Clinical manifestations,diagnosis and long-term prognosis of adult autoimmune enteropathy:Experience from Peking Union Medical College Hospital

BACKGROUND Autoimmune enteropathy(AIE)is a rare disease whose diagnosis and long-term prognosis remain challenging,especially for adult AIE patients.AIM To improve overall understanding of this disease’s diagnosis and prognosis.METHODS We retrospectively analyzed the clinical,endoscopic and histopathological characteristics and prognoses of 16 adult AIE patients in our tertiary medical center between 2011 and 2023,whose diagnosis was based on the 2007 diagnostic criteria.RESULTS Diarrhea in AIE patients was characterized by secretory diarrhea.The common endoscopic manifestations were edema,villous blunting and mucosal hyperemia in the duodenum and ileum.Villous blunting(100%),deep crypt lymphocytic infiltration(67%),apoptotic bodies(50%),and mild intraepithelial lymphocytosis(69%)were observed in the duodenal biopsies.Moreover,there were other remarkable abnormalities,including reduced or absent goblet cells(duodenum 94%,ileum 62%),reduced or absent Paneth cells(duodenum 94%,ileum 69%)and neutrophil infiltration(duodenum 100%,ileum 69%).Our patients also fulfilled the 2018 diagnostic criteria but did not match the 2022 diagnostic criteria due to undetectable anti-enterocyte antibodies.All patients received glucocorticoid therapy as the initial medication,of which 14/16 patients achieved a clinical response in 5(IQR:3-20)days.Immunosuppressants were administered to 9 patients with indications of steroid dependence(6/9),steroid refractory status(2/9),or intensified maintenance medication(1/9).During the median of 20.5 months of followup,2 patients died from multiple organ failure,and 1 was diagnosed with non-Hodgkin’s lymphoma.The cumulative relapse-free survival rates were 62.5%,55.6%and 37.0%at 6 months,12 months and 48 months,respectively.CONCLUSION Certain histopathological findings,including a decrease or disappearance of goblet and Paneth cells in intestinal biopsies,might be potential diagnostic criteria for adult AIE.The long-term prognosis is still unsatisfactory despite corticosteroid and immunosuppressant medications,which highlights the need for early diagnosis and novel medications.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Long-term outcomes after endoscopic removal of malignant colorectal polyps:Results from a 10-year cohort

BACKGROUND Choosing an optimal post-polypectomy management strategy of malignant colorectal polyps is challenging,and evidence regarding a surveillance-only strategy is limited.AIM To evaluate long-term outcomes after endoscopic removal of malignant colorectal polyps.METHODS A single-center retrospective cohort study was conducted to evaluate outcomes after endoscopic removal of malignant colorectal polyps between 2010 and 2020.Residual disease rate and nodal metastases after secondary surgery and local and distant recurrence rate for those with at least 1 year of follow-up were invest-igated.Event rates for categorical variables and means for continuous variables with 95%confidence intervals were calculated,and Fisher’s exact test and Mann-Whitney test were performed.Potential risk factors of adverse outcomes were RESULTS In total,135 lesions(mean size:22.1 mm;location:42%rectal)from 129 patients(mean age:67.7 years;56%male)were enrolled.The proportion of pedunculated and non-pedunculated lesions was similar,with en bloc resection in 82%and 47%of lesions,respectively.Tumor differentiation,distance from resection margins,depth of submucosal invasion,lymphovascular invasion,and budding were reported at 89.6%,45.2%,58.5%,31.9%,and 25.2%,respectively.Residual tumor was found in 10 patients,and nodal metastasis was found in 4 of 41 patients who underwent secondary surgical resection.Univariate analysis identified piecemeal resection as a risk factor for residual malignancy(odds ratio:1.74;P=0.042).At least 1 year of follow-up was available for 117 lesions from 111 patients(mean follow-up period:5.59 years).Overall,54%,30%,30%,11%,and 16%of patients presented at the 1-year,3-year,5-year,7-year,and 9-10-year surveillance examinations.Adverse outcomes occurred in 9.0%(local recurrence and dissemination in 4 patients and 9 patients,respectively),with no difference between patients undergoing secondary surgery and surveillance only.CONCLUSION Reporting of histological features and adherence to surveillance colonoscopy needs improvement.Long-term adverse outcome rates might be higher than previously reported,irrespective of whether secondary surgery was performed.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

Tailoring Iron-Ion Release of Cellulose-Based Aerogel-Coated Iron Foam for Long-Term High-Power Microbial Fuel Cells

The presence of iron(Fe) has been found to favor power generation in microbial fuel cells(MFCs). To achieve long-term power production in MFCs, it is crucial to effectively tailor the release of Fe ions over extended operating periods. In this study, we developed a composite anode(A/IF) by coating iron foam with cellulose-based aerogel. The concentration of Fe ions in the anode solution of A/IF anode reaches 0.280 μg/mL(Fe^(2+) vs. Fe^(3+) = 61%:39%) after 720 h of aseptic primary cell operation. This value was significantly higher than that(0.198 μg/mL, Fe^(2+) vs. Fe^(3+) = 92%:8%) on uncoated iron foam(IF), indicating a continuous release of Fe ions over long-term operation. Notably, the resulting MFCs hybrid cell exhibited a 23% reduction in Fe ion concentration(compared to a 47% reduction for the IF anode) during the sixth testing cycle(600-720 h). It achieved a high-power density of 301 ± 55 mW/m^(2) at 720 h, which was 2.62 times higher than that of the IF anode during the same period. Furthermore, a sedimentary microbial fuel cell(SMFCs) was constructed in a marine environment, and the A/IF anode demonstrated a power density of 103 ± 3 mW/m^(2) at 3240 h, representing a 75% improvement over the IF anode. These findings elucidate the significant enhancement in long-term power production performance of MFCs achieved through effective tailoring of Fe ions release during operation.

Modeling injection-induced fault slip using long short-term memory networks

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

Long-Term Performance and Microstructural Characterization of Dam Concrete in the Three Gorges Project

This study investigates the long-term performance of laboratory dam concrete in different curing environments over ten years and the microstructure of 17-year-old laboratory concrete and actual concrete cores drilled from the Three Gorges Dam.The mechanical properties of the laboratory dam concrete,whether cured in natural or standard environments,continued to improve over time.Furthermore,the laboratory dam concrete exhibited good resistance to diffusion and a refined microstructure after 17 years.However,curing and long-term exposure to the local natural environment reduced the frost resistance.Microstructural analyses of the laboratory concrete samples demonstrated that moderate-heat cement and fine fly ash(FA)particles were almost fully hydrated to form compact micro structures consisting of large quantities of homogeneous calcium(alumino)silicate hydrate(C-(A)-S-H)gels and a few crystals.No obvious interfacial transition zones were observed in the microstructure owing to the longterm pozzolanic reaction.This dense and homogenous microstructure was the crucial reason for the excellent long-term performance of the dam concrete.A high FA volume also played a significant role in the microstructural densification and performance growth of dam concrete at a later age.The concrete drilled from the dam surface exhibited a loose microstructure with higher microporosity,indicating that concrete directly exposed to the actual service environment suffered degradation caused by water and wind attacks.In this study,both macro-performance and microstructural analyses revealed that the application of moderate-heat cement and FA resulted in a dense and homogenous microstructure,which ensured the excellent long-term performance of concrete from the Three Gorges Dam after 17 years.Long-term exposure to an actual service environment may lead to microstructural degradation of the concrete surface.Therefore,the retained long-term dam concrete samples need to be further researched to better understand its microstructural evolution and development of its properties.

Soil bacterial and fungal communities resilience to long-term nitrogen addition in subtropical forests in China

Atmospheric nitrogen(N)deposition is predicted to increase,especially in the subtropics.However,the responses of soil microorganisms to long-term N addition at the molecular level in N-rich subtropical forests have not been clarified.A long-term nutrient addition experiment was conducted in a subtropical evergreen old-growth forest in China.The four treatments were:control,low N(50 kg N ha^(-1)a^(-1)),high N(100 kg N ha^(-1)a^(-1)),and combined N and phosphorus(P)(100 kg N ha^(-1)a^(-1)+50 kg P ha^(-1)a^(-1)).Metagenomic sequencing characterized diversity and composition of soil microbial communities and used to construct bacterial/fungal co-occurrence networks.Nutrient-treated soils were more acidic and had higher levels of dissolved organic carbon than controls.There were no significant differences in microbial diversity and community composition across treatments.The addition of nutrients increased the abundance of copiotrophic bacteria and potentially beneficial microorganisms(e.g.,Gemmatimonadetes,Chaetomium,and Aureobasidium).Low N addition increased microbiome network connectivity.Three rare fungi were identified as module hubs under nutrient addition,indicating that low abundance fungi were more sensitive to increased nutrients.The results indicate that the overall composition of microbial communities was stable but not static to long-term N addition.Our findings provide new insights that can aid predictions of the response of soil microbial communities to long-term N addition.

Long-term thinning decreases the contribution of heterotrophic respiration to soil respiration in subalpine plantations

Interest in the dynamics of soil respiration(R_(S))in subalpine forest ecosystems is increasing due to their high soil carbon density and potential sensitivity to environmental changes.However,as a principal silvicultural practice,the long-term impacts of thinning on R_(S) and its heterotrophic and autotrophic respiration components(R_(h) and Ra,respectively)in subalpine plantations are poorly understood,espe-cially in winter.A 3-year field observation was carried out with consideration of winter CO_(2) efflux in middle-aged sub-alpine spruce plantations in northwestern China.A trench-ing method was used to explore the long-term impacts of thinning on Rs,Rn and R_(a).Seventeen years after thinning,mean annual Rs,Rn and R_(a) increased,while the contribu-tion of R_(h) to R_(s) decreased with thinning intensity.Thinning significantly decreased winter R,because of the reduction in R_(n) but had no significant effect on Ra.The temperature sensitivity(Q_(10))of R_(h) and R_(a) also increased with thinning intensity,with lower Q_(10) values for R_(h)(2.1-2.6)than for Ra(2.4-2.8).The results revealed the explanatory variables and pathways related to R_(n) and R_(a) dynamics.Thinning increased soil moisture and nitrate nitrogen(NO_(3)^(-)-N),and the enhanced nitrogen and water availability promoted R_(h) and R_(a) by improving fine root biomass and microbial activity.Our results highlight the positive roles of NO_(3)^(-)-N in stimulating R_(s) components following long-term thinning.Therefore,applications of nitrogen fertilizer are not recommended while thinning subalpine spruce plantations from the perspective of reducing soil CO_(2) emissions.The increased Q_(10) values of R_(s) components indicate that a large increase in soil CO_(2) emissions would be expected following thinning because of more pronounced climate warming in alpineregions.

A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND Hepatectomy is the first choice for treating liver cancer.However,inflammatory factors,released in response to pain stimulation,may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.AIM To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.METHODS Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed.Thirty-five patients underwent laparoscopic hepatectomy for liver cancer(liver cancer resection group)and 35 patients underwent medical image-guided microwave ablation(liver cancer ablation group).The short-term efficacy,complications,liver function,and immune function indices before and after treatment were compared between the two groups.RESULTS One month after treatment,19 patients experienced complete remission(CR),8 patients experienced partial remission(PR),6 patients experienced stable disease(SD),and 2 patients experienced disease progression(PD)in the liver cancer resection group.In the liver cancer ablation group,21 patients experienced CR,9 patients experienced PR,3 patients experienced SD,and 2 patients experienced PD.No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups(P>0.05).After treatment,total bilirubin(41.24±7.35 vs 49.18±8.64μmol/L,P<0.001),alanine aminotransferase(30.85±6.23 vs 42.32±7.56 U/L,P<0.001),CD4+(43.95±5.72 vs 35.27±5.56,P<0.001),CD8+(20.38±3.91 vs 22.75±4.62,P<0.001),and CD4+/CD8+(2.16±0.39 vs 1.55±0.32,P<0.001)were significantly different between the liver cancer ablation and liver cancer resection groups.CONCLUSION The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar,but liver function recovers quickly after microwave ablation,and microwave ablation may enhance immune function.

RuO_(2)/CoMO_(2)O_(x) Catalyst with Low Ruthenium Loading for Long-Term Acidic Oxygen Evolution

We must urgently synthesize highly efficient and stable oxygen-evolution reaction(OER) catalysts for acidic media. Herein, we constructed a series of Ti mesh(TM)-supported RuO_(2)/CoMo_(y)O_(x) catalysts(RuO_(2)/CoMo_(y)O_(x)/TM) with heterogeneous structures. By optimizing the ratio of Co to Mo, RuO_(2)/CoMO_(2)O_(x)/TM with low Ru loading(0.079 mg/cm^(2)) achieves remarkable OER performance(η = 243 mV at 10 mA/cm^(2)) and high stability(300 h @ 10 mA/cm^(2)) in 0.5 mol/L H_(2)SO_(4) electrolyte. The activity of RuO_(2)/CoMo_yO_x/TM can be maintained for 50 h at 100 mA/cm^(2), and a water electrolyzer with RuO_(2)/CoMO_(2)O_(x)/TM as anode can operate for 40 h at 100 mA/cm~2, suggesting the remarkable OER durability of RuO_(2)/CoMo__(y)O__(x)/TM in acidic electrolyte. Owing to the heterogeneous interface between CoMO_(2)O_(x) and RuO_(2), the electronic structure of Ru atoms was optimized and electron-rich Ru was formed. With modulated electronic properties, the dissociation energy of H_(2)O is weakened, and the OER barrier is lowered. This study provides the design of low-cost noble metal catalysts with long-term stability in an acidic environment.

Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Imperative for long-term management and surveillance in Kawasaki disease

Kawasaki disease(KD)is a significant pediatric vasculitis known for its potential to cause severe coronary artery complications.Despite the effectiveness of initial treatments,such as intravenous immunoglobulin,KD patients can experience long-term cardiovascular issues,as evidenced by a recent case report of an adult who suffered a ST-segment elevation myocardial infarction due to previous KD in the World Journal of Clinical Cases.This editorial emphasizes the critical need for long-term management and regular surveillance to prevent such complications.By drawing on recent research and case studies,we advocate for a structured approach to follow-up care that includes routine cardiac evaluations and preventive measures.