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Introduction: Lysinuric protein intolerance (LPI) is a rare genetic disorder caused by mutations in the solute carrier family 7A member 7 (SLC7A7) gene. Case presentation: We presented two siblings with LPI, carrying novel mutations of c.776delT (p.L259Rfs*18) and c.155G>T (p.G52V) in SLC7A7. The younger sibling, preferring protein‐rich foods, showed severe symptoms, including alveolar proteinosis, macrophage activation syndrome, severe diarrhea, and disturbance of consciousness with involuntary movements. In contrast, the elder sibling only had mild symptoms, likely due to aversion to protein‐rich food since toddler age. Conclusion: LPI is a congenital genetic metabolic disease with multi‐system involvement. Initiating appropriate protein‐restricted diet therapy as soon as possible could help prevent the progression of LPI. [ABSTRACT FROM AUTHOR]

The accurate prediction of highway passenger volume is very important for China's transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume data and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020–2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020–2022 due to various factors such as the epidemic situation and policies, which have had an impact on China's economic development. [ABSTRACT FROM AUTHOR]

Thick coal seam fracture stimulations were conducted to enhance pre-gas drainage efficiency through the use of a highly pressurized multidischarge carbon dioxide gas fracturing technique. This method also offers potential as a strategy for carbon dioxide sequestration, aiding in the reduction of atmospheric carbon dioxide levels and thereby contributing to the fight against climate change. This paper discusses findings from both field experiments and numerical simulations. Data from the field show that the multidischarge fracturing approach significantly improves permeability in thick coal seams, thereby boosting gas drainage effectiveness. Additionally, the impact of increasing the number of fracturing devices is more pronounced at distances of 2.5 or 7.5 m from the borehole but becomes more complex at 12.5 m or further. The numerical simulations reveal that this technique primarily enhances coal seam gas drainage by improving the seam permeability and establishing a gas pressure gradient within the seam. It is noted that the radius of failure around the borehole wall expands with higher discharge pressures, while the radius of effective drainage narrows as the gap between discharge heads increases. Moreover, adding more discharge sets significantly influences the deformation and permeability of the coal seam within a 5 m radius of the fracturing borehole, but the influence is not obvious after 10 m from the fracturing borehole., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)