Interleukin-36γ is expressed by neutrophils and can activate microglia, but has no role in experimental autoimmune encephalomyelitis

Primers used for genotyping. (PDF 39 kb

The Maker-Maker domination game in forests

We study the Maker-Maker version of the domination game introduced in 2018 by Duch\^ene et al. Given a graph, two players alternately claim vertices. The first player to claim a dominating set of the graph wins. As the Maker-Breaker version, this game is PSPACE-complete on split and bipartite graphs. Our main result is a linear time algorithm to solve this game in forests. We also give a characterization of the cycles where the first player has a winning strategy

Auto-encodeur optimisé au sens débit-distorsion : indépendant de la quantification?

National audienceThis work relates to image compression via a transform learned by an auto-encoder. It tries to adapt the quantization to this transform instead of fixing it. We propose to jointly learn the transform and the quantization. Moreover, we analyze whether different quantization steps can be applied to a transform learned for one step only. We show that the second approach corrects the aw of the state-of-the-art auto-encoder for image compression: having to learn one transform per compression rate.Ce travail s'inscrit dans le cadre de la compression d'image via une transformée apprise par un auto-encodeur. Il essaie d'adapter la quantification à cette transformée au lieu de la figer. Nous proposons d'une part d'apprendre conjointement la transformée et la quantification. D'autre part, nous analysons si une multitude de pas de quantification peut s'appliquer lors du test sur une transformée apprise pour un pas. Nous montrons que la seconde approche corrige le défaut du meilleur auto-encodeur pour la compression d'image : devoir effectuer un apprentissage par débit de compression

Context-adaptive neural network based prediction for image compression

International audienceThis paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra image prediction. The choice of neural network for predicting a given image block depends on the block size, hence does not need to be signalled to the decoder. It is shown that, while fully-connected neural networks give good performance for small block sizes, convolutional neural networks provide better predictions in large blocks with complex textures. Thanks to the use of masks of random sizes during training, the neural networks of PNNS well adapt to the available context that may vary, depending on the position of the image block to be predicted. When integrating PNNS into a H.265 codec, PSNR-rate performance gains going from 1.46% to 5.20% are obtained. These gains are on average 0.99% larger than those of prior neural network based methods. Unlike the H.265 intra prediction modes, which are each specialized in predicting a specific texture, the proposed PNNS can model a large set of complex textures

CAMINHOS DE VOLTA: Reflexões afrorreferenciadas sobre dançares de herança ‘gengibreira’

Como diriam sábios mestres, ativistas, brincantes e educadores da terra preta, bandeira vermelha, broa com café, negros e Puris da Zona da Mata Mineira, Seu Nonô e Tião Farinhada, o “caminho de volta” é um movimento necessário. O artigo que segue apresenta memórias dos dançares (ensino, pesquisa, extensão e criação) artístico- metodológicos e didático-pedagógicos que guardo/ partilho como heranças do Grupo Gengibre (UFV, 2004- 2014) acompanhadas de reflexões afrorreferenciadas (NOGUERA, 2014), (ASANTE, 2009) sobre esse ambiente de criar e forjar métodos a partir de “corpo e ancestralidade” (SANTOS, 2006). Discutindo, neste caminho de volta, epistemologia (KAPHAGAWANI; MALHERBE 2002), oralitura (MARTINS, 2003), cosmopercepção (OYEWUMI, 1997) e ancestralidade (OLIVEIRA, 2009), amparados por referenciais afrodiaspóricos a partir de indagações oriundas de corpus negros em espaços de trocas de saberes. PALAVRAS-CHAVE: Afrocentricidade. Dança. Processos de criação em dança

CXCL1 can be regulated by IL-6 and promotes granulocyte adhesion to brain capillaries during bacterial toxin exposure and encephalomyelitis

<p>Abstract</p> <p>Background</p> <p>Granulocytes generally exert protective roles in the central nervous system (CNS), but recent studies suggest that they can be detrimental in experimental autoimmune encephalomyelitis (EAE), the most common model of multiple sclerosis. While the cytokines and adhesion molecules involved in granulocyte adhesion to the brain vasculature have started to be elucidated, the required chemokines remain undetermined.</p> <p>Methods</p> <p>CXCR2 ligand expression was examined in the CNS of mice suffering from EAE or exposed to bacterial toxins by quantitative RT-PCR and <it>in situ </it>hybridization. CXCL1 expression was analyzed in IL-6-treated endothelial cell cultures by quantitative RT-PCR and ELISA. Granulocytes were counted in the brain vasculature after treatment with a neutralizing anti-CXCL1 antibody using stereological techniques.</p> <p>Results</p> <p>CXCL1 was the most highly expressed ligand of the granulocyte receptor CXCR2 in the CNS of mice subjected to EAE or infused with lipopolysaccharide (LPS) or pertussis toxin (PTX), the latter being commonly used to induce EAE. IL-6 upregulated CXCL1 expression in brain endothelial cells by acting transcriptionally and mediated the stimulatory effect of PTX on CXCL1 expression. The anti-CXCL1 antibody reduced granulocyte adhesion to brain capillaries in the three conditions under study. Importantly, it attenuated EAE severity when given daily for a week during the effector phase of the disease.</p> <p>Conclusions</p> <p>This study identifies CXCL1 not only as a key regulator of granulocyte recruitment into the CNS, but also as a new potential target for the treatment of neuroinflammatory diseases such as multiple sclerosis.</p

Impact of Anti-Inflammatory Agents on the Gene Expression Profile of Stimulated Human Neutrophils: Unraveling Endogenous Resolution Pathways

Adenosine, prostaglandin E2, or increased intracellular cyclic AMP concentration each elicit potent anti-inflammatory events in human neutrophils by inhibiting functions such as phagocytosis, superoxide production, adhesion and cytokine release. However, the endogenous molecular pathways mediating these actions are poorly understood. In the present study, we examined their impact on the gene expression profile of stimulated neutrophils. Purified blood neutrophils from healthy donors were stimulated with a cocktail of inflammatory agonists in the presence of at least one of the following anti-inflammatory agents: adenosine A2A receptor agonist CGS 21680, prostaglandin E2, cyclic-AMP-elevating compounds forskolin and RO 20-1724. Total RNA was analyzed using gene chips and real-time PCR. Genes encoding transcription factors, enzymes and regulatory proteins, as well as secreted cytokines/chemokines showed differential expression. We identified 15 genes for which the anti-inflammatory agents altered mRNA levels. The agents affected the expression profile in remarkably similar fashion, suggesting a central mechanism limiting cell activation. We have identified a set of genes that may be part of important resolution pathways that interfere with cell activation. Identification of these pathways will improve understanding of the capacity of tissues to terminate inflammatory responses and contribute to the development of therapeutic strategies based on endogenous resolution

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio