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The endocannabinoidome (eCBome) is the expanded endocannabinoid system (ECS) and studies show that there is a link between this system and how it modulates alcohol induced neuroinflammation. Using conditional knockout (cKO) mice with selective deletion of cannabinoid type 2 receptors (CB2Rs) in dopamine neurons (DAT-Cnr2) and in microglia (Cx3Cr1-Cnr2), we investigated how CB2Rs modulate behavioral and neuroinflammation induced by alcohol. Behavioral tests including locomotor and wheel running activity, rotarod performance test, and alcohol preference tests were used to evaluate behavioral changes induced by alcohol. Using ELISA assay, we investigated the level of pro-inflammatory cytokines, tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1α (IL-1α), and interleukin-1β (IL-1β) in the hippocampus of mice. The findings demonstrated that locomotor activity, wheel running, and rotarod performance activities were significantly affected by cell-type specific deletion of CB2Rs in dopamine neurons and microglia. The non-selective CB2R agonist, WIN 55,212-2, reduced alcohol preference in the wild type and cell-type specific CB2R cKO mice. In addition, the result showed that cell-type specific deletion of CB2Rsper seand administration of alcohol to CB2R cKO mice increased the expression of proinflammatory cytokines in the hippocampus. These findings suggest the involvement of CB2Rs in modulating behavioral and immune alterations induced by alcohol.

 

With the increasing problem complexity, more irregular applications are deployed on high-performance clusters due to the parallel working paradigm, and yield irregular memory access behaviors across nodes. However, the irregularity of memory access behaviors is not comprehensively studied, which results in low utilization of the integrated hybrid memory system compositing of stacked DRAM and off-chip DRAM. To address this problem, we devise a novel method called Similarity-Managed Hybrid Memory System (SM-HMS) to improve the hybrid memory system performance by leveraging the memory access similarity among nodes in a cluster. Within SM-HMS, two techniques are proposed, Memory Access Similarity Measuring and Similarity-based Memory Access Behavior Sharing. To quantify the memory access similarity, memory access behaviors of each node are vectorized, and the distance between two vectors is used as the memory access similarity. The calculated memory access similarity is used to share memory access behaviors precisely across nodes. With the shared memory access behaviors, SM-HMS divides the stacked DRAM into two sections, the sliding window section and the outlier section. The shared memory access behaviors guide the replacement of the sliding window section while the outlier section is managed in the LRU manner. Our evaluation results with a set of irregular applications on various clusters consisting of up to 256 nodes have shown that SM-HMS outperforms the state-of-the-art approaches, Cameo, Chameleon, and Hyrbid2, on job finish time reduction by up to 58:6%, 56:7%, and 31:3%, with 46:1%, 41:6%, and 19:3% on average, respectively. SM-HMS can also achieve up to 98:6% (91:9% on average) of the ideal hybrid memory system performance. 

Most, if not all, green plant (Virdiplantae) species including angiosperms and ferns are polyploids themselves or have ancient polyploid or whole genome duplication signatures in their genomes. Polyploids are not only restricted to our major crop species such as wheat, maize, potato and the brassicas, but also occur frequently in wild species and natural habitats. Polyploidy has thus been viewed as a major driver in evolution, and its influence on genome and chromosome evolution has been at the centre of many investigations. Mechanistic models of the newly structured genomes are being developed that incorporate aspects of sequence evolution or turnover (low-copy genes and regulatory sequences, as well as repetitive DNAs), modification of gene functions, the re-establishment of control of genes with multiple copies, and often meiotic chromosome pairing, recombination and restoration of fertility.

World-wide interest in how green plants have evolved under different conditions – whether in small, isolated populations, or globally – suggests that gaining further insight into the contribution of polyploidy to plant speciation and adaptation to environmental changes is greatly needed. Forward-looking research and modelling, based on cytogenetics, expression studies, and genomics or genome sequencing analyses, discussed in this Special Issue of the Annals of Botany, consider how new polyploids behave and the pathways available for genome evolution. They address fundamental questions about the advantages and disadvantages of polyploidy, the consequences for evolution and speciation, and applied questions regarding the spread of polyploids in the environment and challenges in breeding and exploitation of wild relatives through introgression or resynthesis of polyploids.

Chromosome number, genome size, repetitive DNA sequences, genes and regulatory sequences and their expression evolve following polyploidy – generating diversity and possible novel traits and enabling species diversification. There is the potential for ever more polyploids in natural, managed and disturbed environments under changing climates and new stresses.

 

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity.