Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability

Rubber Material Properties of Several Rubber Tree Strains

：The rubber from rubber tree strain reyan 8-79 (hainan), zhanshi 218-6 (guangdong), yunyan 73-46 and yunyan 75-11 (yunnan) were tested to determine the physical and chemical properties, processing properties of raw rubber and physical and mechanical properties of vulcanized rubber. The results showed that raw rubber from different tree strains had different physical and chemical properties, processing properties,andthe physical and mechanical properties of vulcanized rubber were different as well. Yunyan 75-11 had the highest mooney viscosity, Reyan 8-79 had the highest protein content, Zhanshi 218-6 had the best tensile and tearing strength, Yunyan 73-46 had small elastic modulus, large loss factor and good processing properties

“One-stop” synergistic strategy for hepatocellular carcinoma postoperative recurrence

Residual tumor recurrence after surgical resection of hepatocellular carcinoma (HCC) remains a considerable challenge that imperils the prognosis of patients. Notably, intraoperative bleeding and postoperative infection are potential risk factors for tumor recurrence. However, the biomaterial strategy for the above problems has rarely been reported. Herein, a series of cryogels (coded as SQ-n) based on sodium alginate (SA) and quaternized chitosan (QC) were synthesized and selected for optimal ratios. The in vitro assays showed that SQ-50 possessed superior hemostasis, excellent antibacterial property, and great cytocompatibility. Subsequently, SQAP was constructed by loading black phosphorus nanosheets (BPNSs) and anlotinib hydrochloride (AL3818) based on SQ-50. Physicochemical experiments confirmed that near-infrared (NIR)-assisted SQAP could control the release of AL3818 in photothermal response, significantly inhibiting the proliferation and survival of HUVECs and H22 cells. Furthermore, in vivo studies indicated that the NIR-assisted SQAP prevented local recurrence of ectopic HCC after surgical resection, achieved through the synergistic effect of mPTT and molecular targeted therapy. Thus, the multifunctional SQAP provides a “one-stop” synergistic strategy for HCC postoperative recurrence, showing great potential for clinical application

Proteomics Reveals the Obstruction of Cellular ATP Synthesis in the Ruminal Epithelium of Growth-Retarded Yaks

Growth-retarded yaks are of a high proportion on the Tibetan plateau and reduce the economic income of farmers. Our previous studies discovered a maldevelopment in the ruminal epithelium of growth-retarded yaks, but the molecular mechanisms are still unclear. This study aimed to reveal how the proteomic profile in the ruminal epithelium contributed to the growth retardation of yaks. The proteome of the ruminal epithelium was detected using a high-resolution mass spectrometer. There were 52 proteins significantly differently expressed between the ruminal epithelium of growth-retarded yaks and growth-normal yaks, with 32 downregulated and 20 upregulated in growth-retarded yaks. Functional analysis showed the differently expressed proteins involved in the synthesis and degradation of ketone bodies (p = 0.012), propanoate metabolism (p = 0.018), pyruvate metabolism (p = 0.020), and mineral absorption (p = 0.024). The protein expressions of SLC26A3 and FTH1, enriched in the mineral absorption, were significantly downregulated in growth-retarded yaks. The key enzymes ACAT2 and HMGCS2 enriched in ketone bodies synthesis and key enzyme PCCA enriched in propanoate metabolism had lower protein expressions in the ruminal epithelium of growth-retarded yaks. The ATP concentration and relative mitochondrial DNA copy number in the ruminal epithelium of growth-normal yaks were dramatically higher than those of growth-retarded yaks (p p COQ9, COX4, and LDHA, which are the encoding genes in MRCC I, IV and anaerobic respiration, were also significantly decreased in the ruminal epithelium of growth-retarded yaks (p p p p p p < 0.01, r = 0.770). These results suggested that growth-retarded yaks had a lower VFA metabolism, ketone bodies synthesis, ion absorption, and ATP synthesis in the ruminal epithelium; it also indicated that the growth retardation of yaks is related to the obstruction of cellular ATP synthesis in rumen epithelial cells

Biomarker Discovery and Verification of Esophageal Squamous Cell Carcinoma Using Integration of SWATH/MRM

We propose an efficient integration of SWATH with MRM for biomarker discovery and verification when the corresponding ion library is well established. We strictly controlled the false positive rate associated with SWATH MS signals and carefully selected the target peptides coupled with SWATH and MRM. We collected 10 samples of esophageal squamous cell carcinoma (ESCC) tissues paired with tumors and adjacent regions and quantified 1758 unique proteins with FDR 1% at protein level using SWATH, in which 467 proteins were abundance-dependent with ESCC. After carefully evaluating the SWATH MS signals of the up-regulated proteins, we selected 120 proteins for MRM verification. MRM analysis of the pooled and individual esophageal tissues resulted in 116 proteins that exhibited similar abundance response modes to ESCC that were acquired with SWATH. Because the ESCC-related proteins consisted of a high percentile of secreted proteins, we conducted the MRM assay on patient sera that were collected from pre- and postoperation. Of the 116 target proteins, 42 were identified in the ESCC sera, including 11 with lowered abundances postoperation. Coupling SWATH and MRM is thus feasible and efficient for the discovery and verification of cancer-related protein biomarkers

Systematic Analysis of Missing Proteins Provides Clues to Help Define All of the Protein-Coding Genes on Human Chromosome 1

Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32–39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535

Systematic Analysis of Missing Proteins Provides Clues to Help Define All of the Protein-Coding Genes on Human Chromosome 1

Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32–39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535