information

In this study, an improved particle swarm optimization (PSO) algorithm, including 4 types of new velocity updating formulae (each is equal to the traditional PSO), was introduced. This algorithm was called the reverse direction supported particle swarm optimization (RDS-PSO) algorithm. The RDS-PSO algorithm has the potential to extend the diversity and generalization of traditional PSO by regulating the reverse direction information adaptively. To implement this extension, 2 new constants were added to the velocity update equation of the traditional PSO, and these constants were regulated through 2 alternative procedures, i.e. max min-based and cosine amplitude-based diversity-evaluating procedures. The 4 most commonly used benchmark functions were used to test the general optimization performances of the RDS-PSO algorithm with 3 different velocity updates, RDS-PSO without a regulating procedure, and the traditional PSO with linearly increasing/decreasing inertia weight. All PSO algorithms were also implemented in 4 modes, and their experimental results were compared. According to the experimental results, RDS-PSO 3 showed the best optimization performance

Integration of Hi-C with short and long-read genome sequencing reveals the structure of germline rearranged genomes

Here the authors characterize structural variations (SVs) in a cohort of individuals with complex genomic rearrangements, identifying breakpoints by employing short- and long-read genome sequencing and investigate their impact on gene expression and the three-dimensional chromatin architecture. They find breakpoints are enriched in inactive regions and can result in chromatin domain fusions.Structural variants are a common cause of disease and contribute to a large extent to inter-individual variability, but their detection and interpretation remain a challenge. Here, we investigate 11 individuals with complex genomic rearrangements including germline chromothripsis by combining short- and long-read genome sequencing (GS) with Hi-C. Large-scale genomic rearrangements are identified in Hi-C interaction maps, allowing for an independent assessment of breakpoint calls derived from the GS methods, resulting in >300 genomic junctions. Based on a comprehensive breakpoint detection and Hi-C, we achieve a reconstruction of whole rearranged chromosomes. Integrating information on the three-dimensional organization of chromatin, we observe that breakpoints occur more frequently than expected in lamina-associated domains (LADs) and that a majority reshuffle topologically associating domains (TADs). By applying phased RNA-seq, we observe an enrichment of genes showing allelic imbalanced expression (AIG) within 100 kb around the breakpoints. Interestingly, the AIGs hit by a breakpoint (19/22) display both up- and downregulation, thereby suggesting different mechanisms at play, such as gene disruption and rearrangements of regulatory information. However, the majority of interpretable genes located 200 kb around a breakpoint do not show significant expression changes. Thus, there is an overall robustness in the genome towards large-scale chromosome rearrangements

Integration of Hi-C with short and long-read genome sequencing reveals the structure of germline rearranged genomes

Structural variants are a common cause of disease and contribute to a large extent to inter-individual variability, but their detection and interpretation remain a challenge. Here, we investigate 11 individuals with complex genomic rearrangements including germline chromothripsis by combining short- and long-read genome sequencing (GS) with Hi-C. Large-scale genomic rearrangements are identified in Hi-C interaction maps, allowing for an independent assessment of breakpoint calls derived from the GS methods, resulting in >300 genomic junctions. Based on a comprehensive breakpoint detection and Hi-C, we achieve a reconstruction of whole rearranged chromosomes. Integrating information on the three-dimensional organization of chromatin, we observe that breakpoints occur more frequently than expected in lamina-associated domains (LADs) and that a majority reshuffle topologically associating domains (TADs). By applying phased RNA-seq, we observe an enrichment of genes showing allelic imbalanced expression (AIG) within 100 kb around the breakpoints. Interestingly, the AIGs hit by a breakpoint (19/22) display both up- and downregulation, thereby suggesting different mechanisms at play, such as gene disruption and rearrangements of regulatory information. However, the majority of interpretable genes located 200 kb around a breakpoint do not show significant expression changes. Thus, there is an overall robustness in the genome towards large-scale chromosome rearrangements

entropy-based clustering

An algorithm proposed using Renyi entropy clustering to improve the searching ability of traditional particle swarm optimization (PSO) is introduced in this study. Modified PSO consists of two steps. In the first step, particles in initial population are sorted according to Renyi entropy clustering method, and in the second step, some particles are removed from population and some new particles are added instead of them based on the sorted list. Thus, a reliable new initial population is created. When using sorted list from first to last with decreasing inertia weight parameter, or from last to first with increasing inertia weight parameter, a little improved search performances have been observed on three commonly used benchmark functions. However, in other two combinations of the proposed algorithm (from last to first with decreasing inertia weight and from first to last with increasing inertia weight), little worse optimization performances than traditional PSO have been noted. These four types of the proposed algorithm were run with different exchanging rate values. Thus, the representation ability of Renyi entropy clustering on initial population and the effect of organizing inertia weight parameter were evaluated together. Experimental results which were surveyed at different exchanging rate values showed the efficiency of such evaluation

Heart Valve Diseases

Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and sigma). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure

Application

This paper presents a study on predicting academically at-risk engineering students at the early stage of their education. For this purpose, some soft computing tools namely support vectors machines and artificial neural networks have been employed. The study population included all students enrolled in Pamukkale University, Faculty of Engineering at 2008-2009 and 2009-2010 academic years as freshmen. The data are retrieved from various institutions and questionnaires conducted on the students. Each input data point is of 38-dimension, which includes demographic and academic information about the students, while the output based on the first-year GPA of the students falls into either at-risk or not. The results of the study have shown that either support vector machine or artificial neural network methods can be used to predict first-year performance of a student in a priori manner. Thus, a proper course load and graduation schedule can be transcribed for the student to manage their graduation in a way that potential dropout risks are reduced. Moreover, an input sensitivity analysis has been conducted to determine the importance of each input used in the study

TRCP-6 Mutation Causing FSGS in Childhood

WOS: 00038208260034

Transplantation in pediatric aHUS within the era of eculizumab therapy

aHUS is caused by the over-activation and dysregulation of the alternative complement pathway. Data regarding outcomes of pediatric aHUS patients after kidney transplantation are still very scarce. Accordingly, the aim of this study was to describe the clinical findings and outcomes of pediatric aHUS patients after renal transplantation. This is a retrospective, multicenter study including 12 patients from the national registry system. Among the 12 patients, eight had received prophylactic eculizumab and none of those patients (except one) had experienced aHUS recurrence during a median follow-up period of 58.5 (min-max, 4-94) months. Although eculizumab had been started on the day before transplantation in one of them, aHUS recurrence occurred during the transplantation procedure. Eculizumab had been stopped in only one patient who had no complement gene mutation after 35 months of therapy, and recurrence had not been observed during the 19 months of follow-up. In three patients, maintenance doses had been spaced out without any recurrence. One additional patient with anti-CFH antibody received only two doses of eculizumab for transplantation and had been followed for 46 months without aHUS recurrence. The remaining three patients had not received anti-C5 therapy and none of those patients experienced aHUS recurrence during a median follow-up period of 21 (min-max, 9-42) months. Prophylactic eculizumab is a safe and effective treatment for the prevention of aHUS recurrence. Eculizumab interval prolongation, discontinuation, and transplantation without eculizumab prophylaxis can be tried in selected patients with close follow-up.C1 [Ozcakar, Zeynep Birsin] Ankara Univ, Div Pediat Nephrol, Dept Pediat, Sch Med, Ankara, Turkey.[Ozaltin, Fatih; Gulhan, Bora; Topaloglu, Rezan] Hacettepe Univ, Div Pediat Nephrol, Dept Pediat, Fac Med, Ankara, Turkey.[Ozaltin, Fatih] Hacettepe Univ, Nephrogenet Lab, Fac Med, Ankara, Turkey.[Comak, Elif] Akdeniz Univ, Div Pediat Nephrol, Dept Pediat, Fac Med, Antalya, Turkey.[Parmaksiz, Gonul] Baskent Univ, Adana Teaching & Res Hosp, Pediat Nephrol, Adana, Turkey.[Baskin, Esra] Baskent Univ, Div Pediat Nephrol, Dept Pediat, Sch Med, Ankara, Turkey.[Kasap Demir, Belde] Izmir Katip Celebi Univ, Div Nephrol & Rheumatol, Dept Pediat, Fac Med, Izmir, Turkey.[Canpolat, Nur] Istanbul Univ, Div Pediat Nephrol, Dept Pediat, Cerrahpasa Fac Med, Istanbul, Turkey.[Yuruk Yildirim, Zeynep] Istanbul Univ, Div Pediat Nephrol, Dept Pediat, Istanbul Fac Med, Istanbul, Turkey.[Demircioglu Kilic, Beltinge] Gaziantep Univ, Div Pediat Nephrol, Dept Pediat, Fac Med, Gaziantep, Turkey.[Yuksel, Selcuk] Pamukkale Univ, Div Pediat Nephrol, Dept Pediat, Sch Med, Denizli, Turkey.[Soylemezoglu, Oguz] Gazi Univ, Div Pediat Nephrol, Dept Pediat, Sch Med, Ankara, Turkey