Magnetic field topology of the RS CVn star II Pegasi

The dynamo processes in cool active stars generate complex magnetic fields responsible for prominent surface stellar activity and variability at different time scales. For a small number of cool stars magnetic field topologies were reconstructed from the time series of spectropolarimetric observations using the Zeeman Doppler imaging (ZDI) method. In this study we follow a long-term evolution of the magnetic field topology of the RS CVn binary star II Peg. We collected high-resolution circular polarisation observations of II Peg using the SOFIN spectropolarimeter at the Nordic Optical Telescope. These data cover 12 epochs spread over 7 years. A multi-line diagnostic technique in combination with a new ZDI code is applied to interpret these observations. Magnetic inversions using these data reveals evolving magnetic fields with typical local strengths of 0.5-1.0 kG and complex topologies. Despite using a self-consistent magnetic and temperature mapping technique, we do not find a clear correlation between magnetic and temperature features in the ZDI maps. Neither do we confirm the presence of persistent azimuthal field rings found in other RS CVn stars. Reconstruction of the magnetic field topology of II Peg reveals significant evolution of both the surface magnetic field structure and the extended magnetospheric field geometry. From 2004 to 2010 the total field energy drastically declined and the field became less axisymmetric. This also coincided with the transition from predominantly poloidal to mainly toroidal field topology. A qualitative comparison of the ZDI maps of II Peg with the prediction of dynamo theory suggests that the magnetic field in this star is produced mainly by the turbulent alpha^2 dynamo rather than the solar alphaOmega dynamo. Our results do not show a clear active longitude system, nor is there an evidence of the presence of an azimuthal dynamo wave.Comment: 20 pages, 10 figures; accepted for publication in Astronomy & Astrophysic

FPGA-implementation of PID-controller by differential evolution optimization

We will describe an FPGA implementation of PID-controller that uses differential evolution to optimize the coefficients of the PID controller, which has been implemented in VHDL. The original differential evolution algorithm was improved by ranking based mutation operation and self-adaptation of mutation and crossover parameters. Ranking-based mutation operation improves the quality of solution, convergence rate and success of optimization. Due to the self adaptive control parameters, the user does not have to estimate the mutation and crossover rates. Optimization have been performed by calculating for each generation fitness value by means of trial parameters. The final optimal parameters are selected based on the minimum fitness.fi=vertaisarvioitu|en=peerReviewed

Anti-neuronal anti-bodies in patients with early psychosis

It may be challenging to distinguish autoimmune encephalitis associated with anti-neuronal autoantibodies from primary psychiatric disorders. Here, serum was drawn from patients with a first-episode psychosis (n = 70) or a clinical high-risk for psychosis (n = 6) and controls (n = 34). We investigated the serum prevalence of 24 antineuronal autoantibodies: IgG antibodies for anti-N-methyl-D-aspartate-type glutamate receptor (anti-NMDAR), glutamate and gamma-aminobutyric acid alpha and beta receptors (GABA-a, GABA-b), alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA), glycine receptor (GlyR), metabotropic glutamate receptor 1 and 5 (mGluR1, mGluR5), anti-Tr/Delta/notch-like epidermal growth factor-related receptor (DNER), contactin-associated protein-like 2 (CASPR2), myelin oligodendrocyte glycoprotein (MOG), glutamic acid decarboxylase-65 (GAD65), collapsin response mediator protein 5/crossveinless-2 (CV2), aquaporin-4 (AQP4), anti-dipeptidyl-peptidase-like protein-6 (DPPX), type 1 anti-neuronal nuclear antibody (ANNA-1, Hu), Ri, Yo, IgLON5, Ma2, zinc finger protein 4 (ZIC4), Rho GTPase-activating protein 26, amphiphysin, and recoverin, as well as IgA and IgM for dopamine-2-receptor (DRD2). Anti-NMDA IgG antibodies were positive with serum titer 1:320 in one patient with a clinical high risk for psychosis. He did not receive a diagnosis of encephalitis after comprehensive neurological evaluation. All other antineuronal autoantibodies were negative and there were no additional findings with immunohistochemistry of brain issues. (C) 2017 Elsevier B.V. All rights reserved.Peer reviewe

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features—like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis—are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognosticmarkers in FEP. Combination of differentmarkers inMLmodels with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.Peer reviewe

Flip-flops of FK Comae Berenices

Context.FK Coma

Doppler images of II Pegasi for 2004-2010

Aims. We study the spot activity of II Peg during the years 2004-2010 to determine long- and short-term changes in the magnetic activity. In a previous study, we detected a persistent active longitude, as well as major changes in the spot configuration occurring on a timescale of shorter than a year. The main objective of this study is to determine whether the same phenomena persist in the star during these six years of spectroscopic monitoring. Methods. The observations were collected with the high-resolution SOFIN spectrograph at the Nordic Optical Telescope. The temperature maps were calculated using a Doppler imaging code based on Tikhonov regularization. Results. We present 12 new temperature maps that show spots distributed mainly over high and intermediate latitudes. In each image, 1-3 main active regions can be identified. The activity level of the star is clearly lower than during our previous study for the years 1994-2002. In contrast to the previous observations, we detect no clear drift of the active regions with respect to the rotation of the star. Conclusions. Having shown a systematic longitudinal drift of the spot-generating mechanism during 1994-2002, the star has clearly switched to a low-activity state for 2004-2010, during which the spot locations appear more random over phase space. It could be that the star is near to a minimum of its activity cycle.Comment: Accepted for publication in Astron. and Astrophys., 8 pages, 5 figure

Serum metabolite profile associates with the development of metabolic co-morbidities in first-episode psychosis

Psychotic patients are at high risk for developing obesity, metabolic syndrome and type 2 diabetes. These metabolic co-morbidities are hypothesized to be related to both treatment side effects as well as to metabolic changes occurring during the psychosis. Earlier metabolomics studies have shown that blood metabolite levels are predictive of insulin resistance and type 2 diabetes in the general population as well as sensitive to the effects of antipsychotics. In this study, we aimed to identify the metabolite profiles predicting future weight gain and other metabolic abnormalities in psychotic patients. We applied comprehensive metabolomics to investigate serum metabolite profiles in a prospective study setting in 36 first-episode psychosis patients during the first year of the antipsychotic treatment and 19 controls. While corroborating several earlier findings when comparing cases and controls and the effects of the antipsychotic medication, we also found that prospective weight gain in psychotic patients was associated with increased levels of triacylglycerols with low carbon number and double-bond count at baseline, that is, lipids known to be associated with increased liver fat. Our study suggests that metabolite profiles may be used to identify the psychotic patients most vulnerable to develop metabolic co-morbidities, and may point to a pharmacological approach to counteract the antipsychotic-induced weight gain

State and trait hopelessness in a prospective five-year study of patients with depressive disorders

Background: Hopelessness is a common experience of patients with depressive disorders (DD) and an important predictor of suicidal behaviour. However, stability and factors explaining state and trait variation of hopelessness in patients with DD over time are poorly known. Methods: Patients with DD (n = 406) from the Vantaa Depression Study and the Vantaa Primary Care Depression Study filled in the Beck Hopelessness Scale (BHS), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Perceived Social Support Scale-Revised (PSSS-R), and Eysenck Personality Inventory-Q (EPI-Q) at baseline, at six and eighteen months, and at five years. We conducted a multilevel linear regression analyses predicting BHS with these covariates. Results: During the five-year follow-up half of the variance in BHS was attributable to between-patient variance (50.6%, CI = 41.2-61.5%), and the rest arose from within-patient variance and measurement errors. BDI and BAI explained 5.6% of within-patient and 28.4% of between-patient variance of BHS. High Neuroticism and low Extraversion explained 2.6% of the between-patient variance of BHS. PSSS-R explained 5% of between-patient variance and 1.7% of within-patient variance of BHS. Limitations: No treatment effects were controlled. Conclusions: Hopelessness varies markedly over time both within and between patients with depression; it is both state-and trait-related. Concurrent depressive and anxiety symptoms and low social support explain both state and trait variance, whereas high Neuroticism and low Extraversion explain only trait variance of hopelessness. These variations influence the utility of hopelessness as an indicator of suicide risk.Peer reviewe