BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifyi...BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.展开更多
AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflamma...AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflammation rather than systemic autoimmunity or generalized orbital pressure.METHODS:Data included ophthalmic assessments and a panel of thyroid function and autoimmune biomarkers.Blinded radiological analysis of orbital computed tomography(CT)scans was performed to quantify sinus abnormalities and extraocular muscles(EOMs)involvement.Patients were categorized into two groups based on CT findings,those with no radiological evidence of sinus abnormalities(non-PSA control group)and those with identifiable PSA.Furthermore,ethmoid sinus mucosal biopsies from a subset of TAO patients and noninflammatory controls were subjected to histopathological analysis.RESULTS:Totally 121 TAO patients(mean age 42.4±12.8y,range 10-78y),male:female=42:79,were included.PSA was identified in 44.6%(n=54)of patients,with a distribution anatomically restricted to the maxillary(50.0%isolated)and ethmoid sinuses(18.5%isolated;29.6%combined).Compared to the non-PSA group(n=67),patients with PSA were significantly older(45.1±11.8 vs 40.3±13.2y;P=0.040)and were more likely to be male(55.6%vs 17.9%;P<0.001).They also had significantly higher proptosis(22.1±3.2 vs 20.7±2.9 mm;P<0.001).Medial/inferior rectus involvement was most frequent(88.4%vs 89.3%).Histopathological analysis of sinus mucosa from PSA patients provided direct evidence of pathology,revealing a dense,chronic lymphoplasmacytic infiltrate and submucosal edema,validating the radiological findings as a true inflammatory process.No significant correlation was found with systemic autoimmune markers,including thyroid-stimulating hormone(TSH)receptor antibodies(TRAb,median 4.86 vs 2.71 IU/L,P=0.104).CONCLUSION:TAO is associated with a high prevalence of PSA in a pattern consistent with the orbital anatomy.The correlation with ipsilateral muscle thickening combined with the lack of association with proptosis laterality or systemic biomarkers lend strong support to a model of contiguous inflammation over systemic autoimmunity,a hypothesis that warrants further validation through longitudinal and mechanistic studies.展开更多
Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-cont...Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-control study,we analyzed bisphenol and metal concentrations in amniotic fluid of high-risk pregnant women undergoing amniocentesis.Concentrations of bisphenols and metals were measured using ultra-performance liquid chromatography-tandem mass spectrometry and inductively coupled plasma-mass spectrometry,respectively.Logistic regression and quantile-based g-computation were applied to evaluate individual and combined effects,while dose-response relationships were assessed using restricted cubic splines.Our findings indicated that bisphenol S(BPS),bisphenol Z(BPZ),bisphenol AF(BPAF),antimony(Sb),and vanadium(V)were significantly associated with an increased risk of CNA when analyzed individually,whereas manganese,iron,copper(Cu),nickel(Ni),and zinc(Zn)were significantly and inversely associated with CNA risk.Combined exposure to bisphenol and metal mixtures was associated with an increased risk of CNA in multi-pollutant models.Cu and Ni exhibited a positive additive interaction.Furthermore,BPS,BPZ,and BPAF were individually associated with an increased risk of Down syndrome,while Zn was associated with a decreased risk of Down syndrome.BPS,Sb,V,and Zn were individually associated with an increased risk of Klinefelter syndrome.These findings underscore the potential role of prenatal bisphenol and metal exposure in the pathogenesis of fetal CNA,highlighting both additive and synergistic effects.展开更多
Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ...Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.展开更多
Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nai...Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nail bed related changes encountered in UET recipients in the literature.The differential diagnosis of nail abnormalities in UET includes a wide range of systemic,local and iatrogenic conditions other than immune responses to the allograft.It requires interdisciplinary evaluation by primary transplant surgeons,pathologists,dermatologists and immunologists.The possible underlying mechanisms of nail pathology in UET and the management are discussed.It also underscores the importance of onychodystrophy and need for timely intervention and to improve outcomes in UET recipients.展开更多
Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreou...Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.展开更多
To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and ...To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven si...As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine...Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.展开更多
Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play p...Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
Cervical cancer is the fourth most common cancer worldwide, accounting for 6.8% of new cancer cases and 8.1% of cancer-related deaths. About 85% of these deaths occurred in low- and middle-income countries. The aim of...Cervical cancer is the fourth most common cancer worldwide, accounting for 6.8% of new cancer cases and 8.1% of cancer-related deaths. About 85% of these deaths occurred in low- and middle-income countries. The aim of this study was to assess the frequency and distribution of the human papillomavirus (HPV) genotypes in women showing cytological abnormalities of the cervix at the Sourô SANOU University Hospital (CHUSS) in Bobo-Dioulasso, Burkina Faso. This is a descriptive study of women recruited at the CHUSS. The cervico-uterine smear examination was carried out at the CHUSS Anatomy and Pathology Department for cervical cancer screening. The data were collected from women with atypical cells on their cervico-uterine smear. Cervicovaginal samples were taken from consenting women and HPV genotyping was performed using the HPV Direct FLOW CHIP kit at CERBA. We obtained approval from the ethics committee. The data were analyzed using the SPSS 26 software. The results of the study showed that 67.79% of the participants were aged between 50 and 65, a group that is particularly vulnerable to persistent infection with high-risk oncogenic HPV genotypes. Of the women screened, 40.7% were HPV positive and 29.2% had multiple infections. The most common genotypes were HPV 35, followed by HPV 18, 52, 58 and 66. These data highlight the need for increased surveillance and targeted prevention strategies among this female population.展开更多
Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included p...Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
Human-elephant conflict(HEC)poses a major socio-ecological challenge across elephant range states.Since 2015,the National Forest Ecosystem Research Station of China based in Xishuangbanna has developed the Elephant Ea...Human-elephant conflict(HEC)poses a major socio-ecological challenge across elephant range states.Since 2015,the National Forest Ecosystem Research Station of China based in Xishuangbanna has developed the Elephant Early-warning System(EEWS),a novel approach that has demonstrably reduced the risk of HEC incidents-particularly those involving direct encounters between people and elephants.By dynamically maintaining safety buffers,this system safeguards endangered elephants while mitigating human safety risks during livelihood activities and ensuring uninterrupted elephant movement.Building upon the C4ISR framework(Command,Control,Communications,Computers,Intelligence,Surveillance,and Reconnaissance),EEWS integrates key technological and institutional innovations-including the widespread adoption of mobile internet,deployment of camera traps,use of drones,and cross-sectoral governance reforms.The EEWS’s conceptual framework and technical architecture have been already recognized by local government and are now being scaled up from Xishuangbanna to the entire Asian elephant range in China,establishing a replicable“China model”for achieving harmonious human-elephant coexistence.This study reviews the conceptual foundations,development,and field implementation of EEWS,and offers recommendations to guide future refinement and broader application.展开更多
Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first t...Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first time,to identify abnormal trends in nuclearβ-decay half-lives based on deviations between experimental values and the predictions of neural networks.Nuclei exhibiting anomalous increases,abrupt peaks,sharp decreases,abnormal odd-even oscillations,and excessively large experimental errors in theirβ-decay half-lives,which deviate from systematic patterns,were identified through deviations.These anomalous phenomena may be associated with shell effects,shape coexistence,or discrepancies in the experimental data.The discovery and analysis of these abnormal nuclei provide a valuable reference for further investigations using sophisticated microscopic theories,potentially offering insights into new physics through studies of nuclearβ-decay half-lives.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study in...Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study investigates AGG evolution in a heat-treatable Mg-1Al-0.3Ca-0.5Mn(wt.%)alloy,revealing its fundamental mechanism through phase interaction analysis.The AGG initiation is predominantly driven by Zener pinning force attenuation around abnormally coarsened Al_(8)Mn_(5) precipitates.Mechanistically,this heterogeneous coarsening stems from preferential Al_(8)Mn_(5) phase growth kinetics adjacent to Al_(2)Ca phase during homogenization treatment,creating localized pinning force discontinuities.However,addition of 0.2 wt.%Gd facilitates phase transformation from Al_(8)Mn_(5) to thermally stable Al_(8)Mn_(4)Gd with lower Gibbs free energy,thereby promoting a more uniform and refined precipitate distribution.Consequently,the Gd-containing alloy exhibits enhanced grain thermal stability,maintaining a refined microstructure with average grain size of∼7.7μm even after T4 treatment at 500℃ for 1 h,which simultaneously improves strength and ductility compared to the Gd-free alloy.展开更多
文摘BACKGROUND An echocardiogram is an essential tool in the evaluation of potential kidney transplant recipients(KTRs).Despite cardiac clearance,potential KTRs still have structural and functional abnormalities.Identifying the prevalence of these abnormalities and understanding their predictors is vital for optimizing pretransplant risk stratification and improving post-transplant outcomes.AIM To determine the prevalence of left ventricular hypertrophy(LVH),left ventricular systolic dysfunction(LVSD),diastolic dysfunction(DD),pulmonary hypertension(PH),and their predictors,and to assess their impact on graft function in pre-transplant candidates.METHODS The study included all successful transplant candidates older than 14 who had a baseline echocardiogram.Binary logistic regression models were constructed to identify factors associated with LVH,LVSD,DD,and PH.RESULTS Out of 259 patients,LVH was present in 64%(166),12%(31)had LVSD,27.5%(71)had DD,and 66(25.5%)had PH.Independent predictors of LVH included male gender[odds ratio(OR):2.51;95%CI:1.17-5.41 P=0.02],PH(OR=2.07;95%CI:1.11-3.86;P=0.02),DD(OR:2.47;95%CI:1.29-4.73;P=0.006),and dyslipidemia(OR=1.94;95%CI:1.07-3.53;P=0.03).Predictors for LVSD included patients with DD(OR=3.3,95%CI:1.41-7.81;P=0.006)and a family history of coronary artery disease(OR=4.50,95%CI:1.33-15.20;P=0.015).Peritoneal dialysis was an independent predictor for DD(OR=10.03;95%CI:1.71-58.94,P=0.011).The presence of LVH(OR=3.32,95%CI:1.05-10.55,P=0.04)and mild to moderate or moderate to severe mitral regurgitation(OR=4.63,95%CI:1.45-14.78,P=0.01)were significant factors associated with PH.These abnormalities had no significant impact on estimated glomerular filtration at discharge,6 months,1 year,or 2 years post-transplant.CONCLUSION Significant echocardiographic abnormalities persist in a potential transplant candidate despite cardiac clearance,although they don’t affect future graft function.Understanding the risk factors associated with these abnormalities may help clinicians address these factors pre-and post-transplant to achieve better outcomes.
基金Supported by The National Natural Science Foundation of China(No.82101180)the Fund for Beijing Science&Technology Development of TCM(No.BJZYYB-2023-17)the Beijing Municipal Natural Science Foundation grant(No.7252093).
文摘AIM:To define the prevalence and anatomical patterns of paranasal sinus abnormalities(PSA)in thyroid-associated ophthalmopathy(TAO)and to test the hypothesis that TAO is partially driven by contiguous orbital inflammation rather than systemic autoimmunity or generalized orbital pressure.METHODS:Data included ophthalmic assessments and a panel of thyroid function and autoimmune biomarkers.Blinded radiological analysis of orbital computed tomography(CT)scans was performed to quantify sinus abnormalities and extraocular muscles(EOMs)involvement.Patients were categorized into two groups based on CT findings,those with no radiological evidence of sinus abnormalities(non-PSA control group)and those with identifiable PSA.Furthermore,ethmoid sinus mucosal biopsies from a subset of TAO patients and noninflammatory controls were subjected to histopathological analysis.RESULTS:Totally 121 TAO patients(mean age 42.4±12.8y,range 10-78y),male:female=42:79,were included.PSA was identified in 44.6%(n=54)of patients,with a distribution anatomically restricted to the maxillary(50.0%isolated)and ethmoid sinuses(18.5%isolated;29.6%combined).Compared to the non-PSA group(n=67),patients with PSA were significantly older(45.1±11.8 vs 40.3±13.2y;P=0.040)and were more likely to be male(55.6%vs 17.9%;P<0.001).They also had significantly higher proptosis(22.1±3.2 vs 20.7±2.9 mm;P<0.001).Medial/inferior rectus involvement was most frequent(88.4%vs 89.3%).Histopathological analysis of sinus mucosa from PSA patients provided direct evidence of pathology,revealing a dense,chronic lymphoplasmacytic infiltrate and submucosal edema,validating the radiological findings as a true inflammatory process.No significant correlation was found with systemic autoimmune markers,including thyroid-stimulating hormone(TSH)receptor antibodies(TRAb,median 4.86 vs 2.71 IU/L,P=0.104).CONCLUSION:TAO is associated with a high prevalence of PSA in a pattern consistent with the orbital anatomy.The correlation with ipsilateral muscle thickening combined with the lack of association with proptosis laterality or systemic biomarkers lend strong support to a model of contiguous inflammation over systemic autoimmunity,a hypothesis that warrants further validation through longitudinal and mechanistic studies.
基金supported by the Key Project of Natural Science Foundation of Tianjin(No.23JCZDJC00330)Tianjin Municipal Education Commission Scientific Research Program(No.2022ZD056).
文摘Prenatal exposure to bisphenols and metals has raised significant concerns regarding their potential impact on fetal development,particularly the risk of fetal chromosome numerical abnormalities(CNA).In this case-control study,we analyzed bisphenol and metal concentrations in amniotic fluid of high-risk pregnant women undergoing amniocentesis.Concentrations of bisphenols and metals were measured using ultra-performance liquid chromatography-tandem mass spectrometry and inductively coupled plasma-mass spectrometry,respectively.Logistic regression and quantile-based g-computation were applied to evaluate individual and combined effects,while dose-response relationships were assessed using restricted cubic splines.Our findings indicated that bisphenol S(BPS),bisphenol Z(BPZ),bisphenol AF(BPAF),antimony(Sb),and vanadium(V)were significantly associated with an increased risk of CNA when analyzed individually,whereas manganese,iron,copper(Cu),nickel(Ni),and zinc(Zn)were significantly and inversely associated with CNA risk.Combined exposure to bisphenol and metal mixtures was associated with an increased risk of CNA in multi-pollutant models.Cu and Ni exhibited a positive additive interaction.Furthermore,BPS,BPZ,and BPAF were individually associated with an increased risk of Down syndrome,while Zn was associated with a decreased risk of Down syndrome.BPS,Sb,V,and Zn were individually associated with an increased risk of Klinefelter syndrome.These findings underscore the potential role of prenatal bisphenol and metal exposure in the pathogenesis of fetal CNA,highlighting both additive and synergistic effects.
基金supported by y the Applied Research Advancement Project in Engineering University of PAP(WYY202304)Research and Innovation Team Project in Engineering University of PAP(KYTD202306)Funding for postgraduate education and teaching.
文摘Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.
文摘Nail changes following upper extremity transplantation(UET)cannot be overlooked as they possess diagnostic and prognostic relevance in allotransplantation of upper limbs.This comprehensive review explores nail and nail bed related changes encountered in UET recipients in the literature.The differential diagnosis of nail abnormalities in UET includes a wide range of systemic,local and iatrogenic conditions other than immune responses to the allograft.It requires interdisciplinary evaluation by primary transplant surgeons,pathologists,dermatologists and immunologists.The possible underlying mechanisms of nail pathology in UET and the management are discussed.It also underscores the importance of onychodystrophy and need for timely intervention and to improve outcomes in UET recipients.
基金supported by the National Natural Science Foundation of China(62271337,62371326,and 62371328)the National Key Research and Development Program of China(2019FYC1710204)+1 种基金the National Clinical Key Specialty Construction Project(10000015Z155080000004)the Natural Science Foundation of Jiangsu Province(BK20231310).
文摘Advances in optical coherence tomography(OCT)technology allow a clear view of the vitreoretinal interface(VRI).The abnormality of the VRI is one of the common symptoms of high myopia,mainly including posterior vitreous detachment(PVD)and epiretinal membrane(ERM).They can cause severe damage to the structure and function of the retina,leading to permanent vision loss.Therefore,fully automated detection of abnormalities at the VRI is crucial for the management of high myopia.This paper presents a DS-YOLOv7 network aimed at accurately identifying abnormalities,including partial PVD,complete PVD,and ERM from retinal OCT images.Built upon the YOLOv7 network,the proposed model integrates the advanced dynamic snake convolution(DSConv)module to capture the curvilinear characteristics of lesions,and the mixture of attention and convolution(ACMix)module to improve the precision and robustness of feature extraction through effective fusion of self-attention mechanisms and convolution.Moreover,the introduction of the efficient complete intersection-over-union(ECIoU)loss function further enhances the coordinate regression capability of the model.Threefold cross-validation on a dataset with 1973 OCT B-scans from 46 patients shows that the DS-YOLOv7 achieved superior performance in vitreoretinal interface abnormality detection,with mAP@0.5 of 0.714,mAP@0.75 of 0.438,and mAP@0.5:0.95 of 0.424.The proposed model can provide an accurate and efficient diagnostic tool for patients with high myopia.
基金support from the National Key R&D Program of China(Grant No.2020YFB1711100).
文摘To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process,an integrated diagnostic method combining the gray wolf optimization(GWO)algorithm,support vector machine(SVM),and K-means clustering was proposed.Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables.A GWO-SVM model was developed to accurately predict the energy consumption of individual heats.Based on the prediction results,the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage.For these heats,the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades,enabling root-cause diagnosis of excessive energy consumption.The proposed method was applied to real production data from a converter in a steel plant.The analysis reveals that heat sample No.44 exhibits abnormal energy consumption,due to gas recovery being 1430.28 kg of standard coal below the benchmark level.A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal.This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
基金Key Research and Development Plan of Shaanxi Province(2023-YBGY-493)。
文摘As-forged WSTi6421 titanium alloy billet afterβannealing was investigated.Abnormally coarse grains larger than adjacent grains could be observed in the microstructures,forming abnormal grain structures with uneven size distribution.Through electron backscattered diffraction(EBSD),the forged microstructure at various locations of as-forged WSTi6421 titanium alloy billet was analyzed,revealing that the strength of theβphase cubic texture generated by forging significantly influences the grain size afterβannealing.Heat treatment experiments were conducted within the temperature range from T_(β)−50°C to T_(β)+10°C to observe the macro-and micro-morphologies.Results show that the cubic texture ofβphase caused by forging impacts the texture of the secondaryαphase,which subsequently influences theβphase formed during the post-βannealing process.Moreover,the pinning effect of the residual primaryαphase plays a crucial role in the growth ofβgrains during theβannealing process.EBSD analysis results suggest that the strength ofβphase with cubic texture formed during forging process impacts the orientation distribution differences ofβgrains afterβannealing.Additionally,the development of grains with large orientations within the cubic texture shows a certain degree of selectivity duringβannealing,which is affected by various factors,including the pinning effect of the primaryαphase,the strength of the matrix cubic texture,and the orientation relationship betweenβgrain and matrix.Comprehensively,the stronger the texture in a certain region,the less likely the large misoriented grains suffering secondary growth,thereby aggregating the difference in microstructure and grain orientation distribution across different regions afterβannealing.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
基金supported by the Budgeted Fund of Shanghai University of Traditional Chinese Medicine(Natural Science)(No.2021LK037)the Open Project of Qinghai Province Key Laboratory of Tibetan Medicine Pharmacology and Safety Evaluation(No.2021-ZY-03).
文摘Objective Rheumatoid arthritis(RA)is a systemic autoimmune disease that affects the small joints of the whole body and degrades the patients’quality of life.Zhengqing Fengtongning(ZF)is a traditional Chinese medicine preparation used to treat RA.ZF may cause liver injury.In this study,we aimed to develop a prediction model for abnormal liver function caused by ZF.Methods This retrospective study collected data from multiple centers from January 2018 to April 2023.Abnormal liver function was set as the target variable according to the alanine transaminase(ALT)level.Features were screened through univariate analysis and sequential forward selection for modeling.Ten machine learning and deep learning models were compared to find the model that most effectively predicted liver function from the available data.Results This study included 1,913 eligible patients.The LightGBM model exhibited the best performance(accuracy=0.96)out of the 10 learning models.The predictive metrics of the LightGBM model were as follows:precision=0.99,recall rate=0.97,F1_score=0.98,area under the curve(AUC)=0.98,sensitivity=0.97 and specificity=0.85 for predicting ALT<40 U/L;precision=0.60,recall rate=0.83,F1_score=0.70,AUC=0.98,sensitivity=0.83 and specificity=0.97 for predicting 40≤ALT<80 U/L;and precision=0.83,recall rate=0.63,F1_score=0.71,AUC=0.97,sensitivity=0.63 and specificity=1.00 for predicting ALT≥80 U/L.ZF-induced abnormal liver function was found to be associated with high total cholesterol and triglyceride levels,the combination of TNF-αinhibitors,JAK inhibitors,methotrexate+nonsteroidal anti-inflammatory drugs,leflunomide,smoking,older age,and females in middle-age(45-65 years old).Conclusion This study developed a model for predicting ZF-induced abnormal liver function,which may help improve the safety of integrated administration of ZF and Western medicine.
基金supported by the National Natural Science Foundation of China(No.82171599 and No.32270901)the National Key Research and Developmental Program of China(2022YFC2702601 and 2022YFA0806303)the Global Select Project(DJKLX-2022010)of the Institute of Health and Medicine,Hefei Comprehensive National Science Center.
文摘Male infertility can result from impaired sperm motility caused by multiple morphological abnormalities of the flagella(MMAF).Distinct projections encircling the central microtubules of the spermatozoal axoneme play pivotal roles in flagellar bending and spermatozoal movement.Mammalian sperm-associated antigen 17(SPAG17)encodes a conserved axonemal protein of cilia and flagella,forming part of the C1a projection of the central apparatus,with functions related to ciliary/flagellar motility,skeletal growth,and male fertility.This study investigated two novel homozygous SPAG17 mutations(M1:NM_206996.2,c.829+1G>T,p.Asp212_Glu276del;and M2:c.2120del,p.Leu707*)identified in four infertile patients from two consanguineous Pakistani families.These patients displayed the MMAF phenotype confirmed by Papanicolaou staining and scanning electron microscopy assays of spermatozoa.Quantitative real-time polymerase chain reaction(PCR)of patients’spermatozoa also revealed a significant decrease in SPAG17 mRNA expression,and immunofluorescence staining showed the absence of SPAG17 protein signals along the flagella.However,no apparent ciliary-related symptoms or skeletal malformations were observed in the chest X-rays of any of the patients.Transmission electron microscopy of axoneme cross-sections from the patients showed incomplete C1a projection and a higher frequency of missing microtubule doublets 1 and 9 compared with those from fertile controls.Immunofluorescence staining and Western blot analyses of spermatogenesis-associated protein 17(SPATA17),a component of the C1a projection,and sperm-associated antigen 6(SPAG6),a marker of the spring layer,revealed disrupted expression of both proteins in the patients’spermatozoa.Altogether,these findings demonstrated that SPAG17 maintains the integrity of spermatozoal flagellar axoneme,expanding the phenotypic spectrum of SPAG17 mutations in humans.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘Cervical cancer is the fourth most common cancer worldwide, accounting for 6.8% of new cancer cases and 8.1% of cancer-related deaths. About 85% of these deaths occurred in low- and middle-income countries. The aim of this study was to assess the frequency and distribution of the human papillomavirus (HPV) genotypes in women showing cytological abnormalities of the cervix at the Sourô SANOU University Hospital (CHUSS) in Bobo-Dioulasso, Burkina Faso. This is a descriptive study of women recruited at the CHUSS. The cervico-uterine smear examination was carried out at the CHUSS Anatomy and Pathology Department for cervical cancer screening. The data were collected from women with atypical cells on their cervico-uterine smear. Cervicovaginal samples were taken from consenting women and HPV genotyping was performed using the HPV Direct FLOW CHIP kit at CERBA. We obtained approval from the ethics committee. The data were analyzed using the SPSS 26 software. The results of the study showed that 67.79% of the participants were aged between 50 and 65, a group that is particularly vulnerable to persistent infection with high-risk oncogenic HPV genotypes. Of the women screened, 40.7% were HPV positive and 29.2% had multiple infections. The most common genotypes were HPV 35, followed by HPV 18, 52, 58 and 66. These data highlight the need for increased surveillance and targeted prevention strategies among this female population.
基金National Natural Science Foundation of China(82305090)Shanghai Municipal Health Commission(20234Y0168)National Key Research and Development Program of China (2017YFC1703301)。
文摘Objective To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features,providing novel insights into the early screening of lung cancer.Methods This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1,2019 to December 31,2024,as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period.The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument,and the facial diagnosis features were extracted from it by deep learning technology.Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics,and the least absolute shrinkage and selection operator(LASSO)regression was used to screen the characteristic variables.Based on the screened feature variables,four machine learning methods:random forest,logistic regression,support vector machine(SVM),and gradient boosting decision tree(GBDT)were used to establish lung cancer classification models independently.Meanwhile,the model performance was evaluated by indicators such as sensitivity,specificity,F1 score,precision,accuracy,the area under the receiver operating characteristic(ROC)curve(AUC),and the area under the precision-recall curve(AP).Results A total of 1275 patients with pulmonary nodules and 1623 patients with lung cancer were included in this study.After propensity score matching(PSM)to adjust for gender and age,535 patients were finally included in the pulmonary nodule group and the lung cancer group,respectively.There were significant differences in multiple color space metrics(such as R,G,B,V,L,a,b,Cr,H,Y,and Cb)and texture metrics[such as gray-levcl co-occurrence matrix(GLCM)-contrast(CON)and GLCM-inverse different moment(IDM)]between the two groups of individuals with pulmonary nodules and lung cancer(P<0.05).To construct a classification model,LASSO regression was used to select 63 key features from the initial 136 facial features.Based on this feature set,the SVM model demonstrated the best performance after 10-fold stratified cross-validation.The model achieved an average AUC of 0.8729 and average accuracy of 0.7990 on the internal test set.Further validation on an independent test set confirmed the model’s robust performance(AUC=0.8233,accuracy=0.7290),indicating its good generalization ability.Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components(including color-B-0,wholecolor-Cr,and lipcolor-Cr)were the core factors in the model’s classification decisions,while texture indicators[GLCM-angular second moment(ASM)_2,GLCM-IDM_1,GLCM-CON_1,GLCM-entropy(ENT)_2]played an important auxiliary role.Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas.The various models constructed based on facial image features all demonstrate good performance,indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction,providing a non-invasive and feasible new approach for early lung cancer screening.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金funded by the Field Station Foundation of CAS,Lancang River Conservation Fund Project of SHANSHUI Conservation Center,the SEE Noah’s Ark Project of Beijing Entrepreneurs’Environmental Protection Foundation,the 14th Five-Year Plan of Xishuangbanna Tropical Botanical Garden(E3ZKFF9B01)the High-End Foreign Expert Recruitment Plan,Ministry of Science and Technology of the People’s Republic of China(E3YN105B01)the Yunnan Provincial Foreign Expert Project(202505AO120035).
文摘Human-elephant conflict(HEC)poses a major socio-ecological challenge across elephant range states.Since 2015,the National Forest Ecosystem Research Station of China based in Xishuangbanna has developed the Elephant Early-warning System(EEWS),a novel approach that has demonstrably reduced the risk of HEC incidents-particularly those involving direct encounters between people and elephants.By dynamically maintaining safety buffers,this system safeguards endangered elephants while mitigating human safety risks during livelihood activities and ensuring uninterrupted elephant movement.Building upon the C4ISR framework(Command,Control,Communications,Computers,Intelligence,Surveillance,and Reconnaissance),EEWS integrates key technological and institutional innovations-including the widespread adoption of mobile internet,deployment of camera traps,use of drones,and cross-sectoral governance reforms.The EEWS’s conceptual framework and technical architecture have been already recognized by local government and are now being scaled up from Xishuangbanna to the entire Asian elephant range in China,establishing a replicable“China model”for achieving harmonious human-elephant coexistence.This study reviews the conceptual foundations,development,and field implementation of EEWS,and offers recommendations to guide future refinement and broader application.
基金supported by the‘Young Scientist Scheme’of the National Key R&D Program of China(No.2021YFA1601500)National Natural Science Foundation of China(Nos.12075104,12375109,11875070,and 11935001)+1 种基金Anhui Project(Z010118169)Key Research Foundation of the Education Ministry of Anhui Province(No.2023AH050095)。
文摘Nuclearβ-decay,a typical decay process for unstable nuclei,is a key mechanism for producing heavy elements in the Universe.In this study,neural networks were employed to predictβ-decay half-lives and,for the first time,to identify abnormal trends in nuclearβ-decay half-lives based on deviations between experimental values and the predictions of neural networks.Nuclei exhibiting anomalous increases,abrupt peaks,sharp decreases,abnormal odd-even oscillations,and excessively large experimental errors in theirβ-decay half-lives,which deviate from systematic patterns,were identified through deviations.These anomalous phenomena may be associated with shell effects,shape coexistence,or discrepancies in the experimental data.The discovery and analysis of these abnormal nuclei provide a valuable reference for further investigations using sophisticated microscopic theories,potentially offering insights into new physics through studies of nuclearβ-decay half-lives.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金supports from The National Natural Science Foundation of China (Nos. 52222409, U24A20104 and 52401049)The National Key Research and Development Program (No. 2024YFB3408900) are greatly acknowledgedPartial financial support came from the Fundamental Research Funds for the Central Universities, JLU
文摘Abnormal grain growth(AGG),a prevalent phenomenon in dilute magnesium(Mg)alloys during elevated-temperature processing,significantly compromises mechanical performance through microstructural degradation.This study investigates AGG evolution in a heat-treatable Mg-1Al-0.3Ca-0.5Mn(wt.%)alloy,revealing its fundamental mechanism through phase interaction analysis.The AGG initiation is predominantly driven by Zener pinning force attenuation around abnormally coarsened Al_(8)Mn_(5) precipitates.Mechanistically,this heterogeneous coarsening stems from preferential Al_(8)Mn_(5) phase growth kinetics adjacent to Al_(2)Ca phase during homogenization treatment,creating localized pinning force discontinuities.However,addition of 0.2 wt.%Gd facilitates phase transformation from Al_(8)Mn_(5) to thermally stable Al_(8)Mn_(4)Gd with lower Gibbs free energy,thereby promoting a more uniform and refined precipitate distribution.Consequently,the Gd-containing alloy exhibits enhanced grain thermal stability,maintaining a refined microstructure with average grain size of∼7.7μm even after T4 treatment at 500℃ for 1 h,which simultaneously improves strength and ductility compared to the Gd-free alloy.
