Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have n...Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.展开更多
P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates...P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.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
BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scan...BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.展开更多
BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-ag...BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.展开更多
Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiograp...Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often resu...Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.展开更多
Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or ...Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.展开更多
Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image d...Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image distortions,which pose challenges for object detection tasks.To address this issue,we propose Yolo-CaSKA(Yolo with Contrastive Learning and Selective Kernel Attention),a novel training method that enhances object detection on fisheye camera images.The standard image and the corresponding distorted fisheye image pairs are used as positive samples,and the rest of the image pairs are used as negative samples,which are guided by contrastive learning to help the distorted images find the feature vectors of the corresponding normal images,to improve the detection accuracy.Additionally,we incorporate the Selective Kernel(SK)attention module to focus on regions prone to false detections,such as image edges and blind spots.Finally,the mAP_(50) on the augmented KITTI dataset is improved by 5.5% over the original Yolov8,while the mAP_(50) on the WoodScape dataset is improved by 2.6% compared to OmniDet.The results demonstrate the performance of our proposed model for object detection on fisheye images.展开更多
Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing ...Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).展开更多
Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.展开更多
Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the lef...Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the left ventricular endocardial borders.Their use during echocardiography has become a valuable tool in non-invasive diagnostics.UCAs provide higher-quality images that may ultimately reduce the length of hospital stays and improve patient care.The higher cost associated with UCAs in many situations has been an impediment to frequent use.However,when used as an initial diagnostic test,UCA during rest echocardiogram is more cost-effective than the traditional diagnostic approach,which frequently includes multiple tests and imaging studies to make an accurate diagnosis.They can be easily performed across multiple patient settings and provide optimal images that allow clinicians to make sound medical decisions.This consequently allows for better diagnostic accuracies and improvement in patient care.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple im...BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple imaging sessions.We hypothesized that the contrast enhancement boost(CE-boost)technique could be used to enhance the contrast in equilibrium phase(EP)images and produce enhancement similar to that of portal vein phase(PVP)images,and if this is possible,EP imaging could play the same role as PVP imaging.We also speculated that this might allow the conversion of three-phase dynamic imaging to biphasic dynamic imaging,reducing patients’radiation exposure.AIM To determine if the CE-boost of EP,CE-boost(EP)is useful compared to a conventional image.METHODS We retrospectively analyzed the cases of 52 patients who were diagnosed with liver cancer between January 2016 and October 2022 at our institution.From these computed tomography images,CE-boost images were generated from the EP and plane images.We compared the PVP,EP,and CE-boost(EP)for blood vessels and hepatic parenchyma based on the contrast-to-noise ratio(CNR),signal-to-noise ratio,and figure-of-merit(FOM).Visual assessments were also performed for vessel visualization,lesion conspicuity,and image noise.RESULTS The CE-boost(EP)images showed significant superiority compared to the PVP images in the CNR,signal-to-noise ratio,and FOM except regarding the hepatic parenchyma.No significant differences were detected in CNR or FOM comparisons within the hepatic parenchyma(P=0.62,0.67).The comparison of the EP and CE-boost(EP)images consistently favored CE-boost(EP).Regarding the visual assessment,the CE-boost(EP)images were significantly superior to the PVP images in lesion conspicuity,and the PVP in image noise.The CE-boost(EP)images were significantly better than the EP images in the vessel visualization of segmental branches of the portal vein and lesion conspicuity,and the EP in image noise.CONCLUSION The image quality of CE-boost(EP)images was comparable or superior to that of conventional PVP and EP.CEboost(EP)images might provide information comparable to the conventional PVP.展开更多
AIM:To measure the contrast sensitivity(CS)using computer-based Chart2020 software pre-and post-white light exposure with and without blue-blocking lenses(BBLs).METHODS:The study included participants aged 18 to 25y(n...AIM:To measure the contrast sensitivity(CS)using computer-based Chart2020 software pre-and post-white light exposure with and without blue-blocking lenses(BBLs).METHODS:The study included participants aged 18 to 25y(n=30 eyes),where baseline CS was measured before the experiment.Following this,the participants were exposed to two white light-emitting diodes(LEDs;450 lx each),placed at a 45-degree angle from the participant’s eye and 80 cm from the light source.All participants were randomly divided into three groups(BBL1-Placebo lens,BBL2-Crizal Prevencia,BBL3-Duravision)by sequential randomisation,which was double-blinded.Post-light exposure,the CS was measured monocularly with a calibrated computer-based CS Chart-2020 software at different log units.RESULTS:CS measured using Chart-2020 software at 0.8,1.5,6,12,and 18 cpd pre-and post-white LED exposure with and without BBLs showed a significant difference(P<0.05)in contrast threshold and log contrast at 6 cpd and 18 cpd(P<0.05)and showed no significant differences in 0.8,1.5,12 cpd(P>0.05).CONCLUSION:This study shows that exposure to white LEDs can diminish CS,while BBLs may ameliorate these negative effects.展开更多
Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs ...Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs that primarily function as T2/T2*negative contrast agents,usSPIONs with core diameters below 5 nm can effectively shorten T1 relaxation times,producing bright signals in T1‐weighted images.This distinct behavior stems from their unique magnetic properties,including single‐domain configurations,surface spin canting,and rapid Néel relaxation dynamics,which are particularly enhanced at low magnetic field strengths.The biocompatibility of iron oxide,efficient renal clearance pathways,and versatility for surface functionalization offer potential advantages over gadolinium‐based agents,especially regarding safety concerns related to nephrogenic systemic fibrosis and gadolinium deposition.These nanoparticles show particular promise for applications in lowfield MRI,vascular imaging,targeted molecular imaging,and theranostic platforms.Although challenges remain in optimizing synthesis methods for consistent production of monodisperse usSPIONs with tailored surface chemistry,ongoing research continues to advance their potential for clinical translation.This review explores the mechanisms,synthesis approaches,applications,and future perspectives of usSPIONs as positive contrast agents in MRI.展开更多
基金supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China(No.2023QN04011)the National Natural Science Foundation of China(Nos.42307092 and 52279067)+1 种基金Ordos Science and Technology Major Project(No.ZD20232303)Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region(No.2022QZBZ0003).
文摘Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.
基金supported by the National Key Research and Development Program of China(Program No.:2022YFF1203003)the National Natural Science Foundation of China(Grant No.:82373791).
文摘P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
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BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.
文摘BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.
文摘Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
基金M.Faheem is supported by VTT Technical Research Center of Finland.
文摘Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.
基金supported by the National Key Research and Development Program of China(No.2022YFB380-7300)the National Natural Science Foundation of China(No.12471455)+2 种基金the Clinical Cohort Construction Program of Peking University Third Hospital(BYSYDL2022005)the Key Clinical Projects of Peking University Third Hospital(BYSYZD2023006)the Innovation&Transfer Fund of Peking University Third Hospital(BYSYZHKC2023-109).
文摘Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.
文摘Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image distortions,which pose challenges for object detection tasks.To address this issue,we propose Yolo-CaSKA(Yolo with Contrastive Learning and Selective Kernel Attention),a novel training method that enhances object detection on fisheye camera images.The standard image and the corresponding distorted fisheye image pairs are used as positive samples,and the rest of the image pairs are used as negative samples,which are guided by contrastive learning to help the distorted images find the feature vectors of the corresponding normal images,to improve the detection accuracy.Additionally,we incorporate the Selective Kernel(SK)attention module to focus on regions prone to false detections,such as image edges and blind spots.Finally,the mAP_(50) on the augmented KITTI dataset is improved by 5.5% over the original Yolov8,while the mAP_(50) on the WoodScape dataset is improved by 2.6% compared to OmniDet.The results demonstrate the performance of our proposed model for object detection on fisheye images.
基金granted by Qin Xin Talents Cultivation Program(No.QXTCP C202115),Beijing Information Science&Technology Universitythe Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Fund(No.GJJ-23),National Social Science Foundation,China(No.21BTQ079).
文摘Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).
基金supported by the ScientificResearch and Innovation Team Program of Sichuan University of Science and Technology(No.SUSE652A006)Sichuan Key Provincial Research Base of Intelligent Tourism(ZHYJ22-03)In addition,it is also listed as a project of Sichuan Provincial Science and Technology Programme(2022YFG0028).
文摘Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
文摘Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the left ventricular endocardial borders.Their use during echocardiography has become a valuable tool in non-invasive diagnostics.UCAs provide higher-quality images that may ultimately reduce the length of hospital stays and improve patient care.The higher cost associated with UCAs in many situations has been an impediment to frequent use.However,when used as an initial diagnostic test,UCA during rest echocardiogram is more cost-effective than the traditional diagnostic approach,which frequently includes multiple tests and imaging studies to make an accurate diagnosis.They can be easily performed across multiple patient settings and provide optimal images that allow clinicians to make sound medical decisions.This consequently allows for better diagnostic accuracies and improvement in patient care.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘BACKGROUND Three-phase dynamic computed tomography imaging is particularly useful in the liver region.However,dynamic imaging with contrast media has the disadvantage of increased radiation exposure due to multiple imaging sessions.We hypothesized that the contrast enhancement boost(CE-boost)technique could be used to enhance the contrast in equilibrium phase(EP)images and produce enhancement similar to that of portal vein phase(PVP)images,and if this is possible,EP imaging could play the same role as PVP imaging.We also speculated that this might allow the conversion of three-phase dynamic imaging to biphasic dynamic imaging,reducing patients’radiation exposure.AIM To determine if the CE-boost of EP,CE-boost(EP)is useful compared to a conventional image.METHODS We retrospectively analyzed the cases of 52 patients who were diagnosed with liver cancer between January 2016 and October 2022 at our institution.From these computed tomography images,CE-boost images were generated from the EP and plane images.We compared the PVP,EP,and CE-boost(EP)for blood vessels and hepatic parenchyma based on the contrast-to-noise ratio(CNR),signal-to-noise ratio,and figure-of-merit(FOM).Visual assessments were also performed for vessel visualization,lesion conspicuity,and image noise.RESULTS The CE-boost(EP)images showed significant superiority compared to the PVP images in the CNR,signal-to-noise ratio,and FOM except regarding the hepatic parenchyma.No significant differences were detected in CNR or FOM comparisons within the hepatic parenchyma(P=0.62,0.67).The comparison of the EP and CE-boost(EP)images consistently favored CE-boost(EP).Regarding the visual assessment,the CE-boost(EP)images were significantly superior to the PVP images in lesion conspicuity,and the PVP in image noise.The CE-boost(EP)images were significantly better than the EP images in the vessel visualization of segmental branches of the portal vein and lesion conspicuity,and the EP in image noise.CONCLUSION The image quality of CE-boost(EP)images was comparable or superior to that of conventional PVP and EP.CEboost(EP)images might provide information comparable to the conventional PVP.
文摘AIM:To measure the contrast sensitivity(CS)using computer-based Chart2020 software pre-and post-white light exposure with and without blue-blocking lenses(BBLs).METHODS:The study included participants aged 18 to 25y(n=30 eyes),where baseline CS was measured before the experiment.Following this,the participants were exposed to two white light-emitting diodes(LEDs;450 lx each),placed at a 45-degree angle from the participant’s eye and 80 cm from the light source.All participants were randomly divided into three groups(BBL1-Placebo lens,BBL2-Crizal Prevencia,BBL3-Duravision)by sequential randomisation,which was double-blinded.Post-light exposure,the CS was measured monocularly with a calibrated computer-based CS Chart-2020 software at different log units.RESULTS:CS measured using Chart-2020 software at 0.8,1.5,6,12,and 18 cpd pre-and post-white LED exposure with and without BBLs showed a significant difference(P<0.05)in contrast threshold and log contrast at 6 cpd and 18 cpd(P<0.05)and showed no significant differences in 0.8,1.5,12 cpd(P>0.05).CONCLUSION:This study shows that exposure to white LEDs can diminish CS,while BBLs may ameliorate these negative effects.
文摘Ultrasmall superparamagnetic iron oxide nanoparticles(usSPIONs)are promising alternatives to gadolinium‐based contrast agents for positive contrast enhancement in magnetic resonance imaging(MRI).Unlike larger SPIONs that primarily function as T2/T2*negative contrast agents,usSPIONs with core diameters below 5 nm can effectively shorten T1 relaxation times,producing bright signals in T1‐weighted images.This distinct behavior stems from their unique magnetic properties,including single‐domain configurations,surface spin canting,and rapid Néel relaxation dynamics,which are particularly enhanced at low magnetic field strengths.The biocompatibility of iron oxide,efficient renal clearance pathways,and versatility for surface functionalization offer potential advantages over gadolinium‐based agents,especially regarding safety concerns related to nephrogenic systemic fibrosis and gadolinium deposition.These nanoparticles show particular promise for applications in lowfield MRI,vascular imaging,targeted molecular imaging,and theranostic platforms.Although challenges remain in optimizing synthesis methods for consistent production of monodisperse usSPIONs with tailored surface chemistry,ongoing research continues to advance their potential for clinical translation.This review explores the mechanisms,synthesis approaches,applications,and future perspectives of usSPIONs as positive contrast agents in MRI.
