Decreased centrality of subcortical regions during the transition to adolescence: a functional connectivity study

Investigations of brain maturation processes are a key step to understand the cognitive and emotional changes of adolescence. Although structural imaging findings have delineated clear brain developmental trajectories for typically developing individuals, less is known about the functional changes of this sensitive development period. Developmental changes, such as abstract thought, complex reasoning, and emotional and inhibitory control, have been associated with more prominent cortical control. The aim of this study is to assess brain networks connectivity changes in a large sample of 7- to 15-year-old subjects, testing the hypothesis that cortical regions will present an increasing relevance in commanding the global network. Functional magnetic resonance imaging (fMRI) data were collected in a sample of 447 typically developing children from a Brazilian community sample who were submitted to a resting state acquisition protocol. The fMRI data were used to build a functional weighted graph from which eigenvector centrality (EVC) was extracted. For each brain region (a node of the graph), the age-dependent effect on EVC was statistically tested and the developmental trajectories were estimated using polynomial functions. Our findings show that angular gyrus become more central during this maturation period, while the caudate; cerebellar tonsils, pyramis, thalamus; fusiform, parahippocampal and inferior semilunar lobe become less central. In conclusion, we report a novel finding of an increasing centrality of the angular gyrus during the transition to adolescence, with a decreasing centrality of many subcortical and cerebellar regions.

The opportunities and challenges of large-scale molecular approaches to songbird neurobiology

High-throughput methods for analyzing genome structure and function are having a large impact in songbird neurobiology. Methods include genome sequencing and annotation, comparative genomics, DNA microarrays and transcriptomics, and the development of a brain atlas of gene expression. Key emerging findings include the identification of complex transcriptional programs active during singing, the robust brain expression of non-coding RNAs, evidence of profound variations in gene expression across brain regions, and the identification of molecular specializations within song production and learning circuits. Current challenges include the statistical analysis of large datasets, effective genome curations, the efficient localization of gene expression changes to specific neuronal circuits and cells, and the dissection of behavioral and environmental factors that influence brain gene expression. The field requires efficient methods for comparisons with organisms like chicken, which offer important anatomical, functional and behavioral contrasts. As sequencing costs plummet, opportunities emerge for comparative approaches that may help reveal evolutionary transitions contributing to vocal learning, social behavior and other properties that make songbirds such compelling research subjects.

RNA-Seq Data: A Complexity Journey

A paragraph from the highlights of “Transcriptomics: Throwing light on dark matter” by L. Flintoft (Nature Reviews Genetics 11, 455, 2010), says: “Reports over the past few years of extensive transcription throughout eukaryotic genomes have led to considerable excitement. However, doubts have been raised about the methods that have detected this pervasive transcription and about how much of it is functional.” Since the appearance of the ENCODE project and due to follow-up work, a shift from the pervasive transcription observed from RNA-Seq data to its functional validation is gradually occurring. However, much less attention has been turned to the problem of deciphering the complexity of transcriptome data, which determines uncertainty with regard to identification, quantification and differential expression of genes and non-coding RNAs. The aim of this mini-review is to emphasize transcriptome-related problems of direct and inverse nature for which novel inference approaches are needed.

Brain network response underlying decisions about abstract reinforcers

Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames.

New insights about enzyme evolution from large scale studies of sequence and structure relationships

Understanding how enzymes have evolved offers clues about their structure-function relationships and mechanisms. Here, we describe evolution of functionally diverse enzyme superfamilies, each representing a large set of sequences that evolved from a common ancestor and that retain conserved features of their structures and active sites. Using several examples, we describe the different structural strategies nature has used to evolve new reaction and substrate specificities in each unique superfamily. The results provide insight about enzyme evolution that is not easily obtained from studies of one or only a few enzymes.

Integrative analysis of cancer imaging readouts by networks

Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time‐space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node‐ and/or edge‐valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre‐clinical and clinical impact.

Computational Multiplexing Imaging merges imaging and networks.

Networks show signatures of tumor heterogeneity and phenotypic profiles observed in‐vivo.

A profile ensemble establishes a tumor fingerprint, and this constitutes a novel type of marker.

Personalized treatment is embedded in a systems medicine approach.

Emergence of semiology in epileptic seizures

Semiology, the manifestation of epilepsy, is dependent upon electrical activity produced by epileptic seizures that are organized within existing neural pathways. Clinical signs evolve as the epileptic discharge spreads in both time and space. Studying the relation between these, of which the temporal component is at least as important as the spatial one, is possible using anatomo-electro-clinical correlations of stereoelectroencephalography (SEEG) data. The period of semiology production occurs with variable time lag after seizure onset and signs then emerge more or less rapidly depending on seizure type (temporal seizures generally propagating more slowly and frontal seizures more quickly). The subset of structures involved in semiological production, the "early spread network", is tightly linked to those constituting the epileptogenic zone. The level of complexity of semiological features varies according to the degree of involvement of the primary or associative cortex, with the former having a direct relation to peripheral sensory and motor systems with production of hallucinations (visual and auditory) or elementary sensorimotor signs. Depending on propagation pattern, these signs can occur in a "march" fashion as described by Jackson. On the other hand, seizures involving the associative cortex, having a less direct relation with the peripheral nervous system, and necessarily involving more widely distributed networks manifest with altered cognitive and/or behavioral signs whose neural substrate involves a network of cortical structures, as has been observed for normal cognitive processes. Other than the anatomical localization of these structures, the frequency of the discharge is a crucial determinant of semiological effect since a fast (gamma) discharge will tend to deactivate normal function, whereas a slower theta discharge can mimic physiological function. In terms of interaction between structures, the degree of synchronization plays a key role in clinical expression, as evidenced, for example, by studies of ictal fear-related behavior (decorrelation of activity between structures inducing "release" phenomena) and of déjà vu (increased synchronization). Studies of functional coupling within networks underlying complex ictal behavior indicate that the clinical semiology of a given seizure depends upon neither the anatomical origin of ictal discharge nor the target areas of its propagation alone but on the dynamic interaction between these. Careful mapping of the ictal network in its full spread offers essential information as to the localization of seizure onset, by deducing that a given network configuration could only be generated by a given area or group of areas.

Six challenges in measuring contact networks for use in modelling

Contact networks are playing an increasingly important role in epidemiology. A contact network represents individuals in a host population as nodes and the interactions among them that may lead to the transmission of infection as edges. New avenues for data collection in recent years have afforded us the opportunity to collect individual- and population-scale information to empirically describe the patterns of contact within host populations. Here, we present some of the current challenges in measuring empirical contact networks. We address fundamental questions such as defining contact; measurement of non-trivial contact properties; practical issues of bounding measurement of contact networks in space, time and scope; exploiting proxy information about contacts; dealing with missing data. Finally, we consider the privacy and ethical issues surrounding the collection of contact network data.

The amygdala as a hub in brain networks that support social life

A growing body of evidence suggests that the amygdala is central to handling the demands of complex social life in primates. In this paper, we synthesize extant anatomical and functional data from rodents, monkeys, and humans to describe the topography of three partially distinct large-scale brain networks anchored in the amygdala that each support unique functions for effectively managing social interactions and maintaining social relationships. These findings provide a powerful componential framework for parsing social behavior into partially distinct neural underpinnings that differ among healthy people and disintegrate or fail to develop in neuropsychiatric populations marked by social impairment, such as autism, antisocial personality disorder, and frontotemporal dementia.

Forensic interpretation of molecular variation on networks of disease transmission and genetic inheritance

This paper describes the inference‐on‐networks (ION) framework for forensic interpretat ION of molecular typing data in cases involving allegations of infectious microbial transmission, association of disease outbreaks with alleged sources, and identifying familial relationships using mitochondrial or Y chromosomal DNA. The framework is applicable to molecular typing data obtained using any technique, including those based on electrophoretic separations. A key insight is that the networks associated with disease transmission or DNA inheritance can be used to define specific testable relationships and avoid the ambiguity and subjectivity associated with the criteria used for inferring genetic relatedness now in use. We discuss specific applications of the framework to the 2003 severe acute respiratory syndrome (SARS) outbreak in Singapore and the 2001 foot‐and‐mouth disease virus (FMDV) outbreak in Great Britain.

Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support.

Spatialising the contentious politics of ADHD: networks and scalar strategies in health social movement activism

This paper explores the spatial dynamics of health social movement activism in the context of a specific condition, Attention Deficit Hyperactivity Disorder (ADHD). Deploying qualitative research conducted with Irish ADHD organisations, it examines how place and space affect activist networks and the dilemmas that emerge when local 'mobilisations' converge at national and transnational levels. ADHD activism in Ireland has been predominantly localist in orientation, but certain organisations have shifted their activism to the European scale as a means of gaining further political and epistemic recognition for the condition. The paper suggests that health social movement studies would benefit from an engagement with the geographies of inter-scalar relations in analysing organisations׳ action repertoires.

Why do medical tourists travel to where they do? The role of networks in determining medical travel

Evidence on medical tourism, including patient motivation, is increasing. Existing studies have focused on identifying push and pull factors across different types of treatment, for example cosmetic or bariatric surgery, or on groups, such as diaspora patients returning 'home' for treatment. Less attention has been on why individuals travel to specific locations or providers and on how this decision is made. The paper focused on the role of networks, defined as linkages - formal and informal - between individual providers, patients and facilitators to explain why and where patients travel. Findings are based on a recently completed, two year research project, which examined the effects of medical tourism on the UK NHS. Research included in-depth interviews with 77 returning medical tourists and over sixty managers, medical travel facilitators, clinicians and providers of medical tourism in recipient countries to understand the medical tourism industry. Interviews were conducted between 2011 and 2012, recorded and transcribed, or documented through note taking. Authors undertook a thematic analysis of interviews to identify treatment pathways by patients, and professional linkages between clinicians and facilitators to understand choice of treatment destination. The results highlight that across a large sample of patients travelling for a variety of conditions from dental treatment, cosmetic and bariatric surgery, through to specialist care the role of networks is critical to understand choice of treatment, provider and destination. While distance, costs, expertise and availability of treatment all were factors influencing patients' decision to travel, choice of destination and provider was largely the result of informal networks, including web fora, personal recommendations and support groups. Where patients were referred by UK clinicians or facilitators these followed informal networks. In conclusion, investigating medical travel through focus on networks of patients and providers opens up novel conception of medical tourism, deepening understanding of patterns of travel by combining investigation of industry with patient motivation.

Is "efficiency" a useful concept in cognitive neuroscience?

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The concept of “efficiency” is often used to describe differences in brain activation between groups or individuals.

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I argue that this concept is empty and simply redescribes the data.

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I review different explanations for differences in activation and highlight the challenges in understanding these differences.

It is common in the cognitive neuroscience literature to explain differences in activation in terms of differences in the “efficiency” of neural function. I argue here that this usage of the concept of efficiency is empty and simply redescribes activation differences rather than providing a useful explanation of them. I examine a number of possible explanations for differential activation in terms of task performance, neuronal computation, neuronal energetics, and network organization. While the concept of “efficiency” is vacuous as it is commonly employed in the neuroimaging literature, an examination of brain development in the context of neural coding, neuroenergetics, and network structure provides a roadmap for future investigation, which is fundamental to an improved understanding of developmental effects and group differences in neuroimaging signals.

Changes in support networks in late middle age: the extension of gender and educational differences

OBJECTIVES

This paper tests whether differences by gender and by educational attainment in contact with friends and family and in support expected from friends and family narrow or widen in late middle age.

METHODS

The data are drawn from about 4,800 members of the Wisconsin Longitudinal Survey who answered questions about their frequency of contact with social ties and expectations of 3 kinds of help in both 1993, when they were in their early 50s, and again in 2004.

RESULTS

Using lagged dependent variable models, we find that between their 50s and 60s women's network advantages over men and college graduates' network advantages over high school graduates in frequency of social contact widened. The same was roughly true as well for expectations of social support, although here the divergences depended partly on the type of the support: Women gained relative to men in "talk" support and in help from nonkin if ill, but lost ground in financial support. The college-educated gained ground in all sorts of support from nonkin.

DISCUSSION

These results reinforce concern that late middle age is a period when men and the less educated become yet more disadvantaged in social support, making attention to connectedness yet more critical.

In vivo architectonics: a cortico-centric perspective

Recent advances in noninvasive structural imaging have opened up new approaches to cortical parcellation, many of which are described in this special issue on In Vivo Brodmann Mapping. In this introductory article, we focus on the emergence of cortical myelin maps as a valuable way to assess cortical organization in humans and nonhuman primates. We demonstrate how myelin maps are useful in three general domains: (i) as a way to identify cortical areas and functionally specialized regions in individuals and group averages; (ii) as a substrate for improved intersubject registration; and (iii) as a basis for interspecies comparisons. We also discuss how myelin-based cortical parcellation is complementary in important ways to connectivity-based parcellation using functional MRI or diffusion imaging and tractography. These observations and perspectives provide a useful background and context for other articles in this special issue.

Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer

Better approaches are needed to evaluate a single patient's drug response at the genomic level. Targeted therapy for signaling pathways in cancer has met limited success in part due to the exceedingly interwoven nature of the pathways. In particular, the highly complex RAS network has been challenging to target. Effectively targeting the pathway requires development of techniques that measure global network activity to account for pathway complexity. For this purpose, we used a gene-expression-based biomarker for RAS network activity in non-small cell lung cancer (NSCLC) cells, and screened for drugs whose efficacy was significantly highly correlated to RAS network activity. Results identified EGFR and MEK co-inhibition as the most effective treatment for RAS-active NSCLC amongst a panel of over 360 compounds and fractions. RAS activity was identified in both RAS-mutant and wild-type lines, indicating broad characterization of RAS signaling inclusive of multiple mechanisms of RAS activity, and not solely based on mutation status. Mechanistic studies demonstrated that co-inhibition of EGFR and MEK induced apoptosis and blocked both EGFR-RAS-RAF-MEK-ERK and EGFR-PI3K-AKT-RPS6 nodes simultaneously in RAS-active, but not RAS-inactive NSCLC. These results provide a comprehensive strategy to personalize treatment of NSCLC based on RAS network dysregulation and provide proof-of-concept of a genomic approach to classify and target complex signaling networks.

Off-line consolidation of motor sequence learning results in greater integration within a cortico-striatal functional network

The consolidation of motor sequence learning is known to depend on sleep. Work in our laboratory and others have shown that the striatum is associated with this off-line consolidation process. In this study, we aimed to quantify the sleep-dependent dynamic changes occurring at the network level using a measure of functional integration. We directly compared changes in connectivity before and after sleep or the simple passage of daytime. As predicted, the results revealed greater integration within the cortico-striatal network after sleep, but not an equivalent daytime period. Importantly, a similar pattern of results was also observed using a data-driven approach; the increase in integration being specific to a cortico-striatal network, but not to other known functional networks. These findings reveal, for the first time, a new signature of motor sequence consolidation: a greater between-regions interaction within the cortico-striatal system.

Choosing your network: social preferences in an online health community

A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts based on characteristics that are common sources of homophily in offline relationships. In light of our findings, we discuss design principles that may be useful for organizations and policy makers trying to improve the value of participants' social capital within online health programs.

Developmental differences in higher-order resting-state networks in Autism Spectrum Disorder

OBJECTIVE

Autism Spectrum Disorder (ASD) has been associated with a complex pattern of increases and decreases in resting-state functional connectivity. The developmental disconnection hypothesis of ASD poses that shorter connections become overly well established with development in this disorder, at the cost of long-range connections. Here, we investigated resting-state connectivity in relatively young boys with ASD and typically developing children. We hypothesized that ASD would be associated with reduced connectivity between networks, and increased connectivity within networks, reflecting poorer integration and segregation of functional networks in ASD.

METHODS

We acquired resting-state fMRI from 27 boys with ASD and 29 age- and IQ-matched typically developing boys between 6 and 16 years of age. Functional connectivity networks were identified using Independent Component Analysis (ICA). Group comparisons were conducted using permutation testing, with and without voxel-wise correction for grey matter density.

RESULTS

We found no between-group differences in within-network connectivity. However, we did find reduced functional connectivity between two higher-order cognitive networks in ASD. Furthermore, we found an interaction effect with age in the DMN: insula connectivity increased with age in ASD, whereas it decreased in typically developing children.

CONCLUSIONS

These results show subtle changes in between network connectivity in relatively young boys with ASD. However, the global architecture of resting-state networks appeared to be intact. This argues against recent suggestions that changes in connectivity in ASD may be the most prominent during development.

Understanding brain networks and brain organization

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal "true" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different "slices" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.

Modeling the sustainability of community health networks: novel approaches for analyzing collaborative organization partnerships across time

Sustainability is important if community health organizations are to be effective in collaborating to achieve long term health goals. We present a multi-method set of longitudinal analyses examining structural markers applied to a group of organizations brought together to reduce cancer disparities among older African American adults. At the overall network level, sustainability was seen in the growth of outgoing connections and multiplexity. Results of hierarchical clustering analyses identified distinct patterns of collaborative activation over time at the relationship level. Growth modeling indicated the effects of continuing network membership and participation in collaborative events on several structural markers of sustainability. Results of these analyses provide longitudinal indicators for how collaborations among partner organizations impacted their likelihood of continuing in the community network program. The strategy presented here introduces novel methods to assist with planning and evaluation of future community based public health endeavors.

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ((11)C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA-AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology.

[Peritoneal pseudomyxoma: an overview emphasizing pathological assessment and therapeutic strategies]

Pseudomyxoma peritonei is a clinical entity characterized by a gelatinous ascite associated with mucinous tumor deposits spreading on peritoneal surface and potentially invading abdominal organs. It is considered as a tumor process linked, in most of cases, to a mucinous appendiceal neoplasm. Pseudomyxoma peritonei may benefit from a therapeutic strategy combining cytoreductive surgery and intra-peritoneal chemotherapy, which has led to a major prognosis improvement. Different classifications are available and the last one corresponds to the WHO 2010 version, which individualizes pseudomyxoma peritonei in two classes: low grade and high grade mucinous carcinoma. The very low frequency of this entity and its specific therapeutic strategy need specific health care centres, as well as physicians and pathologists collaborating through dedicated networks. The aim of this article is to summarize the pathology, causes, mechanisms and therapeutic approaches of pseudomyxoma peritonei, as well as their interfaces with dedicated networks.

Arsenic exposure triggers a shift in microRNA expression

Exposure to inorganic Arsenic (iAs) through drinking water is a major public health problem affecting most countries. iAs has been classified by the International Agency for Research on Cancer as Group 1: "Carcinogenic to humans". Although numerous studies have shown the related adverse effects of iAs, sensitive appropriate biomarkers for studies of environmental epidemiology are still required. The present work aims at investigate the role of microRNAs (miRNAs), powerful negative regulators of gene expression, playing a key role in many physiological and pathological cellular processes, in iAs exposure. To this end, we analyzed miRNA changes in expression profile triggered by iAs exposure in Jurkat cell line. We used microarray technology to profile the expression of miRNAs following 2 μmol/L sodium arsenite treatment at different time points. Moreover, we performed phenotypic analysis of iAs treated cells. Real Time Polymerase Chain Reaction (RT-PCR) was used to validate miRNA microarray data and to assay expression modulation of selected relevant mRNAs. Finally, bioinformatics techniques were applied to reconstruct iAs-relevant molecular pathways and miRNA regulatory networks from the expression data. We report miRNAs modulated after iAs treatment in Jurkat cells. In particular, we highlight 36 miRNAs exhibiting consistent dysregulation and particularly a panel of 8 miRNAs which we also validated by RT-PCR analysis. Computational analysis of lists of putative target genes for these 8 miRNAs points to an involvement in arsenic-response pathways, for a subset of them, that were analyzed by RT-PCR. Furthermore, iAs exposure reveals induction of cell cycle progression and the failure of apoptosis, supporting the idea of iAs carcinogenic activity. Our study provides a list of miRNAs whose expression levels are affected by iAs treatment, corroborating the importance of proceeding with the hunt for specific subset of miRNAs, which can serve as potential biomarkers of iAs effects with useful diagnostic value.

Investment into the future of microbial resources: culture collection funding models and BRC business plans for biological resource centres

Through their long history of public service, diverse microbial Biological Resource Centres (mBRCs) have made myriad contributions to society and science. They have enabled the maintenance of specimens isolated before antibiotics, made available strains showing the development and change of pathogenicity toward animals, humans and plants, and have maintained and provided reference strains to ensure quality and reproducibility of science. However, this has not been achieved without considerable financial commitment. Different collections have unique histories and their support is often tied to their origins. However many collections have grown to serve large constituencies and need to develop novel funding mechanisms. Moreover, several international initiatives have described mBRCs as a factor in economic development and have led to the increased professionalism among mBRCs.

Bias tradeoffs in the creation and analysis of protein-protein interaction networks

Networks constructed from aggregated protein-protein interaction data are commonplace in biology. But the studies these data are derived from were conducted with their own hypotheses and foci. Focusing on data from budding yeast present in BioGRID, we determine that many of the downstream signals present in network data are significantly impacted by biases in the original data. We determine the degree to which selection bias in favor of biologically interesting bait proteins goes down with study size, while we also find that promiscuity in prey contributes more substantially in larger studies. We analyze interaction studies over time with respect to data in the Gene Ontology and find that reproducibly observed interactions are less likely to favor multifunctional proteins. We find that strong alignment between co-expression and protein-protein interaction data occurs only for extreme co-expression values, and use this data to suggest candidates for targets likely to reveal novel biology in follow-up studies.

BIOLOGICAL SIGNIFICANCE

Protein-protein interaction data finds particularly heavy use in the interpretation of disease-causal variants. In principle, network data allows researchers to find novel commonalities among candidate genes. In this study, we detail several of the most salient biases contributing to aggregated protein-protein interaction databases. We find strong evidence for the role of selection and laboratory biases. Many of these effects contribute to the commonalities researchers find for disease genes. In order for characterization of disease genes and their interactions to not simply be an artifact of researcher preference, it is imperative to identify data biases explicitly. Based on this, we also suggest ways to move forward in producing candidates less influenced by prior knowledge. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

Protein-protein interaction networks (PPI) and complex diseases

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.

Network diffusion accurately models the relationship between structural and functional brain connectivity networks

The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain's long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways.

Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues

Purpose

G protein-coupled receptors (GPCRs) are a superfamily of membrane proteins of vast pharmaceutical interest. Here, we describe a graph theory-based analysis of the structure of the β₂ adrenergic receptor (β₂ AR), a prototypical GPCR. In particular, we illustrate the network of direct and indirect interactions that link each amino acid residue to any other residue of the receptor.

Methods

Networks of interconnected amino acid residues in proteins are analogous to social networks of interconnected people. Hence, they can be studied through the same analysis tools typically employed to analyze social networks – or networks in general – to reveal patterns of connectivity, influential members, and dynamicity. We focused on the analysis of closeness-centrality, which is a measure of the overall connectivity distance of the member of a network to all other members.

Results

The residues endowed with the highest closeness-centrality are located in the middle of the seven transmembrane domains (TMs). In particular, they are mostly located in the middle of TM2, TM3, TM6 or TM7, while fewer of them are located in the middle of TM1, TM4 or TM5. At the cytosolic end of TM6, the centrality detected for the active structure is markedly lower than that detected for the corresponding residues in the inactive structures. Moreover, several residues acquire centrality when the structures are analyzed in the presence of ligands. Strikingly, there is little overlap between the residues that acquire centrality in the presence of the ligand in the blocker-bound structures and the agonist-bound structures.

Conclusions

Our results reflect the fact that the receptor resembles a bow tie, with a rather tight knot of closely interconnected residues and two ends that fan out in two opposite directions: one toward the extracellular space, which hosts the ligand binding cavity, and one toward the cytosol, which hosts the G protein binding cavity. Moreover, they underscore how interaction network is by the conformational rearrangements concomitant with the activation of the receptor and by the presence of agonists or blockers.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-16) contains supplementary material, which is available to authorized users.

Does the study of genetic interactions help predict the function of mitochondrial proteins in Saccharomyces cerevisiae?

Mitochondria are complex organelles of eukaryotic cells that contain their own genome, encoding key subunits of the respiratory complexes. The successive steps of mitochondrial gene expression are intimately linked, and are under the control of a large number of nuclear genes encoding factors that are imported into mitochondria. Investigating the relationships between these genes and their interaction networks, and whether they reveal direct or indirect partners, can shed light on their role in mitochondrial biogenesis, as well as identify new actors in this process. These studies, mainly developed in yeasts, are significant because mammalian equivalents of such yeast genes are candidate genes in mitochondrial pathologies. In practice, studies of physical, chemical and genetic interactions can be undertaken. The search for genetic interactions, either aggravating or alleviating the phenotype of the starting mutants, has proved to be particularly powerful in yeast since even subtle changes in respiratory phenotypes can be screened in a very efficient way. In addition, several high throughput genetic approaches have recently been developed. In this review we analyze the genetic network of three genes involved in different steps of mitochondrial gene expression, from the transcription and translation of mitochondrial RNAs to the insertion of newly synthesized proteins into the inner mitochondrial membrane, and we examine their relevance to our understanding of mitochondrial biogenesis. We find that these genetic interactions are seldom redundant with physical interactions, and thus bring a considerable amount of original and significant information as well as open new areas of research.

Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping

Mitochondria are double membraned, dynamic organelles that are required for a large number of cellular processes, and defects in their function have emerged as causative factors for a growing number of human disorders and are highly associated with cancer, metabolic, and neurodegenerative (ND) diseases. Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in ND disease, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. However, high-throughput proteomic and genomic approaches developed in genetically tractable model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins, including cytosolic and membrane proteins. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We discuss how the knowledge from the resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus further clarify the role of mitochondrial biology and the complex etiologies of ND disease.

BIOLOGICAL SIGNIFICANCE

Biochemical and genetic investigations have uncovered small numbers of candidate mitochondrial proteins (MPs) involved in neurodegenerative (ND) diseases, but given the diversity of processes affected by MP function and the difficulty of detecting interactions involving these proteins, many more likely remain unknown. Large-scale proteomic and genomic approaches developed in model prokaryotes and lower eukaryotes have proven to be effective tools for querying the physical (protein-protein) and functional (gene-gene) relationships between diverse types of proteins. Extension of this new framework to the mitochondrial sub-system in human will likewise provide a universally informative systems-level view of the physical and functional landscape for exploring the evolutionary principles underlying mitochondrial function. In this review, we highlight how experimental and computational approaches developed recently by our group and others can be effectively used towards elucidating the mitochondrial interactome in an unbiased and systematic manner to uncover network-based connections. We anticipate that the knowledge from these resulting interaction networks can effectively contribute towards the identification of new mitochondrial disease gene candidates, and thus foster a deeper molecular understanding of mitochondrial biology as well as the etiology of mitochondrial diseases. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Phosphoproteome analysis reveals differences in phosphosite profiles between tumorigenic and non-tumorigenic epithelial cells

Oral cancer disease represents a significant fraction of all human cancer types and its poor early diagnosis contributes to reduced individual survival rate. The identification of proteins modulated in tumorigenic cells and its post-translational modifications may improve our understanding of tumor development in epithelial cells. We have analyzed the phosphoproteome of tumorigenic (SCC-9) and non-tumorigenic (HaCaT) cell lines using MS-based approach in order to identify phosphopeptides with differing patterns of modifications and/or abundance. Our results revealed the identity of 4,206 protein phosphorylation sites with sixty-two sites showing to be significantly modulated between the two cell lines. The phosphoproteome data showed an overrepresentation of proteins with a possible role in nuclear regulatory functions. Pathway analysis was further performed on the phosphoproteome dataset and differences and commonalities of the functional pathways present in tumorigenic and non-tumorigenic cells were identified. Phosphopeptides that belong to the proteins lamina-associated polypeptide 2 isoform alpha and serine-arginine repetitive matrix protein 2 were identified with differential abundance and they appear as promising tumor-related phosphopeptides. These two proteins may be related to the structural alterations generally found in the nucleus of tumorigenic cells. The identification of phosphorylation sites in tumorigenic cells may contribute to disclose novel signaling mechanisms associated with OSCC.

SIGNIFICANCE

Oral Squamous Cell Carcinoma (OSCC) is an important cancer disease affecting thousands of people worldwide. Many cellular processes related to the development of oral cancer remain unknown; however, the studies performed in vitro with cancer cells have contributed to guide more specific research which may be further performed by using in vivo approaches or clinical samples. To our knowledge, only few studies have been published showing the results of phosphoproteome profiling of squamous cell carcinoma models, and many signaling proteins must be identified and functionally characterized in order to increase the knowledge available about the complexity of the signaling networks responsible for oral cancer development and its progression. Furthermore, our knowledge regarding proteins exclusive or very low abundant in cancer cells remains limited. A better understanding of the differences between signaling pathways present in epithelial cell lines may contribute to reveal the processes underlying the OSCC.

A Gateway between Omics Data and Systems Biology

Metabolomics captures the cellular chemistry and thereby the ultimate realization of numerous genetic, transcriptional, and enzymatic events. The mechanistic interpretation of small molecules as substrates, intermediates, or products of biochemical action is necessary to place metabolites into the correct physiological context of a system. Therefore, the tight connection between omics technologies and systems biology is vital.

Retinal connectomics: towards complete, accurate networks

Connectomics is a strategy for mapping complex neural networks based on high-speed automated electron optical imaging, computational assembly of neural data volumes, web-based navigational tools to explore 10(12)-10(15) byte (terabyte to petabyte) image volumes, and annotation and markup tools to convert images into rich networks with cellular metadata. These collections of network data and associated metadata, analyzed using tools from graph theory and classification theory, can be merged with classical systems theory, giving a more completely parameterized view of how biologic information processing systems are implemented in retina and brain. Networks have two separable features: topology and connection attributes. The first findings from connectomics strongly validate the idea that the topologies of complete retinal networks are far more complex than the simple schematics that emerged from classical anatomy. In particular, connectomics has permitted an aggressive refactoring of the retinal inner plexiform layer, demonstrating that network function cannot be simply inferred from stratification; exposing the complex geometric rules for inserting different cells into a shared network; revealing unexpected bidirectional signaling pathways between mammalian rod and cone systems; documenting selective feedforward systems, novel candidate signaling architectures, new coupling motifs, and the highly complex architecture of the mammalian AII amacrine cell. This is but the beginning, as the underlying principles of connectomics are readily transferrable to non-neural cell complexes and provide new contexts for assessing intercellular communication.

Peer group ties and executive compensation networks

Publicly traded firms in the US typically determine C.E.O. compensation by benchmarking the pay of their C.E.O.s against the pay of C.E.O.s in "peer" firms. Consequently, executive compensation is influenced not only by firm-level characteristics, but also by the selection and actions of the firm's immediate peers as well as by the structure of the executive compensation network overall. Analyzing compensation peer group choices made by the same 1183 firms for F.Y. 2007, 2008 and 2009, we find that while the typical compensation peer is similar in size and industry to the firm that chose it, deviations from this norm are common, especially among larger firms, and tend to be towards larger firms with better paid CEOs. Further analysis shows that firms who pay CEOs well relative to the pay that would be predicted from their revenues, return on assets, and industry tend to have greater aspiration bias in their group of named peers.

Systems approaches in understanding evolution and evolvability

Systems and network-based approaches are becoming increasingly popular in cellular biology. One contribution of such approaches has been to shed some light on the evolutionary origins of core organisational principles in biological systems, such as modularity, robustness, and evolvability. Models of interactions between genes (epistasis) have also provided insight into how sexual reproduction may have evolved. Additionally, recent work on viewing evolution as a form of learning from the environment has indicated certain bounds on the complexity of the genetic circuits that can evolve within feasible quantities of time and resources. Here we review the key studies and results in these areas, and discuss possible connections between them. In particular, we speculate on the link between the two notions of 'evolvability': the evolvability of a system in terms of how agile it is in responding to novel goals or environments, and the evolvability of certain kinds of gene network functionality in terms of its computational complexity. Drawing on some recent work on the complexity of graph-theoretic problems on modular networks, we suggest that modularity as an organising principle may have its raison d'etre in its ability to enhance evolvability, in both its senses.

Resting state functional connectivity of five neural networks in bipolar disorder and schizophrenia

BACKGROUND

Bipolar disorder (BPD) and schizophrenia (SCZ) share clinical characteristics and genetic contributions. Functional dysconnectivity across various brain networks has been reported to contribute to the pathophysiology of both SCZ and BPD. However, research examining resting-state neural network dysfunction across multiple networks to understand the relationship between these two disorders is lacking.

METHODS

We conducted a resting-state functional connectivity fMRI study of 35 BPD and 25 SCZ patients, and 33 controls. Using previously defined regions-of-interest, we computed the mean connectivity within and between five neural networks: default mode (DM), fronto-parietal (FP), cingulo-opercular (CO), cerebellar (CER), and salience (SAL). Repeated measures ANOVAs were used to compare groups, adjusting false discovery rate to control for multiple comparisons. The relationship of connectivity with the SANS/SAPS, vocabulary and matrix reasoning was investigated using hierarchical linear regression analyses.

RESULTS

Decreased within-network connectivity was only found for the CO network in BPD. Across groups, connectivity was decreased between CO-CER (p<0.001), to a larger degree in SCZ than in BPD. In SCZ, there was also decreased connectivity in CO-SAL, FP-CO, and FP-CER, while BPD showed decreased CER-SAL connectivity. Disorganization symptoms were predicted by connectivity between CO-CER and CER-SAL.

DISCUSSION

Our findings indicate dysfunction in the connections between networks involved in cognitive and emotional processing in the pathophysiology of BPD and SCZ. Both similarities and differences in connectivity were observed across disorders. Further studies are required to investigate relationships of neural networks to more diverse clinical and cognitive domains underlying psychiatric disorders.

An analysis of a 'community-driven' reconstruction of the human metabolic network

Following a strategy similar to that used in baker's yeast (Herrgård et al. Nat Biotechnol 26:1155-1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419-425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved 'community consensus' reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels (http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.

Investigations on the role of π-π interactions and π-π networks in eNOS and nNOS proteins

π-π Interactions play an important role in the stability of protein structures. In the present study, we have analyzed the influence of π-π interactions in eNOS and nNOS proteins. The contribution of these π-π interacting residues in sequential separation, secondary structure involvement, solvent accessibility and stabilization centers has been evaluated. π-π interactions stabilize the core regions within eNOS and nNOS proteins. π-π interacting residues are evolutionary conserved. There is a significant number of π-π interactions in spite of the lesser natural occurrences of π-residues in eNOS and nNOS proteins. In addition to π-π interactions, π residues also form π-π networks in both eNOS and nNOS proteins which might play an important role in the structural stability of these protein structures.

Cancer-related networks: a help to understand, predict and change malignant transformation

Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.

A comparison of Y-chromosomal lineage dating using either resequencing or Y-SNP plus Y-STR genotyping

We have compared phylogenies and time estimates for Y-chromosomal lineages based on resequencing ∼9 Mb of DNA and applying the program GENETREE to similar analyses based on the more standard approach of genotyping 26 Y-SNPs plus 21 Y-STRs and applying the programs NETWORK and BATWING. We find that deep phylogenetic structure is not adequately reconstructed after Y-SNP plus Y-STR genotyping, and that times estimated using observed Y-STR mutation rates are several-fold too recent. In contrast, an evolutionary mutation rate gives times that are more similar to the resequencing data. In principle, systematic comparisons of this kind can in future studies be used to identify the combinations of Y-SNP and Y-STR markers, and time estimation methodologies, that correspond best to resequencing data.

Connectomic approaches before the connectome

Connectome is a term with a short history but a long past. Since the origins of neuroscience the concept of a 'map of neural connections' has been a constant inspiring idea for those who believed the brain as the organ of intellect. A myriad of proto-connectome maps have been produced throughout the centuries, each one reflecting the theory and method of investigation that prevailed at the time. Even contemporary definitions of the connectome rest upon the formulation of a neuronal theory that has been proposed over a hundred years ago. So, what is new? In this article we attempt to trace the development of certain anatomical and physiological concepts at the origins of modern definitions of the connectome. We argue that compared to previous attempts current connectomic approaches benefit from a wealth of imaging methods that in part could justify the enthusiasm for finally succeeding in achieving the goal. One of the unique advantages of contemporary approaches is the possibility of using quantitative methods to define measures of connectivity where structure, function and behaviour are integrated and correlated. We also argue that many contemporary maps are inaccurate surrogates of the true anatomy and a comprehensive connectome of the human brain remains a far distant point in the history to come.

Apropos of an experience]

Public health action on a territory is complex and requires the involvement of multiple actors, who do not always act coordinately. Networks of organizations structures including the whole of the local actors facilitate the generation of synergies and enable greater effectiveness and efficiency of the joint action from the different actors on a same landscape. We present 3 years experience of four Public Health Committees in a region of Catalonia (Spain), composed by the main actors in public health planning. Each of the committees is organized on a plenary and working groups on issues arising from the regional health diagnosis, and coincident with the Health Plan of the Region. Coordination in no case implies the loss or dilution of the firm of the actor generator of intervention initiative in public health, but their empowerment and collaboration by the other actors. In conclusion welcomes the creation of a culture of collaboration and synergies between the different organizations concerned. Lack of specificity is observed in establishing operational objectives, and the need for greater coordination and involvement of the components of the various working groups.

Metabolic network modeling approaches for investigating the "hungry cancer"

Metabolism is the functional phenotype of a cell, at a given condition, resulting from an intricate interplay of various regulatory processes. The study of these dynamic metabolic processes and their capabilities help to identify the fundamental properties of living systems. Metabolic deregulation is an emerging hallmark of cancer cells. This deregulation results in rewiring of the metabolic circuitry conferring an exploitative metabolic advantage for the tumor cells which leads to a distinct benefit in survival and lays the basis for unbound progression. Metabolism can be considered as a thermodynamic open-system in which source substrates of high value are being processed through a well established interconnected biochemical conversion system, strictly obeying physiochemical principles, generating useful intermediates and finally resulting in the release of byproducts. Based on this basic principle of an input-output balance, various models have been developed to interrogate metabolism elucidating its underlying functional properties. However, only a few modeling approaches have proved computationally feasible in elucidating the metabolic nature of cancer at a systems level. Besides this, statistical approaches have been set up to identify biochemical pathways being more relevant for specific types of tumor cells. In this review, we are briefly introducing the basic statistical approaches followed by the major modeling concepts. We have put an emphasis on the methods and their applications that have been used to a greater extent in understanding the metabolic remodeling of cancer.

Towards multiscale modeling of influenza infection

Aided by recent advances in computational power, algorithms, and higher fidelity data, increasingly detailed theoretical models of infection with influenza A virus are being developed. We review single scale models as they describe influenza infection from intracellular to global scales, and, in particular, we consider those models that capture details specific to influenza and can be used to link different scales. We discuss the few multiscale models of influenza infection that have been developed in this emerging field. In addition to discussing modeling approaches, we also survey biological data on influenza infection and transmission that is relevant for constructing influenza infection models. We envision that, in the future, multiscale models that capitalize on technical advances in experimental biology and high performance computing could be used to describe the large spatial scale epidemiology of influenza infection, evolution of the virus, and transmission between hosts more accurately.

Physiological effects of climate warming on flowering plants and insect pollinators and potential consequences for their interactions

Growing concern about the influence of climate change on flowering plants, pollinators, and the mutualistic interactions between them has led to a recent surge in research. Much of this research has addressed the consequences of warming for phenological and distributional shifts. In contrast, relatively little is known about the physiological responses of plants and insect pollinators to climate warming and, in particular, how these responses might affect plant-pollinator interactions. Here, we summarize the direct physiological effects of temperature on flowering plants and pollinating insects to highlight ways in which plant and pollinator responses could affect floral resources for pollinators, and pollination success for plants, respectively. We also consider the overall effects of these responses on plant-pollinator interaction networks. Plant responses to warming, which include altered flower, nectar, and pollen production, could modify floral resource availability and reproductive output of pollinating insects. Similarly, pollinator responses, such as altered foraging activity, body size, and life span, could affect patterns of pollen flow and pollination success of flowering plants. As a result, network structure could be altered as interactions are gained and lost, weakened and strengthened, even without the gain or loss of species or temporal overlap. Future research that addresses not only how plant and pollinator physiology are affected by warming but also how responses scale up to affect interactions and networks should allow us to better understand and predict the effects of climate change on this important ecosystem service.

STEMI Interventions: The European Perspective and Stent for Life Initiative

The Stent for Life Initiative was launched by the European Association of Percutaneous Cardiovascular Interventions (a registered branch of the ESC) and EuroPCR. The purpose of the initiative is to support the implementation of European Society of Cardiology guidelines on management of acute myocardial infarction, help identify barriers to implementation of guidelines, and define actions to ensure that the majority of ST-segment elevation myocardial infarction (STEMI) patients in Europe have access to primary percutaneous coronary intervention. The key objectives are to define the countries with an unmet medical need in the optimal treatment of STEMI and implement an action program to increase patient access to primary percutaneous coronary intervention.

DANGEROUS LIAISONS? DATING AND DRINKING DIFFUSION IN ADOLESCENT PEER NETWORKS

The onset and escalation of alcohol consumption and romantic relationships are hallmarks of adolescence, yet only recently have these domains jointly been the focus of sociological inquiry. We extend this literature by connecting alcohol use, dating and peers to understand the diffusion of drinking behavior in school-based friendship networks. Drawing on Granovetter's classic concept of weak ties, we argue that adolescent romantic partners are likely to be network bridges, or liaisons, connecting daters to new peer contexts which, in turn, promote changes in individual drinking behaviors and allow these behaviors to spread across peer networks. Using longitudinal data of 459 couples from the National Longitudinal Study of Adolescent Health, we estimate Actor-Partner Interdependence Models and identify the unique contributions of partners' drinking, friends' drinking, and friends-of-partners' drinking to daters' own future binge drinking and drinking frequency. Findings support the liaison hypothesis and suggest that friends-of-partners' drinking have net associations with adolescent drinking patterns. Moreover, the coefficient for friends-of-partners drinking is larger than the coefficient for one's own peers and generally immune to prior selection. Our findings suggest that romantic relationships are important mechanisms for understanding the diffusion of emergent problem behaviors in adolescent peer networks.