Search

Increasing evidence shows the oncogenic function of FAM83D in human cancer, but how FAM83D exerts its oncogenic function remains largely unclear. Here, we investigated the importance of FAM83D/FBXW7 interaction in breast cancer (BC). We systematically mapped the FBXW7-binding sites on FAM83D through a comprehensive mutational analysis together with co-immunoprecipitation assay. Mutations at the FBXW7-binding sites on FAM83D led to that FAM83D lost its capability to promote the ubiquitination and proteasomal degradation of FBXW7; cell proliferation, migration, and invasion in vitro; and tumor growth and metastasis in vivo, indicating that the FBXW7-binding sites on FAM83D are essential for its oncogenic functions. A meta-evaluation of FAM83D revealed that the prognostic impact of FAM83D was independent on molecular subtypes. The higher expression of FAM83D has poorer prognosis. Moreover, high expression of FAM83D confers resistance to chemotherapy in BCs, which is experimentally validated in vitro. We conclude that identification of FBXW7-binding sites on FAM83D not only reveals the importance for FAM83D oncogenic function, but also provides valuable insights for drug target.

Previous studies have shown reduced attention to the eyes in individuals with autism spectrum disorder (ASD). However, most eye-tracking evidence regarding this impairment has been derived from passive viewing tasks. Here, we compared the passive viewing of faces with an active task involving face identification with morphing faces. While typical controls prioritized the eyes over other facial features regardless of viewing condition, autistic children exhibited reduced eye-looking in passive viewing, but displayed increased attention allocation to the eyes when instructed to identify faces. The proportional eye-looking in ASD during facial recognition was negatively related to the autism symptoms severity. These findings provide evidence regarding the specific situations in which diminished eye-looking may rise in young ASD children.

Artificial intelligence develops rapidly and occupies an important position in people’s lives. AI technology can automate many repetitive tasks, thus improving production efficiency and reducing labor costs, promoting economic growth. The extensive adoption of AI will lead to the decline of specific traditional jobs, particularly those that are repetitive and require low skill levels, like assembly line manufacturing and data entry. At the same time, AI technology in workplaces leads to a growing need for employees who can adapt to various tasks and responsibilities in different areas. Therefore, there is a demand for individuals skilled in multiple disciplines to integrate these technologies, such as physicians with expertise in medicine and AI technology. Simultaneously, AI advancements are creating fresh job requirements. This paper looked into AI’s influence on the labor market. By evaluating government policy, this paper aims to discover how the labor market should adjust to keep pace regarding work requirements, education, and working arrangements to mitigate AI’s disruption to the labor market and ensure the country’s economic growth.

Spatiotemporal regression is a crucial method in geography for discerning spatiotemporal nonstationarity in geographical relationships and has found widespread application across diverse research domains. This study implements two innovative spatiotemporal intelligent regression models, i.e., Geographically Neural Network Weighted Regression (GNNWR) and Geographically and Temporally Neural Network Weighted Regression (GTNNWR), which use neural networks to estimate spatiotemporal nonstationarity. Due to the higher accuracy and generalization ability, these models have been widely used in various fields of scientific research. To facilitate the application of GNNWR and GTNNWR in addressing spatiotemporal nonstationary processes, the Python-based package GNNWR has been developed. This article details the implementation of these models and introduces the GNNWR package, enabling users to efficiently apply these cutting-edge techniques. Validation of the package is conducted through two case studies. The first case involves the verification of GNNWR using air quality data from China, while the second employs offshore dissolved silicate concentration data from Zhejiang Province to validate GTNNWR. The results of the case studies underscore the effectiveness of the GNNWR package, yielding outcomes of notable accuracy. This contribution anticipates a significant role for the developed package in supporting future research that will leverage big data and spatiotemporal regression techniques. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a neurodegenerative disease caused by a combination of aging, environmental, and genetic factors. Previous research has implicated both causative and susceptibility genes in PD development. Nogo‐A, a neurite outgrowth inhibitor, has been shown to impact axon growth through ligand‐receptor interactions negatively, thereby involved in the deterioration of dopaminergic neurons. However, rare genetic studies have identified the relationship between neurite outgrowth inhibitor (Nogo)‐associated genes and PD from a signaling pathway perspective. Methods: We enrolled 3959 PD patients and 2931 healthy controls, categorized into two cohorts based on their family history and age at onset: sporadic early Parkinson's disease & familial Parkinson's disease (sEOPD & FPD) cohort and sporadic late Parkinson's disease (sLOPD) cohort. We selected 17 Nogo‐associated genes and stratified them into three groups via their function, respectively, ligand, receptors, and signaling pathway groups. Additionally, we conducted the burden analysis in rare variants, the logistic regression analysis in common variants, and the genotype–phenotype association analysis. Last, bioinformatics analysis and functional experiments were conducted to identify the role of the MTOR gene in PD. Results: Our findings demonstrated that the missense variants in the MTOR gene might increase PD risk, while the deleterious variants in the receptor subtype of Nogo‐associated genes might mitigate PD risk. However, common variants of Nogo‐associated genes showed no association with PD development in two cohorts. Furthermore, genotype–phenotype association analysis suggested that PD patients with MTOR gene variants exhibited relatively milder motor symptoms but were more susceptible developing dyskinesia. Additionally, bioinformatics analysis results showed MTOR gene was significantly decreased in PD, indicating a potential negative role of the mTOR in PD pathogenesis. Experimental data further demonstrated that MHY1485, a mTOR agonist, could rescue MPP+‐induced axon inhibition, further implicating the involvement of mTOR protein in PD by regulating cell growth and axon growth. Conclusions: Our preliminary investigation highlights the association of Nogo‐associated genes with PD onset in the Chinese mainland population and hints at the potential role of the MTOR gene in PD. Further research is warranted to elucidate the mechanistic pathways underlying these associations and their therapeutic implications. [ABSTRACT FROM AUTHOR]