findings have important implications for understanding mental health treatments in adolescents. There is a growing emphasis on the role of the microbiota-gut-brain axis as modulator of host behaviour and as therapeutic target for neuropsychiatric disorders. In addition, accumulating evidence suggests that early-life stress can exert long-lasting changes on the brain and microbiota, and this early adversity is associated with increased risk for developing depression in later life. The maternal separation (MS) model in rats is a robust paradigm to study the effects of early-life stress on the microbiota-gut-brain axis. Recently, we have shown that polyphenols, naturally occurring compounds associated with several health benefits, have anti-stress effects in in vitro models. In this study, we assess the therapeutic potential of a variety of both flavonoid and non-flavonoid polyphenols in reversing the impact of MS on behaviour and the microbiota-gut-brain axis. Rats underwent a dietary intervention with the naturally-derived polyphenols xanthohumol and quercetin, as well as with a phlorotannin extract for ed by HPA regulation, BDNF levels rescue and modulation of the microbiota-gut-brain axis. The adenylyl cyclases (ACs) catalyze the production of the ubiquitous second messenger, cAMP, which in turns acts on a number of effectors and thus regulates a plethora of cellular functions. As the key enzymes in the highly evolutionarily conserved cAMP pathway, the ACs control the physiology of the cells, tissues, organs and organisms in health and disease. A comprehensive understanding of the specific role of the ACs in these processes of life requires a deep mechanistic understanding of structure and mechanisms of action of these enzymes. Here we highlight the exciting recent reports on the biochemistry and structure and higher order organization of the ACs and their signaling complexes. These studies have provided the glimpses into the principles of the AC-mediated homeostatic control of cellular physiology. The production of phycobiliproteins (PBPs) from cyanobacteria represents both the industrial application and their commercial value. In this study, the capability of Anabaena variabilis CCC421 for the production of PBPs was evaluated which was further improved by optimization of selected BG-11 medium components viz. FAC, K2HPO4 and trace metals. A design matrix approach using evolutionary algorithm comprised of genetic-algorithm (GA) and fuzzy-logic-methodology (FLM), i.e., GA-Fuzzy, was used for the optimization. The maximum production of PBPs obtained with combinatory approach of GA-Fuzzy was 408.5 mg/L at an optimum combination of factors (FAC 0.153 g/L, K2HPO4 0.2 g/L and Trace metals 0.5 ml/L) which was a 2.13 fold more than the control medium. This novel approach is very useful for modulating biological processes since various nutrients and metabolites have greater influence on these processes. In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms used in current NMNs restrict them from achieving their full potential as the attention process retrieves information based on a set of static connection weights. This is suboptimal in a setting where there are vast differences among samples in the data domain; such as anomaly detection where there is no consistent criteria for what constitutes an anomaly. In this paper, we propose a plastic neural memory access mechanism which exploits both static and dynamic connection weights in the memory read, write and output generation procedures. We demonstrate the effectiveness and flexibility of the proposed memory model in three challenging anomaly detection tasks in the medical domain abnormal EEG identification, MRI tumour type classification and schizophrenia risk detection in children. In all settings, the proposed approach outperforms the current state-of-the-art. Furthermore, we perform an in-depth analysis demonstrating the utility of neural plasticity for the knowledge retrieval process and provide evidence on how the proposed memory model generates sparse yet informative memory outputs. I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings (i) Artificial Curiosity (AC, 1990) is based on two such networks. One network learns to generate a probability distribution over outputs, the other learns to predict effects of the outputs. Each network minimizes the objective function maximized by the other.  https://www.selleckchem.com/GSK-3.html  (ii) Generative Adversarial Networks (GANs, 2010-2014) are an application of AC where the effect of an output is 1 if the output is in a given set, and 0 otherwise. (iii) Predictability Minimization (PM, 1990s) models data distributions through a neural encoder that maximizes the objective function minimized by a neural predictor of the code components. I correct a previously published claim that PM is not based on a minimax game. In this paper, we propose a novel analysis method to investigate the finite-time synchronization (FTS) control problem of the drive-response inertial memristive neural networks (IMNNs) with mixed time-varying delays (MTVDs). Firstly, an improved control scheme is proposed under the delay-independent conditions, which can work even when the past state cannot be measured or the specific time delay function is unknown. Secondly, based on the assumption of bounded activation functions, we establish a new Lemma, which can effectively deal with the difficulties caused by memristive connection weights and MTVDs. Thirdly, by constructing a suitable Lyapunov functions and using a new inequality method, novel sufficient conditions to ensure the FTS for the discussed IMNNs are obtained. Compared with the existing results, our results obtained in a more general framework are more practical. Finally, some numerical simulations are given to substantiate the effectiveness of the theoretical results.