Numerical Study on the Elastic Deformation and the Stress Field of Brittle Rocks under Harmonic Dynamic Load

Funding: The support of National Natural Science Foundation of China (No. 51704074) and Youth Science Foundation of Heilongjiang Province (No. QC2018049) are gratefully acknowledged. The work is also supported by Talent Cultivation Foundation (No. SCXHB201703; No. ts26180119; No. td26180141) and Youth Science Foundation (No. 2019QNL-07) of Northeast Petroleum University.Peer reviewedPublisher PD

酵母におけるオルガネラ間ステロール輸送機構に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 堀内 裕之, 東京大学教授 佐藤 隆一郎, 東京大学准教授 足立 博之, 東京大学准教授 有岡 学, 東京大学准教授 舘川 宏之University of Tokyo(東京大学

MFAP3L activation promotes colorectal cancer cell invasion and metastasis

AbstractAn abundance of microfibril-associated glycoprotein 3-like (MFAP3L) significantly correlates with distant metastasis in colorectal cancer (CRC), although the mechanism has yet to be explained. In this study, we observed that MFAP3L knock-down resulted in reduced CRC cell invasion and hepatic metastasis. We evaluated the cellular location and biochemical functions of MFAP3L and found that this protein was primarily localized in the nucleus of CRC cells and acted as a protein kinase. When EGFR translocated into the nucleus upon stimulation with EGF, MFAP3L was phosphorylated at Tyr287 within its SH2 motif, and the activated form of MFAP3L phosphorylated ERK2 at Thr185 and Tyr187. Moreover, the metastatic behavior of CRC cells in vitro and in vivo could be partially explained by activation of the nuclear ERK pathway through MFAP3L phosphorylation. Hence, we experimentally demonstrated for the first time that MFAP3L likely participates in the nuclear signaling of EGFR and ERK2 and acts as a novel nuclear kinase that impacts CRC metastasis

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies

Construction and validation of a nomogram of risk factors for new-onset atrial fibrillation in advanced lung cancer patients after non-surgical therapy

ObjectiveRisk factors of new-onset atrial fibrillation (NOAF) in advanced lung cancer patients are not well defined. We aim to construct and validate a nomogram model between NOAF and advanced lung cancer.MethodsWe retrospectively enrolled 19484 patients with Stage III-IV lung cancer undergoing first-line antitumor therapy in Shanghai Chest Hospital between January 2016 and December 2020 (15837 in training set, and 3647 in testing set). Patients with pre-existing AF, valvular heart disease, cardiomyopathy were excluded. Logistic regression analysis and propensity score matching (PSM) were performed to identify predictors of NOAF, and nomogram model was constructed and validated.ResultsA total of 1089 patients were included in this study (807 in the training set, and 282 in the testing set). Multivariate logistic regression analysis showed that age, c-reactive protein, centric pulmonary carcinoma, and pericardial effusion were independent risk factors, the last two of which were important independent risk factors as confirmed by PSM analysis. Nomogram included independent risk factors of age, c-reactive protein, centric pulmonary carcinoma, and pericardial effusion. The AUC was 0.716 (95% CI 0.661–0.770) and further evaluation of this model showed that the C-index was 0.716, while the bias-corrected C-index after internal validation was 0.748 in the training set. The calibration curves presented good concordance between the predicted and actual outcomes.ConclusionCentric pulmonary carcinoma and pericardial effusion were important independent risk factors for NOAF besides common ones in advanced lung cancer patients. Furthermore, the new nomogram model contributed to the prediction of NOAF

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis

BackgroundThe primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis.MethodsWe used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations.ResultsWe discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status.ConclusionThis study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis

Tumor abnormal protein as a promising biomarker for screening solid malignancies and monitoring recurrence and metastasis

BackgroundTumor abnormal protein (TAP), the sugar chain protein released by tumor cells during metabolism, allows the development of a technique that exploits aggregated tumor-associated abnormal sugar chain signals in diagnosing malignancies. Clinically, we have found that TAP detection can well predict some malignancies, but several physicians have not paid attention, and related studies have been minimal.MethodsWe evaluated TAP’s ability to distinguish between malignancies and benign diseases by receiver operating characteristic (ROC) curve analysis and studied the possibility of monitoring malignancy progression by evaluating TAP levels in follow-up. We used Kaplan-Meier survival curves and Cox proportional hazard regression models to investigate the relationship between TAP and prognosis.ResultsTAP levels were higher in whole solid malignancies and every type of solid malignancy than in benign patients. ROC curve analysis showed that TAP levels aid in distinguishing between malignancies and benign diseases. TAP levels decreased in patients with complete remission (CR) after treatment and increased in patients with relapse from CR. Patients with metastases had higher TAP levels than non-CR patients without metastases. There was no difference in overall survival among patients with different TAP levels, and multivariate analysis suggested that TAP was not an independent risk factor for solid malignancies.ConclusionTAP is an effective screening biomarker for many solid malignancies that can be used to monitor the progression of malignancies but not to prognosticate

An iterative network partition algorithm for accurate identification of dense network modules

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks