Analysis of Spatial Travel Association Rules for Rail Transit Based on AFC and POI Data

In order to explore the spatial distribution rules and causes of urban rail transit passenger travel, this paper mines the spatial 1-frequent itemset and 2-frequent itemsets of weekdays and weekends metro passenger travel based on Apriori algorithm using the continuous week of Automatic Fare Collection System (AFC) swipe card. At the same time, the K-Means algorithm is used to cluster the subway stations and explore the causes of association rules by combining the Point of Interest (POI) data of the same period within the radiation range of the subway stations. The study shows that the spatial distribution pattern of inbound and outbound passenger flow of Shanghai rail transit is consistent between weekdays and weekends, and the outbound passenger flow is more concentrated than the inbound passenger flow, and the significance of weekends is higher; the spatial distribution of metro stations is "circled"; the analysis of the high-lift association rules show that a large passenger flow group centered on the type 3 station is formed in the spatial location, and the passenger flow within the group is mainly commuter flow with separation of employment and residence. The association rule mining of metro passenger travel data is beneficial to understanding the spatial distribution pattern and causes of metro ridership, which can provide reference for rail network planning and operation management

The Role of Postoperative Radiotherapy on Stage N2 Non-small Cell Lung Cancer

Background and objective The clinical value of postoperative radiotherapy (PORT) in stage N2 nonsmall-cell lung cancer (NSCLC) is controversy. The aim of this study is to analyze the efficacy of PORT in subgroup of stage N2 NSCLC, which can help clinicians to choose proper patients for PORT. Methods Clinical data of 359 patients with stage N2 NSCLC treated with radical surgery between Mar. 2000 and Jul. 2005 were retrospectively reviewed. Two hundred and seven patients received adjuvant chemotherapy and one hundred and four patients received adjuvant radiotherapy. First, the group of patients were analyzed to evaluate the factors affecting the overall survival. The all patients were divided based on tumor size and the number of lymph node metastasis station (single station or multiple station) so as to evaluate the role of PORT. The endpoint was overall survival (OS) and local recurrence-free survival (LRFS). Kaplan-Meier method was used to calculate the OS, LRFS and Log-rank was used to compare the difference in OS and LRFS between different groups. Results The median duration of follow-up was 2.3 years. 224 patients died. The median survival was 1.5 years and 1, 3, 5-year survival were 78%, 38% and 26%. Univariate analysis showed tumor size, the number of lymph node metastasis station and PORT were correlated with OS. Among patients, 5-year survival rates in PORT and non-PORT were 29% and 24% (P=0.047) respectively. In subgroups, PORT was related with high survival in patients with multiple station N2 compared to non-PORT: 36% vs 20% (P=0.013) and 33% vs 15% (P=0.002) in patients in patients with tumor size > 3 cm. Also, it was related with low local recurrence compared to non-PORT: 65% vs 48% (P=0.006) and 62% vs 48% (P=0.033). Conclusion PORT can improve overall survival for N2 NSCLC, especially the patients with the factors as follows: tumor size > 3 cm and multiple station N2 can benefit from PORT more or less

Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches

ObjectivePneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients.MethodsFrom January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3. Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC). The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method.ResultsOf 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50.80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0.895 (sensitivity: 84.3%; specificity: 80.0%).Pneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS.ConclusionOur study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model's output clinically meaningful

Decreased level of recent thymic emigrants in CD4+ and CD8+T cells from CML patients

<p>Abstract</p> <p>Background</p> <p>T-cell immunodeficiency is a common feature in cancer patients, which may relate to initiation and development of tumor. Based on our previous finding, to further characterize the immune status, T cell proliferative history was analyzed in CD4+ and CD8+ T cells from chronic myeloid leukemia (CML) patients.</p> <p>Methods</p> <p>Quantitative analysis of δRec-ψJα signal joint T cell receptor excision circles (sjTRECs) was performed in PBMCs, CD3+, CD4+ and CD8+T cells by real-time PCR, and the analysis of 23 <it>TRBV-D1 </it>sjTRECs was performed by semi-nested PCR. Forty eight CML cases in chronic phase (CML-CP) were selected for this study and 17 healthy individuals served as controls.</p> <p>Results</p> <p>The levels of δRec-ψJα sjTRECs in PBMCs, CD3+, CD4+, and CD8+ T cells were significantly decreased in CML patients, compared with control groups. Moreover, the numbers of detectable <it>TRBV </it>subfamily sjTRECs, as well as the frequency of particular <it>TRBV-BD</it>1 sjTRECs in patients with CML were significantly lower than those from healthy individuals.</p> <p>Conclusions</p> <p>We observed decreased levels of recent thymic emigrants in CD4+ and CD8+ T cells that may underlay the persistent immunodeficiency in CML patients.</p

Synthesis of Newly Discovered Carbon Nanoframes: A Self‐Assembly Strategy Based on DTAB @ NaCl

Abstract Carbon nanomaterials have attracted much attention in the field of science and technology for their excellent properties; however, developing a simple, environmentally friendly, and versatile synthesis strategy for the preparation of carbon nanomaterials with novel structures remains a great challenge. Herein, a surfactant @ salt (dodecyltrimethylammonium bromide DTAB @ NaCl) self‐assembly strategy for the synthesis of carbon nanomaterials with novel structures, i.e., carbon nanoframes is reported. The synthesis differs from the traditional template method in that it is characterized by the introduction of surfactants for separation and protection, and the salt can be recycled. In addition, the carbon frame size in this system can be adjusted on demand by simply adjusting the concentration of surfactant, thus realizing that the carbon nanoframe size is adjustable in the range of 232.58–322.51 nm. Impressively, the carbon nanoframe has purple, blue, and green PL emission behavior. It is anticipated that this research will provide new insights into the development of novel carbon nanomaterials

Chitosan oligosaccharide inhibits EGF-induced cell growth possibly through blockade of epidermal growth factor receptor/imitogen-activated protein kinase pathway

Chitosan oligosaccharide (COS) has been shown to regulate various cellular and biological functions. Epidermal growth factor (EGF) plays a significant role in tumorigenesis and invasiveness of some cancers. The aim of this study was to evaluate the effects of COS on EGF-induced cell growth and the further mechanisms. The results demonstrated that COS could inhibit EGF-induced epithelial GE11 cells proliferation in a dose dependent manner. In addition, EGF stimulated the epithelial cells to undergo morphological alteration, exhibiting mesenchymal cells higher metastatic and invasive potential, however, COS could partly suppress aforementioned morphological change. Signal transduction studies indicated COS repressed epidermal growth factor receptor (EGFR) phosphorylation and mitogen-activated protein kinase (MAPK) activation, but not Grb2, Ras, and Raf. Taken together, chitosan oligosaccharide inhibited EGF-induced cell growth and migration through blockade of the EGFR/MAPK signal transduction pathway. (C) 2017 Elsevier B.V. All rights reserved.</p

Water quality prediction analysis of Qingyi River based on time series

According to the current situation of water quality in drainage basin, the key to improve the prediction accuracy is to select the appropriate prediction model of water quality. The time series method excellently reflected the continuity of the future data in the case of emphasizing historical data. What’s more, the time series method has the higher short-term prediction accuracy and simple modeling process. So, the time series method was used to establish the Auto-Regressive and Moving Average (ARMA) model for the time series of the concentration of dissolved oxygen (DO), biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH3-N) and total nitrogen (TN) at the Guidu fu section of Qingyi River from January 2011 to December 2015. Then, the concentrations of the five water quality indicators from January to June 2016 were predicted, which were verified and analyzed with the measured values. The results show that the model has fine fitting effect and higher prediction accuracy, which can accurately reflect the current and future change trends of the water quality