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OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

IntroductionPlasmodium falciparum is the most damaging malaria pathogen and brings a heavy burden to global health. Host switching and morphological changes in P. falciparum are dependent on an effective gene expression regulatory system. C5 methylation of cytosines is a common RNA modification in eukaryotes, and the NSUN family are essential m5C modification executors. Currently, little is known about this family in Plasmodium spp. In this study, we focus on exploring the function of PfNSUN1 protein.MethodsAn efficient CRISPR/Cas9 gene editing technique was applied to construct the PfNSUN1 knockdown strain. The knockdown efficiency was confirmed by growth curves and western blot experiments. The knockdown transcriptome data was acquired to find differentially expressed genes, and target genes of PfNSUN1 protein were identified by RNA immunoprecipitation and high-throughput sequencing experiments.ResultsThe efficiency of PfNSUN1 protein down-regulated was about 34%. RNA-seq data revealed that differentially expressed genes were mainly down-regulated. And there were 224, 278, 556 genes that were down-regulated with more than 2-fold changes and p-adj

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

The emergence of the Digital Twin of Earth (DTE) signifies a pivotal advancement in the field of the Digital Earth, which includes observations, simulations, and predictions regarding the state of the Earth system and its temporal evolution. While the Geospatial Data Cubes (GDCs) hold promise in probing the DTE across time, existing GDC approaches are not well-suited for real-time data ingestion and processing. This paper proposes the design and implementation of a real-time ingestion and processing approach in Geospatial Data Cube (RTGDC), achieved by ingesting real-time observation streams into data cubes. The methodology employs a publish/subscribe model for efficient observation ingestion. To optimize observation processing within the data cube, a distributed streaming computing framework is utilized in the implementation of the RTGDC. This approach significantly enhances the real-time capabilities of GDCs, providing an efficient solution that brings the realization of the DTE concept within closer reach. In RTGDC, two cases involving local and global scales were implemented, and their real-time performance was evaluated based on latency, throughput, and parallel efficiency.

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies.

Abstract The human‐environmental system in drylands is centered on oases. Despite its extent and socio‐ecological importance, understanding the dynamic changes of global oases and their human and environmental driving forces is imperative for sustainable development in drylands under global warming. Nevertheless, the dynamic changes of global oases and how they respond to the evolving environment are not well established. In this study, three criteria were summarized (i.e., existing in dryland climates, surrounded or partially surrounded by desert terrain, having a reliable source of freshwater and forming landscape units with higher vegetation coverage/productivity). A global oasis distribution map from 1995 to 2020 was generated using European Space Agency Climate Change Initiative Land Cover and GIMMS‐3G+ data (overall accuracy within a 95% confidence interval is 0.85 ± 0.01) based on overlay analysis and visual interpretation. In addition, we used geographic and temporal weighted regression methods to evaluate the potential macro‐level elements affecting both global and local oasis growth. The result showed that the global oases area in 2020 occupied an area of 191.91 Mha, and most oases existed in Asia (77.3%). The global oases area has significantly increased from 1995 to 2020 (+8.65 Mha). However, about 13.43 Mha of the global oases are desertified, indicating a high risk of desertification. Water resources, contributing 51.36% to the total driver's contribution, are key to the global oasis expansion. In the context of climate (climate variability and climate change), this research highlights the need for improved holistic water resource management for long‐term global oasis growth, particularly in developing countries where the oases' development is threatened by water scarcity and desertification.

The escalating impacts of human activities on the ecological environment underscore the significance of valuing ecological resources within the paradigm of sustainable development. In this context, the ecological service value (ESV) of the Danjiangkou watershed (Hubei section) was assessed using a revised equivalent factor evaluation method. Additionally, the study determined the human activity intensity (HAI) and employed GeoDetector to delineate the spatial heterogeneity of ESV, thereby investigating the underlying causes. Results show that vegetation coverage attains a high level of 94%, with a minor 1.37% land use change between 2016 and 2020. Secondly, ESV exhibited a substantial variation, rising from 75.55 billion to 79.81 billion yuan, a growth rate of 5.64%. Lastly, the explanatory degree for the synergistic interplay of HAI and expenditure on the spatial variation of ESV surpassed 30%, and the average sensitivity coefficient of up to 0.82 further indicated the apparent impacts of land use and human interference activities on ESV. The study makes an excellent effort to clarify the complex coupling relationships between different land use types and ESV, which could serve as an example and model for other studies in the field.

Abstract Quick Access Recorders (QARs) provide an important data source for Flight Operation Quality Assurance (FOQA) and flight safety. It is generally characterized by large volume, high-dimensionality and high frequency, and these features result in extreme complexities and uncertainties in its usage and comprehension. In this study, we proposed a Time-Feature Attention (TFA)-based Convolutional Auto-Encoder (TFA-CAE) network model to extract essential flight features from QAR data. As a case study, we used the QAR data landing at the Kunming Changshui International Airport and Lhasa Gonggar International Airport as the experimental data. The results show that (1) the TFA-CAE model performs the best in extracting representative flight features in comparison to some traditional or similar approaches, such as Principal Component Analysis (PCA), Convolutional Auto-Encoder (CAE), Self-Attention-based CAE (SA-CAE), Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models; (2) flight patterns corresponding to different runways can be recognized; and (3) anomalous flights can effectively deviate from many observations. Overall, the TFA-CAE model provides a well-established technique for further usage of QAR data, such as flight risk detection or FOQA.

The supply and demand of ecosystem services (ES) present apparent spatial heterogeneities within cities, with a higher demand but limited supply in urban centers, and vice versa in suburban and rural areas. However, there is still a lack of theory and relevant quantitative tools to quantify the distribution patterns and spatial mismatches of ES supply and demand across space, e.g., along urban-rural gradients. Here, we adopt a modified sigmoid function to quantify spatial gradients of ES supply and demand of six cities within the Danjiangkou Basin in Hubei Province, China. ES supply and demand are calculated using the value equivalent method and the land development index model, respectively. Results show that as the distance from the city center increases, there is a decrease in ES demand, while ES supply exhibits a gradual increase through the gradient analysis. We then utilized the modified sigmoid model to accurately quantify the S-shape curve for ES supply and the inverse S-shape curve for ES demand. We further observed that the balance point (the intersection of the ES supply and demand curves) of ES supply and demand gradually moved towards the outskirts of the city as it expanded. Our study introduces a quantitative tool for analyzing the spatial gradients of ES supply and demand within cities.

ABSTRACTAs an established spatial analytical tool, Geographically Weighted Regression (GWR) has been applied across a variety of disciplines. However, its usage can be challenging for large datasets, which are increasingly prevalent in today’s digital world. In this study, we propose two high-performance R solutions for GWR via Multi-core Parallel (MP) and Compute Unified Device Architecture (CUDA) techniques, respectively GWR-MP and GWR-CUDA. We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models (GWmodel), Multi-scale GWR (MGWR) and Fast GWR (FastGWR). Results showed that all five solutions perform differently across varying sample sizes, with no single solution a clear winner in terms of computational efficiency. Specifically, solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size. For a large sample size, GWR-MP and FastGWR provided coherent solutions on a Personal Computer (PC) with a common multi-core configuration, GWR-MP provided more efficient computing capacity for each core or thread than FastGWR. For cases when the sample size was very large, and for these cases only, GWR-CUDA provided the most efficient solution, but should note its I/O cost with small samples. In summary, GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones, where for certain data-rich GWR studies, they should be preferred.

Frequency-modulated continuous wave radar is capable of constant, real-time detection of human presence and monitoring of cardiopulmonary signals such as respiration and heartbeat. In highly cluttered environments or when the human body moves randomly, noise signals may be relatively large in some range bins, making it crucial to accurately select the range bin containing the target cardiopulmonary signal. In this paper, we propose a target range bin selection algorithm based on a mixed-modal information threshold. We introduce a confidence value in the frequency domain to determine the state of the human target and employ the range bin variance in the time domain to determine the range bin change status of the target. The proposed method accurately detects the state of the target and effectively selects the range bin containing the cardiopulmonary signal with a high signal-to-noise ratio. Experimental results demonstrate that the proposed method achieves better accuracy in cardiopulmonary signal rate estimation. Moreover, the proposed algorithm is lightweight in data processing and has good real-time performance.

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.

Under global warming, the gradual pattern of spring phenology along elevation gradients (EG) has significantly changed. However, current knowledge on the phenomenon of a more uniform spring phenology is mainly focused on the effect of temperature and neglected precipitation. This study aimed to determine whether a more uniform spring phenology occurs along EG in the Qinba Mountains (QB) and explore the effect of precipitation on this pattern. We used Savitzky-Golay (S-G) filtering to extract the start of season (SOS) of the forest from the MODIS Enhanced Vegetation Index (EVI) during 2001-2018 and determined the main drivers of the SOS patterns along EG by partial correlation analyses. The SOS showed a more uniform trend along EG in the QB with a rate of 0.26 ± 0.01 days 100 m-1 per decade during 2001-2018, but there were differences around 2011. A delayed SOS at low elevations was possibly due to the reduced spring precipitation (SP) and spring temperature (ST) between 2001 and 2011. Additionally, an advanced SOS at high elevations may have been caused by the increased SP and reduced winter temperature (WT). These divergent trends contributed to a significant uniform trend of SOS with a rate of 0.85 ± 0.02 days 100 m-1 per decade. Since 2011, significantly higher SP (especially at low elevations) and rising ST advanced the SOS, and the SOS at lower altitudes was more advanced than at higher altitudes, resulting in greater SOS differences along EG (0.54 ± 0.02 days 100 m-1 per decade). The SP determined the direction of the uniform trend in SOS by controlling the SOS patterns at low elevations. A more uniform SOS may have important effects on local ecosystem stability. Our findings could provide a theoretical basis for establishing ecological restoration measures in areas experiencing similar trends.

Spatial heterogeneity or non-stationarity has become a popular and necessary concern in exploring relationships between variables. In this regard, geographically weighted (GW) models provide a powerful collection of techniques in its quantitative description. We developed a user-friendly, high-performance and systematic software, named GWmodelS, to promote better and broader usages of such models. Apart from a variety of GW models, including GW descriptive statistics, GW regression models, and GW principal components analysis, data management and mapping tools have also been incorporated with well-designed interfaces.

The COVID-19 epidemic widely spread across China from Wuhan, Hubei Province, because of huge migration before 2020 Chinese New Year. Previous studies demonstrated that population outflows from Wuhan determined COVID-19 cases in other cities but neglected spatial heterogeneities of their relationships. Here, we use Geographically Weighted Regression (GWR) model to investigate the spatially varying influences of outflows from Wuhan. Overall, the GWR model increases explanatory ability of outflows from Wuhan by 20%, with the adjusted R2 increasing from ~0.6 of Ordinary Least Squares (OLS) models to ~0.8 of GWR models. The coefficient between logarithmic of outflows from Wuhan and COVID-19 cases in other cities is generally less than 1. The sub-linear scaling relationship indicates the increasing returns of outflows was restrained, proving the epidemic was efficiently controlled outside Hubei at the beginning without obvious local transmissions. Coefficients in GWR models vary in cities. Not only cities around Wuhan but also cities having close connections with Wuhan experienced higher coefficients, showing a higher vulnerability of these cities. The secondary or multi-level transmission networks deserve to be further explored to fully uncover influences of migrations on the COVID-19 pandemic.

Abstract Acute myeloid leukemia (AML) is the most prevalent form of acute leukemia. Patients with AML often have poor clinical prognoses. Hypoxia can activate a series of immunosuppressive processes in tumors, resulting in diseases and poor clinical prognoses. However, how to evaluate the severity of hypoxia in tumor immune microenvironment remains unknown. In this study, we downloaded the profiles of RNA sequence and clinicopathological data of pediatric AML patients from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, as well as those of AML patients from Gene Expression Omnibus (GEO). In order to explore the immune microenvironment in AML, we established a risk signature to predict clinical prognosis. Our data showed that patients with high hypoxia risk score had shorter overall survival, indicating that higher hypoxia risk scores was significantly linked to immunosuppressive microenvironment in AML. Further analysis showed that the hypoxia could be used to serve as an independent prognostic indicator for AML patients. Moreover, we found gene sets enriched in high-risk AML group participated in the carcinogenesis. In summary, the established hypoxia-related risk model could act as an independent predictor for the clinical prognosis of AML, and also reflect the response intensity of the immune microenvironment in AML.

Given the high revenues of financial enterprises, they should carry out their social responsibilities to contribute to sustainable social and economic development. The purpose of this study is to provide a more efficient strategy for China’s listed financial enterprises to improve their green value under the condition of realizing the optimal allocation of their resources in the future. To this end, the present study adopts the fuzzy set qualitative comparative analysis method on 26 domestic listed banks to examine the impact of social responsibility undertaking on the improvement of the green values of enterprises. The findings show that the promotion of green values of listed financial enterprises in China does not simply depend on a one-dimension social responsibility investment but requires a combination of multiple dimensions. Accordingly, policy recommendations are provided to help ensure the long-term development of financial enterprises while achieving sustainable economic development.

Objective The aim of this study was to compare the clinical efficacy of percutaneous endoscopic interlaminar discectomy (PEID) and percutaneous endoscopic transforaminal discectomy (PETD) in treating L5/S1 disc herniation. Methods A retrospective analysis of 76 patients with L5/S1 intervertebral disc herniation was performed. There were two surgical treatment groups: one with patients receiving PEID and the other with patients receiving PETD. The two groups were compared by length of surgery, times of intraoperative X‐ray exposure, postoperative time in bed, length of hospital stay, operative complications, patient's assessment of pain using a visual analogue scale (VAS), and disability using the Oswestry disability index (ODI) before and after surgery. Results Subjects in the PEID group were in surgery for 60.90 ± 13.11 min and needed intraoperative X‐ray exposure 4.10 ± 1.09 times. Patients in this group were ambulatory by 7.52 ± 1.08 h after surgery and were hospitalized for 5.05 ± 0.92 days. In contrast, patients in the PETD group were in surgery for 84.06 ± 15.58 min and needed intraoperative X ray exposure 12.81 ± 8.46 times. These patients were ambulatory by 7.06 ± 0.91 h after surgery and remained in the hospital for 4.94 ± 0.80 days. Based on these data, operation time and fluoroscopy time were significantly less (P

Abstract Background LncRNA can regulate gene at various levels such as apparent genetics, alternative splicing, and regulation of mRNA degradation. However, the molecular mechanism of LncRNA in cholangiocarcinoma is still unclear. This deserves further exploration. Methods We investigated the expression of AGAP2-AS1 in 32 CCA tissues and two CCA cell lines. We found a LncRNA AGAP2-AS1 which induced by SP1 has not been reported in CCA, and Knockdown and overexpression were used to investigate the biological role of AGAP2-AS1 in vitro. CHIP and RIP were performed to verify the putative targets of AGAP2-AS1. Results AGAP2-AS1 was significantly upregulated in CCA tumor tissues. SP1 induced AGAP2-AS1 plays an important role in tumorigenesis. AGAP2-AS1 knockdown significantly inhibited proliferation and caused apoptosis in CCA cells. In addition, we demonstrated that AGAP2-AS1 promotes the proliferation of CCA. Conclusions We conclude that the long non-coding RNA AGAP2-AS1 plays a role in promoting the proliferation of cholangiocarcinoma.

Abstract Background Long noncoding RNAs (lncRNAs) are known to regulate tumorigenesis and cancer progression, but their contributions to non-small-cell lung cancer (NSCLC) metastasis remain poorly understood. Our previous and other studies have revealed the involvement of upregulated LINC01234 in regulating gastric cancer and colon cancer cells proliferation, and we aimed to investigate whether LINC01234 overexpression also contribute to cancer cells metastasis in this study. Methods We collect the NSCLC tissues and adjacent non-tumor tissues and analyzed expression levels of LINC01234 by quantitative reverse-transcription PCR. LINC01234 were knocked down by using siRNAs or shRNAs, and overexpressed by transfection with overexpression vector; RNA levels of miRNA were downregulated or upregulated with inhibitors or mimics. Transwell assays were used to evaluate cell migration and invasive ability; in vivo metastasis experiments were performed to investigate the effect of LINC01234 on NSCLC cells metastasis. Luciferase reporter, RIP, and ChIP assays were used to determine the regulation of LINC01234 on its targets. Results LINC01234 expression is increased in NSCLC tissues, and its upregulation is associated with metastasis and shorter survival in NSCLC. Downregulation of LINC01234 impairs cell migration and invasion in vitro, and inhibits cells metastasis in vivo by acting as a competing endogenous RNA for the miR-340-5p and miR-27b-3p. LINC01234 also interacts with the RNA-binding proteins LSD1 and EZH2, leading to histone modification and transcriptional repression of the anti-proliferative genes BTG2. Conclusions Taken together, our findings identify two oncogenic regulatory axes in NSCLC centering on LINC01234: one involving miR-340-5p/miR-27b-3p in the cytoplasm and the second involving EZH2, LSD1, and BTG2 in the nucleus. Our study indicates that these genes may be targeted to reduce or prevent NSCLC metastasis.

Undifferentiated large-cell lung cancer is a rare type of non-small cell lung cancer (NSCLC) with a poor prognosis. It is insensitive to chemotherapy and easily develops drug resistance. Analysis of the Surveillance, Epidemiology, and End Results (SEER) database showed that patients with stage IV undifferentiated large-cell lung cancer had a median overall survival (OS) of only 4 months and that those who received chemotherapy had a median OS of only 5 months longer than those who did not. For the first time, we report a case of advanced large-cell undifferentiated lung cancer with rare tonsil metastasis. The patient developed resistance after 3 months of platinum-based systemic chemotherapy and local treatment. Antiangiogenic therapy has been continuously progressing and has shown certain efficacy in treating many malignant tumors, such as lung cancer. However, there are no relevant studies or case reports on antiangiogenic therapy in the treatment of undifferentiated large-cell lung cancer. Anlotinib, an orally delivered small-molecule antiangiogenic tyrosine kinase inhibitor (TKI), was administered to this patient after chemotherapy resistance occurred, and the outcome was assessed as continued stable disease (SD). As of the last follow-up evaluation, the progression-free survival (PFS) of the patient was 21.5 months, and the OS was 27.5 months. Retrospective immunohistochemical analysis showed that the patient was positive for one of the targets of anlotinib (PDGFR). In general, the findings in this case suggest that anlotinib may be an option with good efficacy for patients with large-cell undifferentiated lung cancer after chemotherapy resistance that may have good efficacy and also suggest that PDGFR may be the target underlying this effect.

Lung cancer is the most common cancer all around the world, with high morbidity and mortality. Long noncoding RNA (lncRNA) has been reported to have a critical role in non-small-cell lung cancer (NSCLC) proliferation and migration. In the present study, we analyzed The Cancer Genome Atlas (TCGA) data, and we found that lncRNA Small Nucleolar RNA Host Gene 17 (SNHG17) was upregulated in NSCLC driven by the amplification of copy number, indicating the special role of SNHG17 in NSCLC. The full exact length of SNHG17 was determined by rapid amplification of cDNA ends (RACE). We modulated SNHG17 expression by RNAi and a series of functional assays were performed. Flow cytometry was used to explore the involvement of SNHG17 in NSCLC cell apoptosis. Results showed that the knockdown of SNHG17 inhibited the proliferation and migration and promoted the apoptosis of NSCLC cells. We acquired the global gene expression profile regulated by SNHG17 in A549 through RNA sequencing (RNA-seq) assays. We found 637 genes were upregulated while 581 genes were downregulated. We selected three genes (FOXA1, XAF1, and BIK) that were closely related to proliferation and apoptosis, and we confirmed their altered expression in A549 and PC-9 cells treated with small interfering RNA si-SNHG17. Our findings indicated gene amplification-driven lncRNA SNHG17 promotes cell proliferation and migration in NSCLC, suggesting its potential value as a biomarker in NSCLC. Keywords: gene amplification, lncRNA, proliferation, migration, NSCLC

Trajectory data of floating cars form an important data source for the studies on transportation, while map matching is always one essential step for them. Most map matching algorithms perform better with trajectory data of high sampling rates than with those of low sampling rates, but the latter can be commonly accessed because of their low cost. In this article, we proposed a map matching algorithm based on then hidden Markov Model. In this algorithm, we concerned both position and direction information for calculating observation and transition probabilities and solved the labelling problem with the Viterbi algorithm by maximizing the state sequence probabilities. We carried out a case study with the GPS trajectory data of floating cars and road network data of Wuhan. The results show that this algorithm can effectively match trajectory data of low sampling rates with the road network with good topology, and the correct rate can reach up to 86% within an acceptable time cost. In particular, it performs well even in some error-prone scenarios, such as two-way multiple parallel lanes, intersections, overpasses and roundabouts. Furthermore, we also discussed factors that might affect the accuracy and efficiency of this algorithm, particularly investigating the effect of topology correctness of the road network.

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

Malaria remains a major public health and economic burden currently. The mutually exclusive expression of the virulence genes is associated with the pathogenesis and immune evasion of human malaria parasites in the host.

Lung cancer is the most common cancer globally and is associated with high morbidity and mortality. Gefitinib has been widely used for treating advanced non-small-cell lung cancer (NSCLC). However, acquired resistance usually develops, although we still know little about the mechanism underlying this. In the present study, we found that the lncRNA UCA1 was upregulated in NSCLC tissues and cells with acquired gefitinib resistance, indicating the special role of UCA1 in gefitinib resistance. Knockdown of UCA1 promoted the sensitivity to gefitinib both in vitro and in vivo by suppressing cell proliferation and inducing apoptosis. Moreover, UCA1 could interact with EZH2 (enhancer of zeste homolog 2) to epigenetically reduce the expression of CDKN1A. Taking the obtained findings together, our study suggests that UCA1 is important for NSCLC to develop gefitinib resistance, and is a potential biomarker for gefitinib resistance and a therapeutic target for advanced NSCLC.

Identification of RNA targets of RNA-binding proteins (RBPs) is essential for complete understanding of their biological functions. However, it is still a challenge to identify the biologically relevant targets of RBPs through in vitro strategies of RIP-seq, HITS-CLIP, or GoldCLIP due to the potentially high background and complicated manipulation. In malaria parasites, RIP-seq and gene disruption are the few tools available currently for identification of RBP targets. Here, we have adopted the TRIBE (Targets of RNA binding proteins identified by editing) system to in vivo identify the RNA targets of PfDis3, a key exoribonuclease subunit of RNA exosome in Plasmodium falciparum. We generated a transgenic parasite line of PfDis3-ADARcd, which catalyzes an adenosine (A)-to-inosine (I) conversion at the potential interacting sites of PfDis3-targeting RNAs. Most of PfDis3 target genes contain one edit site. The majority of the edit sites detected by PfDis3-TRIBE locate in exons and spread across the entire coding regions. The nucleotides adjacent to the edit sites contain ∼75% of A + T. PfDis3-TRIBE target genes are biases toward higher RIP enrichment, suggesting that PfDis3-TRIBE preferentially detects stronger PfDis3 RIP targets. Collectively, PfDis3-TRIBE is a favorable tool to identify in vivo target genes of RBP with high efficiency and reproducibility. Additionally, the PfDis3-targeting genes are involved in stage-related biological processes during the blood-stage development. Thus PfDis3 appears to shape the dynamic transcriptional transcriptome of malaria parasites through post-transcriptional degradation of a variety of unwanted transcripts from both strands in the asexual blood stage.

A series of non-spatial and spatial hedonic models of feeding and replacement cattle prices at video auctions in Uruguay (2002 to 2009) were specified with predictors measuring marketing conditions (e.g., steer price), cattle characteristics (e.g., breed) and agro-ecological factors (e.g., soil productivity, water characteristics, pasture condition, season). Results indicated that cattle prices produced under extensive production systems were influenced by all of predictor categories, confirming that found previously. Although many of the agro-ecological predictors were inherently spatial in nature, the incorporation of spatial effects into the estimation of the hedonic model itself, through either a spatially-autocorrelated error term or allowing the regression coefficients to vary spatially and at different scales, was able to provide greater insight into the cattle price process. Through the latter extension, using a multiscale geographically weighted regression, which was the most informative and most accurate model, relationships between cattle price and predictors operated at a mixture of global, regional, local and highly local spatial scales. This result is considered a key advance, where uncovering, interpreting, and utilizing such rich spatial information can help improve the geographical provenance of Uruguayan beef and is critically important for maintaining Uruguay’s status as a key exporter of beef with respect to the health and safety benefits of natural, open-sky, grass-fed production systems.

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration provides a better description. The approach uses a moving window weighting technique, where localized models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. Currently, GWmodel includes functions for: GW summary statistics, GW principal components analysis, GW regression, and GW discriminant analysis; some of which are provided in basic and robust forms.

Wilms’ tumor is associated with a high treatment success rate, but there is still a risk of recurrence. Cisplatin, which is one of the chemotherapeutic agents used for its treatment, is associated with a very high rate of resistance. Par-4 (prostate apoptosis response 4) is a tumor suppressor, which is capable of sensitizing tumor cells to chemotherapy. Therefore, the aim of this study was to determine whether combined treatment with Par-4 and cisplatin is effective for inhibiting growth of Wilms’ tumor. Wilms’ tumor and control cell samples were collected and analyzed by immunofluorescence assay and immunohistochemistry. Total proteins extracted from cultured cells were analyzed using western blotting and flow cytometry. In addition, a mouse xenograft model was established. We discovered significantly low expression of Par-4 in the tumor tissue, which was positively correlated with high expression of GRP78 (glucose-regulated protein 78). In addition, we found that ectopic Par-4 co-localized with cell surface GRP78 and induced high expression of the endoplasmic reticulum proteins ATF4 and BAX, which activated the endoplasmic reticulum apoptosis pathway. Moreover, treatment with ectopic Par-4 and cisplatin suppressed xenograft growth in nude mice. In conclusion, our results showed that Par-4 overexpression and cisplatin had a synergistic effect on SK-NEP-1 cells, as a result of which cell growth was inhibited and cellular apoptosis was induced. Thus, in vitro and in vivo upregulation of Par-4 expression is indispensable for the trafficking of GRP78 to the cell membrane and subsequent apoptosis of cancer cells.

A simple method was established for the rapid determination of low molecular carbonyl compounds by the combination of atmospheric pressure chemical ionization tandem mass spectrometry (APCI-MS/MS) and data mining. The ionization was carried out in positive mode, and six low molecular carbonyl compounds of acrolein, acetone, propionaldehyde, crotonaldehyde, butanone, and butyraldehyde were analyzed by both full scan mode and daughter scan mode. To overcome the quantitative difficulties from isomer of acetone/propionaldehyde and butanone/butyraldehyde, the quantitation procedure was performed with the characteristic ion of [CH3O]+ under CID energy of 5 and 15 eV. Subsequently, the established method was successfully applied to analysis of six low molecular carbonyl compounds in tobacco smoke with analytical period less than four minutes. The contents of acrolein, acetone, propionaldehyde, crotonaldehyde, butanone, and butyraldehyde for a cigarette were about 63±5.8, 325±82, 55±9.7, 11±1.4, 67±5.9, and 12±1.8 μg/cig, respectively. The experimental results indicated that the established method had the potential application in rapid determination of low molecular carbonyl compounds.

Background/Aims: Dysregulation of microRNAs is correlated with tumor development. The aim of this study is to investigate the clinicopathologic and prognostic significance of microRNA (miR)-409-3p and its tumor suppressor roles in lung adenocarcinoma (LAD). Methods: Quantitative real-time PCR (qRT-PCR) was performed to detect miR-409-3p expression in LAD tissues and corresponding noncancerous tissues. Additionally, the correlations of miR-409-3p expression with clinicopathologic factors and prognosis of patients were statistically analyzed. Next, we investigated whether miR-409-3p could function as a tumor suppressor in LAD cells via regulation of Akt signaling by targeting receptor tyrosine kinase (c-Met). Results: MiR-409-3p was significantly downregulated in LAD tissues compared with corresponding noncancerous tissues. Low miR-409-3p expression was observed to be significantly correlated with poorer tumor differentiation, advanced pTNM stage and higher incidence of lymph node metastasis. Multivariate Cox regression analyses showed that miR-409-3p expression was an independent prognostic factor for LAD patients. Functional analyses indicated that miR-409-3p could inhibit growth, induce apoptosis, reduce migration and invasion in LAD cells via inactivation of Akt signaling by targeting c-Met. Conclusions: MiR-409-3p was an independent prognostic factor and functioned as a tumor suppressor in LAD via regulation of Akt signaling by targeting c-Met.

Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

Multioperator silk screen printer is automatic silk screen printing equipment. There are many advantages, such as energy saving, reduce the labor intensity and so on. The control system consists of an intermittent movement main motor and eight color working station printing cycle motion motors. The system controls nine frequency converters through protocol macro communication by using PLC. This paper studies the principle and process characteristics of multioperator silk screen printer. Besides, this paper adopts protocol macro sequence generation method for communication between PLC and frequency converter. This method can control the programming of parameters, such as frequency, operation and monitoring. It also can realize the printing of eight color working stations and monitor the printing status in real time. The practical application proves that the method meets the technical requirements and the reliability is good.

Long noncoding RNAs (lncRNAs) have been identified as oncogenes or tumor suppressors that are involved in tumorigenesis and chemotherapy drug resistance. Maternally expressed gene 3 (MEG3) is an imprinted gene located at 14q32 that encodes an lncRNA, and decreased MEG3 expression plays an important role in multiple cancers. However, its biological role in the development of the chemoresistance phenotype of human lung adenocarcinoma (LAD) is unknown. This study aimed to observe the expression of MEG3 in LAD and to evaluate its biological role and clinical significance in the resistance of LAD cells to cisplatin. MEG3 expression was markedly decreased in cisplatin-resistant A549/DDP cells compared with parental A549 cells as shown by an lncRNA microarray. MEG3 overexpression in A549/DDP cells increased their chemosensitivity to cisplatin both in vitro and in vivo by inhibiting cell proliferation and inducing apoptosis. By contrast, MEG3 knockdown in A549 cells decreased the chemosensitivity. Moreover, MEG3 was decreased in cisplatin-insensitive LAD tissues while p53 protein levels were decreased and Bcl-xl protein levels increased. Furthermore, patients with lower levels of MEG3 expression showed worse responses to cisplatin-based chemotherapy. These findings demonstrate that MEG3 is significantly downregulated in LAD and partially regulates the cisplatin resistance of LAD cells through the control of p53 and Bcl-xl expression. Thus, MEG3 may represent a new marker of poor response to cisplatin and could be a potential therapeutic target for LAD chemotherapy.

Introduction: Plasmodium falciparum is the most damaging malaria pathogen and brings a heavy burden to global health. Host switching and morphological changes in P. falciparum are dependent on an effective gene expression regulatory system. C5 methylation of cytosines is a common RNA modification in eukaryotes, and the NSUN family are essential m5C modification executors. Currently, little is known about this family in Plasmodium spp. In this study, we focus on exploring the function of PfNSUN1 protein. Methods: An efficient CRISPR/Cas9 gene editing technique was applied to construct the PfNSUN1 knockdown strain. The knockdown efficiency was confirmed by growth curves and western blot experiments. The knockdown transcriptome data was acquired to find differentially expressed genes, and target genes of PfNSUN1 protein were identified by RNA immunoprecipitation and high-throughput sequencing experiments. Results: The efficiency of PfNSUN1 protein down-regulated was about 34%. RNA-seq data revealed that differentially expressed genes were mainly downregulated. And there were 224, 278, 556 genes that were down-regulated with more than 2-fold changes and p-adj<0.05 at ring, trophozoite and schizont stages, respectively. PfNSUN1 protein was significantly enriched on 154 target genes, including 28S ribosomal RNA and pfap2-g5 transcription factor. Discussion: PfNSUN1 is a crucial RNA post-transcriptional modification protein in P. falciparum. It plays a pivotal role in regulating gene expression and parasite growth by targeting 28S ribosomal RNA and pfap2-g5 transcription factor. [ABSTRACT FROM AUTHOR]