Dearomatization of Indole Derivatives via Palladium‐Catalyzed C?H Bond Functionalization of Pyrroles: Convenient Construction of Spiroindolenines

An intramolecular palladium(II)‐catalyzed dearomative arylation reaction of indoles via C H bond functionalization was developed, providing access to structurally novel spiroindolenines with moderate to good yields. A one‐pot process for the synthesis of spiroindoline derivatives was also realized.

     

Palladium‐Catalyzed Three‐Component Reaction of 3‐(Tri‐n‐ butylstannyl)allyl Acetates,Aldehydes, and Triorganoboranes: An Alternative to the Carbonyl Allylation Using α,γ‐Substituted Allylic Tin Reagents

A three‐component reaction of 3‐(tri‐n‐butylstannyl)allyl acetates, aldehydes, and triorganoboranes in the presence of a palladium‐Xantphos catalyst system predominately gave (E)‐anti‐homoallylic alcohols with high diastereoselectivity and good to high levels of alkene stereocontrol. An efficient chirality transfer was observed when an enantioenriched substrate was employed. The reaction was initiated by the formation of an allylic gem‐palladium/stannyl intermediate, which subsequently underwent allylation of the aldehyde by an allyltributyltin followed by a coupling reaction of the in‐situ‐generated (E)‐vinylpalladium acetate with the triorganoborane.

     

二次曲线的计算方法研究

二次曲线的射影理论是射影几何中极其重要的一个组成部分。利用二次曲线的对应进行物体识别、场景重建及运动分析是计算机视觉中非常活跃的一个研究领域。利用图像坐标计算二次曲线,当图像坐标很大时,就会出现计算故障或者引起计算精度的下降;利用齐次坐标来计算二次曲线,相应地增加计算量。本文从实际计算的角度出发,使用N矢量来表示视平面上所有的点和直线,从二次曲线的定义建立了二次曲线的N矢量计算公式。在此基础上,给出了射影平面上由任意一点或一条直线所确定的二次曲线切点、切线的N矢量计算方法,通过举例分析和实验验证,该算法实用、可靠。     

基于分歧的半监督学习

为提高半监督分类的性能,提出一种基于SOM神经网络的半监督分类算法SSC-SOM。结合SOM的聚类特性,基于先聚类后标记的思想,充分利用有标记样本和未标记样本训练SOM分类器;将聚类的形成和有标记样本分配到各个聚类中同时进行,并根据有标记样本计算各个聚类的聚类中心;在整个未标记样本的范围内,根据聚类中心,使用K近邻算法对未标记样本进行标记,挖掘未标记样本的隐含信息。在UCI数据集中进行分类实验,其结果表明,SSC-SOM的分类率比SSOM提高2.22%,且收敛性较好。     

槲皮素对高脂日粮小鼠学习记忆能力的影响研究

探讨槲皮素对高脂日粮小鼠学习记忆的影响及可能机制。4周龄C57B/L雌性小鼠45只,随机分为三组:对照组（基础日粮）,高脂组（约20%脂肪）,槲皮素组（高脂日粮+0.01%槲皮素）。第26周进行Morris水迷宫实验,实验结束后取小鼠大脑皮层和海马组织测定氧化应激指标,荧光定量PCR检测海马组织中脑源性神经营养因子（BDNF）、核转录因子Nrf2及血红素单加氧酶（HO-1）的基因表达。结果显示,高脂组小鼠学习记忆能力显著下降,脑组织出现明显的氧化应激,Nrf2、HO-1和BDNF基因表达显著下调;槲皮素干预后,大脑氧化应激程度减轻,Nrf2、HO-1和BDNF基因表达上调,学习记忆能力得到显著改善。槲皮素改善高脂日粮小鼠学习记忆,可能与上调Nrf2/HO-1通路及BDNF的基因表达有关。      

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.     

PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability

Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.