Copper-Catalyzed [3 + 2] Annulation of O-Acyl Oximes with 4-Sulfonamidophenols for the Synthesis of 5-Sulfonamidoindoles and 2-Amido-5-sulfonamidobenzofuran-3(2H)-ones.

A copper-catalyzed [3 + 2] annulation of O-acyl oximes with 4-sulfonamidophenols is developed. The advantage of this method lies in the concurrent double activation of two substrates to form nucleophilic enamines and electrophilic quinone monoimines. The substituent on the α-carbon of O-acyl oxime determines two different reaction pathways, thereby leading to the selective generation of 5-sulfonamidoindoles and 2-amido-5-sulfonamidobenzofuran-3(2H)-ones.

Adaptive Fine-Grained Predicates Learning for Scene Graph Generation.

The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., "woman-on/standing on/walking on-beach". As general SGG models tend to predict head predicates and re-balancing strategies prefer tail categories, none of them can appropriately handle hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating hard-to-distinguish objects, we propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. First, we introduce an Adaptive Predicate Lattice (PL-A) to figure out hard-to-distinguish predicates, which adaptively explores predicate correlations in keeping with model's dynamic learning pace. Practically, PL-A is initialized from SGG dataset, and gets refined by exploring model's predictions of current mini-batch. Utilizing PL-A, we propose an Adaptive Category Discriminating Loss (CDL-A) and an Adaptive Entity Discriminating Loss (EDL-A), which progressively regularize model's discriminating process with fine-grained supervision concerning model's dynamic learning status, ensuring balanced and efficient learning process. Extensive experimental results show that our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance. Moreover, experiments on Sentence-to-Graph Retrieval and Image Captioning tasks further demonstrate practicability of our method.

Copper-catalyzed [3+2] annulation of O-acyl ketoximes with 2-aryl malonates for the synthesis of 3-aryl-4-pyrrolin-2-ones.

A copper-catalyzed [3+2] annulation of O-acyl ketoximes with 2-aryl malonates for the concise synthesis of 3-aryl-4-pyrrolin-2-ones has been developed. The advantage of this method lies in the use of O-acyl oximes as an internal oxidant to generate the nucleophilic enamines and electrophilic p-quinone methides concurrently. The subsequent nucleophilic addition undergoes selectively on the α-C of malonates.

[Potential molecular mechanisms of QiZhenYuanDan in treatment of atherosclerosis based on network pharmacology].

Objective: By means of network pharmacology, potential targets and molecular pathways of QiZhenYuanDan in the treatment of atherosclerosis (AS) were studied. Methods: TCMSP database was used to obtain the main active components and target information of Astragali Radix, Fructus Ligustri Lucidi, Corydalis Rhizoma and Salvia Miltiorrhiza in QiZhenYuanDan. Disease targets were retrieved by OMIM and other databases. Molecular networks were constructed using Cytoscape. STRING database was searched and PPI network diagram was drawn to obtain the key targets of QiZhenYuanDan in the treatment of AS; and the targets were uploaded to Metascape data platform for GO and KEGG analysis. Results: There were 118 targets of intersection between QiZhenYuanDan and AS, which were used as the predicted targets of QiZhenYuanDan on AS. GO analysis showed that the biological functions of QiZhenYuanDan in the treatment of AS targets mainly involved biological processes, such as the cytokine-mediated signaling pathway, cytokine receptor binding. KEGG pathway was mainly enriched in 155 signaling pathways, including PI3K-Akt, HIF-1, NF-κB signal pathway and inflammatory bowel disease pathway. Conclusion: Based on the result of network pharmacology study, the mechanisms of Qizhenyuandan for AS treatment was preliminarily revealed. The active ingredients such as quercetin and kaempferol act on targets such as IL-6 and PI3K-Akt, and exert anti-AS effects by inhibiting apoptosis, oxidative stress, as well as inflammatory responses. Our result indicates that QiZhenYuanDan exhibits anti-AS effect via a multi-component, multi-target and multi-route synergistic process.

Synthesis of Stable N-H Imines with a Benzo[7,8]indolizine Core and Benzo[7,8]indolizino[1,2-c]quinolines via Copper-Catalyzed Annulation of α,ß-Unsaturated O-Acyl Ketoximes with Isoquinolinium N-Ylides.

A copper-catalyzed annulation of α,ß-unsaturated O-acyl ketoximes with isoquinolinium N-ylides has been developed for the concise synthesis of stable N-H imines with a benzo[7,8]indolizine core. When ß-(2-bromoaryl)-α,ß-unsaturated O-acyl ketoximes are used as the starting materials, a cascade cyclization occurs to afford the benzo[7,8]indolizino[1,2-c]quinolines.

Copper-Catalyzed Bisannulations of Malonate-Tethered O-Acyl Oximes with Pyridine, Pyrazine, Pyridazine, and Quinoline Derivatives for the Construction of Dihydroindolizine-Fused Pyrrolidinones and Analogues.

A copper-catalyzed bisannulation reaction of malonate-tethered O-acyl oximes with pyridine, pyrazine, pyridazine, and quinoline derivatives has been developed for the concise synthesis of structurally novel dihydroindolizine-fused pyrrolidinones and their analogues. The present reaction shows excellent regioselectivity and stereoselectivity. Theoretical calculations reveal that the coordination effect of the carbonyl group in the nucleophilic substrate determines the excellent regioselectivity. Further functionalization of the generated dihydroindolizine-fused pyrrolidinone could be easily realized through substitution, Michael addition, selective aminolysis, and hydrolysis reactions.

Copper-Catalyzed Annulation of Oxime Acetates with α-Amino Acid Ester Derivatives: Synthesis of 3-Sulfonamido/Imino 4-Pyrrolin-2-ones.

A copper-catalyzed annulation of oxime acetates and α-amino acid ester derivatives for the easy preparation of 4-pyrrolin-2-ones bearing a 3-amino group has been developed. This process features the oxidation of amines with oxime esters as the internal oxidant to produce the active 1,3-dinucleophilic and 1,2-dielectrophilic species concurrently. The subsequent nucleophilic cyclization realizes the efficient construction of 4-pyrrolin-2-one derivatives.

Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition.

Emotion recognition in dyadic communication is challenging because: 1. Extracting informative modality-specific representations requires disparate feature extractor designs due to the heterogenous input data formats. 2. How to effectively and efficiently fuse unimodal features and learn associations between dyadic utterances are critical to the model generalization in actual scenario. 3. Disagreeing annotations prevent previous approaches from precisely predicting emotions in context. To address the above issues, we propose an efficient dyadic fusion network that only relies on an attention mechanism to select representative vectors, fuse modality-specific features, and learn the sequence information. Our approach has three distinct characteristics: 1. Instead of using a recurrent neural network to extract temporal associations as in most previous research, we introduce multiple sub-view attention layers to compute the relevant dependencies among sequential utterances; this significantly improves model efficiency. 2. To improve fusion performance, we design a learnable mutual correlation factor inside each attention layer to compute associations across different modalities. 3. To overcome the label disagreement issue, we embed the labels from all annotators into a k-dimensional vector and transform the categorical problem into a regression problem; this method provides more accurate annotation information and fully uses the entire dataset. We evaluate the proposed model on two published multimodal emotion recognition datasets: IEMOCAP and MELD. Our model significantly outperforms previous state-of-the-art research by 3.8%-7.5% accuracy, using a more efficient model.