No abstract

a b s t r a c tThe existence of peripheral oscillators has been shown, and they are critically important for organizing the metabolism of the whole body. Here we show that mice deficient in mPer2 markedly increase circulatory levels of insulin compared with wild type mice. Insulin secretion was more effectively stimulated by glucose, and alloxan, a glucose analogue, induced more severe hyperglycemia in mPer2-deficient mice. Hepatic insulin degrading enzyme (Ide) displayed an obvious day and night rhythm, which was impaired in mPer2-deficient mice, leading to a decrease in insulin clearance. Deficiency in mPer2 caused increased Clock expression and decreased expression of Mkp1 and Ide1, possibly underlying the observed phenotypes and suggesting that mPer2 plays a role in regulation of circulating insulin levels.

The photoelectrochemical redox battery (PRB) has been regarded as an alternative candidate for large‐scale solar energy capture, conversion, and storage as it combines the superior advantages of photoelectrochemical devices and redox batteries. As an emerging solar energy utilization technology, significant progress has been made towards promoting and proliferating the practical applications of PRBs. However, wide market penetration of PRBs is still being significantly inhibited by limited photocatalytic activity, low efficiency, among other critical issues. Furthermore, the integration of each component, including solar materials, redox couples, and membranes and their interaction in PRBs play vital roles towards achieving smooth operation and high performance. Herein, the materials, mechanisms, recent advances, and challenges in the use of PRBs are presented. The crucial influence of redox couples, photoelectrode materials, membranes on the performance of the system including how they affect solar energy capture, reaction kinetics, and internal losses are systematically discussed. In addition, the recent advances of a single‐battery of photoelectrode mode and an integrated device of solar cell mode are summarized. Furthermore, the state of the art performance of PRBs and their upscaling progress are also discussed. Finally, the challenges and perspectives for the future development of PRBs are highlighted.

DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.

Developing electrocatalysts for electrochemical CO2 reduction reaction (CO2RR) with pre-eminent activity and high selectivity at low overpotentials is very significant, but it still remains a formidable challenge. Herein, we report an in situ-activated indium nanoelectrocatalyst derived from InOOH nanosheets for active and selective CO2RR at ultralow overpotentials. Such a catalyst delivers near-unity CO2RR selectivity with formate as the main product, in a wide low-overpotential window of −0.25∼−0.49 V versus reversible hydrogen electrode (vs RHE). Significantly, the CO2RR activity reaches 151 mA cm–2 at −0.45 V vs RHE, comparable to the state-of-the-art Au-based catalysts. Impressively, full-cell CO2 electrolysis implements a record-high electricity-to-fuel energy-conversion efficiency of 76.0% and solar-to-fuel energy-conversion efficiency of 20.7%. Furthermore, in situ synchrotron X-ray diffraction reveals the dynamic formation of nanosized metallic indium, correlating well with CO2RR activity, also evidenced by cyclic voltammetry. Combined with theoretical calculations, it is confirmed that the in situ-generated metallic indium plays a dominant role in promoting formate formation by accelerating the second proton-coupled electron transfer process (*OCHO+ H+ + e – → *HCOOH). Consistent with experimental results, operando Raman spectra further demonstrate that in situ-activated indium nanocatalysts can facilitate formate production even at the thermodynamic potential. This work uncovers nanosized metallic indium as the highly active site and sheds light on the design of superior indium-based catalysts for CO2 electroreduction.

Salecan is a recently identified water-soluble viscous extracellular b-1,3-D-glucan polysaccharide from an Agrobacterium species. It is a high-molecular-mass polymer (about 2 £ 10 6 Da) and composed of a linear chain of glucosyl residues linked through a repeat unit of seven b-(1,3) and two a-(1,3) glucosidic bonds. In the present study, we examined the effects of dietary Salecan fed at 2 and 5 % in a high-fat diet (64 % energy) in C57BL/6J mice. After 6 weeks, mice fed 2 and 5 % Salecan had significantly lower body weight, fat mass and percentage of body fat mass compared with those fed a high-fat cellulose (control) diet. Both the Salecan groups significantly and dose-dependently improved glucose tolerance, with a 9 and 26 % reduction of glucose AUC, respectively. Liver and adipose tissue weights were also significantly decreased by the Salecan treatment. Supplementation with 5 % Salecan led to lower serum TAG, total cholesterol (TC) and HDL-cholesterol (52, 18 and 19 %, respectively) and lower hepatic TAG by 56 % and TC by 22 % compared with the high-fat cellulose control group. Dietary Salecan intake caused an obvious elevation of fat in the faeces. Supplementation with Salecan disturbed bile acid-promoted emulsification and reduced the size of emulsion droplets in vitro. These results indicate that Salecan decreases fat absorption, improves glucose tolerance and has biologically important, dose-related effects on reducing high-fat diet-induced obesity.Key words: Salecan: b-Glucans: High-fat diet: Obesity: Glucose tolerance Obesity represents one of the most serious global health issues. Environmental and genetic factors play an important role in the increase in obesity that is affecting the whole of mankind on a large scale. Among these, diet-induced obesity has become one of the most critical medical problems in the world (1) . Obesity is defined medically as a state of increased body weight, more specifically adipose tissue, of sufficient magnitude to produce adverse health consequences (2,3) . The excessive fat accumulation in adipose tissue, liver and other organs strongly predisposes obese individuals to the development of metabolic changes that increase overall morbidity risk. Obesity is associated with insulin resistance (4) , a state of low-grade chronic inflammation (5) and the metabolic syndrome. The metabolic syndrome is related to higher circulating levels of inflammatory markers, many of which enhance tumour growth (6) . Clearly, prevention and management of obesity are relevant to health promotion.The predominant obesity-causing factor is energy imbalance. While pharmaceutical treatments for obesity have been extensively researched, only a few drugs have been approved for long-term use in significantly obese patients by the Food and Drug Administration. However, they have adverse effects including gastrointestinal discomfort, flatulence and diarrhoea (7) . In addition to prescription drugs, nutritional supplements for weight loss are popular in the over-the-counter market. Although suc...

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer’s representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue–residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

No abstract