Miracle-dz/UniFAR-FTUs huggingface dataset:
Guided Digital Cognitive Behavioral Program for Anxiety in Primary Care: Propensity-Matched Controlled Trial

Background Cognitive behavioral therapy (CBT) is the gold standard treatment for adult anxiety disorders but is often not readily available in a scalable manner in many clinical settings. Objective This study examines the feasibility, acceptability, and effectiveness of a coach-facilitated digital cognitive behavioral program for anxious adults in primary care. Methods In an open trial, patients who screened positive for anxiety (General Anxiety Disorder-7 [GAD7] score ≥5) were offered the digital cognitive behavioral program (active group, n=593). Primary outcomes included anxiety, quality of life (QoL), and ambulatory medical use over 6 months. Intent-to-treat (ITT) and modified intent-to-treat (mITT) analyses were completed. Subsequently, we compared the outcomes of participants with those of a matched control group receiving primary care as usual (CAU; n=316). Results More than half of the patients downloaded the cognitive behavioral mobile app program and about 60% of these were considered engaged, which was defined as completion of ≥3 techniques. The active group demonstrated medium size effects on reducing anxiety symptoms (effect size d=0.44; P<.001) and improving mental health QoL (d=0.49; P<.001) and showed significantly improved physical health QoL (d=0.39; P=.002) and a decreased likelihood of high utilization of outpatient medical care (odds ratio=0.49; P<.001). The active group did not significantly outperform the CAU group in anxiety reduction or QoL improvement (d=0.20; P=.07). However, intent-to-treat analysis showed that the active group had a significantly lower likelihood of high utilization of outpatient medical care than the enhanced CAU group (P<.0001; odds ratio=0.09). Conclusions A coach-facilitated digital cognitive behavioral program prescribed in primary care is feasible and acceptable. Primary care patients prescribed a digital cognitive behavioral program for anxiety experienced significant improvements in anxiety symptoms, QoL, and reduced medical utilization. This effect was observed even among patients with chronic medical conditions and behavioral health comorbidities. Although the primary outcomes in the active group did not improve significantly more than the CAU group, health care utilization declined, and some secondary outcomes improved in participants who engaged in the program compared to the CAU group. Trial Registration ClinicalTrials.gov NCT03186872; https://clinicaltrials.gov/ct2/show/NCT03186872
Structures of ligand-occupied β-Klotho complexes reveal a molecular mechanism underlying endocrine FGF specificity and activity

Significance The three endocrine fibroblast growth factors (FGFs)—FGF19, FGF21, and FGF23—are circulating hormones that regulate critical metabolic processes in liver, adipocytes, hypothalamus, and kidney. Endocrine FGFs mediate their cellular responses by binding to and stimulating complexes composed of an FGF receptor bound to either an α-Klotho or β-Klotho receptor. This paper describes the three-dimensional structure of FGF19CT bound to sKLB and provides mechanistic insights into the mode of action of endocrine FGFs. Detailed understanding of the molecular mechanism of FGF19 provides valuable information and a roadmap for development of agonists or antagonists for treatment of patients with metabolic diseases or cancers that may benefit from either stimulation (FGF21) or inhibition (FGF19, FGF23) of endocrine FGF cell signaling. The three members of the endocrine fibroblast growth factor (FGF) family designated FGF19, FGF21, and FGF23 mediate their pleiotropic cellular effects by binding to and activating binary complexes composed of an FGF receptor (FGFR) bound to either α-Klotho or β-Klotho receptors. Structural analyses of ligand-occupied Klotho extracellular domains have provided important insights concerning mechanisms underlying the binding specificities of FGF21 and FGF23 to β-Klotho or α-Klotho, respectively. They have also demonstrated that Klotho proteins function as primary high-affinity receptors while FGFRs function as the catalytic subunits that mediate intracellular signaling. Here we describe the crystal structure the C-terminal tail of FGF19 (FGF19CT) bound to sKLB and demonstrate that FGF19CT and FGF21CT bind to the same binding site on sKLB, via a multiturn D-P motif to site 1 and via a S-P-S motif to the pseudoglycoside hydrolase region (site 2). Binding affinities to sKLB and cellular stimulatory activities of FGF19CT, FGF21CT, and a variety of chimeric mutants to cells expressing β-Klotho together with FGFR1c or FGFR4 were also analyzed. These experiments as well as detailed comparison of the structures of free and ligand-occupied sKLB to the structure of ligand-occupied sKLA reveal a general mechanism for recognition of endocrine FGFs by Klotho proteins and regulatory interactions with FGFRs that control their pleiotropic cellular responses.
Evaluating traumatic brain injury using conventional magnetic resonance imaging and susceptibility-weighted imaging in dogs

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) sequence used for evaluating traumatic brain injury (TBI). Although SWI is being increasingly used in veterinary medicine, there are no systematic studies regarding its use. We aimed to evaluate TBI lesions by using conventional MRI and SWI in 11 dogs and determine the correlation between clinical status and conventional MRI or SWI findings. The modified Glasgow coma scale (MGCS) at presentation and a previously used MRI grading system (MRGr; grades 1–6) were used to evaluate the brain lesions, and correlations between MGCS score and each MRGr were assessed. Conventional MRI revealed 23 lesions in 11 dogs with variable MGCS scores (range: 11–17). SWI showed comparable findings for all of the lesions except for subdural hemorrhage, and it revealed additional lesions in four dogs. The median MRGr was 2 on both conventional MRI and SWI. The MRGr of the conventional MRI assessments and the MGCS scores showed a significant negative correlation (r = −0.685). In conclusion, SWI had better TBI lesion-detection ability, but conventional MRI had a better correlation with early clinical status and subdural hemorrhage. Thus, a combination of conventional MRI and SWI examinations can improve TBI diagnosis in dogs.
Examining the relationship between changes in personality and depression in older adult cancer survivors

Abstract Objectives: Despite widespread agreement that personality traits change across the lifespan into older adulthood, the association between changes in personality and depression among older adult cancer survivors is unknown. It was hypothesized that older adults with (vs. without) a past cancer diagnosis would experience an increase in neuroticism, and decreases in conscientiousness, agreeableness, openness, and extraversion, and that changes in these traits would mediate the relationship between receiving a cancer diagnosis and change in depression. Two hypotheses were tested in a cancer survivor sample. First, that increased chronic stressors and decreased physical health would mediate the link between personality change and increased depression. Second, that personality change would mediate the link between changes in chronic stressors/health and increased depression. Method: Secondary data analysis utilizing three waves of data from the Health and Retirement Study. Data was compiled from 5,217 participants, among whom 707 received a cancer diagnosis. Results: Older adults with (vs. without) a cancer diagnosis decreased in conscientiousness, which was associated with increased depression. Among cancer survivors, worsening chronic stressors/health mediated many pathways between personality change and an increased depression. Increased neuroticism mediated the link between worsening health/chronic stressors and increased depression. Conclusion: With the exception of conscientiousness, changes in personality did not mediate the link between cancer survivor status and depression. Among older adult cancer survivors, changes in personality traits may increase depression through worsening physical health and chronic stressors, potentially informing targeted interventions. Interventions that target increased neuroticism may be particularly useful in older adult cancer survivors.
Mapping the use of soft systems methodology for change management in healthcare: a scoping review protocol

Introduction It is notoriously challenging to implement evidence-based care and to update and improve healthcare practices. One reason for the difficulty is the complexity of healthcare and the powerful influence of context on implementation and improvement efforts. Thus, there is a need for multifaceted, flexible change methods that takes these complexities into consideration. One approach that has the potential in this regard is soft systems methodology (SSM). However, little is known about how SSM has been applied in healthcare settings, making it difficult to assess the usefulness of SSM for implementation science or improvement research. The aim of the proposed scoping review is to examine and map the use and outcomes of SSM in healthcare. Methods and analysis The review will adapt the framework outlined by Arksey and O’Malley (2005). Citations will be uncovered through a comprehensive database search of the peer-reviewed literature. Two reviewers will conduct a two-stage review and selection process where the titles/abstracts are examined followed by a screening of full texts of the selected citations. Reference lists of included citations will be snowballed to identify potential additional citations. Inclusion criteria are English language, peer-reviewed empirical papers focusing on the application of SSM in a healthcare setting. Both general information about the citations and information related to the objective of the review will be extracted from the included citations and entered into a data charting form. The extracted information will be reported in diagrams and tables and summarised to present a narrative account of the literature. The proposed review will provide information on the potential for using SSM to affect change in healthcare. Ethics and dissemination No primary data will be collected, and thus ethical permission is unnecessary. Dissemination of results include peer-reviewed publications and conference presentations.
Predictors of Fat Oxidation and Caloric Expenditure With and Without Weighted Vest Running

Abstract Purdom, TM, Mermier, C, Dokladny, K, Moriarty, T, Lunsford, L, Cole, N, Johnson, K, and Kravitz, L. Predictors of fat oxidation and caloric expenditure with and without weighted vest running. J Strength Cond Res 35(7): 1865–1872, 2021—This study evaluated the effect of weighted vest running (WVR) on fatty acid oxidation (FAox) and caloric expenditure at predesignated steady-state exercise intensities. Seventeen recreationally trained runners (9 men and 8 women) performed 4 separate graded exercise tests (GXTs) separated by at least 24 hours. The first GXT was a V̇o2max test to establish running velocities (percentage of max) at the prespecified exercise intensities (60, 65, 70, 75, and 80% V̇o2max). The following randomized WVR trials included a control (no vest), 5% body mass (BM) vest, and 10% BM vest using 3-minute incrementally increasing steady-state stages. Indirect calorimetry was used to measure both FAox (g·min−1) and caloric expenditure (kcal·min−1) at each stage. The ANOVA/ANCOVA analysis revealed that intensity had the largest effect on and FAox (p < 0.001) while WVR had a significant main effect on both caloric expenditure and FAox (p < 0.05). Pairwise comparisons revealed that the 10% BM significantly increased caloric expenditure for all exercise intensities compared with control and 5% BM (except 60% V̇o2max), while FAox decreased in the 10% BM 70 and 75% V̇o2max intensities only. Marginal r-squared (Rm2) demonstrate that when sex + fat-free mass (FFM) + fat mass (FM) were included as covariates, sex was a significant predictor of caloric expenditure but was further explained by the large FFM (kg) differences between the sexes significantly influencing caloric expenditure (Rm2 = 0.87, p < 0.001). Exercise intensity, body composition (FFM + FM), and external mass placement are relevant considerations for runners looking to maximize FAox and caloric expenditure.
IoT-Motion Sensor Device and Data Analysis : (Motion Detection and Step-Count Algorithm)

In the last one decade, with the advancement and developmental changes in artificial intelligence (AI) technology and the usage of smart-phones, motion-detection and step-count applications have increased the public attention. Because they are playing a significant role in the fields of device positioning, energy harvesting and facial and behavior recognition etc. These motion-detection sensors and smart-phone applications are playing a vital role in human’s life. In this article, we are suggesting a novel peak detection motion algorithm to instantaneously detect foot movement and steps are counted by using Nordic Thingy device as a motion sensor by holding an android phone (Note 5A) in hand to save the walk data. The sensor device and unconstrained cell phones would have the ability to place not only arbitrary position but also alterable position. Depending on the foot-step motion periodicity and gyroscopes sensitivity; the projected algorithm extracts the features of time domain from a three dimensional (3D) angular velocity of a cell-phone with the help of Fast Fourier Transform (FFT) and classifies whether the sensor device with the smartphone of user sensed walk or not regardless of its placement or location of Thingy-sensor. The proposed algorithm is developed and experienced in real time environments for walking, running, climbing up and down stairs. It indicates promising results for walking at almost 99% accuracy. Contrasting, the conventional motion detection and step-count approaches, the errors are fixed in proposed algorithm and long distances do not affect its performance.
Mobility-as-a-Service: does it contribute to sustainability?

The promise of Mobility-as-a-Service (MaaS) is that it decreases the need to own a car and contributes to a more sustainable transport system. However, MaaS also offers relatively easy access to car-based travel alternatives which may result in substituting public transport trips by car trips. An important question therefore is: which type of traveler is going to adopt MaaS and which impact is this going to have on their mode choices? This paper explores this question by presenting the results of a stated choice experiment conducted in the Netherlands. Travelers are presented with MaaS bundles that vary in accessibility to transport services and price and they respond to a range of questions about bundle adoption, change in transport mode, and willingness to shed one or more cars. The results suggest that if MaaS bundles are given for free to the travelers, this has the potential to change their frequency of mode use. For example, if the MaaS bundel includes unlimited bus, tram and metro (BTM), even travelers who solely use car will then use BTM more. However, realizing this potential is not very likely, because when travelers have to pay for MaaS, adoption rates are rather low, in particular of those who use car the most. In addition, the willingness of car owners to shed their cars is very low, suggesting that currently MaaS is not conceived as a viable alternative for car-ownership. On the other hand, current public travelers seem most interested in MaaS and results indeed as expected suggest that this increases their car use. Overall, the trends reported in this paper adds to a growing insight that MaaS’ contribution to sustainability may be smaller than generally believed.
Automated Video Monitor Screen Extraction using Semantic Segmentation and CNN

In production lines for monitors and displays, some validation tests are based on visual inspection, whose preliminary step usually consists in detecting the area corresponding to a monitor's screen, which is then followed by evaluation procedures. Nonetheless, depending on the chosen technique, such a detection may consume much of a given test's total time. The present paper addresses the mentioned problem and presents an approach that uses semantic segmentation and convolutional neural networks for screen segmentation, which allows a reduction on elapsed test times about 27.77%, without compromising the accuracy already obtained with traditional approaches.
An intelligent music generation based on Variational Autoencoder

In this paper, GAN and VAE are combined with deep learning network to generate intelligent music based on music theory rules, and to explore intelligent music generation algorithm. Different from the traditional algorithmic composition, it is not necessary to manually add complex rules, but trains the initial music set, evaluates and filters the music collection, and ultimately generates music via the RVAE-GAN neural network. The fitness function calculates a weighted sum of a series of features of a piece of music, such as pitch and rhythm distribution, and can also calculate a series of theoretical rules of music theory from the distance between a particular piece of music. Furthermore, we take the music theory and practical experience into account, put forward the expressions and a rule function of rhythm, which are given in a mathematical way. On this basis, the semi-supervised algorithm is used to form the chord structure model, combined with the feature extraction of music, and the intelligent generation music based on GAN confrontation generation network and VAE network combined with music theory rules is proposed and proposed. And mass production has important theoretical and practical significance.
Utilization of collaborative mapping in parking systems

Nowadays advanced driver-assistance (ADAS) systems are of general use, as they help vehicle drivers while driving or during parking. These systems can perform the parking tasks when the goal configuration is available for the vehicle. However, the urban environment makes the parking lot search an inevitable and unpleasant task, as for ADAS systems a goal configuration is required in order to perform the parking maneuver. The literature provided solutions for the parking lot exploration mainly focus on the use of preinstalled sensors in parking lots. The presented paper proposes an autonomous parking system using alternately bird’s eye view maps and community built maps such as Open Street Map. The alternate use of these maps provides the parking lot exploration subsystem for the autonomous parking system. When an additional environmental sensor is installed on the vehicle, making the detection of parking space candidates possible, the use of preinstalled sensors in parking lots becomes unnecessary.
A Mesopic Lighting Evaluation Model Based on 1D Convolutional Neural Networks

In the study, a mesopic lighting evaluation model that can be used to predict S/P ratio, mesopic luminance and color gamut volume of a white LED spectrum is proposed. The result shows that the one-dimensional convolutional neural network describes all of the indices well especially for mesopic luminance and color gamut volumes with a high coefficient of determination. In contrast, the performance of the S/P ratio is slightly unfavorable.
NOMA-aided generalized pre-coded quadrature spatial modulation for downlink communication systems

Generalised pre-coding quadrature spatial modulation (GPQSM) is recently proposed to increase the spectral efficiency (SE) of GPSM, which extends the transmitted symbols into in-phase/quadrature domains. In this paper, a novel scheme named non-orthogonal multiple access (NOMA)-aided GPQSM (NOMA-GPQSM), which incorporates the GPQSM scheme into the multi-user communication networks with assist of NOMA, is proposed to further improve the SE and system performance. In NOMA-GPQSM, one base station (BS) is set to serve K users, where user 1 is closest to the BS, and user K is farthest from the BS. In addition, a low-complexity detection method is proposed to reduce the high detection complexity of the maximum-likelihood (ML) detection in successive interference cancellation (SIC) method for all users by NOMA-GPQSM. The theoretical analysis of the BER performance for all users is also derived. Simulation results show that near users achieve relatively good performance, and far users achieve acceptable performance by adjusting power factors for all users in NOMA-GPQSM.
Microfluidic Cyberphysical Diagnostic System: An ANN Based Application

The present day advances in Microfluidic and Microfabrication technology resulted in the emergence of a new generation of lab-on-chip (LOC) devices namely digital microfluidic biochips. Such devices find wide application in the area of medical diagnostics, forensics, sequencing of DNA, design of drugs, monitoring of environment and so on. Automated optical detection and analysis plays significant role in interpretation and decision making in medical diagnostics using lab-on-chip based applications. This paper proposes a cyber physical system based on artificial neural networks to perform a sequential multilevel detection for enhanced medical detection and diagnostics for accurate treatment procedure. The Biochip based detection and diagnosis is simulated in an FPGA platform. The simulation results are found to be closely accurate with conventional techniques and highly encouraging.
Lowpass-filtered relative-to-maximum masking for speech enhancement in noise-corrupted environments

In this study, we focus on developing a novel speech enhancement scheme that can improve the quality of speech signals in adverse environments. This novel scheme primarily refines our previously presented method, relative-to-maximum masking (RMM), by applying a moving-average filter to the respective mask matrix on the temporal axis. The underlying idea of this process is to capture the neighboring correlated characteristics of the mask values in RMM. Furthermore, such a moving average process can implicitly highlight the low-varying components of the speech spectrogram, which has been shown to reduce noise effect in the past research. Experiments conducted on a subset of Aurora-2 database show that the newly proposed scheme obtains significantly higher PESQ scores than the baseline results, and it also outperforms the original RMM in all noise levels.
Graph Theory Based Localization of Wireless Sensor Networks for Radio Irregularity Cases

In the aerospace applications, the cases of radio irregularity, obstacle, asymmetrical distribution are disadvantageous for node localization of wireless sensor networks. In this paper, the related principles and methods of Graph Theory is introduced to minimize the localization error caused by radio irregularity. The model of asymmetry communication is established based on Graph Theory. Through the analysis to communication path between nodes in directed network, solution schemes are put forward for locating nodes in case of asymmetric communication. The localization performance is compared in different scenes and conditions. For node localization with radio irregularity, the accuracy can be improved 10% by our solutions when the network topology is regular. While the network topology is irregular, the localization accuracy can also be improved when strongly connected graph comes into being.