Search for heavy resonances decaying into a W or Z boson and a Higgs boson in final states with leptons and b-jets in 36 fb(-1) of root s=13 TeV pp collisions with the ATLAS detector

A search is conducted for new resonances decaying into a W or Z boson and a 125 GeV Higgs boson in the νν¯¯¯bb¯¯, ℓ±νbb¯¯, and ℓ+ℓ−bb¯¯ final states, where ℓ± = e± or μ±, in pp collisions at s√=13 TeV. The data used correspond to a total integrated luminosity of 36.1 fb−1 collected with the ATLAS detector at the Large Hadron Collider during the 2015 and 2016 data-taking periods. The search is conducted by examining the reconstructed invariant or transverse mass distributions of W h and Zh candidates for evidence of a localised excess in the mass range of 220 GeV up to 5 TeV. No significant excess is observed and the results are interpreted in terms of constraints on the production cross-section times branching fraction of heavy W ′ and Z′ resonances in heavy-vector-triplet models and the CP-odd scalar boson A in two-Higgs-doublet models. Upper limits are placed at the 95% confidence level and range between 9.0 × 10−4 pb and 7.3 × 10−1 pb depending on the model and mass of the resonance

Latent structure of self-report negative symptoms in patients with schizophrenia: A preliminary study

Introduction: Negative symptoms are associated with poor outcomes and functioning. Latent structure of negative symptoms is important for identifying potential intervention targets for novel treatments. Self-report instruments have been developed to measure negative symptoms. Previous findings on latent structure of negative symptoms are inconsistently and mainly rely on clinician-rated instruments. Method: We aimed to explore the latent structure of the Self-Evaluation of Negative Symptoms Scale (SNS) in 204 clinically-stable outpatients with schizophrenia. Confirmatory factor analysis (CFA) was used to compare the competing models (i.e., one-factor, two-factor and five-factor models), and estimated goodness-of-fit indexes. Other clinician-rated scales for psychopathology and medication side-effects were also collected. Results: The CFA found the five-factor model performing best, with a comparative fit index (CFI) of &gt; 0.95, a Tucker Lewis Index (TLI) of &gt; 0.95, and a root mean square error of approximation (RMSEA) of &lt; 0.06. The robust chi-square difference test for the weighted least squares with mean and variance adjusted estimation (WLSMV) also indicated a significant better fit for the five-factor model. Discussion: Our preliminary findings support a five-factor latent structure of self-report negative symptoms in schizophrenia patients. Further research in this area should utilize multiple clinician-rated and self-report measures, and recruit large and homogeneous samples with schizophrenia.</p

Validation of the Chinese Dimensional Anhedonia Rating Scale in Depressed Patients in Hong Kong

&lt;jats:p&gt; Abstract. To better understand and gauge the severity of anhedonia, the Dimensional Anhedonia Rating Scale (DARS) was developed which focuses on assessing the four pleasure domains of anhedonia. Lacking any Asian data in the original study, a Chinese version of the DARS (C-DARS) was evaluated in this study. The scale was developed by backward and forward translations and reviewed by an expert panel and a focus group. One hundred fifty-one depressed patients were recruited. The internal consistency and test–retest reliability were confirmed (McDonald’s ω = .82); a confirmatory factor analysis showed a second-order model with adequate fit (RMSEA = .078, CFI = .945). Concurrent validity was examined by the correlations with the Chinese version of the Snaith–Hamilton Pleasure Scale ( r = −.72, p &amp;lt; .001), while discriminant validity was examined with the Hamilton Depression Rating Scale ( r = −.34, p &amp;lt; .001). The C-DARS was shown to be a psychometrically sound and valid measure of anhedonia severity ready for clinical use. &lt;/jats:p&gt

Development of an individualized risk calculator of treatment resistance in patients with first-episode psychosis (TRipCal) using automated machine learning: a 12-year follow-up study with clozapine prescription as a proxy indicator

Abstract About 15–40% of patients with schizophrenia are treatment resistance (TR) and require clozapine. Identifying individuals who have higher risk of development of TR early in the course of illness is important to provide personalized intervention. A total of 1400 patients with FEP enrolled in the early intervention for psychosis service or receiving the standard psychiatric service between July 1, 1998, and June 30, 2003, for the first time were included. Clozapine prescriptions until June 2015, as a proxy of TR, were obtained. Premorbid information, baseline characteristics, and monthly clinical information were retrieved systematically from the electronic clinical management system (CMS). Training and testing samples were established with random subsampling. An automated machine learning (autoML) approach was used to optimize the ML algorithm and hyperparameters selection to establish four probabilistic classification models (baseline, 12-month, 24-month, and 36-month information) of TR development. This study found 191 FEP patients (13.7%) who had ever been prescribed clozapine over the follow-up periods. The ML pipelines identified with autoML had an area under the receiver operating characteristic curve ranging from 0.676 (baseline information) to 0.774 (36-month information) in predicting future TR. Features of baseline information, including schizophrenia diagnosis and age of onset, and longitudinal clinical information including symptoms variability, relapse, and use of antipsychotics and anticholinergic medications were important predictors and were included in the risk calculator. The risk calculator for future TR development in FEP patients (TRipCal) developed in this study could support the continuous development of data-driven clinical tools to assist personalized interventions to prevent or postpone TR development in the early course of illness and reduce delay in clozapine initiation

Marketing mix in company Sellier & Bellot, a.s.

The aim of this bachelor thesis was to evaluate tools of marketing mix in the Sellier & Bellot, Inc. and, where appropriate, suggest improvements to the mix of chosen instruments. The company is primarly known as a manufacturer of ammunition, which are the main topic of this dissertation. In the first part are theoretically defined tools of marketing mix. In the practical part is introduced the company and its history at first. Then are the tools of marketing mix of the company analyzed in the way the company use them now. At the end are suggestions for the company how to use better the tools of marketing mix