Review of Richard Dawkins’ The God Delusion

No abstract availabl

Implementing Secure Group Communications using Key Graphs

While the technical issues of securing unicast communications for client-server computingare fairly well-understood, the technical issues of securing group communications are not. Theexisting approach to improve the scalability is to decompose a large group of clients into manysubgroups and employ a hierarchy of group security agents. In this paper, the secure groupcommunications using key graphs and the implementation of a different hierarchical approachto improve the scalability and secure group communication using key graphs has been presented

Microbiology of chronic suppurative otitis media at Queen Elizabeth Central Hospital, Blantyre, Malawi: A cross-sectional descriptive study

Background Chronic suppurative otitis media (CSOM) is still a significant health problem in developing countries. Therefore, it was pertinent to determine the local Malawian microbiology in order to guide adequate treatment, avoid complications, and provide records for future reference.Aim The study sought to determine the CSOM-causing microorganisms at Queen Elizabeth Central Hospital in Blantyre, Malawi, and establish their relationship signs and symptoms, and with the demographic pattern of the study.Methods This was a hospital-based cross-sectional descriptive study carried out at the ENT outpatient clinic and the Microbiology Department of Queen Elizabeth Central Hospital.The sample comprised 104 patients with unilateral or bilateral active CSOM, who met the inclusion criteria. All patients were evaluated through a detailed history and clinical examination. Pus samples from draining ears were collected by aspiration with a sterile pipette. The specimens were immediately sent for microbiological analysis. Data were analyzed using SPSS.version 20.Results The study found that Proteus mirabilis, Pseudomonas aeruginosa, and Staphylococcus aureus were the most prevalent aerobic bacteria, while Bacteroides spp. and Peptostreptococcus spp. were the commonest anaerobic bacteria causing CSOM. These CSOM-causing microorganisms were predominant among males aged 18 years and below. Some CSOMcausing microorganisms were—significantly more so than the others—characteristically associated with each of the following clinical features: quantity of pus drainage, mode of onset, otalgia, hearing loss, location of tympanic membrane perforation, and mucosal appearance

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

Objective: Freezing of Gait (FOG) often described as the sensation of “the feet being glued to the ground” is prevalent in people with Parkinson's disease (PD) and severely disturbs mobility. In addition to tracking disease progression, precise detection of the exact boundaries for each FOG episode may enable new technologies capable of “breaking” FOG in real time. This study investigates the limits of sensitivity and performance for automatic device-based FOG detection. Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble & Support Vector Machine) were developed using (i) accelerometer and (ii) accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed a walking and mobility task designed to elicit FOG. Two clinicians independently annotated the precise FOG episodes using synchronized video according to international guidelines, which were incorporated into a flowchart algorithm developed for this study. Device-detected FOG episodes were compared to the annotated FOG episodes using 10-fold cross-validation to determine accuracy and with Interclass Correlation Coefficients (ICC) to assess level of agreement. Results: Development used 50,962 windows of data representing over 10 hours of data and annotated activities. Very strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity and ICC 1,1 = 0.97 for total FOG duration). Device-based performance varied by method, complexity and cost matrix. The Neural Network that used only 67 accelerometer features provided a good balance between high sensitivity to FOG (89% sensitivity, 81% specificity and ICC 1,1 = 0.83) and solution stability (validation loss ≤ 5%). Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The superior agreement between clinicians indicates there is room to improve future device-based FOG detection by using larger and more varied data sets. Significance: This study has clinical implications with regard to improving PD care by reducing reliance on clinical FOG assessments and time-consuming visual inspection. It shows high sensitivity to automatically detect FOG is possible

Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device

Objective: Freezing of Gait (FOG) often described as the sensation of “the feet being glued to the ground” is prevalent in people with Parkinson's disease (PD) and severely disturbs mobility. In addition to tracking disease progression, precise detection of the exact boundaries for each FOG episode may enable new technologies capable of “breaking” FOG in real time. This study investigates the limits of sensitivity and performance for automatic device-based FOG detection. Methods: Eight machine-learning classifiers (including Neural Networks, Ensemble & Support Vector Machine) were developed using (i) accelerometer and (ii) accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed a walking and mobility task designed to elicit FOG. Two clinicians independently annotated the precise FOG episodes using synchronized video according to international guidelines, which were incorporated into a flowchart algorithm developed for this study. Device-detected FOG episodes were compared to the annotated FOG episodes using 10-fold cross-validation to determine accuracy and with Interclass Correlation Coefficients (ICC) to assess level of agreement. Results: Development used 50,962 windows of data representing over 10 hours of data and annotated activities. Very strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity and ICC 1,1 = 0.97 for total FOG duration). Device-based performance varied by method, complexity and cost matrix. The Neural Network that used only 67 accelerometer features provided a good balance between high sensitivity to FOG (89% sensitivity, 81% specificity and ICC 1,1 = 0.83) and solution stability (validation loss ≤ 5%). Conclusion: The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The superior agreement between clinicians indicates there is room to improve future device-based FOG detection by using larger and more varied data sets. Significance: This study has clinical implications with regard to improving PD care by reducing reliance on clinical FOG assessments and time-consuming visual inspection. It shows high sensitivity to automatically detect FOG is possible

Human harvesting impacts on managed areas: ecological effects of socially-compatible shellfish reserves

We examined how human harvesting impacts on managed areas affect the abundance and size distribution of the edible mangrove shellfish Anadara granosa and Polymesoda spp. in the Roviana Lagoon, Solomon Islands. We tested two hypotheses: (1) in areas permanently and temporally closed to human exploitation, abundance and size distribution of these shellfish species is significantly greater than in sites open to exploitation and (2) moderate human disturbance of shell beds, particularly of Polymesoda spp., increases their abundance. Firstly, we studied perceptions of environmental states and processes coupled to foraging and management interventions to assess sociocultural influences on harvesting practices and ascertain the types of management regime that people would consider in a context where poaching and interloping are common practices. Secondly, we compared shellfish abundance and shell size from areas that were permanently protected, temporally reserved for communal harvest, and permanently open for exploitation. Thirdly, drawing from women’s local knowledge, we measured the abundance of Polymesoda spp. in relation to mud compactness in quadrats across the three management regimes. Results showed that both species were significantly more abundant in permanent and temporally closed sites than in open sites. In the mud compactness study, however, while shell abundance was greater in moderately compacted quadrats, there was no statistical relationship between mud compactness and shell abundance within or across the three management regimes. Results suggest that even under the strong impacts of poaching, temporally closed areas have more clams than open areas and are as effective as areas that are permanently closed nominally. The results also suggest that human harvesting regimes can influence the effectiveness of local management decisions and thus are important when designing community-based conservation programs in the Solomon Islands and other Pacific Islands

Multiple drivers of local (non-) compliance in community-based marine resource management: Case studies from the South Pacific

The outcomes of marine conservation and related management interventions depend to a large extent on people's compliance with these rule systems. In the South Pacific, community-based marine resource management (CBMRM) has gained wide recognition as a strategy for the sustainable management of marine resources. In current practice, CBMRM initiatives often build upon customary forms of marine governance, integrating scientific advice and management principles in collaboration with external partners. However, diverse socio-economic developments as well as limited legal mandates can challenge these approaches. Compliance with and effective (legally-backed) enforcement of local management strategies constitute a growing challenge for communities—often resulting in considerable impact on the success or failure of CBMRM. Marine management arrangements are highly dynamic over time, and similarly compliance with rule systems tends to change depending on context. Understanding the factors contributing to (non-) compliance in a given setting is key to the design and function of adaptive management approaches. Yet, few empirical studies have looked in depth into the dynamics around local (non-) compliance with local marine tenure rules under the transforming management arrangements. Using two case studies from Solomon Islands and Fiji, we investigate what drives local (non-) compliance with CBMRM and what hinders or supports its effective enforcement. The case studies reveal that non-compliance is mainly driven by: (1) diminishing perceived legitimacy of local rules and rule-makers; (2) increased incentives to break rules due to market access and/ or lack of alternative income; and (3) relatively weak enforcement of local rules (i.e., low perceptions of risk from sanctions for rule-breaking). These drivers do not stand alone but can act together and add up to impair effective management. We further analyze how enforcement of CBMRM is challenged through a range of institutional; socio-cultural and technical/financial constraints, which are in parts a result of the dynamism and ongoing transformations of management arrangements. Our study underlines the importance of better understanding and contextualizing marine resource management processes under dynamic conditions for an improved understanding of compliance in a particular setting

Indigenous Knowledge and Long-term Ecological Change: Detection, Interpretation, and Responses to Changing Ecological Conditions in Pacific Island Communities

When local resource users detect, understand, and respond to environmental change they can more effectively manage environmental resources. This article assesses these abilities among artisanal fishers in Roviana Lagoon, Solomon Islands. In a comparison of two villages, it documents local resource users’ abilities to monitor long-term ecological change occurring to seagrass meadows near their communities, their understandings of the drivers of change, and their conceptualizations of seagrass ecology. Local observations of ecological change are compared with historical aerial photography and IKONOS satellite images that show 56 years of actual changes in seagrass meadows from 1947 to 2003. Results suggest that villagers detect long-term changes in the spatial cover of rapidly expanding seagrass meadows. However, for seagrass meadows that showed no long-term expansion or contraction in spatial cover over one-third of respondents incorrectly assumed changes had occurred. Examples from a community-based management initiative designed around indigenous ecological knowledge and customary sea tenure governance show how local observations of ecological change shape marine resource use and practices which, in turn, can increase the management adaptability of indigenous or hybrid governance systems

Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space

As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One important requirement is transparency, which has been associated with model interpretability. Many machine learning algorithms induce models difficult to interpret, named black box. Moreover, people have difficulty to trust models that cannot be explained. In particular for machine learning, many groups are investigating new methods able to explain black box models. These methods usually look inside the black models to explain their inner work. By doing so, they allow the interpretation of the decision making process used by black box models. Among the recently proposed model interpretation methods, there is a group, named local estimators, which are designed to explain how the label of particular instance is predicted. For such, they induce interpretable models on the neighborhood of the instance to be explained. Local estimators have been successfully used to explain specific predictions. Although they provide some degree of model interpretability, it is still not clear what is the best way to implement and apply them. Open questions include: how to best define the neighborhood of an instance? How to control the trade-off between the accuracy of the interpretation method and its interpretability? How to make the obtained solution robust to small variations on the instance to be explained? To answer to these questions, we propose and investigate two strategies: (i) using data instance properties to provide improved explanations, and (ii) making sure that the neighborhood of an instance is properly defined by taking the geometry of the domain of the feature space into account. We evaluate these strategies in a regression task and present experimental results that show that they can improve local explanations

Nonparametric identification of regulatory interactions from spatial and temporal gene expression data

<p>Abstract</p> <p>Background</p> <p>The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their ability to make correct predictions.</p> <p>Results</p> <p>Here we describe a novel approach which uses nonparametric statistics to generate ordinary differential equation (ODE) models from expression data. Compared to other dynamical methods, our approach requires minimal information about the mathematical structure of the ODE; it does not use qualitative descriptions of interactions within the network; and it employs new statistics to protect against over-fitting. It generates spatio-temporal maps of factor activity, highlighting the times and spatial locations at which different regulators might affect target gene expression levels. We identify an ODE model for <it>eve </it>mRNA pattern formation in the <it>Drosophila melanogaster </it>blastoderm and show that this reproduces the experimental patterns well. Compared to a non-dynamic, spatial-correlation model, our ODE gives 59% better agreement to the experimentally measured pattern. Our model suggests that protein factors frequently have the potential to behave as both an activator and inhibitor for the same <it>cis</it>-regulatory module depending on the factors' concentration, and implies different modes of activation and repression.</p> <p>Conclusions</p> <p>Our method provides an objective quantification of the regulatory potential of transcription factors in a network, is suitable for both low- and moderate-dimensional gene expression datasets, and includes improvements over existing dynamic and static models.</p