The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

This Perspective explores and explains the fundamental dogma of nanoparticle delivery to tumours and answers two central questions: ‘how many nanoparticles accumulate in a tumour?’ and ‘how does this number affect the clinical translation of nanomedicines?’

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Analysis of nanoparticle delivery to tumours

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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An atlas of genetic correlations across human diseases and traits.

AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore

PL02-05 

All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

Patterns of Somatic Mutation in Human Cancer Genomes

The extended abstract is for a Research Demo Session based on our previous article [1]. There is a requirement of Continuous Glucose Measurement (CGM) to control diabetes by insulin secretion or medications. A novel non-invasive measurement device iGLU is introduced to determine continuous blood glucose value with optical detection technique. iGLU model is integrated with Internet of Medical Things (IoMT) framework for remote located diabetic patients to provide prescribed diet and medication with the help of diabetologist.

iGLU: Non-Invasive Device for Continuous Glucose Measurement with IoMT Framework

Pd31-06 dairy intake and prostate cancer risk: results from the california collaborative prostate cancer study

Data-driven electricity theft detectors use consumer reported power consumption measurement to detect energy theft. Most of the detection scheme incorporate machine learning algorithms to classify unknown theft. The correct labelling of the training data is a common implicit assumption in such detectors. Unfortunately, these detectors are vulnerable against data poisoning attacks that assume false labels during training. However, the difficulty of specific and accurate features selection arise due to lack of training data set, existing schemes are unable to detect unknown attacks effectively. To enhance the detectors' robustness against unknown data attacks, we propose a theft detection scheme based on label propagation based semisupervised learning algorithm by using small amount of labeled dataset. The scheme is achieve high accuracy than other existing algorithms. The proposed scheme than compared with the other supervised schemes.

Semi Supervised Cyber Attack Detection System For Smart Grid

Endocrine therapy resistance in breast cancer is a major obstacle in the treatment of patients with estrogen receptor-positive (ER+) tumors. Herein, we demonstrate the feasibility of longitudinal, noninvasive and semiquantitative in vivo molecular imaging of resistance to three endocrine therapies by using an inducible fluorescence-labeled short hairpin RNA (shRNA) system in orthotopic mice xenograft tumors. We employed a dual fluorescent doxycycline (Dox)-regulated lentiviral inducer system to transfect ER+ MCF7L breast cancer cells, with green fluorescent protein (GFP) expression as a marker of transfection and red fluorescent protein (RFP) expression as a surrogate marker of Dox-induced tumor suppressor phosphatase and tensin homolog deleted on chromosome 10 (PTEN) knockdown. Xenografted MCF7L tumor-bearing nude mice were randomized to therapies comprising estrogen deprivation, tamoxifen or an ER degrader (fulvestrant) and an estrogen-treated control group. Longitudinal imaging was performed by a home-built multispectral imaging system based on a cooled image intensified charge coupled device camera. The GFP signal, which corresponds to number of viable tumor cells, exhibited excellent correlation to caliper-measured tumor size (P << .05). RFP expression was substantially higher in mice exhibiting therapy resistance and strongly and significantly (P < 1e-7) correlated with the tumor size progression for the mice with shRNA-induced PTEN knockdown. PTEN loss was strongly correlated with resistance to estrogen deprivation, tamoxifen and fulvestrant therapies.

In vivo longitudinal imaging of RNA interference-induced endocrine therapy resistance in breast cancer.


 This work presents a wide-band active filter for RF receiver. The design uses Carbon Nanotube-FET (CNFET) based differential voltage current conveyor (DVCC) for the implementation of the proposed filter. The filter is designed to operate Ku-band frequencies (12-18 GHz), which is used in satellite communication. Additionally, CMOS based circuit and CNFET-based circuit for DVCC are compared for the performance evaluation. HSPICE simulations have been carried out to test the design aspects of the circuit. The CNFET-based circuit has better results in terms of 60 % reduction in the power consumption and about six times improvement in the bandwidth. The filter utilizes low supply voltage of 0.9 V and consumes 524 µW only. The proposed filter outperforms the existing CMOS-based designs which suggests its usage for low-power high-frequency analog circuits.

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Amit Joshi

Papers

iGLU: Non-Invasive Device for Continuous Glucose Measurement with IoMT Framework

Pd31-06 dairy intake and prostate cancer risk: results from the california collaborative prostate cancer study

Semi Supervised Cyber Attack Detection System For Smart Grid

In vivo longitudinal imaging of RNA interference-induced endocrine therapy resistance in breast cancer.

DVCC-based Low-power RF Filter with 32 nm CNFETs