A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography

BackgroundDiabetic retinopathy (DR) is one of the major causes of blindness in adults suffering from diabetes. With the development of wide-field optical coherence tomography angiography (WF-OCTA), it is to become a gold standard for diagnosing DR. The demand for automated DR diagnosis system based on OCTA images have been fostered due to large diabetic population and pervasiveness of retinopathy cases.Materials and methodsIn this study, 288 diabetic patients and 97 healthy people were imaged by the swept-source optical coherence tomography (SS-OCT) with 12 mm × 12 mm single scan centered on the fovea. A multi-branch convolutional neural network (CNN) was proposed to classify WF-OCTA images into four grades: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR).ResultsThe proposed model achieved a classification accuracy of 96.11%, sensitivity of 98.08% and specificity of 89.43% in detecting DR. The accuracy of the model for DR staging is 90.56%, which is higher than that of other mainstream convolution neural network models.ConclusionThis technology enables early diagnosis and objective tracking of disease progression, which may be useful for optimal treatment to reduce vision loss.     

联合检测血清NGAL、KIM-1和SCr可有效提高慢性肾病分期诊断

为了探讨中性粒细胞明胶酶相关脂质运载蛋白(neutrophil gelatinase-associated lipocalin, NGAL)和肾损伤分子-1 (kidney injury molecule-1, KIM-1)以及血肌酐(serum creatinine, SCr)联合检测对慢性肾病(chronic kidney disease, CKD)的早期诊断价值,本研究收集260例肾病患者和85例健康体检者,检测其血清NGAL、KIM-1和SCr水平。依据肾功能分级标准,CKD患者分为CKD 1期(53例),CKD 2期(68例),CKD 3期(71例),CKD 4期(46例)和CKD 5期(22例),并分析以上指标在各组间的含量差异,及其联合测定对CKD早期的敏感性。与健康对照组相比较,CKD 1期、CKD 2期、3期、4期和5期患者的NGAL、KIM-1水平均明显升高(p<0.001)。血清SCr含量在CKD 3期、4期和5期组较健康对照组显著增加(p<0.001)。以上3项指标均随着CKD严重程度增加而升高。各组指标阳性率分析显示,3项联合检测阳性率高于单项检测阳性率。ROC曲线分析NGAL、KIM-1、SCr对CKD诊断的AUC值F分别是为0.824、0.805、0.856。相关性分析结果显示,GFR和NGAL、KIM-1、SCr相关系数分别是r=-0.784、-0.756、-0.728 (p<0.05)。NGAL与KIM-1、SCr的相关系数分别是r=0.932、0.764 (p<0.05);KIM-1与SCr的相关系数r分别是0.791 (p<0.05)。本研究初步得出结论:血清NGAL、Kim-1可作为CKD早期诊断的重要指标,联合检测血清NGAL、Kim-1、SCr可有效提高CKD早期肾损伤诊断的敏感度,对CKD的分期诊断和治疗具有极其重要的临床价值。     

Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis,Blood Analysis and Disease History

Background

Chronic kidney disease (CKD) is a disorder associated with breakdown of kidney structure and function. CKD can be diagnosed in its early stage only by experienced nephrologists and urologists (medical experts) using the disease history, symptoms and laboratory tests. There are few studies related to the automatic diagnosis of CKD in the literature. However, these methods are not adequate to help the medical experts.

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

ObjectivesAlzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification.Materials and methodIn this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used.ResultsThe experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods.Conclusionsthis paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.     

基于SVM 的药物靶点预测方法及其应用

目的:基于已知药物靶点和潜在药物靶点蛋白的一级结构相似性,结合SVM技术研究新的有效的药物靶点预测方法。方法:构造训练样本集,提取蛋白质序列的一级结构特征,进行数据预处理,选择最优核函数,优化参数并进行特征选择,训练最优预测模型,检验模型的预测效果。以G蛋白偶联受体家族的蛋白质为预测集,应用建立的最优分类模型对其进行潜在药物靶点挖掘。结果:基于SVM所建立的最优分类模型预测的平均准确率为81.03%。应用最优分类器对构造的G蛋白预测集进行预测,结果发现预测排位在前20的蛋白质中有多个与疾病相关。特别的,其中有两个G蛋白在治疗靶点数据库(TTD)中显示已作为临床试验的药物靶点。结论:基于SVM和蛋白质序列特征的药物靶点预测方法是有效的,应用该方法预测出的潜在药物靶点能够为发现新的药靶提供参考。     

Detection of dairy fouling: Combining ultrasonic measurements and classification methods

Fouling and cleaning in heat exchangers are severe and costly (up to 0.3% of gross national product) issues in dairy and food processing. Therefore, reducing cleaning time and cost is urgently needed. In this study, two classification methods [artificial neural network (ANN) and support vector machine (SVM)] for detecting protein and mineral fouling presence and absence based on ultrasonic measurements were presented and compared. ANN is based on a multilayer perceptron feed forward neural network, whereas SVM is based on clustering between fouling and no fouling using a hyperplane. When both fouling types (1239 datasets) were combined, ANN showed an accuracy of 71.9% while SVM displayed an accuracy of 97.6%. Separate fouling detection of mineral/protein fouling by ANN/SVM was comparable: dependent on fouling type detection accuracies of 100% (protein fouling, ANN and SVM), and 98.2% (SVM), and 93.5% (ANN) for mineral fouling was reached. It was shown that it was possible to detect fouling presence and absence offline in a static setup using ultrasonic measurements in combination with a classification method. This study proved the applicability of combining classification methods and fouling measurements to take a step toward reducing cleaning costs and time.     

A non-linear model for measuring grapevine leaf thickness by means of red-edge/near-infrared spectral reflectance

Vegetation is a key element of our ecology system. The leaf area and its thickness provide valuable information about the status of our environment. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly assessing leaf parameter in situ. In the past decades, there were lots of work (Boyd et al.) that focused on measurement of leaf area index, but very few on measurement of leaf thickness. In this paper, reflectance of grape leaves was measured over the spectral range of 350–1010 nm. The corresponding thickness of leaves from four grapevine cultivars was also measured as part of seventeen field campaigns undertaken during the summer of 2007. An artificial-intelligence technique, the support vector machine (SVM) model, was introduced to establish the relationship between the leaf thickness and red-edge/near-infrared (NIR) reflectance, with variability examined among individual cultivars as well as at various growth stages. The best wavelengths were variable depending on the grape cultivar and growth stage. The SVM model allows compilation of factors such as cultivar and growth stage with spectral information to yield a superior result.     

A non-linear model for measuring grapevine leaf thickness by means of red-edge/near-infrared spectral reflectance

Vegetation is a key element of our ecology system. The leaf area and its thickness provide valuable information about the status of our environment. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly assessing leaf parameter in situ. In the past decades, there were lots of work (Boyd et al.) that focused on measurement of leaf area index, but very few on measurement of leaf thickness. In this paper, reflectance of grape leaves was measured over the spectral range of 350–1010 nm. The corresponding thickness of leaves from four grapevine cultivars was also measured as part of seventeen field campaigns undertaken during the summer of 2007. An artificial-intelligence technique, the support vector machine (SVM) model, was introduced to establish the relationship between the leaf thickness and red-edge/near-infrared (NIR) reflectance, with variability examined among individual cultivars as well as at various growth stages. The best wavelengths were variable depending on the grape cultivar and growth stage. The SVM model allows compilation of factors such as cultivar and growth stage with spectral information to yield a superior result.     

基于支持向量机的细菌基因组水平转移基因预测

随着各种生物基因组序列测定工作的完成,大量的DNA序列数据涌现出来,为研究在基因组中寻找水平转移基因提供了极大的便利.将基因序列特征分析和支持向量机技术结合起来,通过分析基因序列的特征差异发现水平转移基因.依据以前研究工作的基础,选取了绝对密码子使用频率(FCU)作为序列特征,主要因为它既包含了基因密码子使用偏性的信息,也包含了基因所编码蛋白的氨基酸组成信息,支持向量机利用这些信息进行水平转移基因分析和预测,可以提高预测的准确性.另外,提出了基于分链的水平转移基因预测新方法,即将细菌基因组前导链和滞后链上的基因区别对待,分别进行水平转移基因预测.结果显示,基本预测方法要优于目前预测结果最好的Tsirigos等提出的基于八联核苷酸频率的打分算法,命中率的相对提高率最高达31.47%,而基于分链的方法对水平转移基因的预测取得了更好的结果.     

马拉河流域植被生态需水特征及估算

生态需水是生态用水控制和区域生态环境恢复建设的基本依据。马拉河流域拥有世界著名的生态系统,植被生态需水占流域总需水量的很大一部分。基于1980—2020年ERA5气象数据、叶面积指数(LAI)与世界土壤数据库数据,采用Penman-Monteith法计算了马拉河流域四个季节(短旱季、长雨季、长旱季、短雨季)植被生态需水量的时空变化特征。在此基础上,使用支持向量机(SVM)、随机森林(RF)和卷积神经网络(CNN)3种机器学习方法与7个环境因子(气温、降水、10 m风速、LAI、太阳辐射、相对湿度、地形)建立了回归模型,分别估算了2011—2020年逐年不同季节的植被生态需水量,并与Penman-Monteith法计算结果进行时间序列拟合度和空间相似性的比较。结果表明：马拉河流域植被生态需水量在过去40年所有季节都呈现为波动变化,植被生态需水量长雨季>长旱季>短雨季>短旱季,长雨季的植被生态需水量约为短旱季的1.5倍。不同季节均呈现出上下游高、中游低的植被生态需水量空间分布格局。LAI为最大的正影响因子,风速为最大的负影响因子。就不同方法估算的植被生态需水量准确性而言,...     

Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine

Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen’s self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.     

Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain–computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.     

A deep survival interpretable radiomics model of hepatocellular carcinoma patients

This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients' electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544–0.621), 0.629 (95%CI: 0.601–0.643), 0.581 (95%CI: 0.553–0.613) and 0.650 (95%CI: 0.635–0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.     

不同病因的慢性肾病患者血清氨基酸谱存在显著差异

慢性肾病(chronic kidney disease, CKD)患者血清氨基酸谱发生了显著性变化,但目前并无不同病因的CKD患者血清氨基酸谱的比较研究。本研究主要探究不同病因CKD患者血清氨基酸谱的差异,以及差异氨基酸与肾疾病发生发展的相互关系。选取79例确诊慢性肾病的成年患者,根据其病因,分糖尿病肾病组、高血压肾病组及慢性肾小球肾炎组。另选取25例性别年龄匹配的健康成年人为正常对照组。收集及处理清晨空腹血清标本,采用反相高效液相色谱法（reverse-phase high-performance liquid chromatography, RP-HPLC）测定血清中22种游离氨基酸的水平。结果显示,与正常对照组相比,3组CKD患者血清赖氨酸、丝氨酸、甘氨酸、伽马氨基丁酸(GABA)、色氨酸、亮氨酸以及酪氨酸水平均显著下降（P<0.05）,而血氨水平显著升高（P<0.05）。糖尿病肾病组患者血清苏氨酸水平明显高于其余3组（P<0.05）,而血清天冬氨酸水平显著低于其余3组（P<0.05）。慢性肾小球肾炎患者血清异亮氨酸水平显著低于糖尿病肾病及高血压肾病患者（P<0.05）。上述结果证实,慢性肾病患者血清氨基酸谱较正常对照发生显著变化,且不同病因CKD患者部分血清氨基酸水平存在显著差异。其中,色氨酸水平的差异可能是不同病因CKD患者肾功能恶化速度不一致的原因之一。     

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods,Datasets, Libraries,and Future Directions

ObjectivesAccurate and reliable segmentation of brain tumors from MRI images helps in planning an enhanced treatment and increases the life expectancy of patients. However, the manual segmentation of brain tumors is subjective and more prone to errors. Nonetheless, the recent advances in convolutional neural network (CNN)-based methods have exhibited outstanding potential in robust segmentation of brain tumors. This article comprehensively investigates recent advances in CNN-based methods for automatic segmentation of brain tumors from MRI images. It examines popular deep learning (DL) libraries/tools for an expeditious and effortless implementation of CNN models. Furthermore, a critical assessment of current DL architectures is delineated along with the scope of improvement.MethodsIn this work, more than 50 scientific papers from 2014-2020 are selected using Google Scholar and PubMed. Also, the leading journals related to our work along with proceedings from major conferences such as MICCAI, MIUA and ECCV are retrieved. This research investigated various annual challenges too related to this work including Multimodal Brain Tumor Segmentation Challenge (MICCAI BRATS) and Ischemic Stroke Lesion Segmentation Challenge (ISLES).ResultAfter a systematic literature search pertinent to the theme, we found that principally there exist three variations of CNN architecture for brain tumor segmentation: single-path and multi-path, fully convolutional, and cascaded CNNs. The respective performances of most automated methods based on CNN are appraised on the BraTS dataset, provided as a part of the MICCAI Multimodal Brain Tumor Segmentation challenge held annually since 2012.ConclusionNotwithstanding the remarkable potential of CNN-based methods, reliable and robust segmentation of brain tumors continues to be an intractable challenge. This is due to the intricate anatomy of the brain, variability in its appearance, and imperfection in image acquisition. Moreover, owing to the small size of MRI datasets, CNN-based methods cannot operate with their full capacity, as demonstrated with large scale datasets, such as ImageNet.     

Di-codon usage for classification of genes

Genes are often classified into biologically related groups so that inferences on their functions can be made. This paper demonstrates that the di-codon usage is a useful feature for gene classification and gives better classification accuracy than the codon usage. Our experiments with different classifiers show that support vector machines performs better than other classifiers in classifying genes by using di-codon usage as features. The method is illustrated on 1841 HLA sequences which are classified into two major classes, HLA-I and HLA-II, and further classified into the subclasses of major classes. By using both codon and di-codon features, we show near perfect accuracies in the classification of HLA molecules into major classes and their sub-classes.