Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Review of Behavioural Interventions Promoting Healthy Eating among Older People

Because eating habits are inseparably linked with people’s physical health, effective behaviour interventions are highly demanded to promote healthy eating among older people. The aim of this systematic review was to identify effective diet interventions for older people and provide useful evidence and direction for further research. Three electronic bibliographic databases—PubMed, Scopus and Web of Science Core Collection were used to conduct a systematic literature search based on fixed inclusion and exclusion criteria. English language peer-reviewed journal articles published between 2011 and 2016 were selected for data extraction and quality assessment. Finally, a total of 16 studies were identified. The studies’ duration ranged from three weeks to seven years. The majority of studies were carried out in European countries. Seven studies had a moderate quality while the remaining studies were at a less than moderate level. Three dietary educational interventions and all meal service related interventions reported improvements in older people’s dietary variety, nutrition status, or other health-related eating behaviours. Multicomponent dietary interventions mainly contributed to the reduction of risk of chronic disease. The results supported that older people could achieve a better dietary quality if they make diet-related changes by receiving either dietary education or healthier meal service. Further high-quality studies are required to promote healthy eating among older people by taking regional diet patterns, advanced information technology, and nudging strategies into account.
Midkine in the mouse ventral tegmental area limits ethanol intake and Ccl2 gene expression

Midkine (MDK) is a cytokine and neurotrophic factor that is more highly expressed in the brains of alcoholics and in mice predisposed to drink large amounts of ethanol, suggesting that MDK may regulate ethanol consumption. Here we measured ethanol consumption in male and female Mdk knockout (−/−) mice using the two‐bottle choice and the drinking in the dark (DID) tests. We found that Mdk −/− mice consumed significantly more ethanol than wild‐type controls in both tests. To determine if MDK acts in the ventral tegmental area (VTA) to regulate ethanol consumption, we delivered lentivirus expressing a Mdk shRNA into the VTA of male C57BL/6J mice to locally knockdown Mdk and performed the DID test. Mice expressing a Mdk shRNA in the VTA consumed more ethanol than mice expressing a control non‐targeting shRNA, demonstrating that the VTA is one site in the brain through which MDK acts to regulate ethanol consumption. Since MDK also controls the expression of inflammatory cytokines in other organs, we examined gene expression of interleukin‐1 beta (Il1b), tumor necrosis factor alpha (Tnfα) and the chemokine (C‐C motif) ligand 2 (Ccl2) in the VTA of Mdk −/− mice and in mice expressing Mdk shRNA in the VTA. Expression of Ccl2 was elevated in the VTA of Mdk −/− mice and in mice expressing Mdk shRNA in the VTA. These results demonstrate that MDK functions in the VTA to limit ethanol consumption and levels of CCL2, a chemokine known to increase ethanol consumption.
Weighted Least Squares Twin Large Margin Distribution Machine

In order to improve training efficiency and generalization performance of twin support vector machine, a weighted least squares twin large margin distribution machine is proposed. In our approach, equality constraint technique is used to improve the training speed. The structural risk minimization principle is implemented by introducing a regularization term to improve classification accuracy. In addition, different weights are put on the error variables in order to eliminate the impact of noise data. The experimental results show that the proposed algorithm has better classification performance in testing accuracy and efficiency.
ScholarWorks at WMU

: The National League for Nursing (2009) states that nurse educators must commit to diversity in education by reviewing practices that favor and exclude students. First generation college students (FGCS) have a background that may hinder success and require support to overcome barriers in a nursing program-including mobile device acceptance. A cross-sectional survey of 37 students showed significant differences; FGCS have higher results than non-FGCS in six of the eight key constructs of mobile device acceptance. Mobile devices may help bridge the digital divide. By increasing emphasis on mobile device functioning in the nursing curriculum, nurse educators can continue to promote diversity.
Journal of Graph Algorithms and Applications a Fast Multi-scale Method for Drawing Large Graphs

We present a multi-scale layout algorithm for the aesthetic drawing of undirected graphs with straight-line edges. The algorithm is extremely fast, and is capable of drawing graphs that are substantially larger than those we have encountered in prior work. For example, the paper contains a drawing of a graph with over 15,000 vertices. Also we achieve " nice " drawings of 1000 vertex graphs in about 1 second. The proposed algorithm embodies a new multi-scale scheme for drawing graphs, which was motivated by the earlier multi-scale algorithm of Hadany and Harel [HH99]. In principle, it could significantly improve the speed of essentially any force-directed method (regardless of that method's ability of drawing weighted graphs or the continuity of its cost-function).
The efficacy of downhill running as a method to enhance running economy in trained distance runners

Abstract Running downhill, in comparison to running on the flat, appears to involve an exaggerated stretch-shortening cycle (SSC) due to greater impact loads and higher vertical velocity on landing, whilst also incurring a lower metabolic cost. Therefore, downhill running could facilitate higher volumes of training at higher speeds whilst performing an exaggerated SSC, potentially inducing favourable adaptations in running mechanics and running economy (RE). This investigation assessed the efficacy of a supplementary 8-week programme of downhill running as a means of enhancing RE in well-trained distance runners. Nineteen athletes completed supplementary downhill (−5% gradient; n = 10) or flat (n = 9) run training twice a week for 8 weeks within their habitual training. Participants trained at a standardised intensity based on the velocity of lactate turnpoint (vLTP), with training volume increased incrementally between weeks. Changes in energy cost of running (EC) and vLTP were assessed on both flat and downhill gradients, in addition to maximal oxygen uptake (⩒O2max). No changes in EC were observed during flat running following downhill (1.22 ± 0.09 vs 1.20 ± 0.07 Kcal kg−1 km−1, P = .41) or flat run training (1.21 ± 0.13 vs 1.19 ± 0.12 Kcal kg−1 km−1). Moreover, no changes in EC during downhill running were observed in either condition (P > .23). vLTP increased following both downhill (16.5 ± 0.7 vs 16.9 ± 0.6 km h−1 , P = .05) and flat run training (16.9 ± 0.7 vs 17.2 ± 1.0 km h−1, P = .05), though no differences in responses were observed between groups (P = .53). Therefore, a short programme of supplementary downhill run training does not appear to enhance RE in already well-trained individuals.
Training Based Two-Step Channel Estimation in Two-Way MIMO Relay Systems

In this paper, we study the performance of the training based two-step channel estimation in two-way multiple-input multiple-output relay systems over Rayleigh channels. We first estimate the forward channel during the first phase. During the second phase for the composite channel estimation, singular value decomposition based maximum likelihood method is applied in two way relay systems, which is extended from one way relay systems. Hence, the backward channel is estimated with the given forward channel and composite channel. In general, the proposed estimation scheme can also be extended from the system with a single relay antenna to multiple relay antennas. Moreover, we derive the proposed training sequences and relay gain that aim to minimizes the trace of the approximate Bayesian Cramér–Rao lower bound with respect to the individual channels. The numerical results are shown that although the training sequence is designed to improve individual channel mean square error performance, it can also improve composite channel estimator performance.
Inferring Emotions from Speech Prosody: Not So Easy at Age Five

Previous research has suggested that children do not rely on prosody to infer a speaker's emotional state because of biases toward lexical content or situational context. We hypothesized that there are actually no such biases and that young children simply have trouble in using emotional prosody. Sixty children from 5 to 13 years of age had to judge the emotional state of a happy or sad speaker and then to verbally explain their judgment. Lexical content and situational context were devoid of emotional valence. Results showed that prosody alone did not enable the children to infer emotions at age 5, and was still not fully mastered at age 13. Instead, they relied on contextual information despite the fact that this cue had no emotional valence. These results support the hypothesis that prosody is difficult to interpret for young children and that this cue plays only a subordinate role up until adolescence to infer others’ emotions.
Health Impact Modelling of Active Travel Visions for England and Wales Using an Integrated Transport and Health Impact Modelling Tool (ITHIM)

Background Achieving health benefits while reducing greenhouse gas emissions from transport offers a potential policy win-win; the magnitude of potential benefits, however, is likely to vary. This study uses an Integrated Transport and Health Impact Modelling tool (ITHIM) to evaluate the health and environmental impacts of high walking and cycling transport scenarios for English and Welsh urban areas outside London. Methods Three scenarios with increased walking and cycling and lower car use were generated based upon the Visions 2030 Walking and Cycling project. Changes to carbon dioxide emissions were estimated by environmental modelling. Health impact assessment modelling was used to estimate changes in Disability Adjusted Life Years (DALYs) resulting from changes in exposure to air pollution, road traffic injury risk, and physical activity. We compare the findings of the model with results generated using the World Health Organization's Health Economic Assessment of Transport (HEAT) tools. Results This study found considerable reductions in disease burden under all three scenarios, with the largest health benefits attributed to reductions in ischemic heart disease. The pathways that produced the largest benefits were, in order, physical activity, road traffic injuries, and air pollution. The choice of dose response relationship for physical activity had a large impact on the size of the benefits. Modelling the impact on all-cause mortality rather than through individual diseases suggested larger benefits. Using the best available evidence we found fewer road traffic injuries for all scenarios compared with baseline but alternative assumptions suggested potential increases. Conclusions Methods to estimate the health impacts from transport related physical activity and injury risk are in their infancy; this study has demonstrated an integration of transport and health impact modelling approaches. The findings add to the case for a move from car transport to walking and cycling, and have implications for empirical and modelling research.
Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

We consider a general multi-user mobile cloud computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.
STELLAR MASSES FROM THE CANDELS SURVEY: THE GOODS-SOUTH AND UDS FIELDS

We present the public release of the stellar mass catalogs for the GOODS-S and UDS fields obtained using some of the deepest near-IR images available, achieved as part of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey project. We combine the effort from 10 different teams, who computed the stellar masses using the same photometry and the same redshifts. Each team adopted their preferred fitting code, assumptions, priors, and parameter grid. The combination of results using the same underlying stellar isochrones reduces the systematics associated with the fitting code and other choices. Thanks to the availability of different estimates, we can test the effect of some specific parameters and assumptions on the stellar mass estimate. The choice of the stellar isochrone library turns out to have the largest effect on the galaxy stellar mass estimates, resulting in the largest distributions around the median value (with a semi interquartile range larger than 0.1 dex). On the other hand, for most galaxies, the stellar mass estimates are relatively insensitive to the different parameterizations of the star formation history. The inclusion of nebular emission in the model spectra does not have a significant impact for the majority of galaxies (less than a factor of 2 for ∼80% of the sample). Nevertheless, the stellar mass for the subsample of young galaxies (age <100 Myr), especially in particular redshift ranges (e.g., 2.2 < z < 2.4, 3.2 < z < 3.6, and 5.5 < z < 6.5), can be seriously overestimated (by up to a factor of 10 for <20 Myr sources) if nebular contribution is ignored.
Classifying song and speech: effects of focal temporal lesions and musical disorder

ABSTRACT Song and speech represent two auditory categories the brain usually classifies fairly easily. Functionally, this classification ability may depend to a great extent on characteristic features of pitch patterns present in song melody and speech prosody. Anatomically, the temporal lobe (TL) has been discussed as playing a prominent role in the processing of both. Here we tested individuals with congenital amusia and patients with unilateral left and right TL lesions in their ability to categorize song and speech. In a forced-choice paradigm, specifically designed auditory stimuli representing sung, spoken and “ambiguous” stimuli (being perceived as “halfway between” song and speech), had to be classified as either “song” or “speech”. Congenital amusics and TL patients, contrary to controls, exhibited a surprising bias to classifying the ambiguous stimuli as “song” despite their apparent deficit to correctly process features typical for song. This response bias possibly reflects a strategy where, based on available context information (here: forced choice for either speech or song), classification of non-processable items may be achieved through elimination of processable classes. This speech-based strategy masks the pitch processing deficit in congenital amusics and TL lesion patients.
The Duration of a Co-Occurring Sound Modulates Visual Detection Performance in Humans

Background The duration of sounds can affect the perceived duration of co-occurring visual stimuli. However, it is unclear whether this is limited to amodal processes of duration perception or affects other non-temporal qualities of visual perception. Methodology/Principal Findings Here, we tested the hypothesis that visual sensitivity - rather than only the perceived duration of visual stimuli - can be affected by the duration of co-occurring sounds. We found that visual detection sensitivity (d’) for unimodal stimuli was higher for stimuli of longer duration. Crucially, in a cross-modal condition, we replicated previous unimodal findings, observing that visual sensitivity was shaped by the duration of co-occurring sounds. When short visual stimuli (∼24 ms) were accompanied by sounds of matching duration, visual sensitivity was decreased relative to the unimodal visual condition. However, when the same visual stimuli were accompanied by longer auditory stimuli (∼60–96 ms), visual sensitivity was increased relative to the performance for ∼24 ms auditory stimuli. Across participants, this sensitivity enhancement was observed within a critical time window of ∼60–96 ms. Moreover, the amplitude of this effect correlated with visual sensitivity enhancement found for longer lasting visual stimuli across participants. Conclusions/Significance Our findings show that the duration of co-occurring sounds affects visual perception; it changes visual sensitivity in a similar way as altering the (actual) duration of the visual stimuli does.
Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system

To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbines CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation (SFDI) greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.
Understanding the carbon dioxide gaps

Purpose of reviewThe current review attempts to demonstrate the value of several forms of carbon dioxide (CO2) gaps in resuscitation of the critically ill patient as monitor for the adequacy of the circulation, as target for fluid resuscitation and also as predictor for outcome. Recent findingsFluid resuscitation is one of the key treatments in many intensive care patients. It remains a challenge in daily practice as both a shortage and an overload in intravascular volume are potentially harmful. Many different approaches have been developed for use as target of fluid resuscitation. CO2 gaps can be used as surrogate for the adequacy of cardiac output (CO) and as marker for tissue perfusion and are therefore a potential target for resuscitation. CO2 gaps are easily measured via point-of-care analysers. We shed light on its potential use as nowadays it is not widely used in clinical practice despite its potential. Many studies were conducted on partial CO2 pressure differences or CO2 content (cCO2) differences either alone, or in combination with other markers for outcome or resuscitation adequacy. Furthermore, some studies deal with CO2 gap to O2 gap ratios as target for goal-directed fluid therapy or as marker for outcome. SummaryCO2 gap is a sensitive marker of tissue hypoperfusion, with added value over traditional markers of tissue hypoxia in situations in which an oxygen diffusion barrier exists such as in tissue oedema and impaired microcirculation. Venous-to-arterial cCO2 or partial pressure gaps can be used to evaluate whether attempts to increase CO should be made. Considering the potential of the several forms of CO2 measurements and its ease of use via point-of-care analysers, it is recommendable to implement CO2 gaps in standard clinical practice.
Emerging Coxsackievirus A6 Causing Hand, Foot and Mouth Disease, Vietnam

Hand, foot and mouth disease (HFMD) is a major public health issue in Asia and has global pandemic potential. Coxsackievirus A6 (CV-A6) was detected in 514/2,230 (23%) of HFMD patients admitted to 3 major hospitals in southern Vietnam during 2011–2015. Of these patients, 93 (18%) had severe HFMD. Phylogenetic analysis of 98 genome sequences revealed they belonged to cluster A and had been circulating in Vietnam for 2 years before emergence. CV-A6 movement among localities within Vietnam occurred frequently, whereas viral movement across international borders appeared rare. Skyline plots identified fluctuations in the relative genetic diversity of CV-A6 corresponding to large CV-A6–associated HFMD outbreaks worldwide. These data show that CV-A6 is an emerging pathogen and emphasize the necessity of active surveillance and understanding the mechanisms that shape the pathogen evolution and emergence, which is essential for development and implementation of intervention strategies.
TextZoo, a New Benchmark for Reconsidering Text Classification

Text representation is a fundamental concern in Natural Language Processing, especially in text classification. Recently, many neural network approaches with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid models with attention mechanisms) claimed that they achieved state-of-art in specific text classification datasets. However, it lacks an unified benchmark to compare these models and reveals the advantage of each sub-components for various settings. We re-implement more than 20 popular text representation models for classification in more than 10 datasets. In this paper, we reconsider the text classification task in the perspective of neural network and get serval effects with analysis of the above results.
Cardiac arrest due to an unexpected acute myocardial infarction during head and neck surgery: A case report

Major cardiac complication such as acute myocardial infarction can occur unexpectedly in patients without risk factors. We experienced cardiac arrest due to an unexpected acute myocardial infarction in a patient without any risk factors during head and neck reconstructive surgery. The patient was diagnosed with acute myocardial infarction after return of spontaneous circulation. With immediate percutaneous coronary intervention, the patient recovered without complications.