Improvement of peptide identification with considering the abundance of mRNA and peptide

Scripts used for data analysis in this study. (DOCX 35 kb

Study on Construction Resource Optimization and Uncertain Risk of Urban Sewage Pipe Network

With considering sewage pipe network upgrading projects in the “villages” in cities, the optimization of construction resources and the assessment of delay risks could be achieved. Based on the schedule-cost hypothetical theory, the mathematical model with constraint indicators was established to obtain the expression of optimal resource input, and conclude the method to analyze the schedule uncertainties. The analysis showed that cyclical footage of pipe could be regarded as a relatively fixed value, and the cost can be regarded as a function that depending on the number of working teams. The optimal number of teams and the optimal schedule occurred when the minimum total cost achieved. In the case of insufficient meteorological data, the Monte Carlo simulation method and uncertainty analysis method can be applied to assess the impact of rainfall on the total construction period, correspondingly the probability of such risk could be derived. The calculation showed that the risk of overdue completion varied significantly according to the construction starting time. It was necessary to take rainfall risk into consideration and make corresponding strategies and measures

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04–4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77–3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients

Forecasting Collector Road Speeds Under High Percentage of Missing Data

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsly cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE

Forecasting Collector Road Speeds Under High Percentage of Missing Data

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsly cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE. </jats:p