Chinese EMR Named Entity Recognition Using Fused Label Relations Based on Machine Reading Comprehension Framework.

Chinese electronic medical records (EMR) presents significant challenges for named entity recognition (NER) due to their specialized nature, unique language features, and diverse expressions. Traditionally, NER is treated as a sequence labeling task, where each token is assigned a label. Recent research has reframed NER within the machine reading comprehension (MRC) framework, extracting entities in a question-answer format, achieving state-of-the-art performance. However, these MRC-based methods have a significant limitation: they extract entities of various types independently, ignoring their interrelations. To address this, we introduce the Fusion Label Relations with MRC (FLR-MRC) model, which enhances the MRC model by implicitly capturing dependencies among entity types. FLR-MRC models interrelations between labels using graph attention networks, integrating these with textual data to identify entities. On the benchmark CMeEE and CCKS2017-CNER datasets, FLR-MRC achieves F1-scores of 0.6652 and 0.9101, respectively, outperforming existing clinical NER methods.

Boundary-aware Dual Biaffine Model for Sequential Sentence Classification in Biomedical Documents.

Assigning appropriate rhetorical roles, such as "background," "intervention," and "outcome," to sentences in biomedical documents can streamline the process for physicians to locate evidence and resources for medical treatment and decision-making. While sequence labeling and span-based methods are frequently employed for this task, the former disregards a document's semantic structure, resulting in a lack of semantic coherence across continuous sentences. Span-based approaches, on the other hand, either necessitate the enumeration of all potential spans, which can be time-consuming, or may lead to the misclassification of sentences over extended spans. Consequently, an approach is required that models the semantic structure of documents explicitly and captures boundary information to achieve precise and effective sentence labeling in biomedical documents. To address these challenges, we propose a new approach, the boundary-aware dual biaffine model, which explicitly models the semantic structure of documents and incorporates boundary information via a dual biaffine layer. We introduce a dynamic programming algorithm to minimize missing labels and overlapping predictions, and achieve globally optimal decoding results. We evaluate our approach on three benchmark datasets, namely PubMed 20k RCT, PubMed-PICO and NICTA-PIBOSO. The experimental results demonstrate that our approach outperforms strong baselines and achieves state-of-the-art performance on PubMed 20k RCT and PubMed-PICO. Additionally, our method also achieves competitive results on NICTA-PIBOSO. Availability: Our codes and data will be available at: https://github.com/CSU-NLP-Group/Sequential-Sentence-Classification.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS: In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

In situ construction of a hierarchical TiO2/Ti3C2 hybrid via water steam etching for high-performance potassium-ion batteries.

Potassium ion batteries (PIBs) have attracted great research interest in new-generation large-scale energy storage considering their abundant source, low cost, and suitable working potential. Herein, a hierarchical TiO2/Ti3C2 hybrid is developed via a green, facile water steam etching method for realizing an efficient and durable anode material for PIBs. In this hierarchical assembly, the TiO2 nanoparticles anchored on the Ti3C2 surface contribute a high pseudocapacitance while mitigating the restacking of the Ti3C2 MXene skeleton, which ensures mechanical robustness to accommodate large K+ ions. Benefiting from the amalgamation of structural properties and the synergistic effects stemming from the individual constituents, the optimized TiO2/Ti3C2 anode harvests remarkable performance in the potassium ion storage, including a high reversible capacity of ∼255 mA h g-1 at 0.2 A g-1 after 1300 cycles as well as an outstanding long-term cycling performance and rate capability (a high capacity of ∼230 mA h g-1 even after intensive 10 000 cycles at 2 A g-1). The excellent TiO2/Ti3C2 anode enables the assembled pouch-cell coupling PTCDA cathode to deliver a capacity of ∼173 mA h g-1 at 0.05 A g-1 and retain 120 mA h g-1 after 30 cycles. The employment of the pouch-cell in successfully powering the LED module showcases its application prospect for advanced PIBs.

Regulated Zn Plating and Stripping by a Multifunctional Polymer-Alloy Interphase Layer for Stable Zn Metal Anode.

Metallic zinc electrode with a high theoretical capacity of 820 mAh g-1 is highly considered as a promising candidate for next-generation rechargeable batteries. However, the unavoidable hydrogen evolution, uncontrolled dendrite growth, and severe passivation reaction badly hinder its practical implementations. Herein, a robust polymer-alloy artificial protective layer is designed to realize dendrite-free Zn metal anode by the integration of zincophilic SnSb nanoparticles with Nafion. In comparison to the bare Zn electrode, the Nafion-SnSb coated Zn (NFSS@Zn) electrode exhibits lower nucleation energy barrier, more uniform electric field distribution and stronger anti-corrosion capability, thus availably suppressing the Zn dendrite growth and interfacial side reactions. As a consequence, the NFSS@Zn electrode exhibits a long cycle life over 1500 h at 1 mA cm-2 with an ultra-low voltage hysteresis (25 mV). Meanwhile, when paired with a MnO2 cathode, the as-prepared full cell also demonstrates stable performance for 1000 cycles at 3 A g-1 . This work provides an inspired approach to boost the performance of Zn anodes.

Multigranularity Label Prediction Model for Automatic International Classification of Diseases Coding in Clinical Text.

International Classification of Diseases (ICD) serves as the foundation for generating comparable global disease statistics across regions and over time. The process of ICD coding involves assigning codes to diseases based on clinical notes, which can describe a patient's condition in a standard way. However, this process is complicated by the vast number of codes and the intricate taxonomy of ICD codes, which are hierarchically organized into various levels, including chapter, category, subcategory, and its subdivisions. Many existing studies focus solely on predicting subcategory codes, ignoring the hierarchical relationships among codes. To address this limitation, we propose a multitask learning model that trains multiple classifiers for different code levels, while also capturing the relations between coarser and finer-grained labels through a reinforcement mechanism. Our approach is evaluated on both English and Chinese benchmark dataset, and we demonstrate that our method achieves competitive performance with baseline models, particularly in terms of macro-F1 results. These findings suggest that our approach effectively leverages the hierarchical structure of ICD codes to improve disease code prediction accuracy. Analysis of attention mechanism shows that multigranularity attention of our model captures crucial feature of input text on different granularity levels, which can provide reasonable explanations for the prediction results.

IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Determining drug-drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data.