A Relation Between Approaches to Integrability in Superconformal Yang-Mills Theory

We make contact between the infinite-dimensional non-local symmetry of the typeIIB superstring on AdS5xS5 worldsheet theory and a non-abelian infinite-dimensional symmetry algebra for the weakly coupled superconformal gauge theory. We explain why the planar limit of the one-loop dilatation operator is the Hamiltonian of a spin chain, and show that it commutes with the g*2 N = 0 limit of the non-abelian charges.Comment: 19 pages, harvma

Mutualistic Coupling Between Vocabulary and Reasoning Supports Cognitive Development During Late Adolescence and Early Adulthood.

One of the most replicable findings in psychology is the positive manifold: the observation that individual differences in cognitive abilities are universally positively correlated. Investigating the developmental origin of the positive manifold is crucial to understanding it. In a large longitudinal cohort of adolescents and young adults ( N = 785; n = 566 across two waves, mean interval between waves = 1.48 years; age range = 14-25 years), we examined developmental changes in two core cognitive domains, fluid reasoning and vocabulary. We used bivariate latent change score models to compare three leading accounts of cognitive development: g-factor theory, investment theory, and mutualism. We showed that a mutualism model, which proposes that basic cognitive abilities directly and positively interact during development, provides the best account of developmental changes. We found that individuals with higher scores in vocabulary showed greater gains in matrix reasoning and vice versa. These dynamic coupling pathways are not predicted by other accounts and provide a novel mechanistic window into cognitive development.The Neuroscience in Psychiatry Network is supported by a strategic award from the Wellcome Trust to the University of Cambridge and University College London (095844/Z/11/Z). R. A. Kievit is supported by the Wellcome Trust (Grant No. 107392/Z/15/Z) and the UK Medical Research Council (MC-A060-5PR61). P. Fonagy is funded by a National Institute for Health Research (NIHR) Senior Investigator Award (NF-SI-0514-10157). P. Fonagy was in part supported by the NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Barts Health National Health Service (NHS) Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the UK Department of Health

Personality dimensions emerging during adolescence and young adulthood are underpinned by a single latent trait indexing impairment in social functioning.

BACKGROUND: Personality with stable behavioural traits emerges in the adolescent and young adult years. Models of putatively distinct, but correlated, personality traits have been developed to describe behavioural styles including schizotypal, narcissistic, callous-unemotional, negative emotionality, antisocial and impulsivity traits. These traits have influenced the classification of their related personality disorders. We tested if a bifactor model fits the data better than correlated-factor and orthogonal-factor models and subsequently validated the obtained factors with mental health measures and treatment history. METHOD: A set of self-report questionnaires measuring the above traits together with measures of mental health and service use were collected from a volunteer community sample of adolescents and young adults aged 14 to 25 years (N = 2443). RESULTS: The bifactor model with one general and four specific factors emerged in exploratory analysis, which fit data better than models with correlated or orthogonal factors. The general factor showed high reliability and validity. CONCLUSIONS: The findings suggest that a selected range of putatively distinct personality traits is underpinned by a general latent personality trait that may be interpreted as a severity factor, with higher scores indexing more impairment in social functioning. The results are in line with ICD-11, which suggest an explicit link between personality disorders and compromised interpersonal or social function. The obtained general factor was akin to the overarching dimension of personality functioning (describing one's relation to the self and others) proposed by DSM-5 Section III

Preference uncertainty accounts for developmental effects on susceptibility to peer influence in adolescence.

Adolescents are prone to social influence from peers, with implications for development, both adaptive and maladaptive. Here, using a computer-based paradigm, we replicate a cross-sectional effect of more susceptibility to peer influence in a large dataset of adolescents 14 to 24 years old. Crucially, we extend this finding by adopting a longitudinal perspective, showing that a within-person susceptibility to social influence decreases over a 1.5 year follow-up time period. Exploiting this longitudinal design, we show that susceptibility to social influences at baseline predicts an improvement in peer relations over the follow-up period. Using a Bayesian computational model, we demonstrate that in younger adolescents a greater tendency to adopt others' preferences arises out of a higher uncertainty about their own preferences in the paradigmatic case of delay discounting (a phenomenon called 'preference uncertainty'). This preference uncertainty decreases over time and, in turn, leads to a reduced susceptibility of one's own behaviour to an influence from others. Neuro-developmentally, we show that a measure of myelination within medial prefrontal cortex, estimated at baseline, predicts a developmental decrease in preference uncertainty at follow-up. Thus, using computational and neural evidence, we reveal adaptive mechanisms underpinning susceptibility to social influence during adolescence

Cybersecurity: Selected Legal Issues

This report discusses selected legal issues that frequently arise in the context of legislation to address vulnerabilities of private critical infrastructure to cyber threats, efforts to protect government networks from cyber threats, and proposals to facilitate and encourage sharing of cyber threat information amongst private sector and government entities. This report also provides an overview of the ways in which federal laws of these types may preempt or affect the applicability of state law

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks

Schizotypy-Related Magnetization of Cortex in Healthy Adolescence Is Colocated With Expression of Schizophrenia-Related Genes.

BACKGROUND: Genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits, but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behavior are unclear. METHODS: We assessed schizotypy using a self-reported questionnaire and measured magnetization transfer as a putative microstructural magnetic resonance imaging marker of intracortical myelination in 68 brain regions in 248 healthy young people (14-25 years of age). We used normative adult brain gene expression data and partial least squares analysis to find the weighted gene expression pattern that was most colocated with the cortical map of schizotypy-related magnetization. RESULTS: Magnetization was significantly correlated with schizotypy in the bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy, also in medial prefrontal cortex; all false discovery rate-corrected ps < .05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole-genome expression map colocated with schizotypy-related magnetization were enriched for genes that were significantly downregulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally upregulated in schizophrenia. Positively weighted (downregulated) genes were enriched for neuronal, specifically interneuronal, affiliations and coded a network of proteins comprising a few highly interactive "hubs" such as parvalbumin and calmodulin. CONCLUSIONS: Microstructural magnetic resonance imaging maps of intracortical magnetization can be linked to both the behavioral traits of schizotypy and prior histological data on dysregulated gene expression in schizophrenia

Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks.

Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.PEV is supported by an MRC Bioinformatics Research Fellowship (MR/K020706/1). Functional MRI data acquisition was supported by a strategic award from the Wellcome Trust to the University of Cambridge (IMG, PBJ, ETB) and University College London (RJD, PF): the Neuroscience in Psychiatry Network (NSPN). Additional support was provided by the NIHR Cambridge Biomedical Research Centre. Access to gene expression data was provided by the Allen Institute for Brain Sciences Website: © 2015 Allen Institute for Brain Science. Allen Human Brain Atlas [Internet]. Available from: http://human.brain-map.org. FV is supported by a Gates Cambridge PhD studentship. KW is supported by the University of Cambridge MB/PhD Programme and the Wellcome Trust. We thank Gita Prabu, Roger Tait, Cinly Ooi, John Suckling and Becky Inkster for fMRI data collection and storage. ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline(GSK).This is the final version of the article. It first appeared from Royal Society Publishing via http://dx.doi.org/10.1098/rstb.2015.036