Health in the sustainable development goals: ready for a paradigm shift?

The Millennium Development Goals (MDGs) galvanized attention, resources and accountability on a small number of health concerns of low- and middle-income countries with unprecedented results. The international community is presently developing a set of Sustainable Development Goals as the successor framework to the MDGs. This review examines the evidence base for the current health-related proposals in relation to disease burden and the technical and political feasibility of interventions to achieve the targets. In contrast to the MDGs, the proposed health agenda aspires to be universally applicable to all countries and is appropriately broad in encompassing both communicable and non-communicable diseases as well as emerging burdens from, among other things, road traffic accidents and pollution.We argue that success in realizing the agenda requires a paradigm shift in the way we address global health to surmount five challenges: 1) ensuring leadership for intersectoral coherence and coordination on the structural (including social, economic, political and legal) drivers of health; 2) shifting the focus from treatment to prevention through locally-led, politically-smart approaches to a far broader agenda; 3) identifying effective means to tackle the commercial determinants of ill-health; 4) further integrating rights-based approaches; and 5) enhancing civic engagement and ensuring accountability. We are concerned that neither the international community nor the global health community truly appreciates the extent of the shift required to implement this health agenda which is a critical determinant of sustainable development

Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

Establishing normative values for 18-35 years age in neuropsychological tests used with head and brain injury patients during cognitive rehabilitation: Benton Visual Retention Test and National Adult Reading Test

The Benton Visual Retention Test (BVRT) is a neuropsychological assessment of visuo-spatial and visuo-memory ability. Claims of high reliability and validity are based on solitary samples representative of a wide age range. This study validated theBVRTagainst the National Adult Reading Test (NART), a highly validated and reliable test of estimated pre-morbid IQ in an age-specific group of participants (18-35 years). Using Between-subjects factorial design, fifty-three participants (24 female, 29 male) aged 18-35 years (inclusive) were administered the NART and 3 administrations of the BVRT. Significant positive correlations were found betweenBVRTError scores and NART Error scores for administrations B and C of theBVRTwhich is when presented stimuli are followed by a short time delay before allowing respondents to recall. Significant negative correlations were found over these administrations forBVRTCorrect scores and NART Error scores. No significant relationship was found between depression and performance on theBVRT. However, a weak, non-significant relationship was found between anxiety andBVRTperformance. The BVRTis a well-validated and highly reliable neuropsychological test of visuo-spatial and visuo-memory abilities. Findings provide new data for the 18-35 years age group as well as providing a cautionary note on the possible influence of anxiety on performance levels in light of the frequent occurrence of anxiety post-neurological injury

Alien Registration- Hawkes, Jessie S. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/24573/thumbnail.jp

Diffeomorphic Demons using Normalised Mutual Information, Evaluation on Multi-Modal Brain MR Images

The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent large-deformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios

Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images

The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios