Regulation of Aldo–Keto Reductases in Human Diseases

The aldo–keto reductases (AKRs) are a superfamily of NAD(P)H-linked oxidoreductases, which reduce aldehydes and ketones to their respective primary and secondary alcohols. AKR enzymes are increasingly being recognized to play an important role in the transformation and detoxification of aldehydes and ketones generated during drug detoxification and xenobiotic metabolism. Many transcription factors have been identified to regulate the expression of human AKR genes, which could have profound effects on the metabolism of endogenous mediators and detoxication of chemical carcinogens. This review summarizes the current knowledge on AKR regulation by transcription factors and other mediators in human diseases

Improving Molecular Pretraining with Complementary Featurizations

Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies -- chirality classification and aromatic ring counting -- we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely relies on one or two featurizations on a wide range of molecular property prediction tasks.Comment: 24 pages, work in progres

Research and Optimization of Marine Diesel Engine Index System

Aiming at the quality evaluation of diesel engine, this paper analyzes the influence factors of diesel engine quality from the function and general quality of diesel engine, and establishes the detailed index system of diesel engine quality evaluation, which covers all aspects of diesel engine quality. Finally, the degree of discrimination is sorted by the entropy weight theory, and the indexes are optimized and screened

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification

Proteomic identification of differentially expressed proteins during alfalfa (Medicago sativa L.) flower development

Flower development, pollination, and fertilization are important stages in the sexual reproduction process of plants; they are also critical steps in the control of seed formation and development. During alfalfa (Medicago sativa L.) seed production, some distinct phenomena such as a low seed setting ratio, serious flower falling, and seed abortion commonly occur. However, the causes of these phenomena are complicated and largely unknown. An understanding of the mechanisms that regulate alfalfa flowering is important in order to increase seed yield. Hence, proteomic technology was used to analyze changes in protein expression during the stages of alfalfa flower development. Flower samples were collected at pre-pollination (S1), pollination (S2), and the post-pollination senescence period (S3). Twenty-four differentially expressed proteins were successfully identified, including 17 down-regulated in pollinated flowers, one up-regulated in pollinated and senesced flowers, and six up-regulated in senesced flowers. The largest proportions of the identified proteins were involved in metabolism, signal transduction, defense response, oxidation reduction, cell death, and programmed cell death (PCD). Their expression profiles demonstrated that energy metabolism, carbohydrate metabolism, and amino acid metabolism provided the nutrient foundation for pollination in alfalfa. Furthermore, there were three proteins involved in multiple metabolic pathways: dual specificity kinase splA-like protein (kinase splALs), carbonic anhydrase (CA), and NADPH: quinone oxidoreductase-like protein (NQOLs). Expression patterns of these proteins indicated that MAPK cascades regulated multiple processes, such as signal transduction, stress response, and cell death. PCD also played an important role in the alfalfa flower developmental process, and regulated both pollination and flower senescence. The current study sheds some light on protein expression profiles during alfalfa flower development and contributes to the understanding of the basic molecular mechanisms during the alfalfa flowering process. These results may offer insight into potential strategies for improving seed yield, quality, and stress tolerance in alfalfa

Multiscale Simulation of Shot-Peening-Assisted Low-Pressure Cold Spraying Based on Al-Zn-Al2O3 Coatings

Low-pressure cold spraying has gained much significance for its good economy in recent years. However, compared with high-pressure cold spraying, the unsatisfactory performance of coatings prepared by this method is a key problem restricting its further development. To improve the properties of the coating without incorporating severe conditions, the paper proposed an original shot-peening-assisted low-pressure cold-spraying method (i.e., SP-LPCS). By proceeding with cold spraying and shot peening alternately, SP-LPCS was proved to enhance the mechanical property of the coating effectively. Mixed particles of Zn, Al, and Al2O3 were adopted as the coating powder. Effects of shot-peening pressure, flow rate, and shot size on the micromorphology and the microhardness variance were studied. Results shows that the thickness of the plastic deformation layer stabilizes as the impact time increases to 6. The microscopic simulation of the deformation shows that according to the different metal characteristics of the powder, brittle grains fracture while plastic grains go through deformation and refinement. Meanwhile, the porosity decreases greatly after the impacts, resulting in a higher denseness of the coating. Several factors mutually contribute to the performance improvement of the coating. The microhardness of the material was increased after SP-LPCS, and obvious strengthening belts were observed, with the highest microhardness being 90.93Hv

Targetoid‐like lesions and chilblain‐like erythema manifested on hands and feet: A case of Rowell syndrome from China

Abstract Background: Rowell syndrome (RS) is an uncommon condition characterized by erythema multiforme (EM)‐like lesions and lupus erythematosus. It is more common in females, and EM may be the first manifestation of the disease with positive autoantibodies, such as antinuclear antibody (ANA), SSA, SSB and rheumatoid factor. The pathogenesis of RS is unknown and is likely caused by drug induction, ultraviolet exposure and infection. Method: We describe a case of RS from China which presented as characteristic targetoid‐like lesions and chilblain‐like erythema on hands and feet. This is a case of RS in a female patient from the inpatient department of dermatology. Results: A 41‐year‐old female with systemic lupus erythematosus exhibited chilblain‐like erythema and characteristic EM lesions on her extremities. She tested positive for serum ANA (1:320) and anti‐double‐stranded DNA, as well as other autoantibodies. Systemic glucocorticoids and hydroxychloroquine worked effectively for her. Conclusion: The present case met diagnostic criteria of RS. Notably, there was a co‑occurrence of facial butterfly erythema, chilblain‐like erythema and EM lesions distributed on the limbs in this case

Adversarial Reconstruction-Classification Networks for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR image classification. Nevertheless, for FCN, there are some problems to solve in PolSAR image classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR image classification. However, only when the labeled training sample is sufficient, can SFCN achieve good classification results. To address the above mentioned problem, we propose adversarial reconstruction-classification networks (ARCN), which is based on SFCN and introduces reconstruction-classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised image classification and unsupervised image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true image and reconstructed image can be detected and revised. Our method can achieve impressive performance in PolSAR image classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR images demonstrate the efficiency of the proposed method