Production of Secondaries in High Energy d+Au Collisions

In the framework of Quark-Gluon String Model we calculate the inclusive spectra of secondaries produced in d+Au collisions at intermediate (CERN SPS) and at much higher (RHIC) energies. The results of numerical calculations at intermediate energies are in reasonable agreement with the data. At RHIC energies numerically large inelastic screening corrections (percolation effects) should be accounted for in calculations. We extract these effects from the existing RHIC experimental data on minimum bias and central d+Au collisions. The predictions for p+Au interactions at LHC energy are also given.Comment: 18 pages and 10 figure

Production of secondaries in soft p+pb collisions at LHC

We calculate the inclusive spectra of secondaries produced in soft (minimum bias) p+Pb collisions in the framework of Quark-Gluon String Model at LHC energy, and by taking into account the inelastic screening corrections (percolation effects). The role of these effects is expected to be very large at very high energies, and they should decrease the spectra about 3 times in the midrapidity region and increase them about 2 times in the fragmentation region at the energy of LHC.Comment: 18 pages and 10 figures. arXiv admin note: text overlap with arXiv:0802.219

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

Expanded phenotypic spectrum of the m.8344A>G "MERRF" mutation: Data from the German mitoNET registry.

The m.8344A>G mutation in the MTTK gene, which encodes the mitochondrial transfer RNA for lysine, is traditionally associated with myoclonic epilepsy and ragged-red fibres (MERRF), a multisystemic mitochondrial disease that is characterised by myoclonus, seizures, cerebellar ataxia, and mitochondrial myopathy with ragged-red fibres. We studied the clinical and paraclinical phenotype of 34 patients with the m.8344A>G mutation, mainly derived from the nationwide mitoREGISTER, the multicentric registry of the German network for mitochondrial disorders (mitoNET). Mean age at symptom onset was 24.5 years ±10.9 (6-48 years) with adult onset in 75 % of the patients. In our cohort, the canonical features seizures, myoclonus, cerebellar ataxia and ragged-red fibres that are traditionally associated with MERRF, occurred in only 61, 59, 70, and 63 % of the patients, respectively. In contrast, other features such as hearing impairment were even more frequently present (72 %). Other common features in our cohort were migraine (52 %), psychiatric disorders (54 %), respiratory dysfunction (45 %), gastrointestinal symptoms (38 %), dysarthria (36 %), and dysphagia (35 %). Brain MRI revealed cerebral and/or cerebellar atrophy in 43 % of our patients. There was no correlation between the heteroplasmy level in blood and age at onset or clinical phenotype. Our findings further broaden the clinical spectrum of the m.8344A>G mutation, document the large clinical variability between carriers of the same mutation, even within families and indicate an overlap of the phenotype with other mitochondrial DNA-associated syndromes