Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in dementia diagnosis rates in UK ethnic groups: analysis of UK primary care data

Objectives We compared incidence of dementia diagnosis by white, black, and Asian ethnic groups and estimated the proportion of UK white and black people developing dementia in 2015 who had a diagnosis for the first time in a UK-wide study. Methods We analyzed primary care electronic health records from The Health Improvement Network database between 2007 and 2015 and compared incidence of dementia diagnosis to dementia incidence from community cohort studies. The study sample comprised of 2,511,681 individuals aged 50–105 years who did not have a dementia diagnosis prior to the start of follow-up. Results A total of 66,083 individuals had a dementia diagnosis (4.87/1,000 person-years at risk, 95% CI 4.83–4.90); this incidence increased from 3.75 to 5.65/1,000 person-years at risk between 2007 and 2015. Compared with white women, the incidence of dementia diagnosis was 18% lower among Asian women (adjusted incidence rate ratio (IRR) 0.82, 95% CI 0.72–0.95) and 25% higher among black women (IRR 1.25, 95% CI 1.07–1.46). For men, incidence of dementia diagnosis was 28% higher in the black ethnic group (IRR 1.28, 95% CI 1.08–1.50) and 12% lower in the Asian ethnic group (IRR 0.88, 95% CI 0.76–1.01) relative to the white ethnic group. Based on diagnosis incidence in The Health Improvement Network data and projections of incidence from community cohort studies, we estimated that 42% of black men developing dementia in 2015 were diagnosed compared with 53% of white men. Conclusion People from the black ethnic group had a higher incidence of dementia diagnosis and those from the Asian ethnic group had lower incidence compared with the white ethnic group. We estimated that black men developing dementia were less likely than white men to have a diagnosis of dementia, indicating that the increased risk of dementia diagnosis reported in the black ethnic group might underestimate the higher risk of dementia in this group. It is unclear whether the lower incidence of dementia diagnosis in the Asian ethnic group reflects lower community incidence or underdiagnosis. A cohort study to determine this is needed.
SNARE‐mediated membrane fusion arrests at pore expansion to regulate the volume of an organelle

Constitutive membrane fusion within eukaryotic cells is thought to be controlled at its initial steps, membrane tethering and SNARE complex assembly, and to rapidly proceed from there to full fusion. Although theory predicts that fusion pore expansion faces a major energy barrier and might hence be a rate‐limiting and regulated step, corresponding states with non‐expanding pores are difficult to assay and have remained elusive. Here, we show that vacuoles in living yeast are connected by a metastable, non‐expanding, nanoscopic fusion pore. This is their default state, from which full fusion is regulated. Molecular dynamics simulations suggest that SNAREs and the SM protein‐containing HOPS complex stabilize this pore against re‐closure. Expansion of the nanoscopic pore to full fusion can thus be triggered by osmotic pressure gradients, providing a simple mechanism to rapidly adapt organelle volume to increases in its content. Metastable, nanoscopic fusion pores are then not only a transient intermediate but can be a long‐lived, physiologically relevant and regulated state of SNARE‐dependent membrane fusion.
How values can reduce conflicts in the design process: Results from a multi-site mixed-method field study

This study investigates values that are positively correlated with a reduction in value conflicts during the process of designing computational models among stakeholders such as modelers, clients, users, and those affected by models. Survey results identify statistically significant differences in the values of those who have experienced various kinds of value conflicts and those who have not, in terms of values such as equality, a spiritual life, forgiving, humble, a world at peace, politeness, responsible, self-discipline, honoring of parents and elders, true friendship, and devout. Interview results shed further light on the role that these values play in reducing value conflicts. Overall, this study illustrates the importance for computational modelers to consider and design for the values of all stakeholders in the computational modeling process, which can ensure that the models that they build will be sensitive to and appropriate for the values of all of the individuals and communities that will be affected by computational models.
Risks of using SSRI / SNRI antidepressants during pregnancy and lactation

Abstract At present, affective disorders are among the most commonly diagnosed mental diseases. In pregnancy, they can occur as pre-delivery depression, recurrent depressive disorder or postnatal depression. The estimated prevalence of depressive disorders in pregnancy is approximately 9–16%, with some statistics reporting up to 20%. Approximately 2–3% of pregnant women take antidepressants during pregnancy, and the number of mothers treated increases by birth to 5–7%. Treatment of depression during pregnancy and breastfeeding is a controversial issue, as antidepressants can negatively affect the developing fetus. According to epidemiological studies, the effects of treated depression in pregnancy are related to premature birth, decreased body weight of the child, intrauterine growth retardation, neonatal adaptive syndrome, and persistent pulmonary hypertension. However, untreated depression can adversely affect maternal health and increase the risk of preeclampsia and eclampsia, as well as of subsequent postnatal depression, which can lead to disruption of the mother-child relationship. Based on the above mentioned facts, the basic question arises as to whether or not to treat depression during pregnancy and lactation.
Faster, better, or both? Looking at both sides of online question-answering coin

Online question-answering (Q&A) services are becoming increasingly popular among information seekers. We divide them in two domains: social Q&A (SQA) and virtual referencing (VR) and ask what the demands and expectations are for both in satisfying information seeking needs. Using more than 30 interviews and their qualitative analysis of both experts (librarians) and end users (students), we present our findings that indicate the mismatch in experts’ and end-users’ understanding of how and when each service should be used. More importantly, we show how SQA and VR differ in their functionalities and offerings, commenting on their pros and cons, and the ways in which one could create better hybrid solutions for online Q&A services.
Measures of health-related quality of life in PCOS women: a systematic review

Introduction Polycystic ovary syndrome (PCOS) is associated with biochemical and hormonal disturbance and adverse cosmetic, reproductive, metabolic, and psychological consequences, resulting in reduced health-related quality of life (HRQoL). Various generic and specific questionnaires have been used for assessing different dimensions of HRQoL in PCOS women. The purpose of this systematic review was to identify those general and specific instruments and to determine the factors that affect HRQoL in PCOS women. Materials and methods The research strategy involved general and specific terms in relation to PCOS women and their QoL. A review was performed on studies that were published between 1945 to 2017 and that were indexed in MEDLINE, ISI Web of Science, and Scopus. A narrative synthesis of the data was provided. Results In total, 52 studies (9 qualitative and 43 quantitative) were included in the review. The analysis indicated that 3 specific and 5 general instruments were used to measure the QoL in PCOS women. Of these, the 36-Item Short Form Health Survey (SF-36) and the Polycystic Ovary Syndrome Health-Related Quality of Life Questionnaire (PCOSQ) were used most frequently. All studies assessed different aspects of QoL in PCOS women and found that PCOS had negative effects on QoL in this population. Conclusion The PCOSQ and the SF-36 were used most frequently for the assessment of QoL in PCOS women. Perhaps using either a specific questionnaire solely or a specific questionnaire in conjunction with a generic measure would be more appropriate when measuring QoL in PCOS women. However, both questionnaires showed that they are able to capture different aspects of QoL in PCOS women and to identify areas that can help to improve QoL in these women.
ANALYSIS OF ANDROID VULNERABILITIES AND MODERN EXPLOITATION TECHNIQUES

Android is an operating system based on the Linux kernel. It is the most widely used and popular operating system among Smartphones and portable devices. Its programmable and open nature attracts attackers to take undue advantage. Android platform allows developers to freely access and modify source code. But at the same time it increases the security issue. A user is likely to download and install malicious applications written by software hackers. This paper focuses on understanding and analyzing the vulnerabilities present in android platform. In this paper firstly we study the android architecture; analyze the existing threats and security weaknesses. Then we identify various exploit mitigation techniques to mitigate known vulnerabilities. A detailed analysis will help us to identify the existing loopholes and it will give strategic direction to make android operating system more secure.
Learning Data Terms for Non-blind Deblurring Supplemental Material

In this supplemental material, we provide the detailed analysis of the proposed method in Section 1. The specific formulas for the derivatives (14) of the manuscript are presented in Section 2. Section 3 demonstrates the effectiveness of the proposed method to improve the performance of existing deblurring approaches. We present more experimental results of the proposed algorithm against the state-of-the-art methods in Section 4.
Exploring manifold structure of face images via multiple graphs

Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.
A review on wearable photoplethysmography sensors and their potential future applications in health care

Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.
A Versatile Strategy to Fabricate 3D Conductive Frameworks for Lithium Metal Anodes

The suppression of lithium dendrite is critical to the realization of lithium metal batteries. 3D conductive framework, among different approaches, has shown very promising results in dendrite suppression. A novel cost‐effective and versatile dip‐coating method is presented here to make 3D conductive framework. Various substrates with different geometries are coated successfully with copper, including electrically insulating glass fiber (GF) or rice paper and conducting Ni foam. In particular, the as‐prepared copper coated GF shows promising results to serve as the lithium metal substrate by the electrochemical battery tests. The method significantly broadens the candidate materials database for 3D conductive framework to include all kinds of intrinsically insulating 3D substrates.
Supplementary Material for Superpixel Sampling Networks

Here, we formally define the Achievable Segmentation Accuracy (ASA) metric that is used in the main paper. Given an image I with n pixels, let H ∈ {0, 1, · · · ,m}n×1 denotes the superpixel segmentation with m superpixels. H is composed of m disjoint segments, H = ⋃m j=1H j , where j segment is represented as H . Similarly, let G ∈ {0, 1, · · · , w}n×1 denotes ground-truth (GT) segmentation with w segments. G = ⋃w l=1G , where G denotes l GT segment.
Terrain machine learning

Predicting terrain trafficability of deformable terrain is a difficult task with applications in e.g, forestry, agriculture, exploratory missions. The currently used techniques are neither practical, efficient, nor sufficiently accurate and inadequate for certain soil types. An online method which predicts terrain trafficability is of interest for any vehicle with purpose to reduce ground damage, improve steering and increase mobility. This thesis presents a novel approach for predicting the model parameters used in modelling a virtual terrain. The model parameters include particle stiffness, tangential friction, rolling resistance and two parameters related to particle plasticity and adhesion. Using multi-body dynamics, both vehicle and terrain can be simulated, which allows for an efficient exploration of a great variety of terrains. A vehicle with access to certain sensors can frequently gather sensor data providing information regarding vehicleterrain interaction. The proposed method develops a statistical model which uses the sensor data in predicting the terrain model parameters. However, these parameters are specified at model particle level and do not directly explain bulk properties measurable on a real terrain. Simulations were carried out of a single tracked bogie constrained to move in one direction when traversing flat, homogeneous terrains. The statistical model with best prediction accuracy was ridge regression using polynomial features and interaction terms of second degree. The model proved capable of predicting particle stiffness, tangential friction and particle plasticity, with moderate accuracy. However, it was deduced that the current predictors and training scenarios were insufficient in estimating particle adhesion and rolling resistance. Nevertheless, this thesis indicates that it should be possible to develop a method which successfully predicts terrain model properties.
NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp. , and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.
Antioxidant potentials and degrees of polymerization of six wild fruits

Aqueous methanolic extracts of six wild fruits, Ximenia caffra (sour plum), Sclerocarya birrea (marula), Parinari curatellifolia (mobola plum), Vitex payos (chocolate berry),Bridelia molis (velvet sweet-berry) and Berchemia zeyheri (red ivory) were assayed for radical scavenging effect on DPPH radical, reducing power, superoxide anion radical scavenging effect and the inhibition of phospholipids peroxidation using colorimetric method. The peels and pulps of sour plum exhibited high activity compared to the peels and pulps of the other fruits. Sour plum showed high reducing capacities both in the peel and pulp compared to all the other fruits. At high concentrations of extract over 75% superoxide anion scavenging effect was observed for velvet sweet-berry whilst at concentrations of 40 and 60 mg sample equivalent/μl red ivory fruits showed a high anion scavenging capacity. Velvet sweet-berry, sour plum peel and pulp and chocolate berry peel showed high capacities to inhibit lipid peroxidation at high concentrations. The degrees of polymerization varied between 7 to 16 monomer units of catechin per polymer of phenolic compounds in the different portions of the six fruits. Key words: Phenolic acids, antioxidant, wild fruits, peroxidation and polymerisation.
Contingent Contact-Based Motion Planning

A robot with contact sensing capability can reduce uncertainty relative to the environment by deliberately moving into contact and matching the resulting contact measurement to different possible states in the world. We present a manipulation planner that finds and sequences these actions by reasoning explicitly about the uncertainty over the robot's state. The planner incrementally constructs a policy that covers all possible contact states during a manipulation and finds contingencies for each of them. In contrast to conformant planners (without contingencies), the planned contingent policies are more robust. We demonstrate this in simulated and real-world manipulation experiments. In contrast to POMDP-based planners, we show that our planner can be directly applied to high-dimensional configuration spaces.
Degradation observations of encapsulated planar CH3NH3PbI3 perovskite solar cells at high temperatures and humidity

The stability of encapsulated planar-structured CH3NH3PbI3 (MAPbI3) perovskite solar cells (PSCs) was investigated under various simulated environmental conditions. The tests were performed under approximately one sun (100 mW cm−2) illumination, varying temperature (up to 85 °C cell temperature) and humidity (up to 80%). The application of advanced sealing techniques improved the device stability, but all devices showed significant degradation after prolonged aging at high temperature and humidity. The degradation mechanism was studied by post-mortem analysis of the disassembled cells using SEM and XRD. This revealed that the degradation was mainly due to the decomposition of MAPbI3, as a result of reaction with H2O, and the subsequent reaction of hydroiodic acid, formed during MAPbI3 decomposition, with the silver back contact electrode layer.
Bayesian Optimization Based on K-Optimality

Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining the trade-off parameter between exploitation and exploration. To solve these problems, we investigate the K-optimality, aiming at minimizing the condition number. Firstly, the Sequentially Bayesian K-optimal design (SBKO) is proposed to ensure the stability of the GP prediction, where the K-optimality is given as the acquisition function. We show that the SBKO reduces the integrated posterior variance and maximizes the hyper-parameters’ information gain simultaneously. Secondly, a K-optimal enhanced Bayesian Optimization (KO-BO) approach is given for the optimization problems, where the K-optimality is used to define the trade-off balance parameters which can be output automatically. Specifically, we focus our study on the K-optimal enhanced Expected Improvement algorithm (KO-EI). Numerical examples show that the SBKO generally outperforms the Monte Carlo, Latin hypercube sampling, and sequential DoE approaches by maximizing the posterior variance with the highest precision of prediction. Furthermore, the study of the optimization problem shows that the KO-EI method beats the classical EI method due to its higher convergence rate and smaller variance.
Metacognitive, Emotional, and Avoidance Predictors of Generalized Anxiety Disorder

Predictors of worrying and generalized anxiety disorder (GAD) were compared. First, variables related to a version of inner experience were examined. Specifically, we examined whether negative appraisals about worrying and emotions can be considered subfacets of the overarching construct of experiential avoidance. Second, we examined the relative predictive power of responsibility to continue thinking (i.e., beliefs about the need for prolonged thinking about stressful problems), a construct relating worrying to problem-solving. In two studies, consistent predictors of worrying and GAD were negative metacognitive beliefs, fear of emotions, and responsibility to continue thinking, even after controlling for neuroticism in Study 2. Experiential avoidance did not explain the independent variance in worry. The structural equation model that experiential avoidance explained negative appraisals about worrying and emotions did not fit the data well. Negative metacognitive beliefs evidenced strong predictive power, therefore, requiring particular attention in treatment. Nonetheless, fear of emotions and perceived need for perseverative thinking are candidate additional components that deserve continued investigation.
Social Influences on Songbird Behavior: From Song Learning to Motion Coordination

Social animals learn during development how to integrate successfully into their group. How do social interactions combine to maintain group cohesion? We first review how social environments can influence the development of vocal learners, such as songbirds and humans (Chapter 1). To bypass the complexity of natural social interactions and gain experimental control, we developed Virtual Social Environments, surrounding the bird with videos of manipulated playbacks. This way we were able to design sensory and social scenarios and test how social zebra finches adjust their behavior (Chapters 2 & 3). A serious challenge is that the color output of a video monitor does not match the color vision of zebra finches. To minimize chromatic distortion, we eliminated all of the colors from the videos, except in the beak and cheeks where we superimposed colors that match the sensitivity of zebra finch photoreceptors (Chapter 2). Birds strongly preferred to watch these manipulated ‘bird appropriate’ videos. We also designed Virtual Social Environments for assessing how observing movement patterns might affect behavior in real-time (Chapter 3). We found that presenting birds with manipulated movement patterns of virtual males promptly affects the mobility of birds watching the videos: birds move more when virtual males increase their movements, and they decrease their