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• Assessment different structures of deep-stacked LSTM networks with performing six models that differ in the number and type of LSTM layers, in addition to different hyperparameter settings. • Implementing a hyperparameter optimization tool for each model. • Investigating the performance of the deep-stacked LSTM layer for both unidirectional LSTM and bidirectional LSTM. • Evaluating the best obtained unidirectional and bidirectional LSTM models in comparison with the traditional SVR model. Electricity load forecasting has been a substantial problem in the electric power system management process. An accurate forecasting model is essential to avoid imprecise predictions that can negatively affect system efficiency, economy, and sustainability. Among several prediction techniques, deep learning methods, especially the Long Short-Term Memory (LSTM), have been shown to have a superior performance in predicting the electricity load consumption. However, the consequences of using these methods have not fully been explored in terms of the various hidden layer structures, the depth of the model architecture, and the impact of tuning the model hyperparameters. In this paper, a systematic experimental methodology has been conducted to investigate the impact of using deep-stacked unidirectional (Uni-LSTM) and bidirectional (Bi-LSTM) networks on predicting electricity load consumption. In particular, two stacked configurations, which include two and three LSTM layers, are compared with the single-layered LSTM for both types to show the significant importance of adding the stacked layers. Moreover, for each proposed configuration, a hyperparameter optimization tool has been implemented to obtain the best model. The results indicate that the deep-stacked LSTM layers have no significant improvement in the prediction accuracy; nevertheless, they consume almost twice the time of the single-layered models. Also, the Bi-LSTM networks outperform the Uni-LSTM networks by 76.25%, 75.49%, and 75.35% in terms of Root Mean Square Error (RMSE), with respect to one, two, and three-layer model configurations, respectively. Furthermore, regarding the prediction accuracy comparison over the total tested period, the optimized Bi-LSTM model outperforms both the optimized Uni-LSTM model by 75.98%, 89.1%, and 89.37%, and the Support Vector Regression (SVR) model by 82.54%, 92.59%, and 92.89% in terms of (RMSE), the Mean Average Percentage Error (MAPE), and Mean Absolute Errors (MAE). [ABSTRACT FROM AUTHOR]

Background: Various populations of regulatory T cells play a central role in the development of peripheral tolerance to allergens. Culturing of CD4+ T cells isolated from peripheral blood of allergic patients with vitamin D induces the generation of stable IL-10 producing CD4+CD25+ Treg cells suppressing the proliferation of T helper cells obtained from the same patients. The immune regulatory role of vitamin D in allergic patients has been controversial and obviously needs a more clarifying research work. Aim of the work: to determine the percentage of induced T regulatory cells producing interleukin 10 after stimulation of T regulatory cells with cow milk allergen in the presence of vitamin D in culture. This aims to further in-vitro study the immune regulatory role of vitamin D in cow milk allergic patients. Results: there is association between decreased level of vitamin D and milk-allergy, as serum level of 25(OH) D3 was insufficient in 16 (80 %) patients (10-29.9 ng/ml) while 4 (20%) patients were sufficient (30-100 ng/ml). Addition of vitamin D, in culture, induces the production of CD4+ CD25hi Foxp3+ IL10+ . Treg cells within peripheral blood mononuclear cells (PMNCs) isolated from allergic children who had insufficient vitamin D, but not in allergic children who had normal level of vitamin D. Conclusion: this work provides further evidence for an important role of 1,25(OH)2D3 as an immune-modulatory molecule and suggests that supplementation of vitamin-D-deficient individuals, who are reported to have reduced numbers of circulating and Foxp3+ IL10+ Treg cells, may represent an attractive therapy for enhancing endogenous populations of Treg cells in allergy. [ABSTRACT FROM AUTHOR]