Skeletal Muscle Health and Cognitive Function: A Narrative Review

Sarcopenia is the loss of skeletal muscle mass and function with advancing age. It involves both complex genetic and modifiable risk factors, such as lack of exercise, malnutrition and reduced neurological drive. Cognitive decline refers to diminished or impaired mental and/or intellectual functioning. Contracting skeletal muscle is a major source of neurotrophic factors, including brain-derived neurotrophic factor, which regulate synapses in the brain. Furthermore, skeletal muscle activity has important immune and redox effects that modify brain function and reduce muscle catabolism. The identification of common risk factors and underlying mechanisms for sarcopenia and cognition may allow the development of targeted interventions that slow or reverse sarcopenia and also certain forms of cognitive decline. However, the links between cognition and skeletal muscle have not been elucidated fully. This review provides a critical appraisal of the literature on the relationship between skeletal muscle health and cognition. The literature suggests that sarcopenia and cognitive decline share pathophysiological pathways. Ageing plays a role in both skeletal muscle deterioration and cognitive decline. Furthermore, lifestyle risk factors, such as physical inactivity, poor diet and smoking, are common to both disorders, so their potential role in the muscle–brain relationship warrants investigation.     

Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

A terrain risk assessment method for military surveillance applications for mobile assets

This study proposes an analytical and flexible terrain risk assessment method for military surveillance applications for mobile assets. Considering the risk as the degree of possibility of insurgent presence, the assessment method offers an efficient evaluation of risk in the surrounding terrain for military combat operating posts or observation posts. The method is designed for unmanned aerial vehicles as the surveillance assets of choice to improve the effectiveness of their use. Starting with the area map and geographical data, the target terrain is first digitized for space representation. Then the data of nine geographical parameters are used to formulate five contributing risk factors. These factors are incorporated in an analytical framework to generate a composite map with risk scores that reveal the potential high-risk spots in the terrain. The proposed method is also applied to a real-life case study of COP Kahler in Afghanistan, which was a target for insurgent attacks in 2008. The results confirm that when evaluated with the developed method, the region that the insurgents used to approach COP Kahler has high concentration of high-risk cells.     

PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees

The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates.     

Creating formative feedback spaces in large lectures

Large lectures are the predominant way of teaching first-year students at universities in Norway. However, this forum for education is seldom discussed as a context for a formative feedback practice. The purpose of this sequential mixed methods study was to address whether and how a student-response system can open for a formative feedback practice in lectures and thereby support students' ability to monitor their own learning, as well as supply insight into how students engage with the feedback in their course work. The context for the study was large lectures (150–200 students) in a qualitative method course for first-year psychology students. Findings from the survey (n = 149) showed a positive correlation between the extent to which students report that they use clickers to monitor their own learning, and the extent to which they report that they used the feedback in their own course work. However, findings indicate that students valued the process of monitoring their own learning during the lectures to a greater extent than they actually used the feedback in their course work. Findings from interviews (n = 6) illustrated various ways students applied feedback in their course work.     

基于灾度辨识的钢闸门巡视检查研究

巡视检查是对钢闸门要点部位开展定期、定点的信息收集,并对警兆信息做出快速辨识和评估的重要内容。应用灾度辨识的警兆信息评估方法,综合钢闸门巡视检查的各类要素信息,明确工程最大隶属的灾度等级(Ⅰ～Ⅴ级),可作为工程日常巡视检查决策的重要软技术手段。     

Evaluation of fumonisin exposure by determination of fumonisin B1 in human hair and in Brazilian corn products

The aim of this study was to evaluate the dietary exposure to fumonisin B₁ (FB₁) through determination of residual FB₁ in hair and corn products consumed by 56 volunteers from Pirassununga and Erval Velho, Brazil. Data from FB₁ analyses in corn products and a Food Frequency Questionnaire (FFQ) were used for estimating the mean probable daily intake (PDI_M) for FB₁. FB₁ was detected in 4 human hair samples (7.2%), at a mean level of 21.3 ± 12.1 ng g⁻¹. The mean FB₁ level found in corn products was 360.4 ± 555.1 μg kg⁻¹. The PDI_M value of FB₁ in volunteers was 159 ± 47 ng kg⁻¹ body weight day⁻¹ which represents 7.9% of the provisional maximum tolerable daily intake (PMTDI) recommended for fumonisins. The FB₁ levels found in human hair samples from each volunteer were associated with their individual PDI of FB₁, indicating that exposure to FB₁ in the sample studied do not represent a health concern. This is the first report on the incidence of FB₁ in individual human hair in Brazil.     

腹腔手术后重症患者心肌损伤的危险因素

目的探讨腹腔手术后重症患者心肌损伤的发生情况及可能的危险因素。方法回顾性分析北京大学人民医院2017年1月至2019年1月腹腔手术后重症患者的一般临床资料及心肌损伤情况,收集并观察基础病史、术中(手术时间、是否急诊手术、术中出血>800 ml和术中低血压等)及术后指标(改良氧合指数、血乳酸、急性肾损伤和术后24 h内使用升压药情况等)。根据术后是否发生心肌损伤,将患者分为心肌损伤组和非心肌损伤组,采用Logistic回归分析腹腔手术后重症患者心肌损伤的危险因素。结果在纳入的803例腹腔手术后重症患者中,心肌损伤发生率为17. 2%(138/803),而急性心肌梗死发生率仅为0. 9%(7/803)。单因素分析显示,慢性肾功能不全病史、手术时间、急诊手术、术中低血压、术后24 h内使用升压药、高APACHEⅡ评分及术后即刻急性肾损伤与术后重症患者心肌损伤相关(P<0. 05)。多因素回归分析显示,急诊手术(OR=3. 14,95%CI:1. 76～5. 60,P<0. 001)、术后24 h内使用升压药(OR=2. 26,95%CI:1. 23～4. 15,P=0. 008)、APACHEⅡ评分(OR=1. 05,95%CI:1. 01～1. 09,P=0. 008)和术后急性肾损伤(OR=3. 18,95%CI:1. 78～5. 69,P <0. 001)与腹腔手术后重症患者发生心肌损伤独立相关。结论重症患者腹腔手术后心肌损伤发生率高,急诊手术、术后24 h内使用升压药、高APACHEⅡ评分和术后急性肾损伤是导致腹腔手术后重症患者发生心肌损伤的独立危险因素。     

网络安全等级保护测评中测评结论的度量方法优化

文章分析了网络安全等级保护2.0时期国家标准的新变化对等级测评结论可能产生的影响,并用实际案例和数据论述了以往描述的基于测评指标和基于测评对象的定量分析方法存在的局限性。根据网络安全等级保护国家标准结构和内容的新特点,结合新的等级测评结论表述方法,文章提出了调整和优化定量计算产生等级测评结论的思路,给出了缺陷扣分的原理和缺陷扣分的定量计算方法,并比较了各种定量计算方法在计算结果上的差异,提出了适合新标准的测评结论定量计算公式。     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.