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Age‐associated obesity and muscle atrophy (sarcopenia) are intimately connected and are reciprocally regulated by adipose tissue and skeletal muscle dysfunction. During ageing, adipose inflammation leads to the redistribution of fat to the intra‐abdominal area (visceral fat) and fatty infiltrations in skeletal muscles, resulting in decreased overall strength and functionality. Lipids and their derivatives accumulate both within and between muscle cells, inducing mitochondrial dysfunction, disturbing β‐oxidation of fatty acids, and enhancing reactive oxygen species (ROS) production, leading to lipotoxicity and insulin resistance, as well as enhanced secretion of some pro‐inflammatory cytokines. In turn, these muscle‐secreted cytokines may exacerbate adipose tissue atrophy, support chronic low‐grade inflammation, and establish a vicious cycle of local hyperlipidaemia, insulin resistance, and inflammation that spreads systemically, thus promoting the development of sarcopenic obesity (SO). We call this the metabaging cycle. Patients with SO show an increased risk of systemic insulin resistance, systemic inflammation, associated chronic diseases, and the subsequent progression to full‐blown sarcopenia and even cachexia. Meanwhile in many cardiometabolic diseases, the ostensibly protective effect of obesity in extremely elderly subjects, also known as the ‘obesity paradox’, could possibly be explained by our theory that many elderly subjects with normal body mass index might actually harbour SO to various degrees, before it progresses to full‐blown severe sarcopenia. Our review outlines current knowledge concerning the possible chain of causation between sarcopenia and obesity, proposes a solution to the obesity paradox, and the role of fat mass in ageing.

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Background Ageing, chronic diseases, prolonged inactivity, and inadequate nutrition pose a severe threat to skeletal muscle health and function. To date, experimental evidence suggests that ageing‐related subclinical inflammation could be an important causative factor in sarcopenia. Although inflammatory signalling has been implicated in the pathogenesis of experimental animal models of sarcopenia, few studies have surveyed the clinical association between circulating factors and muscle mass in patients before and after lifestyle interventions. In this study, we evaluated whether proinflammatory cytokines are associated with the onset of sarcopenia, which circulating factors are associated with the severity of sarcopenia, and how these factors change after lifestyle interventions in sarcopenic elderly persons. Methods A total of 56 elderly subjects (age ≥ 60 years) with sarcopenia and 56 elderly non‐sarcopenic subjects, who met entry criteria and had given informed consent, were selected from the Peking Union Medical College Hospital multicentre prospective longitudinal sarcopenia study for testing relevant circulating factors. Thirty‐two elderly subjects from the sarcopenic cohort completed a 12 week intensive lifestyle intervention programme with whey supplements (30 g/day) and a personalized resistance training regimen. The levels of proinflammatory cytokines and metabolic hormones, pre‐intensive and post‐intensive lifestyle interventions, were measured. Results The sarcopenic group was significantly older (72.05 ± 6.54 years; P < 0.001), more likely to be inactive and female (57.1% of all sarcopenic patients), and had a higher prevalence of type 2 diabetes (16% higher risk). Compared with non‐sarcopenic subjects, serum interleukin (IL)‐6, IL‐18, tumour necrosis factor‐α (TNF‐α), TNF‐like weak inducer of apoptosis (TWEAK), and leptin were significantly higher, while insulin growth factor 1, insulin, and adiponectin were significantly lower in sarcopenic patients (all P < 0.05). Logistic regression analyses revealed that high levels of TNF‐α (>11.15 pg/mL) and TWEAK (>1276.48 pg/mL) were associated with a 7.6‐fold and 14.3‐fold increased risk of sarcopenia, respectively. After adjustment for confounding variables, high levels of TWEAK were still associated with a 13.4‐fold increased risk of sarcopenia. Intensive lifestyle interventions led to significant improvements in sarcopenic patients' muscle mass and serum profiles of TWEAK, TNF‐α, IL‐18, insulin, and adiponectin (all P < 0.05). Conclusions High levels of the inflammatory cytokines TWEAK and TNF‐α are associated with an increased risk of sarcopenia, while the metabolic hormones insulin growth factor 1, insulin, and adiponectin are associated with a decreased risk of sarcopenia in our Chinese patient cohort. Intensive lifestyle interventions could significantly improve muscle mass, reduce inflammation, and restore metabolic hormone levels in sarcopenic patients. This trial was registered at clinicaltrials.gov as NCT02873676.

GetOrganelle is a state-of-the-art toolkit to accurately assemble organelle genomes from whole genome sequencing data. It recruits organelle-associated reads using a modified “baiting and iterative mapping” approach, conducts de novo assembly, filters and disentangles the assembly graph, and produces all possible configurations of circular organelle genomes. For 50 published plant datasets, we are able to reassemble the circular plastomes from 47 datasets using GetOrganelle. GetOrganelle assemblies are more accurate than published and/or NOVOPlasty-reassembled plastomes as assessed by mapping. We also assemble complete mitochondrial genomes using GetOrganelle. GetOrganelle is freely released under a GPL-3 license ( https://github.com/Kinggerm/GetOrganelle ).

Aims To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. Method This was a retrospective study of perioperative medical data from patients undergoing non‐cardiac and non‐neurology surgery over 65 years old from January 2014 to August 2019. Forty‐six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC‐ROC), sensitivity, specificity, and precision. Results In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765–0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. Conclusions The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model. Six prediction models were constructed for POD using logistic regression, RF, AdaBoost, XGBoost, GBM, and stacking ensemble learning based on retrospective analysis of a large sample dataset. The logistic regression model had the same AUC(0.78) with the RF, and performed better than the machine learning models because of its better sensitivity, fewer variables, and easier interpretability.

Error correction is a significant step in postprocessing of continuous-variable quantum key distribution system, which is used to make two distant legitimate parties share identical corrected keys. We propose an experiment demonstration of high speed error correction with multi-edge type low-density parity check (MET-LDPC) codes based on graphic processing unit (GPU). GPU supports to calculate the messages of MET-LDPC codes simultaneously and decode multiple codewords in parallel. We optimize the memory structure of parity check matrix and the belief propagation decoding algorithm to reduce computational complexity. Our results show that GPU-based decoding algorithm greatly improves the error correction speed. For the three typical code rate, i.e., 0.1, 0.05 and 0.02, when the block length is 10 6 and the iteration number are 100, 150 and 200, the average error correction speed can be respectively achieved to 30.39 Mbits/s (over three times faster than previous demonstrations), 21.23 Mbits/s and 16.41 Mbits/s with 64 codewords decoding in parallel, which supports high-speed real-time continuous-variable quantum key distribution system.

Reactive oxygen species (ROS) production in phagocytes is a major defense mechanism against pathogens. However, the cellular self-protective mechanism against such potential damage from oxidative stress remains unclear. Here we show that the kinases Mst1 and Mst2 (Mst1/2) sense ROS and maintain cellular redox balance by modulating the stability of antioxidant transcription factor Nrf2. Site-specific ROS release recruits Mst1/2 from the cytosol to the phagosomal or mitochondrial membrane, with ROS subsequently activating Mst1/2 to phosphorylate kelch like ECH associated protein 1 (Keap1) and prevent Keap1 polymerization, thereby blocking Nrf2 ubiquitination and degradation to protect cells against oxidative damage. Treatment with the antioxidant N-acetylcysteine disrupts ROS-induced interaction of Mst1/2 with phagosomes or mitochondria, and thereby diminishes the Mst-Nrf2 signal. Consistently, loss of Mst1/2 results in increased oxidative injury, phagocyte ageing and death. Thus, our results identify the Mst-Nrf2 axis as an important ROS-sensing and antioxidant mechanism during an antimicrobial response. Immune cells produce reactive oxygen species (ROS) to eliminate pathogens, but cell-spontaneous death and ageing may also be induced. Here the authors show that, upon sensing ROS, Mst1/2 kinases modulate the activity of Nrf2 transcription factor and downstream genetic programs to protect mouse macrophages from death and ageing.

Intrauterine adhesion refers to endometrial repair disorders which are usually caused by uterine injury and may lead to a series of complications such as abnormal menstrual bleeding, recurrent abortion and secondary infertility. At present, therapeutic approaches to intrauterine adhesion are limited due to the lack of effective methods to promote regeneration following severe endometrial injury. Therefore, to develop new methods to prevent endometrial injury and intrauterine adhesion has become an urgent need. For severely damaged endometrium, the loss of stem cells in the endometrium may affect its regeneration. This article aimed to discuss the characteristics of various stem cells and their applications for uterine tissue regeneration.

High repetition frequency (HRF) ultrashort pulse fiber laser has been widely used in laser cold processing. The technical solutions such as short cavity length fiber laser have been proposed to achieve HRF ultrashort pulse output recently. However, the application of material-based saturable absorbers in this field has been astricted due to the low modulation depth, low damage resistance threshold, and high saturation fluence. Here, we designed a one-dimensional asymmetric photonic crystal with defect layer (1D-APCDL) as a novel saturable absorber, where the defect layer is Bi Sb Te with high modulation depth. The harmonic pulse with 3.82 GHz repetition frequency is achieved at the wavelength of 1562 nm, which is the highest repetition frequency of the topological insulator-based ring fiber laser so far to the best of our knowledge. The research provides a new saturable absorber solution, and provides a new idea for the application of material-based nonlinear optical chip in high-repetition frequency ultrashort pulse fiber lasers.

HighlightsRecent progress of active materials in supercapacitors synthesized by electrochemical techniques is reviewed.Electrochemically synthesized nanostructures of various dimensions, compositions, and electrochemical properties are discussed.The advantages and challenges of electrochemical technologies in preparing nano-/microstructured materials for electrochemical energy storage devices are summarized.The article reviews the recent progress of electrochemical techniques on synthesizing nano-/microstructures as supercapacitor electrodes. With a history of more than a century, electrochemical techniques have evolved from metal plating since their inception to versatile synthesis tools for electrochemically active materials of diverse morphologies, compositions, and functions. The review begins with tutorials on the operating mechanisms of five commonly used electrochemical techniques, including cyclic voltammetry, potentiostatic deposition, galvanostatic deposition, pulse deposition, and electrophoretic deposition, followed by thorough surveys of the nano-/microstructured materials synthesized electrochemically. Specifically, representative synthesis mechanisms and the state-of-the-art electrochemical performances of exfoliated graphene, conducting polymers, metal oxides, metal sulfides, and their composites are surveyed. The article concludes with summaries of the unique merits, potential challenges, and associated opportunities of electrochemical synthesis techniques for electrode materials in supercapacitors.