Oncogenic BRAF^(V600E) induces microglial proliferation through extracellular signal-regulated kinase and neuronal death through c-Jun N-terminal kinase

Activating V600E in v-Raf murine sarcoma viral oncogene homolog B(BRAF)is a common driver mutation in cancers of multiple tissue origins,including melanoma and glioma.BRAF^(V600E) has also been implicated in neurodegeneration.The present study aims to characterize BRAF^(V600E) during cell death and proliferation of three major cell types of the central nervous system:neurons,astrocytes,and microglia.Multiple primary cultures(primary cortical mixed culture)and cell lines of glial cells(BV2)and neurons(SH-SY5Y)were employed.BRAF^(V600E) and BRAF^(WT) expression was mediated by lentivirus or retrovirus.Blockage of downstream effectors(extracellular signal-regulated kinase 1/2 and JNK1/2)were achieved by siRNA.In astrocytes and microglia,BRAF^(V600E) induces cell proliferation,and the proliferative effect in microglia is mediated by activated extracellular signal-regulated kinase,but not c-Jun N-terminal kinase.Conditioned medium from BRAF^(V600E)-expressing microglia induced neuronal death.In neuronal cells,BRAF^(V600E) directly induces neuronal death,through c-Jun N-terminal kinase but not extracellular signal-regulated kinase.We further show that BRAF-related genes are enriched in pathways in patients with Parkinson’s disease.Our study identifies distinct consequences mediated by distinct downstream effectors in dividing glial cells and in neurons following the same BRAF mutational activation and a causal link between BRAF-activated microglia and neuronal cell death that does not require physical proximity.It provides insight into a possibly important role of BRAF in neurodegeneration as a result of either dysregulated BRAF in neurons or its impact on glial cells.

AGNP:Network-wide short-term probabilistic traffic speed prediction and imputation

The data-driven Intelligent Transportation System(ITS)provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems.Hence,network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network.Abundant research works have adopted various approaches for traffic prediction and imputation.However,previous methods ignore the reliability analysis of the predicted/imputed traffic information.Thus,this study originally proposes an attentive graph neural process(AGNP)method for network-level short-term traffic speed prediction and imputation,simultaneously considering reliability.Firstly,the Gaussian process(GP)is used to model the observed traffic speed state.Such a stochastic process is further learned by the proposed AGNP method,which is utilized for inferring the congestion state on the remaining unobserved road segments.Data from a transportation network in Anhui Province,China,is used to conduct three experiments with increasing missing data ratio for model testing.Based on comparisons against other machine learning models,the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance.With the probabilistic confidence provided by the AGNP,reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.

Integrating social neuroscience into human-machine mutual behavioral understanding for autonomous driving

Autonomous vehicles(AVs)are advertised to free human drivers,providing a safer and more efficient transport mode.After decades of extensive investment and invention,various types of AVs have been unveiled,but they are still restricted to limited application scenarios because of potential safety concerns.Despite rare sensing or detection failures from corner cases,one of the significant concerns primarily questions whether AVs would interact appropriately with surrounding human-driven vehicles on public roads.

The associations between Parkinson’s disease and cancer: the plot thickens

Epidemiological studies support a general inverse association between the risk of cancer development and Parkinson’s disease(PD).In recent years however,increasing amount of eclectic evidence points to a positive association between PD and cancers through different temporal analyses and ethnic groups.This positive association has been supported by several common genetic mutations in SNCA,PARK2,PARK8,ATM,p53,PTEN,and MC1R resulting in cellular changes such as mitochondrial dysfunction,aberrant protein aggregation,and cell cycle dysregulation.Here,we review the epidemiological and biological advances of the past decade in the association between PD and cancers to offer insight on the recent and sometimes contradictory findings.