这项工作是支持批准号P50NS071675的DE从国家神经紊乱和中风研究所（莫里斯·尤德尔帕金森氏病研究的卓越中心以及Feinstein医学研究所）。内容完全是作者的责任，并不一定代表美国国立神经疾病与中风或美国国立卫生研究院的官方意见。保荐人没有发挥作用，在研究设计，收集，分析和解释数据，撰写报告或在决定提交论文的发表。

1。数据的收集和预处理<ol><li>该SSM / PCA的方法可以被应用于来自的各种来源和方式的获得的，以单一的的磁碟区影像的。具体而言，对于上的-现场的PET显像对的新陈代谢中，准备一个合适的的的放射性核素的示踪剂例如为[18 F]-氟脱氧葡萄糖脱氧葡萄糖（FDG），，和管理，以每一个病人。的患者通常是在休息扫描的与眼睛睁得大大，在至少12小时一个快速的的后，，关的药物。 </li><li>扫描个人或一组的评估的每个主题为。如需“格局的推导”中，扫描对性别问题-和年龄-匹配的的的患者和对照组的的的一个相等数量的。 </li><li>中传输一些数据，到一台工作站，，，，并将其转换在你的的平台上以供分析为适当的格式。我们的Windows PC为基础的：MATLAB的分析软件（scanvp，ssm_pca， <a href="http://www.feinsteinneuroscience.org" target="_blank">www.feinsteinneuroscience.org</a> ）的需要ANALYZE或nifti格式的的图像（梅奥诊所的，罗切斯特，MN的）。它提供了了一个转换例程的Ţ：Ø变换GE垫款（密尔沃基，WI的，USA）扫描仪和其他的格式的的图像，以ANALYZE的格式。 </li><li>每个主题的形象正常化到一个共同的的脑立体定位的空间（ 例如：蒙特利尔神经学研究所[MNI的]），使用的一个标准的的（，如的统计的参数化映射（SPM））的的神经影像学检查软件程序包（ <a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://www.fil.ion.ucl.ac.uk/spm中</a> ），所以，，有是一个one-to -酮的的对应的子民与子民之间的体素值（ 图2）。美纹纸，以限制分析，以的的灰质领域（ 图3）和日志转型顷中描述的接下来的步骤中。 </li></ol> 2。执行多变量的SSM / PCA的进行多变量的基于SSM / PCA（ 图4）的操作可以执行由外部软件。的步骤下文分项列示反映执行的的程序为在大多数的一部分下会自动由我们的在-的房子例程（ 图5）（ 扫描VP / ssmpca， <a href="http://www.feinsteinneuroscience.org" target="_blank">www.feinsteinneuroscience.org</a>也显示有空房的作为一个SPM工具箱ssm_pca）。 <ol><li>使用一个可用的的0/1的图片此的的面膜数据以消除不必要的的领域的的体素的空间（，如脑白质及白质脑室的）。使用该ssmpca例程，用户将被给定的一个选项，以中选择的默认情况下或其他的外部掩码（ 图3），，或，以不得不的程式自动地建立一个面具，通过去除较低比一个选定的固定的的的每个主题的数据的最大中的百分之的值。然后，个别的主题口罩都相乘，以确定一个复合的面具。在的掩模之内的的领域定义了常见的一种非-零体元空间，用于分析。 </li><li>三维上的汇率换算每个检者的屏蔽的图像，体素数据，以一个连续的的的行向量，通过追加从连续的的平面的的表顺序扫描，线（ 图4a）。形成一组数据矩阵（D），以便，每个主题的数据对应到一个特定的R嗷嗷：的基体中。每个Column，然后选择代表一个特定的的的跨科目的体素的。理想的情况下，对于生物标志物的的推导中时，矩阵将被组成的一个的正常的控制的主题的的数据和疾病的的主题数据的的的行的相等数目的。 </li><li>变换每个的数据条目，以对数的形式。 </li><li>中心的数据通过中减去的每一行平均水平或主题的的矩阵的意思是从的行元素中的。的所有为中心的的行的的平均代表的特性基是什么意思日志映像的映像被称为一组均值设定档（ 的GMP，图4a）。中减去的的级的GMP的元素从相应的的矩阵column元素的顷的的列装置​​，，装置。的双重的为中心的矩阵的每一行都代表一个&#39;残余的&#39;的的形象被称为一个主题的残余设定档（SRP），，其代表从双方的主体s及体素的V族装置（ 图4b）的偏差的元素。 SRP SV = LOGD SV -的意思S - GMP v </li><li>构建各方，不对译文的准确性，ECT-受试者由-协方差矩阵 C的的复合的双通过计算的非-每个主题的线对 i 的归一化之间的协方差时的数据中心化的矩阵中，J的SRP矩阵的行的作为一个产品的超过所有的体素v总结出的相应的的体素的元素进行评估（ 图4b） 。 ÇIJ =ΣV（SRP IV所述SRP JV） </li><li>套用PCA到的主题-由-须受到的的协方差矩阵 C。该的结果将是与相关联的的本征值的的主题的得分的特征向量了一套。赋权的每个向量，通过乘以由的平方的根目录中公司相应的的的本征值。上表示的得分特征向量的提取的一组被由所得到的矩阵 S的列[^]在图4c的中 。 </li><li>统为可以在矩阵的特征值的相同的一组的体素的的特征向量通过以下来确定上施加PCA +，以柱中用柱的协方差矩阵，或由这种替代计算上减要求苛刻的的的程序，URE描绘在（ 图4c）。左路乘以的先前的由来的的的由的转置建议零售价矩阵的的得分矢量矩阵的情况，，以派生在按降序请订购矩阵的特征值（ 图4c，图6，图7a）时的体素的的柄的特征向量一个数组P于。 ，P = SRPŢX S 每一个的列向量代表的一个本金组成部分（PC）的的图像图案的SSM /主成分分析（PCA）的归因于一个的总方差的百分之占为（VAF）对应到的相对的它的的本征值大小的。 </li></ol> 3。图案生物标志物的推导<ol><li>检查的的查询结果。如前述的的分析，以确定具有高VAF的值的相关联的的的的个人电脑型态，顷。我们的的例程以内，体素的模式向量是的Z-转化的，以便，他们的的价值观代表正面和负面的的标准的从他们的平均值的偏差。然后，他们正在重塑了成图像格式的，在显示之前（ 图7a）。 </li><li，&gt;在某些情况，与这个疾病相关的所在地区公知的的偏差（RSD）的型态与可以目视被，识别。的的数学制剂认为作为在这种情况下，的相等于的解决方案的两个的个人电脑“和他们的相关联的的破门得分由零下的一方，，以符合到一个物理上正确的的的解释的，可能被乘以的派生的个人电脑的正面和负面的的形式的。 </li><li>对应到每一台PC模式的的的分数被显示为棒图和散点图（ 图7）的中 。一个可选的的ROC情节可以被生成的（ 图7d）。若要识别的一种疾病-特定的的图案中，注意到反映的相应的的两样本t-测试和AUC的值的由的p-部分的的值的患者和对照组的科目之间的恩怨的分化程度。限制本分析，以那些与一个高的的VAF和为一些固定的的截止的值（ 例如：，对&lt;0.05和VAF&gt; 5％）的高的的分化相关联的的个人电脑。通常情况下，只有一个或两个台个人电脑满足该标准，的，，用于PET的的数据。 </li><li>有是VARI适用于PC的选择是可以考虑的44的 OU的其他的的方式。在那里本征值开始退化时，的流石滩的情节的序贯的特征值（ 图6）可能会给予一个尖锐的的的表示的的截止值由一个弯位在的曲线中，并一个尖锐的的减少在的曲线的斜率中的。另一种的做法是，以包括所有，占为顶级的的方差的的50％的的的个人电脑。一条被广泛接受的的程序是要计算的阿凯克的信息准则（AIC）45，以确定的个人电脑的哪个组合定义该机型配备的最低的AIC值，，可以区分患者和对照组之间的的。 </li><li>所选择的的PC（s）可以是的矢量归一化的，并呈线性相结合，以产生一个单一的的与疾病-相关的的图案。我们的软件可选地使用的MATLAB函数glmfit，，，以确定通过基于上的后勤事项或其他的应用到学科的分数的回归模型的出来的系数。虽然的病人组和对照组的的分化通常提高了与在去的考虑的额外的PC设备rivation组，将得到的的的型态顷一个复合的的的表示形式，，表示形式，可能会无法的通讯到一个单一的的的物理网络或者“可能会纳入离群值的偏差（ 图7a和图7c的的 ）。 </li><li>进一步的验证是必需的可靠性和的前瞻性重要的意义。可以执行Bootstrap的重采样的，如下面所讨论8，以确定的最可靠的的的的体素在的原来的的的的推导与的至少为标准的在的重复的花纹的推导中的偏差相关联的的数据集，之内。正向，验证被执行，以测试为的敏感性和特异性的独立的群体的歧视，通过派生使用的单一的-学科的的得分的评价方法（ 图4d）在接下来的分部该协议的描述的的患者和对照组的的准群体的的比分为。 </li></ol> 4。单-学科的的的前瞻性分数评价使用一个预定的的生物标志物的<ol><li>一旦一个显着性的的SSM-GIS生物标志物的格局已被确定，一个的得分为及其在一项前瞻性主题的中的的表达可以进行评估从那个从个人的扫描，并使用一个简单的的计算的内部中断向量的商品被检者的建议零售价，矢量和所述地理信息系统图案矢量（ 图4d次，图7d）时中。 SCORE =（乐谱）SRPS•病毒码</li><li>先前操作的是自动化的由我们的的的TPR例行程序中。然而，为了独立地派生的主题的的SRPS矢量使用相关联的的内在的的GIS掩码上的日志变换后的数据，，并减去双方的的的主题的均值和在步骤2.4中，的相应的体素的值的的prederived的参考组的GMP作为。这确保了的得分情况可使用相比，到所预定的的的的参考值范围。一个新的组的遇分数核给可以被同样地进行评价作为一组的准的单一的主题破门得分。对于一个不同的的参考群组与参数设定的一一起使用，GMP，而模式是不变，可以重新校准。 </li></ol>

博士艾德伯格是列上使用的成像标记筛选神经系统功能不全的患者（不包括财务收益）的两项美国专利的共同发明者。有没有进一步的披露。

SSM / PCA模型最初提出默勒等 4已经演变成一个简单和强大的神经影像学数据分析技术1-3。然而，已经有含糊之处，在应用这种方法，我们试图在这里澄清，并在以前的出版物5-7,10。其中一些问题已得到解决在文本中，但在这里再次强调，因为他们的重要性。 SSM / PCA的详细介绍，主要是在休息状态FDG PET数据有效，但也已经成功地使用其他单量成像技术和扫描平台，?...

神经退行性紊乱的已被广泛研究，本地化和量化异常的的大脑代谢作为以及作为非-推理的的那项研究区域相互作用】17的方法的的的使用技术。 （，如本金成分分析（PCA）1,2,4,18和独立分量分析（ICA）19,20，如以及作为监督的的技术（，如偏至少的广场（PLS）21和序数的发展趋势规范的））的以数据-驱动的的的的多变量的分析战略变元分析（ORT / CVA）22不只可以揭示的特征的的的图案或的相互关联的活动的“网络”。的多变量的的的程序，尤其是缩放后的的子配置文件模型（SSM）的的基础知识1,2,4-6,18先前已经描述在朱庇特的3中 。这家基于PCA-的做法被最初开发它来审查异常的功能性的协方差大脑区域之间的的关系，在的脑的血流量和新陈代谢ACQ的的稳定-状态的单一的的磁碟区影像中uired在静息状态的（如PET和SPECT），表现出高的的信号-，以-噪声的特性的网站的详细的方式，。疾病-特定的的的的SSM图案是反映了整体的的差异在的区域的地形中在予正常的科目7,16相比，，并可以反映一个单一的网络过程或的同化的几个复杂的的异常的功能23的患者中的成像生物标志物，。代谢的协方差图案大脑网络被相关联的与，可以区分正常的组和疾病的组之间的的，，，并提供（与临床的疾病的严重程度的评级吻合）的基于网络-的措施，，的的表达式的值（学科的分数）。通常情况下，这样的模式的科目比分为会增加与疾病进展，，，，并可能甚至可以被表达的在症状，发病时间之前14,24。事实上，疾病关连的的的网络的生物标志物已被被其特征在于为神经退行性疾病的（，如帕金森氏病的10条 （PD）的），亨廷顿舞蹈症（Huntington的病25（“房署”），并阿耳茨海默氏的疾病的8）的 </“燮”&gt;“（AD）的。更重要的是，与疾病-相关的的的代谢拓朴图已经也被确定为非典型的帕金森病的的运动障碍性疾病，，如多个系统萎缩（，MSA）和渐进式的性核上性上性麻痹（PSP）的。这些模式已被用来在演唱会为个人的鉴别诊断中具有临床上相似的“，看看-的一致好评”的证候12,13,26。 在此相反，典型的功能磁共振成像以体素的-基础的的的单变量的方法评估在孤立的的大脑集群在患者和对照组之间的的差异的重要意义。更多最近的是，的方法已被开发来衡量功能性的之间的连通性各种不同的定义的的大脑区域27日至29日 。功能连通性了这个定义评论的被受限制，以〔人〕遭受和地区的，特定的的的相互作用和偏离从原来的的SSM /主成分分析的概念，是指，以的的内在的空间上分布的的的大脑网络区域1,2,23,30的横截面的互联互通。要他们的的的优势，MRI检查平台一个重新很容易地安装，广泛可用的，非侵入性和通常会需要更短的的的传统的放射性示踪剂（如PET或SPECT）导致在一个在最近的文献的描述的潜在的的方法的热潮中的的成像方式的扫描的时间比。然而，由此产生的的依赖于时间-的的fMRI的信号提供了局部神经的活性31,32的的间接措施的。的一般复杂的的分析受雇于的算法已被由的大型的大小（的的数据集），的生理的的噪声fMRI信号中固有的的，如以及作为的高的的的的可变性在的大脑活动中，之间存在着科目和地区的19,23的限制。虽然有趣的的了解关于中的大脑组织可以的详细资讯，请参阅被方面的推断从功能磁共振成像的“网络”的属性，他们还没有一直足够的稳定，以被用来作为可靠的疾病的的生物标志物。此外，本因此而产生之的的网络拓朴图是不一定是相等于到使用已建立的的功能成像（例如作为SSM范围/ PCA）的方法的那些已识别的。对于THë键的大部分的的一部分，严谨的的跨-验证的的由此产​​生的的fMRI拓朴图的一直缺乏与在从单一的情况下，的的的前瞻性的扫描数据中的的派生图案的成功的向前的应用程序的的几个例子。 的PCA协方差分析的一个优点就在于在其能力，以找出的最显着的的的的数据变异来源在的第一个的的的少数几个主组件，，，但它是无效的，如果的突出的特征向量代表，而比实际的的的的内在的的网络响应的的的随机噪声因素的。通过选择只有成为第一个的几年的的特征向量，并。限制，以那些，显示在耐心相对于正常的的控制分数线的中的显着的的差异的的的中，我们大大降低其他的噪声元素的的影响力。然而，对于的基本的此处描述的方法中，这些措施的，可能会无法是足够的，以产生在一个典型的的功能性磁振造影中资料集与在下面的描述的的方式的的例外状况的稳健性估计量。 因此，，因为的的的区域的克的稳定的直接的的恋爱关系lucose的的代谢和突触神经的的活性33，这种方法已已应用于的休息时间状态FDG PET显像的数据的分析主要是为了。然而，给定的，脑血流量（CBF）是紧密耦合，以代谢在的静息状态下10,11,34，SPECT [35,36]和更最近的动脉自旋贴标（海拨）MRI灌注成像方法37,38中的活性，已被用来，以评估在个别情况下的异常的的代谢活性。也就是说说，与静息状态下的fMRI（rsfMRI）的可靠的的空间的协方差图案的推导是如前面所指出的不直截了当的31,32。即便如此的是，初步从PD患者和对照组受试者的的的rsfMRI数据的之基于SSM /对主成分分析（PCA）曾透露的一些之间的的地形的同源性与疾病-相关的的图案确定了，使用的两个方式，的BOLD功能磁共振成像39,40的 PET和调幅的低-频率的波动的影响（ALFF）的振幅。最后，我们还注意到，这种做法已成功地应用于在基于体素的的形态中以及两者混合的起始点，（VBM）的结构性的MRI数据41,42，揭示了与与年龄-相关的的的的的体积损失和在在的相同的科目43中的 VBM的和ASL的图案的进一步的比较的中相关联的的鲜明的的空间的协方差图案。 SSM / PCA的空间使用不同的的分析的方法和成像平台的的的的协方差拓朴图和类似的确定的大脑网络的的之间的关系是正在进行的调查的一个话题。

<table border="1">
 <tbody>
 <tr>
 <td>Name of Equipment</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Image Acquisition </td>
 </tr>
 <tr>
 <td>PET Scanner</td>
 <td>GE Medical Systems</td>
 <td>GE Advance</td>
 <td>Any PET, PET/CT and PET/MRI Scanners from GE, Siemens and Philips </td>
 </tr>
 <tr>
 <td>PC Workstations</td>
 <td>Lenovo</td>
 <td>Any</td>
 <td><a href="http://www.lenovo.com/us/en/" target="_blank">http://www.lenovo.com/us/en/</a></td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Radiopharmaceuticals </td>
 </tr>
 <tr>
 <td>[18F]-fluorodeoxyglucose</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Routine Production</td>
 <td>Also distributed by Cardinal Health <a href="http://www.cardinal.com/" target="_blank">http://www.cardinal.com/</a> </td>
 </tr>
 <tr>
 <td></td>
 <td></td>
 <td></td>
 <td>Software </td>
 </tr>
 <tr>
 <td>ScanVP</td>
 <td>Feinstein Institute for Medical Research</td>
 <td>Version 5.9.1, Version 6.2, To be released</td>
 <td><a href="http://www.feinsteinneuroscience.org/" target="_blank">www.feinsteinneuroscience.org</a></td>
 </tr>
 <tr>
 <td>SPM</td>
 <td>The UCL Institute of Neurology</td>
 <td>spm99-spm8</td>
 <td><a href="http://www.fil.ion.ucl.ac.uk/spm" target="_blank">http://www.fil.ion.ucl.ac.uk/spm</a></td>
 </tr>
 <tr>
 <td>Windows</td>
 <td>Microsoft</td>
 <td>Any</td>
 <td></td>
 </tr>
 <tr>
 <td>Matlab</td>
 <td>Mathworks</td>
 <td>Matlab Version 7.0, 7.3</td>
 <td><a href="http://www.mathworks.com/" target="_blank">http://www.mathworks.com/</a></td>
 </tr>
 <tr>
 <td>JMP</td>
 <td>SAS</td>
 <td>Version 5</td>
 <td><a href="http://www.jmp.com/" target="_blank">http://www.jmp.com/</a></td>
 </tr>
 </tbody>
</table>

identification of disease-related spatial covariance patterns using neuroimaging data

的缩放的子配置文件模型（SSM） 显示1-4，是一个多变量的基于PCA-的算法，该算法识别及确定其主要中的变异来源患者组和对照组群脑的图像数据在拒绝较小的的组件（ 图1）的同时。直接向应用于的稳态-是一张专属图片状态多模态图片包含的体素的-由-的体素的的协方差数据的，整个一个的的组的影像设定可以降低到一个几个的显着的线性独立的协方差图案和相应的的主题的分数。每个模式，都被称为，的一组不变量的子配置文件（GIS）的，是一个正交的的的本金代表一个空间上分布的的的网络的功能上相互关联的的的的大脑区域的组件，它。大型的全球性的均值的标量的的影响，可以掩盖较小的网络-特定的的贡献都由所固有的的对数转换中移除，，，并的意思的数据的定心2,5,6。的的主题表达每个的这些模式给一个变量的表示的程度通过一个简单的的可以与独立的CLIN相关联的的的标量得分，的iCal描述符或心理测量学的描述符7,8。通过使用的主题的分数（ 即图案表达式的值）的因素logistic回归分析，线性相关系数可以推导出，以结合起来多个的主要组件集成到与改进的的从健康的的控制科目5,6的患者的歧视的单一的疾病-相关的的空间的协方差的的图案， 即的复合网络的。使用自举的重采样技术9时，可以执行在的推导集的之内的交叉验证的。正向的验证是很容易证实由直接得分的评价的的派生的的图案在的前瞻性的数据集10中 。可以用来一旦通过验证，疾病关连的的的型态，，以得分从个人的的例相对于到一个固定的的参考样品进行患者，往往是一套的“这是使用的的名健康受试者”（与这个疾病组）在原来的柄的推导第11的 。这些标准化处理的值可以在反过来又被用来，以协助在的差分诊断12,13中，，，并，以评估疾病的的进展和治疗效应在网络级别7,14-16。我们提出了一种的应用程序此的方法来的帕金森氏病患者和正常对照组，使用我们的的的在-的房子的软件的情况，，以推导出一个的特征的协方差图案疾病的的生物标志物的的FDG PET显像的数据的的例子。

一个简单的的应用程序的多变量的SSM /主成分分析（PCA），，以推导出一个为PD人仕的神经影像学检查的生物标志物图案的就是如下图所示。的十的PET的FDG的图像临床确诊的帕金森氏症患者（6M/4F，59Y±部7y的sd）的持续时间可变的害病的的（9y的±5Y的sd）和十个年龄和性别相匹配的了正常对照组（6M/4F，58Y±部7y的sd）进行了分析用我们的的ssmpca例程。该二十个相应的空间上预先-归一的的图像?...

Watch this Scientific Journal Video about Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data at JoVE.com

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

多元技术，包括主成分分析（PCA）已经脑功能图像用于识别签名区域变化模式。我们已经开发了一种算法来识别重复的网络神经退行性疾病的诊断，评估病情恶化，患者人群的治疗效果和客观评价的生物标志物。

鉴定的使用神经影像学数据的与疾病-相关的的空间协方差模式

The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain ...

identification-disease-related-spatial-covariance-patterns-using

Research

JoVE Journal

Medicine

15.5K 次观看.  The Feinstein Institute for Medical Research. 多元技术，包括主成分分析（PCA）已经脑功能图像用于识别签名区域变化模式。我们已经开发了一种算法来识别重复的网络神经退行性疾病的诊断，评估病情恶化，患者人群的治疗效果和客观评价的生物标志物。

视频: Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome compared to matched normal tissue. Based on these analyses it is evident that there are many undiscovered genetic drivers of cancer1. Unfortunately these drivers are hidden within a much larger number of passenger anomalies in the genome that do not directly contribute to tumor formation. Another aspect of the cancer genome is that there is considerable genetic heterogeneity within similar tumor types. Each tumor can harbor different mutations that provide a selective advantage for tumor formation2. Performing an unbiased forward genetic screen in mice provides the tools to generate tumors and analyze their genetic composition, while reducing the background of passenger mutations. The Sleeping Beauty (SB) transposon system is one such method3. The SB system utilizes mobile vectors (transposons) that can be inserted throughout the genome by the transposase enzyme. Mutations are limited to a specific cell type through the use of a conditional transposase allele that is activated by Cre Recombinase. Many mouse lines exist that express Cre Recombinase in specific tissues. By crossing one of these lines to the conditional transposase allele (e.g. Lox-stop-Lox-SB11), the SB system is activated only in cells that express Cre Recombinase. The Cre Recombinase will excise a stop cassette that blocks expression of the transposase allele, thereby activating transposon mutagenesis within the designated cell type. An SB screen is initiated by breeding three strains of transgenic mice so that the experimental mice carry a conditional transposase allele, a concatamer of transposons, and a tissue-specific Cre Recombinase allele. These mice are allowed to age until tumors form and they become moribund. The mice are then necropsied and genomic DNA is isolated from the tumors. Next, the genomic DNA is subjected to linker-mediated-PCR (LM-PCR) that results in amplification of genomic loci containing an SB transposon. LM-PCR performed on a single tumor will result in hundreds of distinct amplicons representing the hundreds of genomic loci containing transposon insertions in a single tumor4. The transposon insertions in all tumors are analyzed and common insertion sites (CISs) are identified using an appropriate statistical method5. Genes within the CIS are highly likely to be oncogenes or tumor suppressor genes, and are considered candidate cancer genes. The advantages of using the SB system to identify candidate cancer genes are: 1) the transposon can easily be located in the genome because its sequence is known, 2) transposition can be directed to almost any cell type and 3) the transposon is capable of introducing both gain- and loss-of-function mutations6. The following protocol describes how to devise and execute a forward genetic screen using the SB transposon system to identify candidate cancer genes (Figure 1).

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR

A method of identifying unknown drivers of carcinogenesis using an unbiased approach is described. The method uses the Sleeping Beauty transposon as a random mutagen directed to specific tissues. Genomic mapping of transposon insertions that drive tumor formation identifies novel oncogenes and tumor suppressor genes

鉴定<em&gt;睡美人</em&gt;转座子插入连接器介导的PCR实体瘤

Genomic, proteomic, transcriptomic, and epigenomic analyses of human tumors indicate that there are thousands of anomalies within each cancer genome ...

科研

医学

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion.
Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

We demonstrate methods for the detection of architectural distortion in prior mammograms. Oriented structures are analyzed using Gabor filters and phase portraits to detect sites of radiating tissue patterns. Each site is characterized and classified using measures to represent spiculating patterns. The methods should assist in the detection of breast cancer.

在检测之前乳房X光检查建筑失真<em&gt;通过</em面向模式的&gt;分析

We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of ...

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to exercise training recommendations. The study of exercise adherence is thus a key step towards the optimization of PR programs. To date, mostly indirect measures, such as rates of participation, completion, and attendance, have been used to determine adherence to PR. The purpose of the present protocol is to describe how continuous data tracking technology can be used to measure adherence to a prescribed aerobic training intensity on a second-by-second basis.
In our investigations, adherence has been defined as the percent time spent within a specified target heart rate range. As such, using a combination of hardware and software, heart rate is measured, tracked, and recorded during cycling second-by-second for each participant, for each exercise session. Using statistical software, the data is subsequently extracted and analyzed. The same protocol can be applied to determine adherence to other measures of exercise intensity, such as time spent at a specified wattage, level, or speed on the cycle ergometer. Furthermore, the hardware and software is also available to measure adherence to other modes of training, such as the treadmill, elliptical, stepper, and arm ergometer. The present protocol, therefore, has a vast applicability to directly measure adherence to aerobic exercise.

Pulmonary rehabilitation is widely recognized in the management of respiratory diseases. A key component to successful pulmonary rehabilitation is adherence to the recommended exercise training. The purpose of the present protocol is to describe how continuous data tracking technology can be used to precisely measure adherence to a prescribed aerobic training intensity.

使用连续数据跟踪技术在肺康复研究锻炼坚持性

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to ...

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, particularly in low dosage values1&#8203;&#8203;. &#8216;How to use PSA&#39; remains a current issue, either for diagnosis as a gray zone corresponding to a concentration in serum of 2.5-10 ng/ml which does not allow a clear differentiation to be made between cancer and noncancer2 or for patient follow-up as analysis of post-operative PSA kinetic parameters can pose considerable challenges for their practical application3,4. Alternatively, noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease, e.g. PCA3 in prostate cancer5,6 and to reveal uncharacterized aspects of tumor biology. Moreover, data from the ENCODE project published in 2012 showed that different RNA types cover about 62% of the genome. It also appears that the amount of transcriptional regulatory motifs is at least 4.5x higher than the one corresponding to protein-coding exons. Thus, long terminal repeats (LTRs) of human endogenous retroviruses (HERVs) constitute a wide range of putative/candidate transcriptional regulatory sequences, as it is their primary function in infectious retroviruses. HERVs, which are spread throughout the human genome, originate from ancestral and independent infections within the germ line, followed by copy-paste propagation processes and leading to multicopy families occupying 8% of the human genome (note that exons span 2% of our genome). Some HERV loci still express proteins that have been associated with several pathologies including cancer7-10. We have designed a high-density microarray, in Affymetrix format, aiming to optimally characterize individual HERV loci expression, in order to better understand whether they can be active, if they drive ncRNA transcription or modulate coding gene expression. This tool has been applied in the prostate cancer field (Figure 1).

Human endogenous retroviruses (HERV), which occupy 8% of the human genome, retain scarce coding capacities but a hundred thousand long terminal repeats (LTRs). A custom Affymetrix microarray was designed to identify individual HERV locus expression and was used on prostate cancer tissues as a proof of concept for future clinical studies.

应用生物标志物发现在前列腺癌：个别赫尔维位点表达微阵列鉴定

The prostate-specific antigen (PSA) is the main diagnostic biomarker for prostate cancer in clinical use, but it lacks specificity and sensitivity, ...

In Vivo Tracking of Edema Development and Microvascular Pathology in a Model of Experimental Cerebral Malaria Using Magnetic Resonance Imaging

Cerebral malaria is a sign of severe malarial disease and is often a harbinger of death. While aggressive management can be life-saving, the detection of cerebral malaria can be difficult. We present an experimental mouse model of cerebral malaria that shares multiple features of the human disease, including edema and microvascular pathology. Using magnetic resonance imaging (MRI), we can detect and track the blood-brain barrier disruption, edema development, and subsequent brain swelling. We describe multiple MRI techniques that can visualize these pertinent pathological changes. Thus, we show that MRI represents a valuable tool to visualize and track pathological changes, such as edema, brain swelling, and microvascular pathology, in vivo.

In Vivo Tracking of Edema Development and Microvascular Pathology in a Model of Experimental Cerebral Malaria Using Magnetic Resonance Imaging

We describe a mouse model of experimental cerebral malaria and show how inflammatory and microvascular pathology can be tracked in vivo using magnetic resonance imaging.

使用磁共振成像实验性脑疟疾模型中的水肿发育​​和微血管病理学的体内追踪

Cerebral malaria is a sign of severe malarial disease and is often a harbinger of death. While aggressive management can be life-saving, the detection of ...

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.

使用电子地图的光映射数据的高通量分析

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we ...

Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke survivors. The diagnosis and characterization of these symptoms in individual patients can be challenging and often requires skilled clinical staff. Virtual reality (VR) presents an opportunity to develop novel assessment tools for patients with USN.
We aimed to design and build a VR tool to detect and characterize subtle USN symptoms, and to test the tool on subjects treated with inhibitory repetitive transcranial magnetic stimulation (TMS) of cortical regions associated with USN.
We created three experimental conditions by applying TMS to two distinct regions of cortex associated with visuospatial processing- the superior temporal gyrus (STG) and the supramarginal gyrus (SMG) - and applied sham TMS as a control. We then placed subjects in a virtual reality environment in which they were asked to identify the flowers with lateral asymmetries of flowers distributed across bushes in both hemispaces, with dynamic difficulty adjustment based on each subject's performance. 
We found significant differences in average head yaw between subjects stimulated at the STG and those stimulated at the SMG and marginally significant effects in the average visual axis.
VR technology is becoming more accessible, affordable, and robust, presenting an exciting opportunity to create useful and novel game-like tools. In conjunction with TMS, these tools could be used to study specific, isolated, artificial neurological deficits in healthy subjects, informing the creation of VR-based diagnostic tools for patients with deficits due to acquired brain injury. This study is the first to our knowledge in which artificially generated USN symptoms have been evaluated with a VR task.

The goal was to design, build, and pilot a novel virtual reality task to detect and characterize unilateral spatial neglect, a syndrome affecting 23-46% of acute stroke survivors, expanding the role of virtual reality in the study and management of neurologic disease.

用于评估单边空间忽视的虚拟现实工具：数据收集的新机遇

Unilateral spatial neglect (USN) is a syndrome characterized by inattention to or inaction in one side of space and affects between 23-46% of acute stroke ...

Detection and Removal of Tooth-Colored Composite Resin Using the Fluorescence-Aided Identification Technique

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) is a noninvasive tool to facilitate the distinction of composite resin material from sound tooth substance. Compared to conventional illumination, FIT is a very accurate, reliable, and fast diagnostic method. When composite resin is illuminated with a wavelength of approximately 398 &plusmn; 5 nm, certain fluorescent components make the composite resin appear brighter than the tooth structure. Any fluorescence-inducing light source with the appropriate wavelength can be used for this method. Optimally, this technique is used without additional natural or artificial lighting. The application of FIT can be used for diagnostic purposes, for example, dental charts, and additionally for the complete and minimally invasive removal of composite resin restorations, bracket debonding, and trauma splint removal. The assessment of volumetric changes after composite removal can be provided by overlapping pre- and postoperative scans and subsequent calculation using suitable software.

The Fluorescence-aided Identification Technique is a practicable, fast, and reliable approach for the differentiation of composite resin restorations from tooth substance, and facilitates the minimally invasive and complete removal of composite resin restorations and composite bonded trauma splints.

使用荧光辅助识别技术检测和去除牙齿颜色的复合树脂

The detection and removal of tooth-colored filling materials is a major challenge for every dentist. The Fluorescence-aided Identification Technique (FIT) ...

Visualization of Metabolites Identified in the Spatial Metabolome of Traditional Chinese Medicine Using DESI-MSI

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has become a crucial topic in plant science. Mass spectrometry imaging can extract huge volumes of data and provide spatial distribution information about these by analyzing tissue slices. With the advantage of high throughput and higher accuracy, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is often used in biological research and in the study of traditional Chinese medicine. However, the procedures used in this research are complicated and not affordable. In this study, we optimized sectioning and DESI imaging procedures and developed a more cost-effective method to identify the distribution of metabolites and categorize these compounds in plant tissues, with a special focus on traditional Chinese medicines. The study will promote the utilization of DESI in metabolite analysis and standardization of traditional Chinese medicine/ethnic medicine for research-related technologies.

In this study, a series of methods are presented to prepare DESI-MSI samples from plants, and a procedure of DESI assembly installation, MSI data acquisition, and processing is described in detail. This protocol can be applied in several conditions for acquiring spatial metabolome information in plants.

使用DESI-MSI鉴定中药空间代谢组中代谢物的可视化

The medicinal use of traditional Chinese medicine is mainly due to its secondary metabolites. Visualization of the distribution of these metabolites has ...

使用 Dixon MRI 的综合 3D 肝脏脂肪功能分布模型

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

作者聚焦：一种使用 3D Dixon MRI 评估和区分肝脏脂肪模式的非侵入性工具 

This study introduces a unique 3D quantification method for liver fat fraction (LFF) distribution using Dixon Magnetic Resonance Imaging (Dixon MRI). LFF maps, derived from in-phase and water-phase images, are integrated with 3D liver contours to differentiate LFF patterns between normal and steatotic livers, enabling precise assessment of liver fat content.

使用Dixon磁共振成像分析肝脏脂肪分数分布的3D定量技术

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. ...