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藻類群落結構掃描成像分析系統—浮標版CytoBuoy
日期:2018-03-21 17:22:29
主要功能




1. 專業分析浮游植物細胞,同時(shi)具備傳統流式細胞儀經典(dian)功能1.jpg
2. 可以掃描記錄(lu)各(ge)種(zhong)光學信號(散射、熒光)的動(dong)態(tai)變化(hua)
3. 可實現高頻、原位分析水(shui)體(ti)微生(sheng)物群落及優勢種變化
4. 可在完整的藻類(lei)粒徑譜范圍內對生物量進(jin)行線性評估
5. 可(ke)直接分析(xi)大(da)尺寸范圍的浮游藻類、團體(ti)(ti)結(jie)構(gou),可(ke)現場分析(xi)微囊藻群(qun)體(ti)(ti)結(jie)構(gou)變化
6. 可調式PMT可根據(ju)檢(jian)(jian)測粒徑(jing)大小調節檢(jian)(jian)測器靈敏度
7. 流動(dong)成像技術可(ke)對(dui)感興趣(qu)感興趣(qu)的(de)聚群進行(xing)圈門(men)設定后專門(men)拍(pai)照
8. 脈沖信號指(zhi)紋(wen)圖譜技術,圈(quan)門直觀方便,更真(zhen)實反(fan)應細胞形態
9. 水下測量(CytoSub)可在整個真(zhen)光層分析浮游植物動態(tai)
10. 可整合入浮標中或其它載體上進行在線監(jian)測(ce),可配合CTD對水體做剖面測(ce)量
11.實現實驗(yan)室遠程(cheng)控制基站式自動(dong)在線監測(ce),可實現完全自動(dong)檢測(ce),無人值(zhi)守在線監測(ce)






測量參數




光學參數:      前向散射FWS、側向散射SWS,熒光散射FLR、 FLY、 FLO
形態參數:      能同時獲得包括細胞和顆粒形態物理特性(數量、長度、大小、形態、粒度、色素、峰數等)、群體特征、脈沖圖譜等在內的9個拓撲學指標及最少45組參數
絕對計數:自然水體總顆粒計數,圈門后可集群計數及濃度計算,可實現鏈狀藻單細胞數計數功能
其他測量(liang)參數:分析(xi)體積、進樣速率(lv)等






應用(yong)領域(yu)




1. 海洋生(sheng)態(tai)學與淡水生(sheng)態(tai)學


2. 流域監(jian)測(ce)與(yu)管理


3. 海洋學(xue)與(yu)湖沼(zhao)學(xue)


4. 有(you)害藻(zao)華(HABs)預警


5. 微藻(zao)生(sheng)物技術(shu)


6. 河流(liu)、水(shui)庫、湖泊、海洋的監測與管理


7. 監(jian)測與管理(li)


8. 水源地、水廠、污水處理廠的水質監測


9. 富營(ying)養化研究


10. 藻類(lei)環境生物學


11. 水產養殖








選購指南:




一、便攜(xie)式(shi)浮游植物流(liu)式(shi)細胞儀(yi)CytoSense

系統(tong)組(zu)成:



流式細(xi)胞儀分(fen)析主機:相干高質量連續固態激光器,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FWS L+R、SWS、YF、RF、OF)。
野外便攜式外殼:儀器(qi)采用碳(tan)素纖(xian)維(wei)外殼,防(fang)濺水設計,更輕(qing)便(<15kg),整機安(an)裝(zhuang)于輕(qing)質鋁質框,帶高質量防(fang)震墊。包裝(zhuang)于便攜式航空箱內。
數(shu)據分析系統:含(han)便攜(xie)式筆記本電腦,預裝數據采集軟件(jian)CytoUSB,和數據分析軟件(jian)CytoClus
批(pi)量處(chu)理(li)數據分析軟件EasyClus : 需(xu)購買MatLab軟件配合使用(yong)
高速流動成像(xiang)模塊:可選。

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3.jpg4.jpg
      便(bian)攜式浮游(you)植物流式細胞儀  &nbsp;                Easyclus 粒徑分(fen)布圖(tu)                      Easyclus 散點圖
系統組成:



主機:淺水版Cytosub (水下20米),含CytoSense所有基(ji)本配(pei)置

浮標(biao)模塊:包括(kuo)浮標、太陽(yang)能電(dian)池板、充電(dian)電(dian)池、浮標燈、電(dian)子系統、無線傳輸裝置(zhi)和采(cai)樣管防水連(lian)接器等。根據用戶(hu)需要(yao),也可擴展為易拆卸(xie)浮標模塊,這樣用戶(hu)可以非(fei)常方便的在CytoSense(室內用)和CytoBuoy(在線監測)間轉換。
注意:野外在線監測時不僅僅限于以浮標作為平(ping)臺(tai),其他平(ping)臺(tai)也可(ke),只要可(ke)以具備放(fang)置CytoSense的(de)空間及供電(dian)即可(ke)。同(tong)時,增加Bacterial staining module,可(ke)實現水體(ti)(ti)異養微(wei)生物(wu)自動染色和在線分析,可(ke)在線檢測藻類、細菌(jun)、浮游動物(wu)及沉積物(wu)等顆(ke)粒。具體(ti)(ti)信息(xi)請來(lai)電(dian)咨(zi)詢。


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      &nbsp;                CytoBuoy 浮體

CytoBuoy通訊模(mo)式(shi):無(wu)線通訊



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三、水下浮游植物流式細胞儀——CytoSub 


主機:臺式機CytoSense是防濺水設(she)計,可以在野(ye)外使用,但不能水下使用。CytoSense加上(shang)一個(ge)水下模塊(SUB MODULE)就(jiu)組成了水下式流(liu)式細胞儀CytoSub。
水下模塊:一個耐受(shou)200 m水深壓力的防水外殼,閥門和進樣(yang)環(huan)路部分(包括(kuo)循環(huan)泵),電子控制單元,數(shu)采,水下(xia)連接器和支架。

cytosub 主機.jpg7.jpg





          &nbsp;    Cytosub 主機                         CytoSense 與(yu)CytoSub 轉換
工作模式一:AUV搭載




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利用英(ying)國(guo)國(guo)家海洋(yang)中心(xin)AutoSubAUV搭載(zai)CytoSub

工作模式二:水下垂直剖面分析




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                         ;    與CTD結(jie)合(he)一起測量

注意:此外,水下型浮游(you)植物流式(shi)細胞(bao)儀CytoSub可應用于浮標,Ferrybox等(deng)(deng)監測(ce)平臺,在垂直剖(pou)面不同(tong)層位獲取浮(fu)游植(zhi)物生(sheng)物量(liang)信息(xi),對研究微囊藻沉浮(fu)機制,浮(fu)游動物、水文、水質等(deng)(deng)因素(su)對浮(fu)游植(zhi)物生(sheng)態(tai)位影響提供數據依據。

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        &nbsp;                  CytoSense 檢測對象

產地(di):荷蘭 CytoBuoy



參考(kao)文獻(xian)
數(shu)據來源: Cytometry ,  Goolge scholar等,截至2016年(nian),共收集相(xiang)關(guan)文(wen)獻近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.&nbsp;

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.&nbsp; [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]

    &nbsp;  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]







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