Integrable deformations of the Bogoyavlenskij-Itoh Lotka-Volterra systems

We construct a family of integrable deformations of the Bogoyavlenskij-Itoh systems and construct a Lax operator with spectral parameter for it. Our approach is based on the construction of a family of compatible Poisson structures for the undeformed system, whose Casimirs are shown to yield a generating function for the integrals in involution of the deformed systems. We show how these deformations are related to the Veselov-Shabat systems.Comment: 23 pages, 14 reference

Research into container reshuffling and stacking problems in container terminal yards

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time

第944回千葉医学会例会・第1外科教室談話会

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Comprehensive assessment of metabolite associations with CKD.

(A) Bar chart displaying the distribution of metabolites, categorized and color-coded by their respective classes. (B) A volcano plot visualizing the relationship between metabolites and CKD as assessed by the IVW algorithm. Individual dots signify specific metabolites, with the dot size reflecting the count of associated SNPs for each metabolite. Red dots indicate a positive association with CKD, while blue signifies a negative association. The red dashed line demarcates the statistical significance threshold (P = 0.05). IVW: inverse variance weighted; SNP: single nucleotide polymorphism; CKD: chronic kidney disease. (TIF)</p

The false discovery rate results in IVW methods.

BackgroundChronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.MethodsUtilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.ResultsOur analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.ConclusionThis study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD’s pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.</div

The funnel plot represents IVs for each significant potentially causal association between CKD and selected metabolites.

The funnel plot represents IVs for each significant potentially causal association between CKD and selected metabolites.</p

Pathways involved metabolites.

BackgroundChronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.MethodsUtilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.ResultsOur analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.ConclusionThis study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD’s pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.</div

Metabolism pathway analysis.

(A) Dot plots show the top 25 metabolic pathways in which significant metabolites participate, selected by the IVW algorithm. (B) Network diagram, individual nodes denote distinct metabolite sets. An edge or a connection between two metabolite sets indicates that they have an overlap, wherein more than 25% of their metabolites are shared. (TIF)</p

The overview of the research workflow.

CKD: chronic kidney disease; SNP: single nucleotide polymorphism; MR: mendelian randomization; IVW: inverse variance weighted.</p

The genetic associations of seven metabolites on the risk of CKD.

IVW: inverse variance weighted; CKD: chronic kidney disease. (TIF)</p