Ethyl acetate as an acetyl source and solvent for acetylation of alcohols by KOH.

A KOH mediated mild, efficient, convenient and gram-scalable protocol for the acetylation of alcohols with EtOAc as acetyl source and solvent. Various types of alcohols were successfully transformed into according acetylated products. Good to excellent yields were offered by primary alcohols and low to moderate yields were offered by secondary alcohols.

Analysis of blood biochemistry and non-targeted metabolomics of endometritis in dairy cows.

The incidence of bovine endometritis, which has a negative impact on the reproduction of dairy cows, has been recently increasing. In this study, the differential markers and metabolites of healthy cows and cows with endometritis were analyzed by measuring blood biochemical indicators and immune factors using biochemical and enzyme-linked immunosorbent assay kits combined with nontargeted metabolomics. The LC-QTOF platform was used to evaluate the serum metabolomics of healthy cows and cows with endometritis after 21-27 days of calving. The results showed that glucose, free fatty acid, calcium, sodium, albumin, and alanine aminotransferase levels were significantly lower in the serum of cows with endometritis than in healthy cows (P < 0.05). However, the serum potassium, interleukin-1, interleukin-6, and tumor necrosis factor levels were significantly higher in cows with endometritis (P < 0.05). In addition, the serum metabolome data analysis of the two groups showed that the expression of 468 metabolites was significantly different (P < 0.05), of which 291 were upregulated and 177 were downregulated. These metabolites were involved in 78 metabolic pathways, including amino acid, nucleotide, carbohydrate, lipid, and vitamin metabolism pathways; signal transduction pathways, and other biological pathways. Taken together, negative energy balance and immune activation, which are related to local abnormalities in amino acid, lipid, and carbohydrate metabolism, were the important causes of endometritis in dairy cows. Metabolites such as glucose, carnosine, dehydroascorbic acid, L-malic acid, tetrahydrofolic acid, and UDP-glucose may be used as key indicators in the hematological diagnosis and treatment of endometritis in dairy cows.

Changes in the Texture and Flavor of Lotus Root after Different Cooking Methods.

The changes in the texture and flavor of lotus root were determined before and after boiling, steaming and frying. Compared to fresh lotus root, all three kinds of cooking decreased the hardness and springiness, and frying significantly enhanced the gumminess, chewiness and cohesiveness. The flavor components, such as flavor amino acids, nucleotides and their taste character in lotus roots, were determined by liquid chromatography and electronic tongue. The amino acids and nucleotide contents of fresh lotus root were 20.9 and 0.07 µg/kg, respectively. The content of flavor substances in lotus roots decreased obviously, and the texture characteristics decreased after boiling and steaming. After deep-frying for 2 min, the free amino acids and nucleotide contents of lotus root were 32.09 and 0.85 µg/kg, respectively, which were the highest in all cooking methods. The contents of volatile flavor components and their smell character in lotus roots were determined by GC-MS and electronic nose. There were 58 kinds of flavor compounds identified in fresh lotus root, mainly alcohols, esters and olefins. The total amount of volatile flavor compounds decreased, and new compounds, such as benzene derivatives, were produced in lotus roots after boiling and steaming. After deep-frying, the content of volatile flavor compounds in lotus root increased significantly, especially the aldehyde volatile flavor compounds. The production of pyran, pyrazine and pyridine volatile flavor compounds made the lotus root flavor unique and delicious. The taste and smell character of lotus roots before and after cooking were effectively separated by an electronic tongue, nose and PCA analysis; the results suggested the boiled lotus root exhibited the most natural and characteristic taste and smell among the four groups.

Lignin Biosynthesis in Postharvest Water Bamboo (Zizania latifolia) Shoots during Cold Storage Is Regulated by RBOH-Mediated Reactive Oxygen Species Signaling.

Lignification is a major cause of senescence in fresh shoots of water bamboo (Zizania latifolia), which is a popular vegetable in southeast Asia; however, its physiological and molecular mechanisms is less understood. In the present study, lignin content and transcriptome change in postharvested water bamboo shoots under cold storage were investigated. We found that lignin significantly accumulated in the epidermis of the shoots with the increase of firmness. In the cold storage shoots, the major up-regulated genes were involved in phenylpropanoid biosynthesis, plant-pathogen interactions, and starch and sucrose metabolism. The lignin biosynthesis genes PAL, 4CL, C4H, CCoAOMT, CCR, F5H, CAD, and POD family were up-regulated during cold storage, while HCT and C3H were down-regulated. The MAPK signaling pathway was also up-regulated and respiratory burst oxidase homologue (RBOH) genes were strongly up-regulated. Therefore, we investigated the RBOH gene family and their expression profile in water bamboo shoots. The results indicated that 10 ZlRBOHs were up-regulated in cold storage shoots. Diphenyleneiodonium chloride (DPI), an inhibitor of RBOH oxidase, significantly inhibited the expression of genes involved in lignin deposition and biosynthesis, while H2O2 enhanced these processes. These results suggest that lignification of water bamboo shoots is regulated by RBOH-mediated ROS signaling.

Uncertainty Guided Collaborative Training for Weakly Supervised and Unsupervised Temporal Action Localization.

In weakly supervised (WSAL) and unsupervised temporal action localization (UAL), the target is to simultaneously localize temporal boundaries and identify category labels of actions with only video-level category labels (WSAL) or category numbers in a dataset (UAL) during training. Among existing methods, attention based methods have achieved superior performance in both tasks by highlighting action segments with foreground attention weights. However, without the segment-level supervision on the attention weight learning, the quality of the attention weight hinders the performance of these methods. In this paper, we propose a novel Uncertainty Guided Collaborative Training (UGCT) strategy to alleviate this problem, which mainly includes two key designs: (1) The first design is an online pseudo label generation module, in which the RGB and FLOW streams work collaboratively to learn from each other. (2) The second design is an uncertainty aware learning module, which can mitigate the noise in the generated pseudo labels. These two designs work together to promote the model performance effectively and efficiently by exchanging information between RGB and FLOW streams. Extensive experimental results on two benchmark datasets with three attention based methods demonstrate the effectiveness of the proposed method, e.g, more than 7.0% performance gain for mAP@IoU=0.5 on THUMOS14 dataset.

Laser-sintering fabrication of integrated Al/Ni anodes for lithium-ion batteries.

Integrated Al/Ni electrodes of lithium-ion batteries (LIBs) with variant atomic ratios were successfully fabricated by a one-step laser-sintering process. The microstructure, phase composition, and pore structure were controlled by the raw material composition and laser parameters. The electrodes showed working merits without any conductive agent and binder, or even the collector used in a traditional battery. It was shown that the electrode consisted of multi-phases, i.e., Al, Al3Ni2, Al3Ni, and Ni, when the Al/Ni atomic ratio was higher than 5 : 5. A lower Al/Ni atomic ratio less than 5 : 5 favored the formation of a dual-phase electrode consisting of Al3Ni2 and Ni. As the Al content increased, the specific surface area of the as-sintered electrodes increased in the initial stage and then decreased. The formation of pores was closely related to the content of the residual Al phase after the laser sintering. The residual Al phase filled the pores when the Al content was high, leading to a lower pore size. In contrast, the liquid Al phase completely reacted with the Ni component, leaving a large number of pores at its original sites. The linked pores can serve as transport channels for Li+ ions, provide mass sites for electrochemical reactions, and also buffer huge volume changes of the active material. Among the electrodes, the one with an Al/Ni ratio of 3 : 7 showed the best cycling/rate performance, i.e., a capacity of 522.8 mA h g-1 by a current of 0.1 A g-1 after 200 cycles, even holding to 338.4 mA h g-1 by a big current impact at 2 A g-1. It formed a metallurgical combination between the conductive network and the active material with multiple porous structures, which is helpful for the electrodes to provide high capacity and maintain structural stability during cycling. In addition, the average laser-sintering time of a single electrode was within 10 s, which is suitable for industrial mass production.

The Preparation of Novel P(OEGMA-co-MEO2MA) Microgels-Based Thermosensitive Hydrogel and Its Application in Three-Dimensional Cell Scaffold.

Thermosensitive hydrogel scaffolds have attracted particular attention in three-dimensional (3D) cell culture. It is very necessary to develop a type of thermosensitive hydrogel material with low shrinkage, and excellent biocompatibility and biodegradability. Here, five types of thermosensitive microgels with different volume phase transition temperature (VPTT) or particle sizes were first synthesized using 2-methyl-2-propenoic acid-2-(2-methoxyethoxy) ethyl ester (MEO2MA) and oligoethylene glycol methyl ether methacrylate (OEGMA) as thermosensitive monomers by free radical polymerization. Their VPTT and particle sizes were investigated by a nanometer particle size meter and an ultraviolet spectrophotometer. The feasibility of using these P(OEGMA-co-MEO2MA) microgels to construct thermosensitive hydrogel by means of the thermal induction method is discussed for the first time. The prepared thermosensitive hydrogel with the optimum performance was screened for in situ embedding and three-dimensional (3D) culture of MCF-7 breast cancer cells. The experimental results of AO/EB and MTT methods indicate that the pioneering scaffold material has prominent biocompatibility, and cells grow rapidly in the 3D scaffold and maintain high proliferative capacity. At the same time, there is also a tendency to aggregate to form multicellular spheres. Therefore, this original P(OEGMA-co-MEO2MA) thermosensitive hydrogel can serve as a highly biocompatible and easily functionalized 3D cell culture platform with great potential in the biomedical area.

A cascade reaction for regioselective construction of pyrazole-containing aliphatic sulfonyl fluorides.

A copper-catalyzed cascade reaction of α-diazocarbonyl compounds with ethenesulfonyl fluoride (ESF) is developed, affording a variety of highly functionalized pyrazolyl aliphatic sulfonyl fluorides in good to excellent yields (66-98%). This transformation features broad substrates, exclusive regioselectivity, high atom economy and operational simplicity, thus providing a straightforward method for the direct construction of pyrazole-containing aliphatic sulfonyl fluorides, which will provide great applicable value in medicinal chemistry and other related disciplines.

Diverse Complementary Part Mining for Weakly Supervised Object Localization.

Weakly Supervised Object Localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, a common limitation for available techniques based on classification networks is that they only highlight the most discriminative part of the object, not the entire object. To alleviate this problem, we propose a novel end-to-end part discovery model (PDM) to learn multiple discriminative object parts in a unified network for accurate object localization and classification. The proposed PDM enjoys several merits. First, to the best of our knowledge, it is the first work to directly model diverse and robust object parts by exploiting part diversity, compactness, and importance jointly for WSOL. Second, three effective mechanisms including diversity, compactness, and importance learning mechanisms are designed to learn robust object parts. Therefore, our model can exploit complementary spatial information and local details from the learned object parts, which help to produce precise bounding boxes and discriminate different object categories. Extensive experiments on two standard benchmarks demonstrate that our PDM performs favorably against state-of-the-art WSOL approaches.

Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection.

Weakly supervised temporal action detection has better scalability and practicability than fully supervised action detection in reality deployment. However, it is difficult to learn a robust model without temporal action boundary annotations. In this paper, we propose an en-to-end Multi-Scale Structure-Aware Network (MSA-Net) for weakly supervised temporal action detection by exploring both the global structure information of a video and the local structure information of actions. The proposed SA-Net enjoys several merits. First, to localize actions with different durations, each video is encoded into feature representations with different temporal scales. Second, based on the multi-scale feature representation, the proposed model has designed two effective structure modeling mechanisms including global structure modeling and local structure modeling, which can effectively learn discriminative structure aware representations for robust and complete action detection. To the best of our knowledge, this is the first work to fully explore the global and local structure information in a unified deep model for weakly supervised action detection. And extensive experimental results on two benchmark datasets demonstrate that the proposed MSA-Net performs favorably against state-of-the-art methods.

Laser ablation of pristine Fe foil for constructing a layer-by-layer SiO2/Fe2O3/Fe integrated anode for high cycling-stability lithium-ion batteries.

In this paper, an integrated SiO2/Fe2O3/Fe anode is fabricated by straightforward laser ablation of the surface of Fe foil in air. The oxidized surface is subsequently coated with tetraethyl orthosilicate (TEOS) and transformed into a SiO2 layer through a calcination process in an argon atmosphere. The surface oxidation is traced by on-line optical emission spectroscopy (OES) diagnosis. With high electron temperature (∼5200 K) in the laser irradiation zone, the nanostructured Fe2O3 layer is formed on the Fe foil, resulting in the pristine Fe2O3/Fe anode. This greatly simplified procedure with respect to the conventional route allows direct connection between the Fe2O3 layer and the Fe substrate (current collector) without any binder or conductive agent. In addition, the SiO2 coating layer greatly improves the cycling stability due to the compensatory contribution to capacity during the cycling process and its compatible elasticity to accommodate the volume expansion of Fe2O3, which is verified by first-principles theoretical calculations. The integrated SiO2/Fe2O3/Fe anode delivers a stable capacity of 651.7 mA h g-1 at 0.2 A g-1 after 100 cycles. This strategy offers a low-cost route for the rapid fabrication of integrated electrodes, broadening their applications in high cycling-stability LIBs.

Local Correspondence Network for Weakly Supervised Temporal Sentence Grounding.

Weakly supervised temporal sentence grounding has better scalability and practicability than fully supervised methods in real-world application scenarios. However, most of existing methods cannot model the fine-grained video-text local correspondences well and do not have effective supervision information for correspondence learning, thus yielding unsatisfying performance. To address the above issues, we propose an end-to-end Local Correspondence Network (LCNet) for weakly supervised temporal sentence grounding. The proposed LCNet enjoys several merits. First, we represent video and text features in a hierarchical manner to model the fine-grained video-text correspondences. Second, we design a self-supervised cycle-consistent loss as a learning guidance for video and text matching. To the best of our knowledge, this is the first work to fully explore the fine-grained correspondences between video and text for temporal sentence grounding by using self-supervised learning. Extensive experimental results on two benchmark datasets demonstrate that the proposed LCNet significantly outperforms existing weakly supervised methods.