A TrkB cleavage fragment in hippocampus promotes Depressive-Like behavior in mice.

Decreased hippocampal tropomyosin receptor kinase B (TrkB) level is implicated in the pathophysiology of stress-induced mood disorder and cognitive decline. However, how TrkB is modified and mediates behavioral responses to chronic stress remains largely unknown. Here the effects and mechanisms of TrkB cleavage by asparagine endopeptidase (AEP) were examined on a preclinical murine model of chronic restraint stress (CRS)-induced depression. CRS activated IL-1ß-C/EBPß-AEP pathway in mice hippocampus, accompanied by elevated TrkB 1-486 fragment generated by AEP. Specifi.c overexpression or suppression of AEP-TrkB axis in hippocampal CaMKIIα-positive cells aggravated or relieved depressive-like behaviors, respectively. Mechanistically, in addition to facilitating AMPARs internalization, TrkB 1-486 interacted with peroxisome proliferator-activated receptor-δ (PPAR-δ) and sequestered it in cytoplasm, repressing PPAR-δ-mediated transactivation and mitochondrial function. Moreover, co-administration of 7,8-dihydroxyflavone and a peptide disrupting the binding of TrkB 1-486 with PPAR-δ attenuated depression-like symptoms not only in CRS animals, but also in Alzheimer's disease and aged mice. These findings reveal a novel role for TrkB cleavage in promoting depressive-like phenotype.

[Clinical features and genetic analysis of a child with acute form of Tyrosinemia type I due to a novel variant of FAH gene].

OBJECTIVE: To analyze the clinical phenotype and genetic basis for a child with acute form of tyrosinemia type I (TYRSN1). METHODS: A child with TYRSN1 who presented at the Gansu Provincial Maternal and Child Health Care Hospital in October 2020 was selected as the subject. The child was subjected to tandem mass spectrometry (MS-MS) and urine gas chromatography-mass spectrometry (GC-MS) for the detection of inherited metabolic disorders, in addition with whole exome sequencing (WES). Candidate variants were validated by Sanger sequencing. RESULTS: The child's clinical features included abdominal distension, hepatomegaly, anemia and tendency of bleeding. By mass spectrometry analysis, her serum and urine tyrosine and succinylacetone levels have both exceeded the normal ranges. WES and Sanger sequencing revealed that she has harbored c.1062+5G>A and c.943T>C (p.Cys315Arg) compound heterozygous variants of the FAH gene, which were inherited from her father and mother, respectively. Among these, the c.943T>C was unreported previously. CONCLUSION: Considering her clinical phenotype and result of genetic testing, the child was diagnosed with TYRSN1 (acute type). The compound heterozygous variants of the FAH gene probably underlay the disease in this child. Above finding has further expanded the spectrum of FAH gene variants, and provided a basis for accurate treatment, genetic counseling and prenatal diagnosis for her family.

Whole-exome sequencing identified a homozygous novel RAG1 mutation in a child with omenn syndrome.

INTRODUCTION AND OBJECTIVES: Omenn syndrome (OS) is a very rare type of severe combined immunodeficiencies manifested with erythroderma, eosinophilia, hepatosplenomegaly, lymph-adenopathy, and elevated level of serum IgE. OS is inherited with an autosomal recessive mode of inheritance. Germline mutations in the human RAG1 gene cause OS. MATERIALS AND METHODS: In this study, we investigated a 2-month-old boy with cough, mild anaemia, pneumonia, immunodeficiency, repeated infection, feeding difficulties, hepatomegaly, growth retardation, and heart failure. Parents of the proband were phenotypically normal. RESULTS: Karyotype analysis and chromosomal microarray analysis found no chromosomal structural abnormalities (46, XY) and no pathogenic copy number variations (CNVs) in the proband. Whole-exome sequencing identified a novel homozygous single nucleotide deletion (c.2662delC) in exon 2 of the RAG1 gene in the proband. Sanger sequencing confirmed that both the proband parents were carrying this variant in a heterozygous state. This variant was not identified in two elder sisters and one elder brother of the proband and in the 100 ethnically matched normal healthy individuals. This novel homozygous deletion (c.2662delC) leads to the frameshift, which finally results in the formation of the truncated protein (p.Leu888Phefs*3) V(D)J recombination-activating protein 1 with 890 amino acids compared with the wildtype V(D)J recombination-activating protein 1 of 1043 amino acids. Hence, it is a loss-of-function variant. CONCLUSIONS: Our present study expands the mutational spectrum of the RAG1 gene associated with OS. We also strongly suggested the importance of whole-exome sequencing for the genetic screening of patients with OS.

Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification.

Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets.

Cooking stoves and risk of birth defects in urban China.

INTRODUCTION: Birth defects are a leading cause of infant death. Pregnant women spend a large amount of time indoors, and little research from population-based studies has investigated the association between indoor air pollution and birth defects. We aimed to examine whether using coal, biomass, or electromagnetic stoves for cooking is associated with risk of birth defects compared to using gas stoves. METHODS: A birth cohort study was conducted from 2010 to 2012 in Lanzhou, China. Cases (n = 264) were singleton births with birth defects, which were defined as abnormalities of structure or function, including metabolism, presented at birth based on the International Classification of Diseases (ICD)-10 codes. Controls (n = 9926) were defined as singleton live births without birth defects. Unconditional logistic regression models were employed to estimate the association adjusting for confounding variables. RESULTS: Compared to gas stoves for cooking, biomass (OR = 2.66, 95%CI: 1.38-5.13), and electromagnetic stove (OR = 1.90, 95%CI: 1.26-2.88) for cooking were associated with an increased risk of birth defects. The significant associations remained among non-congenital heart disease (CHD) defects but not CHDs. CONCLUSIONS: Using biomass or electromagnetic stoves for cooking during pregnancy was associated with an increased risk of birth defects. Additional studies are warranted to confirm these novel findings. Studies with larger sample size or greater statistical power are also warranted to better estimate the associations for individual birth defects.

MTTFsite: cross-cell type TF binding site prediction by using multi-task learning.

MOTIVATION: The prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data. RESULTS: In this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained. AVAILABILITY AND IMPLEMENTATION: The resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Quantum Expectation Value Based Language Model with Application to Question Answering.

Quantum-inspired language models have been introduced to Information Retrieval due to their transparency and interpretability. While exciting progresses have been made, current studies mainly investigate the relationship between density matrices of difference sentence subspaces of a semantic Hilbert space. The Hilbert space as a whole which has a unique density matrix is lack of exploration. In this paper, we propose a novel Quantum Expectation Value based Language Model (QEV-LM). A unique shared density matrix is constructed for the Semantic Hilbert Space. Words and sentences are viewed as different observables in this quantum model. Under this background, a matching score describing the similarity between a question-answer pair is naturally explained as the quantum expectation value of a joint question-answer observable. In addition to the theoretical soundness, experiment results on the TREC-QA and WIKIQA datasets demonstrate the computational efficiency of our proposed model with excellent performance and low time consumption.

Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.

Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. We propose a novel method, referred to as DANN_TF, for TFBS prediction. DANN_TF consists of a feature extractor, a label predictor, and a domain classifier. The feature extractor and the domain classifier constitute an Adversarial Network, which ensures that learned features are common features across different cell types. DANN_TF is evaluated on five TFs in five cell types with a total of 25 cell-type TF pairs and compared to a baseline method which does not use Adversarial Network. For both data augmentation and cross-cell-type prediction, DANN_TF performs better than the baseline method on most cell-type TF pairs. DANN_TF is further evaluated by an additional 13 TFs in the five cell types with a total of 65 cell-type TF pairs. Results show that DANN_TF achieves significantly higher AUC than the baseline method on 96.9% pairs of the 65 cell-type TF pairs. This is a strong indication that DANN_TF can indeed learn common features for cross-cell-type TFBS prediction.

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

BACKGROUND: Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. RESULTS: We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CONCLUSION: CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

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. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. CONCLUSIONS: We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance issue between binding and non-binding residues. A web service of EL_PSSM-RT ( http://hlt.hitsz.edu.cn:8080/PSSM-RT_SVM/ ) is provided for free access to the biological research community.

TLR3 signaling in macrophages is indispensable for the protective immunity of invariant natural killer T cells against enterovirus 71 infection.

Enterovirus 71 (EV71) is the most virulent pathogen among enteroviruses that cause hand, foot and mouth disease in children but rarely in adults. The mechanisms that determine the age-dependent susceptibility remain largely unclear. Here, we found that the paucity of invariant natural killer T (iNKT) cells together with immaturity of the immune system was related to the susceptibility of neonatal mice to EV71 infection. iNKT cells were crucial antiviral effector cells to protect young mice from EV71 infection before their adaptive immune systems were fully mature. EV71 infection led to activation of iNKT cells depending on signaling through TLR3 but not other TLRs. Surprisingly, iNKT cell activation during EV71 infection required TLR3 signaling in macrophages, but not in dendritic cells (DCs). Mechanistically, interleukin (IL)-12 and endogenous CD1d-restricted antigens were both required for full activation of iNKT cells. Furthermore, CD1d-deficiency led to dramatically increased viral loads in central nervous system and more severe disease in EV71-infected mice. Altogether, our results suggest that iNKT cells may be involved in controlling EV71 infection in children when their adaptive immune systems are not fully developed, and also imply that iNKT cells might be an intervention target for treating EV71-infected patients.

Cost Effectiveness of Pembrolizumab for Advanced Melanoma Treatment in Portugal.

BACKGROUND: The aim of this study was to assess the cost-effectiveness of pembrolizumab in treating patients with ipilimumab-naïve advanced melanoma in Portugal. METHODS: A cost-effectiveness model was developed to analyze the costs and consequences of treatment with pembrolizumab compared to treatment with ipilimumab in patients with advanced melanoma not previously treated with ipilimumab. The model was parameterized by using data from a head-to-head phase III randomized clinical trial, KEYNOTE-006. Extrapolation of long-term outcomes was based on approaches previously applied, combining ipilimumab data and melanoma patients' registry data. The analysis was conducted from the perspective of the Portuguese National Health Service, and a lifetime horizon (40 years) was used. Portugal-specific disease management costs were estimated by convening a panel of six clinical experts to derive health state resource use and multiplying the results by national unit costs. To test for the robustness of the conclusions, we conducted deterministic and probabilistic sensitivity analyses. RESULTS: Pembrolizumab increases life expectancy in 1.57 undiscounted life-years (LYs) and is associated with an increase in costs versus that of ipilimumab. The estimated incremental cost-effectiveness ratio is €47,221 per quality-adjusted life-year (QALY) and €42,956 per LY. Deterministic sensitivity analysis showed that the results were robust to the change of most input values or assumptions and were sensitive to time on treatment scenarios. According to the probabilistic sensitivity analysis performed, pembrolizumab is associated with a cost per QALY gained inferior to €50,000 in 75% of the cases. CONCLUSIONS: Considering the usually accepted thresholds in oncology, pembrolizumab is a cost-effective alternative for treating patients with advanced melanoma in Portugal.

Folic acid supplementation, dietary folate intake and risk of preterm birth in China.

PURPOSE: Folic acid supplementation has been suggested to reduce the risk of preterm birth. However, results from previous epidemiologic studies have been inconclusive. We investigated the hypothesis that folic acid supplementation and dietary folate intake during pre- and post-conception reduces the risk of preterm birth. METHODS: We analyzed data from a birth cohort study conducted between 2010 and 2012 in Lanzhou, China, including 10,179 pregnant women with live singleton births. RESULTS: Compared to non-users, folic acid supplement users with >12-week duration had a reduced risk of preterm birth (OR 0.67, 95 % CI 0.55-0.83) with a significant dose-response relationship (P for trend = 0.01). A similar pattern was observed for spontaneous preterm birth. Stronger associations were seen for ever use of folic acid supplement and very preterm birth (OR 0.50, 95 % CI 0.36-0.69) and spontaneous very preterm birth (OR 0.42, 95 % CI 0.29-0.63). Dietary folate intake during preconception and pregnancy were also associated with reduced risk of preterm birth (OR 0.68, 95 % CI 0.56-0.83, OR 0.57, 95 % CI 0.47-0.70 for the highest quartiles, respectively), particularly for spontaneous very preterm (OR 0.41, 95 % CI 0.24-0.72, OR 0.26, 95 % CI 0.15-0.47 for the highest quartiles, respectively). There were also decreased risks of preterm birth observed per 10-µg increase in dietary folate intake, and similar associations were found after stratification by folic acid supplementation status. CONCLUSIONS: Our results suggest that folic acid supplementation and higher dietary folate intake during preconception and pregnancy reduces the risk of preterm birth, and the protective effect varies by preterm subtypes.

Maternal tea consumption and the risk of preterm delivery in urban China: a birth cohort study.

BACKGROUND: Studies investigating the relationship between maternal tea drinking and risk of preterm birth have reached inconsistent results. METHODS: The present study analyzed data from a birth cohort study including 10,179 women who delivered a singleton live birth were conducted in Lanzhou, China between 2010 and 2012. RESULTS: Drinking tea (OR = 1.36, 95 % CI: 1.09-1.69), and specifically green (OR = 1.42, 95 % CI: 1.08-1.85) or scented tea (OR = 1.61, 95 % CI: 1.04-2.50), was associated with an increased risk of preterm birth. Drinking tea was associated with both moderate preterm (OR = 1.41, 95 % CI: 1.12-1.79) and spontaneous preterm birth (OR = 1.41, 95 % CI: 1.09-1.83). Risk of preterm birth increased with decreasing age of starting tea drinking (<20 years, OR = 1.60, 95 % CI: 1.17-2.20) and increasing duration (p for trend < 0.01). The relationship between tea drinking and preterm birth is modified by both maternal age (p < 0.05) and gestational weight gain (p < 0.05). CONCLUSIONS: Despite conflicting findings in the previous literature, we saw a significant association with maternal tea drinking and risk of preterm birth in our cohort. More studies are needed both to confirm this finding and to elucidate the mechanism behind this association.

Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.

MOTIVATION: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. RESULTS: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. AVAILABILITY AND IMPLEMENTATION: For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/

Exposure to cooking fuels and birth weight in Lanzhou, China: a birth cohort study.

BACKGROUND: Early studies have suggested that biomass cooking fuels were associated with increased risk of low birth weight (LBW). However it is unclear if this reduced birth weight was due to prematurity or intrauterine growth restriction (IUGR). METHODS: In order to understand the relationship between various cooking fuels and risk of LBW and small for gestational age (SGA), we analyzed data from a birth cohort study conducted in Lanzhou, China which included 9,895 singleton live births. RESULTS: Compared to mothers using gas as cooking fuel, significant reductions in birth weight were observed for mothers using coal (weight difference = 73.31 g, 95 % CI: 26.86, 119.77) and biomass (weight difference = 87.84 g, 95 % CI: 10.76, 164.46). Using biomass as cooking fuel was associated with more than two-fold increased risk of LBW (OR = 2.51, 95 % CI: 1.26, 5.01), and the risk was mainly seen among preterm births (OR = 3.43, 95 % CI: 1.21, 9.74). No significant associations with LBW were observed among mothers using coal or electromagnetic stoves for cooking. CONCLUSIONS: These findings suggest that exposure to biomass during pregnancy is associated with risk of LBW, and the effect of biomass on LBW may be primarily due to prematurity rather than IUGR.

Using distances between Top-n-gram and residue pairs for protein remote homology detection.

BACKGROUND: Protein remote homology detection is one of the central problems in bioinformatics, which is important for both basic research and practical application. Currently, discriminative methods based on Support Vector Machines (SVMs) achieve the state-of-the-art performance. Exploring feature vectors incorporating the position information of amino acids or other protein building blocks is a key step to improve the performance of the SVM-based methods. RESULTS: Two new methods for protein remote homology detection were proposed, called SVM-DR and SVM-DT. SVM-DR is a sequence-based method, in which the feature vector representation for protein is based on the distances between residue pairs. SVM-DT is a profile-based method, which considers the distances between Top-n-gram pairs. Top-n-gram can be viewed as a profile-based building block of proteins, which is calculated from the frequency profiles. These two methods are position dependent approaches incorporating the sequence-order information of protein sequences. Various experiments were conducted on a benchmark dataset containing 54 families and 23 superfamilies. Experimental results showed that these two new methods are very promising. Compared with the position independent methods, the performance improvement is obvious. Furthermore, the proposed methods can also provide useful insights for studying the features of protein families. CONCLUSION: The better performance of the proposed methods demonstrates that the position dependant approaches are efficient for protein remote homology detection. Another advantage of our methods arises from the explicit feature space representation, which can be used to analyze the characteristic features of protein families. The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp.

Passive smoking and preterm birth in urban China.

Studies investigating the relationship between maternal passive smoking and the risk of preterm birth have reached inconsistent conclusions. A birth cohort study that included 10,095 nonsmoking women who delivered a singleton live birth was carried out in Lanzhou, China, between 2010 and 2012. Exposure to passive smoking during pregnancy was associated with an increased risk of very preterm birth (<32 completed weeks of gestation; odds ratio = 1.98, 95% confidence interval: 1.41, 2.76) but not moderate preterm birth (32-36 completed weeks of gestation; odds ratio = 0.98, 95% confidence interval: 0.81, 1.19). Risk of very preterm birth increased with the duration of exposure (P for trend = 0.0014). There was no variability in exposures by trimester. The associations were consistent for both medically indicated and spontaneous preterm births. Overall, our findings support a positive association between passive smoking and the risk of very preterm birth.

Few-Shot Relation Extraction With Automatically Generated Prompts.

Relation extraction (RE) tends to struggle when the supervised training data is few and difficult to be collected. In this article, we elicit relational and factual knowledge from large pretrained language models (PLMs) for few-shot RE (FSRE) with prompting techniques. Concretely, we automatically generate a diverse set of natural language templates and modulate PLM's behavior through these prompts for FSRE. To mitigate the template bias which leads to unstableness of few-shot learning, we propose a simple yet effective template regularization network (TRN) to prevent deep networks from over-fitting uncertain templates and thus stabilize the FSRE models. TRN alleviates the template bias with three mechanisms: 1) an attention mechanism over mini-batch to weight each template; 2) a ranking regularization mechanism to regularize the attention weights and constrain the importance of uncertain templates; and 3) a template calibration module with two calibrating techniques to modify the uncertain templates in the lowest-ranked group. Experimental results on two benchmark datasets (i.e., FewRel and NYT) show that our model has robust superiority over strong competitors. For reproducibility, we will release our code and data upon the publication of this article.

PAK2 promotes proliferation, migration, and invasion of lung squamous cell carcinoma through LIMK1/cofilin signaling pathway.

Although the p21-activated kinase 2 (PAK2) is an essential serine/threonine protein kinase, its role in lung squamous cell carcinoma (LUSC) progression has yet to be fully understood. We analyzed PAK2 mRNA levels and DNA copy numbers as well as protein levels by quantitative real-time PCR and immunohistochemical staining, respectively, in human LUSC tissues and adjacent normal tissues. Then, we used colony formation assays, cell counting kit-8 assays, matrigel invasion assays, wound healing assays and xenograft models in nude mice to investigate the functions of PAK2 in LUSC progression. We demonstrated that the mRNA levels, DNA copy numbers, and protein levels of PAK2 were up-regulated in human LUSC tissues than in adjacent normal tissues. In addition, a higher PAK2 expression was correlated with a poorer prognosis in LUSC patients. In the in vitro study, we found that PAK2 promoted cell growth, migration, invasion, EMT process, and cell morphology regulation in LUSC cells. Furthermore, PAK2 enhanced tumor cell proliferation, migration, and invasion by regulating actin dynamics through the LIMK1/cofilin signaling. Our findings implicated that the PAK2/LIMK1/cofilin signaling pathway is likely a potential clinical marker and therapeutic target for LUSC.