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Immunotherapy, which consists in using molecules targeting the immune system, has existed for many years in oncology (vaccines, interleukins, monoclonal antibodies) but has recently expanded due to the development of immune checkpoint inhibitors. These monoclonal antibodies help to restore the immunity against cancer by specifically targeting some immune checkpoints such as CTLA-4, PD-1 and PD-L1. Furthermore, in oncology, it is common to use systemic corticosteroids in the management of symptoms linked to the natural history of the disease (pain, spinal cord compression, cerebral edema) and toxicities linked to anticancer treatment. The impact of corticosteroids on the efficacy of immune checkpoint inhibitors is still poorly understood and they should be used cautiously. According to previously published studies, there seems to be a deleterious effect of corticosteroid therapy on the efficacy of immune checkpoint inhibitors when administered before or at the initiation of immunotherapy, while this effect does not seem present when corticosteroids are administered to patients already undergoing immunotherapy. The aim of this work is to analyze the existing data evaluating the impact of corticosteroid use of on the efficacy of immune checkpoint inhibitors.

Psychedelics are powerful psychoactive substances. Natural psychedelics have been used for millennia by human civilizations, in particular in Latin America, while synthetic psychedelics were discovered in the 50s, giving rise to a lot of research before they were prohibited. More recently, their therapeutic properties have been studied especially to help patients with psychiatric conditions, psychological distress or substance use disorders. This article is a systematic review of the literature which aims to provide an overview of all studies that assessed the efficacy of psychedelics, i.e. Kartogenin psilocybin, ayahuasca and lysergic acid diethylamide (LSD), on psychiatric diseases and addictions.

We conducted this literature review following the PRISMA recommendations. MEDLINE, PsycInfo, Web of Science and Scopus were searched from January 1990 to May 2020 with the following keywords “(ayahuasca OR psilocybin OR lysergic acid diethylamide) AND (depression OR anxiety OR major depressive disorder OR bipolar disordeWith the advancement of technology, electric equipment and loads have become more sensitive to problems related to power quality, such as voltage sag, swell, imbalances, and harmonics. To detect faults and to protect sensitive loads from these voltage distortions, a Dynamic Voltage Restorer (DVR) series compensator is among the best available cost-effective solutions. One of the main goals of the DVR is to achieve a control structure that is robust, stable, and can handle properly the disturbances (e.g., grid voltage issues, load current, and fluctuations at the DC link voltage) and model uncertainties (e.g., inverters and filter parameters). In this work, a novel framework control strategy based on Uncertainty and Disturbance Estimator (UDE) is proposed to improve the response of the DVR to properly compensate the load voltage under a variety of power quality issues, particularly the ones associated with the grid voltage disturbances. Additionally, the stability of the proposed control system is analyzed and validated using the Lyapunov stability theory. The advantages of the new control system are robustness, simplified design, good harmonic rejection, low tracking error, fast response, and sinusoidal reference tracking without the need for voltage transformations or specific frequency tuning (e.g., abc-dq0 and Proportional-Resonant). This research uses the MATLAB/Simulink software to validate the effectiveness of the proposed scheme under a diverse set of conditions with no control limitations. Moreover, the designed controller is tested under real conditions using Hardware-In-the-Loop (HIL) validation with OPAL-RT real-time simulator coupled with a TI Launchpad microcontroller. The results demonstrate a good performance of the proposed control strategy for a quick transient response and a great harmonic rejection when subject to grid voltage distortions.Nonlinear process modeling is a primary task in intelligent manufacturing, aiming at extracting high-value features from massive process data for further process analysis like process monitoring. However, it is still a challenge to develop nonlinear process models with robust representation capability for diverse process faults. From the new perspective of the correlation between process variables, this paper develops a nonlinear process modeling algorithm to adaptively preserve the features of both global and local inter-variable structures, in order to fully exploit inter-variable features for enhancing the nonlinear representation of process operating conditions. Specifically, a unidimensional convolutional operation with a self-attention mechanism is proposed to simultaneously extract global and local inter-variable structures, wherein different attentions can be adaptively adjusted to these two structures for the final aggregation of them. Besides, cooperating with a two-dimensional dynamic data extension, the unidimensional convolutional operation can represent the overall temporal relationship between process samples. Through stacking a collection of these convolutional operations, a ResNet-style convolutional neural network then is constructed to extract high-order nonlinear features. Experiments on the Tennessee Eastman process validate the effectiveness of the proposed algorithm for two vital process monitoring problems-fault detection and fault identification.Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many people who supervise CCTV in real time. In this paper, we present an alternative approach to automatic image analysis using a variety of artificial intelligence techniques. Based on collaborative learning, these are punished if they make an incorrect classification. The main advantage is the possibility of continually increasing the amount of knowledge during system operation. However, overtraining is possible, so each time, the best classifier is chosen. Another advantage for practical use is the small database, which allows for the quick and practical implementation of such a system. To verify its effectiveness, this ship classification system was tested on data obtained in a Polish city, Szczecin, as part of a bigger project for classifying inland ships and publicly available databases (for more general ship problems).