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

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

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

Hack3D: Crowdsourcing the assessment of cybersecurity in digital manufacturing

This article summarizes lessons from the past three Hack3D events, including ways in which engineers can launch surprise attacks on digital manufacturing (DM) designs. A key outcome is a taxonomy-guided security benchmark for the DM community

Lessons from bright-spots for advancing knowledge exchange at the interface of marine science and policy

Evidence-informed decision-making is in increasing demand given growing pressures on marine environments. A way to facilitate this is by knowledge exchange among marine scientists and decision-makers. While many barriers are reported in the literature, there are also examples whereby research has successfully informed marine decision-making (i.e., 'bright-spots'). Here, we identify and analyze 25 bright-spots from a wide range of marine fields, contexts, and locations to provide insights into how to improve knowledge exchange at the interface of marine science and policy. Through qualitative surveys we investigate what initiated the bright-spots, their goals, and approaches to knowledge exchange. We also seek to identify what outcomes/impacts have been achieved, the enablers of success, and what lessons can be learnt to guide future knowledge exchange efforts. Results show that a diversity of approaches were used for knowledge exchange, from consultative engagement to genuine knowledge co-production. We show that diverse successes at the interface of marine science and policy are achievable and include impacts on policy, people, and governance. Such successes were enabled by factors related to the actors, processes, support, context, and timing. For example, the importance of involving diverse actors and managing positive relationships is a key lesson for success. However, enabling routine success will require: 1) transforming the ways in which we train scientists to include a greater focus on interpersonal skills, 2) institutionalizing and supporting knowledge exchange activities in organizational agendas, 3) conceptualizing and implementing broader research impact metrics, and 4) transforming funding mechanisms to focus on need-based interventions, impact planning, and an acknowledgement of the required time and effort that underpin knowledge exchange activities

A persistent neutrophil-associated immune signature characterizes post-COVID-19 pulmonary sequelae

Interstitial lung disease and associated fibrosis occur in a proportion of individuals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied individuals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. Individuals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these individuals at 12 months after recovery indicated that a subset of the individuals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications