Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region

Preconception Care in International Settings

Objectives: This literature review briefly describes international programs, policies, and activities related to preconception care and resulting pregnancy outcomes. Methods: Electronic databases were searched and findings supplemented with secondary references cited in the original articles as well as textbook chapters, declarations, reports, and recommendations. Results: Forty-two articles, book chapters, declarations, and other published materials were reviewed. Policies, programs, and recommendations related to preconceptional health promotion exist worldwide and comprise a readily identifiable component of historic and modern initiatives pertaining to women's health, reproductive freedom, and child survival. Conclusions: The integration of preconception care services within a larger maternal and child health continuum of care is well aligned with a prevention-based approach to enhancing global health

Maternal health interventions in resource limited countries: a systematic review of packages, impacts and factors for change

The burden of maternal mortality in resource limited countries is still huge despite being at the top of the global public health agenda for over the last 20 years. We systematically reviewed the impacts of interventions on maternal health and factors for change in these countries. A systematic review was carried out using the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in the English language reporting on implementation of interventions, their impacts and underlying factors for maternal health in resource limited countries in the past 23 years were searched from PubMed, Popline, African Index Medicus, internet sources including reproductive health gateway and Google, hand-searching, reference lists and grey literature. Out of a total of 5084 articles resulting from the search only 58 qualified for systematic review. Programs integrating multiple interventions were more likely to have significant positive impacts on maternal outcomes. Training in emergency obstetric care (EmOC), placement of care providers, refurbishment of existing health facility infrastructure and improved supply of drugs, consumables and equipment for obstetric care were the most frequent interventions integrated in 52%-65% of all 54 reviewed programs. Statistically significant reduction of maternal mortality ratio and case fatality rate were reported in 55% and 40% of the programs respectively. Births in EmOC facilities and caesarean section rates increased significantly in 71%-75% of programs using these indicators. Insufficient implementation of evidence-based interventions in resources limited countries was closely linked to a lack of national resources, leadership skills and end-users factors. This article presents a list of evidenced-based packages of interventions for maternal health, their impacts and factors for change in resource limited countries. It indicates that no single magic bullet intervention exists for reduction of maternal mortality and that all interventional programs should be integrated in order to bring significant changes. State leaders and key actors in the health sectors in these countries and the international community are proposed to translate the lessons learnt into actions and intensify efforts in order to achieve the goals set for maternal health

Removal of non-CO2 greenhouse gases by large-scale atmospheric solar photocatalysis

Large-scale atmospheric removal of greenhouse gases (GHGs) including methane, nitrous oxide and ozone-depleting halocarbons could reduce global warming more quickly than atmospheric removal of CO2. Photocatalysis of methane oxidizes it to CO2, effectively reducing its global warming potential (GWP) by at least 90%. Nitrous oxide can be reduced to nitrogen and oxygen by photocatalysis; meanwhile halocarbons can be mineralized by red-ox photocatalytic reactions to acid halides and CO2. Photocatalysis avoids the need for capture and sequestration of these atmospheric components. Here review an unusual hybrid device combining photocatalysis with carbon-free electricity with no-intermittency based on the solar updraft chimney. Then we review experimental evidence regarding photocatalytic transformations of non-CO2 GHGs. We propose to combine TiO2-photocatalysis with solar chimney power plants (SCPPs) to cleanse the atmosphere of non-CO2 GHGs. Worldwide installation of 50,000 SCPPs, each of capacity 200 MW, would generate a cumulative 34 PWh of renewable electricity by 2050, taking into account construction time. These SCPPs equipped with photocatalyst would process 1 atmospheric volume each 14–16 years, reducing or stopping the atmospheric growth rate of the non-CO2 GHGs and progressively reducing their atmospheric concentrations. Removal of methane, as compared to other GHGs, has enhanced efficacy in reducing radiative forcing because it liberates more °OH radicals to accelerate the cleaning of the troposphere. The overall reduction in non-CO2 GHG concentration would help to limit global temperature rise. By physically linking greenhouse gas removal to renewable electricity generation, the hybrid concept would avoid the moral hazard associated with most other climate engineering proposals

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.