To weight or not to weight, that is the question: the design of a composite indicator of landscape fragmentation

Composite indicators (CIs), i.e., combinations of many indicators in a unique synthetizing measure, are useful for disentangling multisector phenomena. Prominent questions concern indicators’ weighting, which implies time-consuming activities and should be properly justified. Landscape fragmentation (LF), the subdivision of habitats in smaller and more isolated patches, has been studied through the composite index of landscape fragmentation (CILF). It was originally proposed by us as an unweighted combination of three LF indicators for the study of the phenomenon in Sardinia, Italy. In this paper, we aim at presenting a weighted release of the CILF and at developing the Hamletian question of whether weighting is worthwhile or not. We focus on the sensitivity of the composite to different algorithms combining three weighting patterns (equalization, extraction by principal component analysis, and expert judgment) and three indicators aggregation rules (weighted average mean, weighted geometric mean, and weighted generalized geometric mean). The exercise provides the reader with meaningful results. Higher sensitivity values signal that the effort of weighting leads to more informative composites. Otherwise, high robustness does not mean that weighting was not worthwhile. Weighting per se can be beneficial for more acceptable and viable decisional processes

INTEGRATING CLIMATE CHANGE ADAPTATION INTO SEA AN ASSESSMENT FOR SARDINIA, ITALY

Climate Change (CC) is recognized as an urgent concern, which implies negative effects on the environment, such as sea level rise, coastal erosion, fl ooding, droughts, and desertifi cation. It involves not only the environmental, but also the economic, and social sphere. The impacts of CC are addressed through two complementary strategies: mitigation and adaptation. The fi rst one operates on the reasons of CC aiming at preventing or reducing greenhouse gases emissions, while the second one focuses on the damage they can cause, aiming at minimizing it or to take advantage of opportunities that may occur. Strategic Environmental Assessment (SEA) represents a systematic and participatory decisionmaking support process, aiming at integrating environmental considerations in the elaboration of plans and programs. While SEA regards explicitly mitigation strategies, so far it still refers marginally to CC adaptation measures to be carried on when implementing spatial planning tools at the regional and local scale. The integration of SEA processes with concepts inspired to adaptation to CCs represents a powerful tool for mainstreaming the corresponding policies and strategies. In this study, we scrutinize SEA and spatial planning tools issued in Sardinia (Italy), with reference to their attitude to incorporate possible climate adaptation concerns. We are interested in proposing and applying a framework based on internationally acknowledged criteria that need to be met to properly implement climate change adaptation measures and actions in current spatial planning and SEA practices

Food safety considerations in relation to Anisakis pegreffii in anchovies (Engraulis encrasicolus) and sardines (Sardina pilchardus) fished off the Ligurian Coast (Cinque Terre National Park, NW Mediterranean)

Aims: The purpose of this work was to verify whether E. coli is a good indicator of viral contamination in mussels and Adenovirus could represent a better alternative as indicator organism of viral presence to guarantee consumer health protection. Methods and Results: Eighty samples of mussels from La Spezia Gulf were analysed for E. coli, Salmonella, Adenovirus, Norovirus and hepatitis A virus with cultural and biomolecular tests. The results of bacterial parameters showed E. coli within the law’s limits and the absence of Salmonella. Twelve samples were positive for Adenovirus presence, one for Norovirus genogroup II and two for hepatitis A virus. None of these positive mussels was found to be contaminated with more than one virus at the same time. Conclusion: This study showed that there was not a direct correlation between the presence of human pathogenic viruses and bacterial indicators. Significance and Impact of the Study: Both E. coli and Adenovirus cannot be considered valid substitutes for the direct research of human pathogenic viruses in mussels. To improve consumer health protection, the European Commission will provide standardized methods for Norovirus and hepatitis A virus detection as soon as possible

Antidepressant and pro-motivational effects of repeated lamotrigine treatment in a rat model of depressive symptoms

Background: The antiepileptic lamotrigine is approved for maintenance treatment of bipolar disorder and augmentation therapy in treatment-resistant depression. Previous preclinical investigations showed lamotrigine antidepressant-like effects without addressing its possible activity on motivational aspects of anhedonia, a symptom clinically associated with poor treatment response and with blunted mesolimbic dopaminergic responsiveness to salient stimuli in preclinical models. Thus, in rats expressing a depressive-like phenotype we studied whether repeated lamotrigine administration restored behavioral responses to aversive and positive stimuli and the dopaminergic response to sucrose in the nucleus accumbens shell (NAcS), all disrupted by stress exposure. Methods: Depressive-like phenotype was induced in non-food-deprived adult male Sprague-Dawley rats by exposure to a chronic protocol of alternating unavoidable tail-shocks or restraint periods. We examined whether lamotrigine administration (7.5 mg/kg twice a day, i.p.) for 14–21 days restored a) the competence to escape aversive stimuli; b) the motivation to operate in sucrose self-administration protocols; c) the dopaminergic response to sucrose consumption, evaluated measuring phosphorylation levels of cAMP-regulated phosphoprotein Mr 32,000 (DARPP-32) in the NAcS, by immunoblotting. Results: Lamotrigine administration restored the response to aversive stimuli and the motivation to operate for sucrose. Moreover, it reinstated NAcS DARPP-32 phosphorylation changes in response to sucrose consumption. Limitations: The pro-motivational effects of lamotrigine that we report may not completely transpose to clinical use, since anhedonia is a multidimensional construct and the motivational aspects, although relevant, are not the only components. Conclusions: This study shows antidepressant-like and pro-motivational effects of repeated lamotrigine administration in a rat model of depressive symptoms

Gravity model in the Korean highway

We investigate the traffic flows of the Korean highway system, which contains both public and private transportation information. We find that the traffic flow T(ij) between city i and j forms a gravity model, the metaphor of physical gravity as described in Newton's law of gravity, P(i)P(j)/r(ij)^2, where P(i) represents the population of city i and r(ij) the distance between cities i and j. It is also shown that the highway network has a heavy tail even though the road network is a rather uniform and homogeneous one. Compared to the highway network, air and public ground transportation establish inhomogeneous systems and have power-law behaviors.Comment: 13 page

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Dynamical Patterns of Cattle Trade Movements

Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

Change in BMI Accurately Predicted by Social Exposure to Acquaintances

Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R[superscript 2]. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends.MIT Masdar ProgramMIT Media Lab Consortiu