Achieving universal access to antiretroviral therapy in a rural district in Malawi: how was it done?

Mexico AIDS Conference 200

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Deep active learning for autonomous navigation.

Imitation learning refers to an agent's ability to mimic a desired behavior by learning from observations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficient for real time decisions. Creating feature representations is especially challenging when extracted from high dimensional visual data. In this paper, we present a method for imitation learning from raw visual data. The proposed method is applied to a popular imitation learning domain that is relevant to a variety of real life applications; namely navigation. To create a training set, a teacher uses an optimal policy to perform a navigation task, and the actions taken are recorded along with visual footage from the first person perspective. Features are automatically extracted and used to learn a policy that mimics the teacher via a deep convolutional neural network. A trained agent can then predict an action to perform based on the scene it finds itself in. This method is generic, and the network is trained without knowledge of the task, targets or environment in which it is acting. Another common challenge in imitation learning is generalizing a policy over unseen situation in training data. To address this challenge, the learned policy is subsequently improved by employing active learning. While the agent is executing a task, it can query the teacher for the correct action to take in situations where it has low confidence. The active samples are added to the training set and used to update the initial policy. The proposed approach is demonstrated on 4 different tasks in a 3D simulated environment. The experiments show that an agent can effectively perform imitation learning from raw visual data for navigation tasks and that active learning can significantly improve the initial policy using a small number of samples. The simulated test bed facilitates reproduction of these results and comparison with other approaches

Ethical Issues in the Development of Readiness Cohorts in Alzheimer's Disease Research.

There is growing interest in the development of novel approaches to secondary prevention trials in Alzheimer's disease to facilitate screening and recruitment of research participants and to reduce the time and costs associated with clinical trials. Several international research collaborations are setting up research infrastructures that link existing research cohorts, studies or patient registries to establish 'trial-ready' or 'readiness' cohorts. From these cohorts, individuals are recruited into clinical trial platforms. In setting up such research infrastructures, researchers must make ethically challenging design decisions in at least three areas: re-contacting participants in existing research studies, obtaining informed consent for participation in a readiness cohort, and disclosure of Alzheimer's disease-related biomarkers. These ethical considerations have been examined by a dedicated workgroup within the European Prevention of Alzheimer's Dementia (EPAD) project, a trans-European longitudinal cohort and adaptive proof-of-concept clinical trial platform. This paper offers recommendations for the ethical management of re-contact, informed consent and risk disclosure which may be of value to other research collaborations in the process of developing readiness cohorts for prevention trials in Alzheimer's disease and other disease areas.This work was funded through the Ethical Legal and Social Implications work package of the European Prevention of Alzheimer’s Dementia (EPAD) study EPAD receives support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° 115736, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. RM was also funded through the UK National Institute of Health Research grant to the Cambridge Biomedical Research Centre

An analysis of passive earth pressure modification due to seepage flow effects

Using an assumed vertical retaining wall with a drainage system along the soil-structure interface, this paper analyses the effect of anisotropic seepage flow on the development of passive earth pressure. Extremely unfavourable seepage flow inside the backfill, perhaps due to heavy rainfall, will dramatically increase the active earth pressure while reducing the passive earth pressure; thus increasing the probability of instability of the retaining structure. In this paper, a trial and error analysis based on limit equilibrium is applied to identify the optimum failure surface. The flow field is computed using Fourier series expansion, and the effective reaction force along the curved failure surface is obtained by solving a modified Kötter equation considering the effect of seepage flow. This approach correlates well with other existing results. For small values of both the internal friction angle and the interface friction angle, the failure surface can be appropriately simplified with a planar approximation. A parametric study indicates that the degree of anisotropic seepage flow affects the resulting passive earth pressure. In addition, incremental increases in the effective friction angle and interface friction both lead to an increase in the passive earth pressure.National Key Basic Research Program of China (No. 2015CB057801), the National Key R & D program of China (No. 2016YFC0800204), and Natural Science Foundation of China (Nos. 51578499 & 51761130078)

Soluble tumor necrosis factor receptor 1 and 2 predict outcomes in advanced chronic kidney disease : a prospective cohort study

Background : Soluble tumor necrosis factor receptors 1 (sTNFR1) and 2 (sTNFR2) have been associated to progression of renal failure, end stage renal disease and mortality in early stages of chronic kidney disease (CKD), mostly in the context of diabetic nephropathy. The predictive value of these markers in advanced stages of CKD irrespective of the specific causes of kidney disease has not yet been defined. In this study, the relationship between sTNFR1 and sTNFR2 and the risk for adverse cardiovascular events (CVE) and all-cause mortality was investigated in a population with CKD stage 4-5, not yet on dialysis, to minimize the confounding by renal function. Patients and methods : In 131 patients, CKD stage 4-5, sTNFR1, sTNFR2 were analysed for their association to a composite endpoint of all-cause mortality or first non-fatal CVE by univariate and multivariate Cox proportional hazards models. In the multivariate models, age, gender, CRP, eGFR and significant comorbidities were included as covariates. Results : During a median follow-up of 33 months, 40 events (30.5%) occurred of which 29 deaths (22.1%) and 11 (8.4%) first non-fatal CVE. In univariate analysis, the hazard ratios (HR) of sTNFR1 and sTNFR2 for negative outcome were 1.49 (95% confidence interval (CI): 1.28-1.75) and 1.13 (95% CI: 1.06-1.20) respectively. After adjustment for clinical covariables (age, CRP, diabetes and a history of cardiovascular disease) both sTNFRs remained independently associated to outcomes (HR: sTNFR1: 1.51, 95% CI: 1.30-1.77; sTNFR2: 1.13, 95% CI: 1.06-1.20). A subanalysis of the non-diabetic patients in the study population confirmed these findings, especially for sTNFR1. Conclusion : sTNFR1 and sTNFR2 are independently associated to all-cause mortality or an increased risk for cardiovascular events in advanced CKD irrespective of the cause of kidney disease

Feasibility of Follow-Up Studies and Reclassification in Spinocerebellar Ataxia Gene Variants of Unknown Significance

Spinocerebellar ataxia (SCA) is a heterogeneous group of neurodegenerative disorders with autosomal dominant inheritance. Genetic testing for SCA leads to diagnosis, prognosis and risk assessment for patients and their family members. While advances in sequencing and computing technologies have provided researchers with a rapid expansion in the genetic test content that can be used to unravel the genetic causes that underlie diseases, the large number of variants with unknown significance (VUSes) detected represent challenges. To minimize the proportion of VUSes, follow-up studies are needed to aid in their reclassification as either (likely) pathogenic or (likely) benign variants. In this study, we addressed the challenge of prioritizing VUSes for follow-up using (a combination of) variant segregation studies, 3D protein modeling, in vitro splicing assays and functional assays. Of the 39 VUSes prioritized for further analysis, 13 were eligible for follow up. We were able to reclassify 4 of these VUSes to LP, increasing the molecular diagnostic yield by 1.1%. Reclassification of VUSes remains difficult due to limited possibilities for performing variant segregation studies in the classification process and the limited availability of routine functional tests

Modeling predicted that tobacco control policies targeted at lower educated will reduce the differences in life expectancy.

Background and Objective: To estimate the effects of reducing the prevalence of smoking in lower educated groups on educational differences in life expectancy. Methods: A dynamic Markov-type multistate transition model estimated the effects on life expectancy of two scenarios. A "maximum scenario" where educational differences in prevalence of smoking disappear immediately, and a "policy target-scenario" where difference in prevalence of smoking is halved over a 20-year period. The two scenarios were compared to a reference scenario, where smoking prevalences do not change. Five Dutch cohort studies, involving over 67,000 participants aged 20 to 90 years, provided relative mortality risks by educational level, and smoking habits were assessed using national data of more than 120,000 persons. Results: In the reference scenario, the difference in life expectancy at age 40 between highest and lowest educated groups was 5.1 years for men and 2.7 years for women. In the "maximum scenario" these differences were reduced to 3.6 years for men and 1.7 years for women (reduction ≈30%), and in the "policy target-scenario" differences were 4.7 years for men and 2.4 years for women (reduction ≈10%). Conclusion: Theoretically, educational differences in life expectancy would be reduced by 30% at maximum, if variations in smoking prevalence were eliminated completely. In practice, tobacco control policies that are targeted at the lower educated may reduce the differences in life expectancy by approximately 10%. © 2006 Elsevier Inc. All rights reserved

The structuring of production control systems

Co-ordination of the activities of production units is necessary to realise the required delivery performance in the market. These should not conflict with reaching the production economics objectives of each of the units. Production structure is needed to reduce the complexity and should minimise the loss of potential flexibility. Any structure will have some elements in common — the definition of basic elements (e.g. capacities) as a first step in production control structure design; the introduction of product units and the decomposition of the total production control to Goods Flow Control and Production Unit Control; the relationship of sales and manufacturing and the interference of products and capacities as two main determining factors of the Goods Flow Control structure. The generality of these elements means it is possible to develop a small but relatively complete set of reference structures. A reference structure for Goods Flow Control in a repetitive manufacturing situation is discussed. Its main elements are master planning, material co-ordination, workload control and work order release