Blockade of interleukin-6 signaling inhibits the classic pathway and promotes an alternative pathway of macrophage activation after spinal cord injury in mice

Background Recent in vivo and in vitro studies in non-neuronal and neuronal tissues have shown that different pathways of macrophage activation result in cells with different properties. Interleukin (IL)-6 triggers the classically activated inflammatory macrophages (M1 phenotype), whereas the alternatively activated macrophages (M2 phenotype) are anti-inflammatory. The objective of this study was to clarify the effects of a temporal blockade of IL-6/IL-6 receptor (IL-6R) engagement, using an anti-mouse IL-6R monoclonal antibody (MR16-1), on macrophage activation and the inflammatory response in the acute phase after spinal cord injury (SCI) in mice. Methods MR16-1 antibodies versus isotype control antibodies or saline alone were administered immediately after thoracic SCI in mice. SC tissue repair was compared between the two groups by Luxol fast blue (LFB) staining for myelination and immunoreactivity for the neuronal markers growth-associated protein (GAP)-43 and neurofilament heavy 200 kDa (NF-H) and for locomotor function. The expression of T helper (Th)1 cytokines (interferon (IFN)-? and tumor necrosis factor-a) and Th2 cytokines (IL-4, IL-13) was determined by immunoblot analysis. The presence of M1 (inducible nitric oxide synthase (iNOS)-positive, CD16/32-positive) and M2 (arginase 1-positive, CD206-positive) macrophages was determined by immunohistology. Using flow cytometry, we also quantified IFN-? and IL-4 levels in neutrophils, microglia, and macrophages, and Mac-2 (macrophage antigen-2) and Mac-3 in M2 macrophages and microglia. Results LFB-positive spared myelin was increased in the MR16-1-treated group compared with the controls, and this increase correlated with enhanced positivity for GAP-43 or NF-H, and improved locomotor Basso Mouse Scale scores. Immunoblot analysis of the MR16-1-treated samples identified downregulation of Th1 and upregulation of Th2 cytokines. Whereas iNOS-positive, CD16/32-positive M1 macrophages were the predominant phenotype in the injured SC of non-treated control mice, MR16-1 treatment promoted arginase 1-positive, CD206-positive M2 macrophages, with preferential localization of these cells at the injury site. MR16-1 treatment suppressed the number of IFN-?-positive neutrophils, and increased the number of microglia present and their positivity for IL-4. Among the arginase 1-positive M2 macrophages, MR16-1 treatment increased positivity for Mac-2 and Mac-3, suggestive of increased phagocytic behavior. Conclusion The results suggest that temporal blockade of IL-6 signaling after SCI abrogates damaging inflammatory activity and promotes functional recovery by promoting the formation of alternatively activated M2 macrophages

Forecasting the spot price behavior of energy in the Brazilian market with statistical tools

A energia elétrica no mercado de curto prazo, ou mercado spot, é comercializada com base no cálculo do Preço de Liquidação das Diferenças (PLD), e seu cálculo segue uma metodologia complexa e dispendiosa. Buscando uma opção alternativa, o presente trabalho propõe o emprego de métodos estatísticos de previsão dos valores e tendências do PLD, com o intuito de mitigar a insegurança na tomada de decisão dos agentes de mercado. Os modelos propostos empregam como entrada a energia natural afluente, a energia armazenada, as gerações hidrelétrica, térmica e eólica, e a demanda de energia elétrica, divulgadas publicamente pelo ONS, além da série histórica do próprio PLD, divulgada pela CCEE, buscando praticidade para utilização do modelo desenvolvido. As previsões são elaboradas por métodos de análise de séries temporais e de regressão para o horizonte de uma semana no submercado Sul para o patamar de carga pesado. Sequências históricas são empregadas para a construção dos modelos e seus resultados de previsão são validados para os períodos excluídos dessas sequências de dados. O modelo que apresenta melhor acurácia na previsão do PLD é o modelo de análise de séries temporais que emprega a suavização exponencial simples com erros médios de 17,91 % (percentual) e de R$28,01/MWh (absoluto). O melhor percentual de acerto de tendência é de 61,36$ 28.01/MWh (absolute).The best percentage of trend hit is 61.36%, obtained from a multiple linear regression model

Forecasting the spot price behavior of energy in the Brazilian market with statistical tools

A energia elétrica no mercado de curto prazo, ou mercado spot, é comercializada com base no cálculo do Preço de Liquidação das Diferenças (PLD), e seu cálculo segue uma metodologia complexa e dispendiosa. Buscando uma opção alternativa, o presente trabalho propõe o emprego de métodos estatísticos de previsão dos valores e tendências do PLD, com o intuito de mitigar a insegurança na tomada de decisão dos agentes de mercado. Os modelos propostos empregam como entrada a energia natural afluente, a energia armazenada, as gerações hidrelétrica, térmica e eólica, e a demanda de energia elétrica, divulgadas publicamente pelo ONS, além da série histórica do próprio PLD, divulgada pela CCEE, buscando praticidade para utilização do modelo desenvolvido. As previsões são elaboradas por métodos de análise de séries temporais e de regressão para o horizonte de uma semana no submercado Sul para o patamar de carga pesado. Sequências históricas são empregadas para a construção dos modelos e seus resultados de previsão são validados para os períodos excluídos dessas sequências de dados. O modelo que apresenta melhor acurácia na previsão do PLD é o modelo de análise de séries temporais que emprega a suavização exponencial simples com erros médios de 17,91 % (percentual) e de R$28,01/MWh (absoluto). O melhor percentual de acerto de tendência é de 61,36$ 28.01/MWh (absolute).The best percentage of trend hit is 61.36%, obtained from a multiple linear regression model

Electron Transport in One and Two Dimensional Materials

This dissertation presents theoretical and experimental studies in carbon nanotubes (CNTs), graphene, and van der Waals heterostructures. The first half of the dissertation focuses on cutting edge tight-binding-based quantum transport models which are used to study proton irradiation-induced single-event effects in carbon nanotubes, total ionizing dose effects in graphene, quantum hall effect in graded graphene p-n junctions, and ballistic electron focusing in graphene p-n junctions. In each study, tight-binding models are developed, with heavy emphasis on tying to experimental data. Once benchmarked against experiment, properties of each system which are difficult to access in the laboratory, such as local density of states, local current density, and quantum transmission probability, are extracted to build our physical intuition. The second half of the dissertation covers experimental work on transport in van der Waals heterostructures. High-quality samples, evidenced by measurements of quasi-ballistic graphene p-n junctions, are enabled by encapsulation in hexagonal boron nitride, assembled using a modified dry transfer technique. The Schottky-Mott limit, previously only a textbook example, is probed in gated graphene-WSe2 heterojunctions. Schottky barrier measurements as a function of gate voltage reveal perfect barrier tuning, following the Schottky-Mott rule. Enabled by the lack of Fermi-level pinning at the graphene-WSe2 interface, a method for dynamically tuning the Schottky diode ideality factor is demonstrated. Finally, an analytical model describing tuning of the junction is developed

Small and large intestine organoids differ in response to differentiation cues.

<p>Real-time PCR analysis of (<b>A&C</b>) SI and (<b>B&D</b>) LI organoids derived from expanded cells and treated with SC media, Differentiation (Diff) or Diff media + DAPT. SC media treatment values used as control and set to 1. Diff media and Diff media + DAPT expression values are relative to SC media. See media key in panel A. (<b>E</b>) Select marker expression comparison between SI and LI organoids in the three treatments. SI values used as control and set to 1. LI organoid expression values are relative to SI organoids. See text for full gene names. Expression was normalized to <i>GAPDH</i> mRNA. Error bars represent upper and lower error limits based on replicate variability (*<i>P</i><0.05, **<i>P</i><0.01, ***<i>P</i><0.001, # no expression detected). (n = 3 wells per sample/primer pair). One paired SI and LI set serves as a representative of all paired sets examined.</p