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For a subset S of a ring R, an element a is said to have left S-element stable range 1 if whenever Ra + Rb = R there exists r ∈ S such that a + rb is a unit. When S = R (.resp., S = U (R) and S = idem (R) )., this is the notion of left stable range 1 (.resp., left unit-1 stable range and left idempotent stable range 1). This article is concerned with S = nil (R) . [ABSTRACT FROM AUTHOR]

Hypercyclic rings are those rings over which cyclic modules have cyclic injective hulls. Such rings were introduced by Caldwell in 1966 and studied by him and Osofsky in 1968. In this paper, we revisit Osofsky's work on hypercyclic rings, highlighting some of her results and questions on the subject. [ABSTRACT FROM AUTHOR]

Environmental disclosure is a crucial method for companies to demonstrate their environmental efforts. However, in some institutional contexts, companies may be inclined to "greenwash" their environmental disclosures, strategically seeking social rewards. Anchored on impression management theory, this study delves into the relationship between environmental disclosure and corporate value. The research substantiates the premium effect of environmental information while shedding light on the adverse impact of "environmental disclosure greenwashing" (EDG) on corporate value. Moreover, EDG leads to a further devaluation in the premium effect of environmental information. A more profound analysis reveals that the market trust acts as the mediating mechanism in the premium devaluation effect caused by EDG. Notably, when information user capability and media attention are scrutinized as distinct groups, the impact of EDG displays pronounced heterogeneity. These findings provide a theoretical foundation for verifying the authenticity of corporate environmental disclosures. Additionally, they offer valuable policy insights for rectifying EDG practices and enhancing environmental disclosure mechanisms. [ABSTRACT FROM AUTHOR]

Bioinformatics is a rapidly growing field involving the application of computational methods to the analysis and interpretation of biological data. An important task in bioinformatics is the identification of novel drug-target interactions (DTIs), which is also an important part of the drug discovery process. Most computational methods for predicting DTI consider it as a binary classification task to predict whether drug target pairs interact with each other. With the increasing amount of drug-target binding affinity data in recent years, this binary classification task can be transformed into a regression task of drug-target affinity (DTA), which reflects the degree of drug-target binding and can provide more detailed and specific information than DTI, making it an important tool in drug discovery through virtual screening. Effectively predicting how compounds interact with targets can help speed up the drug discovery process. In this study, we propose a deep learning model called TC-DTA for the prediction of the DTA, which makes use of the convolutional neural networks (CNN) and encoder module of the transformer architecture. First, the raw drug SMILES strings and protein amino acid sequences are extracted from the dataset. These are then represented using different encoding methods. We then use CNN and the Transformer's encoder module to extract feature information from drug SMILES strings and protein amino acid sequences, respectively. Finally, the feature information obtained is concatenated and fed into a multi-layer perceptron for prediction of the binding affinity score. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, against methods including KronRLS, SimBoost and DeepDTA. On evaluation metrics such as Mean Squared Error, Concordance Index and r 2 m index, TC-DTA outperforms these baseline methods. These results demonstrate the effectiveness of the Transformer's encoder and CNN in the extraction of meaningful representations from sequences, thereby improving the accuracy of DTA prediction. The deep learning model for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology allows for a more effective and efficient drug discovery process.

Objective: To assess the impact of automated algorithms on the trainees' decision-making capacity and confidence for individualized surgical planning., Methods: At Chinese PLA General Hospital, trainees were enrolled to undergo decision-making capacity and confidence training through three alternative visual tasks of the inferior clivus model formed from an automated algorithm and given consecutively in three exemplars. The rationale of automated decision-making was used to instruct each trainee., Results: Following automated decision-making calculation in 50 skull base models, we screened out three optimal plans, infra-tubercle approach (ITA), trans-tubercle approach (TTA), and supra-tubercle approach (STA) for 41 (82.00%), 8 (16.00%), and 1 (2.00%) subject, respectively. From September 1, 2023, through November 17, 2023, 62 trainees (median age [range]: 27 [26-28]; 28 [45.16%] female; 25 [40.32%] neurosurgeons) made a decision among the three plans for the three typical models (ITA, TTA, and STA exemplars). The confidence ratings had fine test-retest reliability (Spearman's rho: 0.979; 95% CI: 0.970 to 0.988) and criterion validity with time spent (Spearman's rho: -0.954; 95%CI: -0.963 to -0.945). Following instruction of automated decision-making, time spent (initial test: 24.02 vs. 7.13 in ITA; 30.24 vs. 7.06 in TTA; 34.21 vs. 12.82 in STA) and total hits (initial test: 30 vs. 16 in ITA; 37 vs. 17 in TTA; 42 vs. 28 in STA) reduced significantly; confidence ratings (initial test: 2 vs. 4 in ITA; 2 vs. 4 in TTA; 1 vs. 3 in STA) increased correspondingly. Statistically significant differences ( P  < 0.05) were observed for the above comparisons., Conclusions: The education tool generated by automated decision-making considers surgical freedom and injury risk for the individualized risk-benefit assessment, which may provide explicit information to increase trainees' decision-making capacity and confidence., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Tang, Bu, Tian, Li, Jiang, Qain and Zhou.)