Breaking down scientific mono cultures by cross-disciplinary software development
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Huber, Florian
Hooft, Justin van der
Rogers, Simon
Medema, Marnix
Ridder, Lars
At the Netherlands eScience Center we contribute with 50+ domain- and computer scientists to an unusually broad range of scientific projects across all quantitative scientific domains. This triggers cross-disciplinary exchange, the strength of which will be illustrated by presenting an ongoing project on linking metabolomic and genetic data. Computational analysis of genomes can predict biosynthetic gene clusters (BGCs) responsible for producing complex biochemical compounds, many of which are still unknown. Adapting machine-learning tools from different domains, we develop a novel method aiming to pinpoint causal links between predicted BGCs and yet unidentified compound signals detected in rich metabolite mixtures.
 I think like RSEs, like research, software engineers are in a position that's quite unique in the Academy of Landsquake and occasionally they have the chance maybe to break down scientific monocultures by really bridging different disciplines and that is something I would like to strengthen here and to do this I would like to bring an example. And I think this is very much in line with the great keynote lecture we had yesterday from Ellis Brett which also argued for why it makes sense to have RSE groups or clusters for strengthening a lot of these points and this is also something I have in mind here. So to make my point why I think that it's a good chance to really bridge domains from the research software engineer perspective first introduce a place where I work which is currently the Netherlands E-Sign Centre and so we are an organisation based in Amsterdam and we're funded by mostly public research money from the Dutch government and at the moment we have about 50 research software engineers working in our place and they come from a very diverse set of backgrounds so about half of them or a bit less I would say come from computational sciences and the other half or like two thirds come like me from all kind of different domains, quantitative domains so like physics, chemistry, math, biology, life sciences but also linguistics and earth sciences and geosciences so we have quite a broad range of people and we have an even broader range of projects so all this is kind of just taken from the website and it's only half of the current project that run in our centre and all these projects are from really different ranges of quantitative science in the Netherlands so because the system is like this people like groups, research groups in the Netherlands from universities or institutes can apply for funding and help at our centre and if then the project is kind of accepted by an external board we try to work together with these researchers in form of a collaboration for two, three or four years to help them what we call digitally enhanced research and so all these projects they come from like all quantitative domains that you can think of so really from humanities, social sciences, to life sciences, physics, every area you can think of and we do a lot of different tasks as you can imagine on these things and they will really depend on so different projects, different domains have different needs and so we do things like big data analytics, efficient computing so HPC which some people are doing a lot and there's optimized data handling so a lot of different things that we do depending on what the project needs and depending on what we can offer in terms of expertise so of course obviously this is an RSE conference and a lot of these things that we will do in this framework is about research offer but as we heard in many talks yesterday also it's not so easy to define what research offer is or what research offer engineer is and a lot of people will leave it quite open because of course different places people have different definitions of it and I think that's good to keep it like this but there's of course more than writing research software for these projects and it can be quite diverse depending on the needs of the researchers, it can be things like linking researchers to IT infrastructure that will be like the HPC part for example, also big databases but for example what I'm doing a lot is actually more data steward or data scientist so we have a lot of people that work in areas where we try to consult people on using the proper algorithms on the statistics on how to evaluate and analyze data and there's something I would particularly argue for now in my talk it's kind of really there's the opportunity to do cross-disciplinary transport and what I mean by this is kind of that we can, since we are a place where people work with all kinds of different backgrounds on projects from all kinds of different backgrounds we occasionally get the chance to really see that we can apply things that belong to one domain to a completely different domain and I will give you one example of such a case which is a project I'm currently working on and that is about integrated omics analysis so omics actually you can think of like in a nutshell it's kind of that's a big data thing for life sciences so I will go a bit more into the biology so I guess most of you are not biologists so I will spend some time on explaining what actually the key problem here is and how we try to solve it and why this is relevant so this project is about linking metabolomic data so this is about the compounds that living organisms produce to the genetic information so to the DNA and what's encoded in this in these organisms and we are specifically interested in a class of compounds that are called secondary metabolites so this is all these funny chemical symbols here and so secondary metabolites are a funny class of things and they are quite important for many reasons so these are things that living organisms like cells, fungi, bacteria produce based on very specific proteins so it goes like this so the DNA of these organisms contains a specific sequence that encodes by RNA a protein and a protein you can think of is the main functional building block of living organisms so this is something that specifically does a certain job and it's a bit oversimplified but in this case this organism has encoded a specific protein to produce a very specific chemical compound and the other interesting thing about them these are not essential building blocks so the cell doesn't need them for other primal functions these are really things that you can think of deliver an evolutionary advantage to this organism and the most known case I would say is really antibiotics because antibiotics are nothing else than secondary metabolites or for example fungi so the things that we use in medicine now are discovered from fungi because these fungi produce them to keep away bacteria or other invaders and for example this chemical symbol is something that keeps conferences going because this is caffeine so this is where we drink coffee and this is also a plant secondary metabolite and a lot of these things are kind of found in medications and drugs and other things but it has another role in addition and these things can also serve as a fingerprint because like the last decades it really became more apparent that we are not a single species organism but more in a symbiotic way lived together with uncountable microbes that populate all our body and guts and these produce also specific metabolites and these can be used to give an idea or a fingerprint of certain diseases because they will hint at certain populations of bacteria or yeast or other organisms so this is what we like to do, we like to know what these compounds are and how they are linked to specific species but of course this is not the data that we usually have so the data that we have available if we do cell culture or bacteria culture or fungi or something like this then we can take the x-thread and we can do mass spectrometry and this gives these mass spectra and they are not really the same as having the compound they are just more like a vague fingerprint of chemical compounds and on the other side we have the DNA which currently is quite possible to really sequence a lot of these DNA but then you still have a kind of cryptic code at the beginning and you need to apply quite a lot of sophisticated bioinformatics tools to in the end get the structure of potential proteins that we believe are responsible for in the end producing these secondary metabolites so the idea that we have or what we are currently working on is really to link up all this data from a lot of different species and a lot of different experiments and say for example that we can infer if we know one protein or one protein complex actually that produces a certain compound and we see that another one is similar in its structure that we then can infer that another similar looking spectra might correspond to a similar looking chemical and so on so the idea is really that we have a lot of noisy data with unclear information and that by teaming up, by using big data analysis and clever algorithms to sort this out we hope that we can get a more reliable better prediction of what kind of DNA or what kind of proteins are probably responsible for what kind of compounds so this is the main task of the project but the specific thing I'd like to mention where we really can use tools from a very different discipline has to do with this mass spectra, so this part that is here so I guess most of you don't know mass spectra or have never worked with it like me before so just a very brief introduction to what it is so you have your compounds, say you have liquid with a lot of substances and you don't know what's in there so you charge them and you run them through mass spectrometry and then you get a mass or mass over a charge ratio actually and this is not yet good enough so you destroy this compound and then you get a lot of fragments and you measure the mass as well so you get a lot of peaks and all these peaks correspond to masses of fragments or mass ratios of these and this with a lot of analytics you can try to make some sense out of it and if you have comparable spectra in your database you can eventually even guess the right molecule but in our case in the end we will work with a lot of unknown spectra so we don't have this luxury here so again the idea is kind of how can we relate this wake fingerprint to actual chemical structure and functions so the key part about this is kind of if we imagine that we have very complex biological samples say for example from human gut or from anything like this we have uncountable different compounds in there and most of them unknown and we have, so this means we have a lot of spectra we don't know nothing about so some of them we might can compare to database maybe but most of them we cannot so one key step to make sense of this kind of complex data is of course to compare things so to ask for how similar are these spectra so that we can maybe cluster them or group them or figure out similarities and of course I mean we are not the first ones to think about this so there is for decades there has been methods how to compare spectra, these mass spectra and this is you just overlay the spectra and look for okay what masses are identical and what are not and then you decide on you give a score and say how similar is this and this is actually it turns out that for what we want to do it's a very poor measure because you can imagine if you create a cartoon molecule like this AB and this AB with some attachments then most of the fragment masses will be different but the molecules themselves will actually be fairly similar still so it doesn't, this measure doesn't grasp it but let's maybe give to another example and that's where I now switch to another domain namely the domain of natural language processing because probably most of you don't have any intuition about mass spectrometry but all of you have intuition about language so if I would ask you how similar are these two sentences one is like cake with a cappuccino and laughs to have a cookie with a cake sorry have a cookie and a coffee you would also say okay wait if he asks for similarity what do we actually mean you can mean like number of words characters but most of you would probably guess that I mean something like topic and meaning and that's exactly what I'm after for the spectra as well so if we take these sentences and we really only do this measure that I just described before and look for identical words then it would be A so both sentences have in common A which is a word that occurs in probably half of English sentences so it's a very poor measure and it would say these sentences are not similar at all but if you really count they'd say likes and laughs, cake and cookie and cappuccino and coffee are very similar he would grasp their things and so what we now do is kind of, the idea really came because we have a lot of people in the Eastern Center that work with with humanities, digital humanities that work with NLP tools to analyze text and language and they had concept that we found were very applicable to our problem actually so the approach now is very naive in the sense that we just do the same thing just for our data so instead of talking about fragments or masses we now just say this is a word and the entire spectra now is not kind of a spectra anymore like this but it's kind of a sentence or a document and so this is what we now do and you could ask okay I don't know how many people are familiar with NLP tools or word-to-vec things like this, have anybody worked with it? not so much right? so like one of the classical approaches of how you find meaning like compare similar meaning of words is you run an algorithm through the entire Wikipedia say and you count for every word what are the next words or what are the close words before and after this word and you do this for entire Wikipedia and then you find wait, cookie occurs nine times together with monster so you get a huge matrix right? this is like a million words and cake never occurs with monsters, weird but you find that kind of cake and cookie both occur quite often together with sweet and you will find that they occur quite often I guess with dough or baking or eating stuff like this and this gives you some kind of sense okay what do these words actually mean and since they occur quite often together you will find that cake and cookie are kind of similar words in this sense as well and so what you can do then is a nice trick and this is like matrix factorization but this is kind of you just do dimensionality reduction because this million by million matrix is not very usable so there's a special circuit algorithm to do this and in the end what you get is a lower dimensional vector so you have a vector with 100 numbers or 200 numbers and the good thing about this thing is kind of it's based on this kind of matrix of co-occurrence these vectors somehow grasp the essence of what this word means and they will place cookie and cake in a similar part of this vector space so you will have cookie and cake here but something totally unrelated will be pointing in a different direction in this 100 dimensional space and so now we just do exactly the same thing we just say okay our words are now masses so they are now like mass numbers and we do the same thing and then we hope that this algorithm also grasps that two fragments are very different but occur in similar contexts will also be aligned like this and now in the end what you do is you sum up all the words in your document in our cases means all the peaks in our spectrum and then we get one overall vector and we do this for other spectra and then we compare these two by just comparing how similar are the overall vectors and this really is something that we can test on test data so as I said before we are really after a method that works for data where we don't have any further information apart from the spectra but we of course have a test data set for example here we have 11,000 spectra where we know which spectra belongs to which compound and based on this this is kind of like a label we can test how good our algorithm is by just giving it the spectra and then say how good does it perform in terms of real molecules so you can say that if you count you just pick the best scores in terms of the similarity and you see how similar are they really this is a classical method and this says that on this side is really low similarity in terms of molecules so most think that this classical algorithm will say are similar spectra don't really also correspond to similar molecules whereas if we do this word-to-word base as this NLP base natural language processing base tool then most of the things that this algorithm believes are similar spectra also correspond to similar molecules and maybe that's better if I give you one kind of picked out example actually a bad example for the classical one and this is the algorithm doesn't see any of these molecules it's just this is our labels so we ask for you a query for example for this molecule and say we give them the spectra behind this and say pick the nine closest spectra from all these 11,000 spectra and then we plot the molecules and I mean you don't have to be a chemist to see that these are very heterogeneous very different kind of structures and if we do the same thing actually with the language process it turns out like this so it really means that this kind of really finds much better spectra into or it finds better measures for spectra similarity with respect to also molecular similarity which is what we after so this is just one example and with this I'd like to highlight that this is a unique position which is very special in the academic landscape because it's a lot about specialization in academic circles and it's a lot about really working in your subdomain or sub-sub-sub domain I guess and as research engineers you're often exposed to a lot of different projects because they are about the same tools the same methods the same techniques and especially if you cluster them into centers or groups or things like that then you can really have synergistic effects and I think that this is a unique way to in the end create opportunities where you mind spot potential synergies between sub fields or techniques and with this I'd like to end but not before advertising a bit that we are also looking for people and so the Eastern Center is so we are now growing and we plan to also continue growing for the next near future and this means we have really room for hiring people that love to work between programming, software, data science and academic science fields and if you're interested in learning more about the Eastern Center or the jobs that we have then please contact me or Carlos who's also at the conference right now in the session right now or contact us at the Eastern Center so please I'd like to end and thank you for your attention.
 I think like RSEs, like research, software engineers are in a position that's quite unique in the Academy of Landsquake  and occasionally they have the chance maybe to break down scientific monocultures by really bridging different disciplines  and that is something I would like to strengthen here and to do this I would like to bring an example.  And I think this is very much in line with the great keynote lecture we had yesterday from Ellis Brett  which also argued for why it makes sense to have RSE groups or clusters for strengthening a lot of these points  and this is also something I have in mind here.  So to make my point why I think that it's a good chance to really bridge domains from the research software engineer perspective  first introduce a place where I work which is currently the Netherlands E-Sign Centre  and so we are an organisation based in Amsterdam and we're funded by mostly public research money from the Dutch government  and at the moment we have about 50 research software engineers working in our place  and they come from a very diverse set of backgrounds so about half of them or a bit less I would say come from computational sciences  and the other half or like two thirds come like me from all kind of different domains, quantitative domains  so like physics, chemistry, math, biology, life sciences but also linguistics and earth sciences and geosciences  so we have quite a broad range of people and we have an even broader range of projects  so all this is kind of just taken from the website and it's only half of the current project that run in our centre  and all these projects are from really different ranges of quantitative science in the Netherlands  so because the system is like this people like groups, research groups in the Netherlands from universities or institutes  can apply for funding and help at our centre and if then the project is kind of accepted by an external board  we try to work together with these researchers in form of a collaboration for two, three or four years  to help them what we call digitally enhanced research and so all these projects they come from like all quantitative domains  that you can think of so really from humanities, social sciences, to life sciences, physics, every area you can think of  and we do a lot of different tasks as you can imagine on these things and they will really depend on  so different projects, different domains have different needs and so we do things like big data analytics, efficient computing  so HPC which some people are doing a lot and there's optimized data handling  so a lot of different things that we do depending on what the project needs and depending on what we can offer in terms of expertise  so of course obviously this is an RSE conference and a lot of these things that we will do in this framework is about research offer  but as we heard in many talks yesterday also it's not so easy to define what research offer is or what research offer engineer is  and a lot of people will leave it quite open because of course different places people have different definitions of it  and I think that's good to keep it like this but there's of course more than writing research software for these projects  and it can be quite diverse depending on the needs of the researchers, it can be things like linking researchers to IT infrastructure  that will be like the HPC part for example, also big databases but for example what I'm doing a lot is actually more data steward or data scientist  so we have a lot of people that work in areas where we try to consult people on using the proper algorithms on the statistics  on how to evaluate and analyze data and there's something I would particularly argue for now in my talk it's kind of really  there's the opportunity to do cross-disciplinary transport and what I mean by this is kind of that we can, since we are a place  where people work with all kinds of different backgrounds on projects from all kinds of different backgrounds  we occasionally get the chance to really see that we can apply things that belong to one domain to a completely different domain  and I will give you one example of such a case which is a project I'm currently working on and that is about integrated omics analysis  so omics actually you can think of like in a nutshell it's kind of that's a big data thing for life sciences  so I will go a bit more into the biology so I guess most of you are not biologists  so I will spend some time on explaining what actually the key problem here is and how we try to solve it and why this is relevant  so this project is about linking metabolomic data so this is about the compounds that living organisms produce  to the genetic information so to the DNA and what's encoded in this in these organisms  and we are specifically interested in a class of compounds that are called secondary metabolites  so this is all these funny chemical symbols here and so secondary metabolites are a funny class of things  and they are quite important for many reasons so these are things that living organisms like cells, fungi, bacteria  produce based on very specific proteins so it goes like this so the DNA of these organisms contains a specific sequence  that encodes by RNA a protein and a protein you can think of is the main functional building block of living organisms  so this is something that specifically does a certain job and it's a bit oversimplified  but in this case this organism has encoded a specific protein to produce a very specific chemical compound  and the other interesting thing about them these are not essential building blocks so the cell doesn't need them for other primal functions  these are really things that you can think of deliver an evolutionary advantage to this organism  and the most known case I would say is really antibiotics because antibiotics are nothing else than secondary metabolites  or for example fungi so the things that we use in medicine now are discovered from fungi  because these fungi produce them to keep away bacteria or other invaders  and for example this chemical symbol is something that keeps conferences going because this is caffeine  so this is where we drink coffee and this is also a plant secondary metabolite  and a lot of these things are kind of found in medications and drugs and other things  but it has another role in addition and these things can also serve as a fingerprint  because like the last decades it really became more apparent that we are not a single species organism  but more in a symbiotic way lived together with uncountable microbes that populate all our body and guts  and these produce also specific metabolites and these can be used to give an idea or a fingerprint of certain diseases  because they will hint at certain populations of bacteria or yeast or other organisms  so this is what we like to do, we like to know what these compounds are and how they are linked to specific species  but of course this is not the data that we usually have so the data that we have available  if we do cell culture or bacteria culture or fungi or something like this  then we can take the x-thread and we can do mass spectrometry  and this gives these mass spectra and they are not really the same as having the compound  they are just more like a vague fingerprint of chemical compounds  and on the other side we have the DNA which currently is quite possible to really sequence a lot of these DNA  but then you still have a kind of cryptic code at the beginning  and you need to apply quite a lot of sophisticated bioinformatics tools  to in the end get the structure of potential proteins that we believe are responsible for  in the end producing these secondary metabolites  so the idea that we have or what we are currently working on is really to link up all this data  from a lot of different species and a lot of different experiments  and say for example that we can infer if we know one protein or one protein complex  actually that produces a certain compound and we see that another one is similar in its structure  that we then can infer that another similar looking spectra might correspond to a similar looking chemical and so on  so the idea is really that we have a lot of noisy data with unclear information  and that by teaming up, by using big data analysis and clever algorithms to sort this out  we hope that we can get a more reliable better prediction of what kind of DNA or what kind of proteins are probably responsible for what kind of compounds  so this is the main task of the project  but the specific thing I'd like to mention where we really can use tools from a very different discipline  has to do with this mass spectra, so this part that is here  so I guess most of you don't know mass spectra  or have never worked with it like me before  so just a very brief introduction to what it is  so you have your compounds, say you have liquid with a lot of substances and you don't know what's in there  so you charge them and you run them through mass spectrometry and then you get a mass or mass over a charge ratio actually  and this is not yet good enough so you destroy this compound and then you get a lot of fragments  and you measure the mass as well so you get a lot of peaks  and all these peaks correspond to masses of fragments or mass ratios of these  and this with a lot of analytics you can try to make some sense out of it  and if you have comparable spectra in your database you can eventually even guess the right molecule  but in our case in the end we will work with a lot of unknown spectra so we don't have this luxury here  so again the idea is kind of how can we relate this wake fingerprint to actual chemical structure and functions  so the key part about this is kind of if we imagine that we have very complex biological samples  say for example from human gut or from anything like this  we have uncountable different compounds in there and most of them unknown  and we have, so this means we have a lot of spectra we don't know nothing about  so some of them we might can compare to database maybe but most of them we cannot  so one key step to make sense of this kind of complex data is of course to compare things  so to ask for how similar are these spectra so that we can maybe cluster them or group them  or figure out similarities  and of course I mean we are not the first ones to think about this  so there is for decades there has been methods how to compare spectra, these mass spectra  and this is you just overlay the spectra and look for okay what masses are identical and what are not  and then you decide on you give a score and say how similar is this  and this is actually it turns out that for what we want to do it's a very poor measure  because you can imagine if you create a cartoon molecule like this AB and this AB with some attachments  then most of the fragment masses will be different  but the molecules themselves will actually be fairly similar still  so it doesn't, this measure doesn't grasp it but let's maybe give to another example  and that's where I now switch to another domain namely the domain of natural language processing  because probably most of you don't have any intuition about mass spectrometry  but all of you have intuition about language  so if I would ask you how similar are these two sentences  one is like cake with a cappuccino and laughs to have a cookie with a cake  sorry have a cookie and a coffee  you would also say okay wait if he asks for similarity what do we actually mean  you can mean like number of words characters  but most of you would probably guess that I mean something like topic and meaning  and that's exactly what I'm after for the spectra as well  so if we take these sentences and we really only do this measure that I just described before  and look for identical words then it would be A  so both sentences have in common A which is a word that occurs in probably half of English sentences  so it's a very poor measure and it would say these sentences are not similar at all  but if you really count they'd say likes and laughs, cake and cookie and cappuccino and coffee are very similar  he would grasp their things  and so what we now do is kind of, the idea really came because we have a lot of people  in the Eastern Center that work with with humanities, digital humanities that work with NLP tools  to analyze text and language  and they had concept that we found were very applicable to our problem actually  so the approach now is very naive in the sense that we just do the same thing just for our data  so instead of talking about fragments or masses we now just say this is a word  and the entire spectra now is not kind of a spectra anymore like this  but it's kind of a sentence or a document  and so this is what we now do and you could ask okay  I don't know how many people are familiar with NLP tools or word-to-vec  things like this, have anybody worked with it?  not so much right?  so like one of the classical approaches of how you find meaning like compare similar meaning of words  is you run an algorithm through the entire Wikipedia say  and you count for every word what are the next words or what are the close words before and after this word  and you do this for entire Wikipedia and then you find  wait, cookie occurs nine times together with monster  so you get a huge matrix right?  this is like a million words and cake never occurs with monsters, weird  but you find that kind of cake and cookie both occur quite often together with sweet  and you will find that they occur quite often I guess with dough or baking or eating stuff like this  and this gives you some kind of sense okay what do these words actually mean  and since they occur quite often together you will find that cake and cookie  are kind of similar words in this sense as well  and so what you can do then is a nice trick and this is like matrix factorization  but this is kind of you just do dimensionality reduction  because this million by million matrix is not very usable  so there's a special circuit algorithm to do this and in the end what you get is a lower dimensional vector  so you have a vector with 100 numbers or 200 numbers  and the good thing about this thing is kind of it's based on this kind of matrix of co-occurrence  these vectors somehow grasp the essence of what this word means  and they will place cookie and cake in a similar part of this vector space  so you will have cookie and cake here but something totally unrelated  will be pointing in a different direction in this 100 dimensional space  and so now we just do exactly the same thing we just say okay our words are now masses  so they are now like mass numbers and we do the same thing  and then we hope that this algorithm also grasps that two fragments  are very different but occur in similar contexts will also be aligned like this  and now in the end what you do is you sum up all the words in your document  in our cases means all the peaks in our spectrum and then we get one overall vector  and we do this for other spectra and then we compare these two by just comparing  how similar are the overall vectors  and this really is something that we can test on test data  so as I said before we are really after a method that works for data where we don't have any further information  apart from the spectra but we of course have a test data set for example here we have 11,000 spectra  where we know which spectra belongs to which compound  and based on this this is kind of like a label we can test how good our algorithm is  by just giving it the spectra and then say how good does it  perform in terms of real molecules  so you can say that if you count  you just pick the best scores  in terms of the similarity and you see how similar are they really  this is a classical method and this says that on this side is really low similarity  in terms of molecules so most think that this classical algorithm will say are similar spectra  don't really also correspond to similar molecules  whereas if we do this word-to-word base as this NLP base natural language processing base tool  then most of the things that this algorithm believes are similar spectra also correspond to similar  molecules and maybe that's better if I give you one kind of picked out example  actually a bad example for the classical one and this is  the algorithm doesn't see any of these molecules it's just this is our labels  so we ask for you a query for example for this molecule and say  we give them the spectra behind this and say pick the nine closest spectra  from all these 11,000 spectra and then we plot the molecules  and I mean you don't have to be a chemist to see that these are very heterogeneous  very different kind of structures and if we do the same thing actually with the language  process it turns out like this so it really means that this kind of  really finds much better spectra into or it finds better  measures for spectra similarity with respect to also molecular similarity which is what  we after so this is just one example and with this I'd like to highlight  that this is a unique position which is very  special in the academic landscape because it's a lot about specialization in academic circles  and it's a lot about really working in your subdomain or sub-sub-sub  domain I guess and as research engineers you're often exposed to a lot  of different projects because they are about the same tools the same methods the same techniques  and especially if you cluster them into centers or groups or  things like that then you can really have synergistic effects  and I think that this is a unique way to in the  end create opportunities where you mind spot potential synergies between sub fields  or techniques and with this I'd like to end but not  before advertising a bit that we are also looking for people  and so the Eastern Center is so we are now growing  and we plan to also continue growing for the next near future and  this means we have really room for hiring people that  love to work between programming, software, data science  and academic science fields and  if you're interested in learning more about the Eastern Center or the jobs that we have  then please contact me or Carlos who's also at the conference right now  in the session right now or contact us at the Eastern Center  so please I'd like to end and thank you for your attention.
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