Exome sequencing utility in defining the genetic landscape of hearing loss and novel-gene discovery in Iran

Hearing loss (HL) is one of the most common sensory defects affecting more than 466 million individuals worldwide. It is clinically and genetically heterogeneous with over 120 genes causing non-syndromic HL identified to date. Here, we performed exome sequencing (ES) on a cohort of Iranian families with no disease-causing variants in known deafness-associated genes after screening with a targeted gene panel. We identified likely causal variants in 20 out of 71 families screened. Fifteen families segregated variants in known deafness-associated genes. Eight families segregated variants in novel candidate genes for HL: DBH, TOP3A, COX18, USP31, TCF19, SCP2, TENM1, and CARMIL1. In the three of these families, intrafamilial locus heterogeneity was observed with variants in both known and novel candidate genes. In aggregate, we were able to identify the underlying genetic cause of HL in nearly 30 of our study cohort using ES. This study corroborates the observation that high-throughput DNA sequencing in populations with high rates of consanguineous marriages represents a more appropriate strategy to elucidate the genetic etiology of heterogeneous conditions such as HL. © 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Lt

Mammogram Density Assessment Dataset

This dataset consists of mammogram images, complete with corresponding segmentation masks for dense tissue and breast area annotated by an expert radiologist. It is divided into two subsets: training and validation. For the training subset, our expert radiologist has also provided assessments of area-based breast density values in percentages. This dataset can be utilized for tasks such as segmentation and breast density estimation. The mammograms were sourced from the public VinDr-Mammo dataset, which can be found at [this link](^https://vindr.ai/datasets/mammo^). In this dataset, we have provided additional annotations, including both segmentation masks and density values.If you use the data in your research, please cite the following studies:Gudhe, N.R., Behravan, H., Sudah, M. et al. Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning. Sci Rep 12, 12060 (2022). https://doi.org/10.1038/s41598-022-16141-2Hieu T. Nguyen et al. “A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography”. 2022. https://doi.org/10.1101/2022.03.07.22272009THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Mammogram Density Assessment Dataset

This dataset consists of mammogram images, complete with corresponding segmentation masks for dense tissue and breast area annotated by an expert radiologist. It is divided into two subsets: training and validation. For the training subset, the expert radiologist has also provided assessments of area-based breast density values in percentages. This dataset can be utilized for tasks such as segmentation and breast density estimation. The mammograms were sourced from the public VinDr-Mammo dataset, which can be found at [this link](^https://vindr.ai/datasets/mammo^). However, our expert radiologist has provided additional annotations, including both segmentation masks and density values.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

THE INTERACTION OF ELLIPTICINE DERIVATIVES WITH NUCLEIC-ACIDS STUDIED BY OPTICAL AND H-1-NMR SPECTROSCOPY - EFFECT OF SIZE OF THE HETEROCYCLIC RING

The DNA interaction of derivatives of ellipticine with heterocyclic ring systems with three, four, or five rings and a dimethylaminoethyl side chain was studied. Optical spectroscopy of drug complexes with calf thymus DNA, poly [(dA-dT).(dA-dT)], or poly [(dG-dC).(dG-dC)] showed a 10 nm bathochromic shift of the light absorption bands of the pentacyclic and tetracyclic compounds upon binding to the nucleic acids, which indicates binding by intercalation. For the tricyclic compound a smaller shift of 1-3 nm was observed upon binding to the nucleic acids. Flow linear dichroism studies show that the geometry of all complexes is consistent with intercalation of the ring system, except for the DNA and poly [(dG-dC).(dG-dC)] complexes of the tricyclic compound, where the average angle between the drug molecular plane and the DNA helix axis was found to be 65 degrees. One-dimensional H-1-nmr spectroscopy was used to study complexes between d(CGCGATCGCG)(2) and the tricyclic and pentacyclic compounds. The results on the pentacyclic compound show nonselective broadening due to intermediate chemical exchange of most oligonucleotide resonances upon drug binding. The imino proton resonances are in slow chemical exchange, and new resonances with upheld shifts approaching 1 ppm appear upon drug binding, which supports intercalative binding of the pentacyclic compound. The results on the tricyclic compound show more rapid binding kinetics and very selective broadening of resonances. The data suggest that the tricyclic compound is in an equilibrium between intercalation and minor groove binding, with a preference to bind close to the AT base pairs with the side chain residing in the minor groove. (C) 1994 John Wiley and Sons, Inc

Machine learning identifies interacting genetic variants contributing to breast cancer risk:a case study in Finnish cases and controls

Abstract We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER−) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis