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Additional file 1: Figure S1. Flowchart of the study design and identification of eligible patients. Table S1. Patients with NPC suffering RT interruption during or outside the Spring Festival in the matched cohort from the real-world dataset.

Additional file 1: Methods. Figure S1. Study flow. HTA, Human Transcriptome Array; ICI, immune checkpoint inhibitor; NMF, non-negative matrix factorization; NPC, nasopharyngeal carcinoma; PSM, propensity score matching. Figure S2. Identification of an NMF immune factor. (A) We applied NMF (k = 5 factors or expression patterns) to analyze the gene expression profiles of nasopharyngeal carcinoma (NPC) samples in the training cohort (n = 113). One of the five factors (green bar) showed the highest ssGSEA scores in both immune enrichment score and 6-gene IFN-γ signature, as shown in the heatmap, indicating that it is an immune factor (or an immune expression pattern). High and low ssGSEA scores are represented in red and blue, respectively. (B) The top 100 exemplar immune factor genes characterized using DAVID confirmed immune-related functions. (C) NMF consensus-clustering of the training cohort using exemplar immune factor genes was refined by random forest classification. As shown in the heatmap, an immune-enriched subtype and a non-immune subtype. The ssGSEA scores of immune enrichment score and 6-gene IFN-γ signature are indicated; high and low scores are represented in red and blue, respectively. NMF, non-negative matrix factorization; NPC, nasopharyngeal carcinoma; ssGSEA, single-sample gene set enrichment analysis. Figure S3. Association of immune subtypes with tumoural genomic features and survival outcome. (A) Box plot showing similar number of non-synonymous mutations among the immune subtypes. (B) Box plot showing similar numbers of gene-level amplifications and deletions among the immune subtypes. (C) Box plot showing significantly higher cell cycling scores in non-IS. The box plot centre corresponds to the median, with the box and whiskers corresponding to the interquartile range and 1.5× interquartile range, respectively. P-values were based on the Kruskal–Wallis rank-sum test. (D) The proportion of CDKN2A deletions was significantly higher in non-IS. P-values were based on the Fisher’s exact test. (E) Kaplan–Meier curves for progression-free survival according to immune subtypes. A trend of better survival was observed for A-IS compared to E-IS and non-IS in 88 patients with available survival outcomes. P-values were calculated by log-rank test. A-IS, active immune subtype; CD4+ Tconv, conventional CD4+ T cells; CD8+ Tcyt, cytotoxic CD8+ T cells; CD8+ Tdys, dysfunctional CD8+ T cells; CD8+ Tnaï, naïve CD8+ T cells; DCs, dendritic cells; E-IS, evaded immune subtype; NK, natural killer; non-IS, non-immune subtype. Figure S4. Genetic similarity of the immune subtypes in different groups of patients from two melanoma ICI cohorts. (A) SubMap analysis of the immune subtypes in validation cohort 1 and four groups (anti-PD-1 responsive and non-responsive, and anti-CTLA-4 responsive and non-responsive) in melanoma ICI cohort 1. (B) SubMap analysis of the immune subtypes in validation cohort 1 and four groups (CR/PR/SD > 12 months, CR/PR/SD 6–12 months, CR/PR/SD 12 months for anti-PD-1 therapy (P = 0.036). A-IS, active immune subtype; CR, complete response; E-IS, evaded immune subtype; ICI, immune checkpoint inhibitor; non-IS, non-immune subtype; PD, progressive disease; PR, partial response; SD, stable disease. Table S1. Clinical cohorts used in this study. Table S2. Publicly available gene signatures used in this study. Table S7. Clinical characteristics of patients in the validation cohort 1 and validation cohort 2 (ICI).

Supplemental Document

Additional file 1: Table S1. Details of the completion of chemotherapy in the whole cohort (962 patients) and the matched cohort (448 patients). Table S2. Details of the dose density of cisplatin in the whole cohort (737 patients) and the matched cohort (224 patients). Table S3. Nodal volume and dosimetric parameter of the two groups in the matched cohort.

It is an effective method to improve the performance of inertial instrument and the accuracy of inertial navigation system by analyzing the random drift of FOG (Fiber Optic Gyro). Allan variance FOG random error analysis method, through analyze and compare Allan variance of the original data, you can correctly evaluate the gyroscope performance indicators. In this paper, we introduce the several different random error analysis method of FOG based on Allan variance, and analyze the different Allan variance of the same group by writing the program, and more, compare the three common quantities of the FOG random error model. The results show that the modified Allan variance method obviously takes a lot of time in the processing time, but the "2k" Allan variance law is calculated more quickly. In the processing effect, the modified Allan variance method can get better calculation results. In general, the "k" Allan variance method and the smooth Allan variance method are more balanced in the computational time and the computational effect. Several Allan variance methods can effectively identify the random error components of FOG, in the actual situation, according to different conditions of use to choose the appropriate treatment.