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Considering the second climate scenario, the seed bank reaches an equilibrium density of 24,182 ± 253 seeds m-2 (+26% in relation to the current scenario) for the susceptible population and 24,299 ± 254 seeds m-2 (+18% in relation to the current scenario) for the resistant one. The results showed that the effect of the rise in temperature implies an increase in population in all the management strategies in relation to the current climate scenario. In both climate scenarios, the strategies based on herbicides application controlling cohorts 1 and 2 were the most efficient, and cropping systems including winter oat-soybeans rotation had a smaller impact on the L. multiflorum seed bank than crop rotations including winter wheat or summer corn. Crop rotations including wheat and corn for L. multiflorum management as an adaptive strategy under the future climate change are suggested.Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters' data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. click here To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. click here Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting.The adverse influences of elevated ambient temperatures during the summer season on the rabbit industry have received increased global attention. Therefore, this study intended to compare the potential effects of nano-selenium (nano-Se) synthesized by biological (BIO) and chemical (CH) methods on growth performance, carcass variables, serum metabolites, and inflammatory cytokines responses of growing rabbits in the summer season. Two hundred and fifty weaned rabbits (males, 35 days of age) were randomly divided into five treatment groups of 50 rabbits each (each group had five replicates with ten male rabbits). Treatment groups were fed a control diet and four controlled diets supplemented with nano-Se synthesized by biological method (BIO25 and BIO50, with a 25 and 50 mg of nano-Se/kg diet, respectively) and chemical method (CH25 and CH50, with a 25 and 50 mg of nano-Se/kg diet, respectively) for eight weeks. During 11 to 13 weeks of age, a gradual enhancement in live body weight (LBW), feed intake (FI) and antioxidants indices, and inflammatory cytokines of growing rabbits during thermal stress.The spread of viruses among cells and hosts often involves multi-virion structures. For instance, virions can form aggregates that allow for the co-delivery of multiple genome copies to the same cell from a single infectious unit. Previously, we showed that vesicular stomatitis virus (VSV), an enveloped, negative-strand RNA virus, undergoes strong aggregation in the presence of saliva from certain individuals. However, the molecular components responsible for such aggregation remain unknown. Here we show that saliva-driven aggregation is protein dependent, and we use comparative proteomics to analyze the protein content of strongly versus poorly aggregating saliva. Quantitative analysis of over 300 proteins led to the identification of 18 upregulated proteins in strongly aggregating saliva. One of these proteins, the fibrinogen gamma chain, was verified experimentally as a factor promoting VSV aggregation in a dose-dependent manner. This study hence identifies a protein responsible for saliva-driven VSV aggregation. Yet, the possible involvement of additional proteins or factors cannot be discarded.In addition to their use as colorants, anthraquinone derivatives have numerous medical applications, for example, as antibacterial and antiinflammatory agents. We confirmed that physcion (an anthraquinone derivative) induces TNF-alpha production by macrophages and increased the expressions of surface molecules (CD40, CD80, and CD86) and major histocompatibility complex (MHC) II. Based on these results, we hypothesized that physcion might induce the maturation of dendritic cells (DCs) to antigen-presenting cells (APCs), and decided to conduct in vitro experiments using bone-marrow-derived DCs (BMDCs). Physcion was not toxic to DCs and increased the expression of surface molecules (e.g., CD40, CD80, CD86, and MHC II) and the production of cytokines (e.g., IL-12p70, IL-1beta, IL-6, and TNF-alpha), but not of IL-10. To confirm that DCs matured by physcion induce T-cell-immune responses, naive CD4+ T cells were treated with physcion-treated DCs or their supernatants. Physcion induced the maturation of DCs, which promoted the polarization of Th1 cells. Our results show physcion-induced DC maturation via TLR4, and that mature DCs promote the differentiation of Th1 cells without affecting the differentiation of Th2 cells. These findings show that physcion has potential use as a treatment for inflammatory diseases associated with Th1/Th2 cell imbalance.
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