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.

/pdf/an-atlas-of-genetic-correlations-across-human-diseases-and-11fyqwnh4c.pdf

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

A PWM waveform generator with variable duty cycle has been designed and implemented in this paper. The design has been simulated for different frequencies. The waveform is generated on Spartan 3E FPGA starter kit which has on board rotary encoder. The on board clock signal and rotary encoder generates the required PWM signal. The duty cycle of PWM is varied by rotating the encoder in clockwise and anticlockwise directions. Various frequencies of PWM signal is generated by pressing it up and down. The design has been proposed where duty cycle can be varied from 0 to 100%. The frequency of the on board clock signal i.e., 50 MHz and this has been varied to generate different frequencies. The output can be observed on DSO and CRO. The PWM is used in controlling inverters, electrical motors and many communication and control applications. The usage of FPGA provides the reconfigurable architecture and also provides flexibility as well as economically viable.

PWM waveform generation using rotary encoder on Spartan-3E starter kit

MERS-CoV, a zoonotic virus, poses a serious threat to public health globally. Thus, it is imperative to develop an effective vaccination strategy for protection against MERS-CoV. Immunoinformatics and computational biology tools provide a faster and more cost-effective strategy to design potential vaccine candidates. In this work, the spike proteins from different strains of MERS-CoV were selected to predict HTL-epitopes that show affinity for T-helper MHC-class II HTL allelic determinant (HLA-DRB1:0101). The antigenicity and conservation of these epitopes among the selected spike protein variants in different MERS-CoV strains were analyzed. The analysis identified five epitopes with high antigenicity: QSIFYRLNGVGITQQ, DTIKYYSIIPHSIRS, PEPITSLNTKYVAPQ, INGRLTTLNAFVAQQ and GDMYVYSAGHATGTT. Then, a multi-epitope vaccine candidate was designed using linkers and adjuvant molecules. Finally, the vaccine construct was subjected to molecular docking with TLR5 (Toll-like receptor-5). The proposed vaccine construct had strong binding energy of -32.3 kcal/mol when interacting with TLR5.Molecular dynamics simulation analysis showed that the complex of the vaccine construct and TLR5 is stable. Analysis using in silico immune simulation also showed that the prospective multi-epitope vaccine design had the potential to elicit a response within 70 days, with the immune system producing cytokines and immunoglobulins. Finally, codon adaptation and in silico cloning analysis showed that the candidate vaccine could be expressed in the Escherichia coli K12 strain. Here we also designed support vaccine construct MEV-2 by using B-cell and CD8+ CTL epitopes to generate the complete immunogenic effect. This study opens new avenues for the extension of research on MERS vaccine development.Communicated by Ramaswamy H. Sarma.

MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology.

We report the photothermal response and Near Infrared (NIR) imaging sensitivities of magneto-fluorescent silica core gold nanocomplexes designed for molecular image guided thermal therapy of cancer. Approximately 160 nm Silica core gold nanoshells were designed to provide NIR fluorescent and Magnetic Resonance (MR) contrast by incorporating FDA approved dye indocyanine green (ICG) and iron-oxide within an outer silica epilayer. The imaging and therapeutic sensitivity, and the stability of fluorescence contrast for 12 microliters of suspension (containing approximately 7.9 × 108 or 1.3 femtoMole nanoshells) buried at depths of 2–8 mm in tissue mimicking scattering media is reported.

Calibrating the photo-thermal response of magneto-fluorescent gold nanoshells

Hypothesis Peripheral blood eosinophil count increases with the degree of mucosal injury associated with gastroesophageal reflux disease (GERD). Design Retrospective review. Setting Single-institution tertiary hospital. Patients Two hundred ninety-five patients (215 men and 80 women; median age, 57 years [interquartile range (IQR), 46-66 years]). One hundred had GERD without intestinal metaplasia, 100 had GERD with intestinal metaplasia, 40 had GERD with dysplasia, and 55 had GERD with intramucosal carcinoma. Results of complete blood count with differential and serum chemistry studies were compared among the groups using a nonparametric test for trend. Results Patients with a higher degree of mucosal injury were older ( P P  = .04) but lost significance when controlled for age ( P  = .53). Percent eosinophil counts were 2.0 (IQR, 1.3-2.8) in patients with GERD without intestinal metaplasia, 2.5 (IQR, 1.6-3.7) in GERD with intestinal metaplasia, 2.6 (IQR, 1.7-4.4) in GERD with dysplasia, and 2.7 (IQR, 1.5-4.3) in GERD with intramucosal carcinoma. This progressive increase in the percent eosinophil count was statistically significant ( P  = .006), remained significant after controlling for age ( P  = .04), and was also significant when measuring the absolute eosinophil count. Conclusion There is a progressive increase in the percent and absolute peripheral blood eosinophil count associated with progressive mucosal injury in patients with GERD.

https://jamanetwork.com/journals/jamasurgery/articlepdf/405872/soa90040_363_366.pdf

Progressive mucosal injury in patients with gastroesophageal reflux disease and increasing peripheral blood eosinophil counts.

In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 92.18% (53 classes), 91.56% (49 classes), 84.98% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.12% (8 classes with arm position variation) have been observed during the testing. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have been improved significantly. For signal pre-processing and deep learning techniques, Keras which is a high-level API for TensorFlow is utilised to build profound learning models. The proposed method has been tested on the Intel Core i7 3.5GHz, 7th Gen CPU with 8GB DDR4 RAM.

Amit Joshi

Papers

PWM waveform generation using rotary encoder on Spartan-3E starter kit

MERS virus spike protein HTL-epitopes selection and multi-epitope vaccine design using computational biology.

Calibrating the photo-thermal response of magneto-fluorescent gold nanoshells

Progressive mucosal injury in patients with gastroesophageal reflux disease and increasing peripheral blood eosinophil counts.

DLPR: Deep Learning-Based Enhanced Pattern Recognition Frame-Work for Improved Myoelectric Prosthesis Control