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To accelerate development and improve the quality of car navigation systems, we have built a system for automatic generation of evaluation courses. In general, the operation of car navigation systems is verified by driving tests using vehicles. The evaluation courses need to be designed so that inspection sites, such as underground parking lots, tunnels, etc., will be visited during the evaluation period. They should be circuits that include as many inspection sites as possible within a defined distance. However, as the number of the inspection sites increases, the number of courses that can be designed becomes enormous. This makes it difficult to create courses that meet all of the requirements. Hence engineers have spent a lot of time on evaluation course design. For this reason, automatic course generation has become essential for reducing man-hours. We believe that one of the effective approaches is to treat automatic evaluation course generation as a combinatorial optimization problem. In our formulation, inspection sites are grouped into clusters according to the required number of courses, and the shortest circuit is constructed in each cluster.Then, we treat the clustering and shortest circuit generation problems separately as a bi-level combinatorial optimization problem. In other words, the original problem is divided into smaller parts of the combinatorial optimization problems. We then propose a Markov chain Monte Carlo method for solving the bi-level optimization problem, and construct a system for automatic generation of evaluation courses. The proposed method significantly reduces course-design time compared to manual course construction.

Summary Lipids, such as cholesterol and fatty acids, influence cell signaling, energy storage, and membrane formation. Cholesterol is biosynthesized through the mevalonate pathway, and aberrant metabolism causes metabolic diseases. The genetic association of a transcription factor NRF3 with obesity has been suggested, although the molecular mechanisms remain unknown. Here, we show that NRF3 upregulates gene expression in SREBP2-dependent mevalonate pathway. We further reveal that NRF3 overexpression not only reduces lanosterol, a cholesterol precursor, but also induces the expression of the GGPS1 gene encoding an enzyme in the production of GGPP from farnesyl pyrophosphate (FPP), a lanosterol precursor. NRF3 overexpression also enhances cholesterol uptake through RAB5-mediated macropinocytosis process, a bulk and fluid-phase endocytosis pathway. Moreover, we find that GGPP treatment abolishes NRF3 knockdown-mediated increase of neutral lipids. These results reveal the potential roles of NRF3 in the SREBP2-dependent mevalonate pathway for cholesterol uptake through macropinocytosis induction and for lipogenesis inhibition through GGPP production., Graphical abstract, Highlights • NRF3 upregulates gene expression of enzymes in the mevalonate pathway • NRF3 induces SREBP2 gene expression and interacts with active SREBP2 proteins • NRF3 reduces neutral lipid levels through GGPS1-mediated GGPP production • NRF3 enhances cholesterol uptake through RAB5-mediated macropinocytosis, Biochemistry; Biological sciences; Molecular biology

We propose a reconstruction procedure for periodic sequence of K Diracs from noisy uniform measurements based on the maximum likelihood estimation. We first express the noise vector using the measurement vector and estimation parameters. This expression and the probability density function (PDF) for the noise vector allow us to define the (log-) likelihood function. We show that when the PDF is Gaussian, the maximization of the likelihood function is equivalent to finding the nearest sequence to the noisy sequence in the Fourier domain. This problem can be efficiently solved by combining an analytic solution and the so-called particle swarm optimization (PSO) search. Computer simulations show that the proposed method outperforms the conventional methods with computational cost of approximately O(K).