Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition.

Extreme prematurity and pregnancy conditions leading to intrauterine growth restriction (IUGR) affect thousands of newborns every year and increase their risk for poor higher order cognitive and social skills at school age. However, little is known about the brain structural basis of these disabilities. To compare the structural integrity of neural circuits between prematurely born controls and children born extreme preterm (EP) or with IUGR at school age, long-ranging and short-ranging connections were noninvasively mapped across cortical hemispheres by connection matrices derived from diffusion tensor tractography. Brain connectivity was modeled along fiber bundles connecting 83 brain regions by a weighted characterization of structural connectivity (SC). EP and IUGR subjects, when compared with controls, had decreased fractional anisotropy-weighted SC (FAw-SC) of cortico-basal ganglia-thalamo-cortical loop connections while cortico-cortical association connections showed both decreased and increased FAw-SC. FAw-SC strength of these connections was associated with poorer socio-cognitive performance in both EP and IUGR children.

Beyond contact-based transmission networks: the role of spatial coincidence.

Animal societies rely on interactions between group members to effectively communicate and coordinate their actions. To date, the transmission properties of interaction networks formed by direct physical contacts have been extensively studied for many animal societies and in all cases found to inhibit spreading. Such direct interactions do not, however, represent the only viable pathways. When spreading agents can persist in the environment, indirect transmission via 'same-place, different-time' spatial coincidences becomes possible. Previous studies have neglected these indirect pathways and their role in transmission. Here, we use rock ant colonies, a model social species whose flat nest geometry, coupled with individually tagged workers, allowed us to build temporally and spatially explicit interaction networks in which edges represent either direct physical contacts or indirect spatial coincidences. We show how the addition of indirect pathways allows the network to enhance or inhibit the spreading of different types of agent. This dual-functionality arises from an interplay between the interaction-strength distribution generated by the ants' movement and environmental decay characteristics of the spreading agent. These findings offer a general mechanism for understanding how interaction patterns might be tuned in animal societies to control the simultaneous transmission of harmful and beneficial agents.

A macro-ecological perspective on crassulacean acid metabolism (CAM) photosynthesis evolution in Afro-Madagascan drylands: Eulophiinae orchids as a case study.

Crassulacean acid metabolism (CAM) photosynthesis is an adaptation to water and atmospheric CO2 deficits that has been linked to diversification in dry-adapted plants. We investigated whether CAM evolution can be associated with the availability of new or alternative niches, using Eulophiinae orchids as a case study. Carbon isotope ratios, geographical and climate data, fossil records and DNA sequences were used to: assess the prevalence of CAM in Eulophiinae orchids; characterize the ecological niche of extant taxa; infer divergence times; and estimate whether CAM is associated with niche shifts. CAM evolved in four terrestrial lineages during the late Miocene/Pliocene, which have uneven diversification patterns. These lineages originated in humid habitats and colonized dry/seasonally dry environments in Africa and Madagascar. Additional key features (variegation, heterophylly) evolved in the most species-rich CAM lineages. Dry habitats were also colonized by a lineage that includes putative mycoheterotrophic taxa. These findings indicate that the switch to CAM is associated with environmental change. With its suite of adaptive traits, this group of orchids represents a unique opportunity to study the adaptations to dry environments, especially in the face of projected global aridification.

Bayesian Networks for the Age Classification of Living Individuals: a Study on Transition Analysis

Over the past few decades, age estimation of living persons has represented a challenging task for many forensic services worldwide. In general, the process for age estimation includes the observation of the degree of maturity reached by some physical attributes, such as dentition or several ossification centers. The estimated chronological age or the probability that an individual belongs to a meaningful class of ages is then obtained from the observed degree of maturity by means of various statistical methods. Among these methods, those developed in a Bayesian framework offer to users the possibility of coherently dealing with the uncertainty associated with age estimation and of assessing in a transparent and logical way the probability that an examined individual is younger or older than a given age threshold. Recently, a Bayesian network for age estimation has been presented in scientific literature; this kind of probabilistic graphical tool may facilitate the use of the probabilistic approach. Probabilities of interest in the network are assigned by means of transition analysis, a statistical parametric model, which links the chronological age and the degree of maturity by means of specific regression models, such as logit or probit models. Since different regression models can be employed in transition analysis, the aim of this paper is to study the influence of the model in the classification of individuals. The analysis was performed using a dataset related to the ossifications status of the medial clavicular epiphysis and results support that the classification of individuals is not dependent on the choice of the regression model.

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

The Association of H1N1 Pandemic Influenza with Congenital Anomaly Prevalence in Europe: An Ecological Time Series Study.

BACKGROUND: In the context of the European Surveillance of Congenital Anomalies (EUROCAT) surveillance response to the 2009 influenza pandemic, we sought to establish whether there was a detectable increase of congenital anomaly prevalence among pregnancies exposed to influenza seasons in general, and whether any increase was greater during the 2009 pandemic than during other seasons. METHODS: We performed an ecologic time series analysis based on 26,967 pregnancies with nonchromosomal congenital anomaly conceived from January 2007 to March 2011, reported by 15 EUROCAT registries. Analysis was performed for EUROCAT-defined anomaly subgroups, divided by whether there was a prior hypothesis of association with influenza. Influenza season exposure was based on World Health Organization data. Prevalence rate ratios were calculated comparing pregnancies exposed to influenza season during the congenital anomaly-specific critical period for embryo-fetal development to nonexposed pregnancies. RESULTS: There was no evidence for an increased overall prevalence of congenital anomalies among pregnancies exposed to influenza season. We detected an increased prevalence of ventricular septal defect and tricuspid atresia and stenosis during pandemic influenza season 2009, but not during 2007-2011 influenza seasons. For congenital anomalies, where there was no prior hypothesis, the prevalence of tetralogy of Fallot was strongly reduced during influenza seasons. CONCLUSIONS: Our data do not suggest an overall association of pandemic or seasonal influenza with congenital anomaly prevalence. One interpretation is that apparent influenza effects found in previous individual-based studies were confounded by or interacting with other risk factors. The associations of heart anomalies with pandemic influenza could be strain specific.

MetaNetX/MNXref - reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks.

MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments-namely MNXref-and proteins. The MetaNetX.org website (http://www.metanetx.org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available.

Automobile Corporate Networks in Europe : Sectoral Specialization of Central and Eastern European Cities

The global automobile industry is made up of very large corporations and their various subsidiaries containing different functions that create complex locational structures. The networks formed by the 19 largest automobile transnational corporations constitute an automobile "oligopoly" representing more than 90% (OICA, 2012) of the world's production. Since the mid-1990s, Central and Eastern European cities have become attractive for transnational corporations and particularly for the production functions in the automobile sector. This leads to a crucial question. Are strategic functions (such as R&D) within these networks also located in Central and Eastern Europe, or is the region still manufacturing-oriented in the automobile industry? This paper focuses on the patterns and the main factors influencing the role of some of these new central and Eastern European cities that have become integrated in the global value chain of the automobile industry. By analysing the various locations of the specialized functions within the corporations, this study aims to extend the research on global value chains (Gereffi and Korzeniewicz; 1994, Sturgeon, 2000; Krätke, 2014). The spatial patterns of the various functions and the ownerships networks of the automobile industry are constructed in order to identify the cities supporting it. In particular, the way that national metropolises bring their national territories into the globalization of the automobile industry is addressed. For example, are there some specific advantages of capital cities compared to cities that have less integration in globalization terms?

Ecological change predicts population dynamics and genetic diversity over 120 000 years.

While ecological effects on short-term population dynamics are well understood, their effects over millennia are difficult to demonstrate and convincing evidence is scant. Using coalescent methods, we analysed past population dynamics of three lizard species (Psammodromus hispanicus, P. edwardsianus, P. occidentalis) and linked the results with climate change data covering the same temporal horizon (120 000 years). An increase in population size over time was observed in two species, and in P. occidentalis, no change was observed. Temporal changes in temperature seasonality and the maximum temperature of the warmest month were congruent with changes in population dynamics observed for the three species and both variables affected population density, either directly or indirectly (via a life-history trait). These results constitute the first solid link between ecological change and long-term population dynamics. The results moreover suggest that ecological change leaves genetic signatures that can be retrospectively traced, providing evidence that ecological change is a crucial driver of genetic diversity and speciation.