Miracle-dz/UniFAR-FTUs huggingface dataset:
Intraindividual Variability in Symptoms Consistently Predicts Sudden Gains: An Examination of Three Independent Datasets

Objective: Sudden gains are robust predictors of outcome in psychotherapy. However, previous attempts at predicting sudden gains have yielded inconclusive findings. The aim of the present study was to examine a novel, transdiagnostic, transtherapeutic predictor of sudden gains that would replicate in different settings and populations. Specifically, we examined intraindividual variability in symptoms. Method: We examined data from a randomized controlled trial (RCT) of prolonged exposure therapy for posttraumatic stress disorder (PTSD) in children and adolescents (n = 63), an RCT of cognitive and behavioral therapies for obsessive–compulsive disorder (OCD) in adults (n = 91), and psychodynamic therapy delivered under routine clinical conditions in a naturalistic setting for diverse disorders (n = 106). In all 3 data sets, we examined whether a measure of variability in symptoms occurring during the first sessions could predict sudden gains. Results: Variability in symptoms was found to be independent of total change during treatment. Variability in symptoms significantly predicted sudden gains in all 3 data sets and correctly classified 81.0%, 69.2%, and 76.9% of individuals to sudden gain or nonsudden gain status, respectively. Conclusions: The present study represents the first examination of variability in symptoms as a predictor of sudden gains. Findings indicated that sudden gains are significantly predicted by intraindividual variability in symptoms, in diverse settings, contexts, and populations. Advantages of this predictor, as well as clinical and research implications are discussed.
Assessing the bioactivity of crystalline silica in heated high-temperature insulation wools

Abstract High-Temperature Insulation Wools (HTIW), such as alumino silicate wools (Refractory Ceramic Fibers) and Alkaline Earth Silicate wools, are used in high-temperature industries for thermal insulation. These materials have an amorphous glass-like structure. In some applications, exposure to high temperatures causes devitrification resulting in the formation of crystalline species including crystalline silica. The formation of this potentially carcinogenic material raises safety concerns regarding after-use handling and disposal. This study aims to determine whether cristobalite formed in HTIW is bioactive in vitro. Mouse macrophage (J774A.1) and human alveolar epithelial (A549) cell lines were exposed to pristine HTIW of different compositions, and corresponding heat-treated samples. Cell death, cytokine release, and reactive oxygen species (ROS) formation were assessed in both cell types. Cell responses to aluminum lactate-coated fibers were assessed to determine if responses were caused by crystalline silica. DQ12 α-quartz was used as positive control, and TiO2 as negative control. HTIW did not induce cell death or intracellular ROS, and their ability to induce pro-inflammatory mediator release was low. In contrast, DQ12 induced cytotoxicity, a strong pro-inflammatory response and ROS generation. The modest pro-inflammatory mediator responses of HTIW did not always coincide with the formation of cristobalite in heated fibers; therefore, we cannot confirm that devitrification of HTIW results in bioactive cristobalite in vitro. In conclusion, the biological responses to HTIW observed were not attributable to a single physicochemical characteristic; instead, a combination of physicochemical characteristics (cristobalite content, fiber chemistry, dimensions and material solubility) appear to contribute to induction of cellular responses.
Depth Image Inpainting for RGB-D Camera Based on Light Field EPI

RGB-D camera is an active depth measurement device. Depth images collected by RGB-D camera usually have depth errors at objects edges and corners in image. Current researches of depth image inpainting methods were focusing on exploiting existed depth data and making use of corresponding RGB image. This paper proposes a method to inpaint depth image by using Light Field EPI (Epipolar Plane Image), which has a special linear structure. We present a software framework to extra objects edges depth data from light field EPI. Then we merge the depth image and light field EPI edges depth data to an integral depth image. Experimental results demonstrate the accurateness of the proposed depth image inpainting method, especially for thin objects and edges in image.
Evidence that a respiratory shield in Escherichia coli protects a low-molecular-mass FeII pool from O2-dependent oxidation

Iron is critical for virtually all organisms, yet major questions remain regarding the systems-level understanding of iron in whole cells. Here, we obtained Mössbauer and EPR spectra of Escherichia coli cells prepared under different nutrient iron concentrations, carbon sources, growth phases, and O2 concentrations to better understand their global iron content. We investigated WT cells and those lacking Fur, FtnA, Bfr, and Dps proteins. The coarse-grain iron content of exponentially growing cells consisted of iron–sulfur clusters, variable amounts of nonheme high-spin FeII species, and an unassigned residual quadrupole doublet. The iron in stationary-phase cells was dominated by magnetically ordered FeIII ions due to oxyhydroxide nanoparticles. Analysis of cytosolic extracts by size-exclusion chromatography detected by an online inductively coupled plasma mass spectrometer revealed a low-molecular-mass (LMM) FeII pool consisting of two iron complexes with masses of ∼500 (major) and ∼1300 (minor) Da. They appeared to be high-spin FeII species with mostly oxygen donor ligands, perhaps a few nitrogen donors, and probably no sulfur donors. Surprisingly, the iron content of E. coli and its reactivity with O2 were remarkably similar to those of mitochondria. In both cases, a “respiratory shield” composed of membrane-bound iron-rich respiratory complexes may protect the LMM FeII pool from reacting with O2. When exponentially growing cells transition to stationary phase, the shield deactivates as metabolic activity declines. Given the universality of oxidative phosphorylation in aerobic biology, the iron content and respiratory shield in other aerobic prokaryotes might be similar to those of E. coli and mitochondria.
Nonparametric Dynamic Curve Monitoring

ABSTRACT Rapid sequential comparison between the longitudinal pattern of a given subject and a target pattern has become increasingly important in modern scientific research for detecting abnormal activities in many data-rich applications. This article focuses on this problem when observations are collected sequentially with uncorrelated or correlated noise involved. A dynamic monitoring procedure is developed after connecting the curve monitoring problem to curve comparison. Under the framework of generalized likelihood ratio testing, we suggest a new exponentially weighted moving average (EWMA) control chart that can accommodate unequally spaced design points. An adaptive parameter selection feature is built in the proposed control chart so that the chart can detect a wide range of longitudinal pattern shifts effectively. To furnish fast computation, recursive formulas are derived for computing the charting statistic. Numerical studies show that the proposed method can deliver a satisfactory performance, and it outperforms existing methods in various cases. An example from the semiconductor manufacturing industry is used for the illustration of its implementation. Supplementary materials for this article are available online.
A Mask Regional Convolutional Neural Network Model for Segmenting Real Time Traffic Images

This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captured by traffic cameras. The approach developed here comprises of image pre-processing, object detection followed by segmentation. Mask R-CNN model not only segments the image, but also surrounds the image with bounding boxes and assigns class names to the individual objects e.g. Car, Truck, Bus, Bicycle, Person etc. The model is trained with the annotated MS COCO Training dataset. To improve the performance of Mask R-CNN over noisy images, here pre-processing algorithms like Non Local Means (NLM) filter denoising and Median filter denoising are used. The testing is carried out on a subset of MS COCO Test dataset which comprises of only traffic images. The improved performance is demonstrated using parameters: increased correct object detections and corresponding confidence value, reduced incorrect object detections and corresponding confidence value, and an overall enhanced segment mask area accuracy.
Does evidence-based medicine training improve doctors’ knowledge, practice and patient outcomes? A systematic review of the evidence

Abstract Courses in Evidence-Based Medicine (EBM) for doctors have consistently demonstrated short-term improvements in knowledge. However, there is no strong evidence linking EBM training to changes in clinical practice or patient outcomes. This systematic review investigates whether EBM training leads to sustained improvements in doctors’ knowledge and practice behaviors that may also facilitate changes in patient outcomes and experiences. A literature search was undertaken in Ovid Medline, Ovid Embase, The Cochrane Library, ERIC and Scopus. Studies published from 1997 to 2016 that assessed outcomes of EBM educational interventions amongst doctors and used measures of knowledge, skills, attitudes, practice or patient outcomes were included. Fifteen studies were included in the analysis: four randomized controlled trials (RCTs), three non-RCTs, and eight before-after (longitudinal cohort) studies. Heterogeneity among studies prevented meaningful comparisons. Varying degrees of bias due to the use of subjective measures were identified, limiting study validity. Results showed that EBM interventions can improve short-term knowledge and skills, but there is little reliable evidence of changes in long-term knowledge, attitudes, and clinical practice. No study measured improvement in patient outcomes or experiences. EBM training for medical practitioners needs to incorporate measures of behavioral changes while incorporating patient outcomes and experience measures.
Achievable Secrecy Rate of Artificial Fast-Fading Techniques and Secret-Key Assisted Design for MIMO Wiretap Channels With Multiantenna Passive Eavesdropper

Artificial noise (AN) is an efficient way to enhance physical layer security of multiantenna wiretap channels with a passive eavesdropper (ED) whose channel state information is unavailable. However, the AN is known to be vulnerable to the eavesdropping attack with a large antenna array. To tackle the problem, the artificial fast fading (AFF) scheme has been developed to deliberately randomize the ED's effective channel so as to disrupt its channel estimation. Some recent progress has been made for the secrecy-rate performance of the AFF scheme in the multi-input single-output wiretap channels. However, it turns out that the performance is over-estimated. In this correspondence, we provide a comprehensive analysis on the secrecy-rate of the AFF scheme in general multi-input multi-output wiretap channels. Unfortunately, our analysis reveals a negative result that the conventional AFF is no better than the AN methods, without some pre-shared information among the legitimate parties. This is in fact in stark contrast to the previous observations. Motivated by our analysis, we propose a novel cross layer design by combining the AFF in the physical layer with the secret-keys in the upper layer crypto systems. Then, we mathematically prove that our method asymptotically nullifies the information leakage to the ED in the physical layer for any number of ED antennas even without the aid of the wiretap codes, while further enhancing the upper layer security, and thus provides an enhanced security over the layers. Finally, numerical results demonstrate the accuracy of our analysis and the efficiency of the proposed scheme.
Association between changes in the use of psychotropic drugs and behavioural and psychological symptoms of dementia over 1 year among long‐term care facility residents

Pharmacological treatments are often used in treating the behavioural and psychological symptoms of dementia (BPSD) in nursing homes, despite the fact that non‐pharmacological treatments are recommended as a first‐line treatment and can provide a suitable alternative. Because the course of BPSD is known to fluctuate depending on individual symptoms over time, the frequencies of drug use and BPSD, as well as their association, should be observed over a longer period. We investigated the association between the changes in psychotropic drug (PD) use and BPSD, focusing on the number of symptoms, severity, and care burden over 1 year among long‐term care facility residents.
A Selective Rectifier for RF Energy Harvesting Under Non-Stationary Propagation Conditions

In the field of rectifying antenna (rectenna) design, most of the proposed circuits are optimized for a fixed value of RF input power. However, this optimization does not take care to adjust the output characteristics of the rectenna to match environment propagation condition variability. This paper performs an evaluation of the performance of an RF/DC converter, dedicated to RF energy harvesting in complex environments. The random behavior of the amount of recoverable energy is taken into account by the selectivity of the conversion system. A judicious choice of the rectifying diode, as well as the rectifier topology, is made according to the level of the input power. The comparison criteria are the DC output voltage and the conversion efficiency. The obtained results show that the topology of a Single Series Diode (SSD) based on a Schottky diode SMS 7630 is the most efficient circuit for low input power levels below −45 dBm. Whereas for high power levels between 13 dBm and 18 dBm, it is preferable to use a Voltage Doubler (VD) topology with Schottky diode HSMS 2850. Based on Log-distance path loss propagation model, the circuit performance is evaluated according to the path loss exponent for different propagation environment.
Melatonin enhances atherosclerotic plaque stability by inducing prolyl-4-hydroxylase &agr;1 expression

Objective: Melatonin, an endogenous neurohormone secreted predominately by the pineal gland, has a variety of physiological functions. However, its protective role in atherosclerosis is not clear. In this study, we sought to investigate the potential effects of melatonin in modulating atherosclerotic plaque stability in apolipoprotein E knockout (ApoE-/-) mice. Method and results: Smooth muscle cells were treated with melatonin, which significantly increased mRNA and protein levels of a key intracellular enzyme essential for collagen maturation and secretion, prolyl-4-hydroxylase &agr;1 (P4H&agr;1). Mechanistically, melatonin increased Akt phosphorylation and transcriptional activation of specificity protein 1 (Sp1), which bound with the P4H&agr;1 promoter and then induced P4H&agr;1 expression. Pretreatment with either Akt inhibitor LY294002 or Sp1 inhibitor mithramycin A (MTM) could inhibit melatonin-induced P4H&agr;1 expression. Finally, atherosclerotic lesions were induced by placing a perivascular collar on the right common carotid artery of ApoE-/- mice, which were received with or without different doses of melatonin or MTM. High-dose melatonin enhanced atherosclerotic plaque stability in ApoE-/- mice in vivo by inducing the expression of P4H&agr;1, which was reversed by MTM. Conclusion: We propose that melatonin supplementation may provide a novel and promising approach to atherosclerosis treatment.
Physical activity in relation to irritable bowel syndrome among Iranian adults

Background Irritable bowel syndrome (IBS) is the most prevalent functional gastrointestinal disorder worldwide. Physical activity in relation to IBS has been investigated in few studies and data in this regard are conflicting. Aim To investigate the association between physical activity and IBS in a large sample of Iranian adults. Methods This cross-sectional study was done on 4763 Iranian adults in the framework of SEPAHAN (The Study on the Epidemiology of Psycho-Alimentary Health and Nutrition) project. The physical activity of study participants was assessed using the General Practice Physical Activity Questionnaire (GPPAQ). Using a validated self-administered modified Rome III questionnaire, functional gastrointestinal disorders including irritable bowel syndrome was assessed. Results The mean age of study participants was 36.5 years. Irritable bowel syndrome was prevalent among 21.5% of participants. Compared with physically active individuals (≥ 1 hour/wk), those with sedentary physical activity (<1 hour/wk) had 1.27 times greater probability of suffering from IBS (OR: 1.27, 95% CI: 1.08–1.49). However, this association was attenuated after adjusting for age, sex, cigarette smoking and medical history of colitis and diabetes. When the analysis was additionally adjusted for diet-related practices and body mass index (BMI), a non-significant association was found between sedentary physical activity and IBS (OR: 1.18, 95% CI: 0.98–1.41). Gender-stratified analysis revealed similar findings in women either before (OR: 1.29, 95% CI: 1.04–1.61) or after controlling for covariates (OR: 1.27, 95% CI: 0.99–1.62). In BMI-stratified analysis, a significant positive association was seen between sedentary physical activity and IBS among individuals with normal BMI (OR: 1.38, 95% CI: 1.07–1.79). Conclusion We found a significant positive association between sedentary physical activity and IBS, particularly among women and individuals of normal weight.
MALOC: A Fully Pipelined FPGA Accelerator for Convolutional Neural Networks With All Layers Mapped on Chip

Recently, field-programmable gate arrays (FPGAs) have been widely used in the implementations of hardware accelerator for convolutional neural networks (CNNs). However, most of these existing accelerators are designed in the same idea as their ASIC counterparts, in which all operations from different layers are mapped to the same hardware units and working in a multiplexed way. This manner does not take full advantage of reconfigurability and customizability of FPGAs, resulting in a certain degree of computational efficiency degradation. In this paper, we propose a new architecture for FPGA-based CNN accelerator that maps all the layers to their own on-chip units and working concurrently as a pipeline. A comprehensive mapping and optimizing methodology based on establishing roofline model oriented optimization model is proposed, which can achieve maximum resource utilization as well as optimal computational efficiency. Besides, to ease the programming burden, we propose a design framework which can provide a one-stop function for developers to generate the accelerator with our optimizing methodology. We evaluate our proposal by implementing different modern CNN models on Xilinx Zynq-7020 and Virtex-7 690t FPGA platforms. Experimental results show that our implementations can achieve a peak performance of 910.2 GOPS on Virtex-7 690t, and 36.36 GOP/s/W energy efficiency on Zynq-7020, which are superior to the previous approaches.
Demand Response Management for Profit Maximizing Energy Loads in Real-Time Electricity Market

In this paper, we consider the profit-maximizing demand response of an energy load in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy load, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon Markov decision process (MDP) problem, which requires extremely high computational complexity due to continuous state and action spaces. To tackle the high computational complexity, we propose a dual approximate approach that transforms the MDP problem into a linear programing problem by exploiting the threshold structure of the optimal solution. Then, a row-generation-based solution algorithm is proposed to solve the problem efficiently. We demonstrate through extensive simulations that the proposed method significantly reduces the computational complexity of the optimal MDP problem (linear versus exponential complexity), while incurring marginal performance loss. More interestingly, the proposed demand response strategy hits a triple win. It not only maximizes the profit of the energy load, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed quadratic approximation and improved row generation algorithm increases the energy load's profit by 55.9% and saves the bills of other utilities by 80.2% comparing with the benchmark algorithms.
Virus and CTL dynamics in the extrafollicular and follicular tissue compartments in SIV-infected macaques

Data from SIV-infected macaques indicate that virus-specific cytotoxic T lymphocytes (CTL) are mostly present in the extrafollicular (EF) compartment of the lymphoid tissue, with reduced homing to the follicular (F) site. This contributes to the majority of the virus being present in the follicle and represents a barrier to virus control. Using mathematical models, we investigate these dynamics. Two models are analyzed. The first assumes that CTL can only become stimulated and expand in the extra-follicular compartment, with migration accounting for the presence of CTL in the follicle. In the second model, follicular CTL can also undergo antigen-induced expansion. Consistent with experimental data, both models predict increased virus compartmentalization in the presence of stronger CTL responses and lower virus loads, and a more pronounced rise of extra-follicular compared to follicular virus during CD8 cell depletion experiments. The models, however, differ in other aspects. The follicular expansion model results in dynamics that promote the clearance of productive infection in the extrafollicular site, with any productively infected cells found being the result of immigration from the follicle. This is not observed in the model without follicular CTL expansion. The models further predict different consequences of introducing engineered, follicular-homing CTL, which has been proposed as a therapeutic means to improve virus control. Without follicular CTL expansion, this is predicted to result in a reduction of virus load in both compartments. The follicular CTL expansion model, however, makes the counter-intuitive prediction that addition of F-homing CTL not only results in a reduction of follicular virus load, but also in an increase in extrafollicular virus replication. These predictions remain to be experimentally tested, which will be relevant for distinguishing between models and for understanding how therapeutic introduction of F-homing CTL might impact the overall dynamics of the infection.