Genetic trade-offs between complex diseases and longevity.

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle-aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long-lived individuals. We further stratified PRSs into cell-type groups and significance-level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p-values (p ≤ 1x10-5 ) for SCZ and T2D were negatively correlated with longevity. While for the less significant SNPs (1x10-5  < p ≤ 0.05), their effects on disease and longevity were positively correlated. Overall, we identified genetically informed positive and negative factors for human longevity, gained more insights on the accumulation of disease risk alleles during evolution, and provided evidence for the theory of genetic trade-offs between complex diseases and longevity.

Distinct biological ages of organs and systems identified from a multi-omics study.

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

Long term optimal investment with regime switching: inflation, information and short sales.

Financial models are based on the standard assumptions of frictionless markets, complete information, no transaction costs and no taxes and borrowing and short selling without restrictions. Short-selling bans around the world after the global financial crisis and in several exchanges during the COVID 19 period, become more and more important. This paper bridges the gap by providing for the first time in the literature a model that accounting explicitly and simultaneously for inflation, information costs and short sales in the portfolio performance with regime switching. Our model can be used by portfolio managers to assess the impact of these market imperfections on portfolio decisions.

[Atrial fibrillation diagnosis algorithm based on improved convolutional neural network].

Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.

Graphene-covered transition metal halide molecules as efficient and durable electrocatalysts for oxygen reduction and evolution reactions.

Proton exchange fuel cells (PEFCs) are one of the most popular and promising energy conversion devices because of their highly stable and efficient membranes in acidic media, but there is a lack of durable non-noble metal electrocatalysts suitable for acidic environments. Herein, we designed a new type of electrocatalysts consisting of transition metal halide molecules covered by graphene sheets, which is supported by experiments. To rapidly screen the best catalysts from numerous candidate materials, the electronic structures, reaction free energies and overpotentials of those graphene-covered halide catalysts were studied by the first-principles calculations to predict the catalytic activities for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). An intrinsic descriptor, the electrostatic force induced by the metallic ions, was found to well describe the catalytic activities and provide a better understanding of the local electrical field effects on catalytic activities. The spin-down d-band center was also introduced to describe catalytic activities of the catalysts. The results demonstrate that the graphene-covered CrBr2 shows the best bifunctional catalytic activities for fuel cells while graphene-covered CoF2 could well facilitate H2O2 production. These catalysts are better than the best commercial noble metal catalysts (e.g., Pt and RuO2) in terms of overpotentials and activities. This work provides a theoretical base for rationally designing durable electrocatalysts with excellent catalytic activities.

Guiding Principles for Designing Highly Efficient Metal-Free Carbon Catalysts.

Carbon nanomaterials are promising metal-free catalysts for energy conversion and storage, but the catalysts are usually developed via traditional trial-and-error methods. To rationally design and accelerate the search for the highly efficient catalysts, it is necessary to establish design principles for the carbon-based catalysts. Here, theoretical analysis and material design of metal-free carbon nanomaterials as efficient photo-/electrocatalysts to facilitate the critical chemical reactions in clean and sustainable energy technologies are reviewed. These reactions include the oxygen reduction reaction in fuel cells, the oxygen evolution reaction in metal-air batteries, the iodine reduction reaction in dye-sensitized solar cells, the hydrogen evolution reaction in water splitting, and the carbon dioxide reduction in artificial photosynthesis. Basic catalytic principles, computationally guided design approaches and intrinsic descriptors, catalytic material design strategies, and future directions are discussed for the rational design and synthesis of highly efficient carbon-based catalysts for clean energy technologies.

Covalent Organic Framework Electrocatalysts for Clean Energy Conversion.

Covalent organic frameworks (COFs) are promising for catalysis, sensing, gas storage, adsorption, optoelectricity, etc. owning to the unprecedented combination of large surface area, high crystallinity, tunable pore size, and unique molecular architecture. Although COFs are in their initial research stage, progress has been made in the design and synthesis of COF-based electrocatalysis for the oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and CO2 reduction in energy conversion and fuel generation. Design principles are also established for some of the COF materials toward rational design and rapid screening of the best electrocatalysts for a specific application. Herein, the recent advances in the design and synthesis of COF-based catalysts for clean energy conversion and storage are presented. Future research directions and perspectives are also being discussed for the development of efficient COF-based electrocatalysts.