外周血EOS、IL-21、CCL4与慢性鼻-鼻窦炎伴鼻息肉患者术后复发的相关性

目的探究外周血嗜酸性粒细胞数(EOS)、白细胞介素-21(IL-21)、趋化因子4(CCL4)与慢性鼻-鼻窦炎伴鼻息肉(CRSwNP)患者术后复发的相关性。方法回顾性选取青岛市第八人民医院2020年1月至2022年6月收治的86例行慢性鼻-鼻窦炎伴鼻息肉切除术患者,收集所有患者的一般资料,对患者进行外周血EOS、IL-21、CCL4检测;并对患者进行随访1年,统计其复发情况,根据患者的术后复发情况将患者分为复发组(n=34)与正常组(n=52)。比较两组的临床资料和外周血EOS、IL-21、CCL4水平;采用Pearson相关性分析探讨外周血EOS、IL-21、CCL4与CRSwNP患者鼻窦CT Lund-Mackay评分的相关性;采用受试者工作特征(ROC)曲线评估CRSwNP患者术后复发的预测价值。结果随访期间共记录到术后复发患者34例,占比39.53%,未复发患者52例,占比60.47%。两组患者的嗜酸性粒细胞型鼻息肉例数、鼻窦CT Lund-Mackay评分、外周血EOS、IL-21、CCL4水平进行比较,差异有统计学意义(t=4.250、5.300、3.890、3.089、3.499,P<0.05)。多因素logistic回归分析结果显示外周血EOS、IL-21、CCL4水平均是CRSwNP患者术后复发的独立危险因素(P<0.05)。Pearson相关性分析结果显示,外周血EOS、IL-21、CCL4水平与CRSwNP患者鼻窦CT Lund-Mackay评分具有相关性(r=0.433、0.681、0.547,P<0.05)。ROC曲线结果显示,外周血EOS、IL-21、CCL4及三者联合对预测CRSwNP患者术后复发的AUC面积分别为:0.725、0.686、0.709、0.834,且联合预测的灵敏度和特异度最高,效果最好(P<0.05)。结论外周血EOS、IL-21、CCL4与CRSwNP患者病情程度具有相关性,通过外周血EOS、IL-21、CCL4预测慢性鼻-鼻窦炎伴鼻息肉患者术后复发具有一定的预测价值。

EOS^(TM)3D影像系统诊断复发性髌骨脱位的可靠性和稳定性

目的 探讨EOS^(TM)3D影像系统诊断复发性髌骨脱位的可靠性和稳定性。方法 回顾性分析2022年3月至2023年3月西安交通大学附属红会医院运动医学中心收治的22例(26膝)复发性髌骨脱位病人的影像学资料,两位影像学医生同时使用EOS^(TM)系统测量病人下肢力线,使用sterEOS软件对影像图片进行3D模型重建,并在三维模型中测量胫骨结节-股骨滑车沟(tibial tubercle-trochlear groove,TTTG)间距,记录每次测量所需的时间和相关参数。所有病人同期进行常规膝关节CT扫描及三维重建。将EOS^(TM)3D影像和CT扫描测量的TT-TG数值进行比较,采用一致性检验研究和Bland-Altman分析图评价测量结果数据的可靠性和稳定性。结果 进行EOSTM下肢力线测量时,不同测量者间测量的股骨和胫骨长度、膝关节内外翻角度及股骨胫骨旋转角度之间差异无统计学意义(P>0.05)。病人常规下肢CT扫描及三维重建测量时间为(21.8±3.2)min(13~29 min),EOSTM3D测量时间为(6.3±1.8)min(4~11 min),差异有统计学意义(t=12.693,P<0.001)。两位医生使用EOS^(TM)3D测量TT-TG值的组内相关系数为0.791,使用常规CT测量的组内相关系数为0.843,两种测量方法组内一致性均较好。Bland-Altman分析结果显示两位测量者分别有96.2%(25/26)、92.3%(24/26)的点位于±1.96标准差范围内,显示使用常规CT三维重建和EOS^(TM)3D测量TT-TG值具备较好的一致性和稳定性。结论 使用EOS^(TM)3D影像系统测量复发性髌骨脱位病人的TT-TG值,具有良好的可靠性及可重复性,具有检查时间短、辐射低等优势,是评估此类病人下肢力线数据的一种快捷、可靠及稳定的方法。

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.

Wideband spectrum sensing using step-sampling based on the multipath nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

Endoscopic ultrasound-guided tissue sampling induced pancreatic duct leak resolved by the placement of a pancreatic stent:A case report

BACKGROUND Pancreatic ductal leaks complicated by endoscopic ultrasonography-guided tissue sampling(EUS-TS)can manifest as acute pancreatitis.CASE SUMMARY A 63-year-old man presented with persistent abdominal pain and weight loss.Diagnosis:Laboratory findings revealed elevated carbohydrate antigen 19-9(5920 U/mL)and carcinoembryonic antigen(23.7 ng/mL)levels.Magnetic resonance imaging of the pancreas revealed an approximately 3 cm ill-defined space-occupying lesion in the inferior aspect of the head,with severe encasement of the superior mesenteric artery.Pancreatic ductal adenocarcinoma was confirmed after pathological examination of specimens obtained by EUS-TS using the fanning method.Interventions and outcomes:The following day,the patient experienced severe abdominal pain with high amylase(265 U/L)and lipase(1173 U/L)levels.Computed tomography of the abdomen revealed edematous wall thickening of the second portion of the duodenum with adjacent fluid collections and a suspicious leak from either the distal common bile duct or the main pancreatic duct in the head.Endoscopic retrograde cholangiopancreatography revealed dye leakage in the head of the main pancreatic duct.Therefore,a 5F 7 cm linear plastic stent was deployed into the pancreatic duct to divert the pancreatic juice.The patient’s abdominal pain improved immediately after pancreatic stent insertion,and amylase and lipase levels normalized within a week.Neoadjuvant chemotherapy was then initiated.CONCLUSION Using the fanning method in EUS-TS can inadvertently cause damage to the pancreatic duct and may lead to clinically significant pancreatitis.Placing a pancreatic stent may immediately resolve acute pancreatitis and shorten the waiting time for curative therapy.When using the fanning method during EUSTS,ductal structures should be excluded to prevent pancreatic ductal leakage.

Design and Experimentation of Multi-Rod Grain Sampling Machine

In order to enhance grain sampling efficiency, in this work a truss type multi-rod grain sampling machine is designed and tested. The sampling machine primarily consists of truss support mechanism, main carriage mechanism, auxiliary carriage mechanism, sampling rods, and a PLC controller. The movement of the main carriage on the truss, the auxiliary carriage on the main carriage, and the vertical movement of the sampling rods on the auxiliary carriage are controlled through PLC programming. The sampling machine accurately controls the position of the sampling rods, enabling random sampling with six rods to ensure comprehensive and random sampling. Additionally, sampling experiments were conducted, and the results showed that the multi-rod grain sampling machine simultaneously samples with six rods, achieving a sampling frequency of 38 times per hour. The round trip time for the sampling rods is 33 seconds per cycle, and the sampling length direction reaches 18 m. This study provides valuable insights for the design of multi-rod grain sampling machines.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

Underdetermined direction of arrival estimation with nonuniform linear motion sampling based on a small unmanned aerial vehicle platform

Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.

Research on Adaptive Cluster Sampling Method Based on PPS

This paper introduces the principle of PPS-based adaptive cluster sampling method and its modified HH estimator and HT estimator calculation method. It compares PPS-based adaptive cluster sampling method with SRS sampling and SRS-based adaptive group. The difference between the group sampling and the advantages and scope of the PPS adaptive cluster sampling method are analyzed. According to the case analysis, the relevant conclusions are drawn: 1) The adaptive cluster sampling method is more accurate than the SRS sampling;2) SRS adaptive The HT estimator of the cluster sampling is more stable than the HH estimator;3) The two estimators of the PPS adaptive cluster sampling method have little difference in the estimation of the population mean, but the HT estimator variance is smaller and more suitable;4) PPS The HH estimator of adaptive cluster sampling is the same as the HH estimator of SRS adaptive cluster sampling, but the variance is larger and unstable.

固件升级|CINEMA EOS和XF605最新固件开始下载

佳能日前发布了CinemaEOS系列摄影机和专业数码摄像机XF605的最新固件升级,包括EOSC500 MarkIl固件版本1.0.9.1,EOSC300MarkII固件版本1.0.7.1,EOSC70固件版本1.0.8.1,EOSR5C固件版本1.0.6.1,XF605摄像机固件版本1.0.3.1。

佳能EOS C500 Mark II增加多个Cinema RAW Light记录格式

佳能Cinema EOS电影摄影机EOS C500 Mark II日前发布了新固件版本1.1.1.1。经过此次固件升级,EOS C500 Mark II可支持三种新增的Cinema RAW Light记录格式:具有高画质的RAW HQ模式、标准画质的RAW ST模式和精简记录可以延长记录时间的RAW LT模式。

甲氨蝶呤联合托法替布治疗对类风湿关节炎患者血清抗CCP抗体、抗MCV抗体及EOS水平的影响

本研究旨在探讨甲氨蝶呤联合托法替布治疗对类风湿关节炎(RA)患者血清抗CCP抗体、抗MCV抗体及EOS水平的影响。方法 本项研究所涉及的数据源于2021年初至2022年中在特定医疗机构就诊的86位类风湿性关节炎(RA)病患者。这些患者经过筛选之后,被随机地分配到两个不同的治疗小组中:一为对照组,仅采用甲氨蝶呤(MTX)作为主要治疗手段;另一为观察组,患者接受的是甲氨蝶呤加上托法替布(tofacitinib)的综合治疗方案。通过对比这两组患者治疗后在临床疗效上的表现、其症状展现、血清学检验结果以及炎症相关生物标志物的水平变化,评估两种不同治疗方法的效用差异。结果 观察组的治疗有效率达到了90.70%,显著高于对照组的69.77%(P<0.05)。观察组在治疗后,关于关节疼痛、关节肿胀、关节活动度不良等症状评分明显低于对照组,同时晨僵时间也较对照组更短(P<0.05)。此外,观察组治疗后血清中抗CCP抗体、抗MCV抗体及嗜酸性粒细胞的水平均低于对照组(P<0.05)。同时,观察组治疗后血清中的CRP、ESR、TNF-α和IL-1水平均较对照组更低(P<0.05)。结论 甲氨蝶呤联合托法替布治疗对RA的疗效显著,能明显降低炎症反应及抗CCP、抗MCV抗体水平,同时增强对症状的控制力。然而,需要进一步的研究来验证这些结果,并考虑其他可能的影响因素。

基于EOS双平面成像分析Blumensaat线的空间倾斜度与前交叉韧带损伤的相关性

目的:采用EOS双平面成像系统测量非接触性前交叉韧带(anterior cruciate ligament,ACL)损伤患者Blumensaat线与股骨机械轴间的夹角(angle composed by femoral mechanical axis and Blumensaat line,FMBL角),与侧位X线片上Blumensaat线与股骨远端轴线的夹角(α角)进行比较,评估其与ACL损伤的相关性,预测非接触性ACL损伤易发人群的准确性。方法:回顾性分析2018年2月至2020年10月收治的88例(176膝)单侧非接触性ACL损伤患者的双下肢骨骼全长EOS影像资料,男53例,女35例;年龄18~45(30.3±6.2)岁;左侧48例,右侧40例。根据ACL损伤组患者ACL损伤的肢体侧别将测量角度参数分为ACL患侧组和ACL健侧组。并在EOS数据库中选取51例(51膝)非ACL损伤者的完全健康侧肢体作为正常对照组,男28例,女23例;年龄20~44(31.6±5.5)岁;左膝26例,右膝25例。将3组下肢骨骼全长EOS影像3D建模,并测量FMBL角与α角。采用单因素二分类Logistic回归分析角度对ACL状态(正常或撕裂)的影响,并根据受试者工作特征(receiver operating characteristic,ROC)曲线得出最佳临界值。结果:ACL损伤组与正常对照组年龄、性别和侧别比较,差异无统计学意义(P>0.05)。ACL患侧组FMBL角(32.8±2.3)。与ACL健侧组(32.5±2.3)。比较,差异无统计学意义(P>0.05),但均明显低于正常对照组(37.0±2.0)°(P<0.001)。ACL患侧组、ACL健侧组、正常对照组间α角比较,差异无统计学意义(P>0.05)。单因素二分类Logistic回归分析结果显示,FMBL角是非接触性ACL损伤的危险因素[OR=0.433,95%CI(0.330,0.569),P<0.001]。FMBL角的ROC曲线下面积为0.909[95%CI(0.861,0.958),P<0.001],最佳诊断界点值为33.7°,敏感度70.5%,特异度98.0%。结论:Blumensaat线与股骨机械轴形成的FMBL角是非接触性ACL损伤的危险因素之一,具有良好的预测准确性,当角度≤33.7°时发生非接触性ACL损伤的风险增加。

Improved Prediction and Reduction of Sampling Density for Soil Salinity by Different Geostatistical Methods

The spatial estimation for soil properties was improved and sampling intensities also decreased in terms of incorporated auxiliary data. In this study, kriging and two interpolation methods were proven well to estimate auxiliary variables： cokriging and regression-kriging, and using the salinity data from the first two stages as auxiliary variables, the methods both improved the interpolation of soil salinity in coastal saline land. The prediction accuracy of the three methods was observed under different sampling density of the target variable by comparison with another group of 80 validation sample points, from which the root-mean-square error （RMSE） and correlation coefficient （r） between the predicted and measured values were calculated. The results showed, with the help of auxiliary data, whatever the sample size of the target variable may be, cokriging and regression-kriging performed better than ordinary kriging. Moreover, regression-kriging produced on average more accurate predictions than cokriging. Compared with the kriging results, cokriging improved the estimations by reducing RMSE from 23.3 to 29% and increasing r from 16.6 to 25.5%, regression-kriging improved the estimations by reducing RMSE from 25 to 41.5% and increasing r from 16.8 to 27.2%. Therefore, regression-kriging shows promise for improved prediction for soil salinity and reduction of soil sampling intensity considerably while maintaining high prediction accuracy. Moreover, in regression-kriging, the regression model can have any form, such as generalized linear models, non-linear models or tree-based models, which provide a possibility to include more ancillary variables.