Activity

Based on the COR theory, this is the first study to argue that knowledge sharing could be considered as an active activity and that individuals could be eager to perform knowledge sharing when they possess significant personal and external resources. The results of this study provide new insights into knowledge sharing.In network models of spiking neurons, the joint impact of network structure and synaptic parameters on activity propagation is still an open problem. Here, we use an information-theoretical approach to investigate activity propagation in spiking networks with a hierarchical modular topology. We observe that optimized pairwise information propagation emerges due to the increase of either (i) the global synaptic strength parameter or (ii) the number of modules in the network, while the network size remains constant. At the population level, information propagation of activity among adjacent modules is enhanced as the number of modules increases until a maximum value is reached and then decreases, showing that there is an optimal interplay between synaptic strength and modularity for population information flow. This is in contrast to information propagation evaluated among pairs of neurons, which attains maximum value at the maximum values of these two parameter ranges. By examining the network behavior under the increase of synaptic strength and the number of modules, we find that these increases are associated with two different effects (i) the increase of autocorrelations among individual neurons and (ii) the increase of cross-correlations among pairs of neurons. The second effect is associated with better information propagation in the network. Our results suggest roles that link topological features and synaptic strength levels to the transmission of information in cortical networks.Ten articles published in the “Special Issue Salicylic Acid Signalling in Plants” are summarized, in order to get a global picture about the mode of action of salicylic acid in plants, and about its interaction with other stress-signalling routes. Its ecological aspects and possible practical use are also discussed.We have assembled the first genome draft of Anaplasma platys, an obligate intracellular rickettsia, and the only known bacterial pathogen infecting canine platelets. A. platys is a not-yet-cultivated bacterium that causes infectious cyclic canine thrombocytopenia, a potentially fatal disease in dogs. Despite its global distribution and veterinary relevance, no genome sequence has been published so far for this pathogen. Here, we used a strategy based on metagenome assembly to generate a draft of the A. platys genome using the blood of an infected dog. The assembled draft is similar to other Anaplasma genomes in size, gene content, and synteny. Notable differences are the apparent absence of rbfA, a gene encoding a 30S ribosome-binding factor acting as a cold-shock protein, as well as two genes involved in biotin metabolism. We also observed differences associated with expanded gene families, including those encoding outer membrane proteins, a type IV secretion system, ankyrin repeat-containing proteins, and proteins with predicted intrinsically disordered regions. Several of these families have members highly divergent in sequence, likely to be associated with survival and interactions within the host and the vector. The sequence of the A. platys genome can benefit future studies regarding invasion, survival, and pathogenesis of Anaplasma species, while paving the way for the better design of treatment and prevention strategies against these neglected intracellular pathogens.The outbreak of COVID-19 is leading to a tremendous search for curative treatments. The urgency of the situation favors a repurposing of active drugs but not only antivirals. This short communication focuses on four treatments recommended by WHO and included in the first clinical trial of the European Discovery project.None of the models met the four key stages required to create a quality risk prediction model. Further research is needed to either refine the tools developed to date or develop new ones that have good performance and have been externally validated before considering the potential impact and implementation of such tools.This work presents the effect of magnesium (Mg) doping on the sensing properties of tin dioxide (SnO2) thin films. Mg-doped SnO2 films were prepared via a spray pyrolysis method using three doping concentrations (0.8 at.%, 1.2 at.%, and 1.6 at.%) and the sensing responses were obtained at a comparatively low operating temperature (160 °C) compared to other gas sensitive materials in the literature. The morphological, structural and chemical composition analysis of the doped films show local lattice disorders and a proportional decrease in the average crystallite size as the Mg-doping level increases. These results also indicate an excess of Mg (in the samples prepared with 1.6 at.% of magnesium) which causes the formation of a secondary magnesium oxide phase. The films are tested towards three volatile organic compounds (VOCs), including ethanol, acetone, and toluene. The gas sensing tests show an enhancement of the sensing properties to these vapors as the Mg-doping level rises. AG-1478 This improvement is particularly observed for ethanol and, thus, the gas sensing analysis is focused on this analyte. Results to 80 ppm of ethanol, for instance, show that the response of the 1.6 at.% Mg-doped SnO2 film is four times higher and 90 s faster than that of the 0.8 at.% Mg-doped SnO2 film. This enhancement is attributed to the Mg-incorporation into the SnO2 cell and to the formation of MgO within the film. These two factors maximize the electrical resistance change in the gas adsorption stage, and thus, raise ethanol sensitivity.This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor-critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer’s preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing.