咩的资金来自伊利诺斯州大学的 HJ--香槟分校的教授。此外, 为了供资, 我们感谢人类前沿科学项目 (M.E.H. 和 T.G.) 和捷克共和国的欧洲社会基金和国家预算。1.07/2.3. 00/30.0041 (T.G.)。我们感谢海洋光学, 以覆盖出版成本。

这里描述的所有方法都已获得长岛大学 IACUC 机构动物保育和使用委员会的批准。
1. 混合丙烯酸颜料
<ol><li>
混合蛋壳的地面着色, 这是颜色, 将统一覆盖整个蛋壳表面。下面的食谱将使50克油漆, 这将填补一个小超过两个22毫升铝漆管。
	<ol>
<li>产生蓝绿色, 代表蓝绿色蛋壳 (如美国migratorius蛋壳), 使用18.24 克的钴绿松, 20.77 克钛白色6.52 克钴绿色, 2.86 克的钴绿松石和1.61 克烧焦的棕褐色。</li>
		<li>产生褐色的颜色, 代表褐色蛋壳 (例如, 鸡肉鸡鸡鸡内金蛋壳), 使用4.12 克的红氧化铁, 9.75 克的镉橙, 22.15 克的原始棕褐色光和13.97 克钛白色。</li>
		<li>产生一种米色, 代表米色蛋壳 (如鹌鹑鹌鹑), 使用10.60 克褐色鸡蛋色, 8.28 克蓝色绿色, 18.51 克钛白色, 12.61 克黄赭色。</li>
		<li>使用无混合的钛白色生成白色颜色。</li>
	</ol>
</li>
	<li>混合一个暗褐色斑点颜色代表在鹌鹑C. 粳稻的斑点, 使用8.38 克褐色鸡蛋色, 26.04 克的烧伤棕褐色, 15.59 克的火星黑色。</li>
	<li>
混合中间颜色横跨蛋壳颜色色域从蓝绿色到褐色, 通过混合蓝绿色和棕色油漆一起和改变他们的贡献相互 (例如, 蓝色绿色到褐色油漆的部分: 10:0, 9:1, 8:2, 7:3, 6:4, 5:5,4:6、3:7、2:8、1:9 和 0:10, 请参见图 1E)。
	<ol>
<li>若要生成更多中间颜色, 只需将这些中间颜色的偶数混合在一起并重复, 直到创建所需的唯一颜色数。即使是蓝绿色和褐色的混合物也会产生中性的灰色颜色, 但是这种颜色可以根据需要使用白色或米色进行调整。如果需要更精确的颜色 (例如, 特定的宿主卵), 则使用消减颜色混合模型来预测颜料的独特组合的光谱反射率, 并使用产生最低只是显著差异 (JND) 的混合物来所需的颜色 (请参见步骤 3.3–3.7)。</li>
	</ol>
</li>
	<li>
存放在空的22毫升铝漆管油漆。
	<ol>
<li>把颜料放到一个塑料三明治袋里, 把一个角落的一小部分剪掉。把塑料袋的末端放进管子里, 把颜料挤进管子里, 轻轻地敲打着桌子。</li>
		<li>密封管使用的粘合剂边缘和折叠结束本身至少3–4倍。</li>
	</ol>
</li>
</ol>2. 涂装实验鸡蛋模型
<ol><li>
获得实验性鸡蛋模型。
	<ol>
<li>使用三维 (以下 3D) 打印机或从分发服务器购买28, 打印模型鸡蛋。建议采用这种简单的方法, 因为它生成的鸡蛋大小一致, 形状为28。 注: 模型鸡蛋也可以从石膏, 粘土, 木材或其他物质。</li>
	</ol>
</li>
	<li>在每个鸡蛋上添加均匀的钛白色涂层, 以阻止底层的颜色。</li>
	<li>用镊子握住每个鸡蛋, 用高质量的丙烯酸颜料和干净的刷子涂上所需的颜色, 以独特的颜色来着色每个鸡蛋。</li>
	<li>在凉爽的环境中使用吹风机, 以加快每一个新绘鸡蛋的干燥过程。</li>
	<li>
使用砂纸砂下来的任何颠簸, 可能是在鸡蛋, 一旦鸡蛋完全干燥。
	<ol>
<li>重复步骤 2.3, 直到鸡蛋有一个均匀涂层的油漆没有任何肿块。鸡蛋不需要少于两件大衣。</li>
	</ol>
</li>
	<li>添加任何斑点, 以模型鸡蛋, 仔细地应用这些与画笔, 并小心飞溅的油漆与牙刷。只有一件大衣是必要的。 注意: 如果需要复制紫外线 (uv) 反射, 涂上均匀的 uv 涂料;但是, 除非允许使用这些油漆, 否则不建议您从机构、州/省和联邦允许的办公室获得许可。</li>
</ol>3. 量化颜色
<ol><li>按下电源按钮打开光谱仪。</li>
	<li>将 sd 卡插入 sd 卡插槽并通过按红色取消按钮将其链接到系统, 选择 "文件系统" 按绿色接受按钮, 通过按下菜单按钮选择 "查找 SD 卡"。然后, 按 "红色取消" 按钮两次或按 "主键" 按钮。</li>
	<li>
将光缆连接到光谱仪和光源上。
	<ol>
<li>将末端标记的 "光源" 连接到光模块, 并将末端标记的 "光谱仪" 连接到光谱仪模块。</li>
	</ol>
</li>
	<li>
将探头尖端插入光纤探头的末端。 注意: 3D 打印机上可打印的示例探针提示可用作补充代码文件。此对象将需要线程处理您选择的螺钉。<ol>
<li>在样品和测量探头之间建立距离 (如5 毫米), 使信噪比最大化。使用挠性标尺确保测量距离的一致性。 注意: 精确的距离将会因每个独立光谱仪的光栅和狭缝宽度、光学宽度和光源的独特组合而异。为所有测量保持相同的距离。灵活的规则可作为补充代码文件下载。</li>
		<li>使用一个重合的正常测量角度 (90°), 除非天然宿主蛋或模型鸡蛋有光泽表面, 在这种情况下使用45°重合倾斜测量角度。用相同的角度测量所有的卵, 真实的和人工的。</li>
		<li>用95% 乙醇清洗探针尖端。</li>
	</ol>
</li>
	<li>打开光源通过按下按钮三次, 选择PX 灯按绿色接受按钮, 选择安装通过按向右滚动按钮, 选择 "定时控制" 通过按向右滚动按钮, 单击向下按钮三次, 然后按 "接受" 按钮选择 "自由运行"。
	<ol>
<li>在采取任何测量之前至少要等15分钟。</li>
	</ol>
</li>
	<li>校准和配置光谱仪。为此, 请按 "主页" 按钮, 然后按 "向左滚动" 按钮选择 "工具", 通过按下菜单上的按钮选择 "手动控制", 然后按下菜单按钮选择 "获取参数".
	<ol>
<li>通过按下滚动向右键, 将 "大篷车平滑" 设置为五, 通过按下滚动向右键两次将光标移动到右边两个空格, 然后通过按下菜单按钮五次来增加 "大篷车" 设置。选择 "接受", 按绿色接受按钮一旦完成。</li>
		<li>按下菜单向下键, 将平均值设置为 10, 然后通过按下滚动按钮两次并调整十位中的值, 通过按下 "向右滚动" 按钮 o 移动到一个位置, 然后移动到右边两个位置。通过按下菜单按钮一次, 将此值降低为零。选择 "接受", 按绿色接受按钮一旦完成。</li>
		<li>按 "主页" 按钮, 通过按下菜单按钮选择反射率, 并将探针牢牢地放在白色标准上。然后按下菜单按钮来存储参考白色标准。通过按下滚动右键并通过按下菜单按钮查看反射率来存储深色标准。</li>
	</ol>
</li>
	<li>测量每个蛋壳的反射率六倍, 通过测量两次接近钝极 (鸡蛋的宽端), 两次在卵的赤道, 两次接近锋利的极点 (较窄的一端)。一定要报告是否避免了斑点。在实验性的鸡蛋和宿主的卵上进行这项研究。</li>
	<li>使用具有 0.25 nm 平滑跨度的局部加权多项式函数, 利用颜色分析软件29来平滑反射曲线中的噪声。如果颜色分数 (如亮度) 在卵子模型的区域之间没有明显的重复性 (步骤 3.7), 重绘卵子 (步骤 2.2–2.6);否则, 平均鸡蛋模型着色整个鸡蛋。</li>
	<li>决定问题的相对感光灵敏度的最合适的集合。 注: 这可能是一个一般的紫外线敏感或紫色敏感的鸟, 或一次可以选择模型相对灵敏度30,31,32。</li>
	<li>
通过将蛋壳反射率、感光灵敏度和适当的产品集成在一起, 量化每个感光细胞的量子捕捉33 , 分别为单34和双锥35、36。整个鸟类视觉光谱 (即300–700 nm) 的辐照光谱。
	<ol>
<li>使用相对量子捕获估计在禽四面体颜色空间33,37中生成坐标。确保与量子捕获不同, 这些相对量子捕获到1的总和。</li>
	</ol>
</li>
	<li>
使用量子捕捉 (步骤 3.10) 估计 discriminability, 在 JND 中, 主机蛋壳颜色 (见步骤 2.6.1) 和每个外来卵子的感知颜色使用神经噪声有限视觉模型32 ,38, 39.
	<ol>
<li>使用相同的光谱仪和光源测量模型卵 (步骤 3.7), 如果可能的话, 测定主人自己的蛋壳着色。 注意: 实际或后勤方面的考虑可能使这不可能, 在这种情况下, 测量来自不同巢的寄主卵的子集, 以建立一个平均寄主蛋壳着色。</li>
		<li>使用韦伯分数为长波敏感锥 0.140。</li>
		<li>考虑到锥的相对丰度和双锥30的主要成员。 注: 如果使用的鸡蛋颜色仅在与天然蛋壳颜色相对应的梯度上, 则宿主和实验卵之间的 JND 可以乘以1或-1, 以区分两者之间的差异 (如蓝绿色或褐色), 请参阅步骤 1.1. 1–1.1. 2)。如果使用的鸡蛋颜色落在多个渐变或填充的颜色空间, 用感知均匀色度图41来总结鸡蛋之间可感知的颜色差异。这类图中的坐标描述了实验卵和宿主卵颜色之间知觉颜色差异的方向和大小, 这些都可以用于进一步的分析。</li>
	</ol>
</li>
</ol>4. 实地工作
<ol><li>确定要研究的物种。 注意: 考虑的因素包括 (但不限于) 寄主和/或寄生虫种类的丰度, 以及寄主是否掌握42或穿刺43 rejecter, 这将影响使用的鸡蛋类型 (例如, 不使用硬人工模型蛋为穿刺喷射器44)。</li>
	<li>系统地搜索研究区的巢。检查以前的嵌套记录, 可以在某些物种45中提供合理的起始位置。 注: 可见标记或标记可增加捕食46的风险;因此, 考虑使用手持式 GPS 代替。</li>
	<li>
每天使用直接或录像方法监视那些巢, 记录每个宿主卵的存在直到实验开始 (步骤 4.4);例如, 离合器完成后的某一天。 注意: 这个日常监测将持续到实验结束 (步骤 4.6)。<ol>
<li>倾听成人发出的警报, 如果他们持续超过三十年代, 就离开这个地区。当任何潜在的巢捕食者存在时, 不要靠近巢, 特别是如果它是一个视觉上的捕食者 (例如, corvid)。</li>
		<li>方法和离开不同地点的巢,即, 走过去的巢, 所以哺乳动物捕食者不能直接跟随化学线索的巢穴。 注意: 这种方法在某些生境中可能是不可行的, 即致密的芦苇床。</li>
		<li>总是尽量减少对巢和巢周围区域的物理干扰。</li>
		<li>在筑巢期间, 不要靠近鸟巢, 因为如果在产卵46之前, 许多鸟儿被扰乱, 就会弃巢。</li>
	</ol>
</li>
	<li>
轻轻地加入一个实验鸡蛋到一个宿主的巢, 滑动到巢的杯子一侧。不要丢弃实验模型, 因为它会破坏宿主的卵。 注意: 随机分配治疗。<ol>
<li>记录, 如果主机的父母留在附近, 从而有机会亲眼目睹的行为, 人工寄生47。记录和统计控制变量, 指示主机是否从巢47刷新。在父母不在的时候进行鸡蛋介绍。</li>
		<li>如果系统中的寄生虫移除宿主卵, 则收集宿主卵。 注意: 在不影响卵子的宿主对实验卵22的反应中, 这可能是不必要的。</li>
	</ol>
</li>
	<li>
使用一套控制巢 (被参观的巢, 被检查和被处理的蛋, 但没有实验的蛋被增加或交换) 确定自然巢遗弃率。仅当从控制巢中移除宿主卵时, 才将其从实验巢中移走 (参见步骤 4.5.1)。 注意: 这是至关重要的, 因为遗弃可能不是针对某些外国鸡蛋的反应, 但可能是对其他鸡蛋类型的反应。<ol>
<li>根据已知的或预期的样本大小和估计效果, 选择先验的控制巢数。将每个n 个巢分配为一个控制巢, 直到统计确定巢逃兵是否是对实验性卵子的宿主反应 (步骤 5.1)。 注: 如果一个主卵被移除, 取出与实验治疗相同数量的宿主卵, 将一颗卵子放在手上5秒, 然后替换它, 并在巢中保持与治疗巢相同的时间长度。如果不移除宿主卵, 将一颗卵子放在手里5秒, 然后替换它, 留在巢中, 用于实验巢的时间 (例如, 十年代)。</li>
	</ol>
</li>
	<li>
重新访问巢穴, 以确定每个宿主巢 (包括控件) 的响应。在实验操作的几个小时内检查巢。 注意: 在某些物种, 拒绝可能发生在同一天的实验性寄生;因此, 在实验操作48的几个小时内检查巢是很重要的。<ol>
<li>检查巢时使用伸缩镜, 避免与巢或离合器直接接触。</li>
		<li>避免在恶劣天气 (雨, 热, 或冷) 的观察, 因为这可能增加对雏鸟和鸡蛋46的危险。</li>
		<li>执行每日检查, 直到主机对所介绍的鸡蛋作出反应或一定的时间已经过去了。 注: 按惯例, 如果卵在巢中保留5–6天, 宿主被认为是受体22;然而, 一些宿主个体甚至在49以后做出反应, 遗漏了这种反应必然会偏向卵子的排斥率估计。理想的情况是, 研究人员应该在系统中确定95% 个家庭明智的延迟的置信区间, 并以此为标准。</li>
	</ol>
</li>
</ol>5. 统计分析
<ol><li>
在一个完美的随机研究中使用费舍尔的确切测试 (即, 实验和控制巢完全散置, 从而在铺设日期, 离合器或任何其他已知影响巢遗弃的参数没有不同) 比较的数量逃兵控制 (步骤 4.5) 和治疗巢。否则, 使用一个广义线性模型 (GLM) 与潜在相关的变量 (见下文), 作为一个一般更谨慎的方法。
	<ol>
<li>如果实验巢的遗弃率明显高于控制巢, 那么代码就会把鸡蛋和废弃的卵子 "拒绝"。 注意: 根据惯例, 当主人识别出寄生蛋并将其取出或丢弃 (与整个巢) 时, 宿主已经 ' 拒绝 ' 卵子。</li>
		<li>如果遗弃率在实验和控制巢之间没有区别, 排除逃兵从分析, 因为他们不是宿主反应对寄生和代码反应作为 ' 逐出 '。 注意: 按照惯例, "弹出" 指的是卵子实际上是从巢中移除的。</li>
		<li>记录主机拒绝卵子的日期和时间。根据您在步骤5.1 上的发现进行重新编码响应变量. 1–5.1. 2。</li>
	</ol>
</li>
	<li>报告费舍尔的确切测试, 它的关联赔率比, 及其适当的置信区间。</li>
	<li>
决定将添加到预测模型中的任何有意义的变量 (例如, 步骤 5.4–5.5)。
	<ol>
<li>指定每个协的编码 (如连续、分类或序号)。</li>
		<li>代码日期作为序号天和中心日期分开在几年之内去除任何可能的混淆由于变化归因于年或季节性7,47,50,51。</li>
		<li>将参与交互的任何变量居中, 以便在模型输出中更容易地解释其较低的顺序术语。 注: 缩放变量允许直接比较研究之间的效果, 有时可以提高模型收敛性。</li>
	</ol>
</li>
	<li>
利用广义线性模型 (GLM) 或广义线性混合模型 (GLMM), 用二项误差分布和 logit 链接函数预测主机响应 (弹出或拒绝 vs 接受)。 注意: GLM 或 GLMM 之间的选择取决于数据, 如果包含随机效果 (例如, 巢 ID, 年)。随机因素至少应有5的水平, 否则方差可能估计差52。<ol>
<li>报告测定系数 (通常为 R2), 以表明方差的比例是由线性模型53,54解释的。</li>
	</ol>
</li>
	<li>预测用负二项误差分布的 GLM (或如果数据不 overdispersed 的泊松误差分布) 和日志链接, 鸟对实验性寄生的反应需要多长时间。 注意: 研究人员指的是一只鸟作为 "反应潜伏期" 所需的时间长短, 这是精确到数天的报告, 因此实验当天被拒绝的卵子的潜伏期为零。模型响应变量具有太多的零 (> 50%) 使用零充气或跨栏模型55。</li>
	<li>
使用诊断工具检查模型是否满意地预测数据, 并报告模型汇总统计以量化方差模型解释的比例56 , 57. 报告测定系数 (通常为 R2), 见步骤 5.4.2.
	<ol>
<li>使用图形验证来验证负二项模型, 方法是生成分量分量图并根据拟合值绘制皮尔逊残差。 注意: 运行良好的模型不会有异常或意外模式55。</li>
		<li>使用合适的测试 (如霍斯默 Lemeshow 测试) 验证二项模型, 以及在 R 包中可用的其他诊断程序, "binomTools"58包含一整套诊断工具。</li>
	</ol>
</li>
	<li>
考虑控制一致的变量, 以便于研究之间的一致性和可比性。 注: 常见变量包括离合器尺寸22, 铺设日期59, 巢龄在操纵49, 以及主机是否被刷新从巢或不47。许多人, 尤其是早期的研究没有使用任何变量。作者应该考虑另外分析蛋类型 (或不同的梯度) 的作用,不用变量使他们的结果定量地比这些研究缺乏变量。<ol>
<li>使用信息理论的方法, 并报告的结果, 平均许多潜在的模型解释主机行为60。 注: 或者, 使用步骤明智的回归分析作为模型选择算法61。研究人员应该使用一个预定义的标准 (例如, 调整 R2, 锦葵 ' Cp, Akaike 的信息标准, 施瓦茨的 BIC, 或 p 值), 并提供完整的模型 (共同变量) 和最终缩减模型。</li>
	</ol>
</li>
</ol>

海洋光学为这篇手稿提供了页面费用。

虽然卵子排斥实验是研究育雏寄生虫-宿主共同进化66的最常见方法, 但缺乏标准化材料、技术或协议的协同努力。这对于 meta 分析尤其成问题。据我们所知, 目前还没有对宿主卵子排斥的 meta 分析控制了研究67、68中的方法学差异, 包括被认为是模仿的或非模仿的。这是一个主要的问题, 因为模仿 (按人体标准) 鸡蛋可以被主机拒绝比看似?...

育雏寄生虫产卵在其他物种的巢, 然后可以提高他们的幼仔和支付与父母照顾1,2,3相关的费用。这种欺骗行为, 以骗取宿主的寄生虫和侦查, 以检测寄生虫的一部分, 对两个行为者提供强烈的选择性压力。在一些鸟类育雏寄生的情况下, 寄主对不同的寄生卵的识别选择了模拟宿主卵的寄生虫, 这就产生了寄主和寄生虫之间的进化军备竞赛4。研究巢寄生是重要的, 因为它是一个模型系统的研究共同进化动力学和决策在野外5。鸡蛋排斥实验是研究鸟类巢寄生的最常用方法之一, 是生态学家研究6种种间相互作用的重要工具。
在卵子排斥实验过程中, 研究人员通常引入天然或模型卵, 并评估宿主在标准化时期对这些实验卵的反应。这种实验可以涉及在巢7之间交换真正的鸡蛋 (在外观上变化), 或染色或油漆真正的鸡蛋表面 (可选添加模式), 并返回他们原来的巢8, 或生成模型鸡蛋,操纵的特性, 如颜色9, 斑点10, 大小11, 和/或形状12。宿主对不同外观的鸡蛋的反应, 可以提供有价值的洞察他们所使用的信息内容, 以达到一个鸡蛋排斥决定13和只是如何不同的鸡蛋需要得到一个反应14。最优接受阈值理论15指出, 主机应平衡错误接受寄生卵子的风险 (接受错误) 或错误地删除自己的卵子 (拒绝错误), 通过检查他们自己的鸡蛋之间的差异 (或这些卵的内部模板) 和寄生蛋。因此, 一个接受阈值存在的范围之外, 主机决定刺激是太不同的容忍。当寄生风险较低时, 接受误差的风险低于寄生风险高;因此, 决策是特定于上下文的, 当感知到的风险改变14、16、17时, 就会适当地转移。
最优接受阈值理论假定宿主和寄生表型的持续变化决定宿主的基础决策。因此, 测量寄主对不同寄生虫表型的反应是必要的, 以确定宿主种群 (其自身的表型变化) 对一系列寄生形式的耐受程度。然而, 几乎所有先前的研究都依赖于分类鸡蛋颜色和 maculation 处理 (例如, 模拟/不模仿)。只有当寄主蛋壳表型没有变化, 这不是一个生物学上的实际期望, 所有的反应是直接可比的 (无论模仿程度)。否则, 一个 "模拟" 蛋模型将会有所不同, 它是如何相似的宿主鸡蛋内和人口之间, 这可能会导致混淆, 当比较调查结果18。理论表明, 宿主的决定是根据寄生蛋和他们自己的14之间的区别, 不一定是特定的寄生蛋颜色。因此, 使用单卵模型不是一个理想的方法来测试假设的主机决定阈值或歧视能力, 除非只是明显的差异 (以下 JND) 之间的鸡蛋模型类型和个人宿主鸡蛋颜色是变量的兴趣。这也适用于实验研究, 交换或添加天然蛋, 以测试主机响应的自然范围的颜色19。然而, 虽然这些研究确实允许变异的寄主和寄生虫表型, 他们是有限的自然变异发现在性状6, 特别是当使用同种卵7。
相比之下, 那些制造各种颜色的人造蛋的研究人员不受自然变异的限制 (例如, 他们可以调查对 superstimuli20的反应), 让他们去探索寄主感知6的极限。最近的研究使用了新的技术来测量宿主的反应在一个表型范围内, 通过画实验蛋设计, 以匹配和超过自然范围的变化, 蛋壳9和专色21。研究宿主对带有颜色渐变的鸡蛋的反应可以揭示潜在的认知过程, 因为理论预测, 如接受阈值15或协同进化特别是模仿4, 是基于持续的差异性状。例如, 通过使用这种方法, Dainson等。21建立了当蛋壳地面着色和斑点着色之间的色对比更高时, 美国罗宾鸫 migratorius倾向于强烈拒绝蛋。这个发现提供了宝贵的洞察力如何这个宿主处理信息, 在这种情况下通过发现, 以决定是否删除寄生蛋。通过自定义颜料混合物, 研究人员可以精确地操纵实验鸡蛋颜色和宿主卵颜色之间的相似性, 同时标准化其他混杂因素, 如斑点模式10, 蛋大小22和鸡蛋形状23。
为了鼓励进一步的复制和 metareplication24的经典和最近的卵子排斥工作, 重要的是, 科学家使用的方法, 标准化跨系统系统 (不同的寄主物种)7,22,空间 (不同的寄主人口)7,22,25,26和时间 (不同的繁殖季节)7,22,25,26 ,27, 很少做。未标准化28的方法后来被证明导致 artefactual 结果29,30。本文是一组指导研究人员试图复制这种类型的卵子排斥实验, 检查对持续变化的反应, 并强调了一些重要的方法论概念: 控制巢的重要性,先验假说, metareplication, pseudoreplication, 色谱分析。尽管在禽类寄主-寄生虫共同进化领域控制的卵子排斥实验还没有全面的协议存在。因此, 这些准则将是提高实验室间和内部重复性的宝贵资源, 因为任何假说的真正检验都在 metareplication,即在系统系统、空间和时间24中重复整个研究, 这只有在使用一致方法29、30、31时才能有意义地完成。

<table>
	<tbody>
		<tr>
			<td>Replicator Mini +</td>
			<td>Makerbot</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Cobalt Turquoise Light</td>
			<td>Winsor &#38; Newton</td>
			<td>28382</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Titanium White</td>
			<td>Winsor &#38; Newton</td>
			<td>28489</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Cobalt Green</td>
			<td>Winsor &#38; Newton</td>
			<td>28381</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Cobalt Turquoise</td>
			<td>Winsor &#38; Newton</td>
			<td>28449</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Burnt Umber</td>
			<td>Winsor &#38; Newton</td>
			<td>28433</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Red Iron Oxide</td>
			<td>Winsor &#38; Newton</td>
			<td>28486</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Cadmium Orange</td>
			<td>Winsor &#38; Newton</td>
			<td>28437</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Raw Umber Light</td>
			<td>Winsor &#38; Newton</td>
			<td>28391</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Yellow Ochre</td>
			<td>Winsor &#38; Newton</td>
			<td>28491</td>
			<td></td>
		</tr>
		<tr>
			<td>Professional Acrylic Paint Mars Black</td>
			<td>Winsor &#38; Newton</td>
			<td>28460</td>
			<td></td>
		</tr>
		<tr>
			<td>Paint Brush</td>
			<td>Utrecht</td>
			<td>206-FB</td>
			<td>Filbert brush</td>
		</tr>
		<tr>
			<td>Paint Brush</td>
			<td>Utrecht</td>
			<td>206-F</td>
			<td>Flat brush</td>
		</tr>
		<tr>
			<td>Hair Dryer</td>
			<td>Oster</td>
			<td>202</td>
			<td></td>
		</tr>
		<tr>
			<td>Fiber optic cables</td>
			<td>Ocean Optics Inc.</td>
			<td>OCF-103813</td>
			<td>1 m custom bifurcating fiber optic assembly with blue zip tube (PVDF), 3.8mm nominal OD jeacketing and 2 legs</td>
		</tr>
		<tr>
			<td>Spectrometer</td>
			<td>Ocean Optics Inc.</td>
			<td>Jaz</td>
			<td>Spectrometer unit with a 50 um slit width, installed with a 200-850 nm detector (DET2B-200-850), and grating option # 2.</td>
		</tr>
		<tr>
			<td>Battery and SD card module for spectrometer</td>
			<td>Ocean Optics Inc.</td>
			<td>Jaz-B</td>
			<td></td>
		</tr>
		<tr>
			<td>Light source</td>
			<td>Ocean Optics Inc.</td>
			<td>Jaz-PX</td>
			<td>A pulsed xenon light source</td>
		</tr>
		<tr>
			<td>White standard</td>
			<td>Ocean Optics Inc.</td>
			<td>WS-1-SL</td>
			<td>made from Spectralon</td>
		</tr>
		<tr>
			<td>OHAUS Adventurer Pro Scale</td>
			<td>OHAUS</td>
			<td>AV114C</td>
			<td>A precision microbalance</td>
		</tr>
		<tr>
			<td>Gemini-20 portable scale</td>
			<td>AWS</td>
			<td>Gemini-20</td>
			<td>A standard scale</td>
		</tr>
		<tr>
			<td>Empty Aluminum Paint Tubes (22 ml)</td>
			<td>Creative Mark</td>
			<td>NA</td>
			<td></td>
		</tr>
		<tr>
			<td>Telescopic mirror</td>
			<td>SE</td>
			<td>8014TM</td>
			<td></td>
		</tr>
		<tr>
			<td>GPS</td>
			<td>Garmin</td>
			<td>Oregon 600</td>
			<td></td>
		</tr>
		<tr>
			<td>220-grit sandpaper</td>
			<td>3M</td>
			<td>21220-SBP-15</td>
			<td>very fine sandpaper</td>
		</tr>
		<tr>
			<td>400-grit sandpaper</td>
			<td>3M</td>
			<td>20400-SBP-5</td>
			<td>very fine sandpaper</td>
		</tr>
		<tr>
			<td>color analysis software: &#8216;pavo&#8217;, an R package</td>
			<td>for use in, R: A language and environment for statistical computing</td>
			<td>v 1.3.1</td>
			<td>https://cran.r-project.org/web/packages/pavo/index.html</td>
		</tr>
		<tr>
			<td>UV clear transparent</td>
			<td>Flock off!</td>
			<td>UV-001</td>
			<td>A transparent ultraviolet paint</td>
		</tr>
		<tr>
			<td>Plastic sandwich bags</td>
			<td>Ziploc</td>
			<td></td>
			<td>Regular plastic sandwich bags from Ziploc that can be purchased at the supermarket.</td>
		</tr>
		<tr>
			<td>Kimwipes</td>
			<td>Kimberly-Clark Professional</td>
			<td>34120</td>
			<td>11 x 21 cm kimwipes</td>
		</tr>
		<tr>
			<td>Toothbrush</td>
			<td>Colgate</td>
			<td></td>
			<td>Toothbrush</td>
		</tr>
	</tbody>
</table>

probing the limits of egg recognition using egg rejection experiments along phenotypic gradients

育雏的寄生虫产卵在其他女性的巢, 让主人的父母孵化和后方他们的幼仔。研究育雏寄生虫如何操纵宿主, 以提高他们的幼仔和寄主如何检测寄生提供了重要的见解, 在共同进化生物学领域。育雏寄生虫, 如杜鹃和牛鹂, 获得了进化的优势, 因为他们不必支付养育自己的幼仔的成本。然而, 这些成本选择宿主防御的所有发展阶段的寄生虫, 包括鸡蛋, 他们的幼鸟, 和成人。抗鸡蛋实验是研究宿主防御的最常用方法。在这些实验中, 研究者将实验鸡蛋放在宿主巢中, 并监视宿主的反应方式。颜色经常纵 , 并且期望是卵子歧视的可能性和寄主和实验性蛋的不同程度是正相关的。本文从描述创建一致卵色的方法出发, 对实验结果进行分析, 以此为指导进行鸡蛋排斥实验。特别注意一种新的方法, 涉及独特的彩色鸡蛋沿颜色梯度, 有潜力探讨颜色偏差的主机识别。没有标准化, 就不可能以有意义的方式比较研究结果;在这个领域内的标准协议将允许越来越准确和可比的结果, 进一步的实验。

生成五颜六色的鸡蛋模型
自定义涂料混合物和天然卵的反射光谱如图 1A所示。在巢寄生研究中使用的颜料混合物应与光谱形状 (颜色) 和震级 (亮度) 的自然反射率测量紧密对应。如果达到这个目标, 实验鸡蛋的颜色应该被寄主视为天然的蛋色。为了评估主机识别, 应将这些反射率?...

Watch this Scientific Journal Video about Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients at JoVE.com

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

基于表型梯度的鸡蛋排斥试验对鸡蛋识别极限的探讨

本议定书提供了运行卵子排斥实验的指导方针: 概述了绘制实验鸡蛋模型的方法, 以模拟天然鸟卵的颜色, 进行实地调查, 分析收集到的数据。该协议为进行类似的鸡蛋排斥实验提供了统一的方法。

Brood parasites lay their eggs in other females' nests, leaving the host parents to hatch and rear their young. Studying how brood parasites manipulate ...

probing-limits-egg-recognition-using-egg-rejection-experiments-along

Research

JoVE Journal

Behavior

8.2K 次观看.  Long Island University-Post. 本议定书提供了运行卵子排斥实验的指导方针: 概述了绘制实验鸡蛋模型的方法, 以模拟天然鸟卵的颜色, 进行实地调查, 分析收集到的数据。该协议为进行类似的鸡蛋排斥实验提供了统一的方法。

视频: Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Assessing Forelimb Function after Unilateral Cervical SCI using Novel Tasks: Limb Step-alternation, Postural Instability and Pasta Handling

Cervical spinal cord injury (cSCI) can cause devastating neurological deficits, including impairment or loss of upper limb and hand function. A majority of the spinal cord injuries in humans occur at the cervical levels. Therefore, developing cervical injury models and developing relevant and sensitive behavioral tests is of great importance. Here we describe the use of a newly developed forelimb step-alternation test after cervical spinal cord injury in rats. In addition, we describe two behavioral tests that have not been used after spinal cord injury: a postural instability test (PIT), and a pasta-handling test. All three behavioral tests are highly sensitive to injury and are easy to use. Therefore, we feel that these behavioral tests can be instrumental in investigating therapeutic strategies after cSCI.

Three new behavioral tests (forelimb step-alternation, postural instability test, pasta handling test) for evaluating forelimb function after cervical spinal cord injury in rodents are described.

采用新型任务评估前肢功能单侧颈脊髓损伤后:肢体步交替，姿势不稳和面食处理

Cervical spinal cord injury (cSCI) can cause devastating neurological deficits, including impairment or loss of upper limb and hand function. A majority ...

科研

行为学

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.
Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

A fully automated protocol for rodent operant conditioning is proposed. The protocol relies on precise temporal control of behavioral events to investigate the extent to which this control influences neural activity underlying sensorimotor integration and cognitive control experiments.

为感觉运动整合与认知控制实验完全自动化的啮齿动物调理协议

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. ...

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

在日常生活环境评价一个智能手机为基础的人类活动识别系统

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion expressions, and multimodal (i.e., film clips) stimuli that included contextual cues. All stimuli were presented via computer. Participants were required to indicate how the person in each stimulus was feeling using a forced-choice format. Additionally, for the film clips, participants had to indicate how they felt in response to the stimulus, and the level of intensity with which they experienced that emotion.

This paper describes how to implement a battery of behavioral tasks to examine emotion recognition of isolated facial and vocal emotion expressions, and a novel task using commercial television and film clips to assess multimodal emotion recognition that includes contextual cues.

探索孤立的表达式和电影剪辑的使用与创伤性脑损伤的人评价情感识别

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion ...

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

使用小波熵来证明正念练习如何增加不规则脑与心脏活动之间的协调

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

The object recognition test (ORT) is a commonly used behavioral assay for the investigation of various aspects of learning and memory in mice. The ORT is fairly simple and can be completed over 3 days: habituation day, training day, and testing day. During training, the mouse is allowed to explore 2 identical objects. On test day, one of the training objects is replaced with a novel object. Because mice have an innate preference for novelty, if the mouse recognizes the familiar object, it will spend most of its time at the novel object. Due to this innate preference, there is no need for positive or negative reinforcement or long training schedules. Additionally, the ORT can also be modified for numerous applications. The retention interval can be shortened to examine short-term memory, or lengthened to probe long-term memory. Pharmacological intervention can be used at various times prior to training, after training, or prior to recall to investigate different phases of learning (i.e., acquisition, early or late consolidation, or recall). Overall, the ORT is a relatively low-stress, efficient test for memory in mice, and is appropriate for the detection of neuropsychological changes following pharmacological, biological, or genetic manipulations.

The object recognition test (ORT) is a simple and efficient assay for evaluating learning and memory in mice. The methodology is described below.

小鼠学习记忆研究的新对象识别实验

The object recognition test (ORT) is a commonly used behavioral assay for the investigation of various aspects of learning and memory in mice. The ORT is ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

面向多参与者的决策科学与导航实验的网络化桌面虚拟现实设置

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

用生理学方法进行虚拟现实实验

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various physiological or pathological changes. The object location task (OLT) and the novel object recognition task (NORT) are two effective behavioral tasks commonly used to reveal the function and relative health of specific brain regions involved in memory. While both of these tests exploit the inherent preference of mice for the novelty to reveal memory for previously encountered objects, the OLT primarily evaluates spatial learning, which relies heavily on hippocampal activity. The NORT, in contrast, evaluates non-spatial learning of object identity, which relies on multiple brain regions. Both tasks require an open-field-testing arena, objects with equivalent intrinsic value to mice, appropriate environmental cues, and video recording equipment and the software. Commercially available systems, while convenient, can be costly. This manuscript details a simple, cost-effective method for building the arenas and setting up the equipment necessary to perform the OLT and NORT. Furthermore, the manuscript describes an efficient testing protocol that incorporates both OLT and NORT and provides typical methods for data acquisition and analysis, as well as representative results. Successful completion of these tests can provide valuable insight into the memory function of various mouse model systems and appraise the underlying neural regions that support these functions.

Here we provide a protocol which includes comprehensive instructions for the economical establishment of murine object location and novel object recognition behavioral testing, including the design, cost, and construction of required equipment as well as execution of behavioral testing, data collection, and analysis.

一种新的小鼠目标识别与目标定位行为测试

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various ...

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

This protocol introduces a method for generating strictly controlled and objectively defined stimuli for figure recognition experiments. A (6, n) figure consists of n line segments that are spanned between n pairs of points located at the vertices of an invisible regular hexagon. The structural properties (graph invariants) and superficial features (non-graph invariants) of each (6, n) figure with n values ranging from 1 to 6 are calculated and stored in a database. Using this database, experimenters can systematically extract appropriate figures depending on the purpose of the experiment. Furthermore, if the database does not contain necessary information, new feature values can sometimes be calculated ad hoc from the formation of a specific (6, n) figure. Let us call a mirror-reflected pair of figures an axisymmetric (Ax) pair. An Ax pair of figures is known to be more difficult to discriminate than a non-identical pair in the decision of whether the shapes of a given pair are rotated-to-be-identical (Idr). The purpose of the present experiment is to examine whether the sameness of line lengths between two figures in a pair causes the discrimination of the pair to be as difficult as that of an Ax pair. Mutually isomorphic figures share common structural properties despite differences in shape. Ax pairs and Idr pairs are special cases of isomorphic pairs. Furthermore, an Ax pair and Idr pair share most of the superficial feature values, except the relative direction from one location to another location across an axis of symmetry is opposite for an Ax pair. Three types of mutually isomorphic (6, 4) figure pairs were generated: Idr; Ax; and non-identical, non-axisymmetric, isomorphic (Nd) pairs. Nd pairs were further classified into three subcategories according to the superficial feature values of the degree of line length differences.

This protocol describes a method for an experiment that examines whether specific graph and non-graph properties (features) are relevant to the recognition of figures. The method uses a database that stores various feature values of respective figures called (6 point, n line) figures.

为图形识别实验生成严格控制的刺激

This protocol introduces a method for generating strictly controlled and objectively defined stimuli for figure recognition experiments. A (6, n) figure ...