Sexual Fears and Avoidant Sexual Behavior in Medical Students.

UNLABELLED: Sexual fears, sometimes in the form of phobias, lead to aversive or sexually avoidant behavior blocking sexual closeness and resulting in deep personal and interpersonal distress. OBJECTIVE: To determine the types of sexual fears and aversive behavior in young people of reproductive age (students) and their degree of markedness as to encourage a further implementation of prevention programs and interventions. MATERIALS AND METHODS: The study included 116 fifth-year medical students in Plovdiv Medical University. Of these, 55 men and 61 women were assessed with the Sexual Aversion Scale, a 30-item self-rating questionnaire. The Diagnostic and Statistical Manual of Mental Disorders criteria of sexual aversion were used. The statistical analyses used were descriptive statistics and independent samples t-test. RESULTS: Sexual fears and aversive or blocking behavior are mild to moderate, mean score of 1.54 ± 0.04, without statistically significant gender differences. Both sexes have established fear-related sexual aversive motives of sexual behavior related to the risk of unwanted pregnancy and HIV infection. Women have significantly higher average scores for the following statements: fear of sexual intercourse (1.61 vs 1.25), avoidance of situations in which they may be involved sexually (1.95 vs 1.51), avoidance of genital sexual contact (1.44 vs 1.16), fear of catching a sexually transmitted disease (2.46 vs 2.09 ), fear of pregnancy (2.61 vs 2.15) and concerns what other people think of them (2.34 vs 1.93 ). CONCLUSIONS: Sexual fears and aversive or blocking behavior were mild to moderate. In both sexes similar fears--aversive or blocking patterns of sexual behavior were found, mainly associated with the fear of unwanted pregnancy and the risk of HIV infection, more expressed in women.

Vid2CAD: CAD Model Alignment Using Multi-View Constraints From Videos.

We address the task of aligning CAD models to a video sequence of a complex scene containing multiple objects. Our method can process arbitrary videos and fully automatically recover the 9 DoF pose for each object appearing in it, thus aligning them in a common 3D coordinate frame. The core idea of our method is to integrate neural network predictions from individual frames with a temporally global, multi-view constraint optimization formulation. This integration process resolves the scale and depth ambiguities in the per-frame predictions, and generally improves the estimate of all pose parameters. By leveraging multi-view constraints, our method also resolves occlusions and handles objects that are out of view in individual frames, thus reconstructing all objects into a single globally consistent CAD representation of the scene. In comparison to the state-of-the-art single-frame method Mask2CAD that we build on, we achieve substantial improvements on the Scan2CAD dataset (from 11.6% to 30.7% class average accuracy).

Immunological Disturbances and Neuroimaging Findings in Major Depressive Disorder (MDD) and Alcohol Use Disorder (AUD) Comorbid Patients.

Mood disorders and Major Depressive Disorder, in particular, appear to be some of the most common psychiatric disorders with a high rate of comorbidity most frequently of anxiety or substance abuse disorders (alcohol use disorder). In both cases - MDD and AUD, a number of immunological disturbances are observed, such as chronic mild inflammation response, increased level of cytokines, hypercortisolaemia, which lead to specific changes in brain neurotransmitter functions. Some of the contemporary brain imaging techniques are functional magnetic resonance imaging (fMRI) and magnetic spectroscopy which are most commonly used to assess the brain metabolism and functional connectivity changes such as altered responses to emotional stimuli in MDD or overactivation of ventromedial prefrontal areas during delayed and underactivation of dorsolateral prefrontal regions during impulsive reward decisions in AUD and dysfunction of gamma-aminobutyric acid (GABA) and/or glutamate neurotransmitter systems, low NAA and myo-Inositol in both MDD and AUD.