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Consistent individual behavioral differences from other members of local populations are called animal personality, and a suite of these correlated behaviors generates a behavioral syndrome. Differential selection patterns among personality types can be largely influenced by predatory behavior. Prey has a large arsenal of antipredator behaviors at their disposal; However, how closely these behaviors are linked to personality is almost unknown. Using a social model species, the Great Tit, Parus major, we investigated whether antipredator behavior in response to predators is correlated with personality. First, we determined if individual differences in exploratory behavior were consistent across trials, that is, whether they were repeatable. Second, we looked into how antipredator behavior was correlated to personality. Individuals were subjected to two repeated trials of standard personality assays followed by an alternative control (Rock Pigeon, Columba livia domestica) and predator display (Eurasian Sparrowhawk, Accipiter nisus) treatment. We found that exploratory behavior was repeatable across trials and had a greater tendency for positive correlation with antipredator behavior when a predator model was displayed. Individuals that swiftly explored novel environments were slower to react to predators than those who explored the novel room slowly. As a result, our findings provide some of the first experimental evidence for a link between exploratory and antipredator behavior, suggesting that personality traits may impact individual fitness. [ABSTRACT FROM AUTHOR]

This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a "re-fly" mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as "re-fly." To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution. [ABSTRACT FROM AUTHOR]

3D mesh denoising is a crucial pre-processing step in many graphics applications. However, existing data-driven mesh denoising models, primarily trained on synthetic white noise, are less effective when applied to real-world meshes with the noise of complex intensities and distributions. Moreover, how to comprehensively capture information from input meshes and apply suitable denoising models for feature-preserving mesh denoising remains a critical and unresolved challenge. This paper presents a rotation-Equivariant model-based Mesh Denoising (EMD) model and a Realistic Mesh Noise Generation (RMNG) model to address these issues. Our EMD model leverages rotation-equivariant features and self-attention weights of geodesic patches for more effective feature extraction, thereby achieving SOTA denoising results. The RMNG model, based on the Generative Adversarial Networks (GANs) architecture, generates massive amounts of realistic noisy and noiseless mesh pairs data for data-driven mesh denoising model training, significantly benefiting real-world denoising tasks. To address the smooth degradation and loss of sharp edges commonly observed in captured meshes, we further introduce varying levels of Laplacian smoothing to input meshes during the paired training data generation, endowing the trained denoising model with feature recovery capabilities. Experimental results demonstrate the superior performance of our proposed method in preserving fine-grained features while removing noise on real-world captured meshes. • Introduced an EMD method using rotation-equivariant features for SOTA mesh denoising. • Developed an RMNG model to create realistic noisy-clean pairs, enhancing real-world denoising. • Proposed a method to add smoothing degradation in data generation, improving feature recovery. [ABSTRACT FROM AUTHOR]

Objective. This study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA). Methods. The TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed. Results. Based on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307. Conclusion. We constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA. [ABSTRACT FROM AUTHOR]

KIF26B has been identified as an oncogene in several tumors; however, its utility as a prognostic indicator for various cancers has not yet been comprehensively evaluated. Here, we first examined how KIF26B intervenes in thirty-three cancers within the TCGA database, including potential immunological functions, and how it affects the prognosis. Based on the open databases TCGA, TIMER2, GEPIA2, GTEx, CPTAC, and HPA, we found that, when compared with normal tissues, KIF26B is overexpressed in 22 tumor tissues. Following a survival analysis, a relationship between the expression of KIF26B and the prognosis of various cancers was observed. Among the genetic alterations assessed, mutations were the most frequent. On the contrary, high phosphorylation levels of S977 were detected in breast cancer, KIRC, LUAD, and UCEC. We also found positive or negative correlations between KIF26B and the immune infiltration of endothelial cells and cancer-associated fibroblast infiltration. This could imply that patients may benefit from immunotherapy. Finally, KEGG pathways and GO enrichment analyses were implemented to identify the molecular mechanisms of KIF26B. This study illustrates the function of KIF26B from a pan-cancer perspective and offers a new horizon for cancer prognostic and immunotherapeutic investigations. [ABSTRACT FROM AUTHOR]