Uncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

Background: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text.

Effects of the low-speed continuous infusion catheter technique on double-lumen central venous catheters: A randomized controlled trial.

Background: Central venous catheters are widely used in clinical practice, and the incidence of central venous catheter occlusion is between 25 % and 38 %. The turbulence caused by the pulsatile flushing technique is harmful to the vascular endothelium and may lead to phlebitis. The low-speed continuous infusion catheter technique is a new type of continuous infusion that ensures that the catheter is always in a keep-vein-open state by continuous low-speed flushing; hence, avoiding the problem of catheter occlusion.

CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation.

Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists. Recent advancements in Radiology Report Generation (RRG) are largely driven by improving a model's capabilities in encoding single-modal feature representations, while few studies explicitly explore the cross-modal alignment between image regions and words. Radiologists typically focus first on abnormal image regions before composing the corresponding text descriptions, thus cross-modal alignment is of great importance to learn a RRG model which is aware of abnormalities in the image.

Genome-wide SNPs reveal novel patterns of spatial genetic structure in (Diptera Culicidae) population in China.

Introduction: Since the second half of the 20th century, , a vector for more than 20 arboviruses, has spread worldwide. is the main vector of infectious diseases transmitted by mosquitoes in China, and it has caused concerns regarding public health. A comprehensive understanding of the spatial genetic structure of this vector species at a genomic level is essential for effective vector control and the prevention of vector-borne diseases.

Effects of Guangzhou seasonal climate change on the development of Aedes albopictus and its susceptibility to DENV-2.

The susceptibility of Asian tiger mosquitoes to DENV-2 in different seasons was observed in simulated field environments as a reference to design dengue fever control strategies in Guangzhou. The life table experiments of mosquitoes in four seasons were carried out in the field. The susceptibility of Ae.

Understanding patient reviews with minimum supervision.

Understanding patient opinions expressed towards healthcare services in online platforms could allow healthcare professionals to respond to address patients' concerns in a timely manner. Extracting patient opinion towards various aspects of health services is closely related to aspect-based sentiment analysis (ABSA) in which we need to identify both opinion targets and target-specific opinion expressions. The lack of aspect-level annotations however makes it difficult to build such an ABSA system.

Semi-field life-table studies of Aedes albopictus (Diptera: Culicidae) in Guangzhou, China.

Background: Aedes albopictus is a major vector for several tropical infectious diseases. Characterization of Ae. albopictus development under natural conditions is crucial for monitoring vector population expansion, dengue virus transmission, and disease outbreak preparedness.

Research progress in RNA interference against the infection of mosquito-borne viruses.

Mosquito-borne diseases have become an important public health issue of global concern because of their high incidence and transmission rate. As a vector for mosquito-borne diseases, studying the interaction mechanism between mosquitoes and mosquito-borne viruses will help control mosquito-borne diseases. The impaired innate immunity and immune barriers evasion caused by mosquito-borne viruses in mosquitoes pose a potential risk for the persistent infection of the virus in mosquitoes and the outbreak of mosquito-borne diseases.

Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability.

Background: Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens. Many recent dengue fever outbreaks in China have been caused solely by the vector. Mapping of the potential distribution ranges of Ae.

Patterns of spatial genetic structures in Aedes albopictus (Diptera: Culicidae) populations in China.

Background: The Asian tiger mosquito, Aedes albopictus, is one of the 100 worst invasive species in the world and the vector for several arboviruses including dengue, Zika and chikungunya viruses. Understanding the population spatial genetic structure, migration, and gene flow of vector species is critical to effectively preventing and controlling vector-borne diseases. Little is known about the population structure and genetic differentiation of native Ae.

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.

EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.

Background: Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues.

Results: In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues.

Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks.

The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare.

PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification.

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach.

DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool.

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.

enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance.

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.

Improved survival ratios correlate with myeloid dendritic cell restoration in acute-on-chronic liver failure patients receiving methylprednisolone therapy.

Acute-on-chronic liver failure (ACLF) is a severe life-threatening complication. Liver transplantation is the only available therapeutic option; however, several limitations have restricted its use in patients. The use of corticosteroids as an optional therapy for ACLF has received a great deal of interest.

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.