A fully-automated paper ECG digitisation algorithm using deep learning

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects

Suv4-20h Histone Methyltransferases Promote Neuroectodermal Differentiation by Silencing the Pluripotency-Associated Oct-25 Gene

Post-translational modifications (PTMs) of histones exert fundamental roles in regulating gene expression. During development, groups of PTMs are constrained by unknown mechanisms into combinatorial patterns, which facilitate transitions from uncommitted embryonic cells into differentiated somatic cell lineages. Repressive histone modifications such as H3K9me3 or H3K27me3 have been investigated in detail, but the role of H4K20me3 in development is currently unknown. Here we show that Xenopus laevis Suv4-20h1 and h2 histone methyltransferases (HMTases) are essential for induction and differentiation of the neuroectoderm. Morpholino-mediated knockdown of the two HMTases leads to a selective and specific downregulation of genes controlling neural induction, thereby effectively blocking differentiation of the neuroectoderm. Global transcriptome analysis supports the notion that these effects arise from the transcriptional deregulation of specific genes rather than widespread, pleiotropic effects. Interestingly, morphant embryos fail to repress the Oct4-related Xenopus gene Oct-25. We validate Oct-25 as a direct target of xSu4-20h enzyme mediated gene repression, showing by chromatin immunoprecipitaton that it is decorated with the H4K20me3 mark downstream of the promoter in normal, but not in double-morphant, embryos. Since knockdown of Oct-25 protein significantly rescues the neural differentiation defect in xSuv4-20h double-morphant embryos, we conclude that the epistatic relationship between Suv4-20h enzymes and Oct-25 controls the transit from pluripotent to differentiation-competent neural cells. Consistent with these results in Xenopus, murine Suv4-20h1/h2 double-knockout embryonic stem (DKO ES) cells exhibit increased Oct4 protein levels before and during EB formation, and reveal a compromised and biased capacity for in vitro differentiation, when compared to normal ES cells. Together, these results suggest a regulatory mechanism, conserved between amphibians and mammals, in which H4K20me3-dependent restriction of specific POU-V genes directs cell fate decisions, when embryonic cells exit the pluripotent state

A Rapid Assessment Scorecard to Identify Informal Settlements at Higher Maternal and Child Health Risk in Mumbai

The communities who live in urban informal settlements are diverse, as are their environmental conditions. Characteristics include inadequate access to safe water and sanitation, poor quality of housing, overcrowding, and insecure residential status. Interventions to improve health should be equity-driven and target those at higher risk, but it is not clear how to prioritise informal settlements for health action. In implementing a maternal and child health programme in Mumbai, India, we had conducted a detailed vulnerability assessment which, though important, was time-consuming and may have included collection of redundant information. Subsequent data collection allowed us to examine three issues: whether community environmental characteristics were associated with maternal and newborn healthcare and outcomes; whether it was possible to develop a triage scorecard to rank the health vulnerability of informal settlements based on a few rapidly observable characteristics; and whether the scorecard might be useful for future prioritisation. The City Initiative for Newborn Health documented births in 48 urban slum areas over 2 years. Information was collected on maternal and newborn care and mortality, and also on household and community environment. We selected three outcomes—less than three antenatal care visits, home delivery, and neonatal mortality—and used logistic regression and classification and regression tree analysis to test their association with rapidly observable environmental characteristics. We developed a simple triage scorecard and tested its utility as a means of assessing maternal and newborn health risk. In analyses on a sample of 10,754 births, we found associations of health vulnerability with inadequate access to water, toilets, and electricity; non-durable housing; hazardous location; and rental tenancy. A simple scorecard based on these had limited sensitivity and positive predictive value, but relatively high specificity and negative predictive value. The scorecard needs further testing in a range of urban contexts, but we intend to use it to identify informal settlements in particular need of family health interventions in a subsequent program

Harnessing the potential of ligninolytic enzymes for lignocellulosic biomass pretreatment

Abundant lignocellulosic biomass from various industries provides a great potential feedstock for the production of value-added products such as biofuel, animal feed, and paper pulping. However, low yield of sugar obtained from lignocellulosic hydrolysate is usually due to the presence of lignin that acts as a protective barrier for cellulose and thus restricts the accessibility of the enzyme to work on the cellulosic component. This review focuses on the significance of biological pretreatment specifically using ligninolytic enzymes as an alternative method apart from the conventional physical and chemical pretreatment. Different modes of biological pretreatment are discussed in this paper which is based on (i) fungal pretreatment where fungi mycelia colonise and directly attack the substrate by releasing ligninolytic enzymes and (ii) enzymatic pretreatment using ligninolytic enzymes to counter the drawbacks of fungal pretreatment. This review also discusses the important factors of biological pretreatment using ligninolytic enzymes such as nature of the lignocellulosic biomass, pH, temperature, presence of mediator, oxygen, and surfactant during the biodelignification process