这项工作由国家科学技术发展委员会（CNPq，巴西）[资助号428048/2018-8和402556/2022-4]和巴西利亚大学（巴西）资助。作者感谢 Duarte Nuno Carvalho 博士和 Evelyn Santos 博士（i3s，葡萄牙波尔图）提供对他们在本研究中使用的智能手机的访问。

1. Bradford蛋白测定试剂的制备
<ol><li>将 100 mg 考马斯亮蓝 G 溶解在 50 mL 95% （w/v） 乙醇中。混合至考马斯亮蓝G完全溶解。 注意:乙醇易燃，会引起眼睛刺激。避免火焰并使用护目镜。</li>
	<li>在前面的溶液中，小心地加入 100 mL 85% （w/v） 磷酸。 注意: 磷酸对金属有腐蚀性，会导致皮肤腐蚀、严重的眼睛损伤和急性口腔毒性。戴上手套和护目镜。</li>
	<li>将含有考马斯亮蓝 G、乙醇和磷酸的溶液缓慢加入 600 mL 去离子水中。</li>
	<li>将溶液稀释至最终体积为 1,000 mL。根据要分析的样品数量扩大或缩小规模。如原始方法7所述，工作Bradford试剂中的终浓度应为0.01%（w/v）考马斯亮蓝G、4.7%（w/v）乙醇和8.5%（w/v）磷酸。</li>
	<li>通过滤纸（Whatman #1 纸或同等纸）去除任何不溶性物质过滤。</li>
	<li>该试剂在室温 （RT） 下储存并避光时可稳定数周。必要时过滤，因为沉淀物可能会随着时间的推移而形成。 注意:或者，市售的即用型 Bradford 试剂也可随手使用。按照制造商的说明制备试剂，然后继续下一步。</li>
</ol>2.蛋白质标准溶液的制备
<ol><li>制备分离蛋白质的储备溶液（参见步骤2.4）作为标准品使用。牛血清白蛋白 （BSA） 是一种经济实惠且常用的蛋白质。其他选择是卵清蛋白和牛丙种球蛋白。</li>
	<li>如果已知用作标准品的蛋白质的摩尔吸收率，请在分光光度计中检查储备溶液的浓度。</li>
	<li>对于 BSA，常用的公式是 BSA （mg/mL） = （A280/6.6） × 10，其中 A280 是相对于适当空白（即ε 2801% = 6.6）7 处的吸光度，光程长度为 1 cm。例如，0.8 mg/mL BSA 在 280 nm 处的吸光度为 0.528。</li>
	<li>为了生成标准曲线，在0.025 mg / mL和1.0 mg / mL内制备几种稀释的BSA。在每孔添加 10 μL 的样品体积后，每孔将获得 0.25-10 μg BSA。 注意:蛋白质标准溶液应在与用于制备样品的培养基具有相同成分（终浓度）的培养基中制备。</li>
</ol>3. 测定
<ol><li>稀释样品，使蛋白质浓度达到0.025-1.0mg/mL的标准曲线范围内。在该范围内进行多次（至少 3 次）样品稀释。 注意:样品可以用磷酸盐缓冲盐水 （PBS） 或任何其他与 Bradford 试剂兼容的培养基/缓冲液组合物稀释。标准品和样品之间培养基组分的最终浓度不应有差异。</li>
	<li>将 10 μL 每种蛋白质标准溶液加入 96 孔微孔板的三个孔（即一式三份）中。对于 0（零）蛋白点，加入 10 μL 用于制备标准溶液和样品稀释液的缓冲液/培养基。</li>
	<li>在另一组孔中，将每个样品稀释度的 10 μL 加入到相同 96 孔微孔板的三个孔（即一式三份）中。一种方法是添加不同体积的样品，并完成每孔最多 10 μL 的培养基（例如，0.5 μL、1 μL、2 μL、4 μL 和 8 μL 样品加上 9.5 μL、9 μL、8 μL、6 μL 和 2 μL 培养基）。</li>
	<li>向所有孔中加入 250 μL Bradford 蛋白测定试剂。典型的微孔板设置如 图2所示。在本例中，包括一组含有 260 μL（每孔最终体积）水的空白孔，作为酶标仪中的空白孔（步骤 4.5）。仅当还将收集吸光度数据时，才需要这样做。 注意:每次进行测定时，在同一样品微孔板中制备标准曲线。换言之，如果样品数量需要另一块板，则在第二块板中准备另一条标准曲线，依此类推。</li>
	<li>在 5-15 分钟内记录结果（第 4 节）。</li>
</ol><img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65547/65547fig02v2.jpg"> 图 2:Bradford 蛋白检测的典型板布局。 空白是指在酶标仪中用作空白的三孔，其中含有 260 μL 水。STD是指蛋白质标准品。S1-S6 是六个不同的样品。SX_1-SX_4是每个样品的四种不同的样品稀释度。 <a href="https://www.jove.com/files/ftp_upload/65547/65547fig02large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
4. 记录结果
<ol><li>在光线充足的房间内，用一只手将微孔板平行于工作台，放在均匀的白色背景（例如，纸张）上。通过在板上放置气泡水平仪来确保准确对齐。</li>
	<li>另一方面，将智能手机平行握住（某些相机应用程序可以有效地指示设备的倾斜度）到工作台和微孔板上，并拍摄整个微孔板的一张或几张照片（图 3）。在 iOS 设备中，通过启用 网格 选项在相机设置中打开相机电平指示器。在 Android 设备中，通过启用 取景提示来打开相机设置中的相机电平指示器。</li>
	<li>不需要特殊的照明设备，但要注意避免阴影和反射。例如，避免使用智能手机对印版或背景进行着色，并避免使用微孔板对背景进行着色。井区边缘的小反射不是问题;颜色数据可以从每个孔中心的一个非常小的区域中提取。</li>
	<li>简要检查图片的背景均匀性、阴影和反射。另外，看看井的角度;每个井的中心应该是直接可见的（即，而不是在井壁后面）。</li>
	<li>如果需要比较微孔板吸光度读数和图片颜色数据，请在酶标仪21中读取590nm和450nm处的微孔板。</li>
</ol><img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65547/65547fig03v2.jpg"> 图 3:捕获 Bradford 蛋白检测的结果。 在光线充足的房间中，用一只手将微孔板平行放置在均匀的背景下。另一方面，智能手机平行于工作台和微孔板。 <a href="https://www.jove.com/files/ftp_upload/65547/65547fig03large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
5.自动提取颜色数据
<ol><li>下载 ImageJ 和 ReadPlate22，ImageJ 插件可在 https://imagej.nih.gov/ij/plugins/readplate/index.html 获得（这是一个.txt文件）。</li>
	<li>打开 ImageJ，单击 Plugins > Install ，然后选择在步骤 6.1 中下载的文件。</li>
	<li>通过单击 "分析">"设置测量 值"来设置测量参数，然后选中以下选项: 面积;标准差;最小和最大灰度值;平均灰度值;模态灰度值。在窗口底部，将 "重定向为:无 "和 "小数位 （0-9）:3"设置为"重定向"。</li>
	<li>转到 "文件>"打开 "，然后选择步骤4中拍摄的微孔板的图片。</li>
	<li>转到 ReadPlate >插件。阅读说明，然后单击 "确定"。</li>
	<li>选择孔数: 96。</li>
	<li>使用插件自动加载的 矩形选择 工具，制作一个从 A1 孔中心开始到 H12 孔中心结束的矩形。然后，单击 "确定"。</li>
	<li>选择 "蓝色 "通道，然后单击 "确定"。</li>
	<li>单击 "确定"确认默认参数。</li>
	<li>检查软件是否在每个孔内划定了一个区域，以及所选区域是否没有覆盖具有异常阴影或反射的区域。单击 "确定"。</li>
	<li>保存结果，然后对绿色通道重复步骤 5.8-5.10。</li>
	<li>使用每种颜色的模式计算蓝绿比。 注意:计算可以手动完成，也可以使用读者首选项的软件完成，例如 R、Microsoft Excel 或 GraphPad Prism。</li>
</ol>6. 手动提取颜色数据
<ol><li>下载 Inkscape，一个免费的开源图形编辑器。 注意:任何带有颜色选择器工具（通常描述为滴管）的软件都可用于识别颜色并提取 RGB 数据。</li>
	<li>打开 Inkscape，转到 "文件">"打开"。 选择 在步骤4中拍摄的微孔板的图片。</li>
	<li>选择 选择和变换对象 （S） 工具，该工具在左上角显示为箭头，然后单击图片。虚线边框将指示选择。</li>
	<li>选择 从图像中选取颜色 （D） 工具，该工具在左侧显示为吸管。</li>
	<li>点击一口井的中心。底部面板上的颜色（"填充:"）将相应更改。单击颜色，右侧将弹出" 填充和描边 "选项卡。</li>
	<li>将 "纯色 "下拉菜单更改为 "RGB"。记录每个孔的蓝色和绿色通道显示的值。</li>
	<li>使用记录的值计算蓝绿比。 注意:计算可以手动完成，也可以使用读者首选项的软件完成，例如 R、Microsoft Excel 或 GraphPad Prism。</li>
</ol>7. 构建标准曲线并推断未知数
<ol><li>绘制平均绿色与蓝色强度比作为蛋白质标准浓度的函数。 注意:手动绘制数据或使用阅读器首选项的软件，例如 R、Microsoft Excel 或 GraphPad Prism。</li>
	<li>计算每个样品的平均绿蓝强度比和稀释度。</li>
	<li>检查样品获得的信号是否在蛋白质标准品的线性范围内。</li>
	<li>忽略低于或高于标准曲线的最小值或最大值的任何值。</li>
	<li>使用描述标准曲线的线性方程来推断样品中的蛋白质量。相应地将计算值乘以稀释系数。</li>
</ol>

智能手机作为使用 Bradford 分析定量蛋白质的分析设备

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</ol>

作者没有利益冲突需要声明。

本文描述了RGBradford，这是一种使用智能手机摄像头记录来自Bradford蛋白质测定的数据，提取颜色数据并准确定量生物样品中的蛋白质水平的方法，如最近最初描述的那样20。与原始RGBradford方法的一个区别是，这里使用了使用ImageJ插件22 自动获取颜色数据的过程。RGBradford方法的主要新颖之处在于使用RGB数据作为分析信号;因此，人们可以使用实验室中使用的任何?...

无论下游用途如何（例如 ELISA、酶动力学、蛋白质印迹、蛋白质纯化和质谱），蛋白质定量对于生命科学实验室的准确分析都至关重要。除了用作辅助读数（即计算每质量蛋白质的分析物的相对水平）外，样品中的蛋白质水平本身也可以是所需的输出。例如，人们可能对食物资源1 或尿液2 中的蛋白质水平感兴趣。有许多方法可用于测量样品3中的蛋白质浓度，包括直接紫外吸光度读数4、蛋白质-铜螯合5,6、蛋白质-染料结合比色法7和蛋白质-染料结合荧光试验8。在引用次数最多的文献9,10的前三名中，有两篇描述蛋白质测量方法的论文5,7证明了蛋白质定量的相关性。尽管许多作者通过引用非主要参考文献或根本没有引用任何内容而忽略了他们的实际引用，但描述Lowry蛋白测定和Bradford蛋白测定的原始论文各有>200,000次引用10次。
Bradford 检测的受欢迎程度源于其经济性、简单性、速度和灵敏度。该测定基于蛋白质与染料考马斯亮蓝G在酸性条件下的相互作用。在测定条件下（即低pH值），染料以三种形式存在:红色阳离子形式，λmax为470nm;在650 nm处具有λmax的绿色中性形式;以及在590nm处λmax为11,12的蓝色阴离子形式（图1）。阳离子形式在没有蛋白质的情况下占主导地位。当蛋白质与染料相互作用时，它们会稳定蓝色阴离子形式，导致溶液颜色发生明显变化，从棕色变为蓝色。通常，通过分光光度法定量染料蓝色形式浓度的变化，其在590-595nm处的吸光度与测定中的蛋白质量成正比。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65547/65547fig01v2.jpg"> 图 1:Bradford 测定条件下的考马斯亮蓝 G 吸收光谱。 三个主峰用箭头标记，表示染料的红色（470 nm）、绿色（650 nm）和蓝色（590 nm）形式的λmax。在没有蛋白质（黄线）和存在 3 μg（灰线）和 10 μg（蓝线）牛血清白蛋白的情况下记录光谱。 <a href="https://www.jove.com/files/ftp_upload/65547/65547fig01large.jpg" target="_blank">请点击这里查看此图的较大版本.</a>
Bradford测定法的广泛使用导致了几个局限性的识别（例如，对不同蛋白质11的可变反应，以及脂质13和洗涤剂7的干扰）以及改进其性能的修饰（例如，添加洗涤剂14,15，碱化14,16和使用吸光度比率17).除了对测定本身的修改外，还描述了使用替代设备（例如智能手机或相机）来捕获分析信号18,19,20。事实上，利用智能手机作为便携式化学分析仪的方法的开发一直是一个活跃的研究领域。使用智能手机的动机源于这些设备的可负担性、便携性、易用性和广泛可用性。
本文提供了一种使用 RGBradford assay20 进行蛋白质定量的方案，该检测使用智能手机作为分析设备。与最初的 RGBradford 出版物20 相比，这里引入了简化颜色提取过程的程序。它涉及使用免费提供的软件应用程序从微孔板图片的每个孔中自动提取颜色信息，从而节省大量时间和精力。这是使用图形编辑器软件应用程序20从每个孔逐个手动获取颜色数据的先前方法的替代方法。最终，可以使用从智能手机拍摄的微孔板图片中提取的颜色数据来量化样品中的蛋白质水平。

<table><tbody><tr><td>96-well flat-bottom polystyrene microtiter plates&amp;nbsp;</td><td>Jet Biofil, Guangzhou, China</td><td>TCP011096</td><td>Any flat-bottom microplate compativle with optical reading will suffice.&amp;nbsp;</td></tr><tr><td>Bovine serum albumin</td><td>Sigma-Aldrich, St. Louis, MO</td><td>A2153</td><td></td></tr><tr><td>Coomassie Brilliant Blue G</td><td>Sigma-Aldrich, St. Louis, MO</td><td>B0770</td><td></td></tr><tr><td>Ethyl alcohol</td><td></td><td></td><td></td></tr><tr><td>iPhone 11</td><td>Apple</td><td>MWM02BR/A</td><td>Can be substituted with other smartphone equiped with a camera</td></tr><tr><td>iPhone 14 Pro</td><td>Apple</td><td>N/A</td><td></td></tr><tr><td>Phosphoric acid</td><td>Sigma-Aldrich, St. Louis, MO</td><td>695017</td><td></td></tr><tr><td>Redmi Note 9 Pro</td><td>XIAOMI&amp;nbsp;</td><td>N/A</td><td></td></tr><tr><td>S22 Ultra</td><td>Samsung&amp;nbsp;</td><td>N/A</td><td></td></tr><tr><td>SpectraMax 384 Plus. Microplate reader.</td><td>Molecular Devices, San Jose, CA</td><td>PLUS 384</td><td>Any microplate reader capable of reading at 450 nm and 590 nm will work. This is optional. The method was actually created to dismiss the need of a microplate reader.</td></tr></tbody></table>

rgbradford: protein quantitation with a smartphone camera

蛋白质定量是生命科学研究中必不可少的程序。在其他几种方法中，布拉德福德测定法是最常用的方法之一。由于其广泛应用，Bradford测定法的局限性和优点已被详尽报道，包括对原始方法的多次修改以提高其性能。原始方法的改变之一是使用智能手机相机作为分析仪器。利用Bradford测定条件下存在的三种形式的考马斯亮蓝染料，本文描述了如何使用从单张微孔板图片中提取的颜色数据准确定量样品中的蛋白质。在微孔板中进行测定后，使用智能手机相机拍摄照片，并使用免费的开源图像分析软件应用程序从图片中提取RGB颜色数据。然后，使用未知蛋白质浓度样品的蓝色与绿色强度之比（在RGB标度中）基于标准曲线计算蛋白质含量。使用RGB颜色数据计算的值与使用常规吸光度数据计算的值之间没有显著差异。

图4是微孔板的图片，从中提取颜色数据，并记录了450nm和590nm处的吸光度。此处报告的代表性 RGB 颜色数据是自动获取的，如第 5 节所述。颜色数据的典型模式是蓝色值的增加和红色和绿色值的减少（图 5）。请注意，尽管所有孔中都有明显的反射，并且微孔板未完全对齐（图4），但从图片中提取的颜色数据准确地反映了吸光度读...

Watch this Scientific Journal Video about An Alternative Approach to Protein Quantification by Bradford Assay Using a Smartphone at JoVE.com

作者聚焦:使用智能手机通过 Bradford 分析进行蛋白质定量的另一种方法 

本文提供了一种使用 Bradford 测定法和智能手机作为分析设备进行蛋白质定量的方案。样品中的蛋白质水平可以使用从智能手机拍摄的微孔板图片中提取的颜色数据进行量化。

RGBradford:使用智能手机相机进行蛋白质定量

Protein quantitation is an essential procedure in life sciences research. Amongst several other methods, the Bradford assay is one of the most used. ...

我们的研究重点是在生化水平上了解动物如何适应极端环境条件。我们探讨了它们对缺氧和抑制代谢率的反应问题。具体来说，我们研究了动物在不利环境中使用的内源性抗氧化剂、氧化应激和细胞应激反应。为此，我们经常量化样品中的蛋白质水平。该协议解决了在资源有限的情况下获得蛋白质定量数据的替代方法的需求，使其成为教育和研究环境中的宝贵工具。RG Bradford 检测试剂盒为蛋白质浓度测量提供了一种实用且可靠的解决方案，在无法使用酶标仪等传统设备的情况下，该解决方案易于访问、简单且准确。

rgbradford-protein-quantitation-with-a-smartphone-camera

Research

JoVE Journal

Biochemistry

RGBradford: Protein Quantitation with a Smartphone Camera

3.1K 次观看.  University of Brasilia. 我们的研究重点是在生化水平上了解动物如何适应极端环境条件。我们探讨了它们对缺氧和抑制代谢率的反应问题。具体来说，我们研究了动物在不利环境中使用的内源性抗氧化剂、氧化应激和细胞应激反应。为此，我们经常量化样品中的蛋白质水平。该协议解决了在资源有限的情况下获得蛋白质定量数据的替代方法的需求，使其成为教育和研究环境中的宝贵工具。RG B...

视频: 作者聚焦:使用智能手机通过 Bradford 分析进行蛋白质定量的另一种方法 

Quantitative Immunofluorescence to Measure Global Localized Translation

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and organogenesis, as well as in multiple diseases. Numerous publications have convincingly shown that specific mechanisms tightly regulate mRNA translation. Increased interest in the translation-induced regulation of protein expression has led to the development of novel methods to study and follow de novo protein synthesis in cellulo. However, most of these methods are complex, making them costly and often limiting the number of mRNA targets that can be studied. This manuscript proposes a method that requires only basic reagents and a confocal fluorescence imaging system to measure and visualize the changes in mRNA translation that occur in any cell line under various conditions. This method was recently used to show localized translation in the subcellular structures of adherent cells over a short period of time, thus offering the possibility of visualizing de novo translation for a short period during a variety of biological processes or of validating changes in translational activity in response to specific stimuli.

This manuscript describes a method to visualize and quantify localized translation events in subcellular compartments. The approach proposed in this manuscript requires a basic confocal imaging system and reagents and is rapid and cost-effective.

定量荧光测量全球本地化翻译

The mechanisms regulating mRNA translation are involved in various biological processes, such as germ line development, cell differentiation, and ...

科研

生物化学

Real-time Tracking of DNA Fragment Separation by Smartphone

Slab gel electrophoresis (SGE) is the most common method for the separation of DNA fragments; thus, it is broadly applied to the field of biology and others. However, the traditional SGE protocol is quite tedious, and the experiment takes a long time. Moreover, the chemical consumption in SGE experiments is very high. This work proposes a simple method for the separation of DNA fragments based on an SGE chip. The chip is made by an engraving machine. Two plastic sheets are used for the excitation and emission wavelengths of the optical signal. The fluorescence signal of the DNA bands is collected by smartphone. To validate this method, 50, 100, and 1,000 bp DNA ladders were separated. The results demonstrate that a DNA ladder smaller than 5,000 bp can be resolved within 12 min and with high resolution when using this method, indicating that it is an ideal substitute for the traditional SGE method.

Traditional slab gel electrophoresis (SGE) experiments require a complicated apparatus and high chemical consumption. This work presents a protocol that describes a low-cost method to separate DNA fragments within a short timeframe.

通过智能手机实时跟踪DNA片段分离

Slab gel electrophoresis (SGE) is the most common method for the separation of DNA fragments; thus, it is broadly applied to the field of biology and ...

Wide-field Fluorescent Microscopy and Fluorescent Imaging Flow Cytometry on a Cell-phone

Fluorescent microscopy and flow cytometry are widely used tools in biomedical research and clinical diagnosis. However these devices are in general relatively bulky and costly, making them less effective in the resource limited settings. To potentially address these limitations, we have recently demonstrated the integration of wide-field fluorescent microscopy and imaging flow cytometry tools on cell-phones using compact, light-weight, and cost-effective opto-fluidic attachments. In our flow cytometry design, fluorescently labeled cells are flushed through a microfluidic channel that is positioned above the existing cell-phone camera unit. Battery powered light-emitting diodes (LEDs) are butt-coupled to the side of this microfluidic chip, which effectively acts as a multi-mode slab waveguide, where the excitation light is guided to uniformly excite the fluorescent targets. The cell-phone camera records a time lapse movie of the fluorescent cells flowing through the microfluidic channel, where the digital frames of this movie are processed to count the number of the labeled cells within the target solution of interest. Using a similar opto-fluidic design, we can also image these fluorescently labeled cells in static mode by e.g. sandwiching the fluorescent particles between two glass slides and capturing their fluorescent images using the cell-phone camera, which can achieve a spatial resolution of e.g. ~ 10 &mu;m over a very large field-of-view of ~ 81 mm2. This cell-phone based fluorescent imaging flow cytometry and microscopy platform might be useful especially in resource limited settings, for e.g. counting of CD4+ T cells toward monitoring of HIV+ patients or for detection of water-borne parasites in drinking water.

手机上的宽视场荧光显微镜和荧光成像流式细胞术

We review our recent results on the integration of fluorescent microscopy and imaging flow cytometry tools on a cell-phone using compact and cost-effective opto-fluidic attachments. These cell-phone based micro-analysis devices might be useful for cytometric analysis, such as performing various cell counting tasks as well as for high-throughput screening of e.g., water samples in resource limited settings.

一个手机上的宽视场荧光显微镜和荧光成像流式细胞仪

Fluorescent microscopy and flow cytometry are widely used tools in biomedical research and clinical diagnosis. However these devices are in general ...

生物工程

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, representing an important agronomic yield component for estimating grain yield. Therefore, a quick, efficient, and standardized technique for assessing ear density would aid in improving agricultural management, providing improvements in preharvest yield predictions, or could even be used as a tool for crop breeding when it has been defined as a trait of importance. Not only are the current techniques for manual ear density assessments laborious and time-consuming, but they are also without any official standardized protocol, whether by linear meter, area quadrant, or an extrapolation based on plant ear density and plant counts postharvest. An automatic ear counting algorithm is presented in detail for estimating ear density with only sunlight illumination in field conditions based on zenithal (nadir) natural color (red, green, and blue [RGB]) digital images, allowing for high-throughput standardized measurements. Different field trials of durum wheat and barley distributed geographically across Spain during the 2014/2015 and 2015/2016 crop seasons in irrigated and rainfed trials were used to provide representative results. The three-phase protocol includes crop growth stage and field condition planning, image capture guidelines, and a computer algorithm of three steps: (i) a Laplacian frequency filter to remove low- and high-frequency artifacts, (ii) a median filter to reduce high noise, and (iii) segmentation and counting using local maxima peaks for the final count. Minor adjustments to the algorithm code must be made corresponding to the camera resolution, focal length, and distance between the camera and the crop canopy. The results demonstrate a high success rate (higher than 90%) and R2 values (of 0.62-0.75) between the algorithm counts and the manual image-based ear counts for both durum wheat and barley.

We present a protocol for counting durum wheat and barley ears, using natural color (RGB) digital photographs taken in natural sunlight under field conditions. With minimal adjustments for camera parameters and some environmental condition limitations, the technique provides precise and consistent results across a range of growth stages.

利用泽尼塔尔 rgb 图像进行田间作物耳计数

Ear density, or the number of ears per square meter (ears/m2), is a central focus in many cereal crop breeding programs, such as wheat and barley, ...

环境科学

用于 秀丽隐杆线 虫草坪回避测定的基于智能手机的成像

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

作者聚焦:一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法  

This article describes a simple, low-cost method of recording the lawn avoidance behavior of Caenorhabditis elegans, using readily available items such as a smartphone and a light emitting diode (LED) light box. We also provide a Python script to process the video file into a format more amenable for counting.

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually ...

行为学

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of applications. The cell surface proteome ("surfaceome") in human disease is of significant interest, as plasma membrane proteins are the primary target of most clinically approved therapeutics, as well as a key feature by which to diagnostically distinguish diseased cells from healthy tissues. However, focused characterization of membrane and surface proteins of the cell has remained challenging, primarily due to the complexity of cellular lysates, which mask proteins of interest by other high-abundance proteins. To overcome this technical barrier and accurately define the cell surface proteome of various cell types using mass spectrometry proteomics, it is necessary to enrich the cell lysate for cell surface proteins prior to analysis on the mass spectrometer. This paper presents a detailed workflow for labeling cell surface proteins from cancer cells, enriching these proteins out of the cell lysate, and subsequent sample preparation for mass spectrometry analysis.

Here, we describe a proteomics workflow for characterization of the cell surface proteome of various cell types. This workflow includes cell surface protein enrichment, subsequent sample preparation, analysis using an LC-MS/MS platform, and data processing with specialized software.

Over the past decade, mass spectrometry-based proteomics has enabled an in-depth characterization of biological systems across a broad array of ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

视频运动分析使用智能手机（ViMAS）的初步研究

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

RGBradford:使用智能手机相机进行蛋白质定量

副本

摘要

探索更多视频

定量荧光测量全球本地化翻译

通过智能手机实时跟踪DNA片段分离

一个手机上的宽视场荧光显微镜和荧光成像流式细胞仪

一个手机上的宽视场荧光显微镜和荧光成像流式细胞仪

利用泽尼塔尔 rgb 图像进行田间作物耳计数

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

视频运动分析使用智能手机（ViMAS）的初步研究

RGBradford:使用智能手机相机进行蛋白质定量

副本

摘要

探索更多视频

定量荧光测量全球本地化翻译

通过智能手机实时跟踪DNA片段分离

一个手机上的宽视场荧光显微镜和荧光成像流式细胞仪

一个手机上的宽视场荧光显微镜和荧光成像流式细胞仪

利用泽尼塔尔 rgb 图像进行田间作物耳计数

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

一种基于智能手机的秀 丽隐杆线虫 草坪回避测定成像方法

"Cell Surface Capture" Workflow for Label-Free Quantification of the Cell Surface Proteome

视频运动分析使用智能手机（ViMAS）的初步研究

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法

一种基于智能手机的秀丽隐杆线虫草坪回避测定成像方法