S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognition

Named Entity Recognition (NER) remains challenging due to the complex entities, like nested, overlapping, and discontinuous entities. Existing approaches, such as sequence-to-sequence (Seq2Seq) generation and span-based classification, have shown impressive performance on various NER subtasks, but they are difficult to scale to datasets with longer input text because of either exposure bias issue or inefficient computation. In this paper, we propose a novel Sequence-to-Forest generation paradigm, S2F-NER, which can directly extract entities in sentence via a Forest decoder that decode multiple entities in parallel rather than sequentially. Specifically, our model generate each path of each tree in forest autoregressively, where the maximum depth of each tree is three (which is the shortest feasible length for complex NER and is far smaller than the decoding length of Seq2Seq). Based on this novel paradigm, our model can elegantly mitigates the exposure bias problem and keep the simplicity of Seq2Seq. Experimental results show that our model significantly outperforms the baselines on three discontinuous NER datasets and on two nested NER datasets, especially for discontinuous entity recognition

Neural activity dissociation between thought-based and perception-based response conflict

Based on the idea that intentions have different penetrability to perception and thought (Fodor, 1983), four Stroop-like tasks, AA, AW, WA, and WW are used, where the A represents an arrow and the CPPR (closest processing prior to response) is perception, and the W represents a word and the CPPR is thought. Event-related brain potentials were recorded as participants completed these tasks, and sLORETA (standardized low resolution brain electromagnetic tomography) was used to localize the sources at specific time points. These results showed that there is an interference effect in the AA and WA tasks, but not in the AW or WW tasks. The activated brain areas related to the interference effect in the AA task were the PFC and ACC, and PFC activation took place prior to ACC activation; but only PFC in WA task. Combined with previous results, a new neural mechanism of cognitive control is proposed

The role of peptides in reversing chemoresistance of breast cancer: current facts and future prospects

Breast cancer is the first malignant tumor in women, and its incidence is also increasing year by year. Chemotherapy is one of the standard therapies for breast cancer, but the resistance of breast cancer cells to chemotherapy drugs is a huge challenge for the effective treatment of breast cancer. At present, in the study of reversing the drug resistance of solid tumors such as breast cancer, peptides have the advantages of high selectivity, high tissue penetration, and good biocompatibility. Some of the peptides that have been studied can overcome the resistance of tumor cells to chemotherapeutic drugs in the experiment, and effectively control the growth and metastasis of breast cancer cells. Here, we describe the mechanism of different peptides in reversing breast cancer resistance, including promoting cancer cell apoptosis; promoting non-apoptotic regulatory cell death of cancer cells; inhibiting the DNA repair mechanism of cancer cells; improving the tumor microenvironment; inhibiting drug efflux mechanism; and enhancing drug uptake. This review focuses on the different mechanisms of peptides in reversing breast cancer drug resistance, and these peptides are also expected to create clinical breakthroughs in promoting the therapeutic effect of chemotherapy drugs in breast cancer patients and improving the survival rate of patients

Transition Metal Atoms Anchored on CuPS3 Monolayer for Enhancing Catalytic Performance of Hydrogen Evolution Reactions

The noble metal such as Pt has been used as the catalysts for hydrogen evolution reaction (HER), but with problems such as scarcity of resources and high cost. Anchoring transition metal atoms onto the catalysts is regarded as a potential approach to solve this problem and enhance the electrocatalytic performance of HER. For this purpose, two-dimensional materials, such as CuPS3 monolayer, are regarded as one of the most ideal carriers for adsorption of metal atoms. However, there is no previous study on this topic. In this paper, we systematically studied microstructures, electronic properties and electrocatalytic performance of the CuPS3 monolayer anchored with transition-metal atoms (e.g., Sc, Ti, V, Cr, Mn, Fe, Co, and Ni) using a density functional theory (DFT). Results showed that all the transition metal atoms are favorably adsorbed onto the CuPS3 monolayer with large binding energies at the top of the Cu atom. The pristine CuPS3 monolayer has a large catalytic inertia for hydrogen evolution reactions, whereas after anchored with transition metal atoms, their catalytic performances have been significantly improved. The Gibbs free energy (ΔGH) is 0.44 eV for the H atom absorbed onto the pristine CuPS3 monolayer, whereas the ΔGH values for the V, Fe, and Ni atoms anchored onto the CuPS3 monolayer are 0.02, 0.11, and 0.09 eV, respectively, which is close to the ΔGH of H atom adsorbed on Pt (e.g., -0.09 eV). At the same time, the influence of hydrogen coverage rate was calculated. The result shows that V adsorbed on CuPS3 monolayer is catalytic active for HER for a large range of hydrogen coverage. Our results demonstrate that anchoring of V atom onto the CuPS3 monolayer is a potentially superior method for making the catalyst for the HER

A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition

In recent years, researchers have shown an increased interest in recognizing the overlapping entities that have nested structures. However, most existing models ignore the semantic correlation between words under different entity types. Considering words in sentence play different roles under different entity types, we argue that the correlation intensities of pairwise words in sentence for each entity type should be considered. In this paper, we treat named entity recognition as a multi-class classification of word pairs and design a simple neural model to handle this issue. Our model applies a supervised multi-head self-attention mechanism, where each head corresponds to one entity type, to construct the word-level correlations for each type. Our model can flexibly predict the span type by the correlation intensities of its head and tail under the corresponding type. In addition, we fuse entity boundary detection and entity classification by a multitask learning framework, which can capture the dependencies between these two tasks. To verify the performance of our model, we conduct extensive experiments on both nested and flat datasets. The experimental results show that our model can outperform the previous state-of-the-art methods on multiple tasks without any extra NLP tools or human annotations

Diffusion Enhancement to Stabilize Solid Electrolyte Interphase

The damage of the solid electrolyte interphase (SEI) layer during the stripping process in lithium secondary batteries causes the reduction of energy density. The stabilization of the solid electrolyte interphase is as important as the inhibition of lithium dendrites for lithium-based batteries. But the former is largely underestimated, which leads to the unclear damage mechanism and the lack of effective solutions to suppress the damage. Here, in this paper a diffusion-limited damage mechanism of the SEI layer is proposed. The inhomogeneity of the SEI layer results in region-dependent diffusion kinetics of lithium ions (Li+) passing through the layer. The slip lines and kinks having a thicker SEI layer, show slower Li+ conduction than the smooth surface. The uneven stripping process leads to the formation of cracks at the boundary between the slip lines and the smooth surface, which further causes collapse and serious damage of SEI. Upon this assumption, it is proposed to enhance the diffusion of Li+ at the local areas of SEI layer by applying parallel magnetic fields on the outside of electrodes. Both the electrochemical characterizations and long-term stability examination confirm the effectiveness of the magnetic field in enhancing the diffusion of Li+ and suppressing the damage of SEI

WWOX CNV-67048 Functions as a Risk Factor for Epithelial Ovarian Cancer in Chinese Women by Negatively Interacting with Oral Contraceptive Use

Copy number variations (CNVs) have attracted increasing evidences to represent their roles as cancer susceptibility regulators. However, little is known about the role of CNV in epithelia ovarian cancer (EOC). Recently, the CNV-67048 of WW domain-containing oxidoreductase (WWOX) was reported to alter cancer risks. Considering that WWOX also plays a role in EOC, we hypothesized that the CNV-67048 was associated with EOC risk. In a case-control study of 549 EOC patients and 571 age (±5 years) matched cancer-free controls, we found that the low copy number of CNV-67048 (1-copy and 0-copy) conferred a significantly increased risk of EOC (OR = 1.346, 95% CI = 1.037–1.747) and it determined the risk by means of copy number-dependent dosage effect (P=0.009). Data from TCGA also confirmed the abovementioned association as the frequency of low copies in EOC group was 3.68 times more than that in healthy group (P=0.023). The CNV also negatively interacted with oral contraceptive use on EOC risk (P=0.042). Functional analyses further showed a lower mRNA level of WWOX in tissues with the 0-copy or 1-copy than that in those with the 2-copy (P=0.045). Our data suggested the CNV-67048 to be a risk factor of EOC in Chinese women