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[Music]

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[Applause]

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thank you everyone for coming to the

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talk and thank you to the organizers for

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give me an opportunity to speak the work

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that I'm going to be presenting is the

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result of collaboration with several

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different people all listed here and in

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particular I'd like to highlight Lucy

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Stewart

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Shrishti Kasia and big iam top Xu Alou

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who were former and current PhD students

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in my lab who did this work as well as

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some of my co p is julie huber from

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Woods Hole Oceanographic Darby Dyer from

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Mount Holyoke College and Susan Lang

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from the University of South Carolina

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the work was funded by the Gordon Betty

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Moore Foundation and by NASA and a lot

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of the work that I going to be showing

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is was published just within the last

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year and if you're interested in it

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since it's cited in the psyche on

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abstract alright so we have a rover on

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Mars and we have our Ovie's and a UVs in

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the deep ocean that are searching for

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life and trying to figure out what that

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life is doing and soon we'll have robots

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and ocean worlds in our solar system and

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it's not a big I don't need to work hard

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to convince you all that we need both

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modeling and detection in order to be

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able to determine where that life is and

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what that life is doing but what I've

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been asking myself and what I wanted to

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talk about in this presentation is how

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can we better integrate these two what

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can I do in my lab and what can we do as

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a community to try and bring these two

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things closer together

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so that's what I wanted to to talk about

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so first of all in the area of modeling

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I've been working with Lucy Stewart

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Chris augur Caroline fortunado Julie

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Huber and others to develop

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methanogenesis reactive transport model

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and the questions that we're trying to

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determine is for an individual event how

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many methanogens aren't there needed to

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be at that vente creates a the methane

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anomalies that we see what's the volume

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of rock that's occupied by these

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methanogens how deep is the biosphere

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and what's the residence time of the

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so what we've done is we've developed a

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one-dimensional pipe flow reactive

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transport model with a series of boxes

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350-degree hydrothermal fluid flowing

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through the boxes and each box you have

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a little bit of dilution of seawater and

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then once the temperature permits

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methanogens can then grow in the box

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before the fluid flows to the next one

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we also can vary the size of the box so

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that we can vary the residence time of

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the fluid through the box the the model

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is informed by chemostat work that was

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done in my lab using both a thermophilic

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and a hyperthermophilic methanogens and

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we got we determined hydrogen monoid

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kinetics for methane production and

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Arrhenius kinetics for temperature

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effects for each of these organisms to

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be able to to put plug into this model

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what we found is is that if the pipe is

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straight in other words if residence

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time remains more or less constant with

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fluid flow is that the hyperthermophilic

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methanogens dominate the system they get

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at the method the hydrogen first and

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they consume it all before the thermal

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files really have an opportunity to be

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able to use that however if the pipe is

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more flanged in residence time increases

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with flow rate or with flow then what'll

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happen is that this gives an opportunity

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for the thermal files to be able to to

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dominate the system over

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hyperthermophiles so we wanted to

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actually test this model and we went to

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axial seamount off the coast of Oregon

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and looked at two different hydrothermal

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vents and first two columns you can see

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hydrogen and methane data that was

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collected by Dave Butterfield and in

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this column here you can see a cell

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count estimates for the two different

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antigens that's based on meta genomic

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work that Julie Huber's lab did and fit

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the model to it the black line

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represents the model these circles

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represent the data that what we actually

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measured and in these figures imagine

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that the 350-degree fluid is rising up

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and is getting diluted this is magnesium

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concentration here on the side but the

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the fluid is rising up cooling and

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mixing is it as you go up towards the

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top

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and the model did a pretty good job it

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you can see hydrogen consumption here

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methane production here and in this

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particular system a marker 33 there are

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more hyperthermophilic methanogens so

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that suggests that the pipe flow at the

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particular vent is more straight that

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residence time into each box is more or

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less constant whereas at marker 113

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there are far more thermophilic

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methanogens than hyperthermophilic

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methanogens and that again suggests that

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perhaps as fluid flows the residence

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time at each temperature gets longer and

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longer some of the things we're able to

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determine from this is that there's only

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about 10 to the 11th methanogens that

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are necessary each vent to be able to

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create the methane anomaly that we saw

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that was a surprise we thought that was

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gonna be much higher than it really was

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the residence time of myth antigen

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growth temperatures was about 29 to 33

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hours only about 2 to 18 cubic meters of

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sea floor was occupied depending upon

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the porosity of the rock and the

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biosphere is we think there's only about

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2 to 30 meters deep moving forward we

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need to put more metabolic diversity

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into these types of reactive transport

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models is something that we're working

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on now and also we need to put more

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ecological theory into these into these

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models we're also in terms of the actual

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detection part of it we looked at

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methane fractionation so I work with big

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um top CEO Lou Tran new en susan lying

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and others to look at the methane carbon

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fractionation that occurs in a pure

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hyperthermophilic myth antigen and we

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varied the flux rate again in a

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chemostat to see how the organism grew

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what gene how gene expression changed

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and how carbon fractionation changed

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when hydrogen flux was high what would

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happen is the carbon would flow through

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the wood young-dal pathway and you'd see

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more methane and more energy produced

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and in that particular situation carbon

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fractionation was relatively low about

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an epsilon of about 29 part 1000

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however when hydrogen flux rate was low

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the carbon would actually flow more into

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biosynthesis so there'd be a much higher

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cell yield

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and also carbon fractionation increased

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significantly the epsilon would grow to

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about 70 to 85 per thousand and axial

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cement which I just told you about the

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carbon fractionation we think is pretty

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low so that actually fits the model that

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we think that there's probably a high

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flux of hydrogen that's beating the

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methanogens in that in that system

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moving forward we need to do more in

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terms of understanding carbon use

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efficiency for a variety of different

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organisms how that might vary with

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change in nutrient availability and also

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we need to work on a more biomarker

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information what does carbon

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fractionation look like and say lipids

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and proteins so also in the area of

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detection moving beyond with antigens

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were interested in what happens with

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minerals and could minerals provide say

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a different type of bio signature so I

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work with Shrishti Kashyap Eli Skloot

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and darvid Iyer and others to synthesize

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six different nano phase iron oxides

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that you see listed here and we worked

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with two hyperthermophilic iron reducers

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and Pyrrha dictum Delaney I and Pyrrha

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baculum Icelandic ohm that grow on iron

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oxide they reduced the iron oxides and

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what we found was that the organisms

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grow best on Farah hydrate

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they grow modestly well on the Pittock

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recite and akka gain height and they

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grow rather poorly and Meg he might Gert

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tight and hematite and that pretty much

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fits what we would expect based on the

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increasing order of crystallinity and

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the thermodynamic stability of the of

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the of the minerals themselves and I'd

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like to point out too that my PhD

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student Trish DK chef will be speaking

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in more detail about this particular

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project on on Friday and she's soon to

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be on the pH on the postdoc market so

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so what we would then wanted to do is we

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wanted to try and identify what those

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minerals are that were formed by these

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iron reducers and remember when the

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organisms are growing on Farah hydrate

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they grew best they had the highest fe2

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flux rates production rates and what we

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found was is that the we used by the way

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a combination of v nir FTIR Ramanand

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mossbauer they all pretty much gave us

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the same results but the FTIR data is

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shown here but when the organisms are

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growing on farah hydrate they produced

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magnetite was that was their end product

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however when the organisms were grown on

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the Pittock row site and acting any I

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remember this is slower growth rate

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slower fe2 production rate the mineral

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end product actually changed with

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lepetit Pro site they produced an iron

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carbonate Sidda right or a Siddha right

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like compound mineral and when they were

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growing on a Kagami night the end

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products were vivvy night which is an

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iron phosphate as well as some magnetite

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and it's a little hard to tease out in

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this picture but I certainly be happy to

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talk about in more detail if anybody's

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interested in this but we did see some

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signals that suggest that perhaps there

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is more in some cases a biogenic type of

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mineral transformation product compared

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to just a biotic li synthesized minerals

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but that's something we still are

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working on a need to tease out more so

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moving forward into the future and

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especially the area of detection and

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also in the areas of how can we couple

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modeling and detection together we are

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now in the process of taking pure thermo

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files and hyperthermophiles and getting

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spectra from them using these four

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different spectroscopy techniques this

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is FTIR here and these are all

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metabolically very different thermo

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files and hyperthermophiles and we do

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see differences especially in the near

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IR and mid IR range we we do see some

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differences between these different

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organisms so that's very tantalizing for

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us and we're hoping we can get to the

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point that we might actually be able to

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use spectroscopy in a native rock or

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even on the seafloor to be able to see

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what types of organisms are there

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that's that's still a dream so it still

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weighs off

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we're also interested in hyper spectral

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imaging this is a piece of sulphide that

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was sitting on my shelf in my office for

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ten years and we decided to put it under

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a hyper spectral image gaming camera and

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about three centimeters across this is

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just the RGB image here but what you can

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do is you can do a pixel-by-pixel

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analysis looking at the spectral

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information for each pixel and get a

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spectra for each pixel and then map out

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where you see similar spectra and this

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actually did a fairly decent job of

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mapping out where the different minerals

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were in this long term what we really

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hope you'd be able to do is to be able

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to do something like hyper spectral

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imaging and to be able to co-locate

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different mineral types and different

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microbes or Lisa some sort of a biogenic

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signal and even maybe be able to do this

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not only on fresh collected samples from

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the from hydrothermal vents onboard ship

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but maybe even one day on the seafloor

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itself so take-home message from all

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this is that we really need to improve

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the iterative process of working between

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modeling and detection and these are

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fairly obvious reasons but it's worth

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saying is in order for us to really be

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able to predict where to find life and

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what kinds of life we would find also

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determining the impact of that life and

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really to be able to ground truth the

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modeling resume results that we're

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generating so thank you very much and

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of time for questions hi Aaron Noel from

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JPL really nice talk thank you did you

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all ever do any sort of chemical

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analysis besides genomic work do you

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look for other biomarkers so we have

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done a little bit looking at the methane

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and the carbon fractionation that's

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there and we haven't done any lipid

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analysis or proteins in terms of like

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extracting that and looking directly at

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that but that's something that we hope

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to get into doing more especially if we

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can do some analysis on the carbon

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fractionation that actually occurs in

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the lipids or at the protein level so

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that's one of the areas that we're

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trying to move into in the future great

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hi John how do you know thank you for

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the talk I was wondering in your pure

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culture experiments with those mineral

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phases did you notice any variation in

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the mineral end product if you changed

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up the electron donor we haven't tried

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that but it's a great question

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especially for the pyro baculum species

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it's a facultative autotroph so we've

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grown it with organics but we'd love to

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do it autotrophic ly so who are the the

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power addicted species uses hydrogen and

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the power vacuum uses organics and we do

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see some differences there between the

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two we don't know if that's at the

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organismal level or how much of it is

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because they're using a different

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electron donor source but jill has a

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great question hi great Paul from

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Mississippi State I was curious about

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the flange Piper sister straight pipe

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was there a difference in the

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temperature in the in the straight pipe

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versus the flange pipe that could affect

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thermophiles being dominant in the

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flange five because it broadens how the

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temperature drops were able to measure

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the temperature difference well you can

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see in the modeling in some ways each

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step you might expect that there's the

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temperature inside the box is saying but

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that's really the residence time inside

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that box that really has a big impact so

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for example if hyperthermophiles spend

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very little time in their box before you

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move on to the thermal file temperatures

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then the thermal files have a chance to

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dominate the system but if

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hyperthermophiles get therefore get to

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them hydrogen first and the residence

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time is long enough then they'll

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a real quick question I was wondering

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what the flow rates you were considering

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of in terms of the residence time in

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some of your chemistry experiments and

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how that would be affected by say a

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diffusely flowing system like lost city

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for example where you have very low flow

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rates and really high thermal diffusion

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through the system so we we varied the

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the hydrogen concentration of the

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hydrogen flow rate into the reactor and

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of course as we did that that changed

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growth the temperature of the growth

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rate and we had to vary the the dilution

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rate to match that we did try a couple

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different dilution rates for our chemo

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stats at any given hydrogen

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concentration to see how much dilution

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affected the system and we found that at

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least statistically there wasn't really

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that much difference with change in

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dilution rate it really seemed to be

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primarily driven by by the hydrogen

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concentration but but that is something

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that that you have to integrate into the

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into chemostat experiment since growth

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rate varies as the as the hydrogen flux