Search

Additional file 1: Figure S1. a. Western blot demonstrating CRISPR/CAS9-mediated CD151 knockout in indicated cells. b. Heatmap of the sphingolipid metabolism genes regulated by CD151 depletion in ZOSM cells from RNA-seq. c. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of transcriptomic profiles of osteosarcoma patients based on the GEO database (GSE42352, n = 127). The red indicates the KEGG pathway related to sphingolipid metabolism. d. Heatmap of osteosarcoma patients showed changes of sphingolipid metabolism genes that express a high level of CD151 compared to tumors with low CD151 expression. Figure S2. a. Liquid chromatography coupled to mass spectrometry (LC/MS) was used to measure the concentrations of intermediates in ZOSM cells. Heatmap showing significantly differently expression metabolites altered by CD151 silencing. Shades of red and blue represent higher and lower levels of metabolites, respectively. b. The top 10 enriched pathways from integrated pathway analysis of significantly changed metabolites. The red indicates the KEGG pathway related to sphingolipid metabolism. c. Lipids were extracted from WT or CD151 KO ZOSM cells and analyzed by LC/MS. Bubble plots represent the mean log2-transformed fold-change difference between cell lines. d. The intensity of BODIPY FL-labeled ceramide or sphingomyelin was analyzed by confocal fluorescence imaging in ZOSM cells. Scale bars: 50 μm. e. ZOSM cells with CD151 depletion were incubated with Alexa 555-conjugated CTB (red) to label GM1-containing lipid rafts. Cells were analyzed by confocal microscopy or flow cytometry. Scale bars: 50 μm. Figure S3. a. Expression of genes involved in sphingolipid metabolism was measured by PCR array in ZOSM cells. Data are shown as log2-transformed fold change in CD151 silencing cells relative to control. b. Western blot analysis of SPTCL1 expression in cells with CD151 overexpression in indicated cells. c. The cellular levels of ceramide in vector and CD151 overexpression ZOSM cells with or without myriocin treatment were analyzed by BODIPY FL-labeled ceramide confocal imaging. Scale bars: 50 μm. d. The cellular levels of lipid rafts in vector and CD151 overexpression ZOSM cells with or without myriocin treatment were analyzed by Alexa 555-conjugated CTB confocal imaging. Scale bars: 50 μm. e. Mice weight quantification of established tumors with CD151 overexpression treated with vehicle or myriocin (0.5 mg/kg). Data are presented as mean ± SD, n = 5. Figure S4. a. Gene set enrichment analysis (GSEA) based on the RNA-seq using Hallmarks gene sets between WT and CD151 KO ZOSM cells. b. The c-myc expression was detected in the indicated cells with CD151 overexpression by western blot. c. Time-course analysis of c-myc protein levels in CD151 depletion ZOSM cells. c-myc band density relative to β-actin was quantified, and the ratio of c-myc protein/actin protein was artificially set as 1.0 for samples untreated with CHX to obtain half-time (T1/2) of c-myc. d. c-myc ubiquitination was analyzed in CD151 depletion cells. ZOSM cells were transfected with the indicated plasmids followed by treatment with MG132 for 6 h. Cell extracts were immunoprecipitated with an anti-HA antibody, and ubiquitination c-myc was detected by western blot. Figure S5. a-b. SPTLC1 mRNA and protein levels in CD151 depletion cells stably infected with vector or c-myc-expressing lentiviruses were analyzed by RT-qPCR (A) and Western blot (B). Data are shown as mean ± SD of triplicate experiments. c-d. Schematic representation of the promoter region in the human SPTLC1 gene (C). ChIP from ZOSM cells was performed with control IgG or c-myc antibody as indicated. The presence of the SPTLC1 binding sites was detected by qPCR using primers. Quantification of enrichments is represented as fold-enrichment over lgG control. e. ZOSM cells were transfected with an empty vector or c-myc plus the wild-type SPTLC1 promoter (WT) or mutated promoter (MUT) for measuring luciferase activity. Data are the mean ± SD, and the data are representative of three independent experiments. Figure S6. a. The representative images of Ki67 staining from SA3831 and SA4009 PDX tumor-bearing mice treated with vehicle or myriocin. Scale bars: 100 μm. Table S1.

This paper proposes a method for predicting the vibration environment of aircraft external stores based on deep belief network, which based on the relationship between the vibration environment, pressure altitude and Mach number of the aircraft external stores in flight. Firstly, GJB 150.16A empirical formulas were analyzed and then generating simulation data, which a total of 7470 sets. Secondly, linear regression and deep belief network models were set up respectively, and deep belief network number of hidden layer node was optimized, then the expected errors of two methods were compared and analyzed, including root mean square error (RMSE), mean absolute error (MAE) and mean relative error (MRE). Finally, DBN method was applied to a case for prediction the vibration of a certain aircraft store, intending to verify the feasibility and accuracy of DBN method. The results show that the DBN model can fully characterize the non-linear relationship between the pressure altitude, Mach number and RMS value of the aircraft external stores in the flight environment, and the overall expected effect is better than linear regression. The expected mean relative error is less than ± 3 dB. This method has provided a new way to predict the vibration environment of aircraft external stores, and is of great significance for PHM in the whole life of aviation weapon equipment.