Phosphorylation of ERK1/2, mTOR and FoxO4.

<p>Western blot analysis (up) and quantification of the results (down) are shown. H - heavy (48.3±0.1 g); L - light (41.7±0.1 g); CM – control methionine levels (0.50% and 0.43% methionine during 1–21 d and 22–42 d, respectively); HM - high methionine levels (0.60% and 0.53% methionine during 1–21 d and 22–42 d, respectively). Different superscripts indicate significant differences (<i>P</i><0.05).</p

Composition and nutrient level of diets (as fed basis).

a<p>Providing the following per mg/kg diet: retinyl acetate, 3.44; cholecalciferol, 0.075; all-rac-α-tocopherol acetate, 30; menadione, 1.3; thiamin, 2.2; riboflavin, 8; nicotinamide, 40; choline chloride, 600; calcium pantothenate, 10; pyridoxine·HCl, 4; biotin, 0.04; folic acid, 1; cobalamin, 0.013; Fe (as FeSO₄.H₂O), 80; Cu (as CuSO₄.5H₂O), 8; Mn (as MnSO₄.H₂O), 110; Zn (as ZnO), 65; I (as KIO₃), 1.1; Se (as Na₂SeO₃), 0.3.</p><p>Composition and nutrient level of diets (as fed basis).</p

Myostatin (MSTN) mRNA level and DNA methylation of gene exon 1 region.

<p>Heavy −48.3±0.1 g; Light −41.7±0.1 g; CM – control methionine levels (0.50% and 0.43% methionine during 1–21 d and 22–42 d, respectively); HM - high methionine levels (0.60% and 0.53% methionine during 1–21 d and 22–42 d, respectively); HW - hatching weight.</p><p>SEM - standard error of the mean.</p><p>Within the same row, different superscripts indicate significant differences (<i>P</i><0.05).</p><p>Myostatin (MSTN) mRNA level and DNA methylation of gene exon 1 region.</p

Descriptive statistical of the three main grading indicators for each category of delayed trains.

Descriptive statistical of the three main grading indicators for each category of delayed trains.</p

Example of calculated train delay status.

This paper focuses on optimizing the management of delayed trains in operational scenarios by scientifically categorizing train delay levels. It employs static and dynamic models grounded in real-world train delay data from high-speed railways. This classification aids dispatchers in swiftly identifying and predicting delay extents, thus enhancing mitigation strategies’ efficiency. Key indicators, encompassing initial delay duration, station impacts, average station delay, delayed trains’ cascading effects, and average delay per affected train, inform the classification. Applying the K-means clustering algorithm to standardized delay indicators yields an optimized categorization of delayed trains into four levels, reflecting varying risk levels. This static classification offers a comprehensive overview of delay dynamics. Furthermore, utilizing Markov chains, the study delves into sequential dynamic analyses, accounting for China’s railway context and specifically addressing fluctuations during the Spring Festival travel rush. This research, combining static and dynamic approaches, provides valuable insights for bolstering railway operational efficiency and resilience amidst diverse delay scenarios.</div

Distances between cluster centers for each train delay classification.

Distances between cluster centers for each train delay classification.</p

Sample of high-speed train delay static grading results of train D902.

Sample of high-speed train delay static grading results of train D902.</p

Determination of optimal cluster numbers for high-speed train delay data using relative evaluation methods.

Determination of optimal cluster numbers for high-speed train delay data using relative evaluation methods.</p

Example of raw train operational data.

This paper focuses on optimizing the management of delayed trains in operational scenarios by scientifically categorizing train delay levels. It employs static and dynamic models grounded in real-world train delay data from high-speed railways. This classification aids dispatchers in swiftly identifying and predicting delay extents, thus enhancing mitigation strategies’ efficiency. Key indicators, encompassing initial delay duration, station impacts, average station delay, delayed trains’ cascading effects, and average delay per affected train, inform the classification. Applying the K-means clustering algorithm to standardized delay indicators yields an optimized categorization of delayed trains into four levels, reflecting varying risk levels. This static classification offers a comprehensive overview of delay dynamics. Furthermore, utilizing Markov chains, the study delves into sequential dynamic analyses, accounting for China’s railway context and specifically addressing fluctuations during the Spring Festival travel rush. This research, combining static and dynamic approaches, provides valuable insights for bolstering railway operational efficiency and resilience amidst diverse delay scenarios.</div

DataSheet_1_Genetic polymorphism in HTR2A rs6313 is associated with internet addiction disorder.docx

IntroductionInternet addiction disorder (IAD) has grown into public health concern of global proportions. Previous studies have indicated that individuals with IAD may exhibit altered levels of serotonin and dopamine, which are known to play crucial roles in depression, anxiety, impulsivity, and addiction. Therefore, polymorphisms in the receptors that mediate the effects of serotonin and dopamine and affect their functional states as well as their activities are suspect. In this study, we aimed to investigate the association between IAD and rs6313 (T102C) polymorphism in the serotonin 2A receptor (5-HT2A) gene, (HTR2A).MethodsTwenty patients with IAD and twenty healthy controls (HCs) were included in this study. Young’s Internet Addiction Test (IAT), Self-Rating Anxiety Scale, Self-Rating Depression Scale, Yale-Brown Obsessive-Compulsive Scale (Y-BOCS), Barratt Impulse Scale, Pittsburgh Sleep Quality Index (PSQI), and Social Support Rating Scale (SSRS) were used to assess the severity of internet addiction, mental status, impulsive traits, sleep quality, and social support. Genotyping was performed to identify rs6313 polymorphisms in the HTR2A gene of all participants.ResultsThe frequencies of the C and T alleles of HTR2A T102C were 28% and 72% in the IAD group and 53% and 47% in the HCs group, respectively, indicating that the differences between these two groups were significant. No significant difference was observed in the distribution of the CC, CT, and TT genotypes of HTR2A gene T102C between the IAD and the HCs groups. Additionally, there was no difference in the distribution of the frequencies of the HTR2A gene T102C CC and CT+TT genotypes between the two groups. However, the distribution between the TT and CC+CT genotypes showed an apparent statistical difference in the HTR2A gene T102C between the two groups. Correlation analysis indicated that the IAT score was positively correlated with the Y-BOCS and BIS scores for the CC+CT genotype in patients with IAD. Moreover, the IAT score was positively correlated with the PSQI score in patients with IAD carrying the TT genotype.ConclusionThe present study demonstrates that rs6313 in HTR2A is associated with IAD, and that the T allele of rs6313 in HTR2A may be a risk factor for IAD.</p