Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethical Evaluation of a Value Sensitive Persuasive System: Case Milky Way

Although persuasive technology is about influencing individuals without coercion, deception, or manipulation, ethical issues still must be considered in designing persuasive systems. In this paper, we utilized value sensitive analysis in evaluating a mobile application to examine how value sensitive analysis can benefit persuasive system design. The evaluated Milky Way application is meant for women with the intention to breastfeed. It supports the users in continuing to breastfeed for a longer period of time, being faithful to the recommendations by World Health Organization. Our results show, that value sensitive analysis brings new and valuable information to the design process of a persuasive system, which in turn can make it more meaningful for users as well as more efficient in supporting behavior change.
geneXplainR: An R interface for the geneXplain platform

The geneXplain platform (A. Kel et al. 2011) is an online toolbox and workflow management system for a broad range of bioinformatic and systems biology applications. The platform is well-known for its upstream analysis (Koschmann et al. 2015), that has been developed to identify causal signalling molecules on the basis of experimental data like expression measurements. Methods integrated into the system include
An Evaluation of Environmental Governance in Urban China Based on a Hesitant Fuzzy Linguistic Analytic Network Process

The aim of this study is to evaluate the performance of urban environmental governance by developing hesitant fuzzy linguistic analytic network process (HFL-ANP). The study bridges the gaps in current knowledge in the following ways: the study methodically develops the HFL-ANP method to evaluate and pick the optimal environmental governance strategy from alternatives; theoretically, network structure of evaluation indicators system on environmental governance is constructed, and the objective and subjective information in the evaluation process of environmental governance is combined. In detail, based on the environmental Kuznets curve (EKC) and the pollution haven hypothesis, the paper constructs the evaluation indexes system of environmental governance and takes observation time length into consideration. Then, we choose three urban cases of environmental governance by exploring the existing literature. Furthermore, we develop the HFL-ANP method and apply it to the cases. The study calculates the initial weights of all indexes by using multiplicative consistency of the HFL preference relation, and derives the decision matrix through combining objective information with subjective information of environmental governance. Finally, we come to the following conclusions: ANP network stricture is close to real-world practical problems and provides the basis for HFL-ANP method; HFL-ANP is a very suitable method of assessing environmental governance; and based on the urban cases of environmental governance, Shanghai is the optimal alternative. In addition, this indicator system can only be applied to cities in China, and the administrative hierarchy of policies has not been considered by this method. Thus, future studies should expand this method and indicator network to contain different countries and different administrative hierarchy.
Hyperspectral Image Classification Based on Capsule Network

In this paper, we propose two novel classification frameworks for hyperspectral image (HSI) based on capsule network (CapsNet), which could address the drawbacks of convolutional neural network (CNN) and problem of limited training samples by introducing affine transformation matrix. Specifically, the proposed framework first performs the classification of HSI based on spectral information. Second, considering the importance of spatial information for HSI processing, we integrate the spatial and spectral information into the proposed framework to further improve the classification performance. Experimental results on real HSI data demonstrate the effectiveness of the proposed framework.
Identifying Patients Who Are Likely to Receive Most of Their Care From a Specific Health Care System: Demonstration via Secondary Analysis

Background In the United States, health care is fragmented in numerous distinct health care systems including private, public, and federal organizations like private physician groups and academic medical centers. Many patients have their complete medical data scattered across these several health care systems, with no particular system having complete data on any of them. Several major data analysis tasks such as predictive modeling using historical data are considered impractical on incomplete data. Objective Our objective was to find a way to enable these analysis tasks for a health care system with incomplete data on many of its patients. Methods This study presents, to the best of our knowledge, the first method to use a geographic constraint to identify a reasonably large subset of patients who tend to receive most of their care from a given health care system. A data analysis task needing relatively complete data can be conducted on this subset of patients. We demonstrated our method using data from the University of Washington Medicine (UWM) and PreManage data covering the use of all hospitals in Washington State. We compared 10 candidate constraints to optimize the solution. Results For UWM, the best constraint is that the patient has a UWM primary care physician and lives within 5 miles of at least one UWM hospital. About 16.01% (55,707/348,054) of UWM patients satisfied this constraint. Around 69.38% (10,501/15,135) of their inpatient stays and emergency department visits occurred within UWM in the following 6 months, more than double the corresponding percentage for all UWM patients. Conclusions Our method can identify a reasonably large subset of patients who tend to receive most of their care from UWM. This enables several major analysis tasks on incomplete medical data that were previously deemed infeasible.
High Resolution SAR Image Classification with Deeper Convolutional Neural Network

Deeper architectures are proven to be beneficial for the classification performance obviously in computer vision field. Inspired by this, deep CNN s are expected to make progress in the SAR target classification problem as well. However, it is hard to train deeper CNNs for SAR images. Such CNNs have millions of parameters to be determined in the network (for example the VGGNet has more than 130 million parameters), hence large-scale dataset is indispensable when training a deep CNN. But there is no large-scale annotated SAR target dataset, and data acquisition and annotation is much more costly for SAR images. With inadequate data, the network is easy to be overfitting. Several methods based on deep learning have been proposed for SAR image classifications, but they cannot get rid of the aforementioned data limitation of labelled SAR images. To solve this problem, this paper proposes a microarchitecture called CompressUnit (CU). With CU, we design a deeper CNN. Compared with the network with the fewest parameters for SAR image classification in literature so far, our network is 2X deeper with only about 10% of parameters. In this way, we get a deeper network with much fewer parameters. This network is easier to be trained with limited SAR data and is more likely to get rid of overfitting.
Understanding Health Information System Implementation in an Indonesian Primary Health Centre: A Sociotechnical Perspective

The implementation of health information systems (HIS) faces major challenges in developing countries. These challenges are due to the complex nature of the healthcare sector that involves complex interactions between various professional groups, technologies and organisational interest. This paper explores socio-technical challenges during a HIS implementation in a primary health centre (PHC) in Bandung City, Indonesia. An Actor-Network Theory (ANT) informed qualitative case study is applied to explore how a variety of human and non-human actions influences the stability of the HIS implementation network and what factors preserve the durability of the HIS implementation. We found un-finished translation processes; vendor support and external encounters affect the stability of the network. However, organisational factors (management authority and support, staff adaptation, and local policy) can preserve implementation durability regardless of the limitations of the HIS. How do different non-human actions influence the stability of implementation networks? What factors do preserve the durability of HIS implementation ? A qualitative case study approach was used. It will be explained in the research methodology section. Actor-Network Theory (ANT) is used as a method and theoretical lens to analyse the collected data and to explain the research findings and implications for practice.
Laparoscopic Gastric Plication versus Laparoscopic Sleeve Gastrectomy: An Up-to-Date Systematic Review and Meta-Analysis

Introduction A meta-analysis was conducted in order to provide an up-to-date comparison of laparoscopic sleeve gastrectomy (LSG) and laparoscopic gastric plication (LGP) for morbid obesity. Materials and Methods The PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions were used for the conduction of this study. A systematic literature search was performed in the electronic databases (MEDLINE, CENTRAL, and Web of Science and Scopus). The fixed effects or random effects model was used according to the Cochran Q test. Results Totally, 12 eligible studies were extracted. LSG displayed a statistically significant lower rate of overall complications (OR: 0.35; 95% CI: 0.17, 0.68; p=0.002) and a sustainable higher %EWL through all time endpoints (OR: 4.86, p=0.04; OR: 7.57, p < 0.00001; and OR: 13.74; p < 0.00001). There was no difference between the two techniques in terms of length of hospital stay (p=0.16), operative duration (p=0.81), reoperation rate (p=0.51), and cost (p=0.06). Conclusions LSG was demonstrated to have a lower overall complications and a higher weight loss rate, when compared to LGP. Further RCTs of a higher methodological quality level, with a larger sample size, are required in order to validate these findings.
Sentryn Acts with a Subset of Active Zone Proteins To Optimize the Localization of Synaptic Vesicles in Caenorhabditis elegans

Synaptic vesicles (SVs) transmit signals by releasing neurotransmitters from specialized synaptic regions of neurons. In the synaptic region, SVs are tightly clustered around small structures called active zones. The motor KIF1A transports SVs outward through axons until they are captured in the synaptic region. This transport must be guided in the forward direction because it is opposed by the dynein motor, which causes SVs to reverse direction multiple times en route. The core synapse stability (CSS) system contributes to both guided transport and capture of SVs. We identified Sentryn as a CSS protein that contributes to the synaptic localization of SVs in Caenorhabditis elegans. Like the CSS proteins SAD Kinase and SYD-2 (Liprin-α), Sentryn also prevents dynein-dependent accumulation of lysosomes in dendrites in strains lacking JIP3. Genetic analysis showed that Sentryn and SAD Kinase each have at least one nonoverlapping function for the stable accumulation of SVs at synapses that, when combined with their shared functions, enables most of the functions of SYD-2 (Liprin-α) for capturing SVs. Also like other CSS proteins, Sentryn appears enriched at active zones and contributes to active zone structure, suggesting that it is a novel, conserved active zone protein. Sentryn is recruited to active zones by a process dependent on the active zone-enriched CSS protein SYD-2 (Liprin-α). Our results define a specialized group of active zone enriched proteins that can affect motorized transport throughout the neuron and that have roles in both guided transport and capture of SVs.
Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks

In this paper, we advocate edge caching in cloud radio access networks (C-RAN) to facilitate the ever-increasing mobile multimedia services. In our framework, central offices will cooperatively allocate cloud resources to cache popular contents and satisfy user requests for those contents, so as to minimize the system costs in terms of storage, VM reconfiguration, content access latency, and content migration. However, this joint resource allocation, content placement and request routing, is nontrivial, since it needs to be continuously adjusted to accommodate system dynamics, such as user movement and content slashdot effect, while taking into account the time-correlated adjustment costs for VM reconfiguration and content migration. To this end, we build a comprehensive model to capture the key components of edge caching in C-RAN and formulate a joint optimization problem, aiming at minimizing the system costs over time and meanwhile satisfying the time-varying user requests and respecting various practical constraints (e.g., storage and bandwidth). Then, we propose a novel online approximation algorithm by resorting to the regularization, rounding, and decomposition technique, which can be proved to have a parameterized competitive ratio with a polynomial running time. Extensive trace-driven simulations corroborate the efficiency, flexibility, and lightweight of our proposed online algorithm; for instance, it achieves an empirical competitive ratio around 2 – 4 and gains over 30% improvement compared with many state-of-the-art algorithms in various system settings.
First Experimental Assessment of a Microwave Imaging Prototype for Cerebrovascular Diseases Monitoring

In the present work we describe recent developments in the realization of a novel device for monitoring continuously a patient after a cerebrovascular event. The proposed technology is microwave imaging (MWI) exploiting the difference in dielectric properties of ischemic and hemorrhagic areas with respect to the other tissues present in the head. The goal of our work is to realize a prototype device that can be complementary to other imaging techniques, as support for physicians in the choice and control of therapies.
‘Interaction structures’ between depressed adolescents and their therapists in short-term psychoanalytic psychotherapy and cognitive behavioural therapy

Objective: Identify the core ‘interaction structures’ between therapists and depressed adolescents within and across two common forms of psychotherapy. Method: A total of 70 audio-recorded psychotherapy sessions representing short-term psychoanalytic psychotherapy (STPP) and cognitive behavioural therapy (CBT) with youth aged 12–18 years old were coded with the Adolescent Psychotherapy Q-set (APQ), a newly developed instrument. Data included different therapist-patient dyads and stages in treatment and were analysed with cluster analysis. Results: Three distinct interaction structures between therapists and depressed adolescents: two influenced by the therapists’ techniques and one more influenced by the young people’s attitude to therapy. Conclusion: When there is a collaborative working relationship between therapists and depressed young people, the therapy process is influenced by the therapists’ techniques; while when there is a poor working relationship, the techniques used by therapists of different theoretical orientation become more similar with the aim of engaging the young person in the process.
Acyl-CoA synthetase 6 enriches the neuroprotective omega-3 fatty acid DHA in the brain

Significance Neurodegenerative diseases are a leading cause of morbidity and mortality among older adults, the fastest-growing segment of the US population. Neurodegenerative disease risk is reduced by high dietary intake of the omega-3 fatty acid, docosahexaenoic acid (DHA), a highly abundant lipid in the brain. Yet the fundamental mechanisms regulating brain DHA enrichment remain unknown. Here, we have made a key discovery that Acyl-CoA synthetase 6 (Acsl6) is required to specifically enrich DHA in the brain. Of importance, mice lacking Acsl6 have impaired motor function and increased astrogliosis, demonstrating the critical need for Acsl6-mediated lipid metabolism in neurological health. This work provides critical insight into longstanding mysteries surrounding brain DHA metabolism and has broad-reaching health implications. Docosahexaenoic acid (DHA) is an omega-3 fatty acid that is highly abundant in the brain and confers protection against numerous neurological diseases, yet the fundamental mechanisms regulating the enrichment of DHA in the brain remain unknown. Here, we have discovered that a member of the long-chain acyl-CoA synthetase family, Acsl6, is required for the enrichment of DHA in the brain by generating an Acsl6-deficient mouse (Acsl6−/−). Acsl6 is highly enriched in the brain and lipid profiling of Acsl6−/− tissues reveals consistent reductions in DHA-containing lipids in tissues highly abundant with Acsl6. Acsl6−/− mice demonstrate motor impairments, altered glutamate metabolism, and increased astrogliosis and microglia activation. In response to a neuroinflammatory lipopolysaccharide injection, Acsl6−/− brains show similar increases in molecular and pathological indices of astrogliosis compared with controls. These data demonstrate that Acsl6 is a key mediator of neuroprotective DHA enrichment in the brain.
Association Between Endothelial Function and Cognitive Performance in Patients With Coronary Artery Disease During Cardiac Rehabilitation

Objective Subtle cognitive deficits indicating early neural risk are common in the clinical presentation of coronary artery disease (CAD). Although deterioration may be mitigated by exercise, cognitive response to exercise is heterogeneous. Vasculopathy including endothelial dysfunction is a hallmark of CAD and may play an important role in impairing neural adaptation to exercise. This study aimed to assess peripheral measurements of endothelial function as predictors of cognitive performance in CAD participants undertaking cardiac rehabilitation (CR). Methods CAD patients (N = 64) undergoing CR were recruited for this prospective observational study. Neuropsychological and endothelial function assessments were performed at baseline and after 3 months of CR. Z-scores for overall cognitive performance and specific cognitive domains (verbal and visuospatial memory, processing speed, and executive function) were calculated. Endothelial function was measured by the reactive hyperemia index (RHI) using peripheral arterial tonometry. Cross-sectional and longitudinal associations between RHI and overall cognition were assessed using linear regressions and mixed models, respectively. Domain-specific associations were also explored. Results Although lower RHI was not associated with overall cognition at baseline (b = 0.26, p = .10), an increased RHI was significantly associated with an improvement in overall cognition (b = 0.55, p = .030) over 3 months. Lower RHI was associated with poorer verbal memory (&bgr; = 0.28, p = .027) at baseline and an increased RHI over 3 months was associated with an improvement in processing speed (b = 0.42, p = .033). Conclusions RHI may be a clinically useful predictor of cognitive change and might provide insight into the etiology of cognitive dysfunction in patients with CAD.
AIS Electronic

: We built the efficiency evaluation index system of home improvement website’s inputs and outputs. The SBM - DEA model is applied to evaluate the efficiency of 30 home improvement websites and the stability of relatively effective websites. Finally, we put forward some specific website optimization suggestions with the results. The purposes of this paper are to study the efficiency of home improvement websites and provide webmasters with optimization suggestions under limited input resources.
Persuasive Serious Social Health Games for Managing Diabetes

Prevalence of chronic diseases is becoming a serious problem facing our society. Many of these diseases are caused due to dietary and lifestyle risk factors and can be perceived as hard to cure. However, behavioural changes such as adopting a healthy diet and lifestyle are effective in reducing chronic conditions. Lifestyle-related diseases are now being combated through the use of ICT-based systems and persuasive technologies to help change lifestyle and manage diseases. We can observe the adoption of serious health games as an aid to persuade individuals to adopt healthy lifestyles. This research leverages information technologies and type 2 diabetes studies to design games that can act as a catalyst to educate, prevent and reverse type 2 diabetes, especially among the Maori and Pacific Island communities. These games not only support the formation of healthy habits but also helps individuals to work together collectively towards a healthier life that embraces their uniqueness. self-evaluative one’s health behaviour and health status” their behaviours. They do things that them satisfaction and avoid things that do not. be to change his or her behaviour where he or she is shown how changes to behaviour serve both self-interest and valued goals.
Nested-Greedy Search for Adversarial Real-Time Games

Churchill and Buro (2013) launched a line of research through Portfolio Greedy Search (PGS), an algorithm for adversarial real-time planning that uses scripts to simplify the problem's action space. In this paper we present a problem in PGS's search scheme that has hitherto been overlooked. Namely, even under the strong assumption that PGS is able to evaluate all actions available to the player, PGS might fail to return the best action. We then describe an idealized algorithm that is guaranteed to return the best action and present an approximation of such algorithm, which we call Nested-Greedy Search (NGS). Empirical results on MicroRTS show that NGS is able to outperform PGS as well as state-of-the-art methods in matches played in small to medium-sized maps.
Distinction between Chronic Enteropathy Associated with the SLCO2A1 Gene and Crohn’s Disease

Background/Aims We recently identified recessive mutations in the solute carrier organic anion transporter family member 2A1 gene (SLCO2A1) as causative variants of chronic nonspecific multiple ulcers of the small intestine (chronic enteropathy associated with SLCO2A1, CEAS). The aim of this study was to investigate the gastroduodenal expression of the SLCO2A1 protein in patients with CEAS and Crohn’s disease (CD). Methods Immunohistochemical staining for SLCO2A1 was performed with a polyclonal antibody, HPA013742, on gastroduodenal tissues obtained by endoscopic biopsy from four patients with CEAS and 29 patients with CD. Results The expression of SLCO2A1 was observed in one of four patients (25%) with CEAS and in all 29 patients (100%) with CD (p<0.001). The three patients with CEAS without SLCO2A1 expression had a homozygous splice-site mutation in SLCO2A1, c.1461+1G>C (exon 7) or c.940+1G>A (exon 10). The remaining one CEAS patient with positive expression of SLCO2A1 had compound heterozygous c.664G>A and c.1807C>T mutations. Conclusions Immunohistochemical staining for SLCO2A1 in gastroduodenal tissues obtained by endoscopic biopsy is considered useful for the distinction of CEAS from CD.