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The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems. [ABSTRACT FROM AUTHOR]

Porcine epidemic diarrhea virus (PEDV) is a highly pathogenic swine coronavirus that causes diarrhea and high mortality in piglets, resulting in significant economic losses within the global swine industry. Nonstructural protein 3 (Nsp3) is the largest in coronavirus, playing critical roles in viral replication, such as the processing of polyproteins and the formation of replication-transcription complexes (RTCs). In this study, three monoclonal antibodies (mAbs), 7G4, 5A3, and 2D7, targeting PEDV Nsp3 were successfully generated, and three distinct linear B-cell epitopes were identified within these mAbs by using Western blotting analysis with 24 truncations of Nsp3. The epitope against 7G4 was located on amino acids 31-TISQDLLDVE-40, the epitope against 5A3 was found on amino acids 141-LGIVDDPAMG-150, and the epitope against 2D7 was situated on amino acids 282-FYDAAMAIDG-291. Intriguingly, the epitope 31-TISQDLLDVE-40 recognized by the mAb 7G4 appears to be a critical B-cell linear epitope due to its high antigenic index and exposed location on the surface of Nsp3 protein. In addition, bioinformatics analysis unveiled that these three epitopes were highly conserved in most genotypes of PEDV. These findings present the first characterization of three novel linear B-cell epitopes in the Nsp3 protein of PEDV and provide potential tools of mAbs for identifying host proteins that may facilitate viral infection. [ABSTRACT FROM AUTHOR]

Background: To evaluate the accuracy of implant placement assisted by a dynamic navigation system, as well as its influencing factors and learning curve. Methods: At Macao We Care Dental Center, 55 cases of implant placement using dynamic navigation were retrospectively evaluated. To evaluate their accuracy, the apex, tip, and angle deviations of preoperatively planned and postoperatively placed implants were measured. The effects of the upper and lower jaws, different sites or lateral locations of dental implants, and the length and diameter of the implants on accuracy were analyzed, as well as the variation in accuracy with the increase in the number of surgical procedures performed by dentists. Results: The implant had an apex deviation of 1.60 ± 0.94 mm, a tip deviation of 1.83 ± 1.03 mm, and an angle deviation of 3.80 ± 2.09 mm. Statistical differences were observed in the tip deviation of implants at different positions based on three factors: jaw position, lateral location, and tooth position (P < 0.05). The tip deviation of the anterior teeth area was significantly greater than those of the premolar and molar areas. There were no statistically significant differences in apex deviation, tip deviation, or angle deviation between the implants of different diameters and lengths (P > 0.05). There were significant differences in the angle deviation between the final 27 implants and the first 28 implants. Learning curve analysis revealed that angle deviation was negatively correlated with the number of surgical procedures, whereas the regression of apex deviation and tip deviation did not differ statistically. Conclusions: The accuracy of dynamic navigation-assisted dental implants meets the clinical needs and is higher than that of traditional implants. Different jaw positions, lateral locations, and implant diameters and lengths had no effect on the accuracy of the dental implants guided by the dynamic navigation system. The anterior teeth area had a larger tip deviation than the posterior teeth area did. As the number of dynamic implantation procedures performed by the same implant doctor increased, the angle deviation gradually decreased. [ABSTRACT FROM AUTHOR]

Background: Proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) metastasizes to a greater extent than MMR-deficient CRAC. Prognostic biomarkers are preferred in clinical practice. However, traditional biomarkers screened directly from sequencing are often not robust and thus cannot be confidently utilized. Methods: To circumvent the drawbacks of blind screening, we established a new strategy to identify prognostic biomarkers in the conserved and specific oncogenic pathway and its regulatory RNA network. We performed RNA sequencing (RNA-seq) for messenger RNA (mRNA) and noncoding RNA in six pMMR CRAC patients and constructed a glycosylation-related RNA regulatory network. Biomarkers were selected based on the network and their correlation with the clinicopathologic information and were validated in multiple centers (n = 775). Results: We constructed a competing endogenous RNA (ceRNA) regulatory network using RNA-seq. Genes associated with glycosylation pathways were embedded within this scale-free network. Moreover, we further developed and validated a seven-glycogene prognosis signature, GlycoSig (B3GNT6 , GALNT3 , GALNT8 , ALG8 , STT3B , SRD5A3 , and ALG6) that prognosticate poor-prognostic subtype for pMMR CRAC patients. This biomarker set was validated in multicenter datasets, demonstrating its robustness and wide applicability. We constructed a simple-to-use nomogram that integrated the risk score of GlycoSig and clinicopathological features of pMMR CRAC patients. Conclusions: The seven-glycogene signature served as a novel and robust prognostic biomarker set for pMMR CRAC, highlighting the role of a dysregulated glycosylation network in poor prognosis. [ABSTRACT FROM AUTHOR]