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

Factoring Electrochemical and Full-Lifecycle Aging Modes of Battery Participating in Energy and Transportation Systems

Transportation electrification emerges as a pivotal strategy to realize deep decarbonization for many countries, and the central part of this is battery. However, a key challenge often overlooked is the impact of battery aging on the economy and longevity of electric vehicles (EVs). To address this issue, the paper proposes a novel battery full-life degradation (FLD) model and energy management framework that substantially improves the overall economic efficiency of Battery Energy Storage Systems (BESS). In the first stage, battery electrochemical aging features are modeled by learning cell fading rate under various healthy states, capitalized on the Stanford experimental open dataset. Accordingly, a lifecycle degradation model is then developed considering various operational conditions and aging stages to quantitatively assess the effects of depth of discharge, C-rate, state of health, and state of charge. In the second stage, battery electrochemical aging features are integrated into vehicle energy management so that batteries under different fading rates can be flexibly utilized during whole lifecycles. The proposed methods are validated on a practical UK distribution network and a hybrid vehicles hardware-in-the-loop platform. With the proposed methods, EV users can make informed decisions to optimize energy usage and prolong the lifespan of vehicle BESS, thereby fostering a more sustainable and efficient transportation infrastructure.</p

Causal association of circulating cholesterol levels with dementia: a mendelian randomization meta-analysis

Prospective studies have shown that abnormally circulating cholesterol is associated with the risk of dementia. However, whether the association is causal or not remains unclear. We attempt to infer the causal association in a MR meta-analysis by using ApoE gene polymorphisms as instrument variables. Studies with dementia risk (27 studies) or circulating lipid levels (7 studies) were included, with totally 3136 dementia patients and 3103 healthy controls. The analyses showed that carriers of ε2 allele significantly were of decreased risk of AD (OR = 0.70; 95% CI: 0.58–0.84; P \u3c 0.01), whereas carriers of ε4 allele were of increased risk of AD (OR = 3.62; 95% CI: 3.03–4.32; P \u3c 0.05), compared to these of ε3 allele. Circulating TC was significantly reduced in carriers of ε2 allele (WMD = − 0.29 mmol/L; 95% CI: −0.54 to −0.03; P \u3c 0.05) and increased in carriers of ε4 allele (WMD = 0.42 mmol/l; 95% CI: 0.001–0.84; P \u3c 0.05). In addition, carriers of ε4 allele had reduction in circulating HDL-C (WMD = − 0.04 mmol/L; 95% CI: − 0.07 to −0.001; P \u3c 0.05). In comparing allele ε2 with ε3, the predicted OR of having AD for 1 mg/dL increment in circulating TC was 0.97 (95% CI: 0.86–0.98; P \u3c 0.05). Comparing allele ε4 with ε3, the predicted OR for a 1 mg/dL increment in TC was 1.08 (95% CI: 1.05–17.58; P \u3c 0.05), and reduction in HDL-C was 2.30 (95% CI: 1.51–43.99; P \u3c 0.05). Our findings demonstrate that high circulating TC and reduced HDL-C levels might be potential risk factors of the development of AD

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