Lateralized effects of hand and eye on anticipatory postural adjustments in visually guided aiming movements

This study investigated hemisphere-specific processing of visually aimed movements and associated postural adjustments while controlling for handedness and eyedness. Eleven right-handed, right-eyed and right-footed healthy adult volunteers performed, from a standing position, an aiming task under two hand (right and left hand) and three visual conditions (binocular vision, right and left eye monocular vision). Centre of pressure (CoP) displacement, hand kinematics and the target's position were synchronously recorded during performance of the aiming task. Analysis revealed a lower RMS error, a later postural adjustment onset and a smaller centre of pressure dispersion when aiming was performed with the dominant right compared to the non-dominant left hand. On the other hand, no differences on either aiming performance or postural adjustments were noted under the three visual conditions. These results suggest a strong handedness and absence of an eyedness effect on the accuracy of aiming and associated postural adjustments.     

基于免疫磁珠法的自动化粪便脱落细胞提取仪的研制与应用

目的 根据免疫磁珠法的原理设计研制自动化粪便脱落细胞提取仪,实现大肠癌脱落细胞的自动化富集.方法 根据粪便组分、理化特性以及粪便脱落细胞分布特点,设计制造包括粪便样本采集装置、粪便样本预处理器、脱落细胞-免疫磁珠结合装置和脱落细胞-免疫磁珠复合物洗涤装置的自动化粪便脱落细胞提取仪.采用抗大肠癌特异表面抗原表皮细胞黏附分子(EpCAM)的单抗Ber-EP4包被至磁珠表面制备免疫磁珠,用于从细胞培养物中富集大肠癌细胞,并在显微镜下观察免疫磁珠与大肠癌细胞的结合效果.收集10例大肠癌患者的癌组织(或癌旁组织),并应用自动化粪便脱落细胞仪提取患者粪便样本的脱落细胞,应用实时荧光定量PCR检测癌组织(或癌旁组织)以及粪便样本的脱落细胞肿瘤相关基因c-myc、cox-2和CD44v6的表达,并对比两种不同来源的样本检出的3种基因的阳性表达率.结果 设计制作了自动化粪便脱落细胞提取仪样机,并且结合自行制备包被特异抗体的免疫磁珠成功提取到直肠癌细胞,细胞回收率达27%.在10例癌组织或癌旁组织中,c-myc、cox-2、CD44v6基因的阳性表达率分别为20%、10%、20%,而在粪便脱落细胞中均为100%,差异有统计学意义(均P＜0.01).自动粪便脱落细胞仪提取的患者粪便样本的脱落细胞为肿瘤细胞.结论 利用免疫磁珠富集大肠癌脱落细胞是可行的,以此为依据制作的自动化脱落细胞提取仪可以用于临床大肠癌的早期筛查.     

ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user‐dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact‐specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event‐related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal.     

基于颜色与纹理的痤疮痧象证型自动分类初探

目的 走罐是中医治疗系统中一种非药物治疗手段.痧象是走罐后在背部显现的图像,含有很多疾病的信息,患者的证型就是其中一种.本文主要完成3种痤疮痧象证型的自动分类.方法 首先分析中医走罐后,背部痤疮痧象图像的特点,然后对7个穴位模块提取Tamura纹理特征,同时针对背部整体痧象图像提取Tamura纹理特征和颜色特征,再通过支持向量机分类方法进行分类.结果 提取的痧象特征可以较好地完成湿热、心火炽盛、脾气虚三种痧象证型的自动分类.结论 痤疮痧象自动分类具有可行性,为中医客观诊断和验证打下初步基础.     

一种基于小波变换的配准特征点自动标记算法

以颅脑CT图像为研究对象,提出了一种基于小波变换的自动标记非刚性配准所需对应特征点的算法.这种算法充分考虑了颅脑CT图像的像素点及其临域的纹理特征,通过进行小波变换建立对应于每个像素点的多分辨率小波特征向量,并以小波特征向量间的差异作为判别依据,在目标图像中标记非刚性配准所需的对应特征点.一系列的实验结果表明,这种基于小波变换的算法能够准确地在目标图像中标记出配准所需的对应特征点,可以作为基于特征的非刚性配准对应特征点自动标记的参量之一.     

基于经血管介入的预测自动推药动物实验研究

本文着重讨论了经血管介入的预测自动推药动物实验,研究了动物实验中存在的参数时变性与个体差异性,改进、完善了体外脉动流模拟实验中的模型和算法,增强了模型的时变适应性及容错性。实验结果表明,在心舒张期推药较之收缩期推药,靶目标血药浓度更高;经改进的与心动周期同步的预测控制推药方法,可进入临床试验。     

A framework for diagnosis of urinary incontinence disease based on scoring measures and automatic classifiers

This work develops a decision support system based on machine learning and scoring measures to determine the type of urinary incontinence in women with low urinary tract symptoms. This system has two main branches. The former consists of selecting the feature set which best defines the UI type from the set of features (age, weight, etc.) characterizing a patient. This feature set is computed from several scoring measures. The patients characterized by the optimum feature set are then classified according to C4.5 and SVM classifiers. The results are evaluated according to Sensitivity and Specificity evaluation measures. The management of the final system is simple and its performance is high, getting Sensitivities over 80% and Specificities near 100% for some configurations.     

神经外科导航系统基于迭代局部最近点法技术的形态学三维数据场的快速配准与融合

目的：拟开发一种全自动快速配准技术,用于临床形态学三维数据场的配准与融合。方法：用本研究提出的基于迭代局部最近点法（ILCP）技术的形态学三维数据场的快速配准技术,用临床实际病例的CT、MRI图像进行配准实验。结果：实验得到了满意的全自动快速配准效果。结果显示ILCP法比普通ICP法快10-30倍,比最大互信息法快8～10倍;耗时可以为临床医生所接受。结论;所提出的方法是切实可行的,为进一步应用于临床打下了坚实的基础。     

基于有限训练样本的融合网络模型用于盆腔危及器官自动分割的研究

将深度学习应用到医学影像中危及器官自动分割领域时,为解决训练样本不足时三维卷积神经网络优化出现的退化、梯度消失等问题,本研究将Dense Net与V-Net两个网络模型进行融合,开发一种用于三维计算机断层扫描(CT)图像自动分割的Dense V-Network算法,勾画女性盆腔危及器官。采用戴斯相似性系数(DSC)、豪斯多夫距离(HD)、杰卡德距离(JD)三个参数来定量评估分割效果。结果显示膀胱、小肠、直肠、股骨头和脊髓自动分割的DSC值均在0.87以上(平均值是0.9);JD值均在2.3以内(平均值是0.18);除小肠外,HD值均在0.9 cm以内(平均值是0.62 cm)。经验证,Dense V-Network网络可精准地勾画盆腔危及器官。     

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.     

基于MEMS传感器的可穿戴式老年人跌倒监测系统的设计

为了缩短老年人跌倒后的救助时间和提高安全保障,我们设计了一种基于加速度和角速度传感器的跌倒监测装置。该装置结合机械微电子系统(microelectro mechanical system,MEMS)传感器、数字信号处理及无线传输技术应用于智能手机上。为区分人体跌倒事件和日常行为,结合人体三轴加速度和角速度联合对跌倒事件进行判断,通过大量实验选取合适阈值。针对老年人的生活特点,数据通过蓝牙装置发送至手机上进行处理,通过手机语音报警、手机GPS定位系统和短信通知医院和用户监护人,使得老年人跌倒后能够在第一时间获得救治,减小伤害。经过大量的实验测试结果统计,相比较于传统的三轴加速度判断跌倒事件,结合三轴加速度和加速度联合判断使该系统的准确率从84.29%提高至100%。结果表明:基于MEMS传感器的可穿戴式跌倒监测系统具有便捷、准确、功耗低的特点,能够准确监测出跌倒事件并进行报警。     

Nano-assemblies of J-aggregates based on a NIR dye as a multifunctional drug carrier for combination cancer therapy

The combination of chemotherapy with photothermal therapy, which may lead to improved therapeutic efficacies and reduced side effects of conventional chemotherapy, would require safe drug delivery systems (DDSs) with strong near-infrared (NIR) absorbance, efficient drug loading, and effective tumor homing ability. Herein, we fabricate nano-assemblies containing J-aggregates of a NIR dye, IR825, for drug delivery and combined photothermal & chemotherapy of cancer. It is found that IR825 could be complexed with a low-molecular-weight cationic polymer polyethylenimine (PEI), forming IR825@PEI J-aggregates with greatly enhanced NIR absorbance red-shifted to 915 nm. Those nano-assemblies of J-aggregates are further modified with polyethylene glycol (PEG), obtaining IR825@PEI-PEG nano-complex which exhibits great dispersity in physiological solutions, excellent photostability, and is able to efficiently load chemotherapeutic drug doxorubicin (DOX) via a unique strategy different from drug loading in conventional amphiphilic polymer-based DDSs. In vivo animal experiments uncover that IR825@PEI-PEG/DOX upon intravenous injection into tumor-bearing mice shows rather high tumor uptake as illustrated by photoacoustic imaging. In vivo combined photothermal & chemotherapy is then carried out, demonstrating great synergistic anti-tumor therapeutic effect remarkably superior to those achieved by the respective mono-therapies. Hence, we present a novel type of nanoscale DDSs based on nano-assemblies of small molecules without involving amphiphilic polymers, promising for imaging-guided combination cancer therapy.     

SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information

Interictal spike detection is a time-consuming, low-efficiency task, but is important to epilepsy diagnosis. Automated systems reported to date usually have their practical efficacy compromised by elevated rates of false-positive detections per minute, which are caused mainly by the influence of artifacts (such as noise activity and ocular movements) and by the adoption of single or simple approaches. This work describes the development of a hybrid system for automatic detection of spikes in long-term electroencephalogram (EEG), named System for Automatic Detection of Epileptiform Events in EEG (SADE(3)), which uses wavelet transform, neural networks and artificial intelligence procedures to recognize epileptic and to reject non-epileptic activity. The system's pre-processing stage filters the EEG epochs with the Coiflet wavelet function, which showed the closest correlation to epileptogenic (EPG) activity, in opposition to some other wavelet functions that did not correlate with these events. In contrast to current attempts using continuous wavelet transform, we chose to work with fast wavelet transform to reduce processing time and data volume. Detail components at appropriate decomposition levels were used to accentuate spikes, sharp waves, high-frequency noise activity and ocular artifacts. These four detailed components were used to train four specialized neural networks, designed to detect and classify the EPG and non-EPG events. An expert module analyzes the networks' outputs, together with multichannel and context information and concludes the detection. The system was evaluated with 126,000 EEG epochs, obtained from seven different patients during long-term monitoring, under diverse behavior and mental states. More than 6,721 spikes and sharp waves were previously identified by three experienced human electroencephalographers. In these tests, the SADE(3) system simultaneously achieved 70.9% sensitivity, 99.9% specificity and a rate of 0.13 false-positives per minute, indicating its usefulness and low vulnerability to artifact influence. After tests, the SADE(3) system showed itself to be able to process bipolar cortical EEG records, from long-term monitoring, up to 32 channels, without any data preparation or event positioning. At the same time, SADE(3) revealed a high capacity to reject non-epileptic paroxysms, robustness in relation to a variety of spike morphologies, flexibility in adjustment of performance rates and the capacity to actually save time during EEG reading. Furthermore, it can be adapted to other applications for pattern recognition, with simple adjustments.     

A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case

Eye movement artifacts represent a critical issue for quantitative electroencephalography (EEG) analysis and a number of mathematical approaches have been proposed to reduce their contribution in EEG recordings. The aim of this paper was to objectively and quantitatively evaluate the performance of ocular filtering methods with respect to spectral target variables widely used in clinical and functional EEG studies. In particular the following methods were applied: regression analysis and some blind source separation (BSS) techniques based on second-order statistics (PCA, AMUSE and SOBI) and on higher-order statistics (JADE, INFOMAX and FASTICA). Considering blind source decomposition methods, a completely automatic procedure of BSS based on logical rules related to spectral and topographical information was proposed in order to identify the components related to ocular interference. The automatic procedure was applied in different montages of simulated EEG and electrooculography (EOG) recordings: a full montage with 19 EEG and 2 EOG channels, a reduced one with only 6 EEG leads and a third one where EOG channels were not available. Time and frequency results in all of them indicated that AMUSE and SOBI algorithms preserved and recovered more brain activity than the other methods mainly at anterior regions. In the case of full montage: (i) errors were lower than 5% for all spectral variables at anterior sites; and (ii) the highest improvement in the signal-to-artifact (SAR) ratio was obtained up to 40dB at these anterior sites. Finally, we concluded that second-order BSS-based algorithms (AMUSE and SOBI) provided an effective technique for eye movement removal even when EOG recordings were not available or when data length was short.     

Accuracy of an immune diagnostic assay based on RD1 selected epitopes for active tuberculosis in a clinical setting: a pilot study

A previous case-control study reported that an in-vitro interferon (IFN)-γ response to early secreted antigenic target (ESAT)-6 selected peptides was associated with active tuberculosis (A-TB). The objective of the present pilot study was to evaluate the diagnostic accuracy of this assay for TB disease in a clinical setting. An IFN-γ ELISPOT assay was performed on samples from patients with suspected A-TB using two peptides selected from ESAT-6 protein and three peptides selected from culture filtrate 10 (CFP-10) proteins. The results were compared with those obtained by two commercially available assays approved for diagnosis of TB infection (T SPOT-TB and QuantiFERON-TB Gold) which use ESAT-6/CFP-10 (RD1) overlapping peptides. Sensitivity to the RD1 selected peptides was 70% (positive for 16 of 23 patients with microbiologically diagnosed A-TB) and specificity was 91% (positive for three of 32 controls). In contrast, the sensitivity and specificity were 91%and 59%, respectively, for T SPOT-TB, and were 83% and 59%, respectively, for QuantiFERON-TB Gold. The RD1 selected peptides assay had the highest diagnostic odds ratio for A-TB. Thus, the results suggest that an assay based on RD1 selected peptides has a higher diagnostic accuracy for A-TB in a clinical setting compared with commercially available assays based on RD1 overlapping peptides.     

A decision support system based on support vector machines for diagnosis of the heart valve diseases

In this paper, a decision support system that classifies the Doppler signals of the heart valve to two classes (normal and abnormal) is presented to support the cardiologist. The paper uses our previous paper where ANN is used as a classifier, as feature extractor from measured Doppler signal. To make this, it uses wavelet transforms and short time Fourier transform methods. Before it classifies these features, it applies Wavelet entropy to them. In this paper, our aim is to develop our previous work by using least-squares support vector machine (LS-SVM) classifier instead of ANN. We use LS-SVM and backpropagation artificial neural network (BP-ANN) to classify the extracted features. In addition, we use receiver operator characteristic (ROC) curves to compare sensitivities and specificities of these classifiers and compute the area under the curves. Finally, we evaluate two classifiers in all aspects.