Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.

[This corrects the article DOI: 10.1371/journal.pone.

MIT: Mutual Information Topic Model for Diverse Topic Extraction.

To automatically mine structured semantic topics from text, neural topic modeling has arisen and made some progress. However, most existing work focuses on designing a mechanism to enhance topic coherence but sacrificing the diversity of the extracted topics. To address this limitation, we propose the first neural-based topic modeling approach purely based on mutual information maximization, called the mutual information topic (MIT) model, in this article.

Does green finance reduce environmental pollution?-a study based on China's provincial panel data.

As a bridge between economy and ecology, green finance is vital in improving environmental quality and promoting sustainable development. Based on the building of an environmental pollution index system, this paper constructs the [Formula: see text] model to deeply explore the specific impact of green finance on environmental pollution using China's provincial panel data from 2007 to 2020. This paper constructs an intermediary model to test the impact mechanism of green finance on reducing environmental pollution and discusses the regional heterogeneity of green finance in reducing environmental pollution.

Older adults' refusal speech act in cognitive assessment: A multimodal pragmatic perspective.

This paper explores how older adults with different cognitive abilities perform the refusal speech act in the cognitive assessment in the setting of memory clinics. The refusal speech act and its corresponding illocutionary force produced by nine Chinese older adults in the Montreal Cognitive Assessment-Basic was annotated and analyzed from a multimodal perspective. Overall, regardless of the older adults' cognitive ability, the most common discursive device to refuse is the demonstration of their inability to carry out or continue the cognitive task.

Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.

The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task.

Biomedical Argument Mining Based on Sequential Multi-Task Learning.

Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification.

Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning.

Background: Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD.

Methods: Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls).

Health issue identification in social media based on multi-task hierarchical neural networks with topic attention.

Objective: Health issue identification in social media is to predict whether the writers have a disease based on their posts. Numerous posts and comments are shared on social media by users. Certain posts may reflect writers' health condition, which can be employed for health issue identification.

Multivariate Machine Learning Analyses in Identification of Major Depressive Disorder Using Resting-State Functional Connectivity: A Multicentral Study.

Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients.

Named Entity Aware Transfer Learning for Biomedical Factoid Question Answering.

Biomedical factoid question answering is an important task in biomedical question answering applications. It has attracted much attention because of its reliability. In question answering systems, better representation of words is of great importance, and proper word embedding can significantly improve the performance of the system.

Bone metabolic biomarker-based diagnosis of type 2 diabetes osteoporosis by support vector machine.

Background: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes.

Potential Value of Plasma Amyloid-β, Total Tau, and Neurofilament Light for Identification of Early Alzheimer's Disease.

The objective of the study was to explore the potential value of plasma indicators for identifying amnesic mild cognitive impairment (aMCI) and determine whether levels of plasma indicators are related to the performance of cognitive function and brain tissue volumes. In total, 155 participants (68 aMCI patients and 87 health controls) were recruited in the present cross-sectional study. The levels of plasma amyloid-β (Aβ) 40, Aβ42, total tau (t-tau), and neurofilament light (NFL) were measured using an ultrasensitive quantitative method.

LSTM-Based End-to-End Framework for Biomedical Event Extraction.

Biomedical event extraction plays an important role in the extraction of biological information from large-scale scientific publications. However, most state-of-the-art systems separate this task into several steps, which leads to cascading errors. In addition, it is complicated to generate features from syntactic and dependency analysis separately.

Lysine-specific demethylase 1 cooperates with BRAF-histone deacetylase complex 80 to enhance HIV-1 Tat-mediated transactivation.

Despite the notable success of combination antiretroviral therapy, how to eradicate latent HIV-1 from reservoirs poses a challenge. The Tat protein plays an indispensable role in HIV reactivation and histone demethylase LSD1 promotes Tat-mediated long terminal repeats (LTR) activation. However, the role of LSD1 in remodeling chromatin and the role of its component BHC80 in activation of latent HIV-1 in T cells are unknown.

HIV-1 Protein Tat Impairs Neuronal Dendrites via Activation of PP1 and Regulation of the CREB/BDNF Pathway.

Despite the success of combined antiretroviral therapy in recent years, the prevalence of human immunodeficiency virus (HIV)-associated neurocognitive disorders in people living with HIV-1 is increasing, significantly reducing the health-related quality of their lives. Although neurons cannot be infected by HIV-1, shed viral proteins such as transactivator of transcription (Tat) can cause dendritic damage. However, the detailed molecular mechanism of Tat-induced neuronal impairment remains unknown.

Position-aware deep multi-task learning for drug-drug interaction extraction.

Objective: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prevent adverse drug events. Although some known DDIs can be found in purposely-built databases such as DrugBank, most information is still buried in scientific publications.

Stability of HIV-1 subtype B and C Tat is associated with variation in the carboxyl-terminal region.

The multifunctional trans-activator Tat is an essential regulatory protein for HIV-1 replication and is characterized by high sequence diversity. Numerous experimental studies have examined Tat in HIV-1 subtype B, but research on subtype C Tat is lacking, despite the high prevalence of infections caused by subtype C worldwide. We hypothesized that amino acid differences contribute to functional differences among Tat proteins.

The 57th amino acid conveys the differential subcellular localization of human immunodeficiency virus-1 Tat derived from subtype B and C.

The multifunctional transactivator Tat protein is an essentially regulatory protein for HIV-1 replication and it plays a role in pathogenesis of HIV-1 infection. At present, numerous experimental studies about HIV-1 Tat focus on subtype B, very few has been under study of subtype C-Tat. In view of the amino acid variation of the clade-specific Tat proteins, we hypothesized that the amino acid difference contributed to differential function of Tat proteins.

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

Objectives: Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing.

Biomedical relation extraction: from binary to complex.

Biomedical relation extraction aims to uncover high-quality relations from life science literature with high accuracy and efficiency. Early biomedical relation extraction tasks focused on capturing binary relations, such as protein-protein interactions, which are crucial for virtually every process in a living cell. Information about these interactions provides the foundations for new therapeutic approaches.

Semi-supervised learning of statistical models for natural language understanding.

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations.

Event trigger identification for biomedical events extraction using domain knowledge.

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently.

Biomedical events extraction using the hidden vector state model.

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events.

Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model.

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure.

Extracting interactions between proteins from the literature.

During the last decade, biomedicine has witnessed a tremendous development. Large amounts of experimental and computational biomedical data have been generated along with new discoveries, which are accompanied by an exponential increase in the number of biomedical publications describing these discoveries. In the meantime, there has been a great interest with scientific communities in text mining tools to find knowledge such as protein-protein interactions, which is most relevant and useful for specific analysis tasks.