BackgroundLeprosy-related disability is a challenge to public health, and social and rehabilitation services in endemic countries. Disability is more than a mere physical dysfunction, and includes activity limitations, stigma, discrimination, and social participation restrictions. We assessed the extent of disability and its determinants among persons with leprosy-related disabilities after release from multi drug treatment.MethodsWe conducted a survey on disability among persons affected by leprosy in Indonesia, using a Rapid Disability Appraisal toolkit based on the International Classification of Functioning, Disability and Health. The toolkit included the Screening of Activity Limitation and Safety Awareness (SALSA) scale, Participation Scale, Jacoby Stigma Scale (anticipated stigma), Explanatory Model Interview Catalogue (EMIC) stigma scale and Discrimination assessment. Community members were interviewed using a community version of the stigma scale. Multivariate linear regression was done to identify factors associated with social participation.ResultsOverall 1,358 persons with leprosy-related disability (PLD) and 931 community members were included. Seventy-seven percent of PLD had physical impairments. Impairment status deteriorated significantly after release from treatment (from 59% to 77%). Around 60% of people reported activity limitations and participation restrictions and 36% anticipated stigma. As for participation restrictions and stigma, shame, problems related to marriage and difficulties in employment were the most frequently reported problems. Major determinants of participation were severity of impairment and level of education, activity and stigma. Reported severity of community stigma correlated with severity of participation restrictions in the same districts.DiscussionThe majority of respondents reported problems in all components of disability. The reported physical impairment after release from treatment justifies ongoing monitoring to facilitate early prevention. Stigma was a major determinant of social participation, and therefore disability. Stigma reduction activities and socio-economic rehabilitation are urgently needed in addition to strategies to reduce the development of further physical impairment after release from treatment.

This report describes the conceptual steps in reaching the design of the AWAKE experiment currently under construction at CERN. We start with an introduction to plasma wakefield acceleration and the motivation for using proton drivers. We then describe the self-modulation instability -a key to an early realization of the concept. This is then followed by the historical development of the experimental design, where the critical issues that arose and their solutions are described. We conclude with the design of the experiment as it is being realized at CERN and some words on the future outlook. A summary of the AWAKE design and construction status as presented in this conference is given in [1].

The Advanced Proton Driven Plasma Wakefield Acceleration Experiment (AWAKE) aims at studying plasma wakefield generation and electron acceleration driven by proton bunches. It is a proof-of-principle R&D experiment at CERN and the world's first proton driven plasma wakefield acceleration experiment. The AWAKE experiment will be installed in the former CNGS facility and uses the 400 GeV/c proton beam bunches from the SPS. The first experiments will focus on the self-modulation instability of the long (rms ∼ 12 cm) proton bunch in the plasma. These experiments are planned for the end of 2016. Later, in 2017/2018, low energy (∼ 15 MeV) electrons will be externally injected to sample the wakefields and be accelerated beyond 1 GeV. The main goals of the experiment will be summarized. A summary of the AWAKE design and construction status will be presented.

AWAKE is a proton-driven plasma wakefield acceleration experiment. We show that the experimental setup briefly described here is ready for systematic study of the seeded self-modulation of the 400 GeV proton bunch in the 10 m-long rubidium plasma with density adjustable from 1 to 10×10 14 cm −3 . We show that the short laser pulse used for ionization of the rubidium vapor propagates all the way along the column, suggesting full ionization of the vapor. We show that ionization occurs along the proton bunch, at the laser time and that the plasma that follows affects the proton bunch.

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate sci-ence, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

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This paper presents a study of early epidemiological assessment of COVID-19 transmission dynamics in Indonesia. The aim is to quantify heterogeneity in the numbers of secondary infections. To this end, we estimate the basic reproduction number $$\mathscr {R}_0$$ R 0 and the overdispersion parameter $$\mathscr {K}$$ K at two regions in Indonesia: Jakarta–Depok and Batam. The method to estimate $$\mathscr {R}_0$$ R 0 is based on a sequential Bayesian method, while the parameter $$\mathscr {K}$$ K is estimated by fitting the secondary case data with a negative binomial distribution. Based on the first 1288 confirmed cases collected from both regions, we find a high degree of individual-level variation in the transmission. The basic reproduction number $$\mathscr {R}_0$$ R 0 is estimated at 6.79 and 2.47, while the overdispersion parameter $$\mathscr {K}$$ K of a negative-binomial distribution is estimated at 0.06 and 0.2 for Jakarta–Depok and Batam, respectively. This suggests that superspreading events played a key role in the early stage of the outbreak, i.e., a small number of infected individuals are responsible for large numbers of COVID-19 transmission. This finding can be used to determine effective public measures, such as rapid isolation and identification, which are critical since delay of diagnosis is the most common cause of superspreading events.

Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development times can be shortened and calculations can be simplified. For these reasons, automatic differentiation has fueled the rapid growth of a variety of sophisticated machine learning techniques over the past decade, but is now also increasingly showing its value to support ab initio simulations of quantum systems and enhance computational quantum chemistry. Here, we present an open-source differentiable quantum chemistry simulation code and explore applications facilitated by automatic differentiation: (1) calculating molecular perturbation properties, (2) reoptimizing a basis set for hydrocarbons, (3) checking the stability of self-consistent field wave functions, and (4) predicting molecular properties via alchemical perturbations.