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The Randomized, Multicenter Test Evaluating the Effects regarding Rapastinel In comparison to Ketamine, Alprazolam, and also Placebo upon Simulated Driving a car Efficiency.
Prostate cancer is classified into different stages, each stage is related to a different Gleason score. The labeling of a diagnosed prostate cancer is a task usually performed by radiologists. In this paper we propose a deep architecture, based on several convolutional layers, aimed to automatically assign the Gleason score to Magnetic Resonance Imaging (MRI) under analysis. We exploit a set of 71 radiomic features belonging to five categories First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and Gray Level Size Zone Matrix. The radiomic features are gathered directly from segmented MRIs using two free-available dataset for research purpose obtained from different institutions. The results, obtained in terms of accuracy, are promising they are ranging between 0.96 and 0.98 for Gleason score prediction.The present study aims to evaluate the relationship in women between a history of physical/sexual abuse and the preferences regarding the choice of a partner for a short/long-term relationship in terms of male facial dimorphism, and to assess their sexual functioning. We enrolled 48 abused women and 60 non-abused women. Facial preferences were evaluated with the Morphing test. TrichostatinA Sexual functioning was measured with the Female Sexual Function Index (FSFI). Regarding the choice for a short-term partner, abused and non-abused women did not show any differences, and both groups chose a less masculine male face. On the other hand, regarding the choice for a long-term partner, abused women showed a preference for an average male face, whilst non-abused women preferred a less masculine face. The sexual functioning of abused women was found significantly dysfunctional in all domains of the FSFI. These data, generated from a small but highly selected cohort, demonstrated that physical/sexual abuse may be associated with a more rational and conscious choice of a male partner for a long-term relationship, but not with an instinctive one, as the choice of an occasional partner. In addition, the sexual functioning of abused women appears to be compromised by the traumatic experience.Background We aimed to find the difference between girls with clinical features of Polycystic ovary syndrome (PCOS), divided into two groups Overweight/obesity (Ov/Ob) and normal weight (N), related to diet, disordered eating attitudes (DEA), metabolic and hormonal differences, and to identify the risk factors of being overweight or obese. Methods Seventy-eight adolescents with PCOS, aged 14-18 years, were divided into Ov/Ob and N groups. Patients underwent blood tests for determination of follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone, DHEA-S, estradiol, of sex hormone-binding globulin (SHBG), fasting glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), and lipid profile. Nutrition was evaluated using a 3-day food record. To examine the level of DEA, the Eating Attitudes Test-26 (EAT-26) was used. We defined an EAT-26 score ≥20 as positive for DEA. Logistic regression was carried out to identify the independent predictors of being overweight and obese. Results An increase of 10 g in plant protein intake decreased the probability of being overweight and of obesity (OR = 0.54; p = 0.036). EAT-26 score ≥20 was correlated with a 7-fold (OR = 6.88; p = 0.02) increased odds of being overweight or of obesity. Conclusion Being overweight and obesity in adolescents with PCOS may be associated with DEA and the type and amount of protein intake.Endogenous and exogenous signals are perceived and integrated by plants to precisely control defense responses. As a crucial environmental cue, light reportedly plays vital roles in plant defenses against necrotrophic pathogens. Phytochrome-interacting factor (PIF) is one of the important transcription factors which plays essential roles in photoreceptor-mediated light response. In this study, we revealed that PIFs negatively regulate plant defenses against Botrytis cinerea. Gene expression analyses showed that the expression level of a subset of defense-response genes was higher in pifq (pif1/3/4/5) mutants than in the wild-type control, but was lower in PIF-overexpressing plants. Chromatin immunoprecipitation assays proved that PIF4/5 binds directly to the ETHYLENE RESPONSE FACTOR1 (ERF1) promoter. Moreover, genetic analyses indicated that the overexpression of ERF1 dramatically rescues the susceptibility of PIF4-HA and PIF5-GFP transgenic plants, and that PIF controls the resistance to B. cinerea in a COI1- and EIN2-dependent manner. Our results provide compelling evidence that PIF, together with the jasmonate/ethylene pathway, is important for plant resistance to B. cinerea.Digital fingerprints are being used more and more to secure applications for logical and physical access control. In order to guarantee security and privacy trends, a biometric system is often implemented on a secure element to store the biometric reference template and for the matching with a probe template (on-card-comparison). In order to assess the performance and robustness against attacks of these systems, it is necessary to better understand which information could help an attacker successfully impersonate a legitimate user. The first part of the paper details a new attack based on the use of a priori information (such as the fingerprint classification, sensor type, image resolution or number of minutiae in the biometric reference) that could be exploited by an attacker. In the second part, a new countermeasure against brute force and zero effort attacks based on fingerprint classification given a minutiae template is proposed. These two contributions show how fingerprint classification could have an impact for attacks and countermeasures in embedded biometric systems. Experiments show interesting results on significant fingerprint datasets.This work presents the development and implementation of a distributed navigation system based on object recognition algorithms. The main goal is to introduce advanced algorithms for image processing and artificial intelligence techniques for teaching control of mobile robots. The autonomous system consists of a wheeled mobile robot with an integrated color camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that performs a computer vision algorithm to recognize the objects. The computer calculates the corresponding speeds of the robot according to the object detected. The speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. Three different algorithms have been tested in simulation and a practical mobile robot laboratory. The results show an average of 84% success rate for object recognition in experiments with the real mobile robot platform.
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