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Objective: Many low-income and middle-income countries (LMICs) struggle to provide the health services investment required for life-saving congenital heart disease (CHD) surgery. We explored associations between risk-adjusted CHD surgical mortality from 17 LMICs and global development indices to identify patterns that might inform investment strategies., Design: Retrospective analysis: country-specific standardised mortality ratios were graphed against global development indices reflective of wealth and healthcare investment. Spearman correlation coefficients were calculated., Setting and Participants: The International Quality Improvement Collaborative (IQIC) keeps a volunteer registry of outcomes of CHD surgery programmes in low-resource settings. Inclusion in the IQIC is voluntary enrolment by hospital sites. Patients in the registry underwent congenital heart surgery. Sites that actively participated in IQIC in 2013, 2014 or 2015 and passed a 10% data audit were asked for permission to share data for this study. 31 sites in 17 countries are included., Outcome Measures: In-hospital mortality: standardised mortality ratios were calculated. Risk adjustment for in-hospital mortality uses the Risk Adjustment for Congenital Heart Surgery method, a model including surgical risk category, age group, prematurity, presence of a major non-cardiac structural anomaly and multiple congenital heart procedures during admission., Results: The IQIC registry includes 24 917 congenital heart surgeries performed in children<18 years of age. The overall in-hospital mortality rate was 5.0%. Country-level congenital heart surgery standardised mortality ratios were negatively correlated with gross domestic product (GDP) per capita (r=-0.34, p=0.18), and health expenditure per capita (r=-0.23, p=0.37) and positively correlated with under-five mortality (r=0.60, p=0.01) and undernourishment (r=0.39, p=0.17). Countries with lower development had wider variation in mortality. GDP per capita is a driver of the association between some other measures and mortality., Conclusions: Results display a moderate relationship among wealth, healthcare investment and malnutrition, with significant variation, including superior results in many countries with low GDP per capita. These findings provide context and optimism for investment in CHD procedures in low-resource settings., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Motivation: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available., Results: The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects')., Availability and Implementation: Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd., Contact: dcraft@broadinstitute.org., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Scanning Electron Microscopy (SEM) has long been the standard in imaging the sub-micrometer surface ultrastructure of both hard and soft materials. In the case of biological samples, it has provided great insights into their physical architecture. However, three of the fundamental challenges in the SEM imaging of soft materials are that of limited imaging resolution at high magnification, charging caused by the insulating properties of most biological samples and the loss of subtle surface features by heavy metal coating. These challenges have recently been overcome with the development of the Helium Ion Microscope (HIM), which boasts advances in charge reduction, minimized sample damage, high surface contrast without the need for metal coating, increased depth of field, and 5 angstrom imaging resolution. We demonstrate the advantages of HIM for imaging biological surfaces as well as compare and contrast the effects of sample preparation techniques and their consequences on sub-nanometer ultrastructure.

As the miniaturization of semiconductor devices continues, characterization of dopant distribution within the structures becomes increasingly challenging. One potential solution is the use of the secondary electron signal produced in scanning electron (SEMs) or helium ion microscopes (HeIMs) to image the changes in electrical potential caused by the dopant atoms. In this article, the contrast mechanisms and resolution limits of secondary electron dopant contrast are explored. It is shown that the resolution of the technique is dependent on the extent of electrical potential present at a junction and that the resolution of dopant contrast can be improved in the HeIM after an in-situ plasma cleaning routine, which causes an oxide to form on the surface altering the contrast mechanism from electrical potential to material contrast.