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6%) and 35 (19.2%) developed new d2 and d3 lesions, respectively. Both varnishes reduced the incidence of caries compared with the control group, but there was no significant difference between group A and group B. Compared with group C, the relative risk for developing cavitated lesions was 0.39 (95% CI 0.22-0.62) in group A and 0.26 (95% CI 0.14-0.50) in group B. The total prevented fraction (Δd2d3mft) for group A and group B was 19.9 and 22.5% (p less then 0.05), respectively. No adverse effects were observed or reported during the study period. In conclusion, the 2 fluoride varnishes demonstrated an equal capacity to reduce the incidence of caries in caries-active preschool children over a 12-month period in comparison with a control group.

To date, there has been little investigation on how motivational and cognitive mechanisms interact to influence problematic drinking behaviours. Towards this aim, the current study examined whether reward-related attentional capture is associated with reward, fear (relief), and habit drinking motives, and further, whether it interacts with these motives in relation to problematic drinking patterns.

Ninety participants (mean age = 34.8 years, SD = 9.1, 54% male) who reported having consumed alcohol in the past month completed an online visual search task that measured reward-related attentional capture as well as the Habit Reward Fear Scale, a measure of drinking motives. Participants also completed measures of psychological distress, impulsivity, compulsive drinking, and consumption items of Alcohol Use Disorders Identification Test. Regression analyses examined the associations between motives for alcohol consumption and reward-related attentional capture, as well as the associations between reward-relatributor to addictive behaviours.

These findings have implications for understanding how cognition may interact with motives in association with problematic drinking. Specifically, the findings highlight different potential pathways to problematic drinking according to an individual's cognitive-motivational profile and may inform tailored interventions to target profile-specific mechanisms. Finally, these findings offer support for contemporary models of addiction that view excessive goal-directed behaviour under negative affect as a critical contributor to addictive behaviours.Quinary and senary non-stoichiometric double perovskites such as Ba2Ca0.66Nb1.34-xFexO6-δ (BCNF) have been utilized for gas sensing, solid oxide fuel cells and thermochemical CO2 reduction. Herein, we examined their potential as narrow bandgap semiconductors for use in solar energy harvesting. A cobalt co-doped BCNF, Ba2Ca0.66Nb0.68Fe0.33Co0.33O6-δ (BCNFCo), exhibited an optical absorption edge at ~ 800 nm, p-type conduction and a distinct photoresponse upto 640 nm while demonstrating high thermochemical stability. A nanocomposite of BCNFCo and g-C3N4 (CN) was prepared via a facile solvent assisted exfoliation/blending approach using dichlorobenzene and glycerol at a moderate temperature. The exfoliation of g-C3N4 followed by wrapping on perovskite established an effective heterojunction between the materials for charge separation. The conjugated 2D sheets of CN enabled better charge migration resulting in increased photoelectrochemical performance. A blend composed of 40 wt% perovskite and CN performed optimally, whilst achieving a photocurrent density as high as 1.5 mA cm-2 for sunlight-driven water-splitting with a Faradaic efficiency as high as ~ 88%.In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. find more The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.Objective. Intracortical microstimulation of the primary somatosensory cortex (S1) has shown great progress in restoring touch sensations to patients with paralysis. Stimulation parameters such as amplitude, phase duration, and frequency can influence the quality of the evoked percept as well as the amount of charge necessary to elicit a response. Previous studies in V1 and auditory cortices have shown that the behavioral responses to stimulation amplitude and phase duration change across cortical depth. However, this depth-dependent response has yet to be investigated in S1. Similarly, to our knowledge, the response to microstimulation frequency across cortical depth remains unexplored.Approach. To assess these questions, we implanted rats in S1 with a microelectrode with electrode-sites spanning all layers of the cortex. A conditioned avoidance behavioral paradigm was used to measure detection thresholds and responses to phase duration and frequency across cortical depth.Main results. Analogous to other cortical areas, the sensitivity to charge and strength-duration chronaxies in S1 varied across cortical layers. Likewise, the sensitivity to microstimulation frequency was layer dependent.Significance. These findings suggest that cortical depth can play an important role in the fine-tuning of stimulation parameters and in the design of intracortical neuroprostheses for clinical applications.