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

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

Remote-controlled experiments with cloud chemistry

Developing cleaner chemical processes often involves sophisticated flow-chemistry equipment that is not available in many economically developing countries. For reactions where it is the data that are important rather than the physical product, the networking of chemists across the internet to allow remote experimentation offers a viable solution to this problem

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

Paralel tornalama işlevini gerçekleştiren CNC torna tezgahının tasarımı, kesici uç ömrü ve kesme kararlılığı incelemeleri (Cutting tool life, cutting stability and process time review on designed and manufactured CNC turning machine which performs the parallel turning function)

Bu çalışmada, tornalama ile üretilen parça esas alınarak, bilgisayar destekli nümerik kontrol (CNC) sistemine sahip, paralel tornalama işlevini gerçekleştiren tezgah tasarlanmış ve imalatı yapılmıştır. İmalatı yapılan CNC paralel tornalama tezgahında paralel tornalama yönteminin, esas işleme zamanı, kesici uç ömrü, parça kalitesi, sistem kararlılığı ve kendinden kaynaklı titreşimler (tırlama) üzerindeki etkileri incelenmiştir. Bu çalışma sonuçlarında elde edilen verilerden yararlanarak; takım ömründe, işleme kalitesinde ve işleme sürelerinde iyileşme sağladığı görülen CNC paralel tornalama tezgahının, seri imalat yapan işletmeler için uygun olduğu değerlendirilmektedir

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

EVALUATION OF PATIENTS WITH PRIMARY HYPEROXALURIA TYPE 1: A MULTICENTER STUDY

WOS: 000443998400363

Is FDG-PET/CT used correctly in the combined approach for nodal staging in NSCLC patients?

BACKGROUND: The most widely accepted approach nowadays in nodal staging of non-small cell lung cancer (NSCLC) is the combined use of 18-Fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) and endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). However, this approach may not be sufficient, especially for early stages. AIMS: Our aim was to assess whether more satisfactory results can be obtained with standardized uptake value maximum lymph node/standardized uptake value mean mediastinal blood pool (SUVmax LN/SUVmean MBP), SUVmax LN/Primary tumor, or a novel cut-off value to SUVmax in this special group. SUBJECTS AND METHODS: Patients with diagnosed NSCLC and underwent FDG-PET/CT were reviewed retrospectively. 168 LNs of 52 early stage NSCLC patients were evaluated. The LNs identified in surgery/pathology reports were found in the FDG-PET/CT images. Anatomic and metabolic parameters were measured. Statistical analysis was performed by using of MedCalc Statistical Software. RESULTS: Regardless of LNs size; sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SUVmax >2.5 were 91.5%, 65.9%, 58.2%, and 95.1%, respectively. Optimum cut-off value of SUVmax was >4.0. Sensitivity, specificity, PPV, and NPV were found as 81.0%, 90.0%, 81.0%, and 90.0% respectively. Optimum cut-off value of SUVmax LN/SUVmean MBP was >1.71. Sensitivity, specificity, PPV, and NPV were found as 94.7%, 80.0%, 71.1%, and 96.7%, respectively. Optimum cut-off value of SUVmax LN/Primary tumor was >0.28. Sensitivity, specificity, PPV, and NPV were found as 81.1%, 85.1%, 72.9% and 90.1%, respectively. CONCLUSION: SUVmax LN/SUVmean MBP >1.71 has higher PPV than currently used, with similar NPV and sensitivity. This can provide increase in the accuracy of combined approach. In this way, faster nodal staging/treatment decisions, cost savings for healthcare system and time saving of medical professionals can be obtained