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Genetic studies have revealed a critical role of the distal-less homeobox gene 5 (Dlx5) in the pathogenesis of ovarian cancer, lung cancer, and T-cell lymphoma; however, the role and underlying mechanisms of Dlx5 in oral squamous cell carcinoma (OSCC) are largely unknown. In this study, we demonstrated that Dlx5 is up-regulated in OSCC tissues and cell lines, compared with their control groups. The results from our immunohistochemistry (IHC) analyses show that high expression levels of Dlx5 correlated with advanced TNM stages (P = 0.0001), lymph node metastasis (P = 0.0049), poor cellular differentiation (P = 0.0491), location of the tumors (P = 0.0132), and poor prognosis for the patient. We also demonstrated that knockdown of Dlx5 inhibited the viability, proliferation, and colony formation of OSCC cell lines CAL-27 and WSU-HN6 cells, probably by blocking cell cycle in the G1 phase. Furthermore, we revealed that Dlx5 exerts its biological functions via direct regulation of CCND1 in CAL-27 and WSU-HN6 cells. Ultimately, we have demonstrated that silencing of Dlx5 inhibits the growth of xenograft tumors in vivo, and that Dlx5 affects the progression of OSCC both in vitro and in vivo via directly regulating CCND1, providing a potential diagnostic biomarker and therapeutic target for OSCC. Key words: oral squamous cell carcinoma, Dlx5, CCND1, cell proliferation. Des etudes genetiques ont revele un role essentiel de Dlx5 dans la pathogenese du cancer de l'ovaire, du cancer du poumon et dans le lymphome a cellules T. Cependant, le role et le mecanisme sous-jacent de Dlx5 dans le carcinome epi-dermo'ide oral (CEO) ne sont vraiment pas clairs. Dans cette etude, les auteurs ont d'abord montre que Dlx5 etait regule a la hausse dans les tissus et les lignees cellulaires de CEO par rapport a leurs groupes temoins, respectivement. En parallele, l'analyse immunohistochimique a montre qu'une expression elevee de Dlx5 etait correlee avec les stades TNM avances (P = 0,0001), les metastases ganglionnaires (P = 0,0049), une faible differentiation pathologique (P = 0,0491),le sitedela maladie (P = 0,0132) et un mauvais pronostic. Ils ont ensuite demontre que le knockdown du Dlx5 inhibait la viabilite cellulaire, la proliferation et la capacite de formation de colonies des lignees cellulaires de CEO CAL-27 et WSU-HN6, probablement en bloquant le cycle cellulaire en phase G1. En outre, ils ont revele que Dlx5 exercait ses fonctions biologiques par la regulation directe de l'expression de CCND1 dans les cellules CAL-27 et WSU-HN6. Enfin, ils ont demontre que le silencage de Dlx5 inhibait la croissance de xenogreffes tumorales in vivo. En somme, ils ont revele que Dlx5 affectait la progression du CEO a la fois in vitro et in vivo en regulant directement l'expression de CCND1, fournissant ainsi un biomarqueur diagnostique potentiel et une cible therapeutique du CEO. [Traduit par la Redaction] Mots-cles: carcinome epidermo'ide oral, Dlx5, CCND1, proliferation cellulaire., Introduction The incidence of oral squamous cell carcinoma (OSCC) has increased significantly in recent years. OSCC is now the 10th most common cancer, with approximately 500 000 new cases reported [...]

In this paper,using 304 stainless steel material 1 mm in diameter to fuse electron beam deposition rapid prototyping,by controlling the wire feed speed,electronic beam intensity and molding parameters such as the same,choose different forming path was 180 mm × 40 mm × 20 mm of print samples,after tensile test,Its microstructure and mechanical properties were observed and studied. By electron beam additive manufacturing,dense block material without macroscopic defects can be obtained. The average particle size of 304 stainless steel printed is no more than 10 μm,and contains fine uniform precipitation phase. Sample to print,staggered reciprocating print and improved staggered reciprocating print under the path of tensile strength of about 683 MPa,878 MPa,970 MPa,respectively the three print path molding samples can reach the hardness and tensile strength of 515 MPa or requirements,tensile fracture analysis,staggered print mode fracture to ductile fracture,staggered reciprocating type fracture to brittle fracture. The staggered reciprocating printing and the improved staggered reciprocating printing method have better density,better tensile strength and other mechanical properties.

Purpose: To present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What's more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced. Design/methodology/approach: Programming completely in C++. Findings: Five compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm. Originality/value: The detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning paradigm under privacy preservation. However, data heterogeneity among clients leads to the shared global model obtained after training, which cannot fit the distribution of each client's dataset, and the performance of the model degrades. To address this problem, we proposed a personalized federated learning method based on clustering and knowledge distillation, called pFedCK. In this algorithm, each client has an interactive model that participates in global training and a personalized model that is only trained locally. Both of the models perform knowledge distillation with each other through the feature representation of the middle layer and the soft prediction of the model. In addition, in order to make an interaction model only obtaining the model information from the client, which has similar data distribution and avoids the interference of other heterogeneous information, the server will cluster the clients according to the similarity of the amount of parameter variation uploaded by different interaction models during every training round. By clustering clients, interaction models with similar data distributions can cooperate with each other to better fit the local dataset distribution. Thereby, the performance of personalized model can be improved by obtaining more valuable information indirectly. Finally, we conduct simulation experiments on three benchmark datasets under different data heterogeneity scenarios. Compared to the single model algorithms, the accuracy of pFedCK improved by an average of 23.4% and 23.8% over FedAvg and FedProx, respectively; compared to typical personalization algorithms, the accuracy of pFedCK improved by an average of 0.8% and 1.3%, and a maximum of 1.0% and 2.9% over FedDistill and FML. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive performance evaluation algorithm explicitly designed for secondary equipment in substations, specifically targeting the relay protection system. In contrast to the current evaluation systems, this novel method navigates the complex internal interconnections and mechanisms inherent within secondary system equipment. Such complications have previously impeded the accuracy and breadth of evaluations, thereby limiting the degree of precision and innovation attainable within substations. The proposed approach effectively integrates the improved Analytic Hierarchy Process entropy weight (IAHP‐EW) method with the Learning Vector Quantization (LVQ) neural network. Initially, the IAHP‐EW method identified the comprehensive evaluation indicators and their corresponding weights for relay protection devices. Following weight allocation, these evaluation indicators are scrutinized and computed utilizing the multivariate regression analysis algorithm, resulting in performance evaluation outcomes for the relay protection system. These outcomes are subsequently classified and utilized in training the LVQ neural network, promoting the network's capacity to autonomously evaluate the performance status of the relay protection system. To corroborate the viability and effectiveness of this proposed performance evaluation and prediction algorithm, empirical operating data from a local substation is used. The results suggest a significant improvement in the evaluation accuracy of secondary equipment performance, indicating potential for practical application and a valuable contribution to the field through the introduction of a novel approach to performance assessment of substation relay protection systems. [ABSTRACT FROM AUTHOR]