Rectilinear crossing number of the double circular complete bipartite graph

In this work, we study a mathematically rigorous metric of a graph visualization quality under conditions that relate to visualizing a bipartite graph. Namely we study rectilinear crossing number in a special arrangement of the complete bipartite graph where the two parts are placed on two concentric circles. For this purpose, we introduce a combinatorial formulation to count the number of crossings. We prove a proposition about the rectilinear crossing number of the complete bipartite graph. Then, we introduce a geometric optimization problem whose solution gives the optimum radii ratio in the case that the number of crossings for them is minimized. Later on, we study the magnitude of change in the number of crossings upon change in the radii of the circles. In this part, we present and prove a lemma on bounding the changes in the number of crossings of that is followed by a theorem on asymptotics of the bounds.Comment: 15 pages, 11 figure

Mixed coordinate Node link Visualization for Co_authorship Hypergraph Networks

We present an algorithmic technique for visualizing the co-authorship networks and other networks modeled with hypergraphs (set systems). As more than two researchers can co-author a paper, a direct representation of the interaction of researchers through their joint works cannot be adequately modeled with direct links between the author-nodes. A hypergraph representation of a co-authorship network treats researchers/authors as nodes and papers as hyperedges (sets of authors). The visualization algorithm that we propose is based on one of the well-studied approaches representing both authors and papers as nodes of different classes. Our approach resembles some known ones like anchored maps but introduces some special techniques for optimizing the vertex positioning. The algorithm involves both continuous (force-directed) optimization and discrete optimization for determining the node coordinates. Moreover, one of the novelties of this work is classifying nodes and links using different colors. This usage has a meaningful purpose that helps the viewer to obtain valuable information from the visualization and increases the readability of the layout. The algorithm is tuned to enable the viewer to answer questions specific to co-authorship network studies.Comment: 10 pages, 3 figures, 1 tabl

Urinary Proteomics in Nephrotic Syndrome

Nephrotic syndrome is the commonest glomerular disease. Typical symptoms could be proteinuria, low serum albumin and oedema. The mechanism of proteinuria in nephrotic syndrome is a defective glomerular filtration barrier. Renal biopsy is the gold standard for diagnosis of nephrotic syndrome currently which is invasive and based on histopathological features, therefore it seems to be necessary to search for noninvasive biomarkers to be used as the complementary tests in the diagnostics and prognostics of glomerular diseases, particularly when renal biopsy is limited or contraindicated. While a big proportion of urinary proteins originate from kidney tissue and these tissue specific proteins excrete more in kidney injury, therefore the identification of urinary proteins can further our understanding of renal dysfunction and renal disease including nephrotic syndrome. The interest of scientist to  urinary proteomics is also growing for biomarker discovery. This review focuses on some types of nephrotic syndrome and proteomic studies applying urine specimen which have been reported

Exploring Biomarkers Beyond Exercise Testing: The Impact of Smoking on Cardiovascular and Pulmonary Health among CKD Patients

Background: Chronic Kidney Disease (CKD) patients often face complex health challenges, including cardiovascular and pulmonary issues. Smoking is a recognized risk factor for these conditions, but its specific impact on CKD patients remains less understood. Materials and Methods: In this cross-sectional study, we investigated the relationship between smoking habits and cardiopulmonary health among CKD patients. We examined baseline characteristics, including demographics, medical history, and biochemical markers, in a cohort of CKD patients. Cardiopulmonary parameters were assessed during exercise testing, including oxygen consumption, ventilation rates, ventilation-perfusion matching markers, and oxygen saturation levels. Results: Our findings revealed no statistically significant differences in cardiopulmonary parameters between smokers and non-smokers within the CKD patient population. This suggests that the relationship between smoking and exercise capacity in CKD patients is complex and influenced by multiple factors. Our analysis of demographics, comorbidities, and medication history provided critical context for interpreting these results. Conclusion: This study contributes to our understanding of the intricate relationship between smoking habits and cardiopulmonary health in CKD patients. While smoking is recognized as a risk factor, its specific impact on exercise capacity within this population may be influenced by individual variables. Further research is needed to explore these relationships in larger and more diverse cohorts. These findings underscore the importance of considering multiple variables when assessing the impact of smoking on the health of CKD patients

Using Clustering to Strengthen Decision Diagram Bounds for Discrete Optimization

Offering a generic approach to obtaining both upper and lower bounds, decision diagrams (DDs) are becoming an increasingly important tool for solving discrete optimization problems. In particular, they provide a powerful and often complementary alternative to other well-known generic bounding mechanisms such as the LP relaxation. A standard approach to employ DDs for discrete optimization is to formulate the problem as a Dynamic Program and use that formulation to compile a DD top-down in a layer-by-layer fashion. To limit the size of the resulting DD and to obtain bounds, one typically imposes a maximum width for each layer which is then enforced by either merging nodes (resulting in a so-called relaxed DD that provides a dual bound) or by dropping nodes (resulting in a so-called restricted DD that provides a primal bound). The quality of the DD bounds obtained from this top-down compilation process heavily depends on the heuristics used for the selection of the nodes to merge or drop. While it is sometimes possible to engineer problem-specific heuristics for this selection problem, the most generic approach relies on sorting the layer’s nodes based on objective function information. In this paper, we propose a generic and problem-agnostic approach that relies on clustering nodes based on the state information associated with each node. In a set of computational experiments with different knapsack and scheduling problems, we show that our approach generally outperforms the classical generic approach, and often achieves drastically better bounds both with respect to the size of the DD and the time used for compiling the DD

Using Clustering to Strengthen Decision Diagram Bounds for Discrete Optimization

Nafar M, Römer M. Using Clustering to Strengthen Decision Diagram Bounds for Discrete Optimization. Proceedings of the AAAI Conference on Artificial Intelligence. 2024;38(8):8082-8089.Offering a generic approach to obtaining both upper and lower bounds, decision diagrams (DDs) are becoming an increasingly important tool for solving discrete optimization problems. In particular, they provide a powerful and often complementary alternative to other well-known generic bounding mechanisms such as the LP relaxation. A standard approach to employ DDs for discrete optimization is to formulate the problem as a Dynamic Program and use that formulation to compile a DD top-down in a layer-by-layer fashion. To limit the size of the resulting DD and to obtain bounds, one typically imposes a maximum width for each layer which is then enforced by either merging nodes (resulting in a so-called relaxed DD that provides a dual bound) or by dropping nodes (resulting in a so-called restricted DD that provides a primal bound). The quality of the DD bounds obtained from this top-down compilation process heavily depends on the heuristics used for the selection of the nodes to merge or drop. While it is sometimes possible to engineer problem-specific heuristics for this selection problem, the most generic approach relies on sorting the layer’s nodes based on objective function information. In this paper, we propose a generic and problem-agnostic approach that relies on clustering nodes based on the state information associated with each node. In a set of computational experiments with different knapsack and scheduling problems, we show that our approach generally outperforms the classical generic approach, and often achieves drastically better bounds both with respect to the size of the DD and the time used for compiling the DD

Lookahead, Merge and Reduce for Compiling Relaxed Decision Diagrams for Optimization

Nafar M, Römer M. Lookahead, Merge and Reduce for Compiling Relaxed Decision Diagrams for Optimization. In: Dilkina B, ed. Integration of Constraint Programming, Artificial Intelligence, and Operations Research. 21st International Conference, CPAIOR 2024, Uppsala, Sweden, May 28–31, 2024, Proceedings, Part II. Lecture Notes in Computer Science. Vol 14743. Cham: Springer Nature ; 2024: 74-82

Classification of Chronic Kidney Disease Patients via k-important Neighbors in High Dimensional Metabolomics Dataset

Background: Chronic kidney disease (CKD), characterized by progressive loss of renal function, is becoming a growing problem in the general population. New analytical technologies such as “omics”-based approaches, including metabolomics, provide a useful platform for biomarker discovery and improvement of CKD management. In metabolomics studies, not only prediction accuracy is attractive, but also variable importance is critical because the identified biomarkers reveal pathogenic metabolic processes underlying the progression of chronic kidney disease. We aimed to use k-important neighbors (KIN), for the analysis of a high dimensional metabolomics dataset to classify patients into mild or advanced progression of CKD. Methods: Urine samples were collected from CKD patients (n=73). The patients were classified based on metabolite biomarkers into the two groups: mild CKD (glomerular filtration rate (GFR)> 60 mL/min per 1·73 m2) and advanced CKD (GFR<60 mL/min per 1·73 m2). Accordingly, 48 and 25 patients were in mild (class 1) and advanced (class 2) groups respectively. Recently, KIN was proposed as a novel approach to high dimensional binary classification settings. Through employing a hybrid dissimilarity measure in KIN, it is possible to incorporate information of variables and distances simultaneously. Results: The proposed KIN not only selected a few number of biomarkers, it also reached a higher accuracy compared to traditional k-nearest neighbors (61.2% versus 60.4%) and random forest (61.2% versus 58.5%) which are currently known as the best classifieres. Conclusion: Real metabolomics dataset demonstrate the superiority of proposed KIN versus KNN in terms of both classification accuracy and variable importance. Keywords Chronic kidney disease Classification High dimensional data KNN SCA

Renal Allograft in a Professional Boxer

Significant health benefits result from regular physical activity for kidney transplant recipients. Nevertheless, some adverse effects also have been shown to be associated with highly intensive exercises. We report a kidney transplant professional boxer whose kidney allograft has remained in good health, despite his violent sport activities

Oxidative stress in kidney transplantation: causes, consequences, and potential treatment.

Oxidative stress is a major mediator of adverse outcomes throughout the course of transplantation. Transplanted kidneys are prone to oxidative stress-mediated injury by pre-transplant and post-transplant conditions that cause reperfusion injury or imbalance between oxidants and antioxidants. Besides adversely affecting the allograft, oxidative stress and its constant companion, inflammation, cause cardiovascular disease, cancer, metabolic syndrome, and other disorders in transplant recipients. Presence and severity of oxidative stress can be assessed by various biomarkers produced from interaction of reactive oxygen species with lipids, proteins, nucleic acids, nitric oxide, glutathione, etc. In addition, expression and activities of redox-sensitive molecules such as antioxidant enzymes can serve as biomarkers of oxidative stress. Via activation of nuclear factor kappa B, oxidative stress promotes inflammation which, in turn, amplifies oxidative stress through reactive oxygen species generation by activated immune cells. Therefore, inflammation markers are indirect indicators of oxidative stress. Many treatment options have been evaluated in studies conducted at different stages of transplantation in humans and animals. These studies have provided useful strategies for use in donors or in organ preservation solutions. However, strategies tested for use in post-transplant phase have been largely inconclusive and controversial. A number of therapeutic options have been exclusively examined in animal models and only a few have been tested in humans. Most of the clinical investigations have been of short duration and have provided no insight into their impact on the long-term survival of transplant patients. Effective treatment of oxidative stress in transplant population remains elusive and awaits future explorations