Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis. This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e.g., left and right eyes). The study initiates with a comprehensive image pre-processing procedure, including circular border cropping, image resizing, contrast enhancement, noise removal, and data augmentation. Subsequently, discriminative deep feature representations are extracted using multiple deep learning blocks, namely the High-Resolution Network (HRNet) and Attention Block, which serve as feature descriptors. The SENet Block is then applied to further enhance the quality and robustness of feature representations from a pair of fundus images, ultimately consolidating them into a single feature representation. Finally, a sophisticated classification model, known as a Discriminative Restricted Boltzmann Machine (DRBM), is employed. By incorporating a Softmax layer, this DRBM is adept at generating a probability distribution that specifically identifies eight different ocular diseases. Extensive experiments were conducted on the challenging Ophthalmic Image Analysis-Ocular Disease Intelligent Recognition (OIA-ODIR) dataset, comprising diverse fundus images depicting eight different ocular diseases. The Fundus-DeepNet system demonstrated F1-scores, Kappa scores, AUC, and final scores of 88.56%, 88.92%, 99.76%, and 92.41% in the off-site test set, and 89.13%, 88.98%, 99.86%, and 92.66% in the on-site test set.In summary, the Fundus-DeepNet system exhibits outstanding proficiency in accurately detecting multiple ocular diseases, offering a promising solution for early diagnosis and treatment in ophthalmology. 

Fundus-DeepNet: Multi-Label Deep Learning Classification System for Enhanced Detection of Multiple Ocular Diseases through Data Fusion of Fundus Images

Controlling post-prandial hyperglycemia and hyperlipidemia, particularly by regulating the activity of digestive enzymes, allows managing type 2 diabetes and obesity. The aim of this study was to assess the effects of TOTUM-63, a formulation of five plant extracts (Olea europaea L., Cynara scolymus L., Chrysanthellum indicum subsp. afroamericanum B.L.Turner, Vaccinium myrtillus L., and Piper nigrum L.), on enzymes involved in carbohydrate and lipid absorption. First, in vitro inhibition assays were performed by targeting three enzymes: α-glucosidase, α-amylase, and lipase. Then, kinetic studies and binding affinity determinations by fluorescence spectrum changes and microscale thermophoresis were performed. The in vitro assays showed that TOTUM-63 inhibited all three digestive enzymes, particularly α-glucosidase (IC50 of 13.1 µg/mL). Mechanistic studies on α-glucosidase inhibition by TOTUM-63 and molecular interaction experiments indicated a mixed (full) inhibition mechanism, and higher affinity for α-glucosidase than acarbose, the reference α-glucosidase inhibitor. Lastly, in vivo data using leptin receptor-deficient (db/db) mice, a model of obesity and type 2 diabetes, indicated that TOTUM-63 might prevent the increase in fasting glycemia and glycated hemoglobin (HbA1c) levels over time, compared with the untreated group. These results show that TOTUM-63 is a promising new approach for type 2 diabetes management via α-glucosidase inhibition.

/pdf/inhibitory-potential-of-a-amylase-a-glucosidase-and-3ve0ouwc.pdf

Inhibitory Potential of α-Amylase, α-Glucosidase, and Pancreatic Lipase by a Formulation of Five Plant Extracts: TOTUM-63

To meet the requirements of vibration control for composite structures, the NiTiNOL steel wire ropes (NiTi-ST) is selected to control the vibration of the composite laminate. The dynamical modeling is built by integrating the composite laminate with the modified Bouc-Wen model. The dynamical model is discretized by Galerkin truncation method (GTM), and theoretical analysis is carried out using the harmonic balance method (HBM), Runge-Kutta method (RK) and finite element method (FEM). Based on the results of dynamical modeling and theoretical analysis, an experimental device is designed and manufactured. The results show that the NiTi-ST has excellent vibration control ability for composite laminate, and the impact on the natural frequency of the system is small. 

Vibration control of composite laminate via NiTiNOL-steel wire ropes: Modeling, analysis, and experiment

Benggangs are a special type of severely eroded landform in southern China. They cause severe damage to soil resources, which has had a large negative impact on local ecological environments and people's lives. Benggang erosion has received abundant attention in the past few decades, but few studies have systematically summarized and analyzed the progress, deficiencies and future directions of research of Benggang erosion. The purpose of this paper is therefore to analyze the progress of research on Benggang erosion, summarize the research deficiencies and propose future research directions and priorities. Benggangs gained attention in the 1960s, and studies of Benggang erosion have achieved rich results, including the characteristics of parental materials, spatial variation of soil properties in Benggang areas, mechanisms of the occurrence and process of Benggang erosion, dynamic monitoring of Benggang erosion, estimation of erosion volume, measures for controlling Benggang erosion and evaluation of their effectiveness. These studies have improved our knowledge and understanding of the basic attributes, occurrence, development, and factors influencing Benggang erosion and have provided a scientific basis for the optimal arrangement of measures for conserving soil and water and the rational development and effective management of Benggangs. We nevertheless believe that deficiencies remain in the aspects of the optimization of monitoring methods, mechanism of composite erosion, control of erosion and evaluation of the effectiveness of controlling Benggang erosion under the current level of development of research on soil erosion and the high standard of ecological protection. Further studies are necessary in the following areas: space-air-ground integrated technology for optimizing the monitoring of Benggang erosion; mechanism of hydraulic-gravitational composite erosion and the construction of models; research, development and application of new material for the prevention and control of Benggang erosion and development of a system for evaluating the effectiveness of erosion control. These research topics can supplement and enrich the content of traditional research on Benggang erosion and are very important for the restoration of ecological environments and improving ecological services and functions in areas with Benggang erosion. 

Progress and prospect of studies of Benggang erosion in southern China

Owing to the short picking period of the fresh Zanthoxylum bungeanum, the postharvest drying has become an essential operation before the storage and transportation of Z. bungeanum. To explore the effects of drying methods on volatile characteristics, the volatilomic profiling of five different dried Z. bungeanum was investigated by E-nose, HS-SPME-GC/MS, GC-IMS in combination with chemometrics. The results indicated that W1W, W2W and W5S sensors within E-nose analysis showed the strongest responses in both fresh and dried Z. bungeanum. According to the identification of volatile organic compounds (VOCs), terpenes, esters and alcohols played the major roles in the volatile formation of the fresh and dried Z. bungeanum. The samples derived from hot air drying showed the relatively similar features with the fresh sample based on the relative abundances of these major VOCs. According to the results of multiple factor analysis (MFA), GC-IMS showed the strongest ability in distinguishing the fresh and different dried samples. Compared with the high levels of terpenes in fresh group, the significant increasement of terpene alcohols and terpene esters from the degradation and transformation of bound terpenoids was the main characteristics of all dried Z. bungeanum. Using the GC-IMS datasets, a weighted correlation network analysis (WCNA) model was constructed to clarify the VOC characteristics in all detetected samples. Thereinto, 6 significantly correlated modules were identified in fresh and five different dried samples. Additionally, a total of 23 hub VOCs can be recognized as the potential biomarkers for better distinguishing the fresh and five different dried Z. bungeanum. 

Combining with volatilomic profiling and chemometrics to explore the volatile characteristics in five different dried Zanthoxylum bungeanum Maxim

http://www.cell.com/article/S2405844022022216/pdf

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

http://www.cell.com/article/S2405844022030729/pdf

Effects of different drying methods on chemical compositions, antioxidant activity and anti-α-glucosidase activity of Coreopsis tinctoria flower tea

Multi-Directional Compression Behaviors and Failure Mechanisms of 3d Orthogonal Woven Composites: Parametric Modeling and Strength Prediction

Multi-classification of fundus diseases based on DSRA-CNN

Pervaporation Separation of C6 Alkane Isomers by Al-Bttotb Membrane

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