Failure mechanism and infrared radiation characteristic of hard siltstone induced by stratification effect

The deformation in sedimentary rock induced by train loads has potential threat to the safe operation of tunnels. This study investigated the influence of stratification structure on the infrared radiation and temporal damage mechanism of hard siltstone. The uniaxial compression tests, coupled with acoustic emission(AE) and infrared radiation temperature(IRT) were conducted on siltstones with different stratification effects. The results revealed that the stratigraphic structure significantly affects the stress-strain response and strength degradation characteristics. The mechanical parameters exhibit anisotropy characteristics, and the stratification effect exhibits a negative correlation with the cracking stress and peak stress. The failure modes caused by the stratification effect show remarkable anisotropic features, including splitting failure(Ⅰ: 0°-22.50°, Ⅱ: 90°), composite failure(45°), and shearing failure(67.50°). The AE temporal sequences demonstrate a stepwise response characteristic to the loading stress level. The AE intensity indicates that the stress sensitivity of shearing failure and composite failure is generally greater than that of splitting failure. The IRT field has spatiotemporal migration and progressive dissimilation with stress loading and its dissimilation degree increases under higher stress levels. The stronger the stratification effect, the greater the dissimilation degree of the IRT field. The abnormal characteristic points of average infrared radiation temperature(AIRT) variance at local stress drop and peak stress can be used as early and late precursors to identify fracture instability. Theoretical analysis shows that the competitive relationship between compaction strengthening and fracturing damage intensifies the dissimilation of the infrared thermal field for an increasing stress level. The present study provides a theoretical reference for disaster warnings in hard sedimentary rock mass.

Risk stratification for radioactive iodine refractoriness using molecular alterations in distant metastatic differentiated thyroid cancer

Objective: Patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC) are often diagnosed with delay and constrained to limited treatment options. The correlation between RAI refractoriness and the underlying genetic characteristics has not been extensively studied.Methods: Adult patients with distant metastatic DTC were enrolled and assigned to undergo next-generation sequencing of a customized 26-gene panel(Thyro Lead). Patients were classified into RAIR-DTC or non-RAIR groups to determine the differences in clinicopathological and molecular characteristics. Molecular risk stratification(MRS) was constructed based on the association between molecular alterations identified and RAI refractoriness, and the results were classified as high, intermediate or low MRS.Results: A total of 220 patients with distant metastases were included, 63.2% of whom were identified as RAIRDTC. Genetic alterations were identified in 90% of all the patients, with BRAF(59.7% vs. 17.3%), TERT promoter(43.9% vs. 7.4%), and TP53 mutations(11.5% vs. 3.7%) being more prevalent in the RAIR-DTC group than in the non-RAIR group, except for RET fusions(15.8% vs. 39.5%), which had the opposite pattern. BRAF and TERT promoter are independent predictors of RAIR-DTC, accounting for 67.6% of patients with RAIR-DTC. MRS was strongly associated with RAI refractoriness(P<0.001), with an odds ratio(OR) of high to low MRS of 7.52 [95%confidence interval(95% CI), 3.96-14.28;P<0.001] and an OR of intermediate to low MRS of 3.20(95% CI,1.01-10.14;P=0.041).Conclusions: Molecular alterations were associated with RAI refractoriness, with BRAF and TERT promoter mutations being the predominant contributors, followed by TP53 and DICER1 mutations. MRS might serve as a valuable tool for both prognosticating clinical outcomes and directing precision-based therapeutic interventions.

Convection and Stratification of Temperature and Concentration

This study is devoted to an analysis of natural convection and the emergence of delamination in an incompressible fluid encapsulated in a closed region heated from the side.Weak,medium and intensive modes of stationary laminar thermal and thermo-concentration convection are considered.It is shown that nonlinear flow features can radically change the flow structure and characteristics of heat and mass transfer.Moreover,the temperature and concentration segregation in the center of the square region display a non-monotonic dependence on the Grashof number(flow intensity).The formation of a nonstationary periodic structure of thermal convection in boundary layers and in the core of a convective flow in the closed region is also examined.Details of the formation of countercurrents inside the region with the direction opposite to the main convective flow are given.Finally,the influence of vertical and horizontal vibrations on oscillatory convection is analyzed in detail.

Stability of the Stratification of Water Bodies in the North Passage of the Yangtze River Estuary Based on the EFDC Model

To study the stratified stability of a water column in the North Passage of the Yangtze River Estuary,a numerical model of the hydrodynamics of this estuary is established using the EFDC model.On the basis of EFDC results,this paper derives and pro-vides the discriminative index of water body stability caused by salinity and analyzes the along-range variation in water body strati-fication stability in the North Passage of the Yangtze River Estuary and the periodic variation at a key location(bend area)based on the simulation results of the numerical model.This work shows that the water body in the bend area varies between mixed and strati-fied types,and the vertical average flow velocity has a good negative correlation with the differential velocity between the surface and bottom layers of the water body.The model simulation results validate the formulae for the stratified stability discriminant during spring tides.

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Risk Stratification and Prognosis of Pulmonary Arterial Hypertension Associated with Congenital Heart Disease

Background:Current guidelines for managing pulmonary arterial hypertension(PAH)recommend a risk strati-fication approach.However,the applicability and accuracy of these strategies for PAH associated with congenital heart disease(PAH-CHD)require further validation.This study aims to validate the reliability and predictive accuracy of a simplified stratification strategy for PAH-CHD patients over a three-year follow-up.Additionally,new prognostic variables are identified and novel risk stratification methods are developed for assessing and managing PAH-CHD patients.Methods:This retrospective study included 126 PAH-CHD patients.Clinical and biochemical variables across risk groups were assessed using Kruskal-Wallis and Fisher’s exact tests.Indepen-dent risk factors were identified using ordered logistic regression,while Kaplan-Meier and Cox proportional hazards regression analyses evaluated their impact on all-cause mortality.A new stratification model for the PAH-CHD population was constructed based on these analyses.Results:Significant survival differences across stratified risk groups were observed(p<0.001),validating the effectiveness of the simplified risk stratification method in PAH-CHD patients.Prothrombin activity was a strong independent predictor of adverse outcomes of PAH-CHD patients(Hazard ratio 0.95,p<0.001,C-index 0.70).A model combining N-terminal pro-brain natriuretic peptide,prothrombin activity,albumin,and right atrial area achieved an area under the curve of 0.89 and a C-index of 0.85.Conclusions:The simplified risk stratification method is applicable to PAH-CHD patients.Prothrombin activity is a strong independent predictor of adverse outcomes.A comprehensive risk stratification approach,incorporating both established and novel biomarkers,enhances accessibility and offers predictive efficacy during follow-up for PAH-CHD patients,comparable to established models.

Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors

BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pressing need to accurately evaluate risk stratification preoperatively.AIM To assess the application of a deep learning model(DLM)combined with computed tomography features for predicting risk stratification of GISTs.METHODS Preoperative contrast-enhanced computed tomography(CECT)images of 551 GIST patients were retrospectively analyzed.All image features were independently analyzed by two radiologists.Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy.Patients were randomly assigned to the training(n=386)and validation cohorts(n=165).A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.RESULTS Among the analyzed CECT image features,tumor size,ulceration,and enlarged feeding vessels were identified as significant risk predictors(P<0.05).In DLM,the overall area under the receiver operating characteristic curve(AUROC)was 0.88,with the accuracy(ACC)and AUROCs for each stratification being 87%and 0.96 for low-risk,79%and 0.74 for intermediate-risk,and 84%and 0.90 for high-risk,respectively.The overall ACC and AUROC were 84%and 0.94 in the combined model.The ACC and AUROCs for each risk stratification were 92%and 0.97 for low-risk,87%and 0.83 for intermediate-risk,and 90%and 0.96 for high-risk,respectively.Differences in AUROCs for each risk stratification between the two models were significant(P<0.05).CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data,demonstrating superiority compared to DLM.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Solar-assisted two-stage catalytic membrane reactor for coupling CO_(2) splitting with methane oxidation reaction

A two-stage catalytic membrane reactor(CMR)that couples CO_(2) splitting with methane oxidation reactions was constructed based on an oxygen-permeable perovskite asymmetric membrane.The asymmetric membrane comprises a dense SrFe_(0.9)Ta_(0.1)O_(3-σ)(SFT)separation layer and a porous Sr_(0.9)(Fe_(0.9)Ta_(0.1))_(0.9)Cu_(0.1)O_(3-σ)(SFTC)catalytic layer.In thefirst stage reactor,a CO_(2) splitting reaction(CDS:2CO_(2)→2CO+O_(2))occurs at the SFTC catalytic layer.Subsequently,the O_(2) product is selectively extracted through the SFT separation layer to the permeated side for the methane combustion reaction(MCR),which provides an extremely low oxygen partial pressure to enhance the oxygen extraction.In the second stage,a Sr_(0.9)(Fe_(0.9)Ta_(0.1))_(0.9)Ni_(0.1)O_(3-σ)(SFTN)catalyst is employed to reform the products derived from MCR.The two-stage CMR design results in a remarkable 35.4%CO_(2) conversion for CDS at 900℃.The two-stage CMR was extended to a hollowfiber configuration combining with solar irradiation.The solar-assisted two-stage CMR can operate stably for over 50 h with a high hydrogen yield of 18.1 mL min^(-1) cm^(-2).These results provide a novel strategy for reducing CO_(2) emissions,suggesting potential avenues for the design of the high-performance CMRs and catalysts based on perovskite oxides in the future.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

Analyzing Thermal Stratification and Nanoparticle Shapes Influence on an EMHD Ternary Nanofluid Flow amidst Two Spinning Disks

The present study examines the thermal distribution of ternary nanofluid flow amid two spinning disks influenced by electric and magnetic fields. Keeping in view the shape of the particles, the electrically conducting ternary nanofluid is analyzed with variable thermophysical features. Three types of nanoparticles namely Copper, Aluminum Oxide, and Graphene with spherical, cylindrical, and platelet shapes are taken respectively and are immersed in a (50-50)% ratio of water and ethylene glycol mixture which acts as a base fluid. The anticipated problem is addressed by employing a reliable and user-friendly numerical bvp4c built-in collocation scheme. This solution is then showcased through illustrations and tables. Strengthening the radiation results in an enhanced heat transfer rate. Radial and azimuthal velocities once rotation of disks is enhanced. The key findings provide a strong theoretical background in photovoltaic cells, solar collectors, radiators, solar water heaters, and many other applications.

Influence of two-stage harvesting on the properties of cold-pressed rapeseed(Brassica napus L.)oils

Rapeseed(Brassica napus L.)harvesting method is critical since it significantly determines the seed yield,oil quality,and industrial efficiency.This study investigated the influences of harvesting methods on the quality of cold-pressed rapeseed oil of two varieties.Oil color,peroxide value(POV),tocopherol content,fatty acid composition,and polarity of total polyphenols(PTP)contents of two rapeseed varieties in Huanggang and Xiangyang were compared through artificially simulated combined harvesting and two-stage harvesting.Results showed significant differences in the quality of rapeseed oil between the two harvesting methods.The red value(R-value),POV,total tocopherol contents,linoleic and linolenic acid content,and PTP content of the pressed rapeseed oil prepared by the combined harvesting method were about 27.6,5.7,15.8,2.0,0.5,and 28.6%lower than those of the oil produced from the two-stage harvesting method,respectively.Xiangyang and Huayouza62 performed better in the two regions and two varieties,respectively.To sum up,the rapeseed oil obtained 41–44 days after final flowering of combined harvesting,35 days after final flowering,and six days of post-ripening of the two-stage harvesting had the best quality.

Ground Penetrating Radar (GPR) Identification Method for Agricultural Soil Stratification in a Typical Mollisols Area of Northeast China

In order to achieve a rapid and accurate identification of soil stratification information and accelerate the development of smart agriculture,this paper conducted soil stratification experiments on agricultural soils in the Mollisols area of Northeast China using Ground Penetrating Radar(GPR)and obtained different types of soil with frequencies of 500 MHz,250 MHz,and 100 MHz antennas.The soil profile data were obtained for 500 MHz,250 MHz,and 100 MHz antennas,and the dielectric properties of each type of soil were analyzed.In the image processing procedure,wavelet analysis was first used to decompose the pre-processed radar signal and reconstruct the high-frequency information to obtain the reconstructed signal containing the stratification information.Secondly,the reconstructed signal is taken as an envelope to enhance the stratification information.The Hilbert transform is applied to the envelope signal to find the time-domain variation of the instantaneous frequency and determine the time-domain location of the stratification.Finally,the dielectric constant of each soil horizon is used to obtain the propagation velocity of the electromagnetic wave at the corresponding position to obtain the stratification position of each soil horizon.The research results show that the 500 MHz radar antenna can accurately delineate Ap/Ah,horizon and the absolute accuracy of the stratification is within 5 cm.The effect on the soil stratification below the tillage horizon is not apparent,and the absolute accuracy of the 250 MHz and 100 MHz radar antennas on the stratification is within 9 cm.The overwhelming majority of the overall calculation errors are kept to within 15%.Based on the three central frequency antennas,the soil horizon detection rate reaches 93.3%,which can achieve accurate stratification of soil profiles within 1 m.The experimental and image processing methods used are practical and feasible;however,the GPR will show a missed detection for soil horizons with only slight differences in dielectric properties.Overall,this study can quickly and accurately determine the information of each soil stratification,ultimately providing technical support for acquiring soil configuration information and developing smart agriculture.

The effect of adjuvant transarterial chemoembolization for hepatocellular carcinoma after liver resection based on risk stratification

Background:There is currently no standard adjuvant treatment proven to prevent hepatocellular carcinoma(HCC)recurrence.Recent studies suggest that postoperative adjuvant transarterial chemoembolization(PA-TACE)is beneficial for patients at high risk of tumor recurrence.However,it is difficult to select the patients.The present study aimed to develop an easy-to-use score to identify these patients.Methods:A total of 4530 patients undergoing liver resection were recruited.Independent risk factors were identified by Cox regression model in the training cohort and the Primary liver cancer big data transarterial chemoembolization(PDTE)scoring system was established.Results:The scoring system was composed of ten risk factors including alpha-fetoprotein(AFP),albuminbilirubin(ALBI)grade,operative bleeding loss,resection margin,tumor capsular,satellite nodules,tumor size and number,and microvascular and macrovascular invasion.Using 5 points as risk stratification,the patients with PA-TACE had higher recurrence-free survival(RFS)compared with non-TACE in>5 points group(P<0.001),whereas PA-TACE patients had lower RFS compared with non-TACE in≤5 points group(P=0.013).In the training and validation cohorts,the C-indexes of PDTE scoring system were 0.714[standard errors(SE)=0.010]and 0.716(SE=0.018),respectively.Conclusions:The model is a simple tool to identify PA-TACE for HCC patients after liver resection with a favorable performance.Patients with>5 points may benefit from PA-TACE.

Effect of cold stratification on the temperature range for germination of Pinus koraiensis

Germination at low spring temperatures may offer a competitive advantage for the growth and survival of plant species inhabiting temperate forest ecosystems.Pinus koraiensis is a dominant species in temperate forests of northeastern China.Its seeds exhibit primary morphophysiological dormancy following dispersal in autumn,limiting natural or artificial regeneration:direct seeding and planting seedlings in spring.The aim of this study was to determine the optimum cold stratification temperature that induces germination to increase towards lower temperatures.Seeds from two populations(Changbaishan and Liangshui)were cold stratified at 0,5 and 10℃.Germination to incubation temperatures(10/5,20/10,25/15 and 30/20℃;14/10 h day/night)were determined after 2 and 4 weeks,and 5.5 and6.5 months of cold stratification.After 5.5 months,approximately 68-91%of seeds from both populations germinated at incubation temperatures of 25/15℃and 30/20℃,regardless of cold stratification temperatures.When the cold stratification temperature was reduced to 0℃and the period increased to 6.5 months,germination at 10/5℃significantly improved,reaching 37%and 64%for the Changbaishan and Liangshui populations,respectively.After 6.5 months of cold stratification,there was a significant linear regression between cold stratification temperatures and germination at10/5℃.The range in temperatures allowing for germination gradually expanded to include lower temperatures with decreasing cold stratification temperatures from 10 to 5℃and further to 0℃.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors

BACKGROUND Computed tomography(CT)imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors(GISTs).AIM To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.METHODS The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed.All patients had received dynamic contrast-enhanced CT(CECT)followed by surgical resection.According to the modified National Institutes of Health criteria,147 lesions were classified into the low malignant potential group(very low and low risk;101 lesions)and high malignant potential group(medium and high-risk;46 lesions).The association between malignant potential and CT characteristic features(including tumor location,size,growth pattern,contour,ulceration,cystic degeneration or necrosis,calcification within the tumor,lymphadenopathy,enhancement patterns,unenhanced CT and CECT attenuation value,and enhancement degree)was analyzed using univariate analysis.Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential.The receiver operating curve(ROC)was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.RESULTS There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs.Univariate analysis showed no significant differences in age,gender,tumor location,calcification,unenhanced CT and CECT attenuation values,and enhancement degree between the two groups(P>0.05).However,a significant difference was observed in tumor size(3.14±0.94 vs 6.63±3.26 cm,P<0.001)between the low-grade and high-grade groups.The univariate analysis further revealed that CT imaging features,including tumor contours,lesion growth patterns,ulceration,cystic degeneration or necrosis,lymphadenopathy,and contrast enhancement patterns,were associated with risk stratification(P<0.05).According to binary logistic regression analysis,tumor size[P<0.001;odds ratio(OR)=26.448;95%confidence interval(CI):4.854-144.099)],contours(P=0.028;OR=7.750;95%CI:1.253-47.955),and mixed growth pattern(P=0.046;OR=4.740;95%CI:1.029-21.828)were independent predictors for risk stratification of gastric GISTs.ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919(95%CI:0.863-0.975)and 0.940(95%CI:0.893-0.986),respectively.The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm,and the sensitivity and specificity were 93.5%and 84.2%,respectively.CONCLUSION CT features,including tumor size,growth patterns,and lesion contours,were predictors of malignant potential for primary gastric GISTs.

Two-stage revision in periprosthetic knee joint infections

Periprosthetic joint infection(PJI)following total knee arthroplasty is one of the most catastrophic and costly complications that carries significant patient wellness as well as economic burdens.The road to efficiently diagnosing and treating PJI is challenging,as there is still no gold standard method to reach the diagnosis as early as desired.There are also international controversies with respect to the best approach to manage PJI cases.In this review,we highlight recent advances in managing PJI following knee arthroplasty surgery and discuss in depth the two-stage revision method.

Comparison of multiple salinity datasets:upper ocean salinity and stratification in the tropical Pacific during the Argo period

Ocean salinity is an important variable that affects the ocean stratification.We compared the salinity and ocean stratification in the tropical Pacific derived from the Argo(Array for Real-time Geostrophic Oceanography data),EN4(Ensemble 4 analysis),SODA(the Simple Ocean Data Assimilation reanalysis),IAP(Institute of Atmospheric Physics data),and ORAS4(Ocean Reanalysis System 4)over 2005–2017.Results show that the spatial distribution of climatological mean of sea surface salinity(SSS)in all the products is consistent,and the low salinity region showed large deviation and strong dispersion.The Argo has the smallest RMSE and the highest correlation with the ensemble mean,while the IAP shows a high-salinity deviations relative to other datasets.All the products show high positive correlations between the sea surface density(SSD)and SSS with respect to the deviations of climatological mean from ensemble mean,suggesting that the SSD deviation may be mainly influenced by the SSS deviation.In the aspect of the ocean stratification,the mixed layer depth(MLD)climatological mean in the Argo shows the highest correlation with the ensemble mean,followed by EN4,IAP,ORAS4,and SODA.The Argo and EN4 show thicker barrier layer(BL)relative to the ensemble mean while the SODA displays the largest negative deviation in the tropical western Pacific.Furthermore,the EN4,ORAS4,and IAP underestimate the stability in the upper ocean at the depths of 20–140 m,while Argo overestimates ocean stability.The salinity fronts in the western-central equatorial Pacific from Argo,EN4,and ORAS4 are consistent,while those from SODA and IAP show large deviations with a westward position in amplitude of 0°–6°and 0°–10°,respectively.The SSS trend patterns from all the products are consistent in having ensemble mean with high spatial correlations of 0.95–0.97.