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主要功能
1. 專業(yè)分析浮游植物細胞，同時具備傳統(tǒng)流式細胞儀經(jīng)典功能
2. 可以掃描記錄各種光學信號（散射、熒光）的動態(tài)變化
3. 可實現(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)對生物量進行線性評估
5. 可直接分析大尺寸范圍的浮游藻類、團體結構，可現(xiàn)場分析微囊藻群體結構變化
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. 流動成像技術可對感興趣感興趣的聚群進行圈門設定后專門拍照
8. 脈沖信號指紋圖譜技術，圈門直觀方便，更真實反應細胞形態(tài)
9. 水下測量(CytoSub)可在整個真光層分析浮游植物動態(tài)
10. 可整合入浮標中或其它載體上進行在線監(jiān)測，可配合CTD對水體做剖面測量
11.實現(xiàn)實驗室遠程控制基站式自動在線監(jiān)測，可實現(xiàn)完全自動檢測，無人值守在線監(jiān)測

測量參數(shù)
光學參數(shù)：前向散射FWS、側向散射SWS，熒光散射FLR、 FLY、 FLO
形態(tài)參數(shù)：能同時獲得包括細胞和顆粒形態(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個拓撲學指標及最少45組參數(shù)
絕對計數(shù)：自然水體總顆粒計數(shù)，圈門后可集群計數(shù)及濃度計算，可實現(xiàn)鏈狀藻單細胞數(shù)計數(shù)功能
其他測量參數(shù)：分析體積、進樣速率等

應用領域
1. 海洋生態(tài)學與淡水生態(tài)學
2. 流域監(jiān)測與管理
3. 海洋學與湖沼學
4. 有害藻華（HABs）預警
5. 微藻生物技術
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、污水處理廠的水質監(jiān)測
9. 富營養(yǎng)化研究
10. 藻類環(huán)境生物學
11. 水產(chǎn)養(yǎng)殖

選購指南：
一、便攜式浮游植物流式細胞儀CytoSense
系統(tǒng)組成：
流式細胞儀分析主機：相干高質量連續(xù)固態(tài)激光器，標配波長488nm, 可選波長445nm、635nm、640nm、660nm等，最多可配置7個檢測器（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，防濺水設計，更輕便（<15kg）,整機安裝于輕質鋁質框，帶高質量防震墊。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。





便攜式浮游植物流式細胞儀		Easyclus 粒徑分布圖		Easyclus 散點圖
系統(tǒng)組成：
主機：淺水版Cytosub （水下20米），含CytoSense所有基本配置
浮標模塊：包括浮標、太陽能電池板、充電電池、浮標燈、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，也可擴展為易拆卸浮標模塊，這樣用戶可以非常方便的在CytoSense（室內(nèi)用）和CytoBuoy（在線監(jiān)測）間轉換。
注意：野外在線監(jiān)測時不僅僅限于以浮標作為平臺，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，增加Bacterial staining module，可實現(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、細菌、浮游動物及沉積物等顆粒。具體信息請來電咨詢。


		CytoBuoy 浮體
CytoBuoy通訊模式：無線通訊


三、水下浮游植物流式細胞儀——CytoSub
主機：臺式機CytoSense是防濺水設計，可以在野外使用，但不能水下使用。CytoSense加上一個水下模塊（SUB MODULE）就組成了水下式流式細胞儀CytoSub。
水下模塊：一個耐受200 m水深壓力的防水外殼，閥門和進樣環(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		CytoSense 與CytoSub 轉換
工作模式一：AUV搭載



		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


		與CTD結合一起測量
注意：此外，水下型浮游植物流式細胞儀CytoSub可應用于浮標，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制，浮游動物、水文、水質等因素對浮游植物生態(tài)位影響提供數(shù)據(jù)依據(jù)。


		CytoSense 檢測對象
產(chǎn)地：荷蘭 CytoBuoy

參考文獻

數(shù)據(jù)來源： Cytometry , Goolge scholar等，截至2016年，共收集相關文獻近100篇。

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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]

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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]

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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]

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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.

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