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Mesenchymal stem cells (MSCs) possess strong immunoregulatory functions, one aspect of which is recruiting monocytes from peripheral vessels to local tissue by secreting monocyte chemoattractant protein 1 (MCP1). However, the regulatory mechanisms of MCP1 secretion in MSCs are still unclear. Recently, the N6-methyladenosine (m6A) modification was reported to be involved in the functional regulation of MSCs. In this study, we demonstrated that methyltransferase-like 16 (METTL16) negatively regulated MCP1 expression in MSCs through the m6A modification. Specifically, the expression of METTL16 in MSCs decreased gradually and was negatively correlated with the expression of MCP1 after coculture with monocytes. Knocking down METTL16 markedly enhanced MCP1 expression and the ability to recruit monocytes. Mechanistically, knocking down METTL16 decreased MCP1 mRNA degradation, which was mediated by the m6A reader YTH N6-methyladenosine RNA-binding protein 2 (YTHDF2). We further revealed that YTHDF2 specifically recognized m6A sites on MCP1 mRNA in the CDS region and thus negatively regulated MCP1 expression. Moreover, an in vivo assay showed that MSCs transfected with METTL16 siRNA showed greater ability to recruit monocytes. These findings reveal a potential mechanism by which the m6A methylase METTL16 regulates MCP1 expression through YTHDF2-mediated mRNA degradation and suggest a potential strategy to manipulate MCP1 expression in MSCs.

Background: Mesenchymal stem cells (MSCs) selectively differentiate into adipocytes or osteoblasts, and several molecules control the fate determination of MSCs. Understanding these key checkpoints greatly contributes to the ability to induce specific MSC differentiation for clinical applications. In this study, we aimed to explore whether TNF receptor-associated factor 4 (TRAF4) affects MSC adipogenic differentiation, which we previously reported that could positively regulated the osteogenic differentiation. Methods: Western blotting and Real-time Polymerase Chain Reaction were used to detected the expression pattern of TRAF4 during adipogenic differentiation. Lentivirus was constructed to regulate TRAF4 expression, and oil red O staining and Western blotting were used to assess its role in adipogenesis, which was confirmed in vivo by implanting an MSC-matrigel mixture into nude mice. Western blotting was used to detect the activated signaling pathways, and a specific inhibitor and agonist were used to clear the roles of the key signaling pathways. Additionaly, Co-Immunoprecipitation was conducted to find that Pyruvate kinase isozyme type M2 (PKM2) interacts with TRAF4, and to further explore their binding and functional domains. Finally, an RNA-binding protein immunoprecipitation assay and Western blotting were used to detect whether N6-methyladenosine mediates the decreased TRAF4 expression during adipogenic differentiation. Findings: The results demonstrated that TRAF4 negatively regulates MSC adipogenesis in vitro and in vivo. Mechanistically, we revealed that TRAF4 binds to PKM2 to activate the kinase activity of PKM2, which subsequently activates β-catenin signaling and then inhibits adipogenesis. Furthermore, TRAF4 downregulation during adipogenesis is regulated by ALKBH5-mediated N6-methyladenosine RNA demethylation. Interpretation: TRAF4 negatively regulates the adipogenesis of MSCs by activating PKM2 kinase activity, which may act as a checkpoint to fine-tune the balance of adipo-osteogenic differentiation, and suggests that TRAF4 may be a novel target of MSCs in clinical use and may also illuminate the underlying mechanisms of bone metabolic diseases. Funding: This study was supported by the National Natural Science Foundation of China (81871750 and 81971518) and the Science and Technology Project of Guangdong Province (2019B02023600 and 2017A020215070). Keywords: TRAF4, Mesenchymal stem cells, Adipogenic differentiation, PKM2

Several different feature extraction methods and classifiers are investigated to solve feature extraction and classification for rear-view vehicle detection, and an on-board, real-time monocular vehicle detection system capable of acquiring grey-scale images is developed. The system is tested in Ford's concept vehicle under different traffic conditions, like structured highway, complex urban streets and varying weather conditions, illustrating good performance.

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research. [ABSTRACT FROM AUTHOR]

Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera. [ABSTRACT FROM PUBLISHER]