Retrospective analysis of pathological types and imaging features in pancreatic cancer: A comprehensive study

BACKGROUND Pancreatic cancer remains one of the most lethal malignancies worldwide,with a poor prognosis often attributed to late diagnosis.Understanding the correlation between pathological type and imaging features is crucial for early detection and appropriate treatment planning.AIM To retrospectively analyze the relationship between different pathological types of pancreatic cancer and their corresponding imaging features.METHODS We retrospectively analyzed the data of 500 patients diagnosed with pancreatic cancer between January 2010 and December 2020 at our institution.Pathological types were determined by histopathological examination of the surgical spe-cimens or biopsy samples.The imaging features were assessed using computed tomography,magnetic resonance imaging,and endoscopic ultrasound.Statistical analyses were performed to identify significant associations between pathological types and specific imaging characteristics.RESULTS There were 320(64%)cases of pancreatic ductal adenocarcinoma,75(15%)of intraductal papillary mucinous neoplasms,50(10%)of neuroendocrine tumors,and 55(11%)of other rare types.Distinct imaging features were identified in each pathological type.Pancreatic ductal adenocarcinoma typically presents as a hypodense mass with poorly defined borders on computed tomography,whereas intraductal papillary mucinous neoplasms present as characteristic cystic lesions with mural nodules.Neuroendocrine tumors often appear as hypervascular lesions in contrast-enhanced imaging.Statistical analysis revealed significant correlations between specific imaging features and pathological types(P<0.001).CONCLUSION This study demonstrated a strong association between the pathological types of pancreatic cancer and imaging features.These findings can enhance the accuracy of noninvasive diagnosis and guide personalized treatment approaches.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

基于特征增强的双重注意力去雾网络

针对现有去雾方法处理的图像细节模糊和色彩偏差等问题,提出了一种基于特征增强的双重注意力去雾网络。该网络采用编码器-解码器结构,设计了一个双重注意力特征增强模块,其中,利用Ghost模块替代非线性卷积,实现模型轻量化处理,通过RFB充分融合不同尺度的特征,实现均匀去雾,引入双重注意力实现信息跨通道与空间交互,保证模型性能和抑制噪声特征。使用RESIDE数据集对网络进行训练和测试。实验结果表明,所提算法在主观视觉和客观评价指标上均有优异表现,能有效地提升网络的特征提取能力,实现对不同场景雾图的色彩恢复,增强图像的对比度和清晰度。

多尺度特征增强的街景绿色景观分割方法

针对街景图像中景观复杂多样且多种景观相互遮挡,绿色景观分割效果存在相似景观错分、边界分割模糊、细节丢失等问题,提出一种多尺度特征增强的城市绿色景观分割网络。在编码部分改进多尺度残差网络提取上下文信息以区分相似景观,同时构建多级特征聚合增强模块增强目标特征的边缘细节信息。增加双注意力机制,在局部特征上建模丰富的上下文联系。最后,将多级特征聚合增强模块同样引入解码器,并融合多层级特征来提高目标信息的恢复能力完善边缘信息。在公共街景数据集Cityscapes与自制数据集StreetData的消融实验表明,该网络与基础网络相比,平均交并比分别提高2.96%和5.57%。此外,在两个数据集上进行对比实验,该网络较对比模型平均交并比分别高1.25%~5.29%和1.52%~6.95%。定量分析与实验结果表明,该方法能够有效识别街景的绿色景观,实现高精度的城市绿色景观数据提取。

基于音频特征的拖拉机发动机状况识别系统设计

拖拉机发动机是保证拖拉机正常运行的关键部件,目前主要采用振动信号开展发动机故障预测与状况识别。为此,提出了一种基于GRU的循环神经网络模型,通过对拖拉机发动机在不同作业条件下产生的音频信号进行分析,提取Mel作为主要特征,构建基于音频特征的拖拉机发动机状况识别系统。预测结果表明:系统能够准确地识别发动机的正常运行状态和不同类型的故障状况,对拖拉机发动机异常的识别率可以达到97.15%。研究结果可以提高拖拉机的运行安全性和可靠性,减少故障停机时间,提高农业生产效率。

发芽糙米特征营养、食味品质提升及功能性评价研究进展

发芽糙米作为一种全谷物,保留了完整的皮层和胚,集中了大部分营养素和功能性活性物质,但由于皮层纤维结构致密,蒸饭过程中阻碍了吸水速率、吸水量和淀粉糊化,与大米相比,其蒸煮性和食用品质较差,且难以同煮同熟,因此一直以来未被广大消费者接受。本文综述糙米发芽的生理代谢反应及发芽过程中营养成分、活性成分和食味品质的变化,总结发芽糙米胁迫富集γ-氨基丁酸(γ-aminobutyric acid,GABA)技术和发芽糙米的功能特性研究现状。糙米发芽过程中,多种生物活性物质在酶促反应下富集,尤其是特征成分GABA含量显著提升。多种逆境胁迫均可通过改变Ca^(2+)、H^(+)或底物水平促进GABA富集,一些预处理技术可进一步提升发芽糙米的食味品质。GABA与其他营养成分和活性成分发挥协同作用,赋予发芽糙米抗高血脂、抗高血糖、保护心脏、改善睡眠、抗炎和抗氧化等一系列功效与作用。

质子入射In靶激发L系特征X射线的角分布研究

本论文实验测量并分析了发射角以10°为间隔,125°-155°范围内,入射能量为250 keV的质子束激发In靶产生的特征L系X射线谱,根据实验测得能谱结果,综合考虑探测器的探测效率后,计算了不同探测角度下特征X射线的相对强度比L_(ι)/L_(γ1);由相对强度比L_(ι)/L_(γ1)与二阶勒让德函数P_(2)(cosθ)之间的函数关系,发现特征X射线Lι在被测能量下呈各向异性发射,推得Lι特征X射线的各向异性参数β为-0.179±0.011,进而得到In靶L_(3)亚壳层的定向度A_(20)为-0.422±0.025,实验结果与理论预测一致.根据PWBA(Plane wave born approximation)模型和ECPSSR(Energy-loss coulomb-repulsion perturbed-stationary-state relativistic)模型,计算了250 keV质子入射下,In靶L亚壳层电离截面σ及Coster-Kronig跃迁矫正因子κ,在根据各向异性参数计算L_(3)亚壳层定向度的过程中考虑了Coster-Kronig跃迁的作用.分析认为本实验碰撞速度下,通过电子转移方式产生空穴的截面比直接电离小的多,所以没有对L_(3)亚壳层定向度进行电子转移因素的修正.

12例非结核分枝杆菌性脊柱炎手术治疗患者临床特征及治疗转归

目的:分析非结核分枝杆菌(non-tuberculous mycobacterial,NTM)性脊柱炎手术治疗患者临床特征,以提高临床诊治水平。方法:采用回顾性研究方法,参照入组标准收集并分析2023年1月至2024年9月首都医科大学附属北京胸科医院、青海省第四人民医院、哈尔滨医科大学附属哈尔滨市胸科医院骨科收治的12例经手术治疗的NTM性脊柱炎患者临床资料,包括患者人口学特征、临床症状及体征、病变部位、入院前诊断及治疗、入院后实验室检查、影像学检查、药物治疗方案、手术治疗方式及预后和随访情况。结果:12例NTM性脊柱炎患者均有1种或多种基础疾病或致免疫力低下的疾病;从发病到确诊的病程范围为12~24个月,其中,外院行抗结核治疗1年及以上者8例、6~9个月者4例。3例患者行MGIT 960液体培养,仅1例患者后续行TB-DNA检测;5例患者行NGS检测;2例患者行病理DNA诊断;2例患者行NGS+病理DNA定性。最终菌种鉴定到8种NTM,以鸟分枝杆菌复合群、胞内分枝杆菌和脓肿分枝杆菌复合群为主。术前抗NTM治疗6周者7例,8周者4例,仅1例合并肺部感染者治疗4周。4例首次手术患者,其中3例行单纯后路手术、1例因骨质破坏严重行单纯前路手术;8例再次手术患者,其中5例行前后路联合手术、1例行单纯前路手术、2例行窦道切除术,但有3例术后行3期手术或单纯窦道切除术。术后继续术前抗NTM方案治疗及康复训练8~12个月,随访2年后8例痊愈、4例有窦道或残腔(其中1例经7次手术后放弃治疗)。结论:NTM性脊柱炎多有明确职业接触史,感染菌种丰富,多因缺乏菌种鉴定而导致长期误诊误治,病程迁延,病理和分子生物学检测可明确诊断。手术治疗是病程迁延、脊柱损害严重患者的主要治疗手段,治愈率约为70%,再手术风险高,故应术前明确致病菌、精准诊断,并及时进行足量且有针对性的抗NTM治疗,以降低患者术后发生窦道及脓肿加重的风险,保证术后良好结局。

未来气候情景下高寒区水稻延迟型冷害演变特征研究

利用中国区域气候模式BCC-CSM 1.0模拟的日平均气温数据,结合国家标准中5—9月温度和的距平(ΔT)指标,判识RCP4.5情景下2024—2060年水稻延迟型冷害的演变特征。结果表明:该模式数据对研究水稻冷害较为可靠;2024—2060年,研究区水稻延迟型冷害识别年数为5~12 a,随时间呈减少趋势,其中2024—2030年、2030s为冷害高发期,2040s为低发期,2050s未发生冷害,与基准时段(1969—2005年)相比,冷害总年数大幅减少;空间分布上,水稻延迟型冷害发生频率总体呈北少南多,冷害发生频率在13.5%~32.3%,高值区位于哈尔滨东部、鸡西南部和牡丹江,而齐齐哈尔、大庆区域为低值中心,与基准时段相比,冷害高频区向南收缩并东移。研究区冷害发生范围在不同年份存在差异,IOC值呈“弹跳式”波动变化,2024、2026、2028、2029、2030、2031、2033、2035、2036、2046、2049年冷害发生范围较大,IOC高于0.42,与基准时段相比,IOC为0的年数增加,IOC高于0.50的年数减少。

盘州地区峨眉山玄武岩组煤夹层储层特征初探

通过松河井田煤层气探井及相关资料,论证了该区峨眉山玄武岩组煤系夹层32^(#)煤层发育情况、煤层特征等;对比分析了玄武岩组煤系夹层32^(#)煤与龙潭组3^(#)煤在煤体结构、煤层厚度、渗透率、含气性等煤储层特征,认为玄武岩组32^(#)煤层气抽采前景较好,可作为龙潭组煤层气地面抽采的有效补充。

银发经济中备老人群消费特征与发展趋势研究

银发经济已成为我国未来经济持续增长的重要领域,发展银发经济需要从供给和需求两方面同时发力。银发经济有特定的年龄针对性,文本关注银发经济中未老阶段的备老经济。备老人群呈现身体健康状况良好、预期寿命持续延长,文化教育水平较高、消费观念开放包容,互联网普及程度较高、消费个性化、注重悦己等群体特征,具有较强的消费能力和消费意愿,备老人群的消费将为银发经济带来巨大的增量空间。深入理解并满足备老人群的消费需求,对推动供需在更高水平实现良性循环、促进银发经济高质量发展具有重要意义。

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China:a hospital-based retrospective study

Traumatic spinal cord injury is potentially catastrophic and can lead to permanent disability or even death.China has the largest population of patients with traumatic spinal cord injury.Previous studies of traumatic spinal cord injury in China have mostly been regional in scope;national-level studies have been rare.To the best of our knowledge,no national-level study of treatment status and economic burden has been performed.This retrospective study aimed to examine the epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China at the national level.We included 13,465 traumatic spinal cord injury patients who were injured between January 2013 and December 2018 and treated in 30 hospitals in 11 provinces/municipalities representing all geographical divisions of China.Patient epidemiological and clinical features,treatment status,and total and daily costs were recorded.Trends in the percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department and cost of care were assessed by annual percentage change using the Joinpoint Regression Program.The percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department did not significantly change overall(annual percentage change,-0.5%and 2.1%,respectively).A total of 10,053(74.7%)patients underwent surgery.Only 2.8%of patients who underwent surgery did so within 24 hours of injury.A total of 2005(14.9%)patients were treated with high-dose(≥500 mg)methylprednisolone sodium succinate/methylprednisolone(MPSS/MP);615(4.6%)received it within 8 hours.The total cost for acute traumatic spinal cord injury decreased over the study period(-4.7%),while daily cost did not significantly change(1.0%increase).Our findings indicate that public health initiatives should aim at improving hospitals’ability to complete early surgery within 24 hours,which is associated with improved sensorimotor recovery,increasing the awareness rate of clinical guidelines related to high-dose MPSS/MP to reduce the use of the treatment with insufficient evidence.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Endoscopic features and treatments of gastric cystica profunda

BACKGROUND Gastric cystica profunda(GCP)represents a rare condition characterized by cystic dilation of gastric glands within the mucosal and/or submucosal layers.GCP is often linked to,or may progress into,early gastric cancer(EGC).AIM To provide a comprehensive evaluation of the endoscopic features of GCP while assessing the efficacy of endoscopic treatment,thereby offering guidance for diagnosis and treatment.METHODS This retrospective study involved 104 patients with GCP who underwent endoscopic resection.Alongside demographic and clinical data,regular patient followups were conducted to assess local recurrence.RESULTS Among the 104 patients diagnosed with GCP who underwent endoscopic resection,12.5%had a history of previous gastric procedures.The primary site predominantly affected was the cardia(38.5%,n=40).GCP commonly exhibited intraluminal growth(99%),regular presentation(74.0%),and ulcerative mucosa(61.5%).The leading endoscopic feature was the mucosal lesion type(59.6%,n=62).The average maximum diameter was 20.9±15.3 mm,with mucosal involvement in 60.6%(n=63).Procedures lasted 73.9±57.5 min,achieving complete resection in 91.3%(n=95).Recurrence(4.8%)was managed via either surgical intervention(n=1)or through endoscopic resection(n=4).Final pathology confirmed that 59.6%of GCP cases were associated with EGC.Univariate analysis indicated that elderly males were more susceptible to GCP associated with EGC.Conversely,multivariate analysis identified lesion morphology and endoscopic features as significant risk factors.Survival analysis demonstrated no statistically significant difference in recurrence between GCP with and without EGC(P=0.72).CONCLUSION The findings suggested that endoscopic resection might serve as an effective and minimally invasive treatment for GCP with or without EGC.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

While single-modal visible light images or infrared images provide limited information,infrared light captures significant thermal radiation data,whereas visible light excels in presenting detailed texture information.Com-bining images obtained from both modalities allows for leveraging their respective strengths and mitigating individual limitations,resulting in high-quality images with enhanced contrast and rich texture details.Such capabilities hold promising applications in advanced visual tasks including target detection,instance segmentation,military surveillance,pedestrian detection,among others.This paper introduces a novel approach,a dual-branch decomposition fusion network based on AutoEncoder(AE),which decomposes multi-modal features into intensity and texture information for enhanced fusion.Local contrast enhancement module(CEM)and texture detail enhancement module(DEM)are devised to process the decomposed images,followed by image fusion through the decoder.The proposed loss function ensures effective retention of key information from the source images of both modalities.Extensive comparisons and generalization experiments demonstrate the superior performance of our network in preserving pixel intensity distribution and retaining texture details.From the qualitative results,we can see the advantages of fusion details and local contrast.In the quantitative experiments,entropy(EN),mutual information(MI),structural similarity(SSIM)and other results have improved and exceeded the SOTA(State of the Art)model as a whole.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.