495 research outputs found

    Postwar village hydro-power rehabilitation

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    Postwar village hydro-power rehabilitatio

    Macrocycle Therapeutics to Treat Life-threatening Diseases

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    Polyphor's macrocycle platform led to the discovery of novel antibiotics addressing specifically Gramnegative bacteria by targeting outer membrane proteins. Furthermore, POL6014, an inhibitor of neutrophile elastase and balixafortide, a CXCR4 inhibitor have been discovered and developed from the platform. Currently a combination of balixafortide and eribulin is in Phase III clinical trial for the treatment of patients with advanced metastatic HER2-negative breast cancer

    Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study.

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    BACKGROUND New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming. PURPOSE We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. METHOD We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. RESULTS Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d = 0.3) as well as continuous improvement (r = 0.49, p < 0.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. CONCLUSIONS Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy
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