Strongly Bound Dibaryon with Maximal Beauty Flavor from Lattice QCD.

We report the first lattice QCD study of the heavy dibaryons in which all six quarks have the bottom (beauty) flavor. Performing a state-of-the-art lattice QCD calculation we find clear evidence for a deeply bound Ω_{bbb}-Ω_{bbb} dibaryon in the ^{1}S_{0} channel, as a pole singularity in the S-wave Ω_{bbb}-Ω_{bbb} scattering amplitude with a binding energy -81( _{-16}^{+14}) MeV. With such a deep binding, Coulomb repulsion serves only as a perturbation on the ground state wave function of the parametrized strong potential and may shift the strong binding only by a few percent. Considering the scalar channel to be the most bound for single flavored dibaryons, we conclude this state is the heaviest possible most deeply bound dibaryon in the visible universe.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach.

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

Non-verbal speech cues as objective measures for negative symptoms in patients with schizophrenia.

Negative symptoms in schizophrenia are associated with significant burden and possess little to no robust treatments in clinical practice today. One key obstacle impeding the development of better treatment methods is the lack of an objective measure. Since negative symptoms almost always adversely affect speech production in patients, speech dysfunction have been considered as a viable objective measure. However, researchers have mostly focused on the verbal aspects of speech, with scant attention to the non-verbal cues in speech. In this paper, we have explored non-verbal speech cues as objective measures of negative symptoms of schizophrenia. We collected an interview corpus of 54 subjects with schizophrenia and 26 healthy controls. In order to validate the non-verbal speech cues, we computed the correlation between these cues and the NSA-16 ratings assigned by expert clinicians. Significant correlations were obtained between these non-verbal speech cues and certain NSA indicators. For instance, the correlation between Turn Duration and Restricted Speech is -0.5, Response time and NSA Communication is 0.4, therefore indicating that poor communication is reflected in the objective measures, thus validating our claims. Moreover, certain NSA indices can be classified into observable and non-observable classes from the non-verbal speech cues by means of supervised classification methods. In particular the accuracy for Restricted speech quantity and Prolonged response time are 80% and 70% respectively. We were also able to classify healthy and patients using non-verbal speech features with 81.3% accuracy.

Automated Verbal and Non-verbal Speech Analysis of Interviews of Individuals with Schizophrenia and Depression.

Schizophrenia and depression are the two most common mental disorders associated with negative symptoms that contribute to poor functioning and quality of life for millions of patients globally. This study is part of a larger research project. The overall aim of the project is to develop an automated objective pipeline that aids clinical diagnosis and provides more insights into symptoms of mental illnesses. In our previous work, we have analyzed non-verbal cues and linguistic cues of individuals with schizophrenia. In this study, we extend our work to include participants with depression. Powered by natural language processing techniques, we extract verbal features, both dictionary-based and vector-based, from participants' interviews that were automatically transcribed. We also extracted conversational, phonatory, articulatory and prosodic features from the interviews to understand the conversational and acoustic characteristics of schizophrenia and depression. Combining these features, we applied ensemble learning with leave-one-out cross-validation to classify healthy controls, schizophrenic and depressive patients, achieving an accuracy of 69%-75% in paired classification. From those same features, we also predict the subjective Negative Symptoms Assessment 16 scores of patients with schizophrenia or depression, yielding an accuracy of 90.5% for NSA2 but lower accuracy for other NSA indices. Our analysis also revealed significant linguistic and non-verbal differences that are potentially symptomatic of schizophrenia and depression respectively.