonance Spectroscopy

Lithium-intercalating materials such as graphite are of great interest, especially for application in lithium-ion batteries. In this work we present an investigation of the electrochemical performance of mesocarbon microbeads (MCMB) modified with copper to reveal the basic electrochemical mechanisms. Copper-modified graphite is known to have better long-term cycling behavior as well as higher capacity compared to the pristine material. Several reasons for these effects were postulated but not proven. Solid-state nuclear magnetic resonance (NMR) spectroscopy provides structural and dynamic information on lithium in ionic conductors. To elucidate the changes in structure and dynamics for the pristine and the modified material, we have employed multi-nuclear solid-state NMR spectroscopy as well as 7Li spin-lattice relaxation measurements and were able to clarify some reasons for the improved characteristics of copper-modified graphite compared to the pristine material, which include increased solid–electrolyte interface (SEI) formation, a facilitated diffusion of lithium ions through the SEI, and reduced moisture.
Failure Mechanism of Very High Cycle Fatigue for High Strength Steels

In recent years, the core engineering components of high-speed train, automobiles and aircrafts are required to endure fatigue loads up from 108 to 1010 cycles. The present study results show that in the very high cycle fatigue (VHCF) regimes of more than 107 cycles, the fatigue failure of high strength steel materials can occur below the traditional fatigue limit, hence the VHCF investigations of high strength steels not only help to further understand the fatigue essence and mechanism, but also do research on the fatigue design and life assessment method. This paper summarizes works of VHCF researches for high strength steels in recent years, such as the characteristics of S-N curve, the observations on fish-eye, which is one of the typical characteristics of fracture surface, crack initiation, crack propagation, etc. The present work also analyzes the fatigue mechanisms and briefly discusses several factors that affect VHCF properties, such as hydrogen effect, inclusion effect, frequency effect. Some possible and prospective aspects of future researches are also proposed.
On lower bounds for MAC layer contention in CSMA/CA-based wireless networks

Wireless LANs operating within unlicensed frequency bands require random access schemes such as CSMA/CA, so that wireless networks from different administrative domains (for example wireless community networks) may co-exist without central coordination, even when they happen to operate on the same radio channel. Yet, it is evident that this lack of coordination leads to an inevitable loss in efficiency due to contention on the MAC layer. The interesting question is, which efficiency may be gained by adding coordination to existing, unrelated wireless networks, for example by self-organization. In this paper, we present a methodology based on a mathematical programming formulation to determine the parameters (assignment of stations to access points, signal strengths and channel assignment of both access points and stations) for a scenario of co-existing CSMA/CA-based wireless networks, such that the contention between these networks is minimized. We demonstrate how it is possible to solve this discrete, non-linear optimization problem exactly for small problems. For larger scenarios, we present a genetic algorithm specifically tuned for finding near-optimal solutions, and compare its results to theoretical lower bounds. Overall, we provide a benchmark on the minimum contention problem for coordination mechanisms in CSMA/CA-based wireless networks.
Exploring Determinants of Patient Adherence to a Portal-Supported Oncology Rehabilitation Program: Interview and Data Log Analyses

Background Telemedicine applications often do not live up to their expectations and often fail once they have reached the operational phase. Objective The objective of this study was to explore the determinants of patient adherence to a blended care rehabilitation program, which includes a Web portal, from a patient’s perspective. Methods Patients were enrolled in a 12-week oncology rehabilitation treatment supported by a Web portal that was developed in cooperation with patients and care professionals. Semistructured interviews were used to analyze thought processes and behavior concerning patient adherence and portal use. Interviews were conducted with patients close to the start and the end of the treatment. Besides, usage data from the portal were analyzed to gain insights into actual usage of the portal. Results A total of 12 patients participated in the first interview, whereas 10 participated in the second round of interviews. Furthermore, portal usage of 31 patients was monitored. On average, 11 persons used the portal each week, with a maximum of 20 in the seventh week and a drop toward just one person in the weeks in the follow-up period of the treatment. From the interviews, it was derived that patients’ behavior in the treatment and use of the portal was primarily determined by extrinsic motivation cues (eg, stimulation by care professionals and patient group), perceived severity of the disease (eg, physical and mental condition), perceived ease of use (eg, accessibility of the portal and the ease with which information is found), and perceived usefulness (eg, fit with the treatment). Conclusions The results emphasized the impact that care professionals and fellow patients have on patient adherence and portal usage. For this reason, the success of blended care telemedicine interventions seems highly dependent on the willingness of care professionals to include the technology in their treatment and stimulate usage among patients.
Genomic and phenotypic characterization of an atypical Aeromonas salmonicida strain isolated from a lumpfish and producing unusual granular structures.

Aeromonas salmonicida strains are roughly classified into two categories, typical and atypical strains. The latter mainly regroup isolates that present unusual phenotypes or hosts, comparatively to the typical strains that belong to the salmonicida subspecies. This study focuses on an uncharacterized atypical strain, M18076-11, isolated from lumpfish (Cyclopterus lumpus) and not part of the four recognized Aeromonas salmonicida subspecies. This isolate presents an unreported phenotype in the A. salmonicida species: the formation of large granular aggregates. Granules are formed of a heterogeneous mix of live and dead cells, with live cells composing the majority of the population. Even if no mechanism was determined to cause cellular aggregation, small globular structures at the cell surface were observed, which might affect granular formation. Pan-genome phylogenetic analysis indicated that this strain groups alongside the masoucida subspecies. However, phenotypic tests showed that these strains have diverging phenotypes, suggesting that M18076-11 might belong to a new subspecies. Also, a pAsal1-like plasmid, which was only reported in strains of the subspecies salmonicida, was discovered in M18076-11. This study sheds light on unsuspected diversity in A. salmonicida subspecies and stresses the need of thorough identification when a new strain is encountered, as unique traits might be discovered.
Snap & Play

In this article, by taking a popular game, the Find-the-Difference (FiDi) game, as a concrete example, we explore how state-of-the-art image processing techniques can assist in developing a personalized, automatic, and dynamic game. Unlike the traditional FiDi game, where image pairs (source image and target image) with five different patches are manually produced by professional game developers, the proposed Personalized FiDi (P-FiDi) electronic game can be played in a fully automatic Snap & Play mode. Snap means that players first take photos with their digital cameras. The newly captured photos are used as source images and fed into the P-FiDi system to autogenerate the counterpart target images for users to play. Four steps are adopted to autogenerate target images: enhancing the visual quality of source images, extracting some changeable patches from the source image, selecting the most suitable combination of changeable patches and difference styles for the image, and generating the differences on the target image with state-of-the-art image processing techniques. In addition, the P-FiDi game can be easily redesigned for the im-game advertising. Extensive experiments show that the P-FiDi electronic game is satisfying in terms of player experience, seamless advertisement, and technical feasibility.
Optimal bidding strategy for a smart microgrid in day-ahead electricity market with demand response programs considering uncertainties

This paper computes the optimal bids that the smart microgrid energy management system (SMEMS) submits to the day-ahead electricity market. This smart microgrid consists of dispatchable generation resources, renewable generation resources, storage system and the loads that can be participate in the demand response (DR) programs. In this study we intend to maximize the expected profit earned by trading in day-ahead electricity market as well as optimal scheduling of smart microgrid for energy dispatching on the operating day. The bidding problem can be difficult due to different uncertainties in generations, loads and market prices forecasts amounts. To deal with these uncertainties, two-stage stochastic programming is employed. Various stochastic scenarios are generated by Monte Carlo simulation and then a scenario reduction algorithm based on kantorovich distance is performed. Nonlinear terms of the objective function are recast into linear forms. Numerical results have confirmed the profitability of the proposed smart microgrid.
Exploiting Unencrypted Mobile Application Traffic for Surveillance Technical Report

Over the last decade, mobile devices and mobile applications have become pervasive in their usage. Although many privacy risks associated with mobile applications have been investigated, prior work mainly focuses on the collection of user information by application developers and advertisers. Inspired by the Snowden revelations, we study the ways mobile applications enable mass surveillance by sending unique identifiers over unencrypted connections. Applying passive network fingerprinting, we show how a passive network adversary can improve his ability to target mobile users’ traffic. Our results are based on a large-scale automated study of mobile application network traffic. The framework we developed for this study downloads and runs mobile applications, captures their network traffic and automatically detects identifiers that are sent in the clear. Our findings show that a global adversary can link 57% of a user’s unencrypted mobile traffic. Evaluating two countermeasures available to privacy aware mobile users, we find their effectiveness to be very limited against identifier leakage.
Impact of an innovated storage technology on the quality of preprocessed switchgrass bales

The purpose of this study was to determine the effects of three particle sizes of feedstock and two types of novel bale wraps on the quality of switchgrass by monitoring the chemical changes in cellulose, hemicellulose, lignin, extractives, and ash over a 225-day period. Using NIR (Near-infrared) modeling to predict the chemical composition of the treated biomass, differences were found in cellulose, lignin, and ash content across switchgrass bales with different particle sizes. Enclosing bales in a net and film impacted the cellulose, lignin, and ash content. Cellulose, hemicellulose, lignin, extractives, and ash were different across the 225-day storage period. A quadratic response function made better prediction about cellulose, lignin, and ash response to storage, and a linear response function best described hemicellulose and extractives response to storage. This study yields valuable information regarding the quality of switchgrass at different intervals between the start and end date of storage, which is important to conversion facilities when determining optimal storage strategies to improve quality of the biomass feedstock, based on potential output yield of a bale over time.
Default Mode Functional Connectivity Is Associated With Social Functioning in Schizophrenia

Individuals with schizophrenia display notable deficits in social functioning. Research indicates that neural connectivity within the default mode network (DMN) is related to social cognition and social functioning in healthy and clinical populations. However, the association between DMN connectivity, social cognition, and social functioning has not been studied in schizophrenia. For the present study, the authors used resting-state neuroimaging data to evaluate connectivity between the main DMN hubs (i.e., the medial prefrontal cortex [mPFC] and the posterior cingulate cortex-anterior precuneus [PPC]) in individuals with schizophrenia (n = 28) and controls (n = 32). The authors also examined whether DMN connectivity was associated with social functioning via social attainment (measured by the Specific Levels of Functioning Scale) and social competence (measured by the Social Skills Performance Assessment), and if social cognition mediates the association between DMN connectivity and these measures of social functioning. Results revealed that DMN connectivity did not differ between individuals with schizophrenia and controls. However, connectivity between the mPFC and PCC hubs was significantly associated with social competence and social attainment in individuals with schizophrenia but not in controls as reflected by a significant group-by-connectivity interaction. Social cognition did not mediate the association between DMN connectivity and social functioning in individuals with schizophrenia. The findings suggest that fronto-parietal DMN connectivity in particular may be differentially associated with social functioning in schizophrenia and controls. As a result, DMN connectivity may be used as a neuroimaging marker to monitor treatment response or as a potential target for interventions that aim to enhance social functioning in schizophrenia.
Comparison of risk factors for, and prevalence of, antibiotic resistance in contaminating and pathogenic urinary Escherichia coli in children in primary care: prospective cohort study

Abstract Background All-cause antibiotic prescribing affects bowel flora antimicrobial susceptibility, and may increase risk of urinary autoinoculation with antibiotic-resistant microbes. However, little is known about relative prevalence of, or risk factors for, antimicrobial resistance among potentially pathogenic microbes thought to be contaminating and infecting urine. Methods Secondary analysis of 824 children under 5 years of age consulting in primary care for an acute illness and their Escherichia coli isolates cultured at ≥103 cfu/mL from the Diagnosis of Urinary Tract infection in Young children (DUTY) study. Multivariable logistic regression investigating risk factors for resistance to amoxicillin, co-amoxiclav, cefalexin, ciprofloxacin, trimethoprim, nitrofurantoin and cefpodoxime in microbes meeting the laboratory criteria for urinary tract infection: ‘pathogens’ (>105 cfu/mL, n = 79) and ‘contaminants’ (103 to 105 cfu/mL, n = 745). Results Forty-three percent of E. coli were resistant to at least one tested antibiotic, with resistance highest to amoxicillin (49.37% pathogenic versus 37.32% contaminant, P = 0.04), trimethoprim (27.85% versus 16.52%, P = 0.01) and co-amoxiclav (16.46% versus 21.48%, P = 0.30). Multidrug resistance (to ≥3 antibiotic groups) was present in 17.07% of pathogens and 30.13% of contaminants (P = 0.04). No isolates were resistant to nitrofurantoin. Recent (0–3 months) exposure to antibiotics was associated with resistance in both pathogens (aOR: 1.10, 95% CI: 1.01–4.39) and contaminants (1.69, 1.09–2.67). Conclusions Prevalence of resistance (including multidrug) was high, but there was no consistent relationship between isolate pathogen/contamination status and resistance. Recent all-cause antibiotic prescribing increased the probability of antimicrobial resistance in both pathogenic and contaminating urinary E. coli in children in primary care.