Aqueous extracts from dietary supplements influence the production of inflammatory cytokines in immortalized and primary T lymphocytes

<p>Abstract</p> <p>Background</p> <p>Congaplex^®and Immuplex^®are dietary supplements that have been traditionally used to support immune system function. The purpose of these experiments was to determine whether Congaplex^®and Immuplex^®affect immune function using primary and immortalized T lymphocytes.</p> <p>Methods</p> <p>Immortalized CEM and Jurkat T lymphocytes and primary peripheral mononuclear blood cells (PBMCs) were treated with the aqueous extracts from Congaplex^®and Immuplex^®to determine the effects of these products on cytokine production in activated T lymphocytes.</p> <p>Results</p> <p>Congaplex^®enhanced phytohemagglutinin/phorbol 12-myristate 13-acetate (PHA/PMA) stimulation of both CEM and Jurkat cells as measured by the production of cytokines, while Immuplex^®suppressed PHA/PMA-induced production of cytokines, with the exception of interleukin (IL)-8 which was enhanced by Immuplex^®. <it>In vitro </it>treatment of PBMCs from 10 healthy donors with Congaplex^®or Immuplex^®decreased PHA-stimulated production of interferon (IFN)-γ but increased the production of IL-13.</p> <p>Conclusions</p> <p>While the effects of Congaplex^®and Immuplex^®differed in these two models, these data demonstrate that the aqueous extracts from these two dietary supplements can affect the inflammatory response of T lymphocytes.</p

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiking neurons on a learning task in continuous space inspired by the Morris Water maze. The synaptic update rule modifies the release probability of synaptic transmission and depends on the timing of presynaptic spike arrival, postsynaptic action potentials, as well as the membrane potential of the postsynaptic neuron. The family of learning rules includes an optimal rule derived from policy gradient methods as well as reward modulated Hebbian learning. The synaptic update rule is implemented in a population of spiking neurons using a network architecture that combines feedforward input with lateral connections. Actions are represented by a population of hypothetical action cells with strong mexican-hat connectivity and are read out at theta frequency. We show that in this architecture, a standard policy gradient rule fails to solve the Morris watermaze task, whereas a variant with a Hebbian bias can learn the task within 20 trials, consistent with experiments. This result does not depend on implementation details such as the size of the neuronal populations. Our theoretical approach shows how learning new behaviors can be linked to reward-modulated plasticity at the level of single synapses and makes predictions about the voltage and spike-timing dependence of synaptic plasticity and the influence of neuromodulators such as dopamine. It is an important step towards connecting formal theories of reinforcement learning with neuronal and synaptic properties

Solution-Processed Cd-Substituted CZTS Nanocrystals for Sensitized Liquid Junction Solar Cells

The Earth-abundant kesterite Cu2ZnSnS4 (CZTS) exhibits outstanding structural, optical, and electronic properties for a wide range of optoelectronic applications. However, the efficiency of CZTS thin-film solar cells is limited due to range of factors, including electronic disorder, secondary phases, and the presence of anti-site defects, which is key factor limiting the Voc. The complete substitution of Zn lattice sites in CZTS nanocrystals (NCs) with Cd atoms offers a promising approach to overcome several of these intrinsic limitations. Herein, we investigate the effects of substitution of Cd2+ into Zn2+ lattice sites in CZTS NCs through a facile solution-based method. The structural, morphological, optoelectronic, and power conversion efficiencies (PCEs) of the NCs synthesized have been systematically characterized using various experimental techniques, and the results are corroborated by first-principles density functional theory (DFT) calculations. The successful substitution of Zn by Cd is demonstrated to induce a structural transformation from the kesterite phase to the stannite phase, which results in the bandgap reducing from 1.51 eV (kesterite) to 1.1 eV (stannite), which is closer to the optimum bandgap value for outdoor photovoltaic applications. Furthermore, the PCE of the novel Cd-substituted liquid junction solar cell underwent a four-fold increase, reaching 1.1%. These results highlight the importance of substitutional doping strategies in optimizing existing CZTS-based materials to achieve improved device characteristics