Grand unified theories without the desert

We present a grand unified theory (GUT) that has GUT fields with masses of the order of a TeV, but at the same time preserves (at the one-loop level) the success of gauge-coupling unification of the minimal supersymmetric standard model (MSSM) and the smallness of proton decay operators. This scenario is based on a five-dimensional theory with the extra dimension compactified as in the Randall-Sundrum model. The MSSM gauge sector and its GUT extension live in the 5D bulk, while the matter sector is localized on a 4D boundary

EFT approach to the electron electric dipole moment at the two-loop level

Altres ajuts: the Catalan ICREA Academia program

Anomalous U(1) as a mediator of supersymmetry breaking

We point out that an anomalous gauge U(1) symmetry is a natural candidate for being the mediator and messenger of supersymmetry breaking. It facilitates dynamical supersymmetry breaking even in the flat limit. Soft masses are induced by both gravity and the U(1) gauge interactions giving an unusual mass hierarchy in the sparticle spectrum which suppresses flavor violations. This scenario does not suffer from the Polonyi problem

Facial emotion processing in schizophrenia : a non-specific neuropsychological deficit?

Original article can be found at : http://journals.cambridge.org/ Copyright Cambridge University PressBackground: Identification of facial emotions has been found to be impaired in schizophrenia but there are uncertainties about the neuropsychological specificity of the finding. Method: Twenty-two patients with schizophrenia and 20 healthy controls were given tests requiring identification of facial emotion, judgement of the intensity of emotional expressions without identification, familiar face recognition and the Benton Facial Recognition Test (BFRT). The schizophrenia patients were selected to be relatively intellectually preserved. Results: The patients with schizophrenia showed no deficit in identifying facial emotion, although they were slower than the controls. They were, however, impaired on judging the intensity of emotional expression without identification. They showed impairment in recognizing familiar faces but not on the BFRT. Conclusions: When steps are taken to reduce the effects of general intellectual impairment, there is no deficit in identifying facial emotions in schizophrenia. There may, however, be a deficit in judging emotional intensity. The impairment found in naming familiar faces is consistent with other evidence of semantic memory impairment in the disorder.Peer reviewe

Towards the ultimate SM fit to close in on Higgs physics

With the discovery of the Higgs at the LHC, experiments have finally addressed all aspects of the Standard Model (SM). At this stage, it is important to understand which windows for beyond the SM (BSM) physics are still open, and which are instead tightly closed. We address this question by parametrizing BSM effects with dimension-six operators and performing a global fit to the SM. We separate operators into different groups constrained at different levels, and provide independent bounds on their Wilson coefficients taking into account only the relevant experiments. Our analysis allows to assert in a model-independent way where BSM effects can appear in Higgs physics. In particular, we show that deviations from the SM in the differential distributions of $a re related to other observables, such as triple gauge-boson couplings, and are then already constrained by present data. On the contrary, BR(h → Zγ) can still hide large deviations from the SM.$h \ \to\ V\overline{f}

Conditional Mutual Information Maps as Descriptors of Net Connectivity Levels in the Brain

There is a growing interest in finding ways to summarize the local connectivity properties of the brain through single brain maps. Here we propose a method based on the conditional mutual information (CMI) in the frequency domain. CMI maps quantify the amount of non-redundant covariability between each site and all others in the rest of the brain, partialling out the joint variability due to gross physiological noise. Average maps from a sample of 45 healthy individuals scanned in the resting state show a clear and symmetric pattern of connectivity maxima in several regions of cortex, including prefrontal, orbitofrontal, lateral–parietal, and midline default mode network components; and in subcortical nuclei, including the amygdala, thalamus, and basal ganglia. Such cortical and subcortical hotspots of functional connectivity were more clearly evident at lower frequencies (0.02–0.1 Hz) than at higher frequencies (0.1–0.2 Hz) of endogenous oscillation. CMI mapping can also be easily applied to perform group analyses. This is exemplified by exploring effects of normal aging on CMI in a sample of healthy controls and by investigating correlations between CMI and positive psychotic symptom scores in a sample of 40 schizophrenic patients. Both the normative aging and schizophrenia studies reveal functional connectivity trends that converge with reported findings from other studies, thus giving further support to the validity of the proposed method

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction

A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods

There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly

Role of neurotrophins in depressive symptoms and executive function: Association analysis of NRN1 gene and its interaction with BDNF gene in a non-clinical sample

Background Neuritin-1 is a neurotrophic factor involved in synaptic plasticity that has been associated with depressive disorders, schizophrenia and cognitive performance. The study of genotype-phenotype relationships in healthy individuals is a useful framework to investigate the etiology of brain dysfunctions. We therefore aimed to investigate in a non-clinical sample whether NRN1 gene contributes to the psychopathological profile, with a particular focus on the clinical dimensions previously related to the NRN1 gene (i.e. depressive and psychotic). Furthermore, we aimed to analyze: i) the role of NRN1 on executive functions, ii) whether the association between either NRN1-psychopathological profile or NRN1-cognitive performance is moderated by the BDNF gene. Methods The sample is comprised of 410 non-clinical subjects who filled in the self-reported Brief Symptom Inventory (BSI) and were assessed for executive performance (Verbal Fluency, Wisconsin Card Sorting Test (WCST) and Letter-Number subscale (WAIS-III)). Genotyping included nine SNPs in NRN1 and one in BDNF. Results i) GG homozygotes (rs1475157-NRN1) showed higher scores on BSI depressive dimension and on total scores compared to A carriers (corrected p-values: 0.0004 and 0.0003, respectively). ii) A linear trend was detected between GG genotype of rs1475157 and a worse cognitive performance in WCST total correct responses (uncorrected p-value: 0.029). iii) Interaction between rs1475157-NRN1 and Val66Met-BDNF was found to modulate depressive symptoms (p=0.001, significant after correction). Limitations Moderate sample size; replication in a larger sample is needed. Conclusions NRN1 is associated with depressive symptoms and executive function in a non-clinical sample. Our results also suggest that the role of NRN1 seems to be modulated by BDNF.This study was supported by: i) Intramural Project CIBERSAM (P91E), ii) The Network of European Funding for Neuroscience Research, ERA-NET NEURON (PiM2010ERN-00642), iii) Instituto de Salud Carlos III through the project PI15/01420 (co-funded by European Regional Development Fund /European Social Fund, “Investing in your future”). Thanks to: i) the Comissionat per a Universitats i Recerca del DIUE (2014SGR1636), ii) Universitat de Barcelona and APIF-IBUB grant 2014. All funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data