Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioural characteristics and abdominal obesity among Brazilian shift working women

Abstract Objective: To explore the association between behavioural characteristics with the prevalence of abdominal obesity (AO) among a population of Southern Brazilian shift working women. Design: A cross-sectional study was conducted. AO was estimated using waist circumference (WC), and it was used to classify women as having AO (WC ≥ 88 cm). Prevalence ratios were estimated using Poisson regression with robust variance. Setting: A large plastic utensils company in Southern Brazil. Participants: 450 female shift workers. Results: The prevalence of the AO in the women shift workers was 44·5 % (95 % CI 40·0, 49·2 %). In night shift workers, the prevalence of AO was 56·1 % compared with 40·9 % among hybrid shift workers. After adjustments for covariates, women who were current smokers had a decrease in the prevalence of AO compared with those who never smoked. Women who had three or fewer meals per day had a 46 % increase in the AO prevalence compared with those eating more frequent meals. Night shift work was associated with increase in AO prevalence compared with hybrid shift (PR 1·33; 95 % CI: 1·08, 1·64). Conclusions: Our findings indicate that behavioural characteristics are associated with a high prevalence of AO in female shift workers, thus suggesting that behavioural modifications among women working shifts, such as increase in meal frequency and physical activity, may reduce AO.
Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

This study aims to evaluate the accuracy of a wireless tracheal audio sensor including the sleep body position measurement, as a possible tool to screen for obstructive sleep apnea (OSA) and also to distinguish among “positional” and “non-positional” sleep apnea patients. 30 adult subjects were asked to sleep naturally for a single night in the sleep laboratory, having simultaneous polysomnography (PSG) as a reference. The results were compared using, e.g., Pearson’s and Lin’s correlation coefficients, Bland-Altman plots, mean absolute error, intercept of the fitted linear model etc. We found the thresholding approach performed on normalized audio energy data achieved mean absolute error of 5.7, and average difference was −2.0. Pearson’s and Lin’s R coefficients were 0.92 and 0.70, respectively. The qualitative approach reached 86% accuracy (sensitivity of 96%, and specificity of 76%) when setting a binary border at the level of AHI = 15 (combined normal + mild versus combined moderate + severe cases). We also checked the distribution of apneas depending on the body position. Our sensor showed a mean = SE difference between supine apnea index and non-supine apnea index of 8.3 ± 3.2, while PSG reference of 11.2 ± 3.8. The proposed sensor might be a good complement for home sleep studies, being less disturbing and allowing for longitudinal observations and reliably showing positional OSA. PSG is the gold standard in diagnosing OSA; however, it requires to spend a night in a lab with medical staff, it provides short observation, the cost of the study is very high, and it is less suitable for children. This is why a reliable screening method is needed in sleep medicine.
Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

The primary objective of this study is to accumulate, summarize, and evaluate the state-of-the-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author’s knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.
Association between genetic and socioenvironmental risk for schizophrenia during upbringing in a UK longitudinal cohort

Abstract Background Associations of socioenvironmental features like urbanicity and neighborhood deprivation with psychosis are well-established. An enduring question, however, is whether these associations are causal. Genetic confounding could occur due to downward mobility of individuals at high genetic risk for psychiatric problems into disadvantaged environments. Methods We examined correlations of five indices of genetic risk [polygenic risk scores (PRS) for schizophrenia and depression, maternal psychotic symptoms, family psychiatric history, and zygosity-based latent genetic risk] with multiple area-, neighborhood-, and family-level risks during upbringing. Data were from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative cohort of 2232 British twins born in 1994–1995 and followed to age 18 (93% retention). Socioenvironmental risks included urbanicity, air pollution, neighborhood deprivation, neighborhood crime, neighborhood disorder, social cohesion, residential mobility, family poverty, and a cumulative environmental risk scale. At age 18, participants were privately interviewed about psychotic experiences. Results Higher genetic risk on all indices was associated with riskier environments during upbringing. For example, participants with higher schizophrenia PRS (OR = 1.19, 95% CI = 1.06–1.33), depression PRS (OR = 1.20, 95% CI = 1.08–1.34), family history (OR = 1.25, 95% CI = 1.11–1.40), and latent genetic risk (OR = 1.21, 95% CI = 1.07–1.38) had accumulated more socioenvironmental risks for schizophrenia by age 18. However, associations between socioenvironmental risks and psychotic experiences mostly remained significant after covariate adjustment for genetic risk. Conclusion Genetic risk is correlated with socioenvironmental risk for schizophrenia during upbringing, but the associations between socioenvironmental risk and adolescent psychotic experiences appear, at present, to exist above and beyond this gene-environment correlation.
Fault Diagnosis of Uninterruptible Power System Based on Gaussian Mixed Model and XGBoost

Uninterruptible power system (UPS) is an important equipment for guarantee data security. The previous research on UPS fault detection has established a mathematical model of the circuit system and combine with machine learning algorithm for fault diagnosis. Because the UPS circuit structure is complex and easy influence by environment temperature humidity, the research method of establishing mathematical model is suitable for the diagnosis of some internal circuits, but not for the whole UPS machine. Based on the analysis of UPS historical data, this paper has established a method that can catch and classify the UPS fault based on the Gaussian Mixed Model (GMM) and the eXtreme Gradient boost (XGBoost). This design is divided into two parts. The first part has used GMM to calculate the logarithmic summation probability of UPS data, and analyze the relationship between UPS logarithmic sum probability and fault working condition. Then set the threshold to capture the UPS fault according to the fault data recall rate. The second part was fault recognition by XGBoost model. The extremely unbalanced fault data makes it difficult to classify, so XGBoost algorithm has been used to do the classification. combine learning curve with grid search to optimize XGBoost parameters. Experimental results show that GMM can accurately detect equipment faults.
Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

Brushless DC motor, also referred to as BLDC motor, has been a widely used electric machine due to its excellent performance over conventional DC motors. Due to complex operating conditions and overloading, several irregularities can take place in a motor. Stator related faults are among the most commonly occurring faults in BLDC motor. With an initial raise in local heating, a fault in the stator can largely reduce motor efficiency and account for the entire system breakdown. In this study, we present a deep learning-based approach to estimate the remaining useful life (RUL) of BLDC motor affected by different stator related faults. To analyze the motor health degradation, we have investigated two types of stator faults namely inter-turn fault (ITF) and winding short-circuit fault (WSC). A generator was coupled with the motor and using an average value rectifier (AVR), generator’s output voltage was monitored for the entire lifecycle. A proven neural network for effective sequence modeling, recurrent neural network (RNN) is selected to train the voltage degradation data. For a better estimation of nonlinear trends, long-short term memory (LSTM) with attention mechanism is chosen to make predictions of the motor RUL for both types of faults. The main concern that encourages authors of this paper is the proposed method can be used for the real-time condition monitoring and health state estimation of BLDC motors. Also, the proposed AVR-LSTM method is not affected by environmental influences, making it suitable for diverse operating conditions.
Short-term retention of proprioceptive information

The Joint Position Reproduction test (JPR), one of the most widely used measurements to estimate proprioceptive accuracy, requires the short term storage of proprioceptive information. It has been suggested that visuospatial sketchpad plays a fundamental role in the memorization of proprioceptive information. The current study aimed to investigate this assumption. To do so, we developed and used a novel JPR protocol to measure the retention capacity with respect to sequences of different positions. Our goal was to develop the original task further to make it comparable with other widely used short-term memory measurements, in which the memory capacity was determined by the number of the items participants retain (memory span). We compared participants’ (N=39) performance in this task to that of results of Corsi block-tapping task (capacity of the visuospatial sketchpad) and Digit span task (capacity of the phonological loop). Proprioceptive memory capacity did not correlate either with spatial or verbal memory capacity. The exploratory analysis revealed that proprioceptive span correlated positively with the performance if 5 joint positions had to be retained. Further associations with verbal span for 6 or 7 positions, and spatial span for 5 positions were found. Our findings do not support the idea that visuospatial sketchpad plays a fundamental role in the storage of proprioceptive information. The independence of span measures indicates that proprioceptive information might be stored in a subsystem independent of the visuospatial sketchpad or phonological loop.
miR-19a-3p Functions as an Oncogene by Regulating FBXO32 Expression in Multiple Myeloma

Background Multiple myeloma remains a virtually incurable hematologic malignancy, which is featured with the aberrant growth of malignant plasma cells. Aims To elucidate the functions of miR-19a-3p in multiple myeloma. Study Design Cell study. Methods Cell counting kit-8 assay was performed to detect cell viability, and flow cytometry was conducted to detect cell apoptosis. Bioinformatics analysis predicted miR-19a-3p-associated biological function, pathway, core regulatory network, and target genes. Luciferase reporter assay verified the target sequence of miR-19a-3p regulating FBXO32. Results miR-19a-3p is upregulated in multiple myeloma cells (p<0.01) and patients with multiple myeloma (p<0.001). Overexpressed miR-19a-3p significantly increased cell viability (p<0.05) and inhibited cell apoptosis (p<0.01). FBXO32 is a target gene of miR-19a-3p (p<0.01). Moreover, FBXO32 is downregulated in MM, and it significantly decreased cell viability (p<0.05) and promoted cell apoptosis (p<0.01). FBXO32 significantly rescued the influence of miR-19a-3p-inhibiting cell apoptosis (p<0.05). Conclusion miR-19a-3p promoted cell proliferation and inhibited cell apoptosis by degrading the target FBXO32 mRNA in multiple myeloma.
Cathodoluminescent Properties of polycrystalline Ga2O3 thin film and its application UV flat panel light source

Polycrystalline gallium oxide thin film was prepared using electron-beam evaporation and a post-annealing treatment. Cathodoluminescent measurement show the gallium oxide thin film has emission peaks ranging from 300 to 650 nm. Using gallium oxide thin film anode, a flat panel UV light source was fabricated by using ZnO nanowires field emitter arrays.
Blocking of the IL-33/ST2 Signaling Axis by a Single-Chain Antibody Variable Fragment (scFv) Specific to IL-33 with a Defined Epitope

Interleukin 33 (IL-33) is an IL-1 family cytokine that plays a central role in immune system by regulating and initiating inflammatory responses. The binding of IL-33 to the suppressor of tumorigenicity 2 (ST2) receptor induces mitogen-activated protein kinases (MAPK) and nuclear factor κB (NF-κB) pathways, thereby leading to inflammatory cytokines production in type 2 helper T cells and type 2 innate lymphoid cells. To develop an antibody specific to IL-33 with a defined epitope, we characterized a single-chain antibody variable fragments (scFvs) clone specific to IL-33, C2_2E12, which was selected from a human synthetic library of scFvs using phage display. Affinity (Kd) of C2_2E12 was determined to be 38 nM using enzyme-linked immunosorbent assay. C2_2E12 did not show cross-reactivity toward other interleukin cytokines, including closely related IL-1 family cytokines and unrelated proteins. Mutational scanning analysis revealed that the epitope of IL-33 consisted of residues 149–158 with key residues being L150 and K151 of IL-33. Structural modeling suggested that L150 and K151 residues are important for the interaction of IL-33 with C2_2E12, implicating that C2_2E12 could block the binding of ST2 to IL-33. Pull-down and in-cell assays supported that C2_2E12 can inhibit the IL-33/ST2 signaling axis. These results suggest that the scFv clone characterized here can function as a neutralizing antibody.
Multimodal imaging of an idiopathic florid vascularised epiretinal membrane: Course, treatment, and outcome

Idiopathic vascular epiretinal membrane is an extremely rare entity and the pathogenesis and clinical course is not clearly understood. A 53-year-old hypertensive female patient presented with complaints of altered vision in the right eye. On examination, her vision was 20/30 and fundoscopy showed a vascularized epiretinal membrane (ERM), which was confirmed on spectral-domain optical coherence tomography. No primary cause was found after investigations. The symptoms and ERM showed slow progression over the next three years with a visual acuity of 20/60. She underwent surgery for removal of the ERM, which was subjected to histopathological evaluation. This is a unique case of a florid proliferative vascularisation of an ERM in the absence of any identifiable cause, which had a good visual outcome following surgery.
Energy-Efficient Monolithic 3-D SRAM Cell With BEOL MoS2 FETs for SoC Scaling

In this article, we propose an energy-efficient monolithic 3-D (M3D) three-tier SRAM cell with back-end-of-the-line (BEOL) back-gated (BG) MoS2 FETs. The impacts of wire routing resistance and capacitance, gate topology of MoS2 FETs, and the layout optimization of multitier 6T SRAM cells have been comprehensively analyzed for SoC scaling through system-technology co-optimization. SRAM plays an integral role in the performance of SoCs, and the performance can be improved by SRAM on logic integration. Compared with one-tier BG SRAM cell design, the proposed monolithic three-tier BG SRAM cell releases the impact of metal line resistance and shows a 44.3% reduction in cell area, 28.4% improvement in read access time, 21.3% improvement in dynamic energy, and 43.6% improvement in energy-delay product. The energy- and area-efficient three-tier BG SRAM cell enables intelligent functionalities for the area- and energy-constrained edge computing devices.
A Positioning Algorithm Based on Improved Adaptive Unscented Kalman Filter

Aiming at the situation that the positioning accuracy of current mobile robots relying on traditional methods is not accurate enough, taking four-Mecanum-wheel mobile robots as the research object, in this paper, an improved adaptive unscented Kalman filter algorithm (AUKF)is proposed, combined with lidar positioning data and the result of track estimation, which can avoid the local optimal solution that may be trapped by the laser positioning result, and reduce the influence of errors caused by wheel slip and other factors. At the same time, different weights are set for the data obtained by different sensors, combined with the Sage-Husa adaptive thought, the measurement noise characteristics can be updated. The simulation results show that, under the condition that the statistical characteristics of the measurement noise are unknown, AUKF has higher accuracy than the traditional UKF algorithm in positioning results and fits the real trajectory.
Privacy-Preserving Identifiers for IoT: A Systematic Literature Review

The Internet of Things (IoT) paves the way for smart applications such as in E-health, E-homes, transportation, or energy production. However, IoT technologies also pose privacy challenges for their users, as they allow the tracking and monitoring of the users’ behavior and context. The EU General Data Protection Regulation (GDPR) mandates data controller to follow a data protection by design and default approach by implementing for instance pseudonymity for achieving data minimisation. This paper provides a systematic literature review for answering the question of what types of privacy-preserving identifiers are proposed by the literature in IoT environments for implementing pseudonymity. It contributes with classifications and analyses of IoT environments for which privacy-preserving identifiers have been proposed and of the pseudonym types and underlying identity management architectures used. Moreover, it discusses trends and gaps in regard to addressing privacy trade-offs.
A Method of Preference and Utility Elicitation By Pairwise Comparisons and its Application to Intelligent Transportation Recommendation Systems

The paper is devoted to the object ranking problem, one of the preference elicitation tasks considered in machine learning. We analyze this problem in the formulation when it is necessary to reconstruct the utility function using observations. Each observation is the pairwise comparison result of utility function values for two random variables. We propose a unified approach to the utility function elicitation problem. This approach consists of two steps: adaptive transformation to the high-dimensional space and construction of linear (for the utility function elicitation) or non-linear (including non-parametric, for the preference elicitation problem) classifiers. In experimental analysis, we compare different methods of transformation to the high-dimensional space and different classification algorithms. We investigate their effectiveness to solve model and real problems of the utility and/or preference function elicitation problem.
Remote Heart Rate and Heart Rate Variability Detection and Monitoring from Face Video with Minimum Resources

This paper describes a cloud-based heart rate (HR) and heart rate variability (HRV) monitor, which can monitor an individual's heart rate and heart rate variability using minimal resources at the user's end. This HR and HRV monitor does not involve any contact sensors or costly medical equipment, and it does not require a high-definition camera to video record the user's face. This HR and HRV monitoring system simply requires the webcam of a personal computer or the camera of a mobile device such as a cell phone or tablet in order to video record the face of the user. The system utilizes the resources of the user's device while recording and encoding the video segments before transferring them to a server. All the processes to calculate the HR and HRV occur at the server.
Specific and generic unsupervised algorithms for NILM applications

In a domestic context, NILM applications (Non-Intrusive Load Monitoring) allow users to know their electric consumption per appliance without having to install sensors for each appliance in their house. The aim of this paper is to present an unsupervised approach of the disaggregation problem. This paper follows 3 papers on the same topic. Each of these papers corresponds to a specific building block of the approach described in this paper. This paper aims at giving a top view of our general approach and draws the links between our 3 previous papers.
Effects of aging and exercise training on eNOS uncoupling and reactive oxygen species signaling in the endothelium of skeletal muscle arterioles

The purpose of the first study was to determine the effects of aging and exercise training on endothelial nitric oxide synthase (eNOS) uncoupling in skeletal muscle arterioles. The results of this study confirmed our previous findings that aging impairs endothelium-dependent, NO-mediated vasodilation and tetrahydrobiopterin (BH4) levels in rat skeletal muscle arterioles. Limited availability of BH4, as observed in old sedentary rats, may contribute to eNOS uncoupling, which decreases NO signaling and increases eNOS-derived O2- generation. Exercise training restored BH4 levels and improved flow-induced NO production in arterioles from aged rats. Furthermore, exercise training increased both NO and reactive oxygen species (ROS)-mediated signaling in skeletal muscle arterioles, suggesting that exercise training-induced enhancement of flow-induced vasodilation in skeletal muscle arterioles involves a balance between NO and O2 – derived ROS.The second study determined the role of O2 – derived ROS in mediating endothelium-dependent vasodilation in skeletal muscle arterioles from young and old, sedentary and exercise trained rats. The results of the second study implicated O2- and O2 – derived ROS (H2O2) as necessary signaling molecules required for endothelium-dependent vasodilation in soleus muscle arterioles. The dependence of ACh-induced vasodilation on H2O2 increased with age and decreased with exercise training. Exercise training contributed to appropriate regulation of the relative production of O2- and H2O2, which must be maintained for robust endothelium-dependent vasodilation to occur in skeletal muscle arterioles.