Porous CuO Microspheres as Long-Lifespan Cathode Materials for Aqueous Zinc-Ion Batteries.

Cathode materials with conversion mechanisms for aqueous zinc-ion batteries (AZIBs) have shown a great potential as next-generation energy storage materials due to their high discharge capacity and high energy density. However, improving their cycling stability has been the biggest challenge plaguing researchers. In this study, CuO microspheres were prepared using a simple hydrothermal reaction, and the morphology and crystallinity of the samples were modulated by controlling the hydrothermal reaction time. The as-synthesized materials were used as cathode materials for AZIBs. The electrochemical experiments showed that the CuO-4h sample, undergoing a hydrothermal reaction for 4 h, had the longest lifecycle and the best rate of capability. A discharge capacity of 131.7 mAh g-1 was still available after 700 cycles at a current density of 500 mA g-1. At a high current density of 1.5 A g-1, the maintained capacity of the cell is 85.4 mA h g-1. The structural evolutions and valence changes in the CuO-4h cathode material were carefully explored by using ex situ XRD and ex situ XPS. CuO was reduced to Cu2O and Cu after the initial discharge, and Cu was oxidized to Cu2O instead of CuO during subsequent charging processes. We believe that these findings could introduce a novel approach to exploring high-performance cathode materials for AZIBs.

New insight into the mechanism of biofouling-resistant thiazole-linked covalent organic frameworks for selective uranium capture from seawater.

The extraction of uranium from seawater is crucial for the sustainable production of nuclear fuel. Traditional amidoxime-functionalized adsorbents suffer from competitive adsorption of vanadium ion and biofouling. These challenges motivate the development of novel adsorbents for selective uranium extraction from seawater. Herein, four kinds of thiazole-linked covalent organic frameworks (COFs) were investigated to harvest uranium from seawater. The selectivity and anti-biofouling performance were systematically investigated through the molecular dynamics (MD) simulations. Driven by the pore size sieving effect and electrostatic interaction, the Ca2UO2(CO3)3 complex and vanadate anions were selectively separated by different COFs in special areas. On one hand, benefits from the small steric partition factor, the Ca2UO2(CO3)3 complex can stick on the surface of COFs. On the other hand, the dispersive negatively and positively charged areas of studied COFs work as potential binding sites for the Ca2UO2(CO3)3 complex and vanadate anions, respectively. Moreover, an analysis of pulling force and desorption time between uranium and vanadium ions further confirmed the selectivity of various thiazole-linked COFs. The anti-biofouling property was comparatively investigated by dynamic trajectory and solvent accessible surface area. Our outcomes illustrate that the hydroxyl and zwitterionic groups in the thiazole-linked COFs endow their strong surface hydrations to resist marine biofouling. In particular, the TpBdsaPa is identified as a promising candidate due to charge dispersed zwitterionic group as well as remarkable anti-biofouling ability. The present study sheds an atomic-level understanding of the thiazole-linked COFs for selective uranium uptaking from seawater, which will provide aid to design novel adsorbent with highly selective uranium extraction capacity and strong anti-biofouling property.

Vanadium Hexacyanoferrate as a High-Capacity and High-Voltage Cathode for Aqueous Rechargeable Zinc Ion Batteries.

Prussian blue analogs (PBAs) are widely used as electrode materials for secondary batteries because of their cheapness, ease of synthesis, and unique structural properties. Nevertheless, the unsatisfactory capacity and cyclic stability of PBAs are seriously preventing their practical applications. Here, vanadium hexacyanoferrate (VHCF) is successfully prepared and used as a cathode for aqueous zinc-ion batteries (AZIBs). When using 3 M Zn(CF3SO3)2 as the electrolyte, a high capacity of ~230 mA h g-1 and a high voltage of ~1.2 V can be achieved. The XRD result and XPS analysis indicate that the outstanding Zn2+ storage capability is due to the presence of dual electrochemical redox centers in VHCF (Fe2+ ⇋ Fe3+ and V5+ ⇋ V4+ ⇋ V3+). However, the battery shows a short cycle life (7.1% remaining capacity after 1000 cycles) due to the dissolution of VHCF. To elongate the cycle life of the battery, a high-concentration hybrid electrolyte is used to reduce the activity of water molecules. The improved battery exhibits an impressive capacity of 235.8 mA h g-1 and good capacity retention (92.9%) after 1000 cycles.

Polysaccharide from Patinopecten yessoensis Skirt Boosts Immune Response via Modulation of Gut Microbiota and Short-Chain Fatty Acids Metabolism in Mice.

Polysaccharide from marine shellfish has various bioactivities. In this study, the effects of polysaccharide from Patinopecten yessoensis skirt (PS) on boosting immune response in mice were evaluated, and the potential mechanisms were explored. The results showed that PS administration effectively increased the serum IgG and IgM levels, implying that PS had immune response-boosting properties. Moreover, PS administration could modulate the composition of the gut microbiota, and significantly improve short-chain fatty acids (SCFAs) metabolism, especially butyrate metabolism. Of note, the expression of the Tlr2, Tlr7, MyD88, Tnfa, and Il1b genes in toll-like receptor (TLR) signaling pathway was significantly increased. In summary, PS could boost immune response by modulating the gut microbiota and SCFAs metabolism correlating with the activation of the TLR signaling pathway. Therefore, PS can be developed as a special ingredient for functional product.

A novel expression vector for Corynebacterium glutamicum with an auxotrophy complementation system.

Corynebacterium glutamicum is an important industrial strain used for the production of amino acids and vitamins. Most tools developed for overexpression of genes in C. glutamicum are based on the inducible promoter regulated by the lacIq gene or contain an antibiotic resistance gene as a selection marker. These vectors are essential for rapid identification of recombinant strains and detailed study of gene functions, but, as a considerable disadvantage, these vectors are not suitable for large-scale industrial production due to the need for the addition of isopropyl-ß-D-thiogalactopyranoside (IPTG) and antibiotics. In this study, the novel Escherichia coli-C. glutamicum shuttle expression vector pLY-4, derived from the expression vector pXMJ19, was constructed. The constitutive vector pLY-4 contains a large multiple cloning site, the strong promoter tacM and two selective markers: the original chloramphenicol resistance gene cat is used for molecular cloning operations, and the auxotrophy complementation marker alr, which can be stably replicated in the auxotrophic host strain without antibiotic selection pressure, is used for industrial fermentation. Heterologous expression of the gapC gene using the vector pLY-4 in C. glutamicum for L-methionine production indicated the potential application of pLY-4 in the development of C. glutamicum strain engineering and industrial fermentation.

Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features.

BACKGROUND: Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. RESULTS: In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. CONCLUSIONS: The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.