Miracle-dz/UniFAR-FTUs huggingface dataset:
Worse Than Death: Survey of Public Perceptions of Disability Outcomes After Hypothetical Traumatic Brain Injury

Supplemental Digital Content is available in the text Objective: The aim of this study was to determine the health utility states of the most commonly used traumatic brain injury (TBI) clinical trial endpoint, the Extended Glasgow Outcome Scale (GOSE). Summary Background Data: Health utilities represent the strength of one's preferences under conditions of uncertainty. There are insufficient data to indicate how an individual would value levels of disability after a TBI. Methods: This was a cross-sectional web-based online convenience sampling adaptive survey. Using a standard gamble approach, participants evaluated their preferences for GOSE health states 1 year after a hypothetical TBI. The categorical GOSE was studied from vegetative state (GOSE2) to upper good recovery (GOSE8). Median (25th percentile, 75th percentile) health utility values for different GOSE states after TBI, ranging from −1 (worse than death) to 1 (full health), with 0 as reference (death). Results: Of 3508 eligible participants, 3235 (92.22%) completed the survey. Participants rated lower GOSE states as having lower utility, with some states rated as worse than death, though the relationship was nonlinear and intervals were unequal between health states. Over 75% of participants rated a vegetative state (GOSE2, absence of awareness and bedridden) and about 50% rated lower severe disability (GOSE3, housebound needing all-day assistance) as conditions worse than death. Conclusions: In the largest investigation of public perceptions about post-TBI disability, we demonstrate unequally rated health states, with some states perceived as worse than death. Although limited by selection bias, these results may guide future comparative-effectiveness research and shared medical decision-making after neurologic injury.
DeepCount: Crowd Counting with WiFi via Deep Learning

Recently, the research of wireless sensing has achieved more intelligent results, and the intelligent sensing of human location and activity can be realized by means of WiFi devices. However, most of the current human environment perception work is limited to a single person's environment, because the environment in which multiple people exist is more complicated than the environment in which a single person exists. In order to solve the problem of human behavior perception in a multi-human environment, we first proposed a solution to achieve crowd counting (inferred population) using deep learning in a closed environment with WIFI signals - DeepCout, which is the first in a multi-human environment. step. Since the use of WiFi to directly count the crowd is too complicated, we use deep learning to solve this problem, use Convolutional Neural Network(CNN) to automatically extract the relationship between the number of people and the channel, and use Long Short Term Memory(LSTM) to resolve the dependencies of number of people and Channel State Information(CSI) . To overcome the massive labelled data required by deep learning method, we add an online learning mechanism to determine whether or not someone is entering/leaving the room by activity recognition model, so as to correct the deep learning model in the fine-tune stage, which, in turn, reduces the required training data and make our method evolving over time. The system of DeepCount is performed and evaluated on the commercial WiFi devices. By massive training samples, our end-to-end learning approach can achieve an average of 86.4% prediction accuracy in an environment of up to 5 people. Meanwhile, by the amendment mechanism of the activity recognition model to judge door switch to get the variance of crowd to amend deep learning predicted results, the accuracy is up to 90%.
A Narrative Review of Methods for Applying User Experience in the Design and Assessment of Mental Health Smartphone Interventions

Abstract Objectives User experience (UX) plays a key role in uptake and usage of mental health smartphone interventions, yet remains underinvestigated. This review aimed to characterize and compare UX evaluation approaches that have been applied in this specific context, and to identify implications for research and practice. Methods A narrative review was conducted of UX-themed studies published in PubMed, PsycINFO, and Scopus up to February 2019. Eligible studies reported on data reflecting users' interactions with a smartphone intervention for any mental health condition. Studies were categorized into “situated” versus “construct-based” methods according to whether or not an established UX construct was used to acquire and analyze data. Results Situated approaches used bespoke UX metrics, including quantitative measures of usage and performance, as well as grounded interview data. Construct-based approaches such as assessments of usability and acceptability were based on conceptual frameworks, with methodologically stronger versions featuring construct definitions, validated measurement tools, and an ability to compare data across studies. Constructs and measures were sometimes combined to form bespoke construct-based approaches. Conclusions Both situated and construct-based UX data may provide benefits during design and implementation of a mental health smartphone intervention by helping to clarify the needs of users and the impact of new features. Notable however was the omission of UX methods, such as split testing. Future research should consider these unaddressed methods, aim to improve the rigor of UX assessment, integrate their use alongside clinical outcomes, and explore UX assessment of more complex, adaptive interventions.
Solamargine inhibits gastric cancer progression by regulating the expression of lncNEAT1_2 via the MAPK signaling pathway

Solamargine, a derivative from the steroidal solasodine in Solanum species, has exhibited anticancer activities in numerous types of cancer; however, its role in gastric cancer (GC) remains unknown. In the present study, it was demonstrated that Solamargine suppressed the viability of five gastric cancer cell lines in a dose-dependent manner and induced notable alterations in morphology. Treatment with Solamargine promoted cell apoptosis (P<0.01). Solamargine increased the expression of long noncoding RNA (lnc) p53 induced transcript and lnc nuclear paraspeckle assembly transcript 1 (NEAT1)_2 (P<0.01) in GC by reducing the phosphorylation of extracellular signal-regulated kinase (Erk)1/2 mitogen-activated protein kinase (MAPK). To gain insight into the potential mechanism, an Erk1/2 inhibitor (U0126) was applied. The results revealed that lncNEAT1_2 expression levels increased, which was consistent with the effects of Solamargine. Downregulation of lncNEAT1_2 in GC cells revealed no effect on the expression levels of total Erk1/2 and, and counteracted the effect of Solamargine. Solamargine was observed to increase the expression of lncNEAT1_2 via the Erk1/2 MAPK signaling pathway. Of note, the knockdown of lncNEAT1_2 reduced the inhibitory effect of Solamargine (P<0.05). Additionally, experiments in vivo and in primary GC cells from patients demonstrated that Solamargine significantly suppressed tumor growth (P<0.05). In vivo analysis of a xenograft mouse model further supported that Solamargine could induce the apoptosis of cancer cells in tumor tissue as observed by a terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling and H&E staining (P<0.05). Experiments in primary GC cells from patients verified the anti-tumor effect of Solamargine. In summary, the findings of the present study indicated that Solamargine inhibited the progression of GC by regulating lncNeat1_2 via the MAPK pathway.
Cooperative decentralised circumnavigation with application to algal bloom tracking

Harmful algal blooms occur frequently and deteriorate water quality. A reliable method is proposed in this paper to track algal blooms using a set of autonomous surface robots. A satellite image indicates the existence and initial location of the algal bloom for the deployment of the robot system. The algal bloom area is approximated by a circle with time varying location and size. This circle is estimated and circumnavigated by the robots which are able to locally sense its boundary. A multi-agent control algorithm is proposed for the continuous monitoring of the dynamic evolution of the algal bloom. Such algorithm comprises of a decentralised least squares estimation of the target and a controller for circumnavigation. We prove the convergence of the robots to the circle and in equally spaced positions around it. Simulation results with data provided by the SINMOD ocean model are used to illustrate the theoretical results.
Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

In this paper, we introduce machine learning approaches that are used to prioritize outpatients (OP) according to their current health state, resulting in self-optimizing heterogeneous networks (HetNet) that intelligently adapt according to users' needs. We use a naïve Bayesian classifier to analyze data acquired from OPs' medical records, alongside data from medical Internet of Things (IoT) sensors that provide the current state of the OP. We use this machine learning algorithm to calculate the likelihood of a life-threatening medical condition, in this case an imminent stroke. An OP is assigned high-powered resource blocks (RBs) according to the seriousness of their current health state, enabling them to remain connected and send their critical data to the designated medical facility with minimal delay. Using a mixed integer linear programming formulation (MILP), we present two approaches to optimizing the uplink side of a HetNet in terms of user-RB assignment: a Weighted Sum Rate Maximization (WSRMax) approach and a Proportional Fairness (PF) approach. Using these approaches, we illustrate the utility of the proposed system in terms of providing reliable connectivity to medical IoT sensors, enabling the OPs to maintain the quality and speed of their connection. Moreover, we demonstrate how system response can change according to alterations in the OPs' medical conditions.
FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning

: In this paper, we propose FlowScan : a pedestrian ﬂow estimation technique based on a dashboard camera. Grasping ﬂows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian ﬂows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of tra ﬃ c accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people ﬂow estimation of both directions was 18.5%, highlighting its e ﬀ ectiveness compared with the state-of-the-art.
GSOAP: a tool for visualization of gene set over-representation analysis

Abstract Motivation Gene sets over-representation analysis (GSOA) is a common technique of enrichment analysis that measures the overlap between a gene set and selected instances (e.g. pathways). Despite its popularity, there is currently no established standard for visualization of GSOA results. Results Here, we propose a visual exploration of the GSOA results by showing the relationships among the enriched instances, while highlighting important instance attributes, such as significance, closeness (centrality) and clustering. Availability and implementation GSOAP is implemented as an R package and is available at https://github.com/tomastokar/gsoap.
Pulmonary hypertension in chronic obstructive pulmonary disease

Even mild pulmonary hypertension (PH) is associated with increased mortality and morbidity in patients with chronic obstructive pulmonary disease (COPD). However, the underlying mechanisms remain elusive; therefore, specific and efficient treatment options are not available. Therapeutic approaches tested in the clinical setting, including long‐term oxygen administration and systemic vasodilators, gave disappointing results and might be only beneficial for specific subgroups of patients. Preclinical studies identified several therapeutic approaches for the treatment of PH in COPD. Further research should provide deeper insight into the complex pathophysiological mechanisms driving vascular alterations in COPD, especially as such vascular (molecular) alterations have been previously suggested to affect COPD development. This review summarizes the current understanding of the pathophysiology of PH in COPD and gives an overview of the available treatment options and recent advances in preclinical studies.
GNG7 silencing promotes the proliferation and differentiation of placental cytotrophoblasts in preeclampsia rats through activation of the mTOR signaling pathway

Preeclampsia (PE) is a pathological condition that manifests during pregnancy as the occurrence of an abnormal urine protein level and increased blood pressure due to inadequate cytotrophoblast invasion. To elucidate the mechanism underlying PE, the present study primarily focused on the regulatory effects and mechanism of the G protein γ 7 (GNG7) on placental cytotrophoblasts in a rat PE model. Initially, the PE model was established with 45 specific pathogen-free Sprague-Dawley rats (30 females and 15 males). The expression patterns of GNG7, 4E-binding protein 1 (4E-BP1), phosphoprotein 70 ribosomal protein S6 kinase (p70S6K) and mammalian target of rapamycin (mTOR) were examined in the PE rats. Placental cytotrophoblasts isolated from normal and PE rats were treated with a small interfering RNA against GNG7, mTOR signaling pathway activator (HIV-1 Tat) or inhibitor (rapamycin). Following treatment, cell proliferation, differentiation and apoptosis were evaluated, and mTOR signaling pathway-related factors (4E-BP1, p70S6K and mTOR), cell proliferation-related factors (vascular endothelial growth factor and transforming growth factor-β1), differentiation-related factors [activator protein-2 (AP-2)α and AP-2γ], and apoptosis-related factors [B-cell lymphoma 2 (Bcl-2) and Bcl-2-associated X protein] were determined. Finally, soluble fms-like tyrosine kinase 1 (sFlt-1) and soluble endoglin (sEng) levels were measured via enzyme-linked immunosorbent assay. Initially, the mTOR signaling pathway was inactivated in the placental tissues and cytotrophoblasts in the PE rats. Silencing GNG7 reduced the levels of sFlt-1 and sEng and activated the mTOR signaling pathway. Silencing of GNG7 or activation of the mTOR signaling pathway enhanced cell proliferation and differentiation, but inhibited the apoptosis of placental cytotrophoblasts in the PE rats. Taken together, the results showed that GNG7 silencing repressed apoptosis and enhanced the proliferation and differentiation of placental cytotrophoblasts in PE rats through activation of the mTOR signaling pathway.
Bioprinting of Cell‐Laden Microfiber: Can It Become a Standard Product?

Hydrogel microfibers have many fascinating applications as microcarriers for drugs, factors, and cells, such as 3D cell culture, building micro‐organoids, and transplantation therapy due to their simple structures. It is unknown whether cell‐laden fiber can become a standard‐use product like woundplast. Here, from the technical and practical view, the elements required for user‐oriented microfibers are first discussed: i) the materials used should promote cell functionalization and be easily processed; ii) follow a manufacturing method for mass fabrication; iii) have the ability to be stored long‐term and be available for immediate use. Here, it is demonstrated that bioactive microfibers can be simply fabricated with coaxial bioprinting using gelatin methacrylate due to its tunable biological and mechanical properties. Additionally, programmed microfibers and 3D constructs with controllable composition can also be fabricated. These microfibers can be used to directly build organoids and complex co‐culture tissue models. In the present study, vascular organoid, angiogenic sprouts, and tumor angiogenesis are demonstrated. It is also demonstrated, for the first time, that the cell‐laden microfibers can be stored long‐term via cryopreservation. These results show that cell‐laden structures can be developed as a novel type of organoid product, which will open more avenues for tissue engineering and clinical organ repair.
Assessment of literacy and numeracy skills related to non-steroidal anti-inflammatory drug labels

Background: Non-steroidal anti-inflammatory drugs are widely used and have a potential for over-the-counter misuse. Limited health literacy is associated with poor health outcomes. Identification of new strategies to assess literacy and numeracy could be useful in targeting effective education initiatives. Objective: To characterize numeracy and literacy skills related to non-steroidal anti-inflammatory drug labels in primary care patients. Methods: Patients were recruited and consented over an 8-month period after their regular primary care visit. Demographic information was collected and two instruments were administered to assess literacy and numeracy skills: (1) a medication label literacy instrument focused on non-steroidal anti-inflammatory drugs (MedLit-NSAID) and (2) a general healthy literacy-screening tool, the Newest Vital Sign. Two questions on the MedLit-NSAID instrument evaluated understanding of the Food and Drug Administration medication guide for non-steroidal anti-inflammatory drugs and the Food and Drug Administration approved over-the-counter label. Results: A total of 145 patients were enrolled. Mean MedLit-NSAID and Newest Vital Sign scores were 6.8 (scale range 0–8) and 4.2 (scale range 0–6), respectively. Higher education level was associated with higher scores for both tools (p ⩽ 0.05). Total MedLit-NSAID scores on average were higher in females compared with males (6.5 vs 6, p = 0.05). Patients with decreased kidney function (n = 18) had significantly lower MedLit-NSAID scores (p ⩽ 0.05). Test–retest scores were not significantly different for MedLit-NSAID (p = 0.32). The correlation between the tools was 0.54 and internal consistency MedLit-NSAID was 0.61. Conclusion: A medication information focused instrument provided specific information to assess health literacy related to non-steroidal anti-inflammatory drug labels. This information could be utilized to develop patient education initiatives for medication label comprehension.
Declines in skeletal muscle quality vs. size following two weeks of knee joint immobilization

Background Disuse of a muscle group, which occurs during bedrest, spaceflight, and limb immobilization, results in atrophy. It is unclear, however, if the magnitude of decline in skeletal muscle quality is similar to that for muscle size. The purpose of this study was to examine the effects of two weeks of knee joint immobilization on vastus lateralis and rectus femoris echo intensity and cross-sectional area. Methods Thirteen females (mean ± SD age = 21 ± 2 years) underwent two weeks of left knee joint immobilization via ambulating on crutches and use of a brace. B-mode ultrasonography was utilized to obtain transverse plane images of the immobilized and control vastus lateralis and rectus femoris at pretest and following immobilization. Effect size statistics and two-way repeated measures analyses of variance were used to interpret the data. Results No meaningful changes were demonstrated for the control limb and the rectus femoris of the immobilized limb. Analyses showed a large increase in vastus lateralis echo intensity (i.e., decreased muscle quality) for the immobilized limb (p = .006, Cohen’s d = .918). For vastus lateralis cross-sectional area, no time × limb interaction was observed (p = .103), but the effect size was moderate (d = .570). There was a significant association between the increase in vastus lateralis echo intensity and the decrease in cross-sectional area (r = − .649, p = .016). Conclusion In female participants, two weeks of knee joint immobilization resulted in greater deterioration of muscle quality than muscle size. Echo intensity appears to be an attractive clinical tool for monitoring muscle quality during disuse.
Task-oriented Design through Deep Reinforcement Learning

We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task. For this task-oriented design, the 3D design process in product design is assigned to an action space in Deep RL, and the desired 3D model is obtained by training each design action according to the task. By showing that this method achieves satisfactory design even when applied to a task pursuing multiple goals, we suggest the direction of how machine learning can contribute to the design process. Also, we have validated with product designers that this methodology can assist the creative part in the process of design.
Post‐surgical chronic pain and quality of life in children operated for congenital heart disease

Advances in medical technology have resulted in an increased life expectancy in pediatric patients with congenital heart diseases. Assessment of health‐related quality of life is crucial to improving their healthcare status. We aimed to assess post‐surgical pain prevalence and its impact on health‐related quality of life in pediatric patients who underwent cardiac surgery during childhood.