You're tuned into the COVID-19 community report here on KDRT LP 95.7 FM in Davis, California. I'm Autumn Lab A Renault and today is Tuesday, January 19, 2021. We're sharing local news and resources focusing on what's impacting Davis and nearby cities in Yolo County during the COVID-19 pandemic. My guest today is Aaron Donley Marino, Executive Director of Tree Davis, which has a mission to enhance and expand our urban forest by teaching the people of our communities to plant and care for trees. We'll talk about the work Tree Davis does around our region and the various ways they've had to adapt due to the pandemic. And we'll get to that in just a few minutes. Reporting on the pandemic once a week as I do there are weeks that just make my head spin with the pace of developments. Last week we were under a stay at home order and then we weren't leaving even county health officials pondering the wisdom of the state's rapid shift as we are faced with viral mutations. And that has been the major discussion this past week emerging variants of the COVID-19 virus. According to the Centers for Disease Control information about the characteristics of these variants is rapidly emerging. Scientists are working to learn more about how easily they might spread, whether they could cause more severe illness and whether currently authorized vaccines will protect people against them. The CDC says that at this time there's no evidence that these variants cause more severe illness or increased risk of death. They constantly change through mutation and new variants of a virus are expected to occur over time. Sometimes these variants emerge and disappear other times they emerge and persist. Multiple variants of the virus that causes COVID-19 have been documented in the United States and globally during this pandemic. In the United Kingdom, a new variant called B117 has emerged with an unusually large number of mutations. This variant spreads more easily and quickly than other variants. Currently, there is no evidence that it causes increased illness more severe illness or increased risk of death. It has been detected in September 2020 and is now highly prevalent in London and Southeast England. It has since been detected in numerous places in Canada and the United States. And as of this weekend, the CDC had identified 40 cases of the new variant in California and those are largely in the San Diego region. An important takeaway here is that scientists and public health experts agree that reducing transmission remains key and towards that end Davis Mayor Gloria Partida and UC Davis Chancellor Gary May wrote an op-ed in the Saturdays Davis Enterprise. In it they called for frequent asymptomatic testing via healthy Davis together as key to getting normal back to normal in our community. Last year at this time, it would have been hard to imagine how normal it would be to wear a mask every day that we could celebrate with joy, even when we're not together and that we could develop effective vaccines in record time. But here we are doing things we had never imagined we could or would. It's with that same sense of challenge and possibility that we tell you it's possible for Davis to be a town without COVID-19 in 2021. As in the coming weeks, not late summer when most of the country is expected to be vaccinated. A new weekly routine of regular asymptomatic COVID-19 testing can help us get there. Healthy Davis together as a joint project between the city of Davis and UC Davis I've talked about it many times on this program, and it provides free saliva based testing to everyone who lives or works in Davis. Frequent testing is the best tool we have to defect detect infection before it spreads. The point of this approach is catching the asymptomatic positive cases as soon as possible. That's what really makes the biggest difference partita and may note with every positive test result we can quickly provide the quarantine support a person needs and mobilize contact tracing efforts to find the people who might have been unintentionally and unknowingly infected. You can learn more at healthy Davis together.org, and I will share that I've had to test myself, and also to date the UC Davis labs have processed over 100,000 tests. I'm sure that's the, the sum total of answers for how we saw this pandemic, but I do believe that it is a key tool in this fight enforcement and people who don't want to comply are are a much bigger issue than I think is is really being acknowledged. Let's take a moment for music and we'll be right back with our interview. Well the world went on pause during the great interruption, the pandemic brought things like climate crisis and the need for mitigation work didn't just miraculously disappear. Joining me today is Aaron Donnelly Marino executive director of tree Davis, which works to caretake and expand our urban forest thanks so much for joining me today Aaron. I'm delighted to be with you autumn thanks for having me. So, next year will be tree Davis is 30th anniversary, which is really exciting. Let's start off. Tell us a little bit about yourself how you got involved kind of what called you to do this work. Sure. Absolutely. I'll tell you a little bit about tree Davis first. So, the mission of tree Davis is to enhance and expand the urban forest by teaching the people of our community to plant and care for trees. And as you mentioned, almost 30 years ago, tree Davis was created by a collection of very thoughtful and visionary people here in the city of Davis including at the time city forster Bob Cordray, Lois Wolk and city manager john Meyer amongst others. And it's a wonderful public nonprofit partnership where basically we help the city's dollars go farther to care for our green spaces across the city. And the mission of tree Davis really called out to me. I was invited to join tree Davis in 2017. And the reason I'm called to this work is because I believe inside of each one of us. Tree Davis is a really deep connection to our earth. And when we can find that stillness and that calmness and feel that connection with the earth. That's also a connection we can share with each other. So our motto at tree Davis is planting trees and growing community and my personal my life work is connecting people to natural resources and through that connecting people to each other. So I feel very, very fortunate to have this role at tree Davis and to have this opportunity to bring people together in appreciation of the really special green spaces that we have here in yellow county because it really is a very unique place both in California and across the country. I grew up in a very urban environment down in Southern California.  I was really eager to find a place left this that has an abundant green belt system. Wonderful parks, open space agricultural land surrounding us. So yeah, it's my my personal connection to the earth and to the community called me to tree Davis, very nice. You know you, you and I share something in that we both run an organization that has Davis in the title and yet both organizations served the region. So tree Davis, tell us a little bit about the work you do in other communities to I know a lot of your projects are grant funded. I know you use volunteers extensively. And I know that there's had to have been a lot of disruption to your work during the time of the pandemic so tell us more about that. And that on all counts, autumn, even though our name is tree Davis we do work yellow county wide over the last few years we have implemented Cal fire grant programs in West Sacramento and in partnership with the Woodland tree foundation in Woodland. And historically we've also done a lot of work in winters and asparto. So really in the long run tree Davis is considering a name change. Because we don't just work in Davis we work all across the county and sometimes beyond the county. And but we're very, very lucky to have strong partnerships as I mentioned with other local organizations. David Wilkinson is the board president of the Woodland tree foundation and we really value our partnership with our neighbors to the north. We're also very happy to be connected with the Puda Creek Council, and really think about our urban spaces being nested in this larger yellow basin watershed in which we live. You know we've got this amazing resource in Puda Creek, and all of our urban spaces drain and connect to Puda Creek so thinking about how our urban forest. And across watershed boundaries, and, as I said earlier can access to each other. And in a normal year tree Davis would hold volunteer events, just about 40 weekends of the year most weekends of the year. Wow. Yeah. Saturdays are usually very, very busy days for us, and we would normally have a gathering of anywhere from 20 to 40 volunteers. We would fan out across our work area whether that was in Davis or neighboring communities, and work in implementing our own grants or working in partnership with municipalities planting city trees on behalf of the city of Woodland or the city of Davis, or the city of West Sacramento. And so that that was our bread and butter historically since 1992 that has been our model, and then 2020 came. And as with everyone else on the planet, we have had to take really necessary reevaluation of how we do things so in some ways you know this has been an extreme inconvenience. But it's also been a real opportunity for us to take a higher level view of what we do and think about what what our service delivery to our community can be beyond just planting trees. What does that look like what does that look like in the last year. Yeah. So we've tried to do more online engagement we've had some social media campaigns where we've encouraged the community to really take time to observe nature where they're sheltering in place whether that is their backyard whether that is the nearby or whether it's the park that they're spending time at. And we've encouraged the community to use the hashtag beauty in Yolo, and, and really just appreciate and delve into these natural spaces that we have we're so lucky. Have, you know your average Davis citizen has a park or a green belt or a green public space within a half mile of where they reside. And that's that's pretty special to be able to walk to a public space no matter where you live and enjoy greenery. And that's a really wonderful privilege that we have here. So anyway, we've really encouraged folks to be aware of that privilege and really appreciate the benefits of the natural spaces that are around us and that brings me to the mental health benefits of being connected to nature. So there are a lot of physical benefits you know people are more likely to be out and exercising and running and having cleaner air and things like that, in green spaces, but just the, the opportunity to have a reflective mental space is very restorative. It's very restorative, especially when, you know, we've got COVID going on we're all sort of in this cortisol fighter flights space a lot of the time wondering what is going to happen to me what is going to happen to my community. I think we're all sort of burned out in that adrenaline cortisol space, and being able to intentionally step out of that space, and be in nature, and breathe and be present is really one of the greatest gifts, we can give ourselves and getting through this tough time. And so that was sort of the aim of our social media campaign beauty in Yolo to get people to really lean into that space. And we've also found ways for folks to continue to connect in community virtually. And we were so honored autumn that you attended our art and sip in the fall. And I want to tell you I'd never done that before and and doing it virtually I thought how is this going to work but it was great art supplies and wine showed up on my doorstep and then I got to see you and other lovely folks online was fun. Yeah, yeah. So that was an opportunity to gather in community virtually and paint trees and have some conversation and appreciate the value of trees. And we're looking into doing more of these events as we move forward as more of a free benefit. And so being able to lean a little bit more into the arts space arts in connection with nature is a goal of ours moving forward. And I mentioned that we're considering a name chain. And so part of that is the geographic scope we don't just work in Davis, but we're also looking at reimagining the space in which we plant trees. And I say that what I mean is thinking about the opportunities for stormwater capture to be able to provide water for trees, thinking about the understory environment in which trees are planted. You know, turf grass is great for some park areas but we have a lot of opportunity in our public green spaces across town where we can provide pollinator habitat. We have native plants in the understory that are both more visually appealing and have much more habitat value than grass and turf. And it can also be cheaper from a maintenance standpoint. So tree Davis is looking to move into what we're calling climate ready landscapes, and so this is beyond. It includes trees but in addition to trees we're thinking about the space where trees will be planted and how we can better design and better plant and provide habitat in these areas. One of the problems, a big fan of trees, but one of the problems in public spaces with street trees especially is when the roots start to become invasive and can cause problems. I know that has a lot to do with proper tree selection, the first place and I know that the build out of a lot of the housing tracks and Davis from the 60s and the 70s didn't perhaps take that into account. So there's that piece. I've also heard you speak before about, and you just did a really good job and you were reading the benefits of trees and green space in our community and our, our good fortune at that. But when trees are absent. I know that the heat in the surrounding area can go up proportionally. And I know that trees are less prevalent in low income areas can you speak to that for a minute. Absolutely. So this ties to our program that we concluded year and a half ago in West Sacramento. West Sacramento is our wonderful neighbors to the east, but there are portions of the community that are under canopy. And there's definitely correlation, both with income level and with race. And it tends to be that these under canopy communities tend to have more folks of color, and they tend to be lower income folks. And so we worked really hard to come in and offer the benefits of canopy to this community in a really authentic and grassroots way. So we started in the public school system, teaching students about the benefits of trees, and then having students have the opportunity to bring that information home to their parents. And so instead of saying hey lucky you you get a tree now you have one more thing to do with your life and watering this tree and trying to keep this tree, folks were coming to us and requesting trees. We're making sure that we weren't pushing our services on anybody. And we were very, very fortunate that through our partnerships with the Washington Unified School District and the city of West Sacramento, we were able to plant 1000 trees in these under canopy neighborhoods and help folks deal with the urban heat island effect that is so very intense here in our Central Valley. You know the difference between a shaded hardscape and an unshaded greenscape can be as much as 60 degrees Fahrenheit. And that's the difference between being able to have your kids play outside and not play outside or being able to stand and talk to your neighbor or not stand and talk to your neighbor. And so, yeah, part of the idea is being able to mitigate the urban heat island effect and provide micro climates in these areas where people can more comfortably spend time outside and with each other. And I love your approach to that too because I think so often any of us who are doing social change work it's very careful that we not try to impose our agenda and our wishes on communities who may not want them or who may see them as as a burden so Absolutely. I really appreciate the sensibility you brought to that and 60 degrees that's a that's kind of a mind boggling statistic actually it is. I melt. I'll be perfectly honest past about 90 degrees I melt. And so when we get the really hot temperatures in the summer on indoors a lot and that's just how it is, but I have a 60 foot turkey oak in my front yard that share higher house. And it's kind of a pain during bird season but I I derive great benefit from it the rest of the year including that shade so Absolutely. Well, what so lots of pivots lots of things you haven't been able to do lots of ways you're retooling as you think about the months ahead as we continue to move through the pandemic. What do you see on the horizon for for Tree Davis. Yeah. So we're currently implementing a grant with the city of Davis that we call community canopy. And this is an effort to plant 1000 trees across the city of Davis, and also to work with the city in writing a 40 year urban forest management plan to make sure that these trees that are planted are cared for for the duration of their lives and beyond. So I described the program in West Sacramento and we're doing that very same program now here in Davis. So there's opportunity for all of your listeners if anybody would like a tree. Please visit our website of bww dot tree Davis dot org, and you can request a free tree for your front yard, and the city and tree Davis work together to provide establishment care for the first three years of that tree. And we also provide a lot of education on caring for that tree as it matures. So, even though we have not been able to hold the large volunteer events to plant these trees, what we've done is we've pivoted into a space where we're working directly with homeowners as volunteers. So if someone would like to obtain a tree they contact us, we set up a site visit at their home in a masked and socially distant Corvid safe manner. And we work with that homeowner to select the right tree for the right place, taking into consideration tree species that are going to be adapted to the future climate conditions of the Central Valley. And we work directly with that homeowner if they would like to plant the tree we instruct them in the process of doing that. If it's someone that doesn't have the physical ability to do that then we plant the tree for them. We moved from these larger scale events to working more on a one on one basis with homeowners and we're going to continue doing that throughout 2021, and in the first quarter of 2022. We have a little bit less than 500 trees left to plant so please call us today if you would like a tree. And what kind of trees are we are we talking about what kind of varieties of trees. So I worked with a panel of experts both from US Forest Service UC Davis and Arborists at the municipal level to provide a species palette of about 20 different species that are both adapted to the projected climate conditions of our region, and so fit the bill in terms of a good municipal urban fit. So earlier autumn you mentioned invasive roots and things like that so we've won our best to eliminate species that are liability trees, both in terms of roots and in terms of limb drop and branching So some of the species on our list include red push pistach, European hackberry which is not the same as the Chinese hackberry that's the one that the aphids like we're not planting that one. We're also doing Oklahoma redbud, White Shield Osage Orange and a suite of others we have the list on our website. And this is a good mix of large canopy shade trees that are from more in parks and larger wider open spaces, and also medium sized trees that fit well in the city's easement space on folks property next to the sidewalk. So we have a variety of sizes. Well, we are out of time I want to thank you so much for joining us today that I've been speaking with Aaron Donnelly Marino Executive Director of Tree Davis Aaron one more time let's give the website and maybe an email or a phone number where people can get those trees and get more information. Absolutely. So one more time, the website is www.treedavis.org and our our phone number is also on that website. So you can find the phone number there as well. Thanks so much for joining me today. Thank you so much, Autumn. It's been a real pleasure. I appreciate it.


Hi guys, welcome to this channel, it's me Xploma Meks creation, consider subscribing and hitting that notification bell. We are still looking at the latest developments in Kataul, we are looking at the latest developments according to the government that we just remember. We are talking about the Milpo exhibition that was in DCC and a lot of developments that came in. Remember we looked at one development that was the introduction of a DictoID app that will contain what you call a DictoRate that will contain all your documents. In today's video also the Minister of Interior also launched other 17 new features to be on what you call the Metra Ash 2. For those people that have the Metra Ash 2, the 17 new features were added alongside existing features. Remember all these are coming because of the sidelines of what you call the Fortinet Milpo exhibition inauguration. And remember we say the Milpo exhibition we had a conveno of over more than 99 entities to showcase the latest developments in technology including the Homeland Security and the Civil Defence that they have to give out to the citizens and residents of Kataimono. Among the services that we are looking at now there are six new services that we are adding on the Metra Ash 2. And remember when we are talking about these six services that we are adding on Metra Ash we are looking at services that we are originally handled by what you call the H. Partiate Affairs Department. Remember some of these departments, for example renewing your ID or renewing your Diorizins ID, you need it to fast submit documents or you need it to fast submit those documents. What you call the expertise of your department or you need it to go there in person. But for this case now according to the latest information we have with the introduction of these six services on the Metra Ash 2 you can still do all the process with your mobile at hand and you can still get the same services that you get when you go there in person. One of the reasons why these services are handled on Metra Ash according to the latest information is to avoid the time taken up and the queues in the ministry of expertise. Things can be handled in a digital way and all services will be approved and people can get the same services. And one of these services or these six services that we are talking about we are talking about services that include what you call added on Metra Ash that we include what you call the residence fee services. Remember when we are talking about the residence services we are talking about the residence permits that are supposed to be in the country. And what does it mean? What are those services? One of the services that is under what you call the residence permit. If you go in Metra Ash 2 and you check under the new feature that was added which is the residence permit you may find is what you call application of renewal of residence permit and receiving what you call application renewal. Remember this one before to receive application renewal residence permit you needed to first go to what you call the directorate. If you needed to first go to the directorate of expert affairs department to get approval by submitting what we call by submitting what we call the documents that are required for that case. Then also understood the same we look at what you call changing passport details remember for example if you had an old passport and you need to change your details into the new passport in case one expired you needed to first go to what you call the department of the expert affairs department. But right now you can do the documents or you can do all the registration using your Metra Ash 2. That service is being added there that you can do that using what you call your mobile phone or using the login what you call the app where you have that app and you definitely can access that service because it is added. You only need to attach the required documents and the app will be done and you will still get that message that is done. Also what you call issuing or what you call residence permit. This was also remember issuing of residence permit you needed to first take what you call medical test and also what you call finger prints remember. All these now are going to be done in what you call Metra Ash 2. It's going to be done directly because these are some of the services are being added and it's just by your hands that in a few steps submitting the required documents it will be done. Then probably you get approval in that case in case you are approved and definitely the rest of things will be working out and you will be entitled to get your ID. Then also getting your ID and actually that is by actually completing the whole process of it all other than moving from one point to a point and getting into alongside getting to what you call long queues for ID. Then another one is also under seal that we look at another one is cancellation of residence permit that require approval from the expert years department. The experts of the department. Now there is some cancellation of ID where you need to cancel your ID but you need to first get approval from what you call the expert years department in that case. So in that case where you need to get approval by no longer need to go to the main office of the department's office but you can still do it from your phone where you will be guided to the next steps and you follow the steps until you can get the approval. Then still another one is what you call residence violations fines that may be imposed by expert years which need approval by the expert years department will also be done digitally or be done by using metro h2 or under metro h2 where they will review your submissions then also you will get approval by your phone and they will guide you, they will further guide you on the necessary steps to follow after the submissions are being done. So meaning that they will no longer be more time to waste on even lining up for long queues and moving for long hours or waiting for long hours in one or the other. Then also what you call tracking application for residence services is also understood that for example if you are going to do what you call tracking you need to track your residence permit or you need to track your residence permit application or printing then definitely for all those services that you track them in one or the other. Then also in number two under the metro h2 under one of the services that have been provided is we call the service of search and follow up dependence. This one is also when we have the service of complaints if you are complaining for example you need to follow up with something, complaints of heavy work for example employee authorizes a person to submit a complaint against employee. This is quite very important and definitely we shall go into the details but in this services for example search and follow up dependence we shall have complaints heavy work where employee can complain about the worker, he can do it directly on his mobile using the metro h2. Then where we have financial claims for example there is overstage somewhere and companies including find like overstage still that will be done what you call by it will be done by what you call the dictor using what you call the metro h2. I think this is what we call some of that is what we call some of the latest information that is being being being being included in one or the other that it is going to hope out to help is going to hope out to see that people can definitely get out in one or the other can have that one which is quite very good or services that is going to occur. Then also we say what you can also track what you call track the recruitment approval is still under the same thing if you want to track if you submitted forms for recruitment for recruitment and they need to be approved by the directorate of expertise affair department then probably it will be a very good is to just be using your phone under what you call the metro h2. Then also we have another service now service number five is what you call establishment of card services this one is issuing what you call establishment of service card is a card for example those who are dealing commercial businesses you need to pay up licenses electronically you need to get approval. Cansolation of licenses cancellation of penalties paying up formalities having valid their permits for you to start up a business still that service is under what you call this is also a new service under what called establishment of card services still under what you call the metro h2. So these are new services that being added and crucial which are pretty very good in one or the other still under that we need to talk about Cansolation of a establishment licenses where you need to pay over dues you need to pay all the quiet fees is still under that at one or the other. Then also we look at service number ten that is still under that is what you call reporting service is called communicate with us we call it preventive security remember when you see this one metro h2. It's a new service that is being added or say new service that is being added and this one it calls upon all business and call upon all citizens if you see something that is harmful to the community. If you see something that is not right you can call again or you can send you can alert the required authorities directly. However as per the ministry this is this is just only added is that to insert what you call the moral values in the community in one or the other. So meaning that for people who are out there in the public you only need to be a bit very careful because everything can be done but in the fall very fast easy fast. If someone can complain about you he doesn't need to take a little bit of papers to complain but only he needs to take up the number then he can message or directly report using his mobile phone under what you call the metro h2. So these are the new services this was details this was the services that were being added in metro h2. For people who are using new trash I think you can still access them if you are using the new trash right now. Thank you so much for coming back to this channel and thank you so much for getting updates from next. This makes for the next question remember if it's your first time to come across this channel consider subscribing but they don't forget to hit that notification bell. It will be the only way that you will be supporting me and it's the only way that you will be supporting this channel. By the way feel free to leave a comment in the comment section where you feel we need to talk about something. We need to discuss about something. Let us know if you can talk about it and share in one word that remember. This makes for the next question. I will see you in the next video.


Figs and wasps. How are figs pollinated? If you're like me, you've probably given little thoughts to figs, aside from how delicious they are. But a quick look into the lives of figs reveals an entirely different picture. A remarkable and complex partnership between figs and wasps. Plant and pollinator. So what exactly are figs? How are figs pollinated? What is a fig wasp and why are they so special? Figs are flowers, not fruits. Our local supermarkets are a categorical minefield. Granted, that most of us are now well aware that tomatoes and avocados are technically fruits and not vegetables, while an aubergine, interestingly, is actually a kind of berry. And the same goes for bananas. But while this botanical treachery has been exposed in some cases, others have managed to slip under the radar. Right under our noses, the fig has been masquerading as a fruit. That's right. What most of us would have smugly placed in the fruit divide is actually not a fruit at all, but an inflorescence, a coir of hundreds of tiny, delicate flowers, clustered within a bulbous stem. Cut open a fig and the psychedelic maze you see within are individual flowers and the crunchy bits, the seeds of each flower. How are figs pollinated? Because figs are technically the flowers of the fig plant and not fruit, in order for the plant to reproduce, these flowers need to be pollinated. But, as you can imagine, pollinating the fig's internal flowers is nothing short of mission impossible. Only one small but significant visitor has successfully undertaken such a task, the fig wasp. At the size of a mosquito, this tiny invertebrate gives its life in service to pollinating the fig, and in return, each fig provides a safe and accommodating nursery to hatch and raise young. Fig plants need fig wasps. Fig plants have remarkable relationships with fig wasps. It is a relationship so complex it can be difficult for us to understand who initiated it and who is getting the better of the bargain. Of the more than 850 types of fig plant, almost all have their own species of fig wasp, which has uniquely adapted to its partner fig. Although certain wasps species have evolved to be generalists, allowing them to pollinate a number of fig plants, these incredibly synchronized relationships didn't just happen overnight. The fig wasp association is prehistoric, the origins of which have been dated as far back as 90 million years ago, 35 million years prior to the mass extension of the dinosaurs. It's thought that sometime before the plant and wasps for tissues encounter, the flowers on fig plants turned quietly inward, coming to form the floral caverns we know today. This seismic shift changed the course of history for both organisms. For the fig plant, this evolutionary adaptation meant committing the highest form of monogamy. No longer would any other creature, from bees to birds, bats, or even the wind, assist the plant in pollination. By sealing themselves off to the outside world, these blossoms triggered what was to become one of nature's most enduring and mutualistic partnerships. Figs' pollination cycle. When a fig is ready to be pollinated, it emits a scent alluring only to the fig's partner wasps. A female wasp, reaching one of these figs, tunnels her way through a needle-sized opening at the fig's base. Tragically, each time a wasp does this, she unknowingly commits the ultimate sacrifice. As she wriggles her way down the narrow hole, her wings and antenna get ripped away, rendering her flightless. She will live out the rest of her two-day lifespan inside the fig, never to take flight again. Inside the fig's chamber, the mother wasp gets to work laying eggs in as many miniscule flowers as possible before she perishes. The fig plant chemically detects each egg as it is deposited into the flowers and responds by surrounding the eggs in plant tissue. This provides the larvae with food for when they emerge. Over the next few weeks, the eggs develop and the male hatchlings emerge first. Not resembling wasps at all, wingless with warm-like bodies, the males have one life mission to fertilize the female larvae before they hatch. After fertilizing the females, the males use their jaws to bite through the fig's flesh, creating exit routes for their female siblings. Completing their life's work, the males crawl back inside the fig's cavern to die. Soon after, female wasps are born as pregnant queens, carrying with them the next generation of wasps. As the females make their way through the exit channels left by the males, the fig's pollen becomes fixed to their bodies. Surfacing for the first time, the pollen laid in females take to the sky in search of the right fig plant to deposit their eggs. Soon after, female wasps are born as pregnant queens, carrying with them the next generation of wasps. As the females make their way through the exit channels left by the males, the fig's pollen becomes fixed to their bodies. Surfacing for the first time, the pollen laid in females take to the sky in search of the right fig plant to deposit their eggs. Disproportionate to their minute size, fig wasps travel farther than any other known pollinator, covering distances as far as 10 kilometers in less than their 48-hour lifespan. At the end of this epic saga, a female wasp makes her way down into a new fig's dark inner cavity, bringing with her the pollen from her birth fig, pollinating her new abode. The process starts anew. Figs and wasps evolving together. Symbiotic relationships can be found throughout nature. The relationship between most flowering plants and pollinators is symbiotic. Flowering plants provide food for pollinators in the form of nectar or pollen, and in return, pollinators facilitate the dispersal of pollen for the plant to reproduce. Fortunately, in most cases, the diversity of flowering plants and pollinators means this exchange is relatively elastic. If one species of pollinator were not present, flowering plants would still be pollinated by other visiting species, and vice versa. This is where the fig wasp and fig pollinator relationship differs. Figs and their wasps are more than symbiotic benefactors. They are partners. As Nigel Tucker, one of Australia's leading tropical ecologists, tells me, figs and fig wasps have a totally obligate relationship. Through their exclusive devotion to one another, over millions of years, the two species have evolved in near-perfect symmetry, shaping both their physical and behavioral traits. A processed biologist called codependent evolution. The result is a connection so profound, neither organism can exist without the other. Take one away, and the other falls. So, are figs vegan, or do we eat wasps?  And the answer is, maybe. Of the more than 850 fig plants, most wild species require pollination by a wasp. So, if you happen to eat one of these varieties, chances are you're getting a little added protein with that fig, which is why some vegans choose to avoid figs altogether. However, commercial varieties you find at your local supermarket are usually cultivated without pollination, crone from cuttings and producing fruit without seeds. Some figs have even been brought to be parthenocarpic, which means they've reproduced asexually, without any pollen transfer at all. An example of this is the common Californian fig, ficuscarica, which makes up 90% of America's fig consumption. In short, it's unlikely you'll find any little pollinators in your supermarket figs. But even if you were to eat a wild fig, the carnivorous enzyme released by figs as they ripen, known as fikaian, effectively digest the internalized wasps for nourishment, leaving no trace of them by the time you take a bite. So, did you know the relationship between figs and wasps and how figs are pollinated? Will you be inspecting your figs for fig wasps? Let us know in our social media. Thank you very much for listening. This article is written by Molly Melvin and read by me, Ines Ortalanso, originally posted on Food Unfold It. Food Unfold It explores the stories behind the food on our plates, reconnecting us to the origins and sustainability of our food. Food Unfold It is co-funded by the EU and powered by EIT Food.


I was like real deal chill out. I bet costic never thought he would swing come on You saw the tape. You know you got a week left like the plot of the ring Number one the final finale bow of the evening in your room for e3, please basically one last time Get into the I thought Danny jack vs. Roars was the ultimate grudge match. This is the ultimate uber mega super ultra grudge To get into it right now Danny's actually the same as on the right-hand side. Let's get this bow. Let's do this Oh, yeah on my right-hand side mr.  We said now This is don't flop we keep it lyrical baby. All right, who's first? Yeah, one round What's it six minutes time on my mind Rob Wilson? Whatever you want to go?   Yeah I said I wasn't gonna do gumbars. You think this bro. We know what's best, but neither should you wrap about sparking You shouldn't but you've got like nothing on me prepping for Robert was a hassle So instead you mentioned Pistol whipping like you do in almost every battle, but pistol whippings fair because when you haven't got a lot of ammo Then you have to switch and hit me with the bottom of the barrel About sparking clips and all the bars you split or that you've been shot like what 50 times like cool Well done. You're not hard to miss Title shot.  Well, have you not been shot enough?    He's been doing all sorts of stuff He got into beef with John John when I thought he was his buddy all happens at a restaurant John was ordering them lunch He said that he had eaten so John thought he won't get much the menu said a fried chicken bonus Salad with a barn or be double orange French sauteed with a slug the menu went down to Zed He thought you'd be a mug stuff in the bottom of the menu or to all of the above You've made John John Dodon's life literally hell. I bet he's kicking himself.   So here's an easy win They're trying to respite your flame, but I'm on the ball and that's relevant to this spa today because you're washed up with Wilson cast away When this battle's done, he's gonna try befriending the kid who knows he might invite me back to his ends for a drink But I won't ever go there. So don't try mentioning it. I said before my dad's been to Chicago.  I Know you thought I battled you Thought something was a special about you, Rob Huh? This is an interview. I'm 56 favorite battle rapper, but tonight. I'm gonna teach you how to Rob I'm dealing with a pasta. I took this battle for free.  I finally got somebody If you had to pay to get in the venue, then you are not somebody Or who is this guy frisk, please quit stop somebody You gave this we know voice like Seth Rogen sausage Even put this battle in this budget If I'm here my stock really went down upon it I'm disgusting. Let's talk about this for a second.    My girl cheated on me. I was going through it Kill myself.  Okay.


Hi everybody. I have to say that somehow Lauren and I prepared the content for course in machine learning, introduction to machine learning, as opposed to a one-hour lecture. So I will try, but my goal is to actually give you the information that you can then take and use in your practice, and to give you the information that you understand that when others give you the results of some of these applications of methodologies and things like that. So I please feel free to stop me and to ask questions and we'll go at the pace that is hopefully comparable for most of you. And yes, definitely ask if you have any questions. So just briefly, there are many objectives, but one is we'll talk about the data integration technique that we have developed, but it's broadly used, which is called similarity network fusion. Then we'll talk about the cluster analysis, the many techniques that I use in the cluster analysis, and then if there is time, because that will be slide 60, would be the building classifiers. But the slides you have I think Han said that she had shared. So if you have any questions about building classifiers and we don't get to them, I'll be happy to go over them with you. So the reason why we have actually developed this data integration technique, which is called similarity network fusion, is that more and more often there's a lot of different and very heterogeneous kinds of data that is being collected on the same set of patients. So again, the set of patients and you might have methylation data on them, methylation and mRNA and microRNA and other epigenetic data, but you can also have the non gene related data, non even omic related data such as the clinical data, right? So related to here is the diet. So this was the case in our IBD study in inflammatory bowel disease study. And so that's one concern is that the data we get on the same set of patients is very heterogeneous. And the other, the two others is that we have a lot of measurements, but very often very few patients. So in classical statistics, we really like when we have a few measurements and lots of patients. But that's unfortunately not the case in omics data. We have lots of measurements and few patients. So we have to take that into account. And also we, the different data flows, they provide different kinds of information. So sometimes it's one confirms the other and sometimes it contradicts the other. And how do we take advantage of all of that kind of difference in the signal? So this is what we have developed. It's a method with two steps. The first is to generate a network. And here in network is the similarity, represent similarity between individuals according to that data type. So as many of the types of data that you have, you will have as many networks. So the each edge in this net, each dot and node in this network represents a patient. And each edge represents how similar they are. So the stronger, the more colorful in this particular representation, the more similar the closer they are to each other. And so here, actually, this is an example of TCGA glioblastoma data set. This is the real data that you are seeing, not on the left, but this is three come from the real data set. And so once you have constructed these networks, we have a nonlinear approach, which I will talk about in more detail as we go. And it combines all of this networks. So it already doesn't necessarily care where the data has come from. What it cares about is that it's in a space of similarities. And once we're in the space of the similarities, we can actually combine all of this data. That's the key point of this approach. So here's an example. And we have lots of examples, because we work with lots of different kinds of data. So here's an example of nonomics data, but you can imagine the same for for your data, whatever data you have, we were just trying to give very, very different types of examples and different types of data. So here we had a clinical cohort of 80 samples. And all of them were OCD diagnosed. And what we wanted to identify is the subtypes of OCD from this data. So we had the clinical data and we had two types of neuroimaging data, so magnetic resonance and the structural resonance imaging data. So for each individual, we had these three different types of measures. Sometimes it works and sometimes it doesn't work. So this is kind of what the data schematic for what the data looks like, we have a matrix, we have each row represents a patient and the measurement supporting to this particular type of measurement. So here it was brain structure. And we had sometimes it goes three at the time. Maybe I will put this down. So we had a brain structure, we had the metabolite concentrations, and we had the three different matrices represented like this, and the patients are all the same. So what we do is we kind of integrate out the information about what type of measurement it is. So we essentially look at the distance or the similarity between patients and now we'll actually go over the different distance metrics that you can use in this context. And once we have the patient by patient similarity, this in a sense is actually equivalent to a network. So if you were just to look at the correlation, say between all pairs of patients, you will be able to construct such a matrix, but it would be full matrix and your network would be a full network. So what we do is usually we specify this matrix to result in a network that you see here. And the specification is just based on who is most similar. So you just remove the one the pay the links between patients that are not similar enough. So we did this for all the three different types of data. And we got the three different networks. And then we integrate it. And I want to give you a sense of kind of what it does. So the imagine this example where we have just two clusters of points, and we are trying to figure out what what is the true clustering in the data. So this is the true class in this we don't see what we see is the one on top, where where some portion of the points is relabeled. So it looks like it's part of the cluster, whereas it is really not. And on the other type of data, another portion as related from the second cluster. So in some sense, you're getting partial information from both of the types of data, which is quite common. So this is simulated data to simulate the reality that we commonly experience. So when you have such information, you actually can use the similarity network fusion and combine all these networks to result in the joint cluster, which gives you essentially the original clustering that you would want to find. So this is just the performance of the metrics with respect to how much noise there is in the data. So how many points are there? And this are some of them are competing approaches that do not perform as well. So in our original network, what that means is that even if we have information from just one type of data, and it's not kept to the other types, if this information is fairly strong, so the similarity is fairly strong, it can propagate to the joint integrated network without having it being complete and all the others. So here's another example of different kinds of noise in the data also very, very common. So here again, the ground truth is not seen in the top perturbation, there is a Gaussian noise in the bottom perturbation is a gamma noise, all you have to know is that this is kind of a different types of noise like normal white white type noise, but one of them, for example, this gamma noise, it actually has a tail, right? So there are lots of values. And so you see that both of them provide information about the clustering, the original clustering, but there is a lot of this all diagonal noise, which you kind of don't want in your data. So once you use this noise in a confusion, it actually integrates the data removing a lot of this noise. So if these noise were reinforced, so both if they were identical, then, of course, if using you get the same exact measures, right? But if the noise is different, and it's weak, right, compared to the signal, then you'll actually get that noise drop out, and you get much stronger signal popping up. So that's the example here is this edge, the similarity between these two individuals was only one dataset, it was fairly weak. And so it disappeared when we combined the three networks. We applied this approach to several different examples. One was published in Kansas last year was in pancreatic TCGA consortium. Another one is, is a completely different one also using imaging data was in psychiatric disorder subtyping. We used clinical combined clinical, and I think we'll kind of wait for them updating. And where did it go? We hear somewhere, right? Here. So we combine methylation data and clinical data. And also we are doing some similar work for inflammatory disorder. So basically, this is just to tell you that there are many different types of data that you can combine to achieve this similarity and the clustering. So now I will talk about clustering, but I will first talk about distances. So I want to make a distinction before I move to clustering that the similarity network fusion, it actually provides you similarity across all patients, you don't have to cluster that data. Very often, you actually do want to cluster, right? You want to identify discrete subtypes in your population, patient population, or something else that you're doing gene set, right? You want to identify gene module, and you want to cluster your data. But depending on your goals, you don't necessarily have to cluster. That said, clustering is a very important and very integral part of the analysis of the all mixed data. And so that's what we'll discuss next. So you can cluster on single or multiple types of data, you can measure you have to figure out what signal you care about. So that's about the preprocessing of your data and figuring out how you would construct, for example, the similarity based on what, right? etc. So let me let me switch to the distance metrics. So when when clustering, you have to figure out how similar is one instance to another instance, right? And whether they should be clustered together or not. So here's an example of a most commonly used metric. It's a Euclidean distance. And Euclidean distance here is shown in 2d space. And you can see that it looks at the coordinates back to early school. This kind of plus where you look at the difference between coordinates in x axis, y axis, etc. So the more generalized, the the squared error is when you have n dimensions. And it's also still a sum across all of this dimension. So this is the most by far the most common use metric in for continuous variables. I want to bring up another one, you might or might not have heard about it, the Mohallabis distance, which I used to call the Mohallabis, but the point is that this distance is a generalization is a generalization of the Euclidean distance. And very often, it is used instead of the where you would normally use Euclidean distance. Why is because of this term? This term s is showing the covariance matrix between all your measurements. So when your measurements are all on the same space, suppose you measure, I don't know, all weight, right? And it's all in the same space. And that's all fine. But if you're measuring height, weight, and maybe some other measures, other clinical measures, blood test, whatever, then this, these are not on the same scale, this is not on the same. And people do some kind of normalization and standardization to account for that. But another way to do it is to use the Mohallabis distance directly and estimate this covariance matrix. So here, it will depend the ultimate result, the ultimate distance will depend on your data. It will be constructed for your data. And this is very often used in machine learning for those of you who use those kind of approaches. And you can see that the incorporating this covariance s allows you to model the distribution of the data better than the Euclidean distance that is shown here in the red concentric circles. But of course, you might have not continuous but categorical variables, right? So you can have high, medium, low, for example, three categories for one of your measures. And in that case, well, Hamman distance is the number of mismatches. It can actually here it's shown for binary data. And that's what people most commonly use. Which metric you use here for binary categorical data really depends on what you are trying to achieve, right? So for example, in some cases, if you are looking at somatic mutations, and you have a very few mutations that you actually care about, and you only care, but if you have the same somatic mutation between two patients, that's actually the strong signal for you. So then you count just the similarity in terms of the same somatic mutations if they are present, right? Because if you are counting matches or mismatches, you would count also the ones where they are absent, right? There are large amounts of genome where there are no somatic mutations and that would dominate your metric if you just counted matches on mismatches, right? So in very often, you have to stop to think about what distance metric you want to use. And this happens a lot in any kind of clustering that we do in our lab also to kind of ensure that the distance metric will ultimately capture what we care about. Finally, and more recently, there is a lot more longitudinal data that's becoming available. So longitudinal or time series, in my mind, longitudinal, it's called when you have a few measurements over time, time series when you have lots of measurements or like hundreds of measurements over time, but it could be my own definition is just based on my experience. But one of the most common metrics there is this fresh air distance. And this is a cartoon that Lauren found somewhere, which I think is great, which is it's so you are trying to compute the distance between the two curves. So between measurements over time of different types. And it's essentially the minimum leash length that you need to be able to make that curve between the dog and the owner. But it ultimately, it's equivalent to the max distance between the two curves. Okay, between the trajectories. One important point is sometimes the methods require distance, which means the bigger is the number, the more dissimilar the the patients or the genes or whatever you're measuring are. But sometimes people look for similarity or dissimilarity, right? And that is kind of this inverse. So you can see here, we have the Euclidean distance that we have computed, according to before. And if we want a similarity, so the equivalent corresponding radial kernel similarity would be the one where you take one over the exponent, it's an unshaded Euclidean distance. So here, in similarity, the higher the similarity, the more similar they are. So it's the opposite of the distance. Just to just to make sure we actually have a lot of a lot of questions in SNF, because we do cluster. At the end, there were lots of questions from the user saying, I get exactly the opposite kind of clustering and what they were using was distance as opposed to similarity, which is what we were using. So it is important. Okay, are there any questions I'll be switching to the clustering approaches? And I don't have a clock here. So I have no idea how. Excellent. Might be able to go through it all. Questions? All right. So common clustering approaches. The first approach that you have already heard about from Andre, from what I understand is the hierarchical, I will then talk about gamemes, Gaussian mixtures and spectral clustering briefly. So hierarchical clustering. The idea is to cluster all the way to one cluster. So the way that hierarchical clustering works is that you start with every instance or every every item being in its own cluster. And then you're trying to merge them. That's that's the idea behind it. So at the end, you merge it all into one big cluster. So here, it basically you computed your distance in hierarchical clustering, they are often called linkage functions. But you compute your distance. So here, it looks like A and B, the closest to each other and E and D are closest to each other. And so we have what merged A and B into one cluster, E and D into another. In the second step, we identify that C is the closest to the A, B cluster. And now A, B, C is a cluster and D, D, P is a cluster and there is a separate one. So this is not a cluster. This is the dental count in the bottom that corresponds to this cluster procedure. The next one is F and ultimately, we have merged everything into itself. So in some sense, this is, I would guess the most commonly used clustering approach, because it's so unassuming in some way, but it does depend on the distance metric. And it will give you a different clustering. And at some point, you will have to decide how many clusters they are, because you have to cut this dental ground somewhere, right? But this gives you an option of making that decision. After the fact, unlike the K-means, where you have to decide on the number of clusters before you actually cluster. So the steps of K-means, and most of you might have heard of K-means, it's also incredibly, maybe the second most common clustering technique. So you choose the number of clusters and then you set the centers randomly, essentially completely randomly. And then each point, you compute the distance of how close it is to each of the centers. And you assign a point to the cluster with that center, with which it is the closest to. So here, you got the red points assigned to one cluster and the yellow points assigned to another cluster. Then once you have made the assignment of all the points, you recompute the center and you repeat the procedure. So you recompute the center, right? You now have this evidence that all the red points are in the cluster, so you are looking to move the center to here because that's the central point of this cluster. And so you recompute and repeat until convergence. Obviously, this procedure converges intuitively, but there is also a proof. And so you kind of, you have the new center, you recompute the distances to the center, you reassign the points, the points now move between clusters. We assign the points, recompute the center, and you end up with the one final option where the center stop moving.  So that's your final for this particular set. The Gaussian mixtures quite nicely is a generalization of this. So what K means is disjoint assignment procedure. So you assign either to one cluster or to the other. Gaussian mixtures allows for the probabilistic assignment, but it's exactly the same procedure as you can imagine. It's just done with the expectation maximization because we are talking about probabilities. So you have the probability of being assigned. So for example, some of these points, there will be with probability 80%, there will be assigned to one cluster with probability 20% assigned to another cluster once the procedure converges. But essentially, it's a generalization. Also very commonly used. And finally, the spectral clustering. So spectral clustering in some sense, makes fewer assumptions, right? So the Gaussian mixtures, you have to make an assumption that the underlying distribution of your data is a mixture of Gaussians. And then you find the assignment. Here, in spectral clustering, all you do is you compute your distances, you compute PCA on that space, you take just a top PPCA and then use K means on the top PPCA that you have chosen. So that's your your spectral clustering. It's used very commonly for graphs. And in SNF, for example, that's what we use, we use spectral clustering. And you can see the difference here. So while in K means the most the closest point is the one that gets clustered, right? So if you have this two clusters, which are kind of what's called on manifold here, the special clustering is actually able to capture more, more accurately. This is the kind of a Swiss roll. So for the clustering approaches, for all of the ones that I have mentioned, you have to choose the number of clusters. So not beforehand for hierarchical clustering. But you do have to make that decision at some point. In the third class, none of these procedures make a decision for you. And Gaussian mixtures also has a probabilistic asset. So at every point, once you have new points, you will be able to say probabilistically, which clustered belongs to, which is a nice thing, which, which component belongs to. And if they're not linearly separable, in the sense that you can't just draw a line in between those two clusters, very clearly and easily, like in the Swiss roll example, then special clustering is maybe the better choice. I don't talk about the non parametric clustering approaches here, but there are approaches. Actually two things I will say. First is that there was a generalization of K means called X means and X means does the derivation of how many clusters are there for you. It's not as commonly used. I have not seen it used, even though actually it came out from the lab where I did my PhD. And we all like it. It's not very commonly used. So I figured that will not be introducing it here. Another one is actually used, but they all make some kind of assumption. So there's a kind of the richly process clustering where the assumption is that the rich cluster rich get richer cluster. So you might have if one cluster already has a lot of members, then the new member is more likely to be assigned to that cluster than to other clusters. And that's that's an assumption that one makes. And unfortunately, even in that case, it was it was proven that that procedure overestimates the true number of clusters simply due to the stochastic process of the estimation. So deciding on a number of clusters is not a solved problem. And I think it makes sense. It's not a solved problem because it actually depends, right? The number of clusters depends on on your particular objective for for doing cluster, right? And sometimes it's you can see by and it's very clear and that's great. But a lot of times you want to see what are the small clusters that are combining into the larger cluster or something like that. And it makes sense to explore the full space. So one of but but there are metrics that help you to assess what is the best number of clusters and then but still the decision is for the user. So silhouette metric is very, very common. Eigen gap and there are of course, other ones online. So silhouette statistic was introduced in 1987. And basically, it looks at two measures, which makes a lot of sense. So AI is this average distance of the individual point to other clusters to to the other things within the cluster. And the AI is the average distance to other items to other clusters. So you want AI to be small, so things within the cluster to be tight, and vi to be big, and the things and it goes between minus one and one here is the silhouette metric. And it goes between minus one and one, obviously, you want it to be positive. Because that would indicate that these greater than. So here's an example of a particular silhouette plot. So here, there are three clusters. And this one cluster, it looks like there are few points that are actually closer to other clusters than to this cluster. And this, again, is done for classroom techniques, such as k means of hierarchical clustering, where you don't, you don't have an ability to get the probabilistic assignment of clusters, right? So for those kind of classrooms, this is useful. I saw this very nice exploration where people have looked at various kind of simulated distributions, and how the different metrics that how the different metrics compare in identifying the right clusters. So here, there are four scenarios. And in the next, the results, I will show you all four. But here, the scenario A is the three clusters into dimension, which you see in the scenario B is three clusters in 10 dimensions, which I would not really plot. Scenario C is four clusters in 10 dimensions. Scenario D is six clusters in two dimensions. But notice that while you can distinguish the three clusters distinguishing within these clusters is very, very important, right within this two very tight kind of pumps. So the way that it works is that you have this four scenarios. And this is the silhouette measure. And it really is the most commonly used. And you can see that with scenario A, silhouette has done pretty well, pretty much on top of all the other clusters. Whereas in scenario D, it has done the worst. So it was not able to distinguish these clusters within within the tighter groups. So this is this is an important thing to keep in mind that there is no one measure. So in SNF packages, enough tool, we provide two measures, we provide silhouette and we provide the Eigen gap, which is the statistic between the principle components. An important thing to consider when you're clustering your data, and I think it's a very, very useful thing is the stability of your clusters. So the problem with clustering is that if you say there are three clusters in the data, every method will find you with three clusters. Now, whether they were three clusters in the data or not, the method does not concern itself with that. And the way to kind of get to it closer to to assessing what is actually happening with the data is to re samples take 80% of your data. So I say 80, but it can be anything can be 7090 doesn't really matter. But you get it is supposedly 80% of your data sample it. And then cluster, and then record, if you sample the two points, whether they were in the same cluster or not. And you just have this kind of fair wise make matrix that tells you if they were if this individual so this is the entry in that matrix, if I and J were sampled together, were they in the same cluster or not. And so if you have a uniform plane, then if you resample 80% of the data by chance, you're some of the individuals will cluster. But the next time you resample, they will not cluster. And so the whole point if you resample it often enough, you will actually get uniformed this matrix. And if you get a uniformed this matrix, you will know that there is no real, there's one big non cluster in your data. And what it also allows you to do this approach is to get rid of the outliers. So you will see that sometimes the kind of patients, for example, that you are trying to cluster, they go between two clusters. So 50% of the time they are in cluster one, 50% of the time they are in cluster two, it means that you simply don't have enough data to actually make that assignment. And maybe you would consider that outliers separately, you will say, I don't have the information to confirm that yes, this individual is of sub type one, sub type two, whereas some of the individuals will always cluster together. And that's the core of your cluster. And that's something that you can go forth with the analysis and keep that. So we're doing on time, we're doing very well on time. Excellent. Questions for the clustering. So no questions, not always a good thing. There's no, no reason. There's no, not much of a difference. Right? The whole point is to give you enough stochasticity in your sampling to see all kinds of the different clusters. But for to give the individuals or patients to give them enough chance to be sampled together. So that's, that's all. It can be 80%, it can be 60%, 50%. Do you have a favorite number? No, no questions? Yes. I think it really depends on the, a lot of times, you can use your prior information on what distance would make the most sense. So if your data is continuous, but you are pretty sure that it's not normally distributed, then things like Euclidean distance are not the right ones to use. And if people just use that Euclidean distance, because that's the most common thing to use, you might suggest Michalana this distance. But I think the biggest problem is a lot of times this clustering is done for the discovery purposes. And so we don't know what's in the data, right? And so it's really hard to, to pick. But you should know that if you use very different measures, distance metrics, and you get completely different clustering, that something is up. Something is up on the data. So maybe it was too sparsely sampled, it's very high dimensional, was too sparsely sampled. But usually the core, the cores of the clusters should be roughly the same, regardless of their measures, because I think this, this, if the concern is that there may be a lot of outliers, I would definitely recommend the core clustering. And then you can do the different thresholds with how many outliers to select.  So this would give you tighter clusters and more outliers. Yes? Moving on to classifiers. Very good. So the goal of the classification is really to, to build a map from the set of predictors from the set of measurements that you have to the outcome that you care about. It's just a map. It can be a linear map. It can be a nonlinear map. But the point is that you want to have some kind of predictor, which you feed your measurements for the new unseen previously item or individual or patient. And you want to be able to predict which class they should belong to. That's the basic premise we find behind the classifier. So the particular purposes may be predicting tumor grade or predict disease sub type, or even impute missing data. So if you figure out what is the model behind your particular measurements, then you can construct using that model predict what measurement is missing. I want to actually highlight this find most important metabolites for predicting disease status, because that is a feature selection question. And the feature selection question is actually different from classification, you can do unsupervised feature selection. You can do supervised feature selection. In fact, in some cases, it will come up in this, where a classifier does feature selection automatically, like lasso. But in some sense, you want to decouple these questions when you are addressing them. So there are many common classifiers, and you might have heard of all most of them. So supports vector machines, logistic regression, lasso, random forest, canyons, neighbors and naive base. I will only talk about logistic regression and other kind of derivatives of that random forest and the canyons, neighbors, I will not touch on SVMs and naive base today. All right. So the logistic regression is basically a linear, identify it constructs linear relations from the predictor space to the outcome. And what it tries to estimate is the probability of the class being one. That's the key here. So here you have y and that's the probability of the class being one. And it maps this continuum in that space. So if you consider this, you might have seen this, this is a linear regression, right? And that could actually predicts the value. So it transforms this linear regression via logistic function into this kind of the two states between to go between zero and one. Right. So here you have multiple points. I do it from the example where you have weight and you're trying to predict obesity zero. Yeah, no obesity. Yes. And all these red points were considered to be not obese. And all the red points were considered to be obese based on weight. And then you fit the curve, this logistic curve to this data to identify to then be able to classify new points. Now, unlike linear regression here, the fitting of the curve is done using maximum likelihood because there is no direct formula that you can apply to derive the beta coefficients. So depending on where you draw, where you draw the line, maybe your classes are skewed. But if not, you would expect that if the probability of class one is below point is predicted to be below estimated to be below point five, we would say it's class zero. And if it's above point five, you would estimate it to be plus one. But that threshold can change depending on your problem. So lasso is actually a regularization on regression. So lasso is a is a sparse sparse, sparse regularization of a regression.  So what what that does, it actually adds the sum of the absolute values of the coefficients to the, to the likelihood function. And it performs variable selection at the same time as estimating. So that's that's a very important part. So depending on how much you regularize it, so usually we have a lambda parameter that you can either set or estimate. So how much you regularize it, it might give you very few predictors of your outcome. Of course, there will be not as predictive as maybe more, but it gives you a feature selection in addition to estimating the prediction at the same time. So to give you an intuition behind that, imagine your beta coefficients, which are, which are your coefficients of the importance of the variables. Imagine that this is the original line on which they, if you order them from this, say the smallest to the largest, this, this is where your coefficients would lie in your regression. If you apply the lasso regularization L1 regularization, then some of them are set directly to zero. And then the rest of them will be the same as they would have been before. So compared to that, there is a regi regression, which is an L2 penalty, which actually doesn't set anything to zero. So unlike lasso, regi regression actually gives you, it shrinks the coefficients. Where it's helpful is if you have, for example, two predictors, which are both equally predictive of the outcome. Lasso would drop one of them. It's at one of them to zero arbitrarily. Whereas the regi regression will give them half of the initial weight, right? And that's, that's important when, when you actually don't want to drop any of the predictors, but actually want to understand how they are related to each other. So elastic net in some sense was developed as the best of both worlds. It actually uses a combination of the both penalties of one and L2. And it optimizes them simultaneously. There's only, well, there's an alpha parameter for that. And depending, so I wrote here that if alpha is set to zero, you get lasso, if alpha is set to one, you get rich, it depends on the implementation. Sometimes it's reverse. So be careful and check the implementation that you are using. But the point is that one of the extreme cases is you get the specification completely, and the other case you get, the other extreme you get the shrinkage completely and just that. But the reason why elastic net was used is because it gives you an opportunity to not drop one of the predictors randomly, but to shrink them. But to set some of the really small predictors still to zero. So it has that lasso sense. And it's very, very useful. All right. I will go through all of them and then ask questions for questionaries. They're all different. So it's not kind of the same as them. So before we talk about random forest, it's important to mention decision trees because random forest is a collection of decision trees. So here's an example of a decision tree, which is a very old machine learning, we call it machine learning, because I come from machine learning, but statistical, really, technique that was used. So here, the question is, should the baseball game be played? And the leaf nodes give you yes or no? And the decision is based on all the variables that were collected. So you look at outlook, which has three categories, sunny, overcast and rainy, humidity high, normal and windy, false and true. And so there is an optimization that is done to decide which often for decision trees is greedy optimization, where it's decided, okay, outlook is the most predictive. So first we'll split an outlook. If it's sunny, we go and we test humidity and wind. It looks like humidity is the next best classifier. So it gives you if the humidity is higher than no, if the humidity is normal than this. And if windy, rainy and windy with them. But the problem with this approach, it's actually very useful. And it can be used with small datasets, which is also very helpful in a lot of cases and clinical practice. The problem with decision trees is that they tend to overfit, they tend to not be robust enough as a classifier. So what people have thought of is random forest. And random forest is basically a collection of this classifier is the way you get different trees in every branch is that you get you get a bag of features.  So you can get 1000 trees and they will all be different because they all used different features, you sample different features to generate each one of those trees. And then sometimes it's majority voting, sometimes it's a little bit more sophisticated. But basically, you get an ensemble method to put together this various decision trees. Right. So the it's a it's a very it's a very nice and very robust algorithm. It also gives you a feature selection and a way a ranking. So at the end of the day, you can ask, how often was this feature selected when it was given a chance when it was sampled, how often was it selected as an important feature for splitting. And so you get this feature importance ranking at the end. And actually, in our own work, where we tested various methods for feature selection, this was one of the most robust methods. So I would I definitely recommend random forest as one of the lessifiers. Another thing that it does better than logistic regression is it's nonlinear. So we can never guarantee that the relation between our predictors in the outcome is linear. And random forest gives an option to model this nonlinear relationships compared to all this other methods that are out there. Cane nearest neighbors. So cane nearest neighbors is also one of the simple, simple classifiers. It works well sometimes, but it's not used as often again due to overfitting, right. And it's also very, it's not very stable. If you get new samples here, your decision will change. So it does in nearest neighbors, as you can imagine, it's quite intuitive. So if you have a new example, you look at what examples, what is the label of the examples, the nearest to it. But depending on how many examples you take, you might get different classification. So say k equals one, your label is plus one. If you set k equals three, you actually have more plus two examples, and you would set the new example to be classroom. And as you get more and more of these examples, of which you are not sure what the label of, you might get different classification, which is not really optimal. So is this the table which tells you the linear logistic regression loss or region regression and loss signal will, in some sense, derivative of the same logistic regression. And they are they encode linear relationships. So n much less than p, that's the number of measurements is much higher than the number of samples. So that's what that encodes. And logistic regression, you simply cannot run. If you have few features, if you have lots of features and a few samples, the other methods you actually can run, including for random forests, you need more because it actually explores the variance of your data, but it still works on very small samples as well. And for generalization, again, the regularized methods work a lot better. And I have mentioned it before, random forest also works a lot better. And the parametric assumptions about the Gaussian work better than the. So if you if you can make the parametric assumption, that's great. A lot of times our predictors are in such vast high dimensional space that they are almost as if they were independent and the relationship as if it was a linear. So I think we always try to start with a linear classifiers in my lab to to see how well we can predict. But then we also try the random forest as VMs to see if we can improve by introducing non linearity into that mapping. And finally, how do you evaluate the classifiers? Well, the most standard are to the ROC curves. So this is the receiver operating curve that comes from from engineering. And basically, if you were to randomly guess, you would get this this line. So if you were if you had a balanced class, then if you were randomly guessing, you would get this diagonal line. So the further away the line of your predictor is from the the this line, the better is the model. What what what does this line represent? Well, it represents the false positive rate versus the true positive rate. You obviously want to have the most true positives at the fewest with the fewest false positives. So that's why this kind of blue model is really, really good. Now, in my experience, if your model looks like this right away, from the first try, it means there's something wrong with the data, because this this model looks to be true for a lot of the real practical data sets that we see in real life. But it does happen. So yeah. And one example of how you would construct this curve is is if you were moving that threshold. Remember how I mentioned in logistic regression, that you had a threshold at point five. If you you were moving it, you would obviously have more or two positive or false to false false positives and vice versa. And that's that's if you were changing that threshold, how well your model would be for precision recall curve is used more often. In, for example, in clinical practice, when we report, we are usually asked for the positive predictive value, which is precision. And precision is basically the number of true positives compared to all the examples that were classified as a positive versus the recall which is the sensitivity is how many true positive they were out of all the the positives that we did and did not capture. So this is also this area under this curve being 2082. This is an incredibly good model with precision recall we usually get at 30%. And that seems to be where majority of this kind of biological models are. And this is it. I went through all of it is amazing. So question