PND-Net: plant nutrition deficiency and disease classification using graph convolutional network.

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 × : 95.50%, and BreakHis 100 × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net's aptness for agricultural growth as well as human cancer classification.

C-H functionalization of pyridines.

Pyridine and its reduced form (piperidine) are the most common nitrogen heterocycles in FDA-approved drugs. Additionally, their presence in alkaloids, ligands for transition metals, catalysts, and organic materials with various properties makes them among the most important structural cores. Despite its importance, direct and selective functionalization of pyridine remains scarce due to its electron-poor nature and nitrogen coordination power. Instead, functionalized pyridine rings were primarily constructed from suitably substituted acyclic precursors. The focus on sustainable chemistry with minimum waste generation encourages chemists to develop direct C-H functionalization. This review summarizes different approaches to tackle the reactivity and regio- and stereoselectivity aspects for direct pyridine C-H functionalization.

SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization.

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such networks in mining discriminative object pose and parts information from texture and shape. This is often inappropriate for fine-grained visual classification (FGVC) since it exhibits high intra-class and low inter-class variances due to occlusions, deformation, illuminations, etc. Thus, an expressive feature representation describing global structural information is a key to characterize an object/ scene. To this end, we propose a method that effectively captures subtle changes by aggregating context-aware features from most relevant image-regions and their importance in discriminating fine-grained categories avoiding the bounding-box and/or distinguishable part annotations. Our approach is inspired by the recent advancement in self-attention and graph neural networks (GNNs) approaches to include a simple yet effective relation-aware feature transformation and its refinement using a context-aware attention mechanism to boost the discriminability of the transformed feature in an end-to-end learning process. Our model is evaluated on eight benchmark datasets consisting of fine-grained objects and human-object interactions. It outperforms the state-of-the-art approaches by a significant margin in recognition accuracy.

Phosphite mediated asymmetric N to C migration for the synthesis of chiral heterocycles from primary amines.

A phosphite mediated stereoretentive C-H alkylation of N-alkylpyridinium salts derived from chiral primary amines was achieved. The reaction proceeds through the activation of the N-alkylpyridinium salt substrate with a nucleophilic phosphite catalyst, followed by a base mediated [1,2] aza-Wittig rearrangement and subsequent catalyst dissociation for an overall N to C-2 alkyl migration. The scope and degree of stereoretention were studied, and both experimental and theoretical investigations were performed to support an unprecedented aza-Wittig rearrangement-rearomatization sequence. A catalytic enantioselective version starting with racemic starting material and chiral phosphite catalyst was also established following our understanding of the stereoretentive process. This method provides efficient access to tertiary and quaternary stereogenic centers in pyridine systems, which are prevalent in drugs, bioactive natural products, chiral ligands, and catalysts.

Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep Network for Image Recognition.

This article presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modeling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The "usefulness" of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

An organocatalyst bound α-aminoalkyl radical intermediate for controlled aerobic oxidation of iminium ions.

A catalyst bound α-aminoalkyl radical intermediate from iminium is developed to control its formation and reactivity with aerobic oxygen. The influence of the catalyst was demonstrated via the ease of radical intermediate formation and its subsequent reactivity, including the first catalyst-controlled enantioselective aerobic oxidation with a chiral phosphite catalyst.