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In this work, the development of two-dimensional current sheets with respect to tearing modes, in collisionless plasmas with a strong guide field, is analysed. During their nonlinear evolution, these thin current sheets can become unstable to the formation of plasmoids, which allows the magnetic reconnection process to reach high reconnection rates. We carry out a detailed study of the effect of a finite $\\beta _e$, which also implies finite electron Larmor radius effects, on the collisionless plasmoid instability. This study is conducted through a comparison of gyrofluid and gyrokinetic simulations. The comparison shows in general a good capability of the gyrofluid models in predicting the plasmoid instability observed with gyrokinetic simulations. We show that the effects of $\\beta _e$ promotes the plasmoid growth. The effect of the closure applied during the derivation of the gyrofluid model is also studied through the comparison among the variations of the different contributions to the total energy.

Reduced fluid models for collisionless plasmas including electron inertia and finite Larmor radius corrections are derived for scales ranging from the ion to the electron gyroradii. Based either on pressure balance or on the incompressibility of the electron fluid, they respectively capture kinetic Alfvén waves (KAWs) or whistler waves (WWs), and can provide suitable tools for reconnection and turbulence studies. Both isothermal regimes and Landau fluid closures permitting anisotropic pressure fluctuations are considered. For small values of the electron beta parameter $\\unicode[STIX]{x1D6FD}_{e}$ , a perturbative computation of the gyroviscous force valid at scales comparable to the electron inertial length is performed at order $O(\\unicode[STIX]{x1D6FD}_{e})$ , which requires second-order contributions in a scale expansion. Comparisons with kinetic theory are performed in the linear regime. The spectrum of transverse magnetic fluctuations for strong and weak turbulence energy cascades is also phenomenologically predicted for both types of waves. In the case of moderate ion to electron temperature ratio, a new regime of KAW turbulence at scales smaller than the electron inertial length is obtained, where the magnetic energy spectrum decays like $k_{\\bot }^{-13/3}$ , thus faster than the $k_{\\bot }^{-11/3}$ spectrum of WW turbulence.

The linear and nonlinear evolutions of the tearing instability in a collisionless plasma with a strong guide field are analysed on the basis of a two-field Hamiltonian gyrofluid model. The model is valid for a low ion temperature and a finite $\\beta _e$. The finite $\\beta _e$ effect implies a magnetic perturbation along the guide field direction, and electron finite Larmor radius effects. A Hamiltonian derivation of the model is presented. A new dispersion relation of the tearing instability is derived for the case $\\beta _e=0$ and tested against numerical simulations. For $\\beta _e \\ll 1$ the equilibrium electron temperature is seen to enhance the linear growth rate, whereas we observe a stabilizing role when electron finite Larmor radius effects become more relevant. In the nonlinear phase, stall phases and faster than exponential phases are observed, similarly to what occurs in the presence of ion finite Larmor radius effects. Energy transfers are analysed and the conservation laws associated with the Casimir invariants of the model are also discussed. Numerical simulations seem to indicate that finite $\\beta _e$ effects do not produce qualitative modifications in the structures of the Lagrangian invariants associated with Casimirs of the model.

IntroductionEvery year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD.ObjectivesThe present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA).Methods91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC).ResultsModels’ performances are summarized in Table 1.Table 1.Models’ performances.Input featuresAlgorithmSpecificitySensitivityBalanced accuracyAUCVBMSVM55.00%50.00%52.50%0.55FASVM72.00%54.00%63.00%0.62VBM and FAMKL68.00%54.00%61.00%0.58Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry.ConclusionsOverall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients.Disclosure of InterestNone Declared

IntroductionOne of the main obstacles in providing effective treatments for major depressive disorder (MDD) is clinical heterogeneity, whose neurobiological correlates are not clearly defined. A biologically meaningful stratification of depressed patients is needed to promote tailored diagnostic procedures.ObjectivesUsing structural data, we performed an unsupervised clustering to define clinically meaningful clusters of depressed patients.MethodsT1-weighted and diffusion tensor images were obtained from 102 MDD patients. In 64 patients, clinical symptoms, number of stressful life events, severity and exposure to adverse childhood experiences were evaluated using the Beck Depression Inventory (BDI), Schedule of Recent Experiences (SRE), Risky Family Questionnaire (RFQ), and Childhood Trauma Questionnaire (CTQ). Clustering analyses were performed with extracted tract-based fractional anisotropy (TBSS, FSL), cortical thickness, surface area, and regional measures of grey matter volumes (CAT12). Gaussian mixture model was implemented for clustering, considering Support Vector Machine (SVM) as classifier. A 10x2 repeated cross-validation with grid search was performed for hyperparameters tuning and clusters’ stability. The optimal number of clusters was determined by normalized stability, Akaike and Bayesian information criterion. Analyses were adjusted for total intracranial volume, age, and sex. The clinical relevance of the identified clusters was assessed through MANOVA, considering domains of clinical scales as dependent variables and clusters’ labels as fixed factors. Discriminant analysis was subsequently performed to assess the discriminative power of these variables.ResultsCross-validated clustering approach identified 2 highly stable clusters (normalized stability=0.316, AIC=-80292.48, BIC=351329.16). MANOVA showed a significant between-clusters difference in clinical scales scores (p=0.038). Discriminant analysis distinguished the two clusters with an accuracy of 78.1%, with BDI behavioural and CTQ minimisation/denial domains showing the highest discriminant values (0.325 and 0.313).ConclusionsOur results defined two biologically informed clusters of MDD patients associated with childhood trauma and specific clinical profiles, which may assist in targeting effective interventions and treatments.Disclosure of InterestNone Declared

We consider a simple electromagnetic gyrokinetic model for collisionless plasmas and show that it possesses a Hamiltonian structure. Subsequently, from this model we derive a two-moment gyrofluid model by means of a procedure which guarantees that the resulting gyrofluid model is also Hamiltonian. The first step in the derivation consists of imposing a generic fluid closure in the Poisson bracket of the gyrokinetic model, after expressing such bracket in terms of the gyrofluid moments. The constraint of the Jacobi identity, which every Poisson bracket has to satisfy, selects then what closures can lead to a Hamiltonian gyrofluid system. For the case at hand, it turns out that the only closures (not involving integro/differential operators or an explicit dependence on the spatial coordinates) that lead to a valid Poisson bracket are those for which the second order parallel moment, independently for each species, is proportional to the zero order moment. In particular, if one chooses an isothermal closure based on the equilibrium temperatures and derives accordingly the Hamiltonian of the system from the Hamiltonian of the parent gyrokinetic model, one recovers a known Hamiltonian gyrofluid model for collisionless reconnection. The proposed procedure, in addition to yield a gyrofluid model which automatically conserves the total energy, provides also, through the resulting Poisson bracket, a way to derive further conservation laws of the gyrofluid model, associated with the so called Casimir invariants. We show that a relation exists between Casimir invariants of the gyrofluid model and those of the gyrokinetic parent model. The application of such Hamiltonian derivation procedure to this two-moment gyrofluid model is a first step toward its application to more realistic, higher-order fluid or gyrofluid models for tokamaks. It also extends to the electromagnetic gyrokinetic case, recent applications of the same procedure to Vlasov and drift- kinetic systems.

A Hamiltonian six-field gyrofluid model is constructed, based on closure relations derived from the so-called ‘quasi-static’ gyrokinetic linear theory where the fields are assumed to propagate with a parallel phase velocity much smaller than the parallel particle thermal velocities. The main properties captured by this model, primarily aimed at exploring fundamental problems of interest for space plasmas such as the solar wind, are its ability to provide a reasonable agreement with kinetic theory for linear low-frequency modes, and at the same time to ensure a Hamiltonian structure in the absence of explicit dissipation. The model accounts for equilibrium temperature anisotropy, ion and electron finite Larmor radius corrections, electron inertia, magnetic fluctuations along the direction of a strong guide field and parallel Landau damping, introduced through a Landau-fluid modelling of the parallel heat transfers for both gyrocentre species. Remarkably, the quasi-static closure leads to exact and simple expressions for the nonlinear terms involving gyroaveraged electromagnetic fields and potentials. One of the consequences is that a rather natural identification of the Hamiltonian structure of the model becomes possible when Landau damping is neglected. A slight variant of the model consists of a four-field Hamiltonian reduction of the original six-field model, which is also used for the subsequent linear analysis. In the latter, the dispersion relations of kinetic Alfvén waves and the firehose instability are shown to be correctly reproduced, relatively far in the sub-ion range (depending on the plasma parameters), while the spectral range where the slow-wave dispersion relation and the field-swelling instabilities are precisely described is less extended. This loss of accuracy originates from the breaking of the condition of small phase velocity, relative to the parallel thermal velocity of the electrons (for kinetic Alfvén waves and firehose instability) or of the ions (in the case of the field-swelling instabilities).

BackgroundSystemic lupus erythematosus (SLE) is associated with autoimmune and allergic events along with impaired defensive responses. Dysregulated T-cell responses might account for this evidence but antigen-specific T-cell behaviour in SLE has not been characterised.ObjectivesTo test for clinical/pathophysiological correlates of SLE immune dysfunction by characterising CD4+ T-cells selectively recognising key SLE-related, major histocompatibility complex (MHC)-restricted epitopes.MethodsHuman leukocyte antigen (HLA) DRB1*03:01 or 11:01-positive subjects were selected from a cohort of 222 patients with SLE and compared with patients with Takayasu’s arteritis (TAK) and healthy controls (HC). In silico analyses through the Immune Epitope Database identified suitable HLA-peptide epitope pairs from a shortlist of autoantigens (histone H3 and H4), allergens (penicilloylated albumin) and pathogen-derived antigens (Epstein-Barr virus, EBV, nuclear antigens) based on the cohort clinical/serological profile. Fluorochrome-conjugated epitope-bound MHC tetramers were eventually built and used to detect antigen-specific CD4+ T-cells through flow cytometry. T-cell differentiation was assessed by CD45RA, CD62L and CD95 and polarisation by CD25, CD127, CD183, CD196 and CD194 staining. Epitope-specific reactivity was validated by multi-cytokine release assays and measurement of CD40L, CD137, OX40 and CD69 expression upon T-cell stimulation with the study peptides. SLE disease activity index 2000 (SLEDAI-2K) and lupus low disease activity state (LLDAS) were used to assess activity and remission.ResultsTotal stem-cell memory T-cells (TSCM) were expanded in SLE compared to control groups. Histone-specific CD4+ T-cells were selectively found in SLE and clustered with anti-DNA antibodies. Only patients with beta-lactam allergy had anti-penicilloylated albumin T-cells. Anti-EBV-specific T cells were found in patients and controls (Figure 1A-C). Antigen-specific T-cell counts were reciprocally correlated. Histone-specific regulatory T-cells (Treg) were inversely correlated with SLEDAI-2K. Circulating histone- and EBV-specific effector memory T-cells (TEM) and Treg were lower in active SLE (Figure 1D-F). EBV-specific TSCM decreased in patients transitioning from remission to active SLE. Immunosuppressive treatment was associated with expanded CD4+ histone-specific T-cells. In vitro T-cell reactivity assays to study epitopes were consistent with ex vivo evidence. Higher IL17F and IL5 were released in response to histones and higher IL5 and IL22 in response to penicilloylated albumin-peptides in SLE. Defective anti-EBV IFNγ, TNF and IL22 release was observed in active SLE compared to HC.ConclusionHistone-specific T-cell responses constitute a hallmark of SLE and might defectively be regulated during active disease, promoting autoreactive effector peripheralisation into target tissues. Dysfunctional T-cell reactivity to EBV might also subtend a constitutional defect in the control of endogenous or exogenous viral stimuli and possibly contribute to autoreactivity through misdifferentiation of precursors. Aberrant responses to beta-lactam might also synergise with autoreactive responses and can accurately be detected through penicilloylated-albumin peptide T-cell reactivity. Antigen-specific (rather than total) T-cell dynamics might faithfully reflect the occurrence of key pathogenic events accounting for SLE immune dysfunction in response to multiple types of antigens and efficiently be used to track SLE phenotype heterogeneity.References[1]Abdirama D et al., Kidney Int, 2021[2]Dolff S et al., Ann Rheum Dis, 2010[3]Draborg AH et al. Lupus Sci & Med, 2014Figure 1.AcknowledgementsThanks to Dr Francesco Manfredi, Dr Rita El Khoury, Dr Laura Falcone, Dr Zulma Magnani, Prof. Patrizia Rovere-Querini Dr Eddie James and Prof. Bill Kwok for scientific counselling. We also thank Dr. Valeria Beretta, Dr Elisa Cantarelli, Dr Annalisa Capobianco, Dr Michela Grossi, Dr Francesco Manfredi, Dr Norma Maugeri, Dr Elisabetta Messaggio, Dr Antonella Monno, Dr Clara Sciorati, Dr Serenesse Tomasi, Dr Cristina Tresoldi and Dr Veronica Valtolina, Dr. Valentina Canti and Dr. Rebecca De Lorenzo.Disclosure of InterestsGiuseppe Alvise Ramirez Consultant of: Astrazeneca, Elena Tassi: None declared, Maddalena Noviello: None declared, Benedetta Allegra Mazzi: None declared, Luca Moroni Consultant of: Astrazeneca, Andrea Sorce: None declared, Lorena Citterio: None declared, Laura Zagato: None declared, Enrico Tombetti: None declared, Elena Baldissera: None declared, Giselda Colombo: None declared, Mona-Rita Yacoub: None declared, Enrica Bozzolo: None declared, Chiara Bonini: None declared, Lorenzo Dagna Consultant of: Abbvie, Amgen, Astra-Zeneca, Biogen, Boehringer-Ingelheim, Bristol-Myers Squibb, Celltrion, Eli Lilly and Company, Galapagos, GlaxoSmithKline, Janssen, Kiniksa Pharmaceuticals, Novartis, Pfizer, Roche, Sanofi-Genzyme, Swedish Orphan Biovitrium (SOBI), and Takeda, Grant/research support from: Abbvie, Bristol-Myers Squibb, Celgene, GlaxoSmithKline, Janssen, Kiniksa, Merk Sharp & Dohme, Mundipharma Pharmaceuticals, Novartis, Pfizer, Roche, Sanofi-Genzyme, and SOBI., Angelo Manfredi: None declared.

Introduction About 60% of bipolar disorder (BD) cases are initially misdiagnosed as major depressive disorder (MDD), preventing BD patients from receiving appropriate treatment. An urgency exists to identify reliable biomarkers for improving differential diagnosis (DD). Machine learning methods may help translate current knowledge on biomarkers of mood disorders into clinical practice by providing individual-level classification. No study so far has combined biological data with clinical data to provide a multifactorial predictive model for DD. Objectives Define a predictive algorithm for BD and MDD by integrating structural neuroimaging and inflammatory data with neuropsychological measures (NM). Two different algorithms were compared: multiple kernel learning (MKL) and elastic net regularized logistic regression (EN). Methods In a sample of 141 subjects (70 MDD; 71 BD), two different models were implemented for each algorithm: 1) structural neuroimaging measures only (i.e. voxel-based morphometry (VBM), white matter fractional anisotropy (FA), and mean diffusivity (MD)); 2) VBM, FA, and MD combined with NM. In a subsample of 71 subjects (36 BD; 38 MDD), two similar models were implemented: 1) VBM, FA, and, MD combined with only NM; 2) VBM, FA, and MD combined with NM and peripheral inflammatory markers. Finally, the best model was selected for comparison with healthy controls (HC). Results Overall, the EN model based on all the modalities achieved the highest accuracy (AUC = 90.2%), outperforming MKL (AUC=85%). EN correctly classified BD and MDD with a diagnostic accuracy of 78.3%, sensitivity of 75%, and specificity of 81.6%. The most significant predictors of BD (variable inclusion probability (VIP) > 80%) were the parahippocampal cingulate, interleukin 9, chemokine CCL5, posterior thalamic radiation, and internal capsule, whereas MDD was best predicted by chemokine CCL23, the anterior cerebellum, and the sagittal stratum. In contrast, NM did not help to differentiate between MDD and BD. However, they help to distinguish patients from HC. Psychomotor coordination and speed of information processing discriminated between MDD and HC (VIP>90%), whereas fluency, working memory, and executive functions differentiated between BD and HC (VIP>80%). Conclusions In summary, BD was predicted by a strong proinflammatory profile, whereas MDD was identified by structural neuroimaging data. A multimodal approach offers additional instruments to improve personalized diagnosis in clinical practice and enhance the ability to make DD. Disclosure of Interest None Declared

IntroductionBipolar patients (BP) frequently have cognitive deficits, that impact on prognosis and quality of life. Finding biomarkers for this condition is essential to improve patients’ healthcare. Given the association between cognitive dysfunctions and structural brain abnormalities, we used a machine learning approach to identify patients with cognitive deficits.ObjectivesThe aim of this study was to assess if structural neuroimaging data could identify patients with cognitive impairments in several domains using a machine learning framework.MethodsDiffusion tensor imaging and T1-weighted images of 150 BP were acquired and both grey matter voxel-based morphometry (VBM) and tract-based white matter fractional anisotropy (FA) measures were extracted. Support vector machine (SVM) models were trained through a 10-fold nested cross-validation with subsampling. VBM and FA maps were entered separately and in combination as input features to discriminate BP with and without deficits in six cognitive domains, assessed through the Brief Assessment of Cognition in Schizophrenia.ResultsThe best classification performance for each cognitive domain is illustrated in Table 1. FA was the most relevant neuroimaging modality for the prediction of verbal memory, verbal fluency, and executive functions deficits, whereas VBM was more predictive for working memory and motor speed domains.Table 1.Performance of best classification models.Input featureBalance Accuracy (%)Specificity (%)Sensitivity (%)Verbal MemoryFA60.1751.3143Verbal FluencyFA57.676253.33Executive functionsFA6063.3356.67Working MemoryVBM56.505657Motor speedVBM53.5047.6759.33Attention and processing speedVBM + FA58.3349.1767.5ConclusionsOverall, the tested SVM models showed a good predictive performance. Although only partially, our results suggest that different structural neuroimaging data can predict cognitive deficits in BP with accuracy higher than chance level. Unexpectedly, only for the attention and processing speed domain the best model was obtained combining the structural features. Future research may promote data fusion methods to develop better predictive models.Disclosure of InterestNone Declared