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Less attention has been paid to the social determinants of aging and the possible mechanism by which calorie restriction might influence social behavior. The purpose of this review is to discuss current knowledge in the interdisciplinary field of geroscience-immunosenescence, inflamm-aging, and metainflammation-which makes a significant contribution to aging. A substantial part of the review is devoted to frontiers in the brain insulin resistance in relation to neuroinflammation. In addition, we summarize new data on potential mechanisms of calorie restriction that influence as a lifestyle intervention on the social brain. This knowledge can be used to initiate successful aging and slow the onset of neurodegenerative diseases.Neonatal hypoxia-ischemia (nHI) is a major cause of death or subsequent disabilities in infants. Hypoxia-ischemia causes brain lesions, which are induced by a strong reduction in oxygen and nutrient supply. Hypothermia is the only validated beneficial intervention, but not all newborns respond to it and today no pharmacological treatment exists. Among possible therapeutic agents to test, trans-resveratrol is an interesting candidate as it has been reported to exhibit neuroprotective effects in some neurodegenerative diseases. This experimental study aimed to investigate a possible neuroprotection by resveratrol in rat nHI, when administered to the pregnant rat female, at a nutritional dose. Several groups of pregnant female rats were studied in which resveratrol was added to drinking water either during the last week of pregnancy, the first week of lactation, or both. 680C91 chemical structure Then, 7-day old pups underwent a hypoxic-ischemic event. Pups were followed longitudinally, using both MRI and behavioral testing. Finally, a lidant properties, inhibition of apoptosis), has an impact on brain metabolism, and more specifically on the astrocyte-neuron lactate shuttle (ANLS) as suggested by RT-qPCR and Western blot data, that contributes to the neuroprotective effects.Diverse populations of GABAA receptors (GABAARs) throughout the brain mediate fast inhibitory transmission and are modulated by various endogenous ligands and therapeutic drugs. Deficits in GABAAR signaling underlie the pathophysiology behind neurological and neuropsychiatric disorders such as epilepsy, anxiety, and depression. Pharmacological intervention for these disorders relies on several drug classes that target GABAARs, such as benzodiazepines and more recently neurosteroids. It has been widely demonstrated that subunit composition and receptor stoichiometry impact the biophysical and pharmacological properties of GABAARs. However, current GABAAR-targeting drugs have limited subunit selectivity and produce their therapeutic effects concomitantly with undesired side effects. Therefore, there is still a need to develop more selective GABAAR pharmaceuticals, as well as evaluate the potential for developing next-generation drugs that can target accessory proteins associated with native GABAARs. In this review, we briefly discuss the effects of benzodiazepines and neurosteroids on GABAARs, their use as therapeutics, and some of the pitfalls associated with their adverse side effects. We also discuss recent advances toward understanding the structure, function, and pharmacology of GABAARs with a focus on benzodiazepines and neurosteroids, as well as newly identified transmembrane proteins that modulate GABAARs.This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.Medical image fusion, which aims to derive complementary information from multi-modality medical images, plays an important role in many clinical applications, such as medical diagnostics and treatment. We propose the LatLRR-FCNs, which is a hybrid medical image fusion framework consisting of the latent low-rank representation (LatLRR) and the fully convolutional networks (FCNs). Specifically, the LatLRR module is used to decompose the multi-modality medical images into low-rank and saliency components, which can provide fine-grained details and preserve energies, respectively. The FCN module aims to preserve both global and local information by generating the weighting maps for each modality image. The final weighting map is obtained using the weighted local energy and the weighted sum of the eight-neighborhood-based modified Laplacian method. The fused low-rank component is generated by combining the low-rank components of each modality image according to the guidance provided by the final weighting map within pyramid-based fusion. A simple sum strategy is used for the saliency components. The usefulness and efficiency of the proposed framework are thoroughly evaluated on four medical image fusion tasks, including computed tomography (CT) and magnetic resonance (MR), T1- and T2-weighted MR, positron emission tomography and MR, and single-photon emission CT and MR. The results demonstrate that by leveraging the LatLRR for image detail extraction and the FCNs for global and local information description, we can achieve performance superior to the state-of-the-art methods in terms of both objective assessment and visual quality in some cases. Furthermore, our method has a competitive performance in terms of computational costs compared to other baselines.
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