An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches

Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed scheme’s accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively

Flavin-N5OOH: A most powerful nucleophile and base in nature

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The Molecular Mechanisms of Oxygen Activation and Hydrogen Peroxide Formation in Lytic Polysaccharide Monooxygenases

Lytic polysaccharide monooxygenases (LPMOs) are copper-dependent enzymes for the degradation of recalcitrant polysaccharides such as chitin and cellulose. Unlike classical hydrolytic enzymes (cellulases), LPMOs catalyze the cleavage of the glycosidic bond via an oxidative mechanism using oxygen and a reductant. The full enzymatic molecular mechanisms, starting from the initial electron transfer from a reductant to oxygen activation and hydrogen peroxide formation, are not yet understood. Using QM/MM metadynamics simulations, we have uncovered the oxygen activation mechanisms by LPMO in the presence of ascorbic acid, one of the most-used reductants in LPMOs assays. Our simulations capture the sequential formation of Cu(II)-O2- and Cu(II)-OOH- intermediates via facile H-atom abstraction from ascorbate. By investigating all the possible reaction pathways from the Cu(II)−OOH- intermediate, we ruled out Cu(II)-O•- formation via direct O-O cleavage of Cu(II)-OOH-. Meanwhile, we identified a possible pathway in which the proximal oxygen atom of Cu(II)−OOH- abstracts a hydrogen atom from ascorbate, leading to Cu(I) and H2O2. The “in situ” generated H2O2 either converts to LPMO-Cu(II)-O•- via a homolytic reaction, or diffuses into the bulk water in an uncoupled pathway. The competition of these two pathways is strongly dependent on the binding of the carbohydrate substrate, which plays a role in barricading the “in situ” generated H2O2 molecule, preventing its diffusion from the active site into the bulk water. Based on the present results, we propose a catalytic cycle of LPMOs that is consistent with the experimental information available. In particular, it explains the enigmatic substrate-dependence of the reactivity of the LPMO with H2O2

Mechanistic Features in Al(I)-Mediated Oxidative Addition of Aryl C-F Bonds: Insights From Density Functional Theory Calculations.

The oxidative addition of a range of robust aryl C-F bonds to a single Al(I) center supported by a (NacNac)- bidentate ligand ((NacNac)- = [ArNC(Me)CHC(Me)NAr]- and Ar = 2,6- Pr 2 i C6H3) have been explored by density functional theory calculations. Our calculations demonstrate that the Al(I) center-mediated C-F insertion generally proceeds via the concerted mechanism that involve both the donation ( n Al → σ C - F * ) and back-donation ( σ F ( p ) → π Al ( p ) * ) interactions. In addition, the predicted free energy barriers for the C-F bond activation show good agreement with the experimental information available. Finally, the comparative studies show that B(I) is the most active among group III metals (B, Al, Ga), thus supplying a testable prediction for experiments

Sequential covalent bonding activation and general base catalysis: insight into N-heterocyclic carbene catalyzed formylation of N-H bonds using carbon dioxide and silane

Ministry of Science and Technology [2011CB808504, 2012CB214900]; National Science Foundation of China [21133007]The detailed catalytic mechanisms of N-heterocyclic carbenes (NHCs) in the formylation of N-H bonds using carbon dioxide and silane were investigated using density functional theory (DFT) calculations. Among all the examined reaction pathways, we found that the most favorable pathway involves collaboration between the covalent bonding activation and general base catalysis. The overall reaction can be divided into four stages, including silane activation through a covalent bonding mechanism, CO2 insertion into the Si-H bond of silane to yield a key intermediate formoxysilane (FOS), the NHC-catalyzed coupling of amine and FOS through a general base mechanism, and C-O bond breaking through general base catalysis to obtain the final amide product. The carbamic acid anion (Me2NCOO-) is an inevitable intermediate from the side reactions, and its formation is almost barrier free. NHC can act as a base to abstract a proton from the nucleophiles (such as amines or alcohol), and facilitate C-N bond or C-O bond formation or cleavage, and such a general base mechanism is remarkably favorable over the covalent binding mechanism for C-N bond (or C-O) bond formation (or cleavage). The calculated thermodynamic properties are in good agreement with the available experimental findings

Effective multi-class lungdisease classification using the hybridfeature engineering mechanism

The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring

The system of crop intensification: Agroecological innovations for improving agricultural production, food security, and resilience to climate change

There is growing consensus that to meet global food-security requirements, agricultural sectors will need to pursue appropriate strategies for sustainable intensification of production. This volume reports on current ‘work in progress’ to achieve this via an approach known as System of Crop Intensification (SCI). Collated from the contributors’ work with farmers in their respective countries – and illustrated throughout with case studies, data, pictures and feedback – it presents a set of ideas and experiences to encourage people to think ‘outside the box’ of current practices

Chemical synthesis of lactic acid from cellulose catalysed by lead(II) ions in water

该工作是博士生王炎良（实验）和王斌举（理论）以及邓卫平博士紧密合作的成果。The direct transformation of cellulose, which is the main component of lignocellulosic biomass, into building-block chemicals is the key to establishing biomass-based sustainable chemical processes. Only limited successes have been achieved for such transformations under mild conditions. Here we report the simple and efficient chemocatalytic conversion of cellulose in water in the presence of dilute lead(II) ions, into lactic acid, which is a high-value chemical used for the production of fine chemicals and biodegradable plastics. The lactic acid yield from microcrystalline cellulose and several lignocellulose-based raw biomasses is >60% at 463 K. Both theoretical and experimental studies suggest that lead(II) in combination with water catalyses a series of cascading steps for lactic acid formation, including the isomerization of glucose formed via the hydrolysis of cellulose into fructose, the selective cleavage of the C3–C4 bond of fructose to trioses and the selective conversion of trioses into lactic acid.该项研究工作得到国家自然科学基金委、科技部和教育部创新研究团队项目的资助

Therapeutic Inducers of Apoptosis in Ovarian Cancer

Ovarian cancers remain one of the most common causes of gynecologic cancer-related death in women worldwide. The standard treatment comprises platinum-based chemotherapy, and most tumors develop resistance to therapeutic drugs. One mechanism of developing drug resistance is alterations of molecules involved in apoptosis, ultimately assisting in the cells’ capability to evade death. Thus, there is a need to focus on identifying potential drugs that restore apoptosis in cancer cells. Here, we discuss the major inducers of apoptosis mediated through various mechanisms and their usefulness as potential future treatment options for ovarian cancer. Broadly, they can target the apoptotic pathways directly or affect apoptosis indirectly through major cancer-pathways in cells. The direct apoptotic targets include the Bcl-2 family of proteins and the inhibitor of apoptotic proteins (IAPs). However, indirect targets include processes related to homologous recombination DNA repair, micro-RNA, and p53 mutation. Besides, apoptosis inducers may also disturb major pathways converging into apoptotic signals including janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3), wingless-related integration site (Wnt)/β-Catenin, mesenchymal-epithelial transition factor (MET)/hepatocyte growth factor (HGF), mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK), and phosphatidylinositol 3-kinase (PI3K)/v-AKT murine thymoma viral oncogene homologue (AKT)/mammalian target of rapamycin (mTOR) pathways. Several drugs in our review are undergoing clinical trials, for example, birinapant, DEBIO-1143, Alisertib, and other small molecules are in preclinical investigations showing promising results in combination with chemotherapy. Molecules that exhibit better efficacy in the treatment of chemo-resistant cancer cells are of interest but require more extensive preclinical and clinical evaluation