Stochastic Kinetics of Nanocatalytic Systems.

Catalytic reaction events occurring on the surface of a nanoparticle constitute a complex stochastic process. Although advances in modern single-molecule experiments enable direct measurements of individual catalytic turnover events occurring on a segment of a single nanoparticle, we do not yet know how to measure the number of catalytic sites in each segment or how the catalytic turnover counting statistics and the catalytic turnover time distribution are related to the microscopic dynamics of catalytic reactions. Here, we address these issues by presenting a stochastic kinetics for nanoparticle catalytic systems. We propose a new experimental measure of the number of catalytic sites in terms of the mean and variance of the catalytic event count. By considering three types of nanocatalytic systems, we investigate how the mean, the variance, and the distribution of the catalytic turnover time depend on the catalytic reaction dynamics, the heterogeneity of catalytic activity, and communication among catalytic sites. This work enables accurate quantitative analyses of single-molecule experiments for nanocatalytic systems and enzymes with multiple catalytic sites.

GNSS Positioning by CORS and EGM2008 in Jilin Province, China.

The Continuously Operating Reference Station (CORS) technique has been widely applied in land resource management, surveying, mapping, deformation monitoring, precise navigation, etc. This article analyzed the positioning method using EGM2008 and CORS of Jilin Province, China. The vertical transformation of EGM2008 from WGS84 to China's CGCS2000 datum and the horizontal coordinate transformation from CGCS2000 to a triangulation coordinate system were discussed. The results indicated that a local geoid with respect to CGCS2000 can be transferred from EGM2008 with the same accuracy, and the geoid correction between CGCS2000 and WGS84 varied from 0.023 m to 0.111 m. The coordinate transformation method based on the curve surface approximation method indicated that the theoretical error was less than 0.09 m in the grid within 10° longitudinal and 5° latitudinal, and less than 0.3 m in large area and 0.1 m in small area in field validation. The method proposed in this article expanded the positioning result and its application for JLCORS and other CORS with local datum.

TNFR2 promotes pancreatic cancer proliferation, migration, and invasion via the NF-κB signaling pathway.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignant disease with low overall survival; chemotherapy and immunotherapy have limited efficacy. Tumor necrosis factor receptor 2 (TNFR2), a type II transmembrane protein, contributes to the development and progression of several tumors. In this study, we elucidated the effect and molecular mechanisms of TNFR2. METHOD: We used The Cancer Genome Atlas and the Genotype-Tissue Expression database to compare the expression of the TNFR2 gene between normal and malignant pancreatic tissue. Using immunohistochemical staining, we divided the patients into high and low-expression groups, then investigated clinicopathologic data and survival curves of pancreatic cancer patients. We measured TNFR2 protein expression in PANC-1 and ASPC-1 pancreatic cancer cells subjected to TNFR2 small interfering RNA or negative control treatment. We performed proliferation, invasion, and migration assays to study the biological effects of TNFR2 in PDAC. The molecular mechanisms were validated using western blotting. RESULTS: TNFR2 was more highly expressed in PDAC cells and tissues than controls. Abundant expression of TNFR2 was associated with aggressive clinicopathologic characteristics and poor outcomes. Overexpression of TNFR2 promoted PDAC cell proliferation, migration, and invasion in vitro. Mechanistically, TNFR2 binds to TNF-α and activates the NF-κB signaling pathway. CONCLUSION: TNFR2 is a prognostic marker that facilitates the proliferation, migration, and invasion of PDAC via the NF-κB signaling pathway. TNFR2 may become a therapeutic target.

Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm.

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population. Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived. Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05-6.85), platelet packed volume (OR: 2.63, CI:2.31-3.79), leukocyte count (OR: 2.01, CI:1.79-2.19), red blood cell count (OR: 1.99, CI:1.80-2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12-1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram. Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

Super-Gaussian, superdiffusive transport of multimode active matter.

Living matter often exhibits multimode transport that switches between an active, self-propelled motion and a seemingly passive, random motion. Here, we investigate an exactly solvable model of multimode active matter, such as living cells and motor proteins, which alternatingly undergoes active and passive motion. Our model study shows that the reversible transition between a passive mode and an active mode causes super-Gaussian transport dynamics, observed in various experiments. We find the non-Gaussian character of the matter's displacement distribution is essentially determined by the population ratio between active and passive motion. Interestingly, under a certain population ratio of the active and passive modes, the displacement distribution changes from sub-Gaussian to super-Gaussian as time increases. The mean-square displacement of our model exhibits transient superdiffusive dynamics, yet recovers diffusive behavior at both the short- and long-time limits. We finally generalize our model to encompass complex, multimode active matter in an arbitrary spatial dimension.