Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy.

OBJECTIVE: The purpose of the study was to construct the potential diagnostic model of immune-related genes during the development of heart failure caused by idiopathic dilated cardiomyopathy. METHOD: GSE5406 and GSE57338 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). CIBERSORT was used for the evaluation of immune infiltration in idiopathic dilated cardiomyopathy (DCM) of GSE5406. Differently expressed genes were calculated by the limma R package and visualized by the volcano plot. The immune-related genes were downloaded from Immport, TISIDB, and InnateDB. Then the immune-related differential genes (IRDGs) were acquired from the intersection. Protein-protein interaction network (PPI) and Cytoscape were used to visualize the hub genes. Three machine learning methods such as random forest, logical regression, and elastic network regression model were adopted to construct the prediction model. The diagnostic value was also validated in GSE57338. RESULTS: Our study demonstrated the obvious different ratio of T cell CD4 memory activated, T cell regulatory Tregs, and neutrophils between DCM and control donors. As many as 2139 differential genes and 274 immune-related different genes were identified. These genes were mainly enriched in lipid and atherosclerosis, human cytomegalovirus infection, and cytokine-cytokine receptor interaction. At the same time, as many as fifteen hub genes were identified as the IRDGs (IFITM3, IFITM2, IFITM1, IFIT3, IFIT1, HLA-A, HLA-B, HLA-C, ADAR, STAT1, SAMHD1, RSAD2, MX1, ISG20, IRF2). Moreover, we also discovered that the elastic network and logistic regression models had a higher diagnostic value than that of random forest models based on these hub genes. CONCLUSION: Our study demonstrated the pivotal role of immune function during the development of heart failure caused by DCM. This study may offer new opportunities for the detection and intervention of immune-related DCM.

Calculation of excited states of monolayer TPPA-COF based on first principles.

Two-dimensional covalent organic frameworks (COFs) have always been a hot topic in condensed matter physics. Herein, the first 100 excited states of the TPPA-COF are calculated to investigate the optical absorption properties of the materials in the interval. The stable molecular structure of monolayer TPPA-COF is obtained by first-principles calculation, which can be regarded as a hexagon with an aperture size of 18.25 Å. By means of the band structure and density of state analysis, it is found that the monolayer band gap width of the TPPA-COF is 1.52 eV. All excited states of the TPPA-COF exhibit obvious pi → pi* (delocalized π to anti π) local excitation characteristics through analysing the spatial distribution of the electron-hole pairs of the 10 excited states with the highest oscillator strength among the first 100 excited states. In addition, the simulated UV-vis spectra show that the maximum absorption intensity of the TPPA-COF is about 357 684 mol-1 cm-1, indicating that the TPPA-COF is a potential light-absorbing material.

A cluster randomized trial of a comprehensive intervention nesting family and clinic into school centered implementation to reduce myopia and obesity among children and adolescents in Beijing, China: study protocol.

BACKGROUND: Myopia and obesity in children and adolescents have become serious public health problems that endanger public health, especially in China. Unhealthy lifestyle behaviors are environmental drivers of both myopia and obesity. This protocol describes a study to evaluate the effectiveness of "22510SS", that is 2 h of daytime outdoor activities ('2'); Limit screen time to no more than 2 h per day ('2'); Consume at least 5 servings of fruits and vegetables daily ('5'); Attain 1 h of physical activity daily ('1'); Consume 0 sugar-sweetened beverages ('0'); Reasonable sleep duration ('S'); Regular supervision ('S'). A school-based, multifaceted intervention strategy for myopia and obesity prevention, and to assess and explore the implementation of "22510SS" with regards to acceptability, feasibility, adoption, usage and maintenance. METHODS AND ANALYSIS: This study aims to develop a comprehensive intervention strategy "22510SS" based on the socio-ecological model, and A two-arm cluster randomized trial with a parallel-group of a 1:1 allocation ratio in 36 primary and secondary schools to test its evidence-based intervention programs on the effects and implementation of myopia and obesity epidemics in children and adolescents in grades 4 and 7. The primary outcomes will include differences in visual acuity, body mass index, outdoor activity indicators, screen time, fruit and vegetable intake, high-quality protein intake, sugar-sweetened beverage intake, sleep duration, and level of monitoring among children and adolescents. Secondary outcomes will assess the acceptability, feasibility, uptake, use, and maintenance of the intervention. Effects on the primary and secondary outcomes will be analyzed using linear and logistic regression analyses, as well as difference-in-difference analysis, taking into account cluster effects and possible confounding factors. Process assessments will also be conducted through quantitative and qualitative analyses, including acceptability, feasibility, gender, adoption, implementation, and sustainability. DISCUSSION: This study will evaluate the effectiveness of "22510SS" and examine its implementation in the school-based network nesting family and clinic. Following this intervention study, the integrated intervention program focused on myopia and obesity among children and adolescents have great potential to be implemented in China to promote and support healthy lifestyle behavior change and reduce the risk of myopia and obesity in children and adolescents. TRIAL REGISTRATION: NCT05275959. Registered 23 Mach 2022.

Identification of Key Genes Related to Immune Cells in Patients with COVID-19 Via Integrated Bioinformatics-Based Analysis.

COVID-19 has spread all over the world which poses a serious threat to social economic development and public health. Despite enormous progress has been made in the prevention and treatment of COVID-19, the specific mechanism and biomarker related to disease severity or prognosis have not been clarified yet. Our study intended to further explore the diagnostic markers of COVID-19 and their relationship with serum immunology by bioinformatics analysis. The datasets about COVID-19 were downloaded from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were selected via the limma package. Then, weighted gene co-expression network analysis (WGCNA) was conducted to identify the critical module associated with the clinic status. The intersection DEGs were processed for further enrichment analysis. The final diagnostic genes for COVID-19 were selected and verified through special bioinformatics algorithms. There were significant DEGs between the normal and COVID-19 patients. These genes were mainly enriched in cell cycle, complement and coagulation cascade, extracellular matrix (ECM) receptor interaction, and the P53 signaling pathway. As much as 357 common intersected DEGs were selected in the end. These DEGs were enriched in organelle fission, mitotic cell cycle phase transition, DNA helicase activity, cell cycle, cellular senescence, and P53 signaling pathway. Our study also identified CDC25A, PDCD6, and YWAHE were potential diagnostic markers of COVID-19 with the AUC (area under curve), 0.958 (95% CI 0.920-0.988), 0.941(95% CI 0.892-0.980), and 0.929 (95% CI 0.880-0.971). Moreover, CDC25A, PDCD6, and YWAHE were correlated with plasma cells, macrophages M0, T cells CD4 memory resting, T cells CD8, dendritic cells, and NK cells. Our study discovered that CDC25A, PDCD6, and YWAHE can be used as diagnostic markers for COVID-19. Moreover, these biomarkers were also closely associated with immune cell infiltration, which plays a pivotal role in the diagnosis and progression of COVID-19.

A comparative evaluation of biomedical similar article recommendation.

BACKGROUND: Biomedical sciences, with their focus on human health and disease, have attracted unprecedented attention in the 21st century. The proliferation of biomedical sciences has also led to a large number of scientific articles being produced, which makes it difficult for biomedical researchers to find relevant articles and hinders the dissemination of valuable discoveries. To bridge this gap, the research community has initiated the article recommendation task, with the aim of recommending articles to biomedical researchers automatically based on their research interests. Over the past two decades, many recommendation methods have been developed. However, an algorithm-level comparison and rigorous evaluation of the most important methods on a shared dataset is still lacking. METHOD: In this study, we first investigate 15 methods for automated article recommendation in the biomedical domain. We then conduct an empirical evaluation of the 15 methods, including six term-based methods, two word embedding methods, three sentence embedding methods, two document embedding methods, and two BERT-based methods. These methods are evaluated in two scenarios: article-oriented recommenders and user-oriented recommenders, with two publicly available datasets: TREC 2005 Genomics and RELISH, respectively. RESULTS: Our experimental results show that the text representation models BERT and BioSenVec outperform many existing recommendation methods (e.g., BM25, PMRA, XPRC) and web-based recommendation systems (e.g., MScanner, MedlineRanker, BioReader) on both datasets regarding most of the evaluation metrics, and fine-tuning can improve the performance of the BERT-based methods. CONCLUSIONS: Our comparison study is useful for researchers and practitioners in selecting the best modeling strategies for building article recommendation systems in the biomedical domain. The code and datasets are publicly available.

Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager.

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (Tb) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI Tb data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The RMSE of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI Tb data can retrieve the sea ice motion successfully.

Insights into the Bond Behavior and Mechanical Properties of Hafnium Carbide under High Pressure and High Temperature.

Hafnium carbide (HfC) is a potential candidate of ultrahigh-temperature ceramics (UHTCs) and has attracted significantly widespread interest in recent years. Here, we have synthesized high-purity HfC samples with NaCl-type structure by using a high-pressure solid-solid reaction. The structural stability, equation of state, plastic deformation, yield strength, and bonding properties under high pressure are investigated by a series of in situ high-pressure synchrotron-radiation angle-dispersive X-ray diffraction experiments combined with first-principles calculations. The yield strength of HfC (∼18 GPa) is obtained from analyzing the plastic deformation behavior under high pressure. In addition, we have successfully prepared bulk HfC ceramics with high density using a high-pressure and high-temperature method. The synthesized sample possesses a desirable Vickers hardness of 24.2 GPa and an excellent fracture toughness of 5.0 MPa·m1/2. The present results offer insights into the achievable application of HfC ceramics under extreme conditions and provide a powerful guide for the further design and synthesis of other high-performance UHTCs.

Linked Color Imaging Can Improve Detection Rate of Early Gastric Cancer in a High-Risk Population: A Multi-Center Randomized Controlled Clinical Trial.

BACKGROUND: Early diagnosis of gastric cancer is difficult in China due to the lack of a valid method for endoscopic screening. Early gastric cancer, especially flat gastric cancer, lacks specific endoscopic features. Many cases appear to be similar to ordinary gastritis cases under normal white light endoscopy, which can lead to misdiagnosis. AIMS: In order to find a new method to improve detection rate of early gastric cancer in China, we designed a trial to validate linked color imaging (LCI) for screening of early gastric cancer in a high-risk population, as compared to white light imaging (WLI). METHOD: Subjects were randomly allocated to either the LCI + WLI or WLI group and then subjected to gastroscopy and all endoscopies were made after special preparation. All endoscopists had knowledge of this experiment. The main indicator was the rate of detection of gastric neoplastic lesions. The difference in the detection rate between the two groups is reported. RESULTS: The detection rate was 4.31% in the WLI group and 8.01% in the LCI + WLI group. This is a difference of 3.70% with a P value < 0.001 and an OR (95% CI) of 1.934 (1.362, 2.746). The lower limit of the 95% CI was greater than 0, and the superiority margin was 1%. CONCLUSION: The detection rate of gastric neoplastic lesions was higher in the LCI + WLI group than in the WLI group, LCI might be an effective method for screening early gastric cancer.

Demystifying COVID-19 publications: institutions, journals, concepts, and topics.

OBJECTIVE: We analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic-related research. METHODS: We conducted a descriptive analysis of authors' institutions and relationships, automatic content extraction of key words and phrases from titles and abstracts, and topic modeling and evolution. Data visualization techniques were applied to present the results of the analysis. RESULTS: We found that leading research institutions on COVID-19 included the Chinese Academy of Sciences, the US National Institutes of Health, and the University of California. Research studies mostly involved collaboration among different institutions at national and international levels. In addition to bioRxiv, major publication venues included journals such as The BMJ, PLOS One, Journal of Virology, and The Lancet. Key research concepts included the coronavirus, acute respiratory impairments, health care, and social distancing. The ten most popular topics were identified through topic modeling and included human metapneumovirus and livestock, clinical outcomes of severe patients, and risk factors for higher mortality rate. CONCLUSION: Data analytics is a powerful approach for quickly processing and understanding large-scale datasets like CORD-19. This approach could help medical librarians, researchers, and the public understand important characteristics of COVID-19 research and could be applied to the analysis of other large datasets.

Correction to: A novel role of glutathione S-transferase A3 in inhibiting hepatic stellate cell activation and rat hepatic fibrosis.

An amendment to this paper has been published and can be accessed via the original article.

Exploring Physical Properties of Tantalum Carbide at High Pressure and Temperature.

Among the transition metal carbides, tantalum carbide (TaC) has gained significant interest due to its attractive mechanical and electronic properties. Here, we have performed high pressure and high temperature (HPHT) measurements on the physical properties of TaC under 5.5 GPa and rhythmically increased temperatures from 1000 to 1500 °C. The microscopic deviatoric strain, Vickers hardness, fracture toughness, grain size, and microstructures are characterized by X-ray diffraction (XRD), scanning electronic microscopy (SEM), transmission electron microscopy (TEM), and microhardness tests. The results reveal that the HPHT sintering causes the densification, which increases the mechanical properties of TaC. At 5.5 GPa and 1300 °C, the Vickers hardness, fracture toughness, relative density, and Young's modulus of TaC are 21.0 GPa, 7.4 MPa m1/2, 457 GPa, and 97.7%, respectively, which are in good agreement with available experimental and theoretical values. It is found that the mechanical properties of TaC are highly impressible to the microstructures and microscopic deviatoric stress. Our cadent HPHT sintering technique will provide powerful guidance for further synthesis and design of other novel ultrahigh temperature ceramics (UHTCs).

Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor.

Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness.

A novel role of glutathione S-transferase A3 in inhibiting hepatic stellate cell activation and rat hepatic fibrosis.

BACKGROUND AND AIMS: Glutathione S-transferase A3 (GSTA3) is known as an antioxidative protease, however, the crucial role of GSTA3 in liver fibrosis remains unclear. As a recently we developed water-soluble pyridone agent with antifibrotic features, fluorofenidone (AKF-PD) can attenuate liver fibrosis, present studies were designed to explore the role of GSTA3 in liver fibrosis and its modulation by AKF-PD in vivo and in vitro. METHODS: Rats liver fibrosis models were induced by dimethylnitrosamine (DMN) or carbon tetrachloride (CCl4). The two activated hepatic stellate cells (HSCs) lines, rat CFSC-2G and human LX2 were treated with AKF-PD respectively. The lipid peroxidation byproduct malondialdehyde (MDA) in rat serum was determined by ELISA. The accumulation of reactive oxygen species (ROS) was measured by dichlorodihydrofluorescein fluorescence analysis. The expression of α-smooth muscle actin (α-SMA), fibronectin (FN), and phosphorylation of extracellular signal-regulated kinase1/2 (ERK1/2), p38 mitogen-activated protein kinase (p38 MAPK), c-Jun N-terminal kinase (JNK) and glycogen synthase kinase 3 beta (GSK-3ß) were detected by western blotting (WB). RESULTS: GSTA3 was substantially reduced in the experimental fibrotic livers and transdifferentiated HSCs. AKF-PD alleviated rat hepatic fibrosis and potently inhibited HSCs activation correlated with restoring GSTA3. Moreover, GSTA3 overexpression prevented HSCs activation and fibrogenesis, while GSTA3 knockdown enhanced HSCs activation and fibrogenesis resulted from increasing accumulation of ROS and subsequent amplified MAPK signaling and GSK-3ß phosphorylation. CONCLUSIONS: We demonstrated firstly that GSTA3 inhibited HSCs activation and liver fibrosis through suppression of the MAPK and GSK-3ß signaling pathways. GSTA3 may represent a promising target for potential therapeutic intervention in liver fibrotic diseases.

Structural and Physical Properties of ZrSi2 under High Pressure: Experimental Study and First-Principles Calculations.

Zirconium disilicide (ZrSi2) has been investigated experimentally and theoretically for its structural and physical properties at high pressures. In situ compression experiments demonstrate that at low pressure, ZrSi2 adopts the C49 structure (space group Cmcm), which persists up to 54.5 GPa at room temperature, and the unit cell of ZrSi2 along b-axis is at least twice as compressible as along a- and c-axis. A bulk modulus of 170.0 ± 0.7 GPa ( K'0 = 4) is derived from the compression experiment employing methanol-ethanol mixture as the pressure-transmitting medium. Diffraction line-width analysis suggests a yield strength of about 3.0 GPa for ZrSi2 under high pressures at room temperature. The first-principles calculations mostly agree with the experimental results, such as mechanical and dynamic stability and elastic anisotropy ( Kc > Ka ≫ Kb). However, predicted axial modulus Kb by modeling is significantly smaller than the experimentally determined value, resulting in a sizable discrepancy between experimental (170.0 GPa) and theoretical (121.0 GPa) bulk moduli.

A Novel Modification of PSO Algorithm for SML Estimation of DOA.

This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.

A Cooperative Downloading Method for VANET Using Distributed Fountain Code.

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency. For cooperative forwarding, however, processing delay from frequent coding and decoding as well as single feedback mechanism using DFC cannot adapt to the environment of VANET. In this paper, a cooperative downloading method for VANET using concatenated DFC is proposed to solve the problems above. The source vehicle and cooperative vehicles encodes the raw data using hierarchical fountain code before they send to the client directly or indirectly. Although some packets may be lost, the client can recover the raw data, so long as it receives enough encoded packets. The method avoids data retransmission due to packet loss. Furthermore, the concatenated feedback mechanism in the method reduces the transmission delay effectively. Simulation results indicate the benefits of the proposed scheme in terms of increasing amount of downloaded data and data receiving rate.

Effects of wheat protein on hot-extrusion 3D-printing performance and the release behaviours of caffeic acid-loaded wheat starch.

In this study, the effects of wheat protein (WP) on the hot-extrusion 3D-printing (HE-3DP) performance of wheat starch (WS) gels, as well as effects of such gels on the encapsulation of caffeic acid, were investigated for the first time. The HE-3DP results show that the addition of WP can reduce print-line width and improve printing accuracy and fidelity, and the best printing results were achieved when using gels with 10 % WP. The rheological results show that WP reduced the gels' linear viscoelastic region (LVR), yield stress (τy), flow stress (τf) and consistency factor (K) but increased their structural recovery rate, which facilitated smooth extrusion during 3D printing and, thus, improved printing accuracy. The analysis of X-ray diffraction and small-angle X-ray scattering indicates that adding WP to WS could increase the mass fractal dimension and lead to denser gel network structures. The results regarding release kinetics demonstrate that the maximum release of caffeic acid from gels decreased by 28 % with the addition of WP, indicating slow-release behaviour. This study provided valuable information about processing wheat products via 3D printing.

Composite formation of whey protein isolate and OSA starch for fabricating high internal phase emulsion: A comparative study at different pH and their application in biscuits.

The composites formed by whey protein isolate (WPI) and octenyl succinate anhydride (OSA)-modified starch were characterized with a focus on the effect of pH, and their potential in fabricating high internal phase emulsions (HIPEs) as fat substitutes was evaluated. The particles obtained at pH 3.0, 6.0, 7.0, and 8.0 presented a nanosized distribution (122.04 ± 0.84 nm-163.24 ± 4.12 nm) while those prepared at pH 4.0 and 5.0 were remarkably larger. Results from the shielding agent reaction and Fourier transform infrared spectroscopy (FT-IR) showed that the interaction between WPI and OSA starch was mainly hydrophobic at pH 3.0-5.0, while there was a strong electrostatic repulsion at pH 6.0-8.0. A quartz crystal microbalance with dissipation (QCM-D) study showed that remarkably higher ΔD and lower Δf/n were observed at pH 3.0-5.0 after successive deposition of WPI and OSA starch, whereas slight changes were noted for those made at higher pH values. The WPI-OSA starch (W-O) composite-based HIPEs made at pH 3.0 and 6.0-8.0 were physically stable after long-term storage, thermal treatment, or centrifugation. Incorporation of HIPE into the biscuit formula yielded products with a desirable sensory quality.

Detection and attribution of the start of the growing season changes in the Northern Hemisphere.

Global climate change has led to significant changes in land surface phenology. At present, research on the factors influencing the start of the growing season (SOS) mainly focuses on single factor effects, such as temperature and precipitation, ignoring the combined action of multiple factors. The impact of multiple factors on the spatial and temporal patterns of the SOS in the Northern Hemisphere is not clear, and it is necessary to combine multiple factors to quantify the degrees of influence of different factors on the SOS. Based on the GIMMS3g NDVI dataset, CRU climate data and other factor data, we used geographic detector model, random forest regression model, multiple linear regression, partial correlation analysis and Sen + Mann-Kendall trend analysis to explore the variation of the SOS in the Northern Hemisphere to reveal the main driving factors and impact threshold of 17 influencing factors on the SOS. The results showed that (1) during the past 34 years (1982-2015), the SOS in Europe and Asia mainly showed an advancing trend, whereas the SOS in North America mainly showed a delaying trend. (2) The SOS was mainly controlled by frost frequency, temperature and humidity. Increasing frost frequency inhibited the advancement of the SOS, and increasing temperature and humidity promoted the advancement of the SOS. (3) There were thresholds for the influences of the driving factors on the SOS. Outside the threshold ranges, the response mechanism of the SOS to driving factors changed. The results are important for understanding the response of the SOS to global climate change.

Evaluation of the detection rate of high­grade gastric intraepithelial neoplasia using linked color imaging and white light imaging.

As an endoscopic technology for the enhancement of images, linked color imaging (LCI) performs well when used for the early detection and diagnosis of gastrointestinal cancer. However, literature data are lacking for LCI in the detection of high-grade gastric intraepithelial neoplasia. Therefore, the aim of the present study was to investigate the efficacy of LCI compared with traditional white light imaging (WLI) in the detection of high-grade gastric intraepithelial neoplasia via the comparison of detection rates between senior and junior endoscopists using both techniques. Overall, 84 lesions from 81 patients with high-grade gastric intraepithelial neoplasia diagnosed between January 2017 and December 2017 were considered. Following the exclusion of three patients with two lesions, 78 patients who had only one lesion were enrolled. The two types of endoscopy, WLI and LCI, were performed in the same patients under the same conditions. Four senior and four junior endoscopists retrospectively compared the images. The detection rate of high-grade gastric intraepithelial neoplasia was significantly higher with LCI than with WLI when performed by senior and junior endoscopists. With WLI, the detection rate obtained by senior endoscopists was significantly higher than that obtained by junior endoscopists. However, for LCI, the detection rates for junior and senior endoscopists were comparable. Interobserver agreement was good to satisfactory. These findings indicate that LCI is superior to WLI in the detection and identification of gastric cancer and provides highly accurate diagnostic results from endoscopic examinations, regardless of the experience of the endoscopist. LCI may be used to narrow the gap in the detection rate of high-grade gastric intraepithelial neoplasia between junior and senior endoscopists.