Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous largescale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.Cancer forms and progresses through a series of critical transitions-from pre-malignant to malignant states, from locally contained to metastatic disease, and from treatment-responsive to treatment-resistant tumors (Figure 1). Although specifics differ across tumor types and patients, all transitions involve complex dynamic interactions between diverse pre-malignant, malignant, and non-malignant cells (e.g., stroma cells and immune cells), often organized in specific patterns within the tumor

Human Papillomavirus (HPV) type 16 oncoprotein E7 plays a major role in cervical carcinogenesis by interacting with and functionally inactivating various host regulatory molecules. Long noncoding RNA (lncRNA) HOTAIR is one such regulator that recruits chromatin remodelling complex PRC2, creating gene silencing H3K27 me3 marks. Hence, we hypothesized that HOTAIR could be a potential target of E7, in HPV16 related cervical cancers (CaCx). We identified significant linear trend of progressive HOTAIR down-regulation through HPV negative controls, HPV16 positive non-malignants and CaCx samples. Majority of CaCx cases portrayed HOTAIR down-regulation in comparison to HPV negative controls, with corresponding up-regulation of HOTAIR target, HOXD10, and enrichment of cancer related pathways. However, a small subset had significantly higher HOTAIR expression, concomitant with high E7 expression and enrichment of metastatic pathways. Expression of HOTAIR and PRC2-complex members (EZH2 and SUZ12), showed significant positive correlation with E7 expression in CaCx cases and E7 transfected C33A cell line, suggestive of interplay between E7 and HOTAIR. Functional inactivation of HOTAIR by direct interaction with E7 could also be predicted by in silico analysis and confirmed by RNA-Immunoprecipitation. Our study depicts one of the causal mechanisms of cervical carcinogenesis by HPV16 E7, through modulation of HOTAIR expression and function.Cervical carcinoma (CaCx) is the second most prevalent cancer among women in India after breast cancer and the fourth most prevalent cancer among women worldwide 1,2 . Human Papillomavirus (HPV) is considered as the major etiologic contributor to the development of CaCx and is found in 99.7% of all the cases, of which, the high-risk types HPV16 and 18 are the most prevalent ones 3,4 . HPV16 alone contributes to more than 50% of the CaCx cases globally 5 . HPV16 acts by frequently integrating into the host chromosome and replicates along with the host genome, which results in E2 gene disruption and consistent expression of the two HPV oncoproteins E6 and E7 due to loss of E2 repressor activity 6 . The infected epithelial basal cells differentiate from the basal membrane to the superficial zone and the virus particles are shed with the sloughed-off epithelial cells. Moreover, E6 and E7 also facilitate persistence of episomal HPV genomes in undifferentiated cells of the cervical epithelium 7 . It is well established that oncoproteins E6 and E7 are the major transforming agents, leading to carcinogenesis. While E6 regulates the decay of the tumor suppressor p53, E7 leads to cellular

Autism is a developmental disorder characterized by impairments in social interaction and communication associated with repetitive patterns of interest or behavior. Autism is highly influenced by genetic factors. Genome-wide linkage and candidate gene association approaches have been used to try and identify autism genes. A few loci have repeatedly been reported linked to autism. Several groups reported evidence for linkage to a region on chromosome 16p. We have applied a direct physical identity-by-descent (IBD) mapping approach to perform a high-density (0.85 megabases) genome-wide linkage scan in 116 families from the AGRE collection. Our results confirm linkage to a region on chromosome 16p with autism. High-resolution single-nucleotide polymorphism (SNP) genotyping and analysis of this region show that haplotypes in the protein kinase c-beta gene are strongly associated with autism. An independent replication of the association in a second set of 167 trio families with autism confirmed our initial findings. Overall, our data provide evidence that the PRKCB1 gene on chromosome 16p may be involved in the etiology of autism. Molecular Psychiatry (2005) 10, 950-960.

BackgroundPsoriasis is a chronic inflammatory autoimmune skin disorder. Several studies suggested psoriasis to be a complex multifactorial disease, but the exact triggering factor is yet to be determined. Evidences suggest that in addition to genetic factors, epigenetic reprogramming is also involved in psoriasis development. Major histopathological features, like increased proliferation and abnormal differentiation of keratinocytes, and immune cell infiltrations are characteristic marks of psoriatic skin lesions. Following therapy, histopathological features as well as aberrant DNA methylation reversed to normal levels. To understand the role of DNA methylation in regulating these crucial histopathologic features, we investigated the genome-wide DNA methylation profile of psoriasis patients with different histopathological features.ResultsGenome-wide DNA methylation profiling of psoriatic and adjacent normal skin tissues identified several novel differentially methylated regions associated with psoriasis. Differentially methylated CpGs were significantly enriched in several psoriasis susceptibility (PSORS) regions and epigenetically regulated the expression of key pathogenic genes, even with low-CpG promoters. Top differentially methylated genes overlapped with PSORS regions including S100A9, SELENBP1, CARD14, KAZN and PTPN22 showed inverse correlation between methylation and gene expression. We identified differentially methylated genes associated with characteristic histopathological features in psoriasis. Psoriatic skin with Munro’s microabscess, a distinctive feature in psoriasis including parakeratosis and neutrophil accumulation at the stratum corneum, was enriched with differentially methylated genes involved in neutrophil chemotaxis. Rete peg elongation and focal hypergranulosis were also associated with epigenetically regulated genes, supporting the reversible nature of these characteristic features during remission and relapse of the lesions.ConclusionOur study, for the first time, indicated the possible involvement of DNA methylation in regulating the cardinal pathophysiological features in psoriasis. Common genes involved in regulation of these pathologies may be used to develop drugs for better clinical management of psoriasis.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0541-9) contains supplementary material, which is available to authorized users.

An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-theart. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images.INDEX TERMS Edge, edge connectivity, edge detection, edge width uniformity, threshold.SUDIPTA ROY received the Ph.D. degree in computer science and engineering from the Department of Computer Science and Engineering, University of Calcutta. He is currently with the Radiological Chemistry and Imaging Laboratory, Washington University in Saint Louis, USA. He has more than five years of experience in teaching and research. He is an author of more than 30 publications in refereed national and international journals and conferences, including the IEEE, Springer, Elsevier, and many others. He is an author of one book and many book chapters. He holds an U.S. patent in medical image processing and filed an Indian patent in smart agricultural system. His research interests include biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning, and big data technologies.

We re-sequenced HPV16 genome (~6 kb) implicated in cervical carcinogenesis (LCR, E2, E5, E6, E7, L1, L2) to prioritize sequence variants for functional validation as biomarkers, using CaCx cases (n=74) and asymptomatic controls (n=24). Of the nucleotide variations recorded (n=271), non-synonymous changes in L2 region were significantly higher (p=0.005) among cases (2.67%) compared to controls (1.27%). Using SIFT database, 29 non-synonymous changes (frequency=0.01-0.03) predicted as deleterious to protein functions were identified. Haplotype analysis considering 110 polymorphic variations (frequency> or =0.05) within intact viral isolates (53 CaCx cases and 21 controls) using NETWORK software, confirmed Asian-American (AA, 14.86%) and European (E, 85.14%) variants, differing at 78 positions. The E-variants portrayed thirty-six haplotypes, of which, E-12 was most prevalent within cases (38.1%; 16/42) and controls (28.57%; 6/21) harboring polymorphic variations at 10 positions, in contrast to HPV16R. Cases of the E-12 haplotype harbored 7 deleterious mutations distributed within L1 (n=1), E2 (n=1), E5 (n=1), and L2 (n=4), while none within similar controls. Thus rare deleterious variations within genes implicated in productive infection over the E-12 haplotype background of intact HPV16 isolates might be of causal relevance for CaCx development.

An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

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