Nemo: a computational tool for analyzing nematode locomotion

The nematode Caenorhabditis elegans responds to an impressive range of chemical, mechanical and thermal stimuli and is extensively used to investigate the molecular mechanisms that mediate chemosensation, mechanotransduction and thermosensation. The main behavioral output of these responses is manifested as alterations in animal locomotion. Monitoring and examination of such alterations requires tools to capture and quantify features of nematode movement. In this paper, we introduce Nemo (nematode movement), a computationally efficient and robust two-dimensional object tracking algorithm for automated detection and analysis of C. elegans locomotion. This algorithm enables precise measurement and feature extraction of nematode movement components. In addition, we develop a Graphical User Interface designed to facilitate processing and interpretation of movement data. While, in this study, we focus on the simple sinusoidal locomotion of C. elegans, our approach can be readily adapted to handle complicated locomotory behaviour patterns by including additional movement characteristics and parameters subject to quantification. Our software tool offers the capacity to extract, analyze and measure nematode locomotion features by processing simple video files. By allowing precise and quantitative assessment of behavioral traits, this tool will assist the genetic dissection and elucidation of the molecular mechanisms underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8

Developing and applying heterogeneous phylogenetic models with XRate

Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART .Comment: 34 pages, 3 figures, glossary of XRate model terminolog

Trapped in the prison of the mind: notions of climate-induced (im)mobility decision-making and wellbeing from an urban informal settlement in Bangladesh

The concept of Trapped Populations has until date mainly referred to people ‘trapped’ in environmentally high-risk rural areas due to economic constraints. This article attempts to widen our understanding of the concept by investigating climate-induced socio-psychological immobility and its link to Internally Displaced People’s (IDPs) wellbeing in a slum of Dhaka. People migrated here due to environmental changes back on Bhola Island and named the settlement Bhola Slum after their home. In this way, many found themselves ‘immobile’ after having been mobile—unable to move back home, and unable to move to other parts of Dhaka, Bangladesh, or beyond. The analysis incorporates the emotional and psychosocial aspects of the diverse immobility states. Mind and emotion are vital to better understand people’s (im)mobility decision-making and wellbeing status. The study applies an innovative and interdisciplinary methodological approach combining Q-methodology and discourse analysis (DA). This mixed-method illustrates a replicable approach to capture the complex state of climate-induced (im)mobility and its interlinkages to people’s wellbeing. People reported facing non-economic losses due to the move, such as identity, honour, sense of belonging and mental health. These psychosocial processes helped explain why some people ended up ‘trapped’ or immobile. The psychosocial constraints paralysed them mentally, as well as geographically. More empirical evidence on how climate change influences people’s wellbeing and mental health will be important to provide us with insights in how to best support vulnerable people having faced climatic impacts, and build more sustainable climate policy frameworks

Structural efficiency of percolation landscapes in flow networks

Complex networks characterized by global transport processes rely on the presence of directed paths from input to output nodes and edges, which organize in characteristic linked components. The analysis of such network-spanning structures in the framework of percolation theory, and in particular the key role of edge interfaces bridging the communication between core and periphery, allow us to shed light on the structural properties of real and theoretical flow networks, and to define criteria and quantities to characterize their efficiency at the interplay between structure and functionality. In particular, it is possible to assess that an optimal flow network should look like a "hairy ball", so to minimize bottleneck effects and the sensitivity to failures. Moreover, the thorough analysis of two real networks, the Internet customer-provider set of relationships at the autonomous system level and the nervous system of the worm Caenorhabditis elegans --that have been shaped by very different dynamics and in very different time-scales--, reveals that whereas biological evolution has selected a structure close to the optimal layout, market competition does not necessarily tend toward the most customer efficient architecture.Comment: 8 pages, 5 figure

SCI

Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture the topological properties of data succinctly at different spatial resolutions. For graphical data, the shape, and structure of the neighborhood of individual data items (nodes) are an essential means of characterizing their properties. We propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We achieve encouraging results on nine different real-world datasets that attest to the potential of persistent homology-based methods for network analysis

Characterization of complex networks: A survey of measurements

Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of measurements for inclusion are welcomed by the author

The Wiring Economy Principle: Connectivity Determines Anatomy in the Human Brain

Minimization of the wiring cost of white matter fibers in the human brain appears to be an organizational principle. We investigate this aspect in the human brain using whole brain connectivity networks extracted from high resolution diffusion MRI data of 14 normal volunteers. We specifically address the question of whether brain anatomy determines its connectivity or vice versa. Unlike previous studies we use weighted networks, where connections between cortical nodes are real-valued rather than binary off-on connections. In one set of analyses we found that the connectivity structure of the brain has near optimal wiring cost compared to random networks with the same number of edges, degree distribution and edge weight distribution. A specifically designed minimization routine could not find cheaper wiring without significantly degrading network performance. In another set of analyses we kept the observed brain network topology and connectivity but allowed nodes to freely move on a 3D manifold topologically identical to the brain. An efficient minimization routine was written to find the lowest wiring cost configuration. We found that beginning from any random configuration, the nodes invariably arrange themselves in a configuration with a striking resemblance to the brain. This confirms the widely held but poorly tested claim that wiring economy is a driving principle of the brain. Intriguingly, our results also suggest that the brain mainly optimizes for the most desirable network connectivity, and the observed brain anatomy is merely a result of this optimization

Induced abortion, pregnancy loss and intimate partner violence in Tanzania: a population based study

BACKGROUND: Violence by an intimate partner is increasingly recognized as an important public and reproductive health issue. The aim of this study is to investigate the extent to which physical and/or sexual intimate partner violence is associated with induced abortion and pregnancy loss from other causes and to compare this with other, more commonly recognized explanatory factors. METHODS: This study analyzes the data of the Tanzania section of the WHO Multi-Country Study on Women's Health and Domestic Violence, a large population-based cross-sectional survey of women of reproductive age in Dar es Salaam and Mbeya, Tanzania, conducted from 2001 to 2002. All women who answered positively to at least one of the questions about specific acts of physical or sexual violence committed by a partner towards her at any point in her life were considered to have experienced intimate partner violence. Associations between self reported induced abortion and pregnancy loss with intimate partner violence were analysed using multiple regression models. RESULTS: Lifetime physical and/or sexual intimate partner violence was reported by 41% and 56% of ever partnered, ever pregnant women in Dar es Salaam and Mbeya respectively. Among the ever pregnant, ever partnered women, 23% experienced involuntary pregnancy loss, while 7% reported induced abortion. Even after adjusting for other explanatory factors, women who experienced intimate partner violence were 1.6 (95%CI: 1.06,1.60) times more likely to report an pregnancy loss and 1.9 (95%CI: 1.30,2.89) times more likely to report an induced abortion. Intimate partner violence had a stronger influence on induced abortion and pregnancy loss than women's age, socio-economic status, and number of live born children. CONCLUSIONS: Intimate partner violence is likely to be an important influence on levels of induced abortion and pregnancy loss in Tanzania. Preventing intimate partner violence may therefore be beneficial for maternal health and pregnancy outcomes

Social marketing and healthy eating : Findings from young people in Greece

This document is the Accepted Manuscript version. The final publication is available at Springer via http://dx.doi.org/10.1007/s12208-013-0112-xGreece has high rates of obesity and non-communicable diseases owing to poor dietary choices. This research provides lessons for social marketing to tackle the severe nutrition-related problems in this country by obtaining insight into the eating behaviour of young adults aged 18–23. Also, the main behavioural theories used to inform the research are critically discussed. The research was conducted in Athens. Nine focus groups with young adults from eight educational institutions were conducted and fifty-nine participants’ views towards eating habits, healthy eating and the factors that affect their food choices were explored. The study found that the participants adopted unhealthier nutritional habits after enrolment. Motivations for healthy eating were good health, appearance and psychological consequences, while barriers included lack of time, fast-food availability and taste, peer pressure, lack of knowledge and lack of family support. Participants reported lack of supportive environments when deciding on food choices. Based on the findings, recommendations about the development of the basic 4Ps of the marketing mix, as well as of a fifth P, for Policy are proposedPeer reviewe

Infectious Disease Modeling of Social Contagion in Networks

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.National Institutes of Health (U.S.) (grant R01GM078986)National Science Foundation (U.S.)Bill & Melinda Gates FoundationTempleton FoundationNational Institute on Aging (grant P01 AG031093)Framingham Heart Study (contract number N01-HC-25195