AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital ...AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.展开更多
The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localiza...The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish ...Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.展开更多
BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic mal...BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.展开更多
This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-...This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.展开更多
Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-...Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,...In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.展开更多
Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email...Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.展开更多
In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by th...In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by the presence of two typhoons and substantial water transport into Henan.However,a favorable synoptic pattern only does not guarantee the occurrence of heavy precipitation in Henan.This study investigates the key environmental features critical for EP under similar synoptic patterns to the 2021 Henan extreme event.It is found that cold clouds are better aggregated on EP days,accompanied by beneficial environment features like enhanced moisture conditions,stronger updrafts,and greater atmospheric instability.The temporal evolution of these environmental features shows a leading signal by one to three days.These results suggest the importance of combining the synoptic pattern and environmental features in the forecasting of heavy precipitation events.展开更多
AIM:To investigate the potential mechanisms of A-V pattern and evaluate the surgical outcomes used in the treatment of sensory exotropia.METHODS:The medical records of patients with sensory A-V pattern exotropia who u...AIM:To investigate the potential mechanisms of A-V pattern and evaluate the surgical outcomes used in the treatment of sensory exotropia.METHODS:The medical records of patients with sensory A-V pattern exotropia who underwent strabismus surgery between May 2014 to June 2019 was retrospectively reviewed.The control group included sensory exotropia patients without A-V pattern and concomitant A-V pattern exotropia patients with normal vision who undergone strabismus surgery over this same time period.Ocular alignment,best corrected visual acuity,oblique muscle function,and stereopsis records were collected.RESULTS:Among the 843 eligible patients,91(10.79%;39 males and 52 females)had A-pattern(54,6.4%)or V-pattern(37,4.4%).Age at onset of vision impairment was 4±5y and at the time of surgery was 25±9y.Statistically significant negative correlations were present between impaired visual acuity and the pre-operative exodeviation(r=-0.198,P=0.016)and patterns(r=-0.207,P=0.015).Age at surgery and exodeviation in patients with concomitant A-V pattern exotropia was significantly earlier as compared with that of sensory A-V pattern exotropia and sensory exotropia(both P<0.0001).There were no significant differences in these clinical variables between sensory exotropia with or without A-V pattern.Deviation and pattern were significantly reduced in patients receiving horizontal rectus surgery with or without oblique muscle surgery(both P<0.0001).CONCLUSION:The prevalence of sensory A-V pattern exotropia in our study is 10.79%.Visual acuity represents an important factor contributing to the occurrence and development of A-V pattern.Isolated horizontal rectus surgery can provide a good option for the correction of sensory A-V pattern exotropia.展开更多
The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward sl...The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward slopes and the central basin.They occurred from June to September,but especially in July,and peaked at 0300 LST.ERA5 reanalysis data and objective classification were used to investigate the synoptic patterns and their effects.There were three synoptic patterns during EPHR events,all accompanied by a Southwest Vortex.The location and intensity of the Southwest Vortex,thermal forcing of the Tibetan Plateau(TP),and low-level winds can greatly affect the intensity and spatial distribution of EPHR.When the Southwest Vortex was located in the western SCB and there were southerly low-level jets(LLJs),convergence and upslope wind would lead to EPHR over the northwestern or northern windward slopes.If there was no LLJ and the whole SCB was under the center of the Southwest Vortex,nocturnal EPHR was controlled by the internal circulation of the Southwest Vortex and the updraft generated by the thermal forcing of the TP,and the rainfall was weaker.The southeastern entrance of the SCB was a key area where the low-level wind dominated the nocturnal peak of EPHR.The nocturnal strengthened southeasterly wind in the key area is attributable to inertial oscillation,and the topographic friction plays an essential role in transporting momentum and moisture into the basin by generating easterly and northeasterly ageostrophic winds.展开更多
BACKGROUND The microcystic,elongated,and fragmented(MELF)pattern of invasion in endometrioid endometrial carcinoma(EEC)is a special mode of myometrial invasion that has been recently recognized by the pathology commun...BACKGROUND The microcystic,elongated,and fragmented(MELF)pattern of invasion in endometrioid endometrial carcinoma(EEC)is a special mode of myometrial invasion that has been recently recognized by the pathology community.Overex-pression of CXC chemokine receptor 4(CXCR4)in tumor cells contributes to tumor growth,invasion,angiogenesis,metastasis,and recurrence.AIM To explore the correlation between CXCR4 expression in EEC and MELF invasion and clinicopathological features.METHODS A total of 205 EEC patients treated at Peking University People’s Hospital from June 2020 to December 2021 were selected(60 cases with MELF invasion,145 cases without).The clinicopathological features of the two groups were compared,and expression of CXCR4 protein,estrogen receptor,and progesterone receptor was detected and compared by immunohistochemistry.RESULTS EEC with MELF invasion was significantly associated with low tumor grade,lymphovascular space invasion,deep myometrial invasion,cervical stromal involvement,and lymph node metastasis.There was a difference in CXCR4 expression between the two groups,with the MELF group having a significantly higher expression than the non-MELF group.CONCLUSION CXCR4 expression is significantly increased in EEC with MELF invasion and in the MELF invasion area,which may promote tumor invasion and metastasis and has some value for prognostic assessment.展开更多
Fractal theory is becoming an increasingly useful tool to describe soil structure dynamics for a better understanding of the performance of soil systems. Changes in land use patterns significantly affect soil physical...Fractal theory is becoming an increasingly useful tool to describe soil structure dynamics for a better understanding of the performance of soil systems. Changes in land use patterns significantly affect soil physical, chemical and biological properties. However, limited information is available on the fractal characteristics of deep soil layers under different land use patterns. In this study, the fractal dimensions of particle size distribution（PSD） and micro-aggregates in the 0–500 cm soil profile and soil anti-erodibility in the 0–10 cm soil profile for 10 typical land use patterns were investigated in the Zhifanggou Watershed on the Loess Plateau, China. The 10 typical land use patterns were： slope cropland, two terraced croplands, check-dam cropland, woodland, two shrublands, orchard, artificial and natural grasslands. The results showed that the fractal dimensions of PSD and micro-aggregates were all significantly influenced by soil depths, land use patterns and their interaction. The plantations of shrubland, woodland and natural grassland increased the amount of larger micro-aggregates, and decreased the fractal dimensions of micro-aggregates in the 0–40 cm soil profile. And they also improved the aggregate state and aggregate degree and decreased dispersion rate in the 0–10 cm soil profile. The results indicated that fractal theory can be used to characterize soil structure under different land use patterns and fractal dimensions of micro-aggregates were more effective in this regard. The natural grassland may be the best choice for improving soil structure in the study area.展开更多
BACKGROUND Different histological growth patterns(HGPs)of colorectal carcinoma(CRC)liver metastasis are associated with patients’prognosis and response to antiangiogenic therapy.However,the relationship between HGPs ...BACKGROUND Different histological growth patterns(HGPs)of colorectal carcinoma(CRC)liver metastasis are associated with patients’prognosis and response to antiangiogenic therapy.However,the relationship between HGPs of liver metastasis and clinicopathological and genomic characteristics of primary cancer has not been well established.AIM To assess whether certain clinicopathological and genomic features of primary CRC could predict the HGPs of liver metastasis.METHODS A total of 29 patients with paired resections of both primary CRC and liver metastasis were divided into two groups:A(15 cases with desmoplastic liver metastasis)and B(14 cases with replacement liver metastasis).Clinical information was obtained from patients’charts.Mismatch repair proteins,BRAFV600E,and PD-L1 were evaluated by immunohistochemistry.Five cases were selected randomly from each group for whole exome sequencing(WES)analysis.RESULTS In the primary tumor,expanding growth pattern,low tumor budding score(TBS),and Crohn’s disease-like response(CDR)were associated with desmoplastic liver metastasis and better overall survival,whereas infiltrating growth pattern alone of primary carcinoma could predict the replacement liver metastasis and worse overall survival(P<0.05).On WES analysis,primary carcinoma with desmoplastic liver metastasis showed mutations in APC(4/5);TP53(3/5);KRAS,PIK3CA,and FAT4(2/5);BRCA-1,BRCA2,BRAF,and DNAH5(1/5),whereas primary carcinoma with replacement liver metastasis showed mutations in APC and TP53(3/5);KRAS,FAT4,DNH5,SMAD,ERBB2,ERBB3,LRP1,and SDK1(1/5).CONCLUSION The HGPs,TBS,and CDR of primary CRC as well as the presence of specific genetic mutations such as those in PIK3CA could be used to predict the HGPs of liver metastasis,response to therapy,and patients’prognosis.展开更多
Recent exploration results indicate that a significant exploration potential remains in the Dongying Depression of the Bohai Bay Basin and the undiscovered oil and gas are largely reservoired in subtle traps including...Recent exploration results indicate that a significant exploration potential remains in the Dongying Depression of the Bohai Bay Basin and the undiscovered oil and gas are largely reservoired in subtle traps including turbidite litholigcal traps of the Sha-3 Member. In order to effectively guide the exploration program targeting turbidites, this study will focus on the depositional models of the Sha-3 Member turbidites and oil/gas accumulation characteristics in these turbidites. Two corresponding relationships were found. One is that the East African Rift Valley provides a modern analog for the depositional systems in the Dongying Depression. The other is that the depositional models of line-sourced slope aprons, single point-source submarine fan and multiple source ramp turbidite, established for deep-sea turbidites, can be applied to interpret the depositional features of the turbidite fans of three different origins: slope turbidite aprons, lake floor turbidite fans and delta-fed turbidite fans in the Sha-3 Member. Updip sealing integrity is the key factor determining whether oil/gas accumulates or not in the slope aprons and lake floor fans. The factors controlling oil/gas migration and accumulation in the delta-fed turbidite fans are not very clear. Multiple factors rather than a single factor probably played significant roles in these processes.展开更多
In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted...In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples. A method which is based on the xtatislical hypothesis testing and used for feature evaluation and optimum subset selection was explored. Thus, the dimensionality reduction of feature space was brought out, and simultaneously the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feature vectors. The average recognition rate of three type flaws was about 87.6%, and the best recognition rate amounted to 97%.展开更多
In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (...In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (PGF) and adjacent vegetated soils (AVS) in mesic sites from three High Arctic islands in order to characterize microbial dynamics as affected by cryoturbation, and a broad bioclimatic gradient. We also characterize total biomass of soil microorganisms and the most probable number of bacteria along a topographic gradient within each bioclimatic subzone to evaluate whether differences in topography lead to differences in microbial dynamics at a smaller scale. We found total microbial biomass C, the most probable number of heterotrophic bacteria, and fungal genera vary along this bioclimatic gradient. Microbial biomass C decreased with increasing latitude. Overall, microbial biomass C, MPN and the number of fungal isolates were higher in AVS than in PGFs. The effects which topographic position had on microbial biomass C varied across the bioclimatic gradient as there was no effect of topographic position in Isachsen (subzone A) and Mould Bay (subzone B), when compared to Green Cabin (subzone C, warmer site).There was no effect of topographic position on MPN counts at Mould Bay and Green Cabin. However, in Isachsen, MPN counts were highest in the wet topographic position as compared to the mesic and dry. In conclusion, PGFs seem to decouple the effect climate that might have on the total biomass of soil microorganisms along the bioclimatic gradient;and influence gets ameliorated as latitude increases. Similarly, the effect of topography on the total microbial biomass is significant at the warmest bioclimatic zone of the gradient. Thus, climate and topographic effects on total microbial biomass increase with warmer climate.展开更多
基金Supported by the National Key R&D Program of China(No.2023YFC2410203)Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support(No.ZLRK202503).
文摘AIM:To investigate the clinical features and prognosis of patients with orbital inflammatory myofibroblastic tumor(IMT).METHODS:This retrospective study collected clinical data from 22 patients diagnosed with orbital IMT based on histopathological examination.The patients were followed up to assess their prognosis.Clinical data from patients,including age,gender,course of disease,past medical history,primary symptoms,ophthalmologic examination findings,general condition,as well as imaging,laboratory,histopathological,and immunohistochemical results from digital records were collected.Orbital magnetic resonance imaging(MRI)and(or)computed tomography(CT)scans were performed to assess bone destruction of the mass,invasion of surrounding tissues,and any inflammatory changes in periorbital areas.RESULTS:The mean age of patients with orbital IMT was 28.24±3.30y,with a male-to-female ratio of 1.2:1.Main clinical manifestations were proptosis,blurred vision,palpable mass,and pain.Bone destruction and surrounding tissue invasion occurred in 72.73%and 54.55%of cases,respectively.Inflammatory changes in the periorbital site were observed in 77.27%of the patients.Hematoxylin and eosin staining showed proliferation of fibroblasts and myofibroblasts,accompanied by infiltration of lymphocytes and plasma cells.Immunohistochemical staining revealed that smooth muscle actin(SMA)and vimentin were positive in 100%of cases,while anaplastic lymphoma kinase(ALK)showed positivity in 47.37%.The recurrence rate of orbital IMT was 27.27%,and sarcomatous degeneration could occur.There were no significant correlations between recurrence and factors such as age,gender,laterality,duration of the disease,periorbital tissue invasion,bone destruction,periorbital inflammation,tumor size,fever,leukocytosis,or treatment(P>0.05).However,lymphadenopathy and a Ki-67 index of 10%or higher may be risk factors for recurrence(P=0.046;P=0.023).CONCLUSION:Orbital IMT is a locally invasive disease that may recur or lead to sarcomatoid degeneration,primarily affecting young and middle-aged patients.The presence of lymphadenopathy and a Ki-67 index of 10%or higher may signify a poor prognosis.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No. HC-CN-20221107001。
文摘The detection of steel surface anomalies has become an industrial challenge due to variations in production equipment,processes,and characteristics.To alleviate the problem,this paper proposes a detection and localization method combining 3D depth and 2D RGB features.The framework comprises three stages:defect classification,defect location,an d warpage judgment.The first stage uses a dataefficient image Transformer model,the second stage utilizes reverse knowledge distillation,and the third stage performs feature fusion using3D depth and 2D RGB features.Experimental results show that the proposed algorithm achieves relatively high accuracy and feasibility,and can be effectively used in industrial scenarios.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
基金General Program of National Natural Science Foundation of China(82474390)Construction Project of Pudong New Area Famous TCM Studios(National Pilot Zone for TCM Development,Shanghai)(PDZY-2025-0716)Shanghai Municipal Science and Technology Program Project Shanghai Key Laboratory of Health Identification and Assessment(21DZ2271000).
文摘Objective To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine(TCM)with machine learning algorithms.The proposed model seeks to establish a TCM-informed tool for early depression screening,thereby bridging traditional diagnostic principles with modern computational approaches.Methods The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1,2022 to October 1,2023,as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group.Videos of 3–10 s were captured using a Xiaomi Pad 5,and the TCM spirit and expressions were determined by TCM experts(at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions).Basic information,facial images,and interview information were collected through a portable TCM intelligent analysis and diagnosis device,and facial diagnosis features were extracted using the Open CV computer vision library technology.Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data,TCM spirit and expression features,and facial diagnosis feature parameters of the two groups,to compare the differences in TCM spirit and expression and facial features.Five machine learning algorithms,including extreme gradient boosting(XGBoost),decision tree(DT),Bernoulli naive Bayes(BernoulliNB),support vector machine(SVM),and k-nearest neighbor(KNN)classification,were used to construct a depression recognition model based on the fusion of TCM spirit and expression features.The performance of the model was evaluated using metrics such as accuracy,precision,and the area under the receiver operating characteristic(ROC)curve(AUC).The model results were explained using the Shapley Additive exPlanations(SHAP).Results A total of 93 depression patients and 87 healthy individuals were ultimately included in this study.There was no statistically significant difference in the baseline characteristics between the two groups(P>0.05).The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows.(i)Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls(P<0.05),with characteristic features such as sad expressions,facial erythema,and changes in the lip color ranging from erythematous to cyanotic.(ii)Depressed patients exhibited significantly lower values in facial complexion L,lip L,and a values,and gloss index,but higher values in facial complexion a and b,lip b,low gloss index,and matte index(all P<0.05).(iii)The results of multiple models show that the XGBoost-based depression recognition model,integrating the TCM“spirit-expression”diagnostic framework,achieved an accuracy of 98.61%and significantly outperformed four benchmark algorithms—DT,BernoulliNB,SVM,and KNN(P<0.01).(iv)The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm,the complexion b value,categories of facial spirit,high gloss index,low gloss index,categories of facial expression and texture features have significant contribution to the model.Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model,offering a novel paradigm for objective depression diagnosis.
文摘BACKGROUND SMARCB1/INI1-deficient pancreatic undifferentiated rhabdoid carcinoma is a highly aggressive tumor,and spontaneous splenic rupture(SSR)as its presenting manifestation is rarely reported among pancreatic malignancies.CASE SUMMARY We herein report a rare case of a 59-year-old female who presented with acute left upper quadrant abdominal pain without any history of trauma.Abdominal imaging demonstrated a heterogeneous splenic lesion with hemoperitoneum,raising clinical suspicion of SSR.Emergency laparotomy revealed a pancreatic tumor invading the spleen and left kidney,with associated splenic rupture and dense adhesions,necessitating en bloc resection of the distal pancreas,spleen,and left kidney.Histopathology revealed a biphasic malignancy composed of moderately differentiated pancreatic ductal adenocarcinoma and an undifferentiated carcinoma with rhabdoid morphology and loss of SMARCB1 expression.Immunohistochemical analysis confirmed complete loss of SMARCB1/INI1 in the undifferentiated component,along with a high Ki-67 index(approximately 80%)and CD10 positivity.The ductal adenocarcinoma component retained SMARCB1/INI1 expression and was positive for CK7 and CK-pan.Transitional zones between the two tumor components suggested progressive dedifferentiation and underlying genomic instability.The patient received adjuvant chemotherapy with gemcitabine and nab-paclitaxel and maintained a satisfactory quality of life at the 6-month follow-up.CONCLUSION This study reports a rare case of SMARCB1/INI1-deficient undifferentiated rhabdoid carcinoma of the pancreas combined with ductal adenocarcinoma,presenting as SSR-an exceptionally uncommon initial manifestation of pancreatic malignancy.
基金financially supported by the National Natural Science Foundation of China(No.U22A20439)the Shenzhen Fundamental Research Program(No.JCYJ20220818100418040)+2 种基金the Guangdong-Hong Kong-Macao Joint Innovation Fund(No.2024A0505040001)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011122)the Shenzhen ShowMac Network Technology Co.,Ltd.
文摘This study proposes a multimodal deep learning framework for joint prediction of the state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries.Twelve representative impedance features-covering charge-transfer resistance,solid electrolyte interface(SEI)layer impedance,and ion diffusion-are extracted from electrochemical impedance spectroscopy(EIS)and combined with short voltage/current segments to form a compact,interpretable feature set.A residual multi-layer perceptron(ResMLP)is employed for SOH regression,and a temporal convolutional network with attention(TCNAttention)is used for RUL estimation.Lifetime experiments on two battery types with different chemistries and form factors,evaluated through three rounds of paired cross-validation,validate the approach.Results show that the proposed features significantly reduce dimensionality and computational cost while substantially lowering SOH error,achieving an average normalized root mean square error of 2.3%.The RUL prediction reaches an average error of 14.8%.Overall,the framework balances interpretability,robustness,and feasibility,providing a practical solution for battery management systems(BMS)monitoring and life prediction.
基金funded by the National Key Lab Foundation,China(No.2020KLF030101)the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2025031)Shaanxi Innovative Research Team of Artificial Intelligence for Fluid Mechanics,China(No.2024RS-CXTD-16)。
文摘Improved delay detached eddy simulation is performed to explore the flow features and aero-optical effects of turrets with different bottom cylinder height at a freestream Mach number Ma=0.7.Analysis of both the time-averaged and instantaneous flow features demonstrate that the shock motion causes the oscillation of separated shear layer.In flow analysis,two unsteady shock-wake-correlated modes are discerned:the asymmetric shifting mode and the symmetric breathing mode.With the increase of cylinder height,the relative energy of shock gradually increases,which goes from 26%to 59%.The proper orthogonal decomposition analysis yields the single frequency peak for the two dominant modes.The frequency peaks of shifting mode are generally at StD<0.23,while the frequency peaks of breathing mode are generally at StD>0.26.The dynamic mode decomposition analysis gives range of frequency peak.The frequency peaks of shifting mode are in the range of StD=0.11-0.23,and the frequency peaks of breathing mode are in range of StD=0.26-0.41.Optical distortion analysis indicates that the distortion calculated in five cases is linked to the breathing mode.When the beam passes through the turbulent wake,it exhibits the high-frequency and high-amplitude characteristics.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
文摘In the field of intelligent air combat,real-time and accurate recognition of within-visual-range(WVR)maneuver actions serves as the foundational cornerstone for constructing autonomous decision-making systems.However,existing methods face two major challenges:traditional feature engineering suffers from insufficient effective dimensionality in the feature space due to kinematic coupling,making it difficult to distinguish essential differences between maneuvers,while end-to-end deep learning models lack controllability in implicit feature learning and fail to model high-order long-range temporal dependencies.This paper proposes a trajectory feature pre-extraction method based on a Long-range Masked Autoencoder(LMAE),incorporating three key innovations:(1)Random Fragment High-ratio Masking(RFH-Mask),which enforces the model to learn long-range temporal correlations by masking 80%of trajectory data while retaining continuous fragments;(2)Kalman Filter-Guided Objective Function(KFG-OF),integrating trajectory continuity constraints to align the feature space with kinematic principles;and(3)Two-stage Decoupled Architecture,enabling efficient and controllable feature learning through unsupervised pre-training and frozen-feature transfer.Experimental results demonstrate that LMAE significantly improves the average recognition accuracy for 20-class maneuvers compared to traditional end-to-end models,while significantly accelerating convergence speed.The contributions of this work lie in:introducing high-masking-rate autoencoders into low-informationdensity trajectory analysis,proposing a feature engineering framework with enhanced controllability and efficiency,and providing a novel technical pathway for intelligent air combat decision-making systems.
基金supported by the National Key Research and Development Program of China(No.2023YFB3105700).
文摘Phishing email detection represents a critical research challenge in cybersecurity.To address this,this paper proposes a novel Double-S(statistical-semantic)feature model based on three core entities involved in email communication:the sender,recipient,and email content.We employ strategic game theory to analyze the offensive strategies of phishing attackers and defensive strategies of protectors,extracting statistical features from these entities.We also leverage the Qwen large language model to excavate implicit semantic features(e.g.,emotional manipulation and social engineering tactics)from email content.By integrating statistical and semantic features,our model achieves a robust representation of phishing emails.We introduce a hybrid detection model that integrates a convolutional neural network(CNN)module with the XGBoost(Extreme Gradient Boosting)classifier,effectively capturing local correlations in high-dimensional features.Experimental results on real-world phishing email datasets demonstrate the superiority of our approach,achieving an F1-score of 0.9587,precision of 0.9591,and recall of 0.9583,representing improvements of 1.3%–10.6%compared to state-of-the-art methods.
基金supported by the National Key Research and Development Pro-gram of China(Grant No.2022YFC3003902)the National Natu-ral Science Foundation of China(Grant Nos.42075146 and 42275006).
文摘In July 2021,a catastrophic extreme precipitation(EP)event occurred in Henan Province,China,resulting in considerable human and economic losses.The synoptic pattern during this event is distinctive,characterized by the presence of two typhoons and substantial water transport into Henan.However,a favorable synoptic pattern only does not guarantee the occurrence of heavy precipitation in Henan.This study investigates the key environmental features critical for EP under similar synoptic patterns to the 2021 Henan extreme event.It is found that cold clouds are better aggregated on EP days,accompanied by beneficial environment features like enhanced moisture conditions,stronger updrafts,and greater atmospheric instability.The temporal evolution of these environmental features shows a leading signal by one to three days.These results suggest the importance of combining the synoptic pattern and environmental features in the forecasting of heavy precipitation events.
文摘AIM:To investigate the potential mechanisms of A-V pattern and evaluate the surgical outcomes used in the treatment of sensory exotropia.METHODS:The medical records of patients with sensory A-V pattern exotropia who underwent strabismus surgery between May 2014 to June 2019 was retrospectively reviewed.The control group included sensory exotropia patients without A-V pattern and concomitant A-V pattern exotropia patients with normal vision who undergone strabismus surgery over this same time period.Ocular alignment,best corrected visual acuity,oblique muscle function,and stereopsis records were collected.RESULTS:Among the 843 eligible patients,91(10.79%;39 males and 52 females)had A-pattern(54,6.4%)or V-pattern(37,4.4%).Age at onset of vision impairment was 4±5y and at the time of surgery was 25±9y.Statistically significant negative correlations were present between impaired visual acuity and the pre-operative exodeviation(r=-0.198,P=0.016)and patterns(r=-0.207,P=0.015).Age at surgery and exodeviation in patients with concomitant A-V pattern exotropia was significantly earlier as compared with that of sensory A-V pattern exotropia and sensory exotropia(both P<0.0001).There were no significant differences in these clinical variables between sensory exotropia with or without A-V pattern.Deviation and pattern were significantly reduced in patients receiving horizontal rectus surgery with or without oblique muscle surgery(both P<0.0001).CONCLUSION:The prevalence of sensory A-V pattern exotropia in our study is 10.79%.Visual acuity represents an important factor contributing to the occurrence and development of A-V pattern.Isolated horizontal rectus surgery can provide a good option for the correction of sensory A-V pattern exotropia.
基金supported by the National Natural Science Foundation of China(Grant Nos.42330610 and 42075010)。
文摘The Sichuan Basin(SCB),China has a high incidence of extremely persistent heavy rainfall(EPHR)events.The EPHR events from 2009 to 2019 in the SCB were mainly concentrated over the northern and northwestern windward slopes and the central basin.They occurred from June to September,but especially in July,and peaked at 0300 LST.ERA5 reanalysis data and objective classification were used to investigate the synoptic patterns and their effects.There were three synoptic patterns during EPHR events,all accompanied by a Southwest Vortex.The location and intensity of the Southwest Vortex,thermal forcing of the Tibetan Plateau(TP),and low-level winds can greatly affect the intensity and spatial distribution of EPHR.When the Southwest Vortex was located in the western SCB and there were southerly low-level jets(LLJs),convergence and upslope wind would lead to EPHR over the northwestern or northern windward slopes.If there was no LLJ and the whole SCB was under the center of the Southwest Vortex,nocturnal EPHR was controlled by the internal circulation of the Southwest Vortex and the updraft generated by the thermal forcing of the TP,and the rainfall was weaker.The southeastern entrance of the SCB was a key area where the low-level wind dominated the nocturnal peak of EPHR.The nocturnal strengthened southeasterly wind in the key area is attributable to inertial oscillation,and the topographic friction plays an essential role in transporting momentum and moisture into the basin by generating easterly and northeasterly ageostrophic winds.
文摘BACKGROUND The microcystic,elongated,and fragmented(MELF)pattern of invasion in endometrioid endometrial carcinoma(EEC)is a special mode of myometrial invasion that has been recently recognized by the pathology community.Overex-pression of CXC chemokine receptor 4(CXCR4)in tumor cells contributes to tumor growth,invasion,angiogenesis,metastasis,and recurrence.AIM To explore the correlation between CXCR4 expression in EEC and MELF invasion and clinicopathological features.METHODS A total of 205 EEC patients treated at Peking University People’s Hospital from June 2020 to December 2021 were selected(60 cases with MELF invasion,145 cases without).The clinicopathological features of the two groups were compared,and expression of CXCR4 protein,estrogen receptor,and progesterone receptor was detected and compared by immunohistochemistry.RESULTS EEC with MELF invasion was significantly associated with low tumor grade,lymphovascular space invasion,deep myometrial invasion,cervical stromal involvement,and lymph node metastasis.There was a difference in CXCR4 expression between the two groups,with the MELF group having a significantly higher expression than the non-MELF group.CONCLUSION CXCR4 expression is significantly increased in EEC with MELF invasion and in the MELF invasion area,which may promote tumor invasion and metastasis and has some value for prognostic assessment.
基金supported by the Strategic Technology Project of Chinese Academy of Sciences (XDA05060300)the Science and Technology R&D Program of Shaanxi Province (2011KJXX63)
文摘Fractal theory is becoming an increasingly useful tool to describe soil structure dynamics for a better understanding of the performance of soil systems. Changes in land use patterns significantly affect soil physical, chemical and biological properties. However, limited information is available on the fractal characteristics of deep soil layers under different land use patterns. In this study, the fractal dimensions of particle size distribution（PSD） and micro-aggregates in the 0–500 cm soil profile and soil anti-erodibility in the 0–10 cm soil profile for 10 typical land use patterns were investigated in the Zhifanggou Watershed on the Loess Plateau, China. The 10 typical land use patterns were： slope cropland, two terraced croplands, check-dam cropland, woodland, two shrublands, orchard, artificial and natural grasslands. The results showed that the fractal dimensions of PSD and micro-aggregates were all significantly influenced by soil depths, land use patterns and their interaction. The plantations of shrubland, woodland and natural grassland increased the amount of larger micro-aggregates, and decreased the fractal dimensions of micro-aggregates in the 0–40 cm soil profile. And they also improved the aggregate state and aggregate degree and decreased dispersion rate in the 0–10 cm soil profile. The results indicated that fractal theory can be used to characterize soil structure under different land use patterns and fractal dimensions of micro-aggregates were more effective in this regard. The natural grassland may be the best choice for improving soil structure in the study area.
基金the Human Resources Development Program for the Outstanding Talents in The Fifth People’s Hospital of Shanghai,Fudan University,No.2017WYRCJY09the Key Medical Speciality of The Fifth People’s Hospital of Shanghai,Fudan University,No.2017WY202K08
文摘BACKGROUND Different histological growth patterns(HGPs)of colorectal carcinoma(CRC)liver metastasis are associated with patients’prognosis and response to antiangiogenic therapy.However,the relationship between HGPs of liver metastasis and clinicopathological and genomic characteristics of primary cancer has not been well established.AIM To assess whether certain clinicopathological and genomic features of primary CRC could predict the HGPs of liver metastasis.METHODS A total of 29 patients with paired resections of both primary CRC and liver metastasis were divided into two groups:A(15 cases with desmoplastic liver metastasis)and B(14 cases with replacement liver metastasis).Clinical information was obtained from patients’charts.Mismatch repair proteins,BRAFV600E,and PD-L1 were evaluated by immunohistochemistry.Five cases were selected randomly from each group for whole exome sequencing(WES)analysis.RESULTS In the primary tumor,expanding growth pattern,low tumor budding score(TBS),and Crohn’s disease-like response(CDR)were associated with desmoplastic liver metastasis and better overall survival,whereas infiltrating growth pattern alone of primary carcinoma could predict the replacement liver metastasis and worse overall survival(P<0.05).On WES analysis,primary carcinoma with desmoplastic liver metastasis showed mutations in APC(4/5);TP53(3/5);KRAS,PIK3CA,and FAT4(2/5);BRCA-1,BRCA2,BRAF,and DNAH5(1/5),whereas primary carcinoma with replacement liver metastasis showed mutations in APC and TP53(3/5);KRAS,FAT4,DNH5,SMAD,ERBB2,ERBB3,LRP1,and SDK1(1/5).CONCLUSION The HGPs,TBS,and CDR of primary CRC as well as the presence of specific genetic mutations such as those in PIK3CA could be used to predict the HGPs of liver metastasis,response to therapy,and patients’prognosis.
文摘Recent exploration results indicate that a significant exploration potential remains in the Dongying Depression of the Bohai Bay Basin and the undiscovered oil and gas are largely reservoired in subtle traps including turbidite litholigcal traps of the Sha-3 Member. In order to effectively guide the exploration program targeting turbidites, this study will focus on the depositional models of the Sha-3 Member turbidites and oil/gas accumulation characteristics in these turbidites. Two corresponding relationships were found. One is that the East African Rift Valley provides a modern analog for the depositional systems in the Dongying Depression. The other is that the depositional models of line-sourced slope aprons, single point-source submarine fan and multiple source ramp turbidite, established for deep-sea turbidites, can be applied to interpret the depositional features of the turbidite fans of three different origins: slope turbidite aprons, lake floor turbidite fans and delta-fed turbidite fans in the Sha-3 Member. Updip sealing integrity is the key factor determining whether oil/gas accumulates or not in the slope aprons and lake floor fans. The factors controlling oil/gas migration and accumulation in the delta-fed turbidite fans are not very clear. Multiple factors rather than a single factor probably played significant roles in these processes.
文摘In this paper, three types of weld flaw were taken as target, evaluation and recognition of flaw echo features were studied. On the basis of experimental study and theoretical analysis, 26 features have been extracted from each echo samples. A method which is based on the xtatislical hypothesis testing and used for feature evaluation and optimum subset selection was explored. Thus, the dimensionality reduction of feature space was brought out, and simultaneously the amount of calculation was decreased. An intelligent pattern classifier with B-P type neural network was constructed which was characterized by high speed and accuracy for learning. Using a half of total samples as training set and others as testing set, the learning efficiency and the classification ability of network model were studied. The results of experiment showed that the learning rate of different training samples was about 100%. The results of recognition was satisfactory when the optimum feature subset was taken as the sample's feature vectors. The average recognition rate of three type flaws was about 87.6%, and the best recognition rate amounted to 97%.
文摘In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (PGF) and adjacent vegetated soils (AVS) in mesic sites from three High Arctic islands in order to characterize microbial dynamics as affected by cryoturbation, and a broad bioclimatic gradient. We also characterize total biomass of soil microorganisms and the most probable number of bacteria along a topographic gradient within each bioclimatic subzone to evaluate whether differences in topography lead to differences in microbial dynamics at a smaller scale. We found total microbial biomass C, the most probable number of heterotrophic bacteria, and fungal genera vary along this bioclimatic gradient. Microbial biomass C decreased with increasing latitude. Overall, microbial biomass C, MPN and the number of fungal isolates were higher in AVS than in PGFs. The effects which topographic position had on microbial biomass C varied across the bioclimatic gradient as there was no effect of topographic position in Isachsen (subzone A) and Mould Bay (subzone B), when compared to Green Cabin (subzone C, warmer site).There was no effect of topographic position on MPN counts at Mould Bay and Green Cabin. However, in Isachsen, MPN counts were highest in the wet topographic position as compared to the mesic and dry. In conclusion, PGFs seem to decouple the effect climate that might have on the total biomass of soil microorganisms along the bioclimatic gradient;and influence gets ameliorated as latitude increases. Similarly, the effect of topography on the total microbial biomass is significant at the warmest bioclimatic zone of the gradient. Thus, climate and topographic effects on total microbial biomass increase with warmer climate.
