Search

Asteroseismology, the study of intrinsic oscillations in stars, can reveal fundamental properties of cool stars critical in our understanding of stellar rotational evolution. Through space missions such as Kepler and TESS, asteroseismology has seen a surge of new data and research in the past decade. These data have contributed to important results in the field of stellar braking particularly for F, G and K stars, thanks to estimates of stellar age for rotating field stars, and the measurement of stellar rotation through oscillation spectra as important comparisons for estimates from star-spots. In this talk, I will provide an introduction to asteroseismology how it can provide important results for your research. This will be followed by a breakdown of how asteroseismologists have used these techniques to establish the presence of weakened magnetic braking on the main sequence using asteroseismic data, and what we should be looking forward to from these techniques with the TESS mission., {"references":["Hall, Oliver et al. (2021), Nature Astronomy, Volume 5, p. 707-714","Nielsen, Martin et al. (2015), Astronomy & Astrophysics, Volume 582, id.A10, 5 pp.","Davies, Guy et al. (2015), Monthly Notices of the Royal Astronomical Society, Volume 446, Issue 3, p.2959-2966","Benomar, Othman et al. (2018), Science, Volume 361, Issue 6408, pp. 1231-1234","Silva Aguirre, Victor et al. (2015), Monthly Notices of the Royal Astronomical Society, Volume 452, Issue 2, p.2127-2148","Davies, Guy et al. (2016), Monthly Notices of the Royal Astronomical Society, Volume 456, Issue 2, p.2183-2195","Lund, Mikkel et al. (2017), The Astrophysical Journal, Volume 835, Issue 2, article id. 172, 31 pp.","Silva Aguirre, Victor et al. (2017), The Astrophysical Journal, Volume 835, Issue 2, article id. 173, 18 pp.","Lund, Mikkel et al. (2014), The Astrophysical Journal, Volume 790, Issue 2, article id. 121, 28 pp.","David, Trevor et al. (2022), The Astrophysical Journal, Volume 933, Issue 1, id.114, 22 pp."]}

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. We propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry, and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically-validated lens for cortical connectomics using readily-available MRI data.

Adolescence is a time of profound changes in the physical wiring and function of the brain. Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14 to 25 y ( n = 199). Core to our work was an advanced in vivo model of cortical wiring incorporating MRI features of corticocortical proximity, microstructural similarity, and white matter tractography. Longitudinal analyses assessing age-related changes in cortical wiring identified a continued differentiation of multiple corticocortical structural networks in youth. We then assessed structure–function coupling using resting-state functional MRI measures in the same participants both via cross-sectional analysis at baseline and by studying longitudinal change between baseline and follow-up scans. At baseline, regions with more similar structural wiring were more likely to be functionally coupled. Moreover, correlating longitudinal structural wiring changes with longitudinal functional connectivity reconfigurations, we found that increased structural differentiation, particularly between sensory/unimodal and default mode networks, was reflected by reduced functional interactions. These findings provide insights into adolescent development of human brain structure and function, illustrating how structural wiring interacts with the maturation of macroscale functional hierarchies.

Asteroseismology has transformed stellar astrophysics. Red giant asteroseismology is a prime example, with oscillation periods and amplitudes that are readily detectable with time-domain space-based telescopes. These oscillations can be used to infer masses, ages and radii for large numbers of stars, providing unique constraints on stellar populations in our galaxy. The cadence, duration, and spatial resolution of the Roman galactic bulge time-domain survey (GBTDS) are well-suited for asteroseismology and will probe an important population not studied by prior missions. We identify photometric precision as a key requirement for realizing the potential of asteroseismology with Roman. A precision of 1 mmag per 15-min cadence or better for saturated stars will enable detections of the populous red clump star population in the Galactic bulge. If the survey efficiency is better than expected, we argue for repeat observations of the same fields to improve photometric precision, or covering additional fields to expand the stellar population reach if the photometric precision for saturated stars is better than 1 mmag. Asteroseismology is relatively insensitive to the timing of the observations during the mission, and the prime red clump targets can be observed in a single 70 day campaign in any given field. Complementary stellar characterization, particularly astrometry tied to the Gaia system, will also dramatically expand the diagnostic power of asteroseismology. We also highlight synergies to Roman GBTDS exoplanet science using transits and microlensing., Comment: Roman Core Community Survey White Paper, 3 pages, 4 figures

Psychosis is a set of functionally disruptive symptoms that have a profound impact on quality of life and that carry enormous societal costs. As psychosis-related conditions have their onset in adolescence and young adulthood, it has been conceptualised as a symptomatic manifestation of abnormal neurodevelopment. According to this, conditions such as schizophrenia occurs as a consequence of deviations in neurotypical maturational trajectories which are triggered by molecular and cellular processes. Thus, understanding typical and atypical brain maturation represents a critical step to fully comprehend the pathophysiological mechanisms underlying psychotic symptomatology. Over the last two decades, psychiatric neuroimaging has provided a unique insight into the brain structural abnormalities associated to psychosis. However, limited effect sizes and a lack of molecular interpretation has hindered its translational clinical value. Here, we propose a framework for robustly integrating large amounts of neuroimaging and molecular data that can identify atypical maturational trajectories in psychiatric conditions. By integrating Magnetic Resonance Imaging data from 100k+ healthy individuals (the largest one to date), we will first construct Brain Charts of neurotypical development for the whole lifespan. These Brain Charts will model the normative maturational trajectories of four morphometric features (i.e., cortical thickness, regional volume, surface area and curvature) for each brain region. Next, we will extract these features from individuals with psychosis-related conditions, including 474 patients with 22q11.2 Deletion Syndrome, 600 patients with schizophrenia-spectrum condition, and 459 patients with First Episode Psychosis who were longitudinally scanned (252 of them scanned up 10 years follow-up). Alterations on maturational trajectories will be established using the normative Brain Chart as reference curve. Thus, raw morphometric features from psychotic patients will be transformed into age-, sex- and site-corrected centile estimations of abnormal development. Grounded on the neurodevelopmental hypothesis of psychosis, the resulting centiles will illustrate which brain regions deviate from the expected normative value in each patient. We will additionally exploit a publicly available transcriptomic dataset to determine whether regions with abnormal trajectories have particular gene expression profiles. This will allow us to explore which set of genes are over-expressed (and under-expressed) in brain regions with abnormal trajectories. Moreover, we will compute Polygenic Risk Scores (PRS) associated to several psychiatric and neurodevelopmental disorders from blood samples. We will establish whether individuals with higher PRS present more severe deviation on their maturational trajectories. Building on that, we will explore the optimal criterion for combining PRS and abnormal trajectories for patients’ stratification so we can anticipate which individuals will show symptoms improvement and which will have treatment-resistant psychosis.

Recent discovery of approximately 270 common genetic variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. We hypothesized that normal variation in PRS would be associated with magnetic resonance imaging (MRI) phenotypes of brain morphometry and tissue composition. We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macrostructural MRI metrics measured at each of 180 cortical areas, seven subcortical structures, and 15 major white matter tracts. Linear mixed-effect models were used to investigate associations between PRS and brain structure at global and regional scales, controlled for multiple comparisons. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, five subcortical structures, and 14 white matter tracts. Other microstructural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate, and prefrontal cortical areas, insula, and hippocampus. Post-hoc bidirectional Mendelian randomization analyses provided preliminary evidence in support of a causal relationship between (reduced) thalamic NDI and (increased) risk of schizophrenia. Risk-related reduction in NDI is plausibly indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks. Cortical, subcortical, and white matter microstructure may be linked to the genetic mechanisms of schizophrenia., E.-M.S is supported by a PhD studentship awarded by the Friends of Peterhouse. This research was co-funded by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre and a Marmaduke Sheild grant to R.A.I.B. and V.W.. E.T.B is an NIHR Senior Investigator. R.R.G was funded by a Guarantors of Brain Fellowship. R.A.I.B. is supported by a British Academy Post-Doctoral fellowship and the Autism Research Trust. We wish to thank Dr Petra Vertes and Dr Lisa Ronan for their advice on research design and Dr Simon R White for his statistical advice and support. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This research was possible due to an application to the UK Biobank (project 20904).

Funder: Alan Turing Institute; doi: http://dx.doi.org/10.13039/100012338, Funder: Guarantors of Brain; doi: http://dx.doi.org/10.13039/501100000627, The human brain is a highly plastic 'complex' network-it is highly resilient to damage and capable of self-reorganisation after a large perturbation. Clinically, neurological deficits secondary to iatrogenic injury have very few active treatments. New imaging and stimulation technologies, though, offer promising therapeutic avenues to accelerate post-operative recovery trajectories. In this study, we sought to establish the safety profile for 'interventional neurorehabilitation': connectome-based therapeutic brain stimulation to drive cortical reorganisation and promote functional recovery post-craniotomy. In n = 34 glioma patients who experienced post-operative motor or language deficits, we used connectomics to construct single-subject cortical networks. Based on their clinical and connectivity deficit, patients underwent network-specific transcranial magnetic stimulation (TMS) sessions daily over five consecutive days. Patients were then assessed for TMS-related side effects and improvements. 31/34 (91%) patients were successfully recruited and enrolled for TMS treatment within two weeks of glioma surgery. No seizures or serious complications occurred during TMS rehabilitation and 1-week post-stimulation. Transient headaches were reported in 4/31 patients but improved after a single session. No neurological worsening was observed while a clinically and statistically significant benefit was noted in 28/31 patients post-TMS. We present two clinical vignettes and a video demonstration of interventional neurorehabilitation. For the first time, we demonstrate the safety profile and ability to recruit, enroll, and complete TMS acutely post-craniotomy in a high seizure risk population. Given the lack of randomisation and controls in this study, prospective randomised sham-controlled stimulation trials are now warranted to establish the efficacy of interventional neurorehabilitation following craniotomy.

OBJECTIVE: The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS: Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS: Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS: Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.

Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14–24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression.

Funder: The Wellcome Trust funded the ‘Neuroscience in Psychiatry Project’ (NSPN). All NSPN members (S-Table 4) are supported by a Wellcome Strategic Award (ref 095844/7/11/Z). Ray Dolan is supported by a Wellcome Investigator Award (ref 098362/Z/12/Z). The Max Planck – UCL Centre for Computational Psychiatry and Ageing is a joint initiative of the Max Planck Society and UCL. A.M.F. Reiter acknowledges support from the German Research Foundation (Deutsche Forschungsgesellschaft, DFG RE 4449/1-1, SFB 940-3, project B7) and by a 2020 NARSAD Young Investigator Grant from the Brain & Behavior Foundation. M. Moutoussis receives support from the NIHR UCLH Biomedical Research Centre. R.A. Kievit is supported by a Sir Henry Wellcome Trust Grant 107392/Z/15/Z and MRC Programme Grant SUAG/014 RG91365. E. Bullmore is in receipt of an NIHR Senior Investigator Award. P. Fonagy is in receipt of 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 NHS Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. We are grateful to the NSPN management and research assistant teams., 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.

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, “Multi-scAle heteroGeneity analysIs and Clustering” (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N=4403). We then applied MAGIC to imaging data from Alzheimer’s disease (ADNI,N=1728) and schizophrenia (PHENOM,N=1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available athttps://github.com/anbai106/MAGIC.HighlightsWe propose a novel multi-scale semi-supervised clustering method, termed MAGIC, to disentangle the heterogeneity of brain diseases.We perform extensive semi-simulated experiments on large control samples (UK Biobank,N=4403) to precisely quantify performance under various conditions, including varying degrees of brain atrophy, different levels of heterogeneity, overlapping disease subtypes, class imbalance, and varying sample sizes.We apply MAGIC to MCI and Alzheimer’s disease (ADNI,N=1728) and schizophrenia (PHENOM,N=1166) patients to dissect their neuroanatomical heterogeneity, providing guidance regarding the use of the semi-simulated experiments to validate the subtypes found in actual clinical applications.Graphical abstract

Targeted therapies developed for diffuse midline gliomas (DMG) expressing H3K27M have focused on histone deacetylase inhibitors (HDACi). High-throughput drug screening with patient derived DMG cell lines identified the HDACi panobinostat as a prominent clinical agent as well as pre-clinical studies with orthotopic mouse tumors models proving efficacious. Diametrically there is a pronounced lack of measurable panobinostat CSF concentrations in a non-human primate (NHP) non-tumor bearing pre-clinical model and in pediatric brain tumor patients. Notwithstanding, adult and pediatric glioma clinical trials and clinical observation with panobinostat alone or in combination have demonstrated minor responses. Pharmacokinetic models utilize the premise that CSF drug penetration is a surrogate of CNS drug penetration. However, the direct correlation between CSF and CNS drug levels is undefined especially in lieu of geographic CNS extracellular fluid drug variability previously demonstrated in the same NHP pre-clinical model. Utilizing the same NHP model, this study sought to compare panobinostat CSF penetration to CNS penetration via analysis of homogenized normal cerebrum, cerebellum, and brainstem tissue utilizing LC-MS/MS. METHODS: Panobinostat was administered orally as a single dose to three non-human primates. Pre panobinostat plasma and CSF were collected. Following panobinostat administration (1-hr Tmax) CSF, cerebrum, cerebellum, and brain stem tissue were collected as well as plasma to confirm the presence of panobinostat. Tissue slices were individually homogenized and panobinostat extracted via protein precipitation. Plasma, CSF, and tissue panobinostat concentrations were quantified using a LC-MS/MS assay. The lower limit of quantitation (LLOQ) for plasma-0.1 ng/ml, CSF-0.5 ng/ml, and tissue-10.0 pg/mg. RESULTS: Panobinostat was quantifiable in plasma (n=2) at the 1 hour (20.033 ng/mL and 0.153 ng/mL). CSF and CNS tissue samples were below the LLOQ for panobinostat in all samples. CONCLUSIONS: Panobinostat was not measurable from CSF and homogenized brain tissue in a non-tumor bearing NHP model at 1-hour post-administration using LC-MS/MS.

Funder: Canada Research Chairs; FundRef: http://dx.doi.org/10.13039/501100001804, Funder: Fonds de la Recherche du Quebec – Santé, Funder: Autism Research Trust, Funder: Canadian Institutes of Health Research; FundRef: http://dx.doi.org/10.13039/501100000024, Funder: BrainCanada, Funder: MNI-Cambridge collaborative award, Adolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes, with strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development.

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental condition that is accompanied by neuroanatomical differences on the macroscopic and microscopic level. Findings from histological, genetic, and more recently in vivo neuroimaging studies converge in suggesting that neuroanatomical abnormalities, specifically around the gray-white matter (GWM) boundary, represent a crucial feature of ASD. However, no research has yet characterized the GWM boundary in ASD based on measures of diffusion. Here, we registered diffusion tensor imaging data to the structural T1-weighted images of 92 adults with ASD and 92 matched neurotypical controls in order to examine between-group differences and group-by-sex interactions in fractional anisotropy and mean diffusivity sampled at the GWM boundary, and at different sampling depths within the superficial white and into the gray matter. As hypothesized, we observed atypical diffusion at and around the GWM boundary in ASD, with between-group differences and group-by-sex interactions depending on tissue class and sampling depth. Furthermore, we identified that altered diffusion at the GWM boundary partially (i.e., ~50%) overlapped with atypical gray-white matter tissue contrast in ASD. Our study thus replicates and extends previous work highlighting the GWM boundary as a crucial target of neuropathology in ASD, and guides future work elucidating etiological mechanisms.

Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by anatomical and functional variations not related to a disease or pathology of interest. Semi-supervised clustering techniques have been proposed to overcome this and, therefore, capture disease-specific patterns more effectively. An additional limitation of both unsupervised and semi-supervised conventional machine learning methods is that they typically model, learn and infer from data using a basis of feature sets pre-defined at a fixed anatomical or functional scale (e.g., atlas-based regions of interest). Herein we propose a novel method, "Multi-scAle heteroGeneity analysIs and Clustering" (MAGIC), to depict the multi-scale presentation of disease heterogeneity, which builds on a previously proposed semi-supervised clustering method, HYDRA. It derives multi-scale and clinically interpretable feature representations and exploits a double-cyclic optimization procedure to effectively drive identification of inter-scale-consistent disease subtypes. More importantly, to understand the conditions under which the clustering model can estimate true heterogeneity related to diseases, we conducted extensive and systematic semi-simulated experiments to evaluate the proposed method on a sizeable healthy control sample from the UK Biobank (N = 4403). We then applied MAGIC to imaging data from Alzheimer's disease (ADNI, N = 1728) and schizophrenia (PHENOM, N = 1166) patients to demonstrate its potential and challenges in dissecting the neuroanatomical heterogeneity of common brain diseases. Taken together, we aim to provide guidance regarding when such analyses can succeed or should be taken with caution. The code of the proposed method is publicly available at https://github.com/anbai106/MAGIC.

Gifted children learn more rapidly and effectively than others, presumably due to neurophysiological differences that affect efficiency in neuronal communication. Identifying the topological features that support its capabilities is relevant to understanding how the brain structure is related to intelligence. We proposed the analysis of the structural covariance network to assess which organizational patterns are characteristic of gifted children. The graph theory was used to analyse topological properties of structural covariance across a group of gifted children. The analysis was focused on measures of brain network integration, such as, participation coefficient and versatility, which quantifies the strength of specific modular affiliation of each regional node. We found that the gifted group network was more integrated (and less segregated) than the control group network. Brain regional nodes in the gifted group network had higher versatility and participation coefficient, indicating greater inter-modular communication mediated by connector hubs with links to many modules. Connector hubs of the networks of both groups were located mainly in association with neocortical areas (which had thicker cortex), with fewer hubs in primary or secondary neocortical areas (which had thinner cortex), as well as a few connector hubs in limbic cortex and insula. In the group of gifted children, a larger proportion of connector hubs were located in association cortex. In conclusion, gifted children have a more integrated and versatile brain network topology. This is compatible with the global workspace theory and other data linking integrative network topology to cognitive performance.

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.

A characteristic of adaptive behavior is its goal-directed nature. An ability to act in a goal-directed manner is progressively refined during development, but this refinement can be impacted by the emergence of psychiatric disorders. Disorders of compulsivity have been framed computationally as a deficit in model-based control, and have been linked also to abnormal frontostriatal connectivity. However, the developmental trajectory of model-based control, including an interplay between its maturation and an emergence of compulsivity, has not been characterized. Availing of a large sample of healthy adolescents (n = 569) aged 14 to 24 y, we show behaviorally that over the course of adolescence there is a within-person increase in model-based control, and this is more pronounced in younger participants. Using a bivariate latent change score model, we provide evidence that the presence of higher compulsivity traits is associated with an atypical profile of this developmental maturation in model-based control. Resting-state fMRI data from a subset of the behaviorally assessed subjects (n = 230) revealed that compulsivity is associated with a less pronounced change of within-subject developmental remodeling of functional connectivity, specifically between the striatum and a frontoparietal network. Thus, in an otherwise clinically healthy population sample, in early development, individual differences in compulsivity are linked to the developmental trajectory of model-based control and a remodeling of frontostriatal connectivity.

Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: "conservative" and "disruptive." Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearman's correlation between edgewise baseline FC (at 14 y, [Formula: see text]) and adolescent change in FC ([Formula: see text]), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas.

Most previous efforts to calibrate how rotation and magnetic activity depend on stellar age and mass have relied on observations of clusters, where isochrones from stellar evolution models are used to determine the properties of the ensemble. Asteroseismology employs similar models to measure the properties of an individual star by matching its normal modes of oscillation, yielding the stellar age and mass with high precision. We use 27 days of photometry from the Transiting Exoplanet Survey Satellite to characterize solar-like oscillations in the G8 subgiant of the 94 Aqr triple system. The resulting stellar properties, when combined with a reanalysis of 35 yr of activity measurements from the Mount Wilson HK project, allow us to probe the evolution of rotation and magnetic activity in the system. The asteroseismic age of the subgiant agrees with a stellar isochrone fit, but the rotation period is much shorter than expected from standard models of angular momentum evolution. We conclude that weakened magnetic braking may be needed to reproduce the stellar properties, and that evolved subgiants in the hydrogen shell-burning phase can reinvigorate large-scale dynamo action and briefly sustain magnetic activity cycles before ascending the red giant branch.

The Transiting Exoplanet Survey Satellite (TESS) is recording short-cadence, high duty-cycle timeseries across most of the sky, which presents the opportunity to detect and study oscillations in interesting stars, in particular planet hosts. We have detected and analysed solar-like oscillations in the bright G4 subgiant HD 38529, which hosts an inner, roughly Jupiter-mass planet on a 14.3d orbit and an outer, low-mass brown dwarf on a 2136 d orbit. We combine results frommultiple stellarmodelling teams to produce robust asteroseismic estimates of the star's properties, including its mass M = 1.48 +/- 0.04 M-circle dot, radius R = 2.68 +/- 0.03 R-circle dot, and age t = 3.07 +/- 0.39 Gyr. Our results confirm that HD 38529 has a mass near the higher end of the range that can be found in the literature and also demonstrate that precise stellar properties can be measured given shorter timeseries than produced by CoRoT, Kepler, or K2.

Background In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results. Methods We propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain–behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures. Results Both sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain–behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain–behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume. Conclusions Multivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders.

The Transiting Exoplanet Survey Satellite (TESS) is performing a near all-sky survey for planets that transit bright stars. In addition, its excellent photometric precision enables asteroseismology of solar-type and red-giant stars, which exhibit convection-driven, solar-like oscillations. Simulations predict that TESS will detect solar-like oscillations in nearly 100 stars already known to host planets. In this paper, we present an asteroseismic analysis of the known red-giant host stars HD;212771 and HD;203949, both systems having a long-period planet detected through radial velocities. These are the first detections of oscillations in previously known exoplanet-host stars by TESS, further showcasing the mission?s potential to conduct asteroseismology of red-giant stars. We estimate the fundamental properties of both stars through a grid-based modeling approach that uses global asteroseismic parameters as input. We discuss the evolutionary state of HD;203949 in depth and note the large discrepancy between its asteroseismic mass (M-* = 1.23 0.15 MM* = 1.00 0.16 M if in the clump) and the mass quoted in the discovery paper (M-* = 2.1 0.1 M), implying a change >30% in the planet?s mass. Assuming HD;203949 to be in the clump, we investigate the planet?s past orbital evolution and discuss how it could have avoided engulfment at the tip of the red-giant branch. Finally, HD;212771 was observed by K2 during its Campaign 3, thus allowing for a preliminary comparison of the asteroseismic performances of TESS and K2. We estimate the ratio of the observed oscillation amplitudes for this star to be

This paper documents the sixteenth data release (DR16) from the Sloan Digital Sky Surveys; the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the southern hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey (TDSS) and new data from the SPectroscopic IDentification of ERosita Survey (SPIDERS) programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library "MaStar"). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17)., Comment: DR16 release: Monday Dec 9th 2019. This is the alphabetical order SDSS-IV collaboration data release paper. 25 pages, 6 figures, accepted by ApJS on 11th May 2020. Minor changes clarify or improve text and figures relative to v1

Recent advances in structure-from-motion techniques are enabling many scientific fields to benefit from the routine creation of detailed 3D models. However, for a large number of applications, only a single camera is available, due to cost or space constraints in the survey platforms. Monocular structure-from-motion raises the issue of properly estimating the scale of the 3D models, in order to later use those models for metrology. The scale can be determined from the presence of visible objects of known dimensions, or from information on the magnitude of the camera motion provided by other sensors, such as GPS. This paper addresses the problem of accurately scaling 3D models created from monocular cameras in GPS-denied environments, such as in underwater applications. Motivated by the common availability of underwater laser scalers, we present two novel approaches. A fully-calibrated method enables the use of arbitrary laser setups, while a partially-calibrated method reduces the need for calibration by only assuming parallelism on the laser beams, with no constraints on the camera. The proposed methods have several advantages with respect to the existing methods. The need for laser alignment with the optical axis of the camera is removed, together with the extremely error-prone manual identification of image points on the 3D model. The performance of the methods and their applicability was evaluated on both data generated from a realistic 3D model and data collected during an oceanographic cruise in 2017. Three separate laser configurations have been tested, encompassing nearly all possible laser setups, to evaluate the effects of terrain roughness, noise, camera perspective angle and camera-scene distance. In the real scenario, the computation of 6 independent model scale estimates using our fully-calibrated approach, produced values with standard deviation of 0.3%.

Asteroseismology is the only observational tool in astronomy that can probe the interiors of stars, and is a benchmark method for deriving fundamental properties of stars and exoplanets. Over the coming decade, space-based and ground-based observations will provide a several order of magnitude increase of solar-like oscillators, as well as a dramatic increase in the number and quality of classical pulsator observations, providing unprecedented possibilities to study stellar physics and galactic stellar populations. In this white paper, we describe key science questions and necessary facilities to continue the asteroseismology revolution into the 2020's., Comment: 11 pages, 4 figures. White paper submitted to the Astro2020 Decadal Survey

Proceedings for a contributed talk in the splinter session "Stellar Brightness Variations: Building on the Solar Knowledge".

SAFARI is a point source spectrometer for the SPICA mission, which provides far-infrared spectroscopy and high sensitivity. SPICA mission, having a large cold telescope cooled to 6K above absolute zero, will provide an optimum environment where instruments are limited only by the cosmic background. SAFARI is a grating-based spectrometer with two modes of operation, Low Resolution (LR), or nominal mode (R~300) and High Resolution, (HR) (R~2000-11000). The SAFARI shall provide point source spectroscopy with diffraction-limited capability in four spectral bands over 34-230μm and a field of view (FoV) on sky over 2’×2’. Due to the complexity of the optical design of the SAFARI instrument a modular design was selected. Four principal modules are defined: Calibration Module (CS), Input Optics Module (IOM), Beam and Mode Distribution (BMDO) and Grating Modules (GMs). The present work is focused in the last module. Dispersive optical systems inherently demand the need of volume allocation for the optical system, being this fact somehow proportional to the wavelength and the required resolving power. The image sampling and the size of the detector elements are key drivers in this optical modular design. The optimization process has been performed taking into account the conceptual design parameters obtained during this phase such as collimator and camera optics focal lengths, subsystem diameters and periods and AOIs of the diffraction gratings.

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.

Prior research suggests that instructions modify place learning and navigation behaviors in a virtual space. Two pilot studies indicated that under certain instructional sets, participants behaved as if they were navigating in a room without distal cues—even though their ability to identify icons present in the room appears unaffected. In this study, we did a follow-up replicating those findings. Additionally, we attempted to measure the quality of cognitive map formation using two alternative methods (signal detection and rank ordering methods). The results of these methods were mixed; however, the large sample size, and appropriate controls used in this study solidify the interpretations of the two earlier pilot studies. Even with the vast individual differences in instruction adherence, participants given instructions that do not match the environmental contingencies (incongruent) behaved as if they were navigating in a room without distal cues.

Editorial

Cloud Computing has emerged as an economical option to use IT resources when needed without considerations about where they are allocated or how they are delivered. Cloud Computing expands the SOA capabilities by adding scalability, elasticity and other relevant quality attributes. In this context, many companies have started to migrate their SOA applications to Cloud environments without proper support. We conducted a systematic mapping study to gather the current knowledge about existing strategies for migrating SOA applications to cloud computing. 105 papers were identified and the results show that most of the approaches follow a semi-automated (conventional) strategy for migrating to the Cloud (93%) and that most of the reported works follow a hybrid deployment model (60%). We additionally identify several research gaps such as the need for more technology-independent solutions, a common definition for concepts and resources, tool support, and validation.

In PZT (Lead Zirconate Titanate) ceramics, stoichiometric composition is closely related with physical properties and applications. Laser-induced Breakdown Spectroscopy (LIBS) have shown to be a suitable technique for analyzing many types of solid samples, usually in combination with multivariate techniques. In this study, a method for quick determination of Pb, Zr, Ti and Sr in PZT ceramics using LIBS was proposed. Spectral data is analyzed with PLS (Partial Least Squares), providing results better than Multiple Linear Regression (MLR) and external calibration, when compared with previous determination of the same group of ceramics by PIXE (Particle Induced X-ray Emission). Most of predicted individual concentrations were between 98 and 102% of the reference value for Pb, and between 90 and 110% for the rest of the elements. (C) 2016 Elsevier B.V. All rights reserved.

Underwater images suffer from severe perceptual/visual degradation, due to the dense and non-uniform medium, causing scattering and attenuation of the propagated light that is sensed. Typical restoration methods rely on the popular Dark Channel Prior to estimate the light attenuation factor, and subtract the back-scattered light influence to invert the underwater imaging model. However, as a consequence of using approximate and global estimates of the back-scattered light, most existing single-image underwater descattering techniques perform poorly when restoring non-uniformly illuminated scenes. To mitigate this problem, we introduce a novel approach that estimates the back-scattered light locally, based on the observation of a neighborhood around the pixel of interest. To circumvent issue related to selection of the neighborhood size, we propose to fuse the images obtained over both small and large neighborhoods, each capturing distinct features from the input image. In addition, the Laplacian of the original image is provided as a third input to the fusion process, to enhance texture details in the reconstructed image. These three derived inputs are seamlessly blended via a multi-scale fusion approach, using saliency, contrast, and saturation metrics to weight each input. We perform an extensive qualitative and quantitative evaluation against several specialized techniques. In addition to its simplicity, our method outperforms the previous art on extreme underwater cases of artificial ambient illumination and high water turbidity.

International audience; Properly assessing the extent and magnitude of fault ruptures associated with large earthquakes is critical for understanding fault behavior and associated hazard. Submarine faults can trigger tsunamis, whose characteristics are defined by the geometry of seafloor displacement, studied primarily through indirect observations (e.g., seismic event parameters, seismic profiles, shipboard bathymetry, coring) rather than direct ones. Using deep-sea vehicles, we identify for the first time a marker of coseismic slip on a submarine fault plane along the Roseau Fault (Lesser Antilles), and measure its vertical displacement of ∼0.9 m in situ. We also map recent fissuring and faulting of sediments on the hangingwall, along ∼3 km of rupture in close proximity to the fault's base, and document the reactivation of erosion and sedimentation within and downslope of the scarp. These deformation structures were caused by the 2004 M w 6.3 Les Saintes earthquake, which triggered a subsequent tsunami. Their characterization informs estimates of earthquake recurrence on this fault and provides new constraints on the geometry of fault rupture, which is both shorter and displays locally larger coseismic displacements than available model predictions that lack field constraints. This methodology of detailed field observations coupled with near-bottom geophysical surveying can be readily applied to numerous submarine fault systems, and should prove useful in evaluating seismic and tsunamigenic hazard in all geodynamic contexts.

Due to rapid global economic growth and competition, there is an increased pressure on logistic companies. On one hand, they have to be flexible and agile, to meet Service Level Agreements (SLA’s) with clients, while on the other hand they have to comply with government regulations and environment protection laws. Better utilization of different modes of transport and improved decision making can contribute to solving both these challenges. This requires increased co-operation between logistic service providers (LSPs). Our aim is to develop an IT platform which facilitates increased cooperation between logistic partners and allows them to function in a synchromodal way. We use the principles of Service Oriented Architecture (SOA) to design the IT platform. Using this platform, a single consignment can be fulfilled using different modes of transport and unforeseen problems in transportation can be dealt with. A consortium of leading Dutch logistic companies will be the test bed for this research.

e-Infrastructures, and in particular Data Repositories and Open Access Data Infrastructures, are essential platforms for e-Science and e-Research and are being built since several years both in Europe and the rest of the world to support diverse multi/inter-disciplinary Virtual Research Communities. So far, however, it is difficult for scientists to correlate papers to datasets used to produce them and to discover data and documents in an easy way. In this paper, the CHAINREDS project’s Knowledge Base and its Semantic Search Engine are presented, which attempt to address those drawbacks and contribute to the reproducibility of science.

In university studies, it is not unusual for students to drop some of the subjects they have enrolled in for the academic year. They start by not attending lectures, sometimes due to neglect or carelessness, or because they find the subject too difficult, this means that they lose the continuity in the topics that the professor follows. If they try to attend again they discover that they hardly understand anything and become discouraged and so decide to give up attending lectures and study on their own. However some fail to turn up to do their final exams and the failure rate of those who actually do the exams is high. The problem is that this is not only the case with one specific subject, but it is often the same with many subjects. The result is that students arent’s productive enough, wasting time and also prolonging their years of study which entails a great cost for families. Degree courses structured to be conducted and completed in three academic courses, it may in fact take up to an average of six or more academic courses. In this paper, we have studied this problem, which apart from the waste of money and time, produces frustration in the student, who finds that he has not been able to achieve what he had proposed at the beginning of the course. It is quite common, to find students who do not even pass nor 50% of the subjects they had enrolled in for the academic year. If this happens repeatedly to a student, it can be the point when he considers dropping out altogether. This is also a concern for the universities, especially in the early courses. In our experience as professors, we have found that students, who attend lectures regularly and follow the explanations, approach the final exams with confidence and rarely fail the subject. In this proposal we present some techniques and methods carried out to solve in possible, the problem of lack of attendance to lectures. This involves "rewarding students for their assistance and participation in lectures". Rewarding assistance with a "prize" that counts for the final mark on the subject and involving more participation in the development of lectures. We believe that we have to teach students to use the lectures as part of their learning in a non-passive way. We consider the professor's work as fundamental in terms of how to convey the usefulness of these topics explained and the applications that they will have for their professional life in the future. In this way the student see for himself the use and importance of what he is learning. When his participation is required, he will feel more involved and confident participating in the educational system. Finally we present statistical results of studies carried out on different degrees and on different subjects over two consecutive years. In the first year we assessed only the final exams without considering the students attendance, or participation. In the second year, we have applied the techniques and methods proposed here. In addition we have compared the two ways of assessing subjects.

The aim of this paper is to investigate whether there are any 11-yr or quasi-biennial solar cycle-related variations in solar rotational splitting frequencies of low-degree solar p modes. Although no 11-yr signals were observed, variations on a shorter timescale (~2yrs) were apparent. We show that the variations arose from complications/artifacts associated with the realization noise in the data and the process by which the data were analyzed. More specifically, the realization noise was observed to have a larger effect on the rotational splittings than accounted for by the formal uncertainties. When used to infer the rotation profile of the Sun these variations are not important. The outer regions of the solar interior can be constrained using higher-degree modes. While the variations in the low-l splittings do make large differences to the inferred rotation rate of the core, the core rotation rate is so poorly constrained, even by low-l modes, that the different inferred rotation profiles still agree within their respective 1sigma uncertainties. By contrast, in asteroseismology, only low-l modes are visible and so higher-l modes cannot be used to constrain the rotation profile of stars. Furthermore, we usually only have one data set from which to measure the observed low-l splitting. In such circumstances the inferred internal rotation rate of a main sequence star could differ significantly from estimates of the surface rotation rate, hence leading to spurious conclusions. Therefore, extreme care must be taken when using only the splittings of low-l modes to draw conclusions about the average internal rotation rate of a star., Comment: 10 pages, 7 figures, accepted for publication in MNRAS

Una alta productividad en Ingeniería está asociada entre otras cosas a una gestión eficiente del flujo de las enormes cantidades de información y correspondientes tomas de decisiones consubstanciales a los entornos de diseño y producción. Ello exige que nuestros titulados estén formados para ser capaces de manejar esa información de modo integrado, a través de los diferentes estadios en que se genera y procesa. En el contexto de la enseñanza de la Ingeniería existen un buen número de cursos designados a desarrollar competencias específicas, como las requeridas en los currículos académicos, pero muy pocos en los cuales las competencias de integración sean el objetivo principal. En este artículo se documenta una asignatura que tiene esa orientación, con la intención de alimentar el debate sobre la formación, tan necesario en tiempos de fuerte contracción de la actividad económica como el que vivimos en la actualidad y que está promoviendo el COIN. La asignatura se denomina “Tecnologías de la Información Aplicadas a la Construcción Naval” y es impartida en la titulación de Ingeniero Naval y Oceánico, plan 2002, de la Universidad Politécnica de Madrid (UPM). La primera parte de la asignatura está dedicada a la planificación y gestión de proyectos; los estudiantes adquieren competencias en la definición, usando Ms-PROJECT, de la estructura de descomposición de tareas y la asignación de recursos así como en el seguimiento de proyectos, a través de una serie de ejemplos de complejidad creciente, finalizando con la construcción de un buque. La segunda parte está dedicada al uso de un gestor de bases de datos, Ms-ACCESS, y orientada a la gestión de la información relativa a Producción. Otra vez la estrategia pasa por trabajar sobre una serie de ejemplos de complejidad creciente y se finaliza con la administración de una base de datos de tuberías de un buque, para las cuales se establecen hitos de producción, recepción y montaje, lo que conecta está parte con la primera. Finalmente, la tercera parte de la asignatura está dedicada al trabajo con FORAN, con el cual se definen con detalle los elementos estructurales de todo el buque sobre el que se ha elaborado la planificación. En esta parte, el trabajo es cooperativo, dado que los estudiantes operan simultáneamente en el mismo modelo 3D. Las clases se realizan en un aula multimedia en la cual cada estudiante puede utilizar todas las aplicaciones de software tratadas. Se han realizado encuestas a los estudiantes para obtener una retroalimentación desde su experiencia así como para hacer una valoración de su satisfacción con el proceso de aprendizaje. Los resultados de esas encuestas son discutidos en el artículo.

The adaptation to the European Higher Education Area (EHEA) is becoming a great challenge for the University Community, especially for its teaching and research staff, which is involved actively in the teaching-learning process. It is also inducing a paradigm change for lecturers and students. Among the methodologies used for processes of teaching innovation, system thinking plays an important role when working mainly with mind maps, and is focused to highlighting the essence of the knowledge, allowing its visual representation. In this paper, a method for using these mind maps for organizing a particular subject is explained. This organization is completed with the definition of duration, precedence relationships and resources for each of these activities, as well as with their corresponding monitoring. Mind maps are generated by means of the MINDMANAGER package whilst Ms-PROJECT is used for establishing tasks relationships, durations, resources, and monitoring. Summarizing, a procedure and the necessary set of applications for self organizing and managing (timed) scheduled teaching tasks has been described in this paper.

The adaptation to the European Higher Education Area (EHEA) is becoming a great challenge for the University Community, especially for its teaching and research staff, which is involved actively in the teaching-learning process. It is also inducing a paradigm change for lecturers and students. Among the methodologies used for processes of teaching innovation, system thinking plays an important role when working mainly with mind maps, and is focused to highlighting the essence of the knowledge, allowing its visual representation. In this paper, a method for using these mind maps for organizing a particular subject is explained. This organization is completed with the definition of duration, precedence relationships and resources for each of these activities, as well as with their corresponding monitoring. Mind maps are generated by means of the MINDMANAGER package whilst Ms-PROJECT is used for establishing tasks relationships, durations, resources, and monitoring. Summarizing, a procedure and the necessary set of applications for self organizing and managing (timed) scheduled teaching tasks has been described in this paper

A high productivity rate in Engineering is related to an efficient management of the flow of the large quantities of information and associated decision making activities that are consubstantial to the Engineering processes both in design and production contexts. Dealing with such problems from an integrated point of view and mimicking real scenarios is not given much attention in Engineering degrees. In the context of Engineering Education, there are a number of courses designed for developing specific competencies, as required by the academic curricula, but not that many in which integration competencies are the main target. In this paper, a course devoted to that aim is discussed. The course is taught in a Marine Engineering degree but the philosophy could be used in any Engineering field. All the lessons are given in a computer room in which every student can use each all the treated software applications. The first part of the course is dedicated to Project Management: the students acquire skills in defining, using Ms-PROJECT, the work breakdown structure (WBS), and the organization breakdown structure (OBS) in Engineering projects, through a series of examples of increasing complexity, ending up with the case of vessel construction. The second part of the course is dedicated to the use of a database manager, Ms-ACCESS, for managing production related information. A series of increasing complexity examples is treated ending up with the management of the pipe database of a real vessel. This database consists of a few thousand of pipes, for which a production timing frame is defined, which connects this part of the course with the first one. Finally, the third part of the course is devoted to the work with FORAN, an Engineering Production package of widespread use in the shipbuilding industry. With this package, the frames and plates where all the outfitting will be carried out are defined through cooperative work by the studens, working simultaneously in the same 3D model. In the paper, specific details about the learning process are given. Surveys have been posed to the students in order to get feed-back from their experience as well as to assess their satisfaction with the learning process. Results from these surveys are discussed in the paper

The identification of the solar-like oscillation modes, as measured by asteroseismology, is a necessary requirement in order to infer the physical properties of the interior of the stars. Difficulties occur when a large number of modes of oscillations with a low signal-to-noise ratio are observed. In those cases, it is of common use to apply a likelihood-ratio test to discriminate between the possible scenarios. We present here a statistical analysis of the likelihood-ratio test and discuss its accuracy to identify the correct modes. We use the AsteroFLAG artificial stars, simulated over a range of magnitude, inclination angle, and rotation rate. We show that the likelihood-ratio test is appropriate up to a certain magnitude (signal-to-noise ratio)., Comment: 4 pages, 3 figures, Proceedings of the HELAS-IV International Conference, Submitted to Astronomische Nachrichten

This paper addresses invariance principles for a certain class of switched nonlinear systems. We provide an extension of LaSalle's Invariance Principle for these systems and state asymptotic stability criteria. We also present some related results that deal with the compactness of the trajectories of these switched systems and that are interesting by their own.

Over the past 15 years, our knowledge of the interior of the Sun has tremendously progressed by the use of helioseismic measurements. However, to go further in our understanding of the solar core, we need to measure gravity (g) modes. Thanks to the high quality of the Doppler-velocity signal measured by GOLF/SoHO, it has been possible to unveil the signature of the asymptotic properties of the solar g modes, thus obtaining a hint of the rotation rate in the core. However, the quest for the detection of individual g modes is not yet over. In this work, we apply the latest theoretical developments to guide our research using GOLF velocity time series. In contrary to what was thought till now, we are maybe starting to identify individual low-frequency g modes..., Highlight of Astronomy (HoA) proceedings of the JD-11, IAU 2009. 2 pages, 1 figure

The current era of ubiquitous digital cameras, digital cameras integrated into smartphones, and virtually limitless data storage affords exciting newopportunities formedicine in general and specifically dermatology. Digital photography has the potential to dramatically enableandfacilitate improvements indermatologyteaching,clinicaldocumentation,anddiagnosis. One of the barriers to the diffusion of digital imaging intodermatologypractice is the lackofstandards fordigitalphotography.Asnoted in thearticlebyQuigleyet al,1 there are currently no standards for dermatologic photography designated by Digital Imaging and Communications in Medicine. While some organizations, such as the American Teledermatology Association,2 have offered general guidelines, to our knowledge, no consistent actionable standards exist inmedical publications. The absence of standards severely impedes the integration of dermatologic images across systems that support documentation, diagnosis, and clinical practice. In a white paper for the Digital Imaging and Communications in Medicine working group, Madden3 enumerates the many challenges to the development and implementation of dermatologic imaging standards. Foremost among these is the typical relianceon consumeroff-the-shelf cameras andmonitors for dermatologic imaging. Off-the-shelf technologies are designed for consumer satisfaction rather than optimization of clinically relevant imaging. The image processing algorithms,whitebalance, andcolor calibrationusedby thesecameras and monitors are inherently proprietary and continuouslychanging. Inaddition to introducing technical limitations and variability, reliance on off-the-shelf technology has impeded the establishment of a significant capital equipment market in dermatologic imaging. As such, there are no vendors who aremotivated to invest heavily in the development and validation of dermatology-relevant standards across the entire spectrum of image acquisition, storage, and display. Today, patients routinely use “selfies” to communicate with their physicians about lesions they are concerned about, and they are also beginning touse selfies as ameans formonitoring nevi, a variety of skin eruptions, and ulcers. In addition, the widespread use of smartphones has spawned a plethora of dermatology applications (“apps”), with new releases being added to the ever-expanding apps list on a regular basis. Unfortunately, while the public has been quick to adopt these trends, many dermatologists are still documenting their findings via text messaging. However, basic standards could go a longway toward improving theutility of dermatologic photography.While adequate imagequalitymaybe subjective anddiffer by theapplication (eg, diagnosis vsdocumentation), a standard approach to image-associated metadata is needed to have interchangeable systems andmethods for filtering images for quality assurance and control. A prerequisite for consistentmetadata is consistent terminology for technical (eg, image type) and clinical (eg, anatomic site) data. The common terminology shouldbe aligned as closely as possible with existing standards, such as Systematized Nomenclature ofMedicine Clinical Terms4 and Institute of Electrical and Electronics Engineers standards.5 In theabsenceofexternal regulatoryandindustrydriversof dermatology imaging standards, it behooves the dermatology community towork toward theestablishment anddiffusionof some basic standards. Failing to do so creates the risk ofmissingoutonthefullbenefitsof imagingforourpatientsandreflects adverselyonourprofession.Despitemuchresistance fromphysicians, it is clear that theelectronicmedical record ispermeatingmedical practiceowing to requirements set forthby federal and private health care payers. In the process of incorporating a text-basedandmetadata–baseddocumentationsystem, considering theneedtoproperly reflect theelementsofanencounter to support the coding for a given visit, dermatologists and othermedicalprofessionals spendasignificantportionof their dailyclinicaleffort inmeetingtheseexpectations.Unfortunately, standards to incorporate thevaluablepresenceofdigital images into theelectronicmedical recordsystemdonothave thesame level of interest and adoption. The accuracy of dermatological findings documented through digital images far exceedswhat eventhemostdetailedtextcanreflect.Establishingaconsistent approachto imageacquisition, storage,andviewinghas thepotential topreserve, in the long term, themostvaluableaspectof amedical specialty that isbasedonvisualassessmentof thehuman skin. If the dermatology community fails to take a leadershiprole indefiningandsetting imagingstandards, there isarisk that impracticalorspecialty-inappropriatestandardswillbe imposed by external organizations. Anobviousapproach fordermatologyasa specialty is to leverage the effort already set forth by establishedmedical communities. A series of radiologic standards onmedical imaging as it relates to storage, security, and viewing has already been established and could serve as a platform for skin-related imagingstandardization.Withindermatology,establishedcommunities with shared clinical or research goals are best suited to contribute to the evolution of standards. One such community is themelanoma-dermoscopycommunity,acollegialgroup with an impressive record of broad-based collaborative research.6 In addition, an associated cottage industry of vendorsof imaging technologyhas arisenaround this community. Thiscommunityhasestablishedthe InternationalSkin Imaging Collaboration (ISIC)under theauspicesof the International SoRelated article page 883 Opinion

Finding solar-analog stars with fundamental properties as close as possible to the Sun and studying the characteristics of their surface magnetic activity is a very promising way to understand the solar variability and its associated dynamo process. However, the identification of solar-analog stars depends on the accuracy of the estimated stellar parameters. Thanks to the photometric CoROT and Kepler space missions, the addition of asteroseismic data was proven to provide the most accurate fundamental properties that can be derived from stellar modeling today. Here, we present our latest results on the solar--stellar connection by studying 18 solar analogs that we identified among the Kepler seismic sample (Salabert et al., 2016a}. We measured their magnetic activity properties using the observations collected by the Kepler satellite and the ground-based, high-resolution HERMES spectrograph. The photospheric (Sph) and chromospheric (S) magnetic activity proxies of these seismic solar analogs are compared in relation to the solar activity. We show that the activity of the Sun is comparable to the activity of the seismic solar analogs, within the maximum-to-minimum temporal variations of the 11-year solar activity cycle. Furthermore, we report on the discovery of temporal variability in the acoustic frequencies of the young (1 Gyr-old) solar analog KIC10644253 with a modulation of about 1.5 years, which agrees with the derived photospheric activity Sph (Salabert et al., 2016b}. It could be the signature of the short-period modulation, or quasi-biennal oscillation, of its magnetic activity as observed in the Sun and in the 1-Gyr-old solar analog HD30495. In addition, the lithium abundance and the chromospheric activity estimated from HERMES confirms that KIC10644253 is a young and more active star than the Sun.