The reception of M. Yu. Lermontov’s creativity in China: the newest literary criticism (article second)

В статье рассматривается история изучения творческого наследия М. Ю. Лермонтова в Китае на современном этапе, демонстрируются научные достижения китайского лермонтоведения как результат его предшествующего развития. Особое внимание в статье уделяется юбилею Лермонтова, отмеченного в Китае публикацией целого ряда работ, проведением научных конференций. Делается вывод о том, что китайское лермонтоведение находится на новом подъеме, что выражается во все более глубоком осмыслении творчества великого русского поэта.The article discusses the history of the study of the creative heritage of M. Yu. Lermontov in China at the present stage, demonstrates the scientific achievements of the Chinese literary study of Lermontov as a result of his previous development. Special attention is paid to the anniversary of Lermontov, marked in China publishing a number of works, holding of scientific conferences. The conclusion is that the study of Lermontov in China is on the new lift, which is expressed in an ever deeper understanding of the literary creation of the great russian poet

Trade-Offs between Economic Benefits and Ecosystem Services Value under Three Cropland Protection Scenarios for Wuhan City in China

Over the past few decades urbanization and population growth have been the main trend all over the world, which brings the increase of economic benefits (EB) and the decrease of cropland. Cropland protection policies play an important role in the urbanization progress. In this study, we assess the trade-offs between EB and ecosystem services value (ESV) under three cropland protection policy scenarios using the LAND System Cellular Automata for Potential Effects (LANDSCAPE) model. The empirical results reveal that trade-offs between EB and ESV in urbanizing areas are dynamic, and that they considerably vary under different cropland protection policy scenarios. Especially, the results identify certain "turning points" for each policy scenario at which a small to moderate growth in EB would result in greater ESV losses. Among the three scenarios, we found that the cropland protection policy has the most adverse effect on trade-offs between EB and ESV and the results in the business as usual scenario have the least effect on the trade-offs. Furthermore, the results show that a strict balance between requisition and compensation of cropland is an inappropriate policy option in areas where built-up areas are increasing rapidly from the perspective of mitigating conflict between EB and ESV and the numbers of cropland protection that restrained by land use planning policy of Wuhan is a better choice

A novel numerical implementation of electrochemical-thermal battery model for electrified powertrains with conserved spherical diffusion and high efficiency

The performance of batteries in electrified powertrain systems is highly influenced by mass diffusion and electrochemistry which are often ignored in the simulation of these systems due to the lack of a conserved, efficient, and integrable battery model. Therefore, this work numerically implements an electrochemical-thermal battery model with conserved numerical schemes and efficient numerical methods which include Jacobian-based and Jacobian-Free Newton Krylov (JFNK) solvers. The performance of the developed model is evaluated by simulating measurements of a LiFePO 4 battery under constant discharge rates and Urban Dynamometer Driving Schedule (UDDS), as well as by a detailed comparison with existing battery models. The comparison highlights two features of our model: (a) negligible mass imbalances in the spherical diffusion modelling, which are five orders of magnitude smaller than those from a recent battery model in the literature; (b) efficient modelling of real-world driving cycles with the computational time two orders of magnitude shorter than that of the literature model. These advanced features indicate that our model can be applied in both fundamental electrochemical-thermal studies of lithium-ion battery and detailed simulations of electrified powertrains as an accurate and efficient sub-model.</p

Projecting future impacts of cropland reclamation policies on carbon storage

Cropland reclamation policies result in carbon storage loss by the conversion of natural land. However, the future impacts of cropland reclamation policies (CRP) on carbon storage have seldom been explored. Taking Hubei, China as study area, this study assesses the impacts of cropland reclamation policies before and after optimization on carbon storage from 2010 to 2030. The LAND System Cellular Automata model for Potential Effects (LANDSCAPE) was used to simulate the land use patterns in 2030, while the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Carbon Storage and Sequestration model was applied to calculate the changes in carbon storage. Results indicate that carbon storage loss due to cropland reclamation policies is expected to increase from 0.48 Tg·C to 4.34 Tg·C between 2010 and 2030 in Hubei. This increase is related to the loss of wetland and forest. Carbon storage loss can be reduced by 52%–73% by protecting carbon-rich lands. This study highlights the importance of considering the carbon storage loss when implementing cropland reclamation policies

Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder

In this paper, we propose a novel framework for ASD diagnosis using structural magnetic resonance imaging (MRI). Our method deals explicitly with the distributional differences of gray matter (GM) and white matter (WM) features extracted from MR images. We project linearly the GM and WM features onto a canonical space where their correlations are mutually maximized. In this canonical space, features that are highly correlated with the class labels are selected for ASD diagnosis. In addition, graph matching is employed to preserve the geometrical relationships between samples when projected onto the canonical space. Our evaluations based on a public ASD dataset show that the proposed method outperforms all competing methods on all clinically important measures in differentiating ASD patients from healthy individuals

ADCNet: a unified framework for predicting the activity of antibody-drug conjugates

Antibody-drug conjugate (ADC) has revolutionized the field of cancer treatment in the era of precision medicine due to their ability to precisely target cancer cells and release highly effective drug. Nevertheless, the realization of rational design of ADC is very difficult because the relationship between their structures and activities is difficult to understand. In the present study, we introduce a unified deep learning framework called ADCNet to help design potential ADCs. The ADCNet highly integrates the protein representation learning language model ESM-2 and small-molecule representation learning language model FG-BERT models to achieve activity prediction through learning meaningful features from antigen and antibody protein sequences of ADC, SMILES strings of linker and payload, and drug-antibody ratio (DAR) value. Based on a carefully designed and manually tailored ADC data set, extensive evaluation results reveal that ADCNet performs best on the test set compared to baseline machine learning models across all evaluation metrics. For example, it achieves an average prediction accuracy of 87.12%, a balanced accuracy of 0.8689, and an area under receiver operating characteristic curve of 0.9293 on the test set. In addition, cross-validation, ablation experiments, and external independent testing results further prove the stability, advancement, and robustness of the ADCNet architecture. For the convenience of the community, we develop the first online platform (https://ADCNet.idruglab.cn) for the prediction of ADCs activity based on the optimal ADCNet model, and the source code is publicly available at https://github.com/idrugLab/ADCNet

Systematic review of computational methods for drug combination prediction

Synergistic effects between drugs are rare and highly context-dependent and patient-specific. Hence, there is a need to develop novel approaches to stratify patients for optimal therapy regimens, especially in the context of personalized design of combinatorial treatments. Computational methods enable systematic in-silico screening of combination effects, and can thereby prioritize most potent combinations for further testing, among the massive number of potential combinations. To help researchers to choose a prediction method that best fits for various real-world applications, we carried out a systematic literature review of 117 computational methods developed to date for drug combination prediction, and classified the methods in terms of their combination prediction tasks and input data requirements. Most current methods focus on prediction or classification of combination synergy, and only a few methods consider the efficacy and potential toxicity of the combinations, which are the key determinants of therapeutic success of drug treatments. Furthermore, there is a need to further develop methods that enable dose-specific predictions of combination effects across multiple doses, which is important for clinical translation of the predictions, as well as model-based identification of biomarkers predictive of heterogeneous drug combination responses. Even if most of the computational methods reviewed focus on anticancer applications, many of the modelling approaches are also applicable to antiviral and other diseases or indications.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe