Miracle-dz/UniFAR-FTUs huggingface dataset:
Fall Prevention Mobile Clinic: A Novel Fall Prevention Program for Community-Dwelling Older Adults

RÉSUMÉ Chez les personnes âgées, les chutes sont associées à des causes courantes et multifactorielles, et peuvent provoquer des blessures importantes. Cette étude avant-après a évalué les impacts d’une clinique mobile de prévention des chutes (Fall Prevention Mobile Clinic [FPMC]) pour la réduction du risque de chutes chez des personnes âgées de Colombie-Britannique (Canada). Quatre cent soixante-seize participants, dont l’âge moyen était de 83,6 ans, ont pris part à cette étude et ont été suivis pendant 12 mois après leur passage à la FPMC. À leur rendez-vous de suivi de 12 mois, le pourcentage moyen d’adoption des recommandations pour la prévention des chutes était de 48,8 % (ÉT : 25,7 %), la performance au test Timed up and Go (une mesure de mobilité) s’était améliorée, passant d’une médiane de 19,04 s à 17,45 s, et le nombre de participants ayant chuté a diminué, passant de 64,8 % (dans les 12 mois précédant le premier RDV à la clinique) à 55,6 % (au cours des 12 mois suivant le premier RDV à la clinique ; p = 0,012). Le FPMC évalué dans cette étude a fourni des recommandations qui ont été prises en compte par les participants; et qui ont amélioré leur mobilité et diminué leur risque de chutes dans l’année suivante. ABSTRACT Causes of falls in older adults are common, multifactorial, and can lead to significant injury. This before-and-after study evaluated the benefits of a Fall Prevention Mobile Clinic (FPMC) in reducing the risk of falling in older adults in British Columbia, Canada. Four hundred seventy-six participants (average age of 83.6 years) enrolled in the study and were followed for 12 months after attending the FPMC. At 12-month follow-up, the mean percentage uptake of fall prevention recommendations was 48.8 per cent (SD = 25.7%), the Timed Up and Go mobility measure improved from a median of 19.04 seconds to 17.45 seconds and the number of participants falling decreased from 64.8 per cent (in the 12 months before attending the clinic) to 55.6 per cent (in the 12 months after attending the clinic) (p = .012). After attending the FPMC, participants acted on recommendations, improved mobility and decreased their risk of future falls.
Is Social Support a Cause or Consequence of Depression? A Longitudinal Study of Adolescents

A large body of literature has examined the relations between social support and depression. However, the exact nature and direction of these relations are not well understood. This study explored the relations between specific types of social support (peer support and teacher support) and depression. Adolescents (ages 11 to 17) for the first time (N = 2453) participated in a two-wave, 6-month longitudinal study. Structural equation modeling was used to test a social causation model (deficits in social support increase the likelihood of depression), interpersonal theories of depression (depression leads to social erosion), and a reciprocal influence model. Depression influenced peer support significantly and negatively. By contrast, the social causation model was not supported. These results held for males and females. Findings suggested that depression resulted in social support erosion. However, the effect was specific to perceived peer support but not to perceived teacher support.
Smith Predictor Based ℋ∞ Control for Piezoelectric Nano Stages with Time Delays

Nano precision capacitive displacement sensors have been successfully employed to the servo control of piezoelectric nano stages. However the analog-to-digital (A/D) conversion of the ultra high precision signal results in major time delays for the nano control systems. Meanwhile, the behavior of the hysteresis of piezoactuators can be also approximated as a variable gain cascaded with a frequency varying time delay. The infinite dimensionality of the time delays in nano servo systems poses new challenges for the control of nano stages, where traditional control methods can not be applied directly. In this paper, a linear model with time delays and hysteresis approximation is discussed for piezoelectric nano stages, where the system parameters are further determined using an identification method by experimental data. Furthermore a Smith predictor based robust ℋ∞ controller is developed to achieve high performance and robust stability for the control of nano stages. Real time experiments with the proposed control design are conducted, where robustness against model uncertainties and accurate positioning performance are demonstrated, which outperforms the method without considering time delays.
Sensitivity and Noise Evaluation of an Optoelectronic Sensor for Mosquitoes Monitoring

Mosquitoes play a biological role of significant impact on public health, being vectors of several diseases transmission. In particular, the Aedes aegypti mosquito is a vector of potentially fatal diseases such as Dengue, Zika, Chikungunya, and Yellow Fever. The studies progress on these insects biological properties is fundamental for the improvement of more accurate mosquitoes detection sensors. Therefore, the optoelectronic sensors for flying insects detection and classification taking into account the fundamental wingbeat frequency of several insects species database available in the literature. Also, the optoelectronic approach is capable to perform insect sensing without needing uninterrupted data recording. This functionality is essential to reduce the sensor's power consumption and enable the construction of a low-power device ready for an Internet of Things (IoT) scenario. In this regard, this paper aims to design and deploy an optoelectronic sensor module to validate the optoelectronic sensor operating principle and evaluate the main optical interference sources. Thus, it is possible to determine the best sensor's operating bandwidths for an optimal signal-to-noise ratio and efficiency.
Efficient and Lightweight Convolutional Neural Network for Lane Mark and Road Segmentation

Semantic segmentation is one of an important task in computer vision that takes a great part in the perception needs of intelligent autonomous vehicles. ConvNets excel at this task, as they can be adaptively trained end-to-end to yield a set of robust hierarchies of features. The proposed key method is to reduce the unnecessary weights to build an efficient and lightweight network to acquire high accuracy on lane mark and road segmentation at pixel level. The proposed fully convolutional neural network achieves $360\text{x}480 @ 28$ fps and 97.6% accuracy on our in-house pixel-based hand-annotated lane mark and road datasets. All our models and results are trained and evaluated on an NVIDIA GTX 1080 GPU device.
Multi-linear formulas for permanent and determinant are of super-polynomial size

An arithmetic formula is multilinear if the polynomial computed by each of its subformulas is multilinear. We prove that any multilinear arithmetic formula for the permanent or the determinant of an n × n matrix is of size super-polynomial in n. Previously, super-polynomial lower bounds were not known (for any explicit function) even for the special case of multilinear formulas of constant depth.
A new approach to the maximum-flow problem

All previously known efficient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the <italic>preflow</italic> concept of Karzanov is introduced. A preflow is like a flow, except that the total amount flowing into a vertex is allowed to exceed the total amount flowing out. The method maintains a preflow in the original network and pushes local flow excess toward the sink along what are estimated to be shortest paths. The algorithm and its analysis are simple and intuitive, yet the algorithm runs as fast as any other known method on dense graphs, achieving an <italic>O</italic>(<italic>n</italic><supscrpt>3</supscrpt>) time bound on an <italic>n</italic>-vertex graph. By incorporating the dynamic tree data structure of Sleator and Tarjan, we obtain a version of the algorithm running in <italic>O</italic>(<italic>nm</italic> log(<italic>n</italic><supscrpt>2</supscrpt>/<italic>m</italic>)) time on an <italic>n</italic>-vertex, <italic>m</italic>-edge graph. This is as fast as any known method for any graph density and faster on graphs of moderate density. The algorithm also admits efficient distributed and parallel implementations. A parallel implementation running in <italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>log <italic>n</italic>) time using <italic>n</italic> processors and <italic>O</italic>(<italic>m</italic>) space is obtained. This time bound matches that of the Shiloach-Vishkin algorithm, which also uses <italic>n</italic> processors but requires <italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>) space.
Fat composition in infant formula contributes to the severity of necrotising enterocolitis

Abstract Necrotising enterocolitis (NEC) is a devastating disease that typically affects formula-fed premature infants, suggesting that dietary components may influence disease pathogenesis. TAG are the major fat components of infant formula, and their digestion requires pancreatic lipases, which may be naturally deficient in premature neonates. We hypothesise that NEC develops partly from the accumulation of incompletely digested long-chain TAG-containing unsaturated fatty acids within the intestinal epithelial cells, leading to oxidative stress and enterocyte damage. We further hypothesise that the administration of a formula that contains reduced TAG (‘pre-digested fat’) that do not require lipase action may reduce NEC severity. To test these hypotheses, we induced NEC in neonatal mice using three different fat formulations, namely ‘standard fat’, ‘pre-digested fat’ or ‘very low fat’, and determined that mice fed ‘standard fat’ developed severe NEC, which was significantly reduced in mice fed ‘pre-digested fat’ or ‘very low fat’. The expression level of the critical fat-digesting enzyme carboxyl ester lipase was significantly lower in the newborn compared with older pups, leading to impaired fat digestion. The accumulation of mal-digested fat resulted in the significant accumulation of fat droplets within the intestinal epithelium of the distal ileum, resulting in the generation of reactive oxygen species and intestinal inflammation. Strikingly, these changes were prevented in pups fed ‘pre-digested fat’ or ‘very low fat’ formulas. These findings suggest that nutritional formula containing a pre-digested fat system may overcome the natural lipase deficiency of the premature gut, and serve as a novel approach to prevent NEC.
Descriptor : HISDAC-US , historical settlement data compilation for the conterminous United States over 200 years

Human settlement plays a key role in understanding social processes such as urbanization and interactions between human and environmental systems but not much is known about the landscape evolution before the era of operational remote sensing technology. In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. These settlement data will be of great interest in disciplines in which the long-term evolution of human settlement represents crucial information to explore novel research questions.
The Importance of Relevance: Willingness to Share eHealth Data for Family Medicine Research

Objective: To determine the proportion of family medicine patients unwilling to allow their eHealth data to be used for research purposes, and evaluate how patient characteristics and the relevance of research impact that decision. Design: Cross-sectional questionnaire. Setting: Acute care respiratory clinic or an outpatient family medicine clinic in Montreal, Quebec. Participants: Four hundred seventy-four waiting room patients recruited via convenience sampling. Main Outcome Measures: A self-administered questionnaire collected data on age, gender, employment status, education, mother tongue and perceived health status. The main outcome of was self-reported relevance of three research scenarios and willingness or refusal to share their anonymized data. Responses were compared for family practice vs. specialty care patients. Results: The questionnaire was completed by 229 family medicine respondents and 245 outpatient respondents. Almost a quarter of all respondents felt the research was not relevant. Family medicine patients (15.7%) were unwilling to allow their data to be used for at least one scenario vs. 9.4% in the outpatient clinic. Lack of relevance (OR 11.55; 95% CI 5.12–26.09) and being in family practice (OR 2.13; 95% CI 1.06–4.27) increased the likelihood of refusal to share data for research. Conclusion: Family medicine patients were somewhat less willing to share eHealth data, but the overall refusal rate indicates a need to better engage patients in understanding the significance of full access to eHealth data for the purposes of research. Personal relevance of the research had a strong impact on the responses arguing for better efforts to make research more pertinent to patients.
Reliability Modeling and Analysis of Load-Sharing Systems With Continuously Degrading Components

This paper presents a reliability modeling and analysis framework for load-sharing systems with identical components subject to continuous degradation. It is assumed that the components in the system suffer from degradation through an additive impact under increased workload caused by consecutive failures. A log-linear link function is used to describe the relationship between the degradation rate and load stress levels. By assuming that the component degradation is well modeled by a step-wise drifted Wiener process, we construct maximum likelihood estimates (MLEs) for unknown parameters and related reliability characteristics by combining analytical and numerical methods. Approximate initial guesses are proposed to lessen the computational burden in numerical estimation. The estimated distribution of MLE is given in the form of multivariate normal distribution with the aid of Fisher information. Alternative confidence intervals are provided by bootstrapping methods. A simulation study with various sample sizes and inspection intervals is presented to analyze the estimation accuracy. Finally, the proposed approach is illustrated by track degradation data from an application example.
Salient object detection based on HDCT and the prior boundary

It is difficult to detect the accurate boundary of the object from the frequency information of image. In order to solve this problem, a new salient object detection algorithm which is combined Hypercomplex Discrete Cosine Transform (HDCT) and the prior boundary is proposed. Firstly, we divide the images into image blocks, and the HDCT is performed on each image block. Then the features of the image block are represented by the information of low frequency energy. Finally, the saliency map is calculated according to the prior boundary. The experiments show that the performance of the proposed algorithm is much better than the other classical salient object detection algorithms. At the same time, the boundary of the detected object region is much clearer.
Protein Composition of Mycobacterium smegmatis Differs Significantly Between Active Cells and Dormant Cells With Ovoid Morphology

Mycobacteria are able to form dormant cells, which survive for a long time without multiplication. The molecular mechanisms behind prolonged survival of dormant cells are not fully described. In particular, little information is known on biochemical processes which might take place in cells under dormancy. To gain insight into this problem, Mycobacterium smegmatis cells in deep dormant state were obtained after gradual acidification of the growth medium in prolonged stationary phase followed by 1 month of storage at room temperature. Such cells were characterized by low metabolic activity, including respiration, resistance to antibiotics, and altered morphology. The protein composition of cytoplasm and membrane fractions obtained from active and dormant cells were compared by 2D electrophoresis. Almost half of the proteins found in the proteome of dormant cells were absent in that of active cells. This result differs significantly from published results obtained in other studies employing different models of mycobacterium dormancy. This discrepancy could be explained by a deeper dormancy developed in the present model. A feature of a “dormant proteome” is high representation of enzymes involved in glycolysis and defense systems that inactivate or detoxify reactive oxygen and nitrogen species, aldehydes, and oxidized lipids. Dormant mycobacteria are enriched by degradative enzymes, which could eliminate damaged molecules, or the products of such degradation could be reutilized by the cell during prolonged storage. We suggest that some enzymes in dormant cells are inactive, having been used upon transition to the dormant state, or proteins stored in dormant cells for further cell reactivation. At the same time, some proteins could be functional and play roles in maintenance of cell metabolism, albeit at a very slow rate. This study provides a clue as to which biochemical processes could be active under dormancy to ensure long-term viability of dormant mycobacteria.
GACAT3 promoted proliferation of osteoarthritis synoviocytes by IL-6/STAT3 signaling pathway.

OBJECTIVE Osteoarthritis (OA) is one of the most common chronic joint diseases, caused by lesions in articular cartilage and synovial membranes. Synovitis is a major characteristic of OA, due to the proliferation of synoviocytes. Long noncoding RNAs (lncRNAs) have implicated to play an important role in many different diseases. The aim of this study was to verify the role of lncRNA gastric cancer-associated transcript 3 (GACAT3) in osteoarthritis. MATERIALS AND METHODS We utilized the qRT-PCR to detect the expression of lncRNA GACAT3 in osteoarthritis synoviocytes (OAS) and normal synoviocytes (NS). The cell proliferation in NS and OAS after transfection with lncRNA-NC or lncRNA-GACAT3 was detected. The cell cycle and apoptosis rate in NS and OAS were measured by the Flow cytometry analysis. Western blot was used to analyze the possible related mechanism that GACAT3 regulated the cells proliferation in osteoarthritis. RESULTS We found that GACAT3 expression was significantly increased in OAS compared with NS. GACAT3 expression was decreased in OAS after transfection with siRNA and the cell proliferation in OAS after transfection with siRNA was significantly inhibited. The cell cycle was arrested in G0/G1 phase and the apoptosis rate was increased in OAS after transfection with siRNA. Moreover, GACAT3 could impact the proliferation of OAS by interleukin-6/signal transducer and activator of transcription-3 (IL-6/STAT3) signaling pathway. CONCLUSIONS In this study, we found that lncRNA GACAT3 was closely related to the osteoarthritis. GACAT3 may be involved in the development and progression of osteoarthritis and become a potential target for treating.
A Hybrid Artificial Bee Colony Algorithm using Multiple Linear Regression on Time Series Datasets

Heuristic algorithms are successfully being applied to solution of time series datasets. The algorithms can be improved further for an elevated level of success. In this study, a hybrid application of the successful artificial bee colony (ABC) algorithm with the statistical multiple linear regression (MLR) method is presented. The proposed algorithm is applied to 3 benchmark time series datasets commonly used in the literature, and favorable results are obtained compared to other similar studies. Pure multiple linear regression, on the other hand, is shown to be unable to reach the same level of success. The results demonstrate that the hybrid application of ABC and multiple linear regression produces satisfactory results in time series datasets.
Improving social network support for partners facing spousal cancer while caring for minors: Four‐month outcomes of a single‐centre randomised controlled trial

Social support is an important predictor of the well-being of partners of cancer patients. Those who are caring for minors (well parents) may be in special need of such support. The aim of this study was to evaluate the 4-month impact of a psychoeducational social support intervention, named the Cancer-PEPSONE programme (CPP), on well parents' received and perceived social support, as well as on their psychological distress, quality of life (QOL) and parental capacity. The study was an open randomised controlled trial with a parallel-group design (N = 35; Intervention = 17, Controls = 18). The participants in the intervention group received CPP in their homes. Controls received support as usual. Data were collected in Norway using validated self-report questionnaires. Questionnaires were sent by post, before randomisation (T1), and at three- (T2) and six-month (T3) follow-up. Linear mixed models analyses revealed intervention effects on received (p = 0.04, d = 0.6) and perceived (p = 0.01, d = 1.0) social support, as well as on parental capacity (p = 0.02, d = 1.0), but not on psychological distress and QOL. Social support mediated the relationship between receiving CPP and later psychological distress. CPP may help well parents in maintaining social support and enhancing parental capacity. An improvement in social support may alleviate well parents' psychological distress.
Deep Optical Flow Supervised Learning With Prior Assumptions

Traditional methods for estimating optical flow use variational model that includes data term and smoothness term, which can build a constraint relationship between two adjacent images and optical flow. However, most of them are too slow to be used in real-time applications. Recently, convolutional neural networks have been used in optical flow area successfully. Many current learning methods use large data sets that contain ground truth for network training, which can make use of prior knowledge to estimate optical flow directly. However, these methods overemphasize the factor of deep learning and ignore advantages of many traditional assumptions used in variational framework for optical flow estimation. In this paper, inspired by classical energy-based optical flow methods, we propose a novel approach for dense motion estimation, which combines traditional prior assumptions with supervised learning network. During training, the variation in image brightness, gradient and spatial smoothness are embedded in network. Our method is tested on both synthetic and real scenes. The experimental results show that employing the prior assumptions during training can obtain more detailed and smoothed flow fields and can improve the accuracy of optical flow estimation.