人工智能與機(jī)器人 生命科學(xué) 無人機(jī)遙感及激光雷達(dá)掃描系統(tǒng) 植物生理生態(tài)研究 環(huán)境保護(hù)與防治 實(shí)驗(yàn)室自動化定制方案 種業(yè)實(shí)驗(yàn)室建設(shè)與種子研究 溫室自動化與植物培養(yǎng) 植物表型研究 植物樣品采集及儲存 土壤研究 海洋與氣象監(jiān)測 淡水生態(tài)監(jiān)測 動物研究 澤泉?jiǎng)?chuàng)新教育 食品科學(xué) 能量與信息場研究 物聯(lián)網(wǎng)
藻類群落結(jié)構(gòu)掃描成像分析系統(tǒng)—浮標(biāo)版CytoBuoy
日期：2017-03-27 14:21:35
主要功能




1. 專業(yè)分析浮游植物細(xì)胞，同時(shí)具備傳統(tǒng)流式細(xì)胞儀經(jīng)典功能1.jpg
2. 可以掃描記錄各種光學(xué)信號（散射、熒光）的動態(tài)變化
3. 可實(shí)現(xiàn)高頻、原位分析水體微生物群落及優(yōu)勢種變化
4. 可在完整的藻類粒徑譜范圍內(nèi)對生物量進(jìn)行線性評估
5. 可直接分析大尺寸范圍的浮游藻類、團(tuán)體結(jié)構(gòu)，可現(xiàn)場分析微囊藻群體結(jié)構(gòu)變化
6. 可調(diào)式PMT可根據(jù)檢測粒徑大小調(diào)節(jié)檢測器靈敏度
7. 流動成像技術(shù)可對感興趣感興趣的聚群進(jìn)行圈門設(shè)定后專門拍照
8. 脈沖信號指紋圖譜技術(shù)，圈門直觀方便，更真實(shí)反應(yīng)細(xì)胞形態(tài)
9. 水下測量(CytoSub)可在整個(gè)真光層分析浮游植物動態(tài)
10. 可整合入浮標(biāo)中或其它載體上進(jìn)行在線監(jiān)測，可配合CTD對水體做剖面測量
11.實(shí)現(xiàn)實(shí)驗(yàn)室遠(yuǎn)程控制基站式自動在線監(jiān)測，可實(shí)現(xiàn)完全自動檢測，無人值守在線監(jiān)測






測量參數(shù)




光學(xué)參數(shù)：      前向散射FWS、側(cè)向散射SWS，熒光散射FLR、 FLY、 FLO
形態(tài)參數(shù)：      能同時(shí)獲得包括細(xì)胞和顆粒形態(tài)物理特性（數(shù)量、長度、大小、形態(tài)、粒度、色素、峰數(shù)等）、群體特征、脈沖圖譜等在內(nèi)的9個(gè)拓?fù)鋵W(xué)指標(biāo)及最少45組參數(shù)
絕對計(jì)數(shù)：自然水體總顆粒計(jì)數(shù)，圈門后可集群計(jì)數(shù)及濃度計(jì)算，可實(shí)現(xiàn)鏈狀藻單細(xì)胞數(shù)計(jì)數(shù)功能
其他測量參數(shù)：分析體積、進(jìn)樣速率等






應(yīng)用領(lǐng)域




1. 海洋生態(tài)學(xué)與淡水生態(tài)學(xué)


2. 流域監(jiān)測與管理


3. 海洋學(xué)與湖沼學(xué)


4. 有害藻華（HABs）預(yù)警


5. 微藻生物技術(shù)


6. 河流、水庫、湖泊、海洋的監(jiān)測與管理


7. 監(jiān)測與管理


8. 水源地、水廠、污水處理廠的水質(zhì)監(jiān)測


9. 富營養(yǎng)化研究


10. 藻類環(huán)境生物學(xué)


11. 水產(chǎn)養(yǎng)殖








選購指南：




一、便攜式浮游植物流式細(xì)胞儀CytoSense

系統(tǒng)組成：



流式細(xì)胞儀分析主機(jī)：相干高質(zhì)量連續(xù)固態(tài)激光器，標(biāo)配波長488nm, 可選波長445nm、635nm、640nm、660nm等，最多可配置7個(gè)檢測器（檢測通道含F(xiàn)WS L+R、SWS、YF、RF、OF）。
野外便攜式外殼：儀器采用碳素纖維外殼，防濺水設(shè)計(jì)，更輕便（<15kg）,整機(jī)安裝于輕質(zhì)鋁質(zhì)框，帶高質(zhì)量防震墊。包裝于便攜式航空箱內(nèi)。
數(shù)據(jù)分析系統(tǒng)：含便攜式筆記本電腦，預(yù)裝數(shù)據(jù)采集軟件CytoUSB,和數(shù)據(jù)分析軟件CytoClus
批量處理數(shù)據(jù)分析軟件EasyClus : 需購買MatLab軟件配合使用
高速流動成像模塊：可選。

2.jpg

3.jpg4.jpg
      便攜式浮游植物流式細(xì)胞儀                   Easyclus 粒徑分布圖                      Easyclus 散點(diǎn)圖
系統(tǒng)組成：



主機(jī)：淺水版Cytosub （水下20米），含CytoSense所有基本配置

浮標(biāo)模塊：包括浮標(biāo)、太陽能電池板、充電電池、浮標(biāo)燈、電子系統(tǒng)、無線傳輸裝置和采樣管防水連接器等。根據(jù)用戶需要，也可擴(kuò)展為易拆卸浮標(biāo)模塊，這樣用戶可以非常方便的在CytoSense（室內(nèi)用）和CytoBuoy（在線監(jiān)測）間轉(zhuǎn)換。
注意：野外在線監(jiān)測時(shí)不僅僅限于以浮標(biāo)作為平臺，其他平臺也可，只要可以具備放置CytoSense的空間及供電即可。同時(shí)，增加Bacterial staining module，可實(shí)現(xiàn)水體異養(yǎng)微生物自動染色和在線分析，可在線檢測藻類、細(xì)菌、浮游動物及沉積物等顆粒。具體信息請來電咨詢。


5.jpg









                       CytoBuoy 浮體

CytoBuoy通訊模式：無線通訊



6.jpg




三、水下浮游植物流式細(xì)胞儀——CytoSub 


主機(jī)：臺式機(jī)CytoSense是防濺水設(shè)計(jì)，可以在野外使用，但不能水下使用。CytoSense加上一個(gè)水下模塊（SUB MODULE）就組成了水下式流式細(xì)胞儀CytoSub。
水下模塊：一個(gè)耐受200 m水深壓力的防水外殼，閥門和進(jìn)樣環(huán)路部分（包括循環(huán)泵），電子控制單元，數(shù)采，水下連接器和支架。

cytosub 主機(jī).jpg7.jpg





               Cytosub 主機(jī)                         CytoSense 與CytoSub 轉(zhuǎn)換
工作模式一：AUV搭載




8.jpg











利用英國國家海洋中心AutoSub型AUV搭載CytoSub

工作模式二：水下垂直剖面分析




9.jpg







                             與CTD結(jié)合一起測量

注意：此外，水下型浮游植物流式細(xì)胞儀CytoSub可應(yīng)用于浮標(biāo)，Ferrybox等監(jiān)測平臺，在垂直剖面不同層位獲取浮游植物生物量信息，對研究微囊藻沉浮機(jī)制，浮游動物、水文、水質(zhì)等因素對浮游植物生態(tài)位影響提供數(shù)據(jù)依據(jù)。

10.jpg11.jpg




                           CytoSense 檢測對象

產(chǎn)地：荷蘭 CytoBuoy



參考文獻(xiàn)
數(shù)據(jù)來源： Cytometry ,  Goolge scholar等，截至2016年，共收集相關(guān)文獻(xiàn)近100篇。

1.       Simon Bonato a, Elsa Breton , al e: Spatio-temporal patterns in phytoplankton assemblages ininshore–offshore gradients using flow cytometry: A case study in the eastern English Channel, Journal of Marine Systems 2016,76-83.[CytoSense]

2.       Goran Bakalar & Vinko Tomas, Possibility of Using Flow Cytometry in the Treated Ballast Water Quality Detection, Pomorski zbornik 51 (2016), 43-55

3.       Quan Zhou, Wei Chen, al e: A flow cytometer based protocol for quantitative analysis of bloom-forming cyanobacteria (Microcystis) in lake sediments, Journal of Environmental Sciences 2012, 24(9) 1709–1716

4.       A. Mansour, I. Leblond al.e: Invited Paper: Wireless Sensor Networks for Ecosystem Monitoring & Port Surveillance. (WSCN 2013)

5.       Endymion D. Cooper , Bastian Bentlage al e: Metatranscriptome profiling of a harmful algal bloom.Harmful Algea 37(2014)75-83.

6.       SERGIO A. COELHO-SOUZA, FáBIO V. ARAúJO al e: Bacterial and Archaeal Communities Variability Associated with Upwelling and Anthropogenic Pressures in the Protection Area of Arraial do Cabo (Cabo Frio region - RJ). Anais da Academia Brasileira de Ciências (2015) 87(3):1737-1750

7.    Malkassian, A., D. Nerini, al. e: Functional analysis and classification of phytoplankton based on data from an automated flow cytometer. Cytometry Part A 2011, 94A:263-275.  [Cytosense]

8.    Thyssen, M., B. Beker, al. e: Phytoplankton distribution during two contrasted summers in a Mediterranean harbour: combining automated submersible flow cytometry with conventional techniques. Environmental Monitoring and Assessment 2011, 173:1-16. 

9.    Thyssen, M., Denis M: Temporal and Spatial High-Frequency Monitoring of Phytoplankton by Automated Flow Cytometry and Pulse-Shape Analysis. Springer Netherlands 2011:293-298. 

10.   Vidoudez, C., J. C. Nejstgaard, al. e: Dynamics of Dissolved and Particulate Polyunsaturated Aldehydes in Mesocosms Inoculated with Different Densities of the Diatom Skeletonema marinoi. Marine Drugs 2011, 9: 345-358. 

11.   Hansen, B. W., H. H. Jakobsen, al. e: Swimming behavior and prey retention of the polychaete larvae Polydora ciliata. Journal of Experimental Biology 2010:3237-3246. 

12.   Pereira GC, Figuiredo ARd, Jabor PM, Ebecken1 NFF: Assessing the ecological status of plankton in Anjos Bay: a flowcytometry approach. Biogeosciences Discuss 2010, 7:6243–6264.  [cytobuoy]

13.   Barofsky, A., Simonelli P, al e: Growth phase of the diatom Skeletonema marinoi influences the metabolic profile of the cells and the selective feeding of the copepod Calanus spp. J Plankton Res 2009, 32:263-272.  [CytoBuoy]

14.   Donk V, E., Cerbin S, al e: The effect of a mixotrophic chrysophyte on toxic and colony-forming cyanobacteria. Freshwater Biology 2009, 54:1843-1855. 

15.   Pereira, C. G, Granato A, al. e: Virioplankton Abundance in Trophic Gradients of an Upwelling Field. Brazilian Journal of Microbiology 2009, 40:857-865.  [CytoBuoy]

16.   Thyssen, M., Mathieu D, al. e: Short-term variation of phytoplankton assemblages in Mediterranean coastal waters recorded with an automated submerged flow cytometer. J Plankton Res 2008, 30:1027-1040.  [Cytosub]

17.   Thyssen, T. M, Garcia N, al. e: Sub meso scale phytoplankton distribution in the north east Atlantic surface waters determined with an automated flow cytometer. Biogeosciences Discuss 2008, 5:2471-2503.  [Cytosub]

18.   Dubelaar, J. GB, Casotti R, al. e: Phytoplankton and their analysis by flow cytometry. Flow Cytometry with Plant Cells 2007:287-322.  [CytoBuoy]

19.   Takabayashi, M., Lew K, al e: The effect of nutrient availability and temperature on chain length of the diatom, Skeletonema costatum. J Plankton Res 2006, 28:831-840.  [CytoSense]

20.   Takabayashi, M., Wilkerson FP, al. e: Response Of Glutamine Synthetase Gene Transcription And Enzyme Activity To External Nitrogen Sources In The Diatom Skeletonema Costatum (Bacillariophyceae). J Phycol 2005, 41:84-94.  [Cytobuoy]

21.   Dubelaar, J. GB, Geerders PJF: Innovative technologies to monitor plankton dynamics. Sea Technol 2004, 45:15-21.  [CytoSub]

22.   Dubelaar, J. GB, Geerders PJF, al. e: High frequency monitoring reveals phytoplankton dynamics. J Environ Monit 2004, 6:946-952.  [Cytosense]

23    Cunninghama, A., McKeea D, al e: Fine-scale variability in phytoplankton community structure and inherent optical properties measured from an autonomous underwater vehicle. J Mar Syst 2003, 43:51-59. 

24.   Dubelaar, J. GB, Gerritzen PL: CytoBuoy: a step forward towards using flow cytometry in operational oceanography. Sci Mar (Barc) 2000, 64:255-265.  [CytoBuoy]

25.   Dubelaar, J. GB, Jonker RR: Flow cytometry as a tool for the study of phytoplankton. Scientia Marina 2000, 64.  [CytoBuoy]

26.   Jonker R, Droben R, Tarran G, Medlin L, Wilkins M, Garcla L, zabala L, boddy l: Automated identification and characterisation of microbial populations using flow  cytometry: the AIMS project. scientia marina 2000, 64:225-234.  [Cyto]

27.   Woodd-Walker, S. R, Gallienne CP, al e: A test model for optical plankton counter (OPC) coincidence and a comparison of OPC-derived and conventional measures of plankton abundance. J Plankton Res 2000, 22:473-483. 

28.   Dubelaar, J. GB, Gerritzen PL, al e: Design and first results of CytoBuoy: A wireless flow cytometer for in situ analysis of marine and fresh waters. Cytometry 1999, 37:247-254.  [CytoBuoy]

29.   Wilkins, F. M, Boddy L, al e: Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks." Appl Environ Microbiol 1999, 65:4404-4410. 

30.   Jonker, R. R, Meulemans JT, al e: Flow cytometry: A powerful tool in analysis of biomass distributions in phytoplankton. Water SciTechnol 1995, 32:177-182.  [Cytosense]

31.   Jonker, R. R, G. B. J. Dubelaar, al. e: The European Optical Plankton Analyser: A high dynamic range flow cytometer. Scientia Marina 1994. 

32.   Dubelaar, G. B. J., A. Groenewegen ea: Optical plankton analyser: a flow cytometer for plankton analysis, II: Specifications. Cytometry 1989, 10:529-539.  [OPA]

       33.   Peeters, J. C. H., G. B. J. Dubelaar, al e: Optical plankton analyser: A flow cytometer for plankton analysis, I: Design considerations. Cytometry 1989, 10:522-528.  [OPA]







收 藏