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.

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.

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.

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.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

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℃.

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.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Vertical Diffusion Coefficient with Stratification Effect for Silty Sediment Suspension Under Waves

To analyze previous experimental data of suspended sediment concentration for silty sediment with different sediment particle sizes due to waves, a new stratification correction coefficient is presented. The suspended sediment concentration gradient and sediment particle diameter are selected as parameters. Furthermore, a diffusion coefficient model with a stratification effect over the whole water depth for silty sediment suspension under waves is developed. The comparison between the suspended sediment concentration calculated by the presented model and several groups of experimental data shows that the model can reasonably reflect the vertical distribution of silty sediment suspension.The stratification effect calculated by the present model decreases with an increase in the sediment particle diameter,which indicates that the model can be extended to describe the suspended sediment concentration of fine to medium sand when the near-bottom sediment concentration is not very high. Although the original model needs to be iteratively solved, the approximate method without iteration is recommended for applications when the near bottom sediment concentration is between 10 and 20 kg/m~3 due to the small difference between the non-iterative and iterative solution for near bed layer suspended sediment concentration, which plays a major role in sediment transport.

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.

Substrate,Hormone,Winnowing,and Stratification Influence the Seed Germination of Ilex asprella(Hook.et Arn.)Champ.ex Benth

Ilex asprella(Hook.et Arn.)Champ.ex Benth is one of the most important traditional Chinese medicines in southern China.The seeds of Ilex asprella usually have extremely low germination due to their dormancy characteristics,which severely impacts the efficiency of seedling raising and increases labor costs.In this study,to improve the seed germination of I.asprella,the effects of germination substrate,hormone,winnowing,and stratification treatments on the seed germination of I.asprella were investigated.The results of the germination matrix showed that the highest germination percentage of 45.2%was achieved under the 20℃/10℃day/night temperature and vermiculite germination medium conditions.The results of hormone treatments revealed that 100–400 mg/L of gibberellin(GA)and 50–100 mg/L of salicylic acid(SA)were found to be effective in releasing the dormancy of I.asprella seeds.Moreover,winnowing could effectively eliminate unsaturated seeds and impurities,thus improving the seed germination of I.asprella.Furthermore,warm temperature(15℃)stratification could expand the temperature range of I.asprella’s seed germination,which was beneficial for seed germination of I.asprella and for seed nursery at room temperature in production practice.The present study obtained a method to break dormancy and increase seed germination in I.asprella,thereby forming a groundwork for improving the efficiency of large-scale planting of I.asprella.

ON THE RIGOROUS MATHEMATICAL DERIVATION FOR THE VISCOUS PRIMITIVE EQUATIONS WITH DENSITY STRATIFICATION

In this paper, we rigorously derive the governing equations describing the motion of a stable stratified fluid, from the mathematical point of view. In particular, we prove that the scaled Boussinesq equations strongly converge to the viscous primitive equations with density stratification as the aspect ratio goes to zero, and the rate of convergence is of the same order as the aspect ratio. Moreover, in order to obtain this convergence result, we also establish the global well-posedness of strong solutions to the viscous primitive equations with density stratification.

Image super‐resolution via dynamic network

Convolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution.However,obtained information of these con-volutional neural networks cannot completely express predicted high‐quality images for complex scenes.A dynamic network for image super‐resolution(DSRNet)is presented,which contains a residual enhancement block,wide enhancement block,feature refine-ment block and construction block.The residual enhancement block is composed of a residual enhanced architecture to facilitate hierarchical features for image super‐resolution.To enhance robustness of obtained super‐resolution model for complex scenes,a wide enhancement block achieves a dynamic architecture to learn more robust information to enhance applicability of an obtained super‐resolution model for varying scenes.To prevent interference of components in a wide enhancement block,a refine-ment block utilises a stacked architecture to accurately learn obtained features.Also,a residual learning operation is embedded in the refinement block to prevent long‐term dependency problem.Finally,a construction block is responsible for reconstructing high‐quality images.Designed heterogeneous architecture can not only facilitate richer structural information,but also be lightweight,which is suitable for mobile digital devices.Experimental results show that our method is more competitive in terms of performance,recovering time of image super‐resolution and complexity.The code of DSRNet can be obtained at https://github.com/hellloxiaotian/DSRNet.