Understand and Treat History Correctly Is Crucial

Over recent years, Japanese right-wing conservatist forces have launched a media and diplomatic attack of shifting the blame of "hindering the development of Sino-Japanese relations" on to China. They said that China was playing the history card, seizing Japan’s historical fault as a handle against Japan. Some public figures in China also advocate sheltering the issue of the perception of history in order to

Correctly Addressing Your Chinese Counterpart

People doing business in China should try to know how to address their Chinese counter-parts, especially in formal settings so as to better engage with them. Different cultures mean people are addressed differently.

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.

Corrigendum: MicroRNA-502-3p regulates GABAergic synapse function in hippocampal neurons

There is an error in the name of the cell line in the abstract of the published paper“MicroRNA-502-3p regulates GABAergic synapse function in hippocampal neurons”published on pages 2698-2707,Issue 12,Volume 19 of Neural Regeneration Research(Sharma et al.,2024),because of oversight during final proof checking.The correct description should be“human-GABA receptor A-α1/β2/γ2L human embryonic kidney(HEK)recombinant cell line.”The authors apologize for any inconvenience this correction may cause for readers and editors of Neural Regeneration Research.

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.

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.

LETTERS

I am assistant director of the UN&Human Rights division at Sri Lanka’s Ministry of Foreign Affairs.I visited China last December and attended the Hernán Santa Cruz Dialogue on Economic,Social and Cultural Rights in Hangzhou,east China’s Zhejiang Province.Together with other representatives from nearly 50 countries and regions,I shared Sri Lanka’s practices in correcting development imbalances and protecting people’s rights in all areas.

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.

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.

Delineation of urban growth boundary based on FLUS model under the perspective of land use evaluation in hilly mountainous areas

With rapid economic development,the size of urban land in China is expanding dramatically.The Urban Growth Boundary(UGB)is an expandable spatial boundary for urban construction in a certain period in order to control the urban sprawl.Reasonable delineation of UGB can inhibit the disorderly spread of urban space and guide the normal development of the city.It is of practical significance for the construction of green urban space.The study utilizes GIS technology to establish a land construction suitability evaluation system for Nankang city,which is experiencing rapid urban expansion,and outlines the preliminary UGB under the future land use simulation(FLUS)model.At the same time,considering the coupled coordination of"Production-Living-Ecological Space",and based on the suitability evaluation,we revised the preliminary UGB by combining the advantages of the patch-generating land use simulation(PLUS)model and the convex hull model to delineate the final UGB.The results show that:1)the comprehensive score of the evaluation of the suitability of the construction of land from high to low shows the distribution of the center of the city to the surrounding circle type spread,the center of the city has the highest suitability score.The results of convex hull model show that the urban expansion type of Nankang is epitaxial.In the future,the urban expansion will mainly occur in the northern part of the city.The PLUS model predicts an increase of 3359.97 hm^(2)of construction land in Nankang by 2035,of which 2022.97 hm^(2)is urban construction land.2)The FLUS model has a prediction accuracy of 86.3%and delineates a preliminary UGB area of 9215.07 hm^(2).3)We used the results of the construction suitability evaluation,PLUS model simulation results,and convex hull model predictions to revise the originally delineated UGB.The final delineated UGB area is 8895.67 hm^(2)and it is capable of meeting the future development of the study area.The results of the delineation can promote sustainable urban development,and the delineation methodology can provide a reference basis for the preparation of territorial spatial planning.

从中国首台紫外-可见光高光谱卫星仪器反演得到的高空间分辨率臭氧廓线

Understanding the vertical distribution of ozone is crucial when assessing both its horizontal and vertical transport,as well as when analyzing the physical and chemical properties of the atmosphere.One of the most effective ways to obtain high spatial resolution ozone profiles is through satellite observations.The Environmental Trace Gases Monitoring Instrument(EMI)deployed on the Gaofen-5 satellite is the first Chinese ultraviolet-visible hyperspectral spectrometer.However,retrieving ozone profiles using backscattered radiance values measured by the EMI is challenging due to unavailable measurement errors and a low signal-to-noise ratio.The algorithm developed for the Tropospheric Monitoring Instrument did not allow us to retrieve 87%of the EMI pixels.Therefore,we developed an algorithm specific to the characteristics of the EMI.The fitting residuals are smaller than 0.3%in most regions.The retrieved ozone profiles were in good agreement with ozonesonde data,with maximum mean biases of 20%at five latitude bands.By applying EMI averaging kernels to the ozonesonde profiles,the integrated stratospheric column ozone and tropospheric column ozone also showed excellent agreement with ozonesonde data,The lower layers(0-7.5 km)of the EMI ozone profiles reflected the seasonal variation in surface ozone derived from the China National Environmental Monitoring Center(CNEMC).However,the upper layers(9.7-16.7 km)of the ozone profiles show different trends,with the ozone peak occurring at an altitude of 9.7-16.7 km in March,2019.A stratospheric intrusion event in central China from August 11 to 15,2019,is captured using the EMI ozone profiles,potential vorticity data,and relative humidity data.The increase in the CNEMC ozone co ncentration showed that downward transport enhanced surface ozone pollution.

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.

The application of the strip-shaped cymba conchae orthosis in the nonsurgical correction of complex auricular deformity

Objective: This study aims to evaluate the efficacy and safety of using a strip-shaped cymba conchae orthosis for the nonsurgical correction of complex auricular deformities. Methods: Clinical data were collected from 2020 to 2021 for 6 patients who underwent correction using a stripshaped cymba conchae orthosis. The indications, corrective effects, and complications associated with use of the orthosis were analyzed. Results: There were four indications for treatment: cryptotia with helix adhesion;cryptotia with grade I microtia;cryptotia with excessive helix thickness;and auricular deformity beyond the treatment time window(≥6 months). Excellent corrective effects were observed in all 6 patients. Complications occurred in one patient, who recovered after symptomatic treatment. Conclusion: The use of a strip-shaped cymba conchae orthosis alone or combined with a U-shaped helix orthosis presents a feasible approach for correcting complex auricular deformities or deformities beyond the treatment time window in pediatric patients.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach

Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.

Adaptation analysis and fusion correction method of CMIP6 precipitation simulation data on the Qinghai-Tibetan Plateau

In order to obtain more accurate precipitation data and better simulate the precipitation on the Tibetan Plateau,the simulation capability of 14 Coupled Model Intercomparison Project Phase 6(CMIP6)models of historical precipitation(1982-2014)on the Qinghai-Tibetan Plateau was evaluated in this study.Results indicate that all models exhibit an overestimation of precipitation through the analysis of the Taylor index,temporal and spatial statistical parameters.To correct the overestimation,a fusion correction method combining the Backpropagation Neural Network Correction(BP)and Quantum Mapping(QM)correction,named BQ method,was proposed.With this method,the historical precipitation of each model was corrected in space and time,respectively.The correction results were then analyzed in time,space,and analysis of variance(ANOVA)with those corrected by the BP and QM methods,respectively.Finally,the fusion correction method results for each model were compared with the Climatic Research Unit(CRU)data for significance analysis to obtain the trends of precipitation increase and decrease for each model.The results show that the IPSL-CM6A-LR model is relatively good in simulating historical precipitation on the Qinghai-Tibetan Plateau(R=0.7,RSME=0.15)among the uncorrected data.In terms of time,the total precipitation corrected by the fusion method has the same interannual trend and the closest precipitation values to the CRU data;In terms of space,the annual average precipitation corrected by the fusion method has the smallest difference with the CRU data,and the total historical annual average precipitation is not significantly different from the CRU data,which is better than BP and QM.Therefore,the correction effect of the fusion method on the historical precipitation of each model is better than that of the QM and BP methods.The precipitation in the central and northeastern parts of the plateau shows a significant increasing trend.The correlation coefficients between monthly precipitation and site-detected precipitation for all models after BQ correction exceed 0.8.

Real-time model correction using Kalman filter for Raman-controlled cell culture processes

Raman spectroscopy has found extensive use in monitoring and controlling cell culture processes.In this context,the prediction accuracy of Raman-based models is of paramount importance.However,models established with data from manually fed-batch cultures often exhibit poor performance in Raman-controlled cultures.Thus,there is a need for effective methods to rectify these models.The objective of this paper is to investigate the efficacy of Kalman filter(KF)algorithm in correcting Raman-based models during cell culture.Initially,partial least squares(PLS)models for different components were constructed using data from manually fed-batch cultures,and the predictive performance of these models was compared.Subsequently,various correction methods including the PLS-KF-KF method proposed in this study were employed to refine the PLS models.Finally,a case study involving the auto-control of glucose concentration demonstrated the application of optimal model correction method.The results indicated that the original PLS models exhibited differential performance between manually fed-batch cultures and Raman-controlled cultures.For glucose,the root mean square error of prediction(RMSEP)of manually fed-batch culture and Raman-controlled culture was 0.23 and 0.40 g·L^(-1).With the implementation of model correction methods,there was a significant improvement in model performance within Raman-controlled cultures.The RMSEP for glucose from updating-PLS,KF-PLS,and PLS-KF-KF was 0.38,0.36 and 0.17 g·L^(-1),respectively.Notably,the proposed PLS-KF-KF model correction method was found to be more effective and stable,playing a vital role in the automated nutrient feeding of cell cultures.

Correction: Surface Patterning of Metal ZincElectrode with an In-Region Zincophilic Interfacefor High-Rate and Long-Cycle-Life Zinc MetalAnode

Correction to:Nano-Micro Letters(2024)16:112 https://doi.org/10.1007/s40820-024-01327-2 In the supplementary information the following corrections have been carried out:1.Institute of Energy and Climate Research,Materials Synthesis and Processing,Forschungszentrum Jülich GmbH,52425 Jülich,Germany.Corrected:Institute of Energy and Climate Research:Materials Synthesis and Processing(IEK-1),Forschungszentrum Jülich GmbH,52425 Jülich,Germany.