Beyond labels: A review of the application of quantum dots as integrated components of assays, bioprobes, and biosensors utilizing optical transduction

A comprehensive review of the development of assays, bioprobes, and biosensors using quantum dots (QDs) as integrated components is presented. In contrast to a QD that is selectively introduced as a label, an integrated QD is one that is present in a system throughout a bioanalysis, and simultaneously has a role in transduction and as a scaffold for biorecognition. Through a diverse array of coatings and bioconjugation strategies, it is possible to use QDs as a scaffold for biorecognition events. The modulation of QD luminescence provides the opportunity for the transduction of these events via fluorescence resonance energy transfer (FRET), bioluminescence resonance energy transfer (BRET), charge transfer quenching, and electrochemiluminescence (ECL). An overview of the basic concepts and principles underlying the use of QDs with each of these transduction methods is provided, along with many examples of their application in biological sensing. The latter include: the detection of small molecules using enzyme-linked methods, or using aptamers as affinity probes; the detection of proteins via immunoassays or aptamers; nucleic acid hybridization assays; and assays for protease or nuclease activity. Strategies for multiplexed detection are highlighted among these examples. Although the majority of developments to date have been in vitro, QD-based methods for ex vivo biological sensing are emerging. Some special attention is given to the development of solid-phase assays, which offer certain advantages over their solution-phase counterparts.     

螺旋CT成像联合血清copeptin、IL-18水平检测在急性脑梗死患者神经功能及预后评估中的应用价值

本文选取74例急性脑梗死(ACI)患者作为研究对象,入院时根据美国国立卫生院卒中量表(NIHSS)评分分为重度组(NIHSS评分＞15分,n=21)、中度组(NIHSS评分5?15分,n=24)、轻度组(NIHSS评分＜5分,n=29),均接受血清copeptin和IL-18水平检测及螺旋CT成像检查.结果发现,随AC...     

钨合金柱形弹超高速撞击水泥砂浆靶的侵彻深度研究

为探索钨合金柱形弹超高速撞击水泥砂浆靶的侵彻深度随撞击速度变化规律,利用二级轻气炮开展了?3.45 mm×10.5 mm的克级93 W钨合金柱形弹以1.82~3.66 km/s的速度撞击水泥砂浆靶的实验,利用CT图像诊断技术获得了侵彻深度和残余弹长随撞击速度的变化规律,对超高速撞击过程进行了数值模拟,结合数值模拟结果进一步分析了超高速撞击物理过程。结果表明:(1)超高速撞击条件下成坑是弹坑+弹洞型;(2)侵深-速度曲线呈现先增大后减小的现象,在弹速2.6 km/s附近存在侵彻深度极大值,约为8.5倍弹长,相对于中低速侵彻的深度并没有显著优势。(3)通过基于数值模拟得到的弹靶界面压力时程曲线将侵彻过程分为4个阶段,其中准定常侵彻阶段和第三侵彻阶段是决定总侵深的主要阶段。(4)随撞击速度增加,弹体侵蚀逐渐剧烈,此时准定常侵彻阶段的侵深变化不大,而第三侵彻阶段中的刚体侵彻部分大幅降低,导致总侵深大幅降低,使总侵深曲线呈现先增大后减小的现象。     

Luminescence of Molecules with Internal Charge Transfer upon Longwave Excitation

We have studied the spectral properties of luminescence of laurdan molecules in glycerin upon excitation at the red edge of the absorption band at different temperatures. The most significant red-wave shift of the spectra (10 nm) for the longwave band of dual fluorescence is observed depending on the excitation wavelength at a low temperature of 260 K when a solvent forms a fairly rigid matrix. At the same time, at increased temperatures of up to 370 K a small bathochromic shift and a change in the shape of the luminescence bands are also recorded reliably. Changes in the excitation spectra were observed when luminescence was recorded in the bands of the LE- and CT states. The difference spectrum responsible for the additional absorption that does not make a contribution to the longwave luminescence component has been isolated. The decay kinetics of both luminescence components have been measured and their expansions in decay constants have been analyzed. The experimental dependences obtained point to the complex mechanism of inhomogeneous broadening of spectra.     

CT重构中射线硬化的校正研究

在工业CT(ComputedTomography)重构中,由于射束硬化使得重建图像中出现"杯状"伪影。为了消除这种影响,提出了一种校正方法。该方法基于Beer理论,根据多色射束和数据,首先拟合出等效单色射束和数据,然后进行卷积反投影重构。这种方法不仅较好地消除了射线硬化的影响,同时也使重构图像的信息损失不大。     

基于X射线断层成像的逆向工程中轮廓序列分割算法的原理及实现

为了实现基于X射线断层成像(CT)的逆向工程中具有参数识别的三维图像重构, 提出了一种分割轮廓序列的新算法. 首先通过一定角度的射线法来得到轮廓间的嵌套关系,然后采用扫描一次关系矩阵生成轮廓树的方法实现层内轮廓定位, 最后运用轮廓间定量、定性的属性判定来完成层间的轮廓匹配. 通过实例, 本文提供的算法可以准确、快速地分割CT零件中的轮廓序列.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

MCNP Dose Calculations in a CT Phantom for Therapeutic External Photon Beam

In this paper, we have addressed the problem of the radiation transport with the Monte Carlo N particle(MCNP) code. This is a general purpose Monte Carlo tool designed to transport neutron, photon and electron in three dimensional geometries. To examine the performance of MCNP5 code in the field of external radiotherapy, we performed the modeling of an Electron Density phantom (EDP) irradiated by photons from 60Co source. The model was used to calculate the Percent Depth Dose (PDD) at different depths in an EDP. One field size for PDD has been examined. A 60Co photons source placed at 80 cm source to surface distance (SSD). The results of calculations were compared to TPS data obtained at National Institute of Oncology of Rabat.     

螺旋锥束CT中两种解析重建算法的研究

 研究了锥束螺旋Katsevich和FDK重建算法,并对这两种算法进行比较。实验结果表明：当投影数据没有噪声的时候,FDK算法和Katsevich算法均能取得较好的效果,当对实际物体进行重建的时候,投影数据含有噪声, Katsevich算法需要对投影数据求导,而它对投影数据的噪声较敏感,重建质量有所下降。