Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstructive Sleep Apne a Risk Is Associated With Severity of Metabolic Syndrome A Secondary Analysis of the 2015–2018 National Health and Nutrition Examination Survey

Assistant Professor, School of Nursing, University of Pittsburgh, Pennsylvania. Kyeongra Yang, PhD, MPH, RN Associate Professor, School of Nursing, Rutgers, The State University of New Jersey, Newark. Paul W. Scott, PhD Assistant Professor, School of Nursing, University of Pittsburgh, Pennsylvania. Eileen R. Chasens, PhD, RN, FAAN Professor, School of Nursing, University of Pittsburgh, Pennsylvania. Cassandra Godzik, PhD, APRN Postdoctoral Research Fellow, Department of Psychiatry, Dartmouth College and Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire. Faith S. Luyster, PhD Assistant Professor, School of Nursing, University of Pittsburgh, Pennsylvania. Christopher C. Imes, PhD, RN Assistant Professor, School of Nursing, University of Pittsburgh, Pennsylvania. C.G. ha Fellows J.K. has Blood I The Na analyse analyse All auth article. The au Ethics A The Na analyse from pa were u Corres Jacob K St, Ste DOI: 10
FINANCIAL STABILITY AND THE DESIGN OF MONETARY POLICY 1

This paper builds upon the existing empirical literature on the factors behind financial stability, focusing on the role of monetary policy design. In particular, it analyzes a sample of 79 countries in the period 1970 to 1999 to evaluate the effect of the choice of the central bank objectives and the monetary policy strategy on financial stability. We find that focusing the central bank objectives on price stability reduces the likelihood of a banking crisis. This result is robust, in general, to several model specifications and groups of countries. The results are less clear-cut for the monetary policy strategy although for some model specifications, in particular for the group of countries in transition, the choice of an exchange rate-based strategy appears to reduce the likelihood of a banking crisis. Finally, locating regulatory and supervisory responsibilities at the central bank seems to reduce the likelihood of a banking crisis when controlling for this variable.
PGPM_A_361809 743..763

1Key Laboratory of Molecular Mechanism and Intervention Research for Plateau Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China; 2School of Basic Medical Sciences, Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China; 3Department of Emergency, The Affiliated Hospital of Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China; 4Department of Clinical Laboratory, The Affiliated Hospital of Xizang Minzu University, Xianyang, Shaanxi, 712082, People’s Republic of China
Deep Supervised Cross-modal Hashing for Ship Image Retrieval

The retrieval of multimodal ship images obtained by remote sensing satellites is an important content of remote sensing data analysis, which is of great significance to improve the ability of marine monitoring. In this paper, We propose a novel cross-modal ship image retrieval method, called Deep Supervised Cross-modal Hashing(DSCMH). It consists of a feature learning part and a hash learning part used for feature extraction and hash code generation separately, both two parts have modality-invariant constraints to keep the cross-modal invariability, and the label information is also brought to supervise the above process. Furthermore, we design a class attention module based on the cross-modal class center to strengthen class discrimination. The experiment results show that the proposed method can effectively improve the cross-modal retrieval accuracy of ship images and is better than several state-of-the-art methods.
Real-time pulse shape discrimination for neutron scattering science using EJ-270

A detector for neutron scattering applications utilizing the 6Li loaded pulse shape discrimination scintillator EJ-270 was developed. The detector utilises a pulse shape discrimination algorithm developed for high-count rate applications implemented in FPGA to enable real time operation. The implemented algorithm we call Tail Sum was specifically developed for high-rate thermal neutron detection and implemented into a 2 channel 500 MSps 14-bit ADC with a Xilinx Kintex 7 K325T FPGA. The detector consisted of a 48 mm diameter × 15 mm thick cylinder of EJ-270 mounted on a Hamamatsu R6231-100 PMT and an E1198-2 base at -1000 V. The performance of the system was evaluated using a moderated 241Am-Be mixed radionuclide source, a figure of merit of 1.605 ± 0.007 was measured. The γ-ray sensitivity of the system was measured using a 60Co source and found to be 3.23 × 10-6 ± 7 × 10-8. The high neutron count rate capability of the system was tested on the Emma instrument at the ISIS pulsed neutron and muon source based at the Rutherford Appleton Laboratory, a peak count rate of 5 × 105 neutrons per second was measured on a single channel and was found to have no statistically significant deviation from the expected count-rate.
Neuromorphic computing for attitude estimation onboard quadrotors

Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, battery autonomy, available sensors, computing resources, processing time, etc), and particularly in aerial vehicles, severely hamper the performance of fully-autonomous on-board control, including sensor processing and state estimation. In this work, we propose a spiking neural network capable of estimating the pitch and roll angles of a quadrotor in highly dynamic movements from six-degree of freedom inertial measurement unit data. With only 150 neurons and a limited training dataset obtained using a quadrotor in a real world setup, the network shows competitive results as compared to state-of-the-art, non-neuromorphic attitude estimators. The proposed architecture was successfully tested on the Loihi neuromorphic processor on-board a quadrotor to estimate the attitude when flying. Our results show the robustness of neuromorphic attitude estimation and pave the way toward energy-efficient, fully autonomous control of quadrotors with dedicated neuromorphic computing systems.
Coherent manipulation of perfect optical vortex via inelastic four-wave mixing in a cold five-level atomic system

We propose an efficient scheme to manipulate the perfect optical vortex (POV) beam in a cold atomic ensemble with a five-level configuration. The orbital angular momentum can be completely transferred from a POV control beam to the generated mixing field via inelastic four-wave mixing (IFWM) process. It is found that the intensity and helical phase of the generated vortex IFWM field can be coherently controlled by adjusting the relevant parameters, such as the atomic population, and the intensity and detuning of the pumping field. The obtained results can be reasonably explained via the combination of inherent and modal dispersion relation. Our result may find potential applications in POV-based large-particle manipulation and optical communication.
Cloud Resource Scheduling Method based on Markov Process and the Cuckoo Search

With the rapid development of cloud computing, the computing reliability of cloud servers has become an important research area. To improve the load balancing capability of cloud servers, an autonomous adaptive architecture cloud computing framework based on software-defined networking and network function virtualization were proposed. And a cloud resource scheduling method based on the Markov process model and cuckoo search algorithm to realize the load balancing of cloud nodes was designed. The cloud platform verified by the experiment shows that the method can effectively guarantee the service reliability of cloud computing.
Watersaving Cleaning Processing of Sheep Wool and Eco-friendly Extraction of Lanolin

Sheep wool, an important natural raw material for textile industry. This article propose a watersaving sheep wool cleaning processing. For each kilogram of wool, containing about 35% impurities, the water loss in washing is as low as 2.5 liters. Wool washing sewage with high content of COD (chemical oxygen demand), up to 20-30 g/L, is treated by neutral detergent and then a solid hydroxide (Calcium Hydroxide or Alum) with cationic polyacrylamide (CPAM) in order to have a fast flocculation of solid waste. The COD content in wastewater can be as low as 352 mg/L. On the other hand, solid matter contains a large amount of high-value chemical oils, collectively known as lanolin, by extraction of solid matter in hot alcoholic solution (85% Ethanol,15% methanol) at 60 degrees Celsius. About 40 g of lanolin crude product is obtained.
Energy Efficient Operation Optimization of Building Air-conditioners via Simulator-assisted Asynchronous Reinforcement Learning

At present, buildings are the major energy consumers in most developed and developing countries. Energy efficiency is one of the primary objectives of today’s building projects, especially in the context of carbon-peak and carbon-neutral. In buildings, air-conditioning systems account for nearly half of total building energy consumption. Optimal control and operation can significantly improve air-conditioning’s energy efficiency but the key challenge is the complex dynamics of air-conditioning systems. This study focuses the energy efficient operation of air-conditioners in office buildings, and proposes a simulation-assisted reinforcement learning (RL) method to develop energy efficient operation strategies. This study uses a whole building energy simulation model to build the environmental simulator for the air-conditioning system, and uses asynchronous RL algorithm to learn energy efficient operation strategies. Energy saving and Setpoint Not Met is used as the optimization objective as well as the criteria for evaluating the performance of the RL operation strategies. A validation simulator with varied weather conditions is also built to validate the robustness of reinforcement learning. The results show that, compared to common rule-based control strategy, 16.1% energy saving with better thermal comfort can be achieved by the RL operation strategy. In addition, the results also show the RL operation strategy has a certain level of robustness.
A Fully Distributed Hierarchical Control Framework for Coordinated Operation of DERs in Active Distribution Power Networks

In order to effectively accommodate large-scale distributed energy resources (DERs) in active distribution power networks, this paper proposes a fully distributed hierarchical framework to economically manage multiple DERs within three layers: the tertiary, the secondary generation regulation layer, and the primary frequency droop control layer. In the tertiary layer, a fully distributed dispatch strategy is designed to optimally allocate the active power base point of DERs in a center-free manner, with total generation and load imbalance locally estimated by individual DER using the discrete finite-time consensus algorithm. In the secondary layer, a distributed fair generation correction strategy is designed based on power sharing ratio to timely counterbalance the load deviations on top of the tertiary layer. In the primary layer, a droop controller is presented for DER, battery energy storage and controllable load to maintain satisfactory system frequency performance. The proposed hierarchical framework is a fully distributed generation coordinating approach, which could optimally control active power outputs of multiple DERs with the minimal cost while ensuring satisfactory system frequency, and is also robust to DERs’ uncertainties. Case studies have demonstrated the effectiveness of proposed fully distributed hierarchical framework for optimally coordinating DERs power outputs and regulating system frequency.
Machine learning with validation to detect diabetic microvascular complications using clinical and metabolomics data

AIMS: Using machine learning integrated with clinical and metabolomic data to identify biomarkers associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR), and to improve the performance of DKD/DR detection models beyond traditional risk factors. METHODS: We examined a population-based cross-sectional sample of 2,772 adults with type 1 or type 2 diabetes from Singapore Epidemiology of Eye Diseases study (SEED, 2004-2011). LASSO logistic regression (LASSO) and gradient boosting decision tree (GBDT) were used to select markers of prevalent DKD (defined as an eGFR < 60ml/min/1.73m^2) and prevalent DR (defined as an ETDRS severity level >= 20) from an expanded set of 19 established risk factors and 220 NMR-quantified circulating metabolites. Risk assessment models were developed based on the variable selection results and externally validated in UK Biobank (n=5,843, 2007-2010). Model performance (AUC with 95% CI, sensitivity, and specificity) of machine learning was compared to that of traditional logistic regression adjusted for age, gender, diabetes duration, HbA1c%, systolic BP, and BMI. RESULTS: SEED participants had a median age of 61.7 years, with 49.1% female, 20.2% having DKD, and 25.4% having DR. UK Biobank participants had a median age of 61.0 years, with 39.2% female, 6.4% having DKD, and 5.7% having DR. Both algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with CVD, hypertension medication, and three metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density-lipoprotein); While DR was additionally associated with HbA1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional logistic regression in both internal (AUC: 0.832-0.838 vs. 0.743 for DKD, and 0.779-0.790 vs. 0.764 for DR) and external validation (AUC: 0.737-0.790 vs. 0.692 for DKD, and 0.778 vs. 0.760 for DR). CONCLUSIONS: Machine-learned biomarkers suggested insulin resistance to be a primary factor associated with diabetic microvascular complications. Integrating machine learning with biomedical big data enabled biomarker discovery from a wide range of correlated variables, which may facilitate our understanding of the disease mechanisms and improve disease screening.
Clustering of adverse health and educational outcomes in adolescence following early childhood poverty: implications for UK's 'levelling up' agenda

Background Early childhood poverty is associated with poorer health and educational outcomes in adolescence. However, there is limited evidence about the clustering of these adverse outcomes by income group. Methods We analysed five outcomes at age 17 known to limit life chances - psychological distress, self-assessed ill health, smoking, obesity, and poor educational achievement - using data from the longitudinal UK Millennium Cohort Study (N=15,245). We compared how single and multiple outcomes were distributed across quintiles of household income in early childhood (0-5 years) and modelled the maximum potential benefit of tackling the income gradient in these outcomes. Findings Children from the poorest households were 12.7(95% CI 6.4-25.1) times more likely than those from the richest to experience four or five adverse adolescent outcomes, with poor educational achievement and smoking showing the largest single risk ratios - 4.6(95% CI: 4.2-5.0) and 3.6(95% CI 3.0-4.2), respectively. We modelled hypothetical absolute and relative poverty elimination scenarios, as well as an income inequality elimination scenario, and found these would yield maximum reductions in multiple adolescent adversity of 5%, 30%, and 80% respectively. Interpretation Early childhood poverty is more strongly correlated with multiple adolescent adversity than any single adverse outcome. Reducing absolute poverty alone is not sufficient to eliminate the life-long burden of multiple adversity, which disproportionately impacts children across the bottom three-fifths of the income distribution. An ambitious levelling up agenda needs co-ordinated multi-agency action to tackle the complex interacting factors generating the steep social gradient in multiple adolescent adversity.
Common Genetic Variation Important in Early Subcortical Brain Development

Recent genome-wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with subcortical brain volumes. These studies have been undertaken in largely adult cohorts. To better understand the role of genetic variability in foetal and perinatal brain development, we investigate how common genetic variation affects subcortical brain development in a cohort of 208 term-born infants from the Developing Human Connectome Project. We examine six SNPs, that have shown robust associations with subcortical brain volumes in adult studies and ask if these associations exist at birth. We then investigate whether genome-wide polygenic scores (GPSs) for adult subcortical brain volumes are predictive of the corresponding neonatal brain volume. Finally, we explore the relationship between GPSs for psychiatric disorders and subcortical brain volume at birth. We find the association between SNP rs945270 and putamen volume, seen in adults, is present at birth (p=3.67x10-3, {beta}=0.13, SE=0.04). The associations between SNP rs61921502 and hippocampal volume and SNP rs11111090 and brainstem volume are also nominally present in our neonatal cohort. We show that neonatal hippocampal, brainstem, putamen and thalamic volume are all significantly associated with the GPSs for their corresponding volume in adults. Finally, we find that GPSs for five psychiatric disorders and a cross-disorder score are not significantly predictive of subcortical brain volumes or total brain volume at birth. Our results indicate that SNPs important in shaping adult subcortical brain volume are also significant in foetal and perinatal brain development.
Self-modulation of motor cortex activity after stroke: a randomized controlled trial

Abstract Real-time functional MRI neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders that are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial (ClinicalTrials.gov: NCT03775915) in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n = 12) or Sham (n = 12) neurofeedback. Assessments of brain structure, brain function and measures of upper-limb function were carried out before and 1 week after neurofeedback training. Additionally, measures of upper-limb function were repeated 1 month after neurofeedback training. Primary outcome measures were (i) changes in lateralization of motor cortex activity during movements of the stroke-affected hand throughout neurofeedback training days; and (ii) changes in motor performance of the affected limb on the Jebsen Taylor Test (JTT). Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within (P = 0.019), but not across, training days. There was no group effect on the primary behavioural outcome measure, which was average JTT performance across all subtasks (P = 0.116). Secondary analysis found improvements in the performance of the gross motor subtasks of the JTT in the Real neurofeedback group compared to Sham (P = 0.010). However, there were no improvements on the Action Research Arm Test or the Upper Extremity Fugl–Meyer score (both P > 0.5). Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1 week after neurofeedback training (P = 0.008), indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract were positively correlated with participants neurofeedback performance (P = 0.002). Therefore, here we demonstrate that chronic stroke survivors are able to use functional MRI neurofeedback to self-modulate motor cortex activity in comparison to a Sham control, and that training is associated with improvements in gross hand motor performance and with white matter structural changes.
Precision Surgery Guided by Intraoperative Molecular Imaging

Intraoperative molecular imaging (IMI) has recently emerged as an important tool in the armamentarium of surgical oncologists. IMI allows real-time assessment of oncologic resection quality, margin assessment, and occult disease detection during real-time surgery. Numerous tracers have now been developed for use in IMI-guided tissue sampling. Fluorochromes localize to the tumor by taking advantage of their disorganized capillary milieu, overexpressed receptors, or upregulated enzymes. Although fluorescent tracers can suffer from issues of autofluorescence and lack of depth penetration, these challenges are being addressed through hybrid radioactive/fluorescent tracers and new tracers that fluoresce in the near-infrared (NIR-II [wavelength > 1,000 nm]) range. IMI is already being used to treat numerous cancers, with demonstrated improvement in cancer recurrence and patient outcomes without incurring significant burden on either clinicians or patients. In this comprehensive review, we discuss history, mechanism, current oncologic applications, and future directions of IMI-guided optical biopsy.
Psychological interventions to prevent relapse in anxiety and depression: A systematic review and meta-analysis

Objectives The aim of this review is to establish the effectiveness of psychological relapse prevention interventions, as stand-alone interventions and in combination with maintenance antidepressant treatment (M-ADM) or antidepressant medication (ADM) discontinuation for patients with remitted anxiety disorders or major depressive disorders (MDD). Methods A systematic review and a meta-analysis were conducted. A literature search was conducted in PubMed, PsycINFO and Embase for randomised controlled trials (RCTs) comparing psychological relapse prevention interventions to treatment as usual (TAU), with the proportion of relapse/recurrence and/or time to relapse/recurrence as outcome measure. Results Thirty-six RCTs were included. During a 24-month period, psychological interventions significantly reduced risk of relapse/recurrence for patients with remitted MDD (RR 0.76, 95% CI: 0.68–0.86, p<0.001). This effect persisted with longer follow-up periods, although these results were less robust. Also, psychological interventions combined with M-ADM significantly reduced relapse during a 24-month period (RR 0.76, 95% CI: 0.62–0.94, p = 0.010), but this effect was not significant for longer follow-up periods. No meta-analysis could be performed on relapse prevention in anxiety disorders, as only two studies focused on relapse prevention in anxiety disorders. Conclusions In patients with remitted MDD, psychological relapse prevention interventions substantially reduce risk of relapse/recurrence. It is recommended to offer these interventions to remitted MDD patients. Studies on anxiety disorders are needed. Systematic review registration number PROSPERO 2018: CRD42018103142.