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.     

净水水处理剂中有害杂质在水中残留物分析与风险评估

与欧盟和美国标准相比,我国硫酸铝和硫酸铵标准缺少对水处理剂中有害杂质镍、锑、硒、银、吡啶、氰化物、六价铬的检测;次氯酸钠缺少对水处理剂中有害杂质汞、铬、镉、铁、镍、锑、硒、银、六价铬的检测。结合某市2019年上半年13个水厂出厂水和原水的检测数据,剔除了5项始终低于检出限的有害杂质指标,以及2项出厂水浓度低于原水浓度的有害杂质指标。建议该市硫酸铝和硫酸铵的检测项目可以在原有国标基础上增加锑、硒;次氯酸钠可以增加汞、锑、硒。     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net

The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing.     

Prediction of rock mass P wave velocity using blasthole drilling information

In this paper, the development of the models for the prediction of rock mass P wave velocity is presented. For model development, the database of 53 cases including widely used and recorded drilling parameters and P wave velocity was constructed from the field studies conducted in 13 open pit lignite mines. Both conventional linear, non-linear multiple regression and Adaptive Neuro Fuzzy Inference System (ANFIS) were used for model development. Prediction performance indicators showed that ANFIS model presented the best performance and it can successfully be used for the preliminary prediction of P wave velocities of rock masses.     

Quantifying the limits of CAR T-cell delivery in mice and men

CAR (Chimeric Antigen Receptor) T cells have demonstrated clinical success for the treatment of multiple lymphomas and leukaemias, but not for various solid tumours, despite promising data from murine models. Lower effective CAR T-cell delivery rates to human solid tumours compared to haematological malignancies in humans and solid tumours in mice might partially explain these divergent outcomes. We used anatomical and physiological data for human and rodent circulatory systems to calculate the typical perfusion of healthy and tumour tissues, and estimated the upper limits of immune cell delivery rates across different organs, tumour types and species. Estimated maximum delivery rates were up to 10 000-fold greater in mice than humans yet reported CAR T-cell doses are typically only 10–100-fold lower in mice, suggesting that the effective delivery rates of CAR T cells into tumours in clinical trials are far lower than in corresponding mouse models. Estimated delivery rates were found to be consistent with published positron emission tomography data. Results suggest that higher effective human doses may be needed to drive efficacy comparable to mouse solid tumour models, and that lower doses should be tested in mice. We posit that quantitation of species and organ-specific delivery and homing of engineered T cells will be key to unlocking their potential for solid tumours.     

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。     

防振条约束失效对蒸汽发生器传热管面内流弹失稳的影响研究

根据防振条布置以及面内支承连续失效个数，将防振条面内约束失效分析划分为多种工况，分析了不同工况下面内约束失效对传热管面内模态的影响，采用各位置阻尼在振型函数上进行加权平均的方法计算了各阶模态的阻尼比，进而研究了防振条面内不同约束对传热管面内流弹失稳的影响。分析结果表明，随着面内支承连续失效位置的增多，弯管段面内首阶模态频率不断降低，出现在弯管段的振型越加明显；弯管段面内首阶模态不一定是最大流弹失稳比值出现的模态，最大流弹失稳比值出现模态的振型几乎都出现在弯管段；随着面内支承连续失效位置的增多，面内流弹失稳比值不断增大，当连续3个及以上防振条面内约束失效时，将出现流弹失稳现象。     

Type II fuzzy set-based data analytics to explore amino acid associations in protein sequences of Swine Influenza Virus

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset.