The slow rise of technology: Computer vision techniques in fish population connectivity

1. Technological advancements in data collection and analysis are producing a new generation of ecological data. Among these, computer vision (CV) has received increased attention for its robust capabilities for rapidly processing large volumes of digital imagery.
2. In marine ecosystems, the study of fish connectivity provides fundamental information for assessing fisheries stocks, designing and implementing protected areas and understanding the impact of habitat loss. While the field of fish connectivity has benefited from technological advancements, the extent to which novel techniques, such as CV, have been utilized has not been assessed. To inform future directions and developments, this study reviewed the current use of CV in fish connectivity research, quantified how the implementation of such technology in fish connectivity research compared with other areas of marine research and described how this field could benefit from CV.
3. The review found that the use of remote camera systems in fish connectivity research is increasing, but the implementation of automated analysis of digital imagery has been slow. Successful implementation and expansion of CV frameworks in aquaculture and coral reef ecology suggest that CV techniques could greatly benefit fish connectivity research.
4. A case study of potential use of CV in fish connectivity research, scaling up optimal foraging models to predict marine population connectivity, highlights how beneficial it could be.
5. The capacity for CV techniques to be adopted alongside traditional approaches, the unparalleled speed, accuracy and reliability of these approaches and the benefits of being able to study ecosystems along multiple spatial–temporal scales, all make CV a valuable tool for assessing connectivity. Ultimately, these technologies can assist data-driven decisions that directly influence the health and productivity of marine ecosystems.

     

淮安市现代农业远程视频监控平台建设探讨

介绍淮安市现代农业远程视频监控平台建设情况,重点分析了平台框架构成、建设成果和多种效益,突出了经验做法、特色亮点,提出了下一步计划打算。     

公开视频课程模块化体系研究

以控制工程基础课程为研究对象,对公开视频课程体系建设进行研究和探索。针对目前公开视频课存在的问题,在对试点班级需求分析的基础上提出了控制工程基础视频公开课程相应合理的方案。探讨了该公开视频课程体系的框架模块化发展,说明依托现代网络平台示范教学与在线学习成为课堂教学的有益补充,促进了优质教学资源的共享。     

苹果叶片总蛋白提取及其双向电泳分析

蛋白质提取方法和电泳条件的建立是蛋白质组学研究中双向电泳分析的关键。通过比较不同提取方法,包括酚/SDS抽提、TCA-丙酮沉淀法、甲醇/醋酸铵沉淀法以及改良HB-酚抽提方法。此外,还优化了样品上样量、等电聚焦程序设置等条件。从所得2-D图谱分离效果来看,改良HB-酚抽提方法所得蛋白点数最多,2-D图谱背景清晰、分离效果好,明显优于其他3种方法。300μg上样量可作为苹果叶片总蛋白2-DE分析的理想的上样量,同时优化了等电聚焦程序,最终得到的2-D图谱分离效果较为理想。该研究的开展也为苹果叶片蛋白质组学后续研究奠定了基础。     

适用于水稻微粒体蛋白的双向电泳技术及苯达松抗性相关蛋白的初步分析

针对不同来源的样品，建立相应的双向电泳(two-dimensional gel electrophoresis, 2-DE)技术是开展蛋白质组学研究的基础。以野生型水稻(O. sativa L.)农林8号(N8)及其苯达松敏感致死(bentazon sensitive lethal, bsl)突变体农林8号m (N8m)为材料，通过优化条件，建立了适合于水稻微粒体蛋白的双向电泳技术。并通过双向电泳图谱的比对，共找到15个差异蛋白点，分为供试材料间差异、苯达松处理前后差异和N8在苯达松处理后特异表达等3种类型。结合苯达松抗性比较及苯达松残留分析，推测材料间差异是因γ射线辐照引起基因结构变异，从而影响了基因的表达；苯达松处理前后差异与氧自由基清除酶系或系统获得抗性相关酶系有关；N8在苯达松处理后特异表达与苯达松代谢有关。     

基于混合高斯模型的移动奶牛目标实时检测

针对奶牛养殖场背景复杂和环境多变导致现有的目标检测算法无法满足鲁棒性和实时性需求的问题,基于递归背景建模思想,在混合高斯模型中引入惩罚因子,提出了一种动态背景建模方法,采用局部更新策略,以降低模型复杂度和解决前景消融问题;提出基于色度偏差和亮度偏差的二分类算法,避免目标物阴影区域的影响。对不同天气及环境变化剧烈情况下获取的奶牛视频样本进行实验。结果表明,与混合高斯模型相比,平均模型复杂度降低了50.85%,前景误检率和背景误检率分别降低了19.50和13.37个百分点,单帧运行时间降低了29.25%,检测准确率更高、实时性更好,且解决了前景消融问题,能满足在复杂背景和环境条件下实时提取奶牛目标的要求。     

Real-Time Self-Photographing Technology in the Virtual Environment

As a new technology towards 21st century, the occurs of the virtual reality technology is a result of computer graphic, human machine interface technology, sensor technology and artificial intelligence. The real time self photographing technology based on the virtual environment is a typical application of this virtual reality technique. A series of things has been discussed and studied ,such as visual reality,video intercept and so on,and the real time self photographing system based on the technology of blue background image extracting. A new arithmetic is emphasized which is produced during study and exploitation.It is image fusion technique based on the contrast appreciate characteristic.     

盐胁迫对燕麦种子萌发期蛋白质组影响的研究

为探析盐胁迫下燕麦种子萌发时蛋白质的差异表达及其相关生理功能分析,比较了燕麦在不同浓度氯化钠溶液处理下种子的萌发及幼苗生长能力,然后采用二维凝胶电泳结合基质辅助激光解析飞行时间质谱的蛋白质组学方法分析种子在0和1%氯化钠溶液处理种子萌发时的蛋白质组差异表达。结果表明:随着盐胁迫的加剧,燕麦种子的萌发力和成苗率降低。蛋白质组分析显示:1%氯化钠溶液处理使得燕麦种子蛋白质组二维凝胶中11个蛋白质点表达发生变化,其中10个蛋白质点表达量上调。质谱鉴定得到3个可靠蛋白,分别为:燕麦蛋白、燕麦蛋白N9和蛋白质二硫键异构酶。这些蛋白的差异表达可能与盐分胁迫下燕麦种子萌发的生理活动有关联。     

小麦V-CMS和杂种F1代线粒体蛋白质组表达差异

本研究以V型小麦恢复基因的近等基因系为基础材料,运用蛋白质组学技术对V-CMS及杂种F1黄化苗和幼穗时期线粒体蛋白质表达图谱进行了比较,分析了差异蛋白质点与CMS的关系.主要结果如下:(1)比较了黄化苗和幼穗线粒体蛋白质的表达图谱,鉴定了10个差异表达的蛋白质点,包括与能量代谢有关的F0-F1ATP酶复合物的亚基和一些...