Heterogeneous credit impacts of healthcare spending of the poor in peri-urban areas, Vietnam: Quantile treatment effects estimation

Quantile Treatment Effects are estimated to study the impacts of household credit access on health spending by poor households in one District of Ho Chi Minh City, Vietnam. There are significant positive effects of credit on the health budget shares of households with low healthcare spending. In contrast, when an Average Treatment Effect is estimated there is no discernible impact of credit access on health spending. Hence, typical approaches to studying heterogeneous credit impacts that only consider between group differences and not differences over the distribution of outcomes may miss some heterogeneity of interest to policymakers

Impacts of household credit on education and healthcare spending by the poor in peri-urban areas in Vietnam

There is debate about whether microfinance has positive impacts on education and health for borrowing households in developing countries. To provide evidence for this debate we use a new survey designed to meet the conditions for propensity score matching (PSM) and examine the impact of household credit on education and healthcare spending by the poor in peri-urban areas of Ho Chi Minh City, Vietnam. In addition to matching statistically identical non-borrowers with borrowers, our estimates also control for household pre-treatment income and assets, which may be associated with unobservable factors affecting both credit participation and the outcomes of interest. The PSM estimates of binary treatment effect show significant and positive impacts of borrowing on education and healthcare spending. However, multiple ordered treatment effect estimates reveal that only formal credit has significant and positive impacts on education and healthcare spending, while informal credit has insignificant impacts on the spending

Household credit to the poor and its impact on child schooling in peri-urban areas, Vietnam

This paper uses a novelty dataset of poor households in peri-urban areas in Vietnam to estimate impacts of small loans on child schooling. The Probit and Negative Binomial model estimates roughly indicate no strong evidence of the effect, especially of informal credit. Formal credit is likely to have positive impacts on child schooling, but its effect is not strong enough to be conclusive. The paper suggests that to obtain the target of sustainable poverty reduction, easing access to formal credit sources as well as exempting tuition and other school fees are necessary to keep poor children at schools longer

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Ecological IVIS design : using EID to develop a novel in-vehicle information system

New in-vehicle information systems (IVIS) are emerging which purport to encourage more environment friendly or ‘green’ driving. Meanwhile, wider concerns about road safety and in-car distractions remain. The ‘Foot-LITE’ project is an effort to balance these issues, aimed at achieving safer and greener driving through real-time driving information, presented via an in-vehicle interface which facilitates the desired behaviours while avoiding negative consequences. One way of achieving this is to use ecological interface design (EID) techniques. This article presents part of the formative human-centred design process for developing the in-car display through a series of rapid prototyping studies comparing EID against conventional interface design principles. We focus primarily on the visual display, although some development of an ecological auditory display is also presented. The results of feedback from potential users as well as subject matter experts are discussed with respect to implications for future interface design in this field

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

The role of uncertainty intolerance in adjusting to long-term physical health conditions: A systematic review

Long-term physical health conditions (LTPHCs) are associated with poorer psychological well-being, quality of life, and longevity. Additionally, individuals with LTPHCs report uncertainty in terms of condition aetiology, course, treatment, and ability to engage in life. An individual’s dispositional ability to tolerate uncertainty—or difficulty to endure the unknown—is termed intolerance of uncertainty (IU), and may play a pivotal role in their adjustment to a LTPHC. Consequently, the current review sought to investigate the relationship between IU and health-related outcomes, including physical symptoms, psychological ramifications, self-management, and treatment adherence in individuals with LTPHCs. A systematic search was conducted for papers published from inception until 27 May 2022 using the databases PsycINFO, PubMed (MEDLINE), CINAHL Plus, PsycARTICLES, and Web of Science. Thirty-one studies (N = 6,201) met the inclusion criteria. Results indicated that higher levels of IU were associated with worse psychological well-being outcomes and poorer quality of life, though impacts on self-management were less clear. With the exception of one study (which looked at IU in children), no differences in IU were observed between patients and healthy controls. Although findings highlight the importance of investigating IU related to LTPHCs, the heterogeneity and limitations of the existing literature preclude definite conclusions. Future longitudinal and experimental research is required to investigate how IU interacts with additional psychological constructs and disease variables to predict individuals’ adjustment to living with a LTPHC

Modular Damage Detection for Expandable and Inflatable Structures

NASA has identified potential damage from micrometeoroid and orbital debris (MMOD) impacts as a primary threat to Commercial Crew Program vehicles. The International Space Station (ISS) and extraterrestrial habitats also exhibit the risk of damage caused by MMODs. Currently no integrated in-situ or real-time health monitoring damage detection system is being used for expandable and inflatable structures. A novel, modular damage detection system design that incorporates interchangeable and replaceable sensory panels in a foldable architecture is described. The design implements technologies that provide for situational awareness, self-configuration, and damage detection and localization. The system is applicable for the new Gateway and surface and ground support infrastructur

Drilling of shallow marine sulfide-sulfate mineralisation in south-eastern Tyrrhenian Sea, Italy; Seafloor sulfides, Tyrrhenian Sea, highsulfidation; hydrothermal systems, Palinuro

Semi-massive to massive sulfides with abundant late native sulfur were drilled in a shallowwater hydrothermal system in an island arc volcanic setting at the Palinuro volcanic complex in the Tyrrhenian Sea, Italy. Overall, 12.7 m of sulfide mineralisation were drilled in a sediment-filled depression at a water depth of 630 - 650 m using the lander-type Rockdrill I drill rig of the British Geological Survey. Polymetallic (Zn, Pb, Sb, As, Ag) sulfides overlie massive pyrite. The massive sulfide mineralisation contains a number of atypical minerals, including enargite-famatinite, tennantite-tetrahedrite, stibnite, bismuthinite, and Pb-,Sb-, and Ag-sulfosalts, that do not commonly occur in mid-ocean ridge massive sulfides. Analogous to subaerial epithermal deposits, the occurrence of these minerals and the presence of abundant native sulfur suggest an intermediate to high sulfidation and/or high oxididation state of the hydrothermal fluids in contrast to the near-neutral and reducing fluids from which base metal-rich massive sulfides along mid-ocean ridges typically form. Oxidised conditions during sulfide deposition are likely related to the presence of magmatic volatiles in the mineralising fluids that were derived from a degassing magma chamber below the Palinuro volcanic complex