Positron Emission Tomography Lung Image Respiratory Motion Correcting with Equivariant Transformer

In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our studyintroduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learningbasedframework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques,which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency andoveremphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective featureextraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Liegroup domains to highlight fundamental motion patterns, coupled with employing competitive weighting forprecise target deformation field generation. Our empirical evaluations confirm TEMT’s superior performancein handling diverse PET lung datasets compared to existing image registration networks. Experimental resultsdemonstrate that TEMT achieved Dice indices of 91.40%, 85.41%, 79.78%, and 72.16% on simulated geometricphantom data, lung voxel phantom data, cardiopulmonary voxel phantom data, and clinical data, respectively. Tofacilitate further research and practical application, the TEMT framework, along with its implementation detailsand part of the simulation data, is made publicly accessible at https://github.com/yehaowei/temt.

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation Correction

Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.

Clinical Effect Analysis of Posterior Chamber Intraocular Lens Implantation for Correcting High Myopia and Astigmatism

Objective:To explore the corrective effect of posterior chamber intraocular lens implantation with phakic eyes in the treatment of high myopia and astigmatism.Methods:From May 2023,the hospital began to collect the case data of diagnosis and treatment of high myopia and astigmatism.By May 2024,310 cases were included,all of which were treated with posterior chamber intraocular lens implantation.The visual acuity,astigmatism and axial position of the intraocular lens were observed before and after treatment.Results:At different time points after the operation,the patient’s vision was significantly improved compared with that before the operation(P<0.05),and the vision level was equal to or greater than the best-corrected vision before the operation.At different time points after the operation,the average rotation of the intraocular lens was less than 5 degrees.Astigmatism was significantly lower than that before the operation(P<0.05).After the operation,the intraocular pressure increased in 11 cases,accounting for 3.55%,with no adverse complications such as lens turbidity,glare and obvious halo occurring.Conclusion:The posterior chamber intraocular lens implantation with phakic eyes has an ideal correction effect in the treatment of high myopia and astigmatism,which can effectively improve the vision level of patients and reduce the degree of astigmatism,and has high effectiveness and safety.

Advances in 3D correction of adolescent scoliosis:The superiority of scoliocorrector fatma-UI in rotational realignment

This letter compares the clinical efficacy and economic feasibility of the scoliocorrector fatma-UI(SCFUI)with direct vertebral rotation(DVR)in treating adolescent idiopathic scoliosis(AIS).SCFUI has shown promising results in threedimensional spinal correction,providing superior rotational alignment compared to DVR and achieving significant improvements in coronal and sagittal planes.Additionally,SCFUI’s advanced design reduces risks associated with AIS surgeries and enhances overall patient outcomes.Economic analysis reveals SCFUI as a cost-effective option,potentially lowering long-term healthcare costs by minimizing complications and revisions.Our findings suggest that SCFUI is a viable,innovative approach in AIS treatment,meeting clinical and economic demands in orthopedic care.

AI-based Correction of Wave Forecasts Using the Transformer-enhanced UNet Model

Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products,thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity.The traditional method that relies on forecasters'subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting.To address this problem,this paper proposes a Transformer-enhanced UNet(TransUNet)model for wave forecast AI correction,which fuses wind and wave information.The Transformer structure is integrated into the encoder of the UNet model,and instead of using the traditional upsampling method,the dual-sampling module is employed in the decoder to enhance the feature extraction capability.This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data,wave height forecast data,and significant wave height reanalysis data provided by ECMWF.The experimental results indicate that the TransUNet model yields smaller root-meansquare errors,mean errors,and standard deviations of the corrected results for the next 24-h forecasts than does the UNet model.Specifically,the root-mean-square error decreased by more than 21.55%compared to its precorrection value.According to the statistical analysis,87.81%of the corrected wave height errors for the next 24-h forecast were within±0.2m,with only 4.56%falling beyond±0.3 m.This model effectively limits the error range and enhances the ability to forecast wave heights.

Correction to:METTL5 promotes cell proliferation,invasion,and migration by up-regulating Toll-like receptor 8 expression in colorectal cancer

Correction to“METTL5 promotes cell proliferation,invasion,and migration by up-regulating Toll-like receptor 8 expression in colorectal cancer”in World J Gastrointest Oncol 2024;16(5):2006-2017,published by Kong LS,Tao R,Li YF,Wang WB,and Zhao X.In this article,we added the correct images.

Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method

Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.

Correction to:Assessing recent recurrence after hepatectomy for hepatitis B-related hepatocellular carcinoma by a predictive model based on sarcopenia

Correction to“Assessing recent recurrence after hepatectomy for hepatitis Brelated hepatocellular carcinoma by a predictive model based on sarcopenia.World J Gastroenterol 2024;30(12):1727-1738[PMID:38617742 DOI:10.3748/wjg.v30.i12.1727]”.In this article,figure 1 needs to be corrected.

Correction to:High expression of autophagy-related gene EIF4EBP1 could promote tamoxifen resistance and predict poor prognosis in breast cancer

This is an erratum to the published paper titled,“High expression of autophagyrelated gene EIF4EBP1 could promote tamoxifen resistance and predict poor prognosis in breast cancer.”We have removed the citations to certain articles in subsequent revisions of the manuscript.However,owing to our oversight,the citation marker in the upper right corner was not removed.We apologize for any confusion this may have caused.

Influence of Temperature on the Inter-pressure of Soil Water Tension with Correcting Method

When the electronic temperature sensor was incorporated into a system of soil water tension and the insidetube temperature was monitored in real time, it is concluded that the inside temperature increased by 26.9 ℃ and the inside pressure changed about 14.6 Kpa, when the pottery soil was replaced by the sealing plug. When the soil water was relatively stable in the experimental salvers, the in-side pressure stil varied regularly with the temperature. When the inside temperature increased by 22.2 ℃, the inside pressure varied about 7.4 Kpa. Through com-pensation calculation of the inside tension, the temperature in the warming and cooling periods was compensated, which was useful to correct the tension measurement errors induced from the changing temperature. When the measuring interval was 4 hours and the temperature difference was 18.1 ℃, the tension difference of both points was only 0.278 Kpa, compared to the difference up to 6.5 Kpa before compensation.

An Effective Method for Correcting the Seasonal-Interannual Prediction of Summer Climate Anomaly

An effective method was proposed for correcting the seasonal—interannual prediction of the summer climate anomaly. The predictive skill can be substantially improved by applying the method to the seasonal—interannual prediction carried out by a coupled ocean—atmosphere model. Thus the method has the potential to improve the operational summer climate predictions. Key words Correction, Seasonal-interannual prediction - Quasi-biennial signal This research was supported by the National Key Programme for Developing Basic Sciences under Contract G1998040905-2 and the key project “ The Analytical Study on the Seasonal and Interannual Variability of the General Atmospheric Circulation (1998-2001)” of National Natural Science Foundation of China under Contract 49735160.

A New Method for Correcting Vehicle License Plate Tilt

In the course of vehicle license plate （VLP） automatic recognition, tilt correction is a very crucial process. According to Karhunen-Loeve （K-L） transformation, the coordinates of characters in the image are arranged into a two-dimensional covariance matrix, on the basis of which the centered process is carried out. Then, the eigenvector and the rotation angle α are computed in turn. The whole image is rotated by -α. Thus, image horizontal tilt correction is performed. In the vertical tilt correction process, three correction methods, which are K-L transformation method, the line fitting method based on K -means clustering （LFMBKC）, and the line fitting based on least squares （LFMBLS）, are put forward to compute the vertical tilt angle θ. After shear transformation （ST） is imposed on the rotated image, the final correction image is obtained. The experimental results verify that this proposed method can be easily implemented, and can quickly and accurately get the tilt angle. It provides a new effective way for the VLP image tilt correction as well.

CLINICAL APPLICATION OF THE TRAFIXINSTRUMENTATION IN CORRECTING SCOLIOSIS

To evaluate the results of the TRAFIX instrumentation in correcting s coliosis. Methods. Since October 1997, 47 patients with scoliosis received spinal fixation with the TRAFIX instrumentation at the Peking Union Medical College Hospital. T he average age was 14.3 years (range 10 to 38 years). There were 27 idiopathic c ases, 16 congenital cases, 2 cases with Marfan syndrome and 2 with neurofibromat osis. Twelve of the 47 patients underwent anterior release, while 4 patients rec eived the revision approach. The average follow up time was 26 months (13～38 m onths). Results. The measurements of primary coronal deformity before and after surgery were 74°(50°～115°) and 38.7° (11°～95°), respectively. The average curve correction was 54%. The average number of fused segments was 12.5 (7～17) verte brae. The distance between the center of apex and the C7 plumb line was 56.8 mm before surgery (25～107mm) and 31 mm after surgery (10～87mm). Conclusion. The TRAFIX instrumentation provides three dimensional correction wi th refinement, convenience and reliable fixation.

CLINICAL OBSERVATION ON CORRECTING MALPOSITION OF FETUS BY ELECTRO-ACUPUNCTURE

Forty-eight cases of malposition of fetus were treated by electro-acupuncture, using Zhiyin (UB 67) points. 39 cases were corrected with a rate of 81.3%, the average session of treatment being 1.41. Two control groups were set up: moxibustion and blank control group. Statistical analysis shows that efficacy of electro-acupuncture is markedly superior to that of the blank. Sessions of electro-acupuncture were less than that of moxibustion and the difference was statistically significant, though there is no significant difference of efficacy between the two groups.

A method for correcting characteristic X-ray net peak count from drifted shadow peak

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse sequences.The trained model was validated using simulated pulses.The relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM models.As a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping algorithm.For slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping algorithm.The model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,respectively.Ten measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

Self-correcting wavelet neural network control of continuous rotary electro-hydraulic servo motor

In allusion to the problem of friction,leakage,vibration and noise existing in continuous rotary motor electro-hydraulic servo system,highly nonlinearity and uncertainties affecting the system performance,based on the transfer function of electro-hydraulic servo system,a kind of Pol-Ind friction model is proposed.The parameters of Pol-Ind friction model are identified and the accurate mathematical model of friction torque is obtained by experiment.The self-correcting wavelet neural network(WNN)controller is proposed,and Adam optimization algorithm is used to perform gradient optimization on scale factor and displacement factor in wavelet basis function,so as to improve the speed and precision of parameter optimization.Through comparative simulation analysis,it is clearly that the self-correcting WNN controller can effectively improve the frequency response and tracking accuracy of continuous rotary motor electro-hydraulic servo system.

Correction:Nanomaterial-Based Repurposing of Macrophage Metabolism and Its Applications

Following publication of the original article[1],the authors reported an error in the last author’s name,it was mistakenly written as“Jun Den”.The correct author’s name“Jun Deng”has been updated in this Correction.

Evaluating and correcting rain effects on dual-frequency altimeter Jason-1 wind measurements

Rain is one of the main sources of error in dual-frequency altimeter Jason-1 wind measurement. In this study, a new radar altimeter backscatter model is proposed and validated to eliminate rain effects. The model takes into account attenuation, volume backscattering, and sea surface perturbation by raindrops under rain conditions. A match-up dataset is built to evaluate rain effects, in combination with the Jason-1 normalized radar cross section, precipitation radar data from the Tropical Rainfall Measuring Mission, and sea surface wind reanalysis data from the European Centre for Medium-Range Weather Forecasts. The results show that rain-induced surface perturbation backscatter increases with rain rate at Ku-band, but their correlation at C-band is poor. In addition, rain surface perturbation and attenuation have major effects on radar altimeter wind measurements. Finally, a rain correction model for Jason-1 winds is developed and validation results prove its ability to reduce rain-induced inaccuracies in wind retrievals.

Correcting the Expectation of Even Degree of Chicken Body Weight

The even degree of chicken population could be estimated by the coefficient of variation or the percentage of the chicken whose body weights were not higher and lower than average body weight by 15% in the sampling population.In fact,the two indexes were relative to each other.On the basis of analyzing the relationship between the two expectations,the expectations which had been worked out independently were corrected in this paper.

A METHOD FOR CORRECTING INTENSITY IN CONTINU-OUS SCANNING X-RAY STRESS MEASUREMENT

A set of absorption curves was priorly prepared on transparent films to fit the background and peak intensities in continuous scanning X-ray stress measurement.It may be better to correct both background and absorption of pure diffraction intensity.Experimental results revealed this to be a reliable correction method.