Passive Avoidance Training and Recall are Associated With Increased Glutamate Levels in the Intermediate Medial Hyperstriatum Ventrale of the Day-Old Chick

In the young chick, the intermediate medial hyperstriatum ventrale is involved in learning paradigms, including imprinting and passive avoidance learning. Biochemical changes in the intermediate medial hyperstriatum ventrale following learning include an up-regulation of amino-acid transmitter levels and receptor activity. To follow the changes of extracellular amino acid levels during passive avoidance training, we used an in vivo microdialysis technique. Probes were implanted in chicks before training the animals, either on a methyl- anthranylate-or water-coated bead. One hour later, recall was tested in both groups by presenting a similar bead. An increase of extra-cellular glutamate levels accompanied training and testing in both groups; during training, glutamate release was higher in methylanthranylate- trained than in water-trained chicks. When compared with the methylanthranylate-trained chicks during testing, the water-trained chicks showed enhanced extra-cellular glutamate levels. No other amino acid examined showed significant changes. After testing, the chicks were anesthetized and release- stimulated with an infusion of 50 mM potassium. Extra-cellular glutamate and taurine levels were significantly increased in both methylanthranylate-and water-trained chicks. The presentation of methylanthranylate as an. olfactory stimulus significantly enhanced glutamate levels, especially in methylanthranylate-trained chicks. The results suggest that such changes in extra-cellular glutamate levels in the intermediate medial hyperstriatum ventrale accompany pecking at either the water- or the methylanthranylate-bead. The taste of the aversant may be responsible for the greater increases found in methylanthranylate-trained birds

PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis

With the spread of COVID-19 over the world, the need arose for fast and precise automatic triage mechanisms to decelerate the spread of the disease by reducing human efforts e.g. for image-based diagnosis. Although the literature has shown promising efforts in this direction, reported results do not consider the variability of CT scans acquired under varying circumstances, thus rendering resulting models unfit for use on data acquired using e.g. different scanner technologies. While COVID-19 diagnosis can now be done efficiently using PCR tests, this use case exemplifies the need for a methodology to overcome data variability issues in order to make medical image analysis models more widely applicable. In this paper, we explicitly address the variability issue using the example of COVID-19 diagnosis and propose a novel generative approach that aims at erasing the differences induced by e.g. the imaging technology while simultaneously introducing minimal changes to the CT scans through leveraging the idea of deep autoencoders. The proposed prepossessing architecture (PrepNet) (i) is jointly trained on multiple CT scan datasets and (ii) is capable of extracting improved discriminative features for improved diagnosis. Experimental results on three public datasets (SARS-COVID-2, UCSD COVID-CT, MosMed) show that our model improves cross-dataset generalization by up to 11:84 percentage points despite a minor drop in within dataset performance

Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer.

Although targeted therapies often elicit profound initial patient responses, these effects are transient due to residual disease leading to acquired resistance. How tumors transition between drug responsiveness, tolerance and resistance, especially in the absence of preexisting subclones, remains unclear. In epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma cells, we demonstrate that residual disease and acquired resistance in response to EGFR inhibitors requires Aurora kinase A (AURKA) activity. Nongenetic resistance through the activation of AURKA by its coactivator TPX2 emerges in response to chronic EGFR inhibition where it mitigates drug-induced apoptosis. Aurora kinase inhibitors suppress this adaptive survival program, increasing the magnitude and duration of EGFR inhibitor response in preclinical models. Treatment-induced activation of AURKA is associated with resistance to EGFR inhibitors in vitro, in vivo and in most individuals with EGFR-mutant lung adenocarcinoma. These findings delineate a molecular path whereby drug resistance emerges from drug-tolerant cells and unveils a synthetic lethal strategy for enhancing responses to EGFR inhibitors by suppressing AURKA-driven residual disease and acquired resistance

Experimental study on the influence of nanoparticle PCM slurry for high temperature on convective heat transfer and energetic performance in a circular tube under imposed heat flux

International audienc

IL-2/anti-IL-2 antibody complexes show strong biological activity by avoiding interaction with IL-2 receptor α subunit CD25

IL-2 is crucial to T cell homeostasis, especially of CD4+ T regulatory cells and memory CD8+ cells, as evidenced by vigorous proliferation of these cells in vivo following injections of superagonist IL-2/anti-IL-2 antibody complexes. The mechanism of IL-2/anti-IL-2 antibody complexes is unknown owing to a lack of understanding of IL-2 homeostasis. We show that IL-2 receptor α (CD25) plays a crucial role in IL-2 homeostasis. Thus, prolongation of IL-2 half-life and blocking of CD25 using antibodies or CD25-deficient mice led in combination, but not alone, to vigorous IL-2–mediated T cell proliferation, similar to IL-2/anti-IL-2 antibody complexes. These data suggest an unpredicted role for CD25 in IL-2 homeostasis