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Tumor suppressor BRCA2 is known to stabilize and restart stalled DNA replication forks. Here the authors show that BRCA2 is recruited to the replication fork through its interaction with MCM10 and inhibits Primase-Polymerase-mediated repriming, lesion bypass and single strand DNA gap formation after DNA damage.

To investigate the genetic etiology of anophthalmia and microphthalmia, we used exome sequencing in a Caucasian female with unilateral microphthalmia and coloboma, bilateral optic nerve hypoplasia, ventricular and atrial septal defects, and growth delays. We found two sequence variants in SALL4 - c.[575C>A], predicting p.(Ala192Glu), that was paternally inherited, and c.[2053G>C], predicting p.(Asp685His), that was maternally inherited. Haploinsufficiency for SALL4 due to nonsense or frameshift mutations has been associated with acro-renal ocular syndrome that is characterized by eye defects including Duane anomaly and coloboma, in addition to radial ray malformations and renal abnormalities. Our report is the first description of structural eye defects associated with two missense variants in SALL4 inherited in trans; the absence of reported findings in both parents suggests that both sequence variants are hypomorphic mutations and that both are needed for the ocular phenotype. SALL4 is expressed in the developing lens and regulates BMP4, leading us to speculate that altered BMP4 expression was responsible for the eye defects, but we could not demonstrate altered BMP4 expression in vitro after using small interfering RNAs (siRNAs) to reduce SALL4 expression. We conclude that SALL4 hypomorphic variants may influence eye development.

Glioblastoma (GBM) is the most common primary brain malignancy in adults with a dismal prognosis. Despite advances in genomic analysis and surgical technique and the development of targeted therapeutics, most treatment options are ineffective and mainly palliative. Autophagy is a form of cellular self-digestion with the goal of recycling intracellular components to maintain cell metabolism. Here, we describe some recent findings that suggest GBM tumors are more sensitive to the excessive overactivation of autophagy leading to autophagy-dependent cell death. GBM cancer stem cells (GSCs) are a subset of the GBM tumor population that play critical roles in tumor formation and progression, metastasis, and relapse, and they are inherently resistant to most therapeutic strategies. Evidence suggests that GSCs are able to adapt to a tumor microenvironment of hypoxia, acidosis, and lack of nutrients. These findings have suggested that autophagy may promote and maintain the stem-like state of GSCs as well as their resistance to cancer treatment. However, autophagy is a double-edged sword and may have anti-tumor properties under certain conditions. The role of the STAT3 transcription factor in autophagy is also described. These findings provide the basis for future research aimed at targeting the autophagy-dependent pathway to overcome the inherent therapeutic resistance of GBM in general and to specifically target the highly therapy-resistant GSC population through autophagy regulation.

This work presents a method for predicting plasma equilibria in tokamak fusion experiments and reactors. The approach involves representing the plasma current as a linear combination of basis functions using principal component analysis of plasma toroidal current densities ( J t ) from the EFIT-AI equilibrium database. Then utilizing EFIT's Green's function tables, basis functions are created for the poloidal flux (ψ) and diagnostics generated from the toroidal current ( J t). Similar to the idea of a physics-informed neural network (NN), this physically enforces consistency between ψ , J t , and the synthetic diagnostics. First, the predictive capability of a least squares technique to minimize the error on the synthetic diagnostics is employed. The results show that the method achieves high accuracy in predicting ψ and moderate accuracy in predicting J t with median R 2 = 0.9993 and R 2 = 0.978, respectively. A comprehensive NN using a network architecture search is also employed to predict the coefficients of the basis functions. The NN demonstrates significantly better performance compared to the least squares method with median R 2 = 0.9997 and 0.9916 for J t and ψ , respectively. The robustness of the method is evaluated by handling missing or incorrect data through the least squares filling of missing data, which shows that the NN prediction remains strong even with a reduced number of diagnostics. Additionally, the method is tested on plasmas outside of the training range showing reasonable results. [ABSTRACT FROM AUTHOR]

Cloud computing is meant for storing the huge data using third party that ensures that confidential data cannot be accessed by the other users. But with rapid growth of technologies, data in the cloud normally increases which questions its security in storing in the cloud. Hence the protecting the cloud data seeks the strong security levels to counterfeit the different cloud attack. In order to achieve the highest level of security for cloud data, this study suggests a powerful encryption technique that combines chaotic scrolls and logistic maps. The proposed model exhibits the following advantages over the other algorithms: 1) High dynamic key generation 2) ability to counterfeit the multiple attacks 3) High randomness encrypted data which can provide more confusion of hacking from the intruder’s insight. To prove the strength of the proposed model, NIST National Institute of Science and Technology (NIST) is used for significant experiments. in which different statistical tests were carried out to prove the strength of the proposed model. The level of security of the suggested model is also evaluated and investigated using formal analysis using Burrows-Abadi-Needham Logic (BAN). The given model is thoroughly verified using both the Profverif tool and AVISPA. In terms of communication costs and unpredictability, the suggested model's randomness has also been contrasted with that of another existing algorithm. Results demonstrates that the proposed model shows its ability to provide more potent protection to the cloud data than the other existing encryption algorithms. [ABSTRACT FROM AUTHOR]

Background: Down Syndrome is a genetic disorder caused by abnormal cell division, resulting in extra genetic material from chromosome 21. Non-homologous Robertsonian translocation (RT) between chromosomes 13 and 14 is a common genetic abnormality seen in couples with reproductive failure. The present report highlights the co-occurrence of Down Syndrome with RT of chromosomes 13 and 14. Case Presentation: A 6-month-old male child, born to second-degree consanguineous parents, was referred to our institute for the conventional karyotyping method. Peripheral blood cultures were set up following the standard protocol for karyotype analysis, which revealed Down Syndrome and non-homologous RT between chromosomes 13 and 14 in the child, inherited from his mother. A normal karyotype was found in the father. Conclusion: The study highlights the importance of cytogenetic analysis in detecting additional chromosomal abnormalities in syndromic children. [JBCGenetics 2020; 3(2.000): 113-117]

Based on epidemiological commonalities, multiple sclerosis (MS) and Hodgkin lymphoma (HL), two clinically distinct conditions, have long been suspected to be aetiologically related. MS and HL occur in roughly the same age groups, both are associated with Epstein-Barr virus infection and ultraviolet (UV) light exposure, and they cluster mutually in families (though not in individuals). We speculated if in addition to sharing environmental risk factors, MS and HL were also genetically related. Using data from genome-wide association studies (GWAS) of 1816 HL patients, 9772 MS patients and 25 255 controls, we therefore investigated the genetic overlap between the two diseases.From among a common denominator of 404 K single nucleotide polymorphisms (SNPs) studied, we identified SNPs and human leukocyte antigen (HLA) alleles independently associated with both diseases. Next, we assessed the cumulative genome-wide effect of MS-associated SNPs on HL and of HL-associated SNPs on MS. To provide an interpretational frame of reference, we used data from published GWAS to create a genetic network of diseases within which we analysed proximity of HL and MS to autoimmune diseases and haematological and non-haematological malignancies.SNP analyses revealed genome-wide overlap between HL and MS, most prominently in the HLA region. Polygenic HL risk scores explained 4.44% of HL risk (Nagelkerke R(2)), but also 2.36% of MS risk. Conversely, polygenic MS risk scores explained 8.08% of MS risk and 1.94% of HL risk. In the genetic disease network, HL was closer to autoimmune diseases than to solid cancers.HL displays considerable genetic overlap with MS and other autoimmune diseases.

Alternative rescue factor B (ArfB) is an enzyme that releases peptides from stalled ribosomes to allow ribosome recycling. Here the authors carry-out cryo-EM analyses of 70S ribosomes complexed with ArfB on either a short or longer mRNA to reveal distinct modes of ArfB function.

Introduction: Antiglomerular basement membrane disease manifests as rapidly progressive glomerulonephritis and alveolar hemorrhage. It encompasses 10–15% of crescentic glomerulonephritis and is associated with poor outcome. In this study, we have elaborated on the clinical details, morphological features, and outcome of anti-GBM glomerulonephritis. Materials and Methods: All the consecutive biopsy-proven cases of anti-GBM glomerulonephritis over a period of 4½ years were analyzed, retrospectively. Results: Sixteen cases were diagnosed as anti-GBM glomerulonephritis during the study period. Twelve patients presented with rapidly progressive renal failure of which four patients required hemodialysis at the time of presentation. Goodpasture's syndrome was noted in two patients. Thirteen cases were positive for circulating anti-GBM antibodies and two patients showed double positivity for both anti-GBM antibodies and ANCA. Fifteen biopsies revealed crescentic glomerulonephritis with linear deposition of IgG along the glomerular basement membrane in all the 16 cases. Conclusion: Renal biopsy analysis is important in the diagnosis of Anti GBM nephritis. Morphology is an important predictor of disease progression.

Osteoblastoma is considered as a bone tumor, which is benign and arises from non-odontogenic epithelium. This condition is quite rare in children and very few cases of osteoblastoma are reported. The case report describes osteoblastoma affecting mandible in an 8-year-old boy. It was noticed that the lesion began to show gradual enlargement and was painful. An excisional biopsy was carried out. Histopathological examination revealed eosinophilic bony trabeculae, lined by osteoblasts, with osteocytes within lacunae. Intertwining connective tissue showed delicate collagen fibers, numerous vascular channels, and extravasated RBCs. It was finally diagnosed as osteoblastoma based on the features of clinical, histological, and radiographic appearance.

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA , as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to $40\%$ on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.

Anophthalmia/microphthalmia (A/M) is a genetically heterogeneous birth defect for which the etiology is unknown in more than 50% of patients. We used exome sequencing with the ACE Exome(TM) (Personalis, Inc; 18 cases) and UCSF Genomics Core (21 cases) to sequence 28 patients with A/M and four patients with varied developmental eye defects. In the 28 patients with A/M, we identified de novo mutations in three patients (OTX2, p.(Gln91His), RARB, p.Arg387Cys and GDF6, p.Ala249Glu) and inherited mutations in STRA6 in two patients. In patients with developmental eye defects, a female with cataracts and cardiomyopathy had a de novo COL4A1 mutation, p.(Gly773Arg), expanding the phenotype associated with COL4A1 to include cardiomyopathy. A male with a chorioretinal defect, microcephaly, seizures and sensorineural deafness had two PNPT1 mutations, p.(Ala507Ser) and c.401-1G>A, and we describe eye defects associated with this gene for the first time. Exome sequencing was efficient for identifying mutations in pathogenic genes for which there is no clinical testing available and for identifying cases that expand phenotypic spectra, such as the PNPT1 and COL4A1-associated disorders described here.