Understanding the underlying mechanisms of recent Jatropha curcas L. adoption by smallholders in Kenya: A rural livelihood assessment in Bondo, Kibwezi, and Kwale districts

Between 2004 and 2007, NGOs, community based organisations and private investors promoted jatropha in Kenya with the aim of generating additional income and producing biofuel for rural development. By 2008 it became gradually evident that jatropha plantations (both mono- and intercropping) are uneconomical and risky due to competition for land and labour with food crops. Cultivation of jatropha hedges was found to have better chances of economic success and to present only little risks for the adopting farmers. Still, after 2008 a number of farmers went on adopting jatropha in plots rather than as hedges. It is hypothesised that lack of awareness about the low economic prospects of jatropha plantations was the main reason for continued adoption, and that smallholder farmers with higher resource endowments mainly ventured into its cultivation. In this study we provide an empirical basis for understanding the role of households' capital assets in taking up new livelihood strategies by smallholder farmers in three rural districts in Kenya. For that purpose, we assess the motivation and enabling factors that led to the adoption of jatropha as a new livelihood strategy, as well as the context in which promotion and adoption took place. A household survey was conducted in 2010, using a structured questionnaire, to collect information on household characteristics and capital asset endowment. Data were analysed using descriptive statistics and non-parametric statistical tests. We established that access to additional income and own energy supply were the main motivation for adoption of jatropha, and that financial capital assets do not necessarily have a positive influence on adoption as hypothesised. Further, we found that the main challenges that adopting farmers faced were lack of access to information on good management practices and lack of a reliable market. We conclude that continued adoption of on-farm jatropha after 2008 is a result of lacking awareness about the low economic value of this production type. We recommend abandoning on-farm production of jatropha until improved seed material and locally adapted agronomic knowledge about jatropha cultivation becomes available and its production becomes economically competitive.

Shear bond strengths of three glass ionomer cements to enamel and dentine

OBJECTIVES The shear bond strength of three glass ionomer cements (GIC) to enamel and dentine was evaluated. STUDY DESIGN Sound permanent human molars (n=12) were grinded perpendicular to their axial axes, exposing smooth, flat enamel and dentine surfaces. The teeth were embedded in resin and conditioned with polyacrylic acid (25%; 10s). Twenty four specimens of each GIC: Fuji IX (FJ-GC), Ketac Molar Easymix (KM-3M ESPE) and Maxxion (MX-FGM) were prepared according to the Atraumatic Restorative Treatment (ART) (12 enamel and 12 dentine), in a bonding area of 4.91 mm² and immersed in water (37°C, 24h). The shear bond strength was tested in a universal testing machine. Non-parametric statistical tests (Friedman and post-hoc Wilcoxon Signed Ranks) were carried out (p=0.05). RESULTS The mean (±sd) of shear bond strength (MPa), on enamel and dentine, were: KM (6.4±1.4 and 7.6±1.5), FJ (5.9±1.5 and 6.0±1.9) and MX (4.2±1.5 and 4.9±1.5), respectively. There was a statistically significant difference between the GICs in both groups: enamel (p=0.004) and dentine (p=0.002). The lowest shear bond value for enamel was with MX and the highest for dentine was KM (p<0.05). CONCLUSION It is concluded that KM has the best adhesion to both enamel and dentine, followed by FJ and MX.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

On degeneracy and invariances of random fields paths with applications in Gaussian process modelling

We study pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling. The key idea is that a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators. We first show in a second order set-up that almost sure degeneracy of random field paths under some class of linear operators defined in terms of signed measures can be controlled through the two first moments. A special focus is then put on the Gaussian case, where these results are revisited and extended to further linear operators thanks to state-of-the-art representations. Several degeneracy properties are tackled, including random fields with symmetric paths, centred paths, harmonic paths, or sparse paths. The proposed approach delivers a number of promising results and perspectives in Gaussian process modelling. In a first numerical experiment, it is shown that dedicated kernels can be used to infer an axis of symmetry. Our second numerical experiment deals with conditional simulations of a solution to the heat equation, and it is found that adapted kernels notably enable improved predictions of non-linear functionals of the field such as its maximum.

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

Can MRI of the neck compete with clinical findings in assessing danger to life for survivors of manual strangulation? A statistical analysis

The aim of the study was to examine the clinical forensic findings of strangulation according to their ability to differentiate between life-threatening and non-life-threatening strangulation, compare clinical and MRI findings of the neck and discuss a simple score for life-threatening strangulation (SLS).

Does published orthodontic research account for clustering effects during statistical data analysis?

In orthodontics, multiple site observations within patients or multiple observations collected at consecutive time points are often encountered. Clustered designs require larger sample sizes compared to individual randomized trials and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this study to assess to what degree clustering effects are considered during design and data analysis in the three major orthodontic journals. The contents of the most recent 24 issues of the American Journal of Orthodontics and Dentofacial Orthopedics (AJODO), Angle Orthodontist (AO), and European Journal of Orthodontics (EJO) from December 2010 backwards were hand searched. Articles with clustering effects and whether the authors accounted for clustering effects were identified. Additionally, information was collected on: involvement of a statistician, single or multicenter study, number of authors in the publication, geographical area, and statistical significance. From the 1584 articles, after exclusions, 1062 were assessed for clustering effects from which 250 (23.5 per cent) were considered to have clustering effects in the design (kappa = 0.92, 95 per cent CI: 0.67-0.99 for inter rater agreement). From the studies with clustering effects only, 63 (25.20 per cent) had indicated accounting for clustering effects. There was evidence that the studies published in the AO have higher odds of accounting for clustering effects [AO versus AJODO: odds ratio (OR) = 2.17, 95 per cent confidence interval (CI): 1.06-4.43, P = 0.03; EJO versus AJODO: OR = 1.90, 95 per cent CI: 0.84-4.24, non-significant; and EJO versus AO: OR = 1.15, 95 per cent CI: 0.57-2.33, non-significant). The results of this study indicate that only about a quarter of the studies with clustering effects account for this in statistical data analysis.

Changes in respiratory mechanics and gas exchange during the acute respiratory distress syndrome

BACKGROUND: The time course of impairment of respiratory mechanics and gas exchange in the acute respiratory distress syndrome (ARDS) remains poorly defined. We assessed the changes in respiratory mechanics and gas exchange during ARDS. We hypothesized that due to the changes in respiratory mechanics over time, ventilatory strategies based on rigid volume or pressure limits might fail to prevent overdistension throughout the disease process. METHODS: Seventeen severe ARDS patients {PaO2/FiO2 10.1 (9.2-14.3) kPa; 76 (69-107) mmHg [median (25th-75th percentiles)] and bilateral infiltrates} were studied during the acute, intermediate, and late stages of ARDS (at 1-3, 4-6 and 7 days after diagnosis). Severity of lung injury, gas exchange, and hemodynamics were assessed. Pressure-volume (PV) curves of the respiratory system were obtained, and upper and lower inflection points (UIP, LIP) and recruitment were estimated. RESULTS: (1) UIP decreased from early to established (intermediate and late) ARDS [30 (28-30) cmH2O, 27 (25-30) cmH2O and 25 (23-28) cmH2O (P=0.014)]; (2) oxygenation improved in survivors and in patients with non-pulmonary etiology in late ARDS, whereas all patients developed hypercapnia from early to established ARDS; and (3) dead-space ventilation and pulmonary shunt were larger in patients with pulmonary etiology during late ARDS. CONCLUSION: We found a decrease in UIP from acute to established ARDS. If applied to our data, the inspiratory pressure limit advocated by the ARDSnet (30 cmH2O) would produce ventilation over the UIP, with a consequent increased risk of overdistension in 12%, 43% and 65% of our patients during the acute, intermediate and late phases of ARDS, respectively. Lung protective strategies based on fixed tidal volume or pressure limits may thus not fully avoid the risk of lung overdistension throughout ARDS.

Statistical finite element model for bone shape and biomechanical properties

We present a framework for statistical finite element analysis combining shape and material properties, and allowing performing statistical statements of biomechanical performance across a given population. In this paper, we focus on the design of orthopaedic implants that fit a maximum percentage of the target population, both in terms of geometry and biomechanical stability. CT scans of the bone under consideration are registered non-rigidly to obtain correspondences in position and intensity between them. A statistical model of shape and intensity (bone density) is computed by means of principal component analysis. Afterwards, finite element analysis (FEA) is performed to analyse the biomechanical performance of the bones. Realistic forces are applied on the bones and the resulting displacement and bone stress distribution are calculated. The mechanical behaviour of different PCA bone instances is compared.

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.