Circular RNAs (circRNAs) are a class of non‑coding RNAs with a circular, covalent structure that lack both 5' ends and 3' poly(A) tails, which are stable and specific molecules that exist in eukaryotic cells and are highly conserved. The role of circRNAs in viral infections is being increasingly acknowledged, since circRNAs have been discovered to be involved in several viral infections (such as hepatitis B virus infection and human papilloma virus infection) through a range of circRNA/microRNA/mRNA regulatory axes. These findings have prompted investigations into the potential of circRNAs as targets for the diagnosis and treatment of viral infection‑related diseases. The aim of the present review was to systematically examine and discuss the role of circRNAs in several common viral infections, as well as their potential as diagnostic markers and therapeutic targets.
Using the pre-trained language models to understand source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial risks. However, there are several challenges in applying these language models to solve programming language related problems directly. For instance, the shift of domain knowledge between natural language (NL) and programming language (PL) requires understanding the semantic and syntactic information from the data from different perspectives. To this end, we propose the AstBERT model, a pre-trained PL model aiming to better understand the financial codes using the abstract syntax tree (AST). Specifically, we collect a sheer number of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement. Experiment results show that our AstBERT achieves promising performance on three different downstream tasks.
The American feminist movement has experienced three waves of movement since its outbreak in the mid-19th century.Its influence has continued to impact the norms of patriarchal society and has had a huge impact on the development of the film industry.Disney, one of the giants of the animation world, produced a series of films portraying princesses.As a mirror of the social and contemporary context, Disney reflected the image of women in different periods of the feminist movement through its animated films.This paper will analyse the representative Disney princess animations during the three waves through an observational method in the context of the feminist movement in the United States, in an attempt to construct the changes in the image of Disney princesses from the 20th to the 21st century, and thus explore the awakening and development of women's consciousness.
PANDAS - Parallel Adaptive static/dynamic Nonlinear Deformation Analysis System - a novel supercomputer simulation tool has been developing for simulating the highly non-linear coupled geomechanical-fluid flow-thermal systems involving heterogeneously fractured geomaterials at different spatial and temporal scales. This abstract briefly introduces the software system, and then focuses on our recent research outcomes in high performance simulation of enhanced geothermal reservoir system.
Twenty agroforestry systems consisting of different management practices (conventional and organic) and shade types were set up for coffee plantations in 2,000 at the Tropical Agricultural Research and Higher Education Center (CATIE), Turrialba, Costa Rica. The physical (density, bulk density, moisture content, and roasting loss) and chemical attributes (mineral, total lipid, fatty acids, caffeine, and carbohydrate contents) of harvested green coffee beans were investigated. The full sun and Erythrina shade tree systems significantly improved ( p < 0.05) the density of the green coffee beans and decreased ( p < 0.05) the moisture content and roasting loss of the green coffee beans. The intensive organic (IO) management practice significantly increased some mineral contents, such as K, P, and Ca, in green coffee beans. The full sun system also significantly promoted ( p < 0.05) some mineral contents, such as Ca and Mn, in green coffee beans. In terms of total lipid and fatty acids (FAs), compared with the moderate conventional (MC) management practice, the IO management practice was beneficial as it significantly increased ( p < 0.05) the total lipid and FAs contents in the green coffee beans, while the Erythrina shade tree system significantly increased ( p < 0.05) the total lipid and FAs contents of green coffee beans more efficiently than the other shade types. The caffeine content of green coffee beans was significantly higher ( p < 0.05) under the intensive conventional (IC) and IO management practices than under the MC management practice and higher under the full sun system than under the shaded system. The Erythrina shade tree system significantly improved ( p < 0.05) the carbohydrate content of green coffee beans. Overall, in consideration of sustainability, the IO management practice associated with the Erythrina shade tree system would be a useful combination for the local farmers to grow coffee trees.
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electorencephal data rich with spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract hidden spatial-temporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires a colossal amount of training data in order to obtain satisfactory model performance. Considering the above-mentioned challenges, we propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning in this paper. After transforming the raw spatial-temporal data into high quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs capable of capturing the hidden topological and long-term temporal correlation information in these graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module in our framework to leverage diverse data distributions from different participants with attentive aggregations of their models.
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