Hello, everyone. My name is Jane Katten, and I'm the CEO at Age Well. I'd like to welcome you this evening to our show, Aging Matters. I'd also like to take a moment and thank our sponsor, MBP Healthcare, for making this possible. As some of you may know, Age Well is the largest area agency on aging in the state of Vermont. And our goal is to make sure that anyone over the age of 60, or under the age of 60 with a disability on long-term care Medicaid, has all the supports and services they need to remain independent in their homes, wherever that may be. Tonight on Aging Matters, we're going to explore a couple of very interesting topics that have great impact on older Vermonters. And those two topics are food insecurity, and the second one is a concept called food deserts. I'd like to welcome tonight, Chris Moldovan, who is the Director of Nutrition and Wellness Services at Age Well. Welcome, Chris. Happy to be here, Jane. Super. Before we dive into our two topics tonight, maybe what you can do for our viewers is tell them a little bit about your background, your professional experience, and your role at Age Well. Sure. So I am a registered dietitian. I have worked in the field, really all over Northwestern Vermont, for the last 30-plus years, doing lots of different roles within the field of dietetics. I've worked with the aging population, and I worked with the agencies on aging as the consultant dietitian, as well as the last seven-plus years. I've been the Director of Nutrition and Wellness at Age Well. Awesome. Well, you definitely have some pretty amazing credentials for our topics tonight, so I'm excited to dive into those. So as I mentioned earlier, the two things will focus on our food insecurity and food deserts. But before we talk about both, let's hone in on food insecurity. What is this? Yeah, Jane, it's a really good question. You know, food insecurity is something we hear about all the time, and I think there's a little confusion with hunger versus food insecurity. So I just want to state what, you know, the definition of hunger is the actual feeling that someone feels when they don't have access to food. Food insecurity is when they don't have access consistently to healthy food, a variety of healthy foods that would enable them to live a healthy, active lifestyle. Got it. So that's a really important differentiation to make. Absolutely. So, Chris, how exactly are older Vermonters impacted by food insecurity? What does that look like? Well, it's an interesting question. If you look nationally, there are 35 million Americans who experience food insecurity. Of that 35 million, 15 million are older adults. Wow. So in Vermont, you know, it's estimated two out of five individuals are dealing with food insecurity, so lacking the access to consistent food that enables them to live a healthy lifestyle. Two in five. Wow. So let's think about that a little bit more in terms of the work that you're doing. What are some of the barriers that you're seeing that older Vermonters have when trying to access foods, especially healthy foods? Yeah. One of the things to think about, I think, for older Vermonters is, you know, I think it's an issue of not wanting to admit it that they are actually dealing with food insecurity or they actually are experiencing hunger. They have to first kind of acknowledge that, gosh, they do need help. They don't have, you know, a variety of foods that they can plan a healthy meal or get the different nutrients that their body needs. Now, why is it that they don't have it? It may be related to where they live and their, you know, living situation. It may be lack of transportation. It may be directly related to the inability to get out of the house. You know, maybe they used to drive but now they don't and they can't get to the grocery store or if they take a bus and get to the grocery store, they can't physically carry the food back home. So, you know, there's a lot of different reasons why someone would not have access. Of course, you know, income and access to the resources to purchase and get a hold of those foods is a big issue. People are on fixed incomes, limited incomes and, of course, other expenses, like housing, fuel assistance, you know, heating and cooling, transportation costs, medication costs, all of those kinds of things will, you know, have an impact. Yeah, wow. Those are so many issues that we have to be thinking about all the time. So, Chris, is the Nutrition and Wellness Director at AgeWell irresponsible for overseeing numerous programs. There must be many to help with these issues that you just talked about. Can you tell us which AgeWell programs specifically focus on food insecurity? Yes. Well, within the Nutrition and Wellness Department, we have a very extensive home-delivered meals or Meals on Wheels program. So, individuals who live in their homes who are unable to prepare their own meals or get out without considerable assistance to access food can have one meal delivered to their homes Monday through Friday. Those meals meet a third of the DRI or Dietary Reference Intakes for an individual over the age of 60 and are in accordance with the Older Americans Act, which is a federal program which helps to fund that program specifically. I'm really, really proud about the program that AgeWell offers over the last five or so years. We've transformed the whole Meals on Wheels program. The whole nutrition program, really. But we've focused on what the actual healthcare needs or the therapeutic needs of an individual living in their home might be. So, what I mean is we have eight different therapeutic diets available to people in their home. So, our regular diet is considered heart-healthy, which means that it's reduced in fat and sodium. We've looked at saturated fat and we've made sure that it's a well-rounded meal that can meet that individual's needs. A person who might have diabetes, it might need a carb-controlled dessert. So, our regular diet, which is heart-healthy with a carb-controlled dessert, is our diabetic-friendly meal. We also have a renal-friendly meal for those people dealing with renal failure or end-stage renal disease. So, we focus on protein, potassium, fluids, as well as sodium. We also have gluten-free meals, lactose-free meals for those people that have intolerances and can't manage those or digest those foods appropriately. And, of course, we have a vegetarian option. So, that's a lacto-ovo meal, which has milk, eggs, and cheese as good protein sources, meets the dietary requirements for the Older Americans Act, and yet is a well-balanced meal. We also offer texture-modified meals. So, people who might have chewing and swallowing problems, maybe they've had a stroke and they're recovering, maybe they have poor dentition, maybe they're weak from different therapies that they might be undergoing for acute or chronic health problem. So, we have mechanical soft, which is fork-tender food, as well as puree, which is more like an oatmeal consistency. And any combination thereof of all of those meals. So, someone might have diabetes, they might also have problems with chewing, and they might also be gluten intolerant.  Monday through Friday, we deliver those meals, and then on Fridays, we are able to deliver frozen meals for them to reheat over the weekend so that they can have a meal in their home. Wow. So, that sounds amazing. If I knew someone who needed some help with access to food, how would they arrange that through H-Well?  That's the 1-800-642-5119 number. Speak to one of our information and assistance experts, and they'll gladly make the referral and get that person set up. If an individual is not, say, unable to get out of their home but they're still driving, they're out in the community, but they just don't really feel up to or want to or know how to plan a well-balanced meal, they can actually come to one of our community sites. And they're sprinkled all over the four counties that we serve, Chittenden, Franklin, Addison and Grand Isle. We work with senior centers and other meal site locations in some situations in faith communities as well as schools. We work with restaurants to make sure that we can have a nice meal available to people within the community and within a reasonable drive from their home. Sometimes we hear about friends picking up another friend and driving them to the meal site so they can share a meal and not go alone and have someone to sit with. And then we hear of others who just show up at the site and see friends and community members they haven't seen and they just get to socialize and have a great time as well as a good meal. Wow. Fantastic. I've also heard about grab-and-go meals. Yes. But what is that? So we have, again, a wealth of grab-and-go locations. Those are simply grab-and-go. An individual can come to a site, sign up in advance, let us know you're coming so we have an accurate count. And we'll have a meal ready for that person to come and drive through, get out of their car if appropriate. Or during COVID, we had, you know, people wear masks and roll down their window or open their trunk. And we'd put the meal packaged to go in their trunk. They could take it home. They could go to a park. They could eat it at a friend's house and enjoy that same meal but in a safe setting. And the grab-and-go's have been so popular that we've continued on, even though a lot of the meal sites are reopening, we're offering a wide variety so that if there's a day that we don't have a sit-down meal available, they can still go to a grab-and-go location and get a nice meal. Wow. What about restaurants? We have, again, relationships with quite a few community restaurants, again, throughout the four counties. And we have a restaurant ticket program, which is simply a voucher that someone would donate for. All of our meals, it's a suggested donation of $5. And so they would make a suggested donation of $5 for a ticket. They would bring that ticket to the designated restaurant of their choice. I think we have eight right now that are actively providing meals. They would show up on a day and a time that that restaurant is participating. And they would ask for the age-well menu. On that menu, there's a variety of choices. It might be three choices or maybe even eight or 10 choices, where, again, well-balanced meal has a good protein source, a good amount of fruits and vegetables, whole grains, serving of dairy, as well as often a dessert. And that whole package is all served to that individual, just like anyone else, in the restaurant. And they would pay for it with the ticket. So they hand in their ticket. The restaurant then gets reimbursed through age-well at the end of that month. And it's seamless. That's a really nice program. I'm sure people love to get out to restaurants. Absolutely. We also have some sit-down restaurant meals. We work with different partners. We work with places like the Glass Onion, down in Middlebury at Hannaford Greer Center. We work with Rosies, which is also in Middlebury. We work with the professional food tech program over at Essex High School.  Again, menus preset, and they can enjoy a meal. Most importantly, they would call age-well. They can use that 1-800 number that I mentioned, the helpline, and ask to speak to the nutrition coordinator in their county. And then we can get them hooked up and find out about the different programs and meal sites and dates for different programs that they could access. That's wonderful. Chris, I've heard all about age-well service areas and the kinds of programs that we offer. Are these programs also available throughout the state of Vermont? Yeah, that's an interesting point. There are five area agencies on aging in the state. Age-well is one. We serve Northwestern Vermont. So there are four others throughout the state. And they have nutrition and wellness programming as well. It's a little different. Each agency on aging does it slightly different. But they have sit-down meals. They have those home-delivered meals that we talked about as well. That's fantastic. I read some statistics recently that said throughout the state of Vermont, all of the area agencies on aging last year delivered over a million plus home-delivered and congregate meals. That's fantastic, isn't it? It's amazing. It's really amazing. The other nutrition programming, I guess I would like to just mention, we do a great deal of outreach with Three Squares Vermont. So Three Squares Vermont is what's known nationally as the SNAP program. But basically it's a food supplemental program to help people that are within a certain income level expand their food dollar so that they can access and purchase healthy foods. So if you're over the age of 65, the money goes directly into your bank account. So there's no debit card or anything that you have to fumble around with and you can choose to spend the money at any grocery or store you'd like to. However, if you're under that age, 60 to 65, you would have some sort of a card to use. It's a great program and many, many Vermonters access that program and many more should. As the money filters into Vermont, it enables people to access food, and it also stimulates the economy here in Vermont. So if you're eligible, you absolutely should apply and you also could apply for the Three SNAP program, which is a three-year program versus a two-year program. So that means paperwork is less. You do it every three years instead of every two years. So it's an easier way to access that. Nice, and it sounds like Agewell and all the AAAs, as we call them, can help with that. Absolutely. We have experts in the field who can help you with the application access. They can help you fill it out. They can help you troubleshoot any of the documents or paperwork that you might need and answer questions to find out if truly it's worth your while to fill out the application. But in many cases, people will get anywhere from $25 a month to over $100 a month. It just depends on that individual and their income. Super. Chris, I know there are older Vermonters who are taking advantage of these programs, home-delivered or congregate. What if they can still manage at home and cook for themselves but just can't get groceries? How does that work? How do they get access to food? Yeah, and again, that could be a clear barrier for some people. At Agewell, we have an extensive volunteer cohort where we have well over 1,000 volunteers. Some deliver meals on wheels. Some teach Tai Chi. Some do work with the state health insurance program. But others actually will help run errands. They'll take a list from an older Vermonter and they will go to the grocery store, pick up the groceries, bring back the change, as well as the groceries delivered right to the home. Some of the grocery stores we work with also will deliver. It just depends on your location and what you're near. But if you're ever worried about, concerned about, don't think you can access food, I would encourage you to call the helpline, again, 1-800-642-5119 and speak to one of our INA or Information and Assistance Coordinators, and they'll help you get you in the right direction. We don't want anyone to go hungry. We want people to have access to the right foods to help manage their health care. Chris, I love the way that you have talked about all these different programs under one umbrella that come together to help older Vermonters have access to food or the types of services that might be able to get them access to healthy foods. So those are great updates and thank you for sharing that information. So you've shared some really nice resources and information, some challenges and some of the solutions. Let's take a moment now to just shift gears a little bit and move on to this other interesting topic called food deserts. And we're hearing more and more about this in the media, especially in Vermont, where we have very many rural areas and so on. But maybe you can take a moment and just tell us more about food deserts. What are these? Food deserts, it's a fairly new term, probably I would say in the last 5 or 10 years we've heard more and more talk about it. What it really is referring to access to food, does the individual that we're talking about live in a community where there is a grocery store? Do they have a possible transportation that's available to them, maybe a bus route that will get them to the grocery store? Or do they have to drive three towns over? Do they actually have to drive 20 plus or 30 plus miles to get to a major grocery store to have access? There's a lot of communities, particularly outside of Chittenden County. We think of Chittenden County as having some major grocery stores which are wonderful and great and they have a wide variety of foods and people can go to the local city market or Trader Joe's or Hannaford or Price Chopper. But outside of Chittenden County there are few and far between and in some cases just nonexistent. So those people have to do a trip into town to get to that grocery store. They might be able to go into a small mom and pop or a gas station type store that has a variety of foods but again you're not going to see that full variety of healthy options, things like fresh fruits and vegetables, a variety of good lean protein sources, whole grains. We had a quick bag of chips and a soda but you're not going to be able to get something that's really providing a lot of nourishment and again assistance and support for those individuals to manage their health. Yeah, that's a big challenge. And then it goes back to the whole issue of being able to have transportation to get to those locations where there is ample healthy food. Absolutely and the thought of do I have transportation, can I manage it, can I pick up the food, even if I wanted to purchase, say a week's worth of groceries, can I get it into my home, do I have to walk up a flight of stairs or two flights of stairs to get to my apartment. Those are all really big concerns. And then of course, what else is out there? And I think that's really sort of the solution if you will to food deserts is really accessing the resources that are in the communities. And then of course, in the grocery store, well we can't just build one, but there might be a farmers market every week and maybe every Wednesday or every Saturday in your town. You might be able to access again a variety of fresh fruits and vegetables at that kind of a setting. Or maybe you're going to your local food pantry. Sometimes faith communities or other nonprofits will set up a nice food pantry or food shelf available to individuals who are trying to be very helpful. Commodity food distribution. So there's a program where each month a box of foods will be delivered to a location and then that person can pick up the box of foods or in some cases it's directly to a senior housing. Or it might be the building in where they live, the box arrives. They just have to bring it up to their apartment and have access to that. There might also be programs like we talked about Three Squares that people know that they do have the ability to get some assistance. And with those programs there's things like Farm to Family Coupons which double the value of what an individual can get through the Three Squares program at the farmers markets. So it's really looking at those community resources and accessing neighbors, friends, volunteers from age well. If there's somebody that can help you maybe they can run to the store, maybe they can split a large 10-pound bag of potatoes if you're not going to use them all this week. Or help you access and get those foods that you need. Nice. Thank you for sharing that information. It makes me think about the network that we work within our communities. Tell me a little bit about your experience with our senior centers and meal sites and how they may be able to help and support food deserts. Oh, absolutely.   And in some cases they're sort of acting as a distribution site, right? So they might be able to get, let's say, gleaned vegetables from the local farm that just, you know, they produce too much zucchini this year and they have lots of zucchini and so they might bring it to the senior center to distribute or fresh ripe tomatoes or, you know, maybe fresh lettuce or whatever the produce might be in season. They're going to be able to let you know where the local food shelf is and they may indeed actually have a little mini food pantry within the senior center. So working with the staff at the senior center, the volunteers, they're asking and just saying, hey, I need a little help. There's nothing wrong with that and they will direct you in the right direction. Nice. Thank you for sharing that information, too. I think our senior centers, our community centers, all of them are focused on helping with access to healthy foods. Absolutely. Sometimes that's hard in the wintertime, especially to get fresh fruits and veggies, but I know there's other options out there, especially with the supports you talked about. So, Chris, if you could offer one piece of advice on what you think is the most important steps that older adults can take if they're dealing with any of the challenges we talked about this evening associated with food insecurity or access to healthy foods, what would those steps be? Well, I think the first step really would be to acknowledge that, you know, there's a shortfall. I need some help here and I think I need to, you know, look at the resources that are available to me. I would work with my healthcare provider making sure that, you know, first and foremost, I'm following their advice if there's some direct dietary guidance that would help them better manage their chronic health problems, like diabetes or hypertension or heart disease. I would go for that. I would make sure that those become a priority. I would look to access the area agencies on aging, call the 1-800 number, let them know what you're struggling with. It might be as simple as hooking you up with a volunteer, asking a care and service coordinator to come out and work with you in your home, or get you started on Meals on Wheels. So calling that help line is going to be very, very important to sort of make a plan so that you can manage independently. Wonderful. That's great advice and I think so important as we're thinking about food security matters and how to get access to really healthy foods. So I appreciate your time this afternoon and it's been really, really informative. So thank you for joining us. Thank you. And to all of our viewers out there, I hope you enjoyed our show today. Please tune in next time as we probe and dive into some other interesting topics as we think about aging matters and aging well. And as Chris mentioned and as we think about it every day, if any of you need help at home to allow you to stay at home and be independent and stay healthy, please call Agewell or any of the area agencies on aging in Vermont. We're here to help. Agewell's help line is available here as we've mentioned earlier. I'll repeat that number for you. So you have it. It's 1-800-642-5119 and that help line is available five days a week during business hours and we have so many people ready and willing to help you stay healthy and Agewell. Thank you for tuning in next time on Aging Matters. Thank you.


Hi everyone, it's MJ and we're looking at question 6 of the September 2017 paper. And this paper is on hypothesis testing and it's quite an interesting one because it's going to be asking us to do questions around the type 1 and type 2 error. So what I'm going to do is I'm going to read through the question and then we're going to get stuck into it. So it says some tea experts claim that the taste of a cup of tea does not change according to whether the tea or the milk is added first to the cup. To test the hypothesis that people cannot tell the difference, an actuary organizes a tasting experiment where an individual is asked to taste 10 randomly presented cups of tea. Five of these cups were prepared with tea added first and the other five with milk added first. The individual does not know how many cups of tea were prepared in either way. To the null hypothesis that people cannot tell the difference, the probability of correctly recognizing the preparation method purely by trans is considered to be 0.5. The actuary adopts the following decision rules for testing the hypothesis. Reject the null hypothesis if 7 or more cups are correctly identified. Conclude that the individual can tell the difference. Do not reject the null hypothesis if less than 7 cups are correctly identified. Conclude that the individual cannot tell the difference. Determine the probability of the type 1 error for this test. Okay, now just a very quick recap. These are the two types of errors that we can get in hypothesis testing. Type 1 is saying the following. Type 1 is the probability we reject the null hypothesis given that the null hypothesis is true. And then we have type 2 error which is the probability we fail to reject HO given that HO is false. Okay, so with that in mind, let's look at here. Determine the probability of a type 1 error for this test. So let's maybe stop writing this out here. We have, yeah, our null hypothesis is that HO, our P is equal to 0.5. Once again, we're dealing with the binomial distribution. We have a binomial in the sense of 10 as our N and our P which is what's under consideration. So the probability of a type 1 error is as follows. Okay, it is going to be the probability that we reject HO given HO is correct. Okay, and in this case, HO we're using P equals to 0.05 which means it's going to be the probability that we get the correct answers. Okay, so our correct answers are greater than or equal to 7 given that P is equal to 0.5. Now what this means is we're using our binomial. We're going to have the situation here with X equals to 7. Out of the 10, choose the X and we have to add for each one because remember we want to get the answer when it's equal to 7, equal to 8, equal to 9, equal to 10. So that's why we're summing it. When that is equal to 0.5, 1 minus 0.5, 10 minus X. This is coming straight from, I could say, the binomial. And what we're going to be doing is we're getting the following 0.8281 which is equal to 0.1719. And if you're like, whoa, how did you calculate that so quickly? This is like a monster sum to do. I mean, you could do this, especially if you've got the new fancy calculators and if you're allowed those fancy calculators into the exam, you can do it on that. Otherwise, actually, I know we're not allowed to have those fancy calculators in the exam. So we will be using the tables on page 187 to come up with these answers. So you just go to that page and it will have the binomial in the situation and it's quite easy then to apply it. So that is question one done. Not too bad. Let's look at question two based on past experience. We can quantify the probability P that an individual recognizes the T-preparation method correctly. Determine the probability of a type II error assuming that the true value of P is P is equal to 0.7. Okay. So what we're doing here, it's the probability that we fail to reject the null hypothesis given that the null hypothesis is false. So how are we going to do this? Here we can have part two. Probability of our type II error is equal to the probability that we accept or maybe we should probably be saying failing to reject HO. That should actually be better given that HO is false. And we've told in the situation that the HO is 0.07. Okay. So what we're going to have, it is the probability that our correct answers are less than seven given P is equal to 0.7. In the tables, I mean, it's much easier to work it out like this, less than or equal to six given that P is equal to 0.7 because then you can reach straight from the tables because remember the tables deal with that. And if you have that, you need to subtract one. And then bam, you're getting that straight from the table value when N is equal to 10, P is equal to 0.7. And then what we have here for type II error when HO is equal to 0.8, we again, we're going to have the probability correct answers less than or equal to six given that P is equal to 0.8 straight from the tables 0.1209. Okay. So that's not too tricky. Maybe the hard part was adjusting that, but otherwise you're going, this is basically just testing to see, did you read through the material that dealt with type I and type II errors? So this is standard book work and I mean, gosh, they're giving you two marks for each. That's very generous. I would have only given one more for each, but anyway, coming to part three, which is maybe the more harder part, this is testing a more higher level of thinking. This is comment on the power of the test based on your answer in part two. Okay. And what we know is that the power is related to the type II error. It is equal to one minus the probability of our type II error. And what we're seeing is that the power is going to increase when the P increases. And we know that because look over here, the type II error is decreasing, which means the power is increasing when P increases. So in other words, you know, the power is higher, therefore, it is more likely to correctly reject the hypothesis of guessing when the individual is more skilled in identifying the preparation method correctly.  I'm a bit again surprised that they are giving three marks for this when maybe it could have been a two-mark question, but you're, you basically just needed to make that comment and identify that you want a test to have a very large power because you can then minimize this type II error. You could probably even get a mark by saying we cannot minimize both simultaneously by saying when we increase the power, we are also going to be increasing the probability of type I. Anyway, that is the question on hypothesis testing. Let me know if you've got any questions or comments and I'll do my best to reply to them. Thanks guys so much for watching and I'll see you for the next video. Cheers.


Well, welcome to a Monday. So this is the sixth lecture for the evolution part of Bio1B. Are there any questions from the last lectures? Nothing? Everybody enjoyed the Cal UCLA game? It was OK? Yeah, it was a good game. All right, so my plan today is to finish up briefly with migration. So we talked about the four different forces that change allele frequencies in a population, mutation, natural selection, genetic drift. I want to talk briefly about migration, and then we'll talk about some examples of selection, not only experimental evolution, that is to say evolution in the laboratory and then an example of evolution in the wild. So just to finish up on migration, this is not a very complicated idea, but the idea now is that we have two populations from the species. Remember, each species has at least one population. But we imagine a species that has two populations. There's population one and population two. And migration is just movement of individuals from one population to the next. So in this case, you're going to have movement of individuals from this population of that or that population of this. And so migration can change allele frequencies for the obvious reason that if there's a mutation that appears in this population and an individual that bears that mutation moves from this population of that population then the allele frequencies have obviously changed. Now the main thing you should realize about migration is it's a force that homogenizes the frequencies in different populations. It makes the frequencies of alleles more similar to each other. So if you have lots of migration between two populations, the allele frequencies in the two populations can become very similar. If you cut off migration, that is to say, you just all of a sudden don't allow migration between two populations, what can happen? Well, the allele frequencies over time will become more and more different. And that makes sense. If you have a mutation that appears in this population, there's no way for this mutation to get into this population unless it occurs independently over here. And similarly, you're allowing genetic drift. Even if you start the two populations off with the same allele frequencies and then you turn off migration, then genetic drift will occur differently in the two populations. Maybe an allele will become fixed in this population and lost in this population. When we get to speciation, you'll see that migration and specifically turning off migration. So speaking of, think about having little knobs. You turn up natural selection, turn down natural selection, turn up migration, turn down migration. If you turn off migration, rather, you actually will make the two populations over time will become more and more different. And eventually, they can become new species. Now there are examples of migration. One that I'll just give you involves domesticated rice. And there was a variant of rice that was tested in a plot called Liberty Rice. It was what was this strain? It was LL601. That was a strain name. This particular strain of rice was resistant to a herbicide. The herbicide was called Liberty herbicide. So this was called Liberty strain of rice because it was resistant to this herbicide. And it was grown in a few test plots from 1998 to 2001. And it was never approved for human consumption, so that was the end of it. They terminated the experiment. But it turns out that through migration, some of these Liberty Rice genes got into domesticated rice that was approved for human consumption and it was actually exported across the world, around the world. And so this started to creep up a few years after this test plot was done. And people became very concerned about it. That you actually had this particular strain that actually invaded the domesticated rice. And so it actually was such a concern that Japan for one month actually debanned the rice imports from the US. So from that perspective, migration actually had an economic cost to the farmers who exported rice. So it's just one example of migration. Of course, you might hear about lots of concerns that people have currently about basically migration. That is to say, we might have genetically engineered crops, for instance, and the concern is that the genes from these genetically engineered crops might invade wild populations. For instance, if you make specific crops resistant to herbicides, the concern is that maybe those genes will get into the wild populations of weeds, and before you know it, will have weeds that are also resistant to their herbicides. Now that hasn't really occurred very much, and people kind of debate the importance of this force. But it is a potential concern that you should be aware of. OK, so that's all I really wanted to say about. Yes? Where is the question? Oh, yes? Oh, sure. I don't know if I can actually turn it up or I can reposition it. Is that better? OK, so maybe it was too close to my throat. There's another I sort of forgot to mention. I don't want to say complaints, but concerns about the quality of the slide images for the recording. So a lot of people obviously don't show up to the morning lectures, and they watch the lectures on the web. And I'm sorry about that. That's the one downside, I guess, of not being here in person is that the quality of the images in the webcast aren't as crisp as they are here. Of course, those people can download the PDFs, and they have a very good version of it. So there's nothing really I can do about it. OK, so that's all I wanted to say about population genetics. That was it. That's the end of the math part of evolution. Now, like I said, I want to go into examples of selection. And the two examples I want to give are experimental evolution and then natural selection in the wild. There's lots of examples of experimental evolution. So here's an example which you're probably familiar with if you ever eat corn. So this is a domesticated corn. This is the type of corn you find in the supermarket. Of course, corn has not always been like that. What is the wild relative of corn? It's a plant called tiosinte.  And over time, and people have a pretty good record of what corn looked like in the past, because you'll often find ancient corn in archaeological digs. So you can actually document what it looked like in the past. But anyways, what you're seeing here is this is what the female part of the corn plant looks like. This is the male tassel. This is what the male part looks like. And this is what's happened over time between just selecting for larger kernel size and larger cobs of corn is done to tiosinte. And there's actually a really interesting experiment, a very long-term experiment that's been performed since 1896 in Illinois. It's an agricultural station in Illinois. So this is the number of generations, over 100 generations of selection for oil content in corn. And so this is what the oil content was in the late 1890s. Over time, people have selected the corn to have higher oil content. That is to say you let the corn reproduce that has the highest oil content in your field. And you can see here they've got these high selected lines where you've gone from about 5% to over 20% oil content in the kernels. And then at certain times, they actually reverse the experiment. So here somewhere around the 1940s, they said let's try and divide the plots and let's start to reverse the selections in the opposite directions. We'll keep selecting for high oil content, but we'll take this other plot of land and have all the corn on that land will start to select for low oil content. And this is what you see. And they've also had a low oil content plot as well over time. So for almost any domesticated species that we're interested in, we can select for traits as long as there's some variation. We can select for traits and get almost anything we want. Give us enough time, give humans enough time and they can select for traits, domesticated crops that will have almost any feature you want. So before I get to the viral example, let me talk about an example that I really love that comes from Michael Rose's lab at UC Irvine. And what Michael Rose does is he studies senescence. So I kind of debate whether even to talk to you guys about senescence, because all your life, all you've known is getting bigger and stronger and better, so to speak, right? You're about 18, 19, 20. And basically up to about 18, 19 or 20, things are good. I remember those days. You get stronger and bigger, you look better looking than you are, you'll ever be in your life again. But from here on out, let me assure you it's downhill, okay? Every year your memory gets worse. Like if I work out, it takes me forever to recover and I'm miserable. It's just, it's worse. And from my perspective, it only gets worse from here on out, right?  So it's a very, but this is something we all have to face. And there is an interesting question. Why? Why do we senesce? Wouldn't it be better to be 80 years old and have lots of children? Wouldn't natural selection favor that, okay? And so there is this question of what is the evolutionary explanation for aging or senescence? And the example goes like this. Let me, we have to sort of imagine a perfect population or a population of individuals that don't age, if you can imagine that. So that is to say somebody who's 80 looks just as good as somebody who's 20 is just as fit, just as strong, okay? So there's no difference in their fitness or essentially their physiological age. They don't age. But just because you don't age doesn't mean you don't die. So let's imagine you're like a deer, right? That doesn't age. Well, you still can be run down by mountain lion and eaten, right?  Or if humans didn't age, doesn't mean we didn't get run over by a bus or get in a car accident and die, right? There's, just because you don't age doesn't mean you don't die. So keep that distinction in mind. So let's imagine a population of organisms where each year there's a probability of 95.95 that they survive.  Maybe the mountain lions eat 5% of that cohort every year. With me? So what is the probability that you make it to 10 years? I believe I put that down. It's about 0.77. So about 77% of the cohort, that imaginary cohort of non-age organisms will still be alive for 10 years from now. That means over a quarter of them have still died, right? And then what's the chance of them making it 50 years? It's 7%. Only 7% are alive after 50 years. There's still not those 50-year-old animals or plants, whatever they are. Look, just as fit as the one-year-old ones, right? But there's only 7% of that cohort left. And then you can ask, well, what's the probability that you make it to 100 years? It's about 0.005 or about half a percent in this imaginary population. Only half a percent are still alive after 100 years in this ideal non-age. And those half-percent of 100-year-old animals look great, right? But they only represent a small fraction of the cohort that was alive 100 years ago. So this is the scenario we're picking up, I'm trying to describe. So what this means is that if you have a beneficial mutation that acts early in life, there's more individuals on which that beneficial mutation can act. If this beneficial mutation only affects 80-year-old individuals, then only a small fraction of the population can experience that beneficial mutation. And it won't be as strongly selected. So it also means that if you have a beneficial mutation that acts early in life, but has a detrimental effect later in life, that mutation can spread. Because there's more individuals on which it can act and the detrimental effects will be experienced by just a few individuals. So that's the model. That's the theory, okay? So what did Michael Rose do? Well, he said, well, let's test this theory. What, basically the explanation for why organisms age and why you kind of evolution of ageing is because of this model I just described. I should mention too that obviously, senescence or aging evolves, right? Because if you look at humans, we live what, 70 to 80 years? Look at our closest relatives, the chimps. Even in conditions as benign as the ones we live in, they only live 50 or 60 years. Okay, and if you go further out, and you look at a house mouse, right? They're closely related to us and in an evolutionary sense, they're kind of closely related to us. They only live maybe three years, right? So between mice and humans, humans live 80, mice live three. There's been a lot of, you know, obviously aging is something that evolves. So Michael Rose said, well, maybe we can make Drosophila, flies, fruit flies, evolve in terms of aging in the lab. And you should know that the usual, the Drosophila melanogaster, the workhorse for modern genetics, they only live about three or four weeks, right? And flies, they don't suffer from things like, insects typically don't get cancers like mammals do, but they do show signs of senescence, you know, their wings become tattered, they're less active, they're less productive in terms of the reproductive ability. They age like any other organism. So what he did is he said, let's make these late-acting mutations that normally natural selection doesn't care about. Let's make those late-acting mutations important. So how do you do that? Well, he only let the flies reproduce when they got old. So you can think of this as sort of the career woman experiment, but only in flies, right? So at first, he wouldn't let the flies mate until they're two or three weeks old. So you can easily, with flies, you can, you know, they're larving, they kind of dig around in the bottom of the vial, getting food, and then when they metamorphose into flies, you can easily separate the males and the females. You can have a female flask, and you have a male flask of flies. It's then quite easy to say, you know, they can only date, so to speak, when they get to three weeks old, right? So only the oldest flies are reproducing, and then you can raise the bar. So it'd be, instead of three weeks, make it four weeks. Instead of four weeks, make it five weeks, the age at which they're allowed to reproduce. After a while, he had flies that were living 10 days longer on average. Okay, so that'd be kind of equivalent to humans living a lot, 120 years instead of 80, 90 years. Then he continued the experiments even further, and eventually he got flies that were living about six months. He called these the strain of flies, the strain of flies that he selected for it, his so-called Methuselah strain, for obvious reasons. They're very old flies. So there is, you can't actually select for aging, and you can actually, aging is like any other trait, you can actually make it evolve in the lab, okay? But once again, what he did in the lab was make these late-acting deleterious mutations important, so to speak. Normally, natural selection doesn't see these late-acting mutations, because so few individuals are around to experience them, but he made them important, okay? Are there any questions about that? It's a great experiment, and if you're really interested in it, you can go to his website, and you can download his papers. They're quite interesting papers. You can also imagine that he's gotten a lot of funding for this work, often looking at which mutations were responsible for the old age in his strain of flies, with the idea being that whatever mutations are responsible for increased longevity in his fly strains might also be important for, say, us. So there are ramifications for these types of studies for human health. Okay, so now I wanna go to another example of natural selection, but only this time in the lab, once again, but using viruses, and the experiment was performed by these two people. This is Jim Bull, who's at the University of Texas, and this is Holly Wickman, who's at University of Idaho in Moscow. And they were working on these critters, and these critters, what are they? This is a scanning electron micrograph of a virus called Phi X174. So it's Phi X174. And this is just, it's a virus that infects E. coli. This is the bacterium that you can find in your gut. You can raise E. coli quite easily in the lab. In fact, it's a standard strain of bacterium that people often raise in labs for molecular biology type work. And basically, if you had a flask, say, of E. coli that was happily growing at body temperature, 37 degrees Celsius, and you threw in one of these guys, before you know it, the strain would become kind of clear. Basically, you throw in one of these guys that gets into an E. coli and about 200 pop out killing that bacterium, and they infect other bacteria and so forth. And before you know it, a large fraction of the culture of E. coli will be dead, and they have lots of Phi X174. The Phi X174 genome is a double-stranded DNA, and it's circular. So viruses like any other critter have genomes. This is a very small genome. So our genome is about 3 billion ACGs and Ts. This one has a genome that's 5,383 ACGs and Ts. So it's quite small. In fact, this was the first organism to have its genome sequenced, I think, in like 1977. It's the very first organism to have its entire genome sequenced. And the number, or the letters here represent the genes. And so the genes are given actually just letters for their name. So you can talk about the G protein or the H protein or the A protein. And there's a couple of genes here where you see B overlaps with A, star overlapping with A. This is one of these cases where you actually have multiple coding regions in the same piece of DNA, which is unusual. But you don't have to worry about that. You don't have to worry about remembering any of the genes here. What they did is normally, like I said, the E. coli grows well at 37 degrees Celsius. And the bacteria, the Phi X174 is also well adapted for 37 degrees Celsius because that's the temperature at which its host lives, right? So if you're a Phi X174 and you can reproduce rapidly at 37 degrees Celsius, you're golden, right? You're okay. What they did is they raised the temperature of which they incubated the E. coli from 37 degrees Celsius to 43 degrees Celsius. Now what happens? The Phi X174 initially aren't as good. So what you see here is you have this zero line. This is the fitness of the phage. When I say phage, I mean virus. So people call these viruses that infect the E. coli bacteriophage, right, for bacterial viruses. So I've just realized that I'm saying both words here. So bacteriophage or bacterial virus. Initially, their fitness would be here at the zero line, but when you first start off this experiment, you crank the temperature up to 43 degrees Celsius, the phage aren't happy. They don't reproduce as well and they're less fit. That is to say, they produce fewer offspring. Fewer viral particles come out of the bacterium at the 43 degrees, then it does at 37 degrees. But if you continually passage the viruses at 43 degrees Celsius, they get better and better. So what do I mean by passage? It means you have a flask of E. coli, you put in a virus, the virus explodes. Before you know it, you have started off with one viral particle, one 5x174, and before you know it, you have trillions and trillions of them, okay, like over the course of a couple hours. Then you take a small number of the guys from this flask and you put them into a new flask of freshly growing, naive E. coli. That is to say E. coli with no bacteriophage. And you put a few of these guys in here and what happens, they grow up in the lysis one. And then you take a couple of these guys from this flask and you put them into another flask of E. coli. This is what's called passage. You're just taking a few of the viruses from this flask and putting them into a new flask of fresh E. coli that are growing at 43 degrees. So what you're doing is you're favoring mutations that allow the virus to grow well at high temperature. And what you're seeing, this is just a measure of how well the viruses grow. You can see that both in the Texas, they did the experiment twice, once in Texas, once in Idaho. The Texas drained over a few days very rapidly, did much better at 43 degrees and so did the Idaho strain. The Idaho strain for some reasons didn't do quite as well as the Texas strain, but they both increased dramatically in how well they could grow at 43 degrees. So their fitness increased in both cases. Now the neat thing about viruses is, especially a virus like Bix174 that has a small genome, is that you can actually sequence the genome and find out what mutations were responsible for the adaptive response. Which mutations allowed the virus to do well at high temperature? And these are all the changes they saw in the Texas experiment and also in the Idaho experiment. And so what you see here is each, so all of those 5,383 nucleotides in the genome are numbered from one to 5,383. And so what you're seeing here is the position of the change and what type of change it was from. Okay, so this would mean it was from an amino acid T to I. T, tyrosine maybe, I, I forget, isolucine or something like that. I don't have all the 20 amino acid codes remembered, okay, memorized. But this is probably from alanine to valine, for instance. But these are the positions at which you had, had the mutations. Here was a case where you had a deletion in the Texas analysis. And you hear you had a deletion in the Idaho experiment as well. Note that in many of these cases, you see the same exact mutation occurring independently in the Texas experiment and also in the Idaho experiment. So for instance, here's an experiment where, here's a site where we have a change from H to Y, amino acid H to Y at position 4110. And that occurred in both of them. This deletion looks as if it also occurred in the same way in both of these experiments. Now, there's two ways you'd explain this, the fact that you get the same results. One is that there's some sort of wild contamination problem. That doesn't seem likely because the experiments were formed in quite different sites, right? Contamination meaning maybe some of the Texas viruses somehow got into the flasks in Idaho when they're doing this experiment. That's that we can dismiss because they're very careful to do the experiments in widely separated areas. So what that means is these mutations were selected independently in the two different experiments, right? Natural selection or selection in these experiments acted in the same way and favored the same mutations when they occurred. And so here's just the final thing they could do is they could look at the frequencies of these mutations and how they changed over the course of the experiment. So for instance, here's one of the mutations and you can see that kind of like the little computer simulations I showed you where when you had these favored mutations they would sort of go up in frequency and then they'd plateau. We can see the same sort of behavior here but what you're seeing is that these mutations spread through the population and fix incredibly rapidly. They're very large population sizes and these mutations are very favored. That is to say the individuals that bear these mutations have a huge fitness advantage over the individuals that don't. Okay, so that's all I wanted to say about experimental evolution. I gave you an example with the course of corn which isn't so surprising. The senescence experiment that Michael Rose did and then an example with viruses and people are doing these types of experiments all the time in the lab. Often they're not evolutionary biologists interested in testing questions in population genetics say but they're working in companies and they're trying to evolve proteins that say bind something more tightly. So these are for whatever reasons. Maybe a customer wants something that will bind a particular protein for whatever reason and they can actually evolve these things in the lab. So they use evolution or selection in molecular biology labs all over the world. But let's give you some examples from the wild and I'll tell you one of my favorite examples one I'm not gonna show you or describe but one involves a couple, the Rosemary and Peter Grant who are at Princeton University and they've gone back to a particular island in the Galapagos and studied a population of Finch for well, since the 1970s. They go every year, it's a small island and basically what they can do is they can document every single bird on the island. There's not that many of them and how natural selection has favored different beak shapes depending on the seed availability in that year and there's a really good book that I would recommend I actually even recommended it to my mom I thought it was such a good book. It's called the beak of the Finch it's by a fellow named Jonathan Weiner. It's a good readable explanation of these experiments so if you're interested you can check it out of the library and read it. It's not obviously something I would require that you know but just if you're interested. I wanted to give an example not from Princeton but from Cal and so I decided to give you an example of an experiment that was done recently by this woman right here, Hopi Hoekstra. So she was a Bio 1B student she was an integrative biology major here at University of Texas University of California I'm thinking I'm Texas University of California she was on the volleyball team in 1991. There she is. She was in a sorority here. There's a picture of the sorority I took on a game day coming down from the steps from the stadium. Here she is at a party. There she is right there and you should notice there's very discreetly hiding something in their hands I have no idea what it is but I'm sure she's gonna love it that I'm showing those photos of her rather and there here she is in the field so she studies, she's a mnemologist and she studies mice basically in the field and the work she's doing now is actually out in the Gulf of Mexico and she's studying mice that occur in the sand dunes. What I'm gonna describe is the work she did as what's called a postdoc it's what you do after you got your PhD often people have a couple of years where they get to do nothing but research before they get to the job like me where they have all sorts of other responsibilities and she studies cryptic coloration in mice so the cryptic coloration example you've probably gotten in high school is an example with the moths on trees in England this is an experiment or an observation that people made that with industrialization you had trees that became much more sooty and experienced from coal from this they're burning a lot of coal at the time and the wild these moths they're ancient phenotype their primitive phenotype is to be white so they actually match these trees what they're finding is that these melanic forms were actually spreading and increasing in frequency the story isn't quite as little bit more complicated than what I'm saying but not much more complicated I think there's actually another I think there's actually a white moth here and a black moth here but I think that's the black moth maybe I can actually find them they're very cryptic but what she's studying is cryptic coloration in mice and so there's a lot so if you think about the must-must, the must-domesticus the mouse they use in labs all over the world a lot is known about their coat colors and the genes responsible for different coat color polymorphisms so for instance here's a little mouse it has a little it's a piebald mouse and you can actually see the piebald mutation in humans there's a basketball player with the piebald mutation there's the mouse with the piebald mutation this is people know the mutation it's responsible for this right so it's a mutation into endothelian receptor B it's the piebald mutation that's the house mouse she was actually studying here's some more other mutations that you're probably aware of in lots of animals are albinos they have a mutation of tyrosinase mutation this is typically a deleterious mutation because they don't have any melanin so here's an example of albinism in humans and here's some examples this is a gorilla that has this mutation the koala, the zebra and so forth there's an albinos squirrel that's pretty cool and then of course there's the albino mouse very cute little mouse so she was studying adaptive or cryptic coloration in these rock pocket mice that occur in the Southwest the American Southwest and I know, is anybody here from Arizona? Must be a few people I'm not from Arizona but I'll keep my hand down so I guess it's a California university but if you go, if you drive along the southern part of Arizona along say highway eight what you'll see is typically what you see is lots and lots of this hours and hours of this but occasionally when things get very exciting you'll go across these fairly recent lava flows where the rock is more basaltic, darker in color so occasionally you come across these basalt flows so this is an example from the pinnacate lava flow where you see this much darker substrate and what you see in these darker rocks is same species of rock pocket mice but you see this melanic form a darker form and then the typical form you see all over the rest of Arizona has this more light pelage, lighter coat and she was interested in the genes that underlie this and whether or not this coat coloration was of adaptive significance so this is just giving you an example of the different places where she studied her different study sites this is the pinnacates I just showed you located here in Arizona and what she's showing you here is on the lava flow that means this is lava flow, that's not lava flow here's the kensin lava flow, lava flow, not lava flow that is to say the ground just surrounding the lava flows on the lava flows you find lots of these dark mice and right next to the lava flows you find lots of the light mice that's what it means that's what this figure means and that's true for these other lava flows as well here's the kensin all the mice she found on the lava flows were dark and all the mice she found just adjacent to the lava flow were white and that's the true for these others as well oh and then down here is an estimate of the age of these lava flows and for reasons I'll describe later you can actually very precisely date lava flows using what's called radiometric age dating so the age of these lava flows is well established and here's some of the mice in the lab so you can actually see the differences from each of these different lava flows this is the type of mouse you find on the lava flow this is the mouse coloration you'll find just adjacent to the lava flow and the pinnacades and so forth this is what, if you go upstairs and to go through the drawers of the museum here you find lots of examples of mice that look like this kind of flattened out with tags on them not very happy so what she did is she took what's called an association state so she looked at these mice and she noticed and I should say too that this work was done not only with Hopi Hoxtra but with this fellow Michael Nockman who's at the University of Arizona so it's a Nockman Hoxtra what she did is she looked at the hairs on these pinnacati mice the lava flow mice and noticed that the hairs look reminiscent of a mutation that you find in the mice that the house mouse the mouse that everybody studies and labs all over the world and she thought well maybe the same mutations or mutations in the same genes are responsible for the dark coloration in these rock pocket mice as the ones that cause dark coloration in laboratory mice that was the thought so you have 30,000 genes that you can think of out there in the genome she tried to limit which ones to really study a lot by using what's called the candidate gene approach so concentrate your attention based on what you know in a related species and the basic idea is this in these association studies you have some mutation that's responsible for the phenotype and so in this case the mutation in the gene that's responsible for the phenotype is indicated by a star each one of these lines represents a chromosome so these chromosomes might come from would come from different individuals these different mice say now you may not actually have sequenced or actually know what the mutation is you may never actually see the star but what you can get is places nearby that are polymorphic that is to say nucleotide sites, the SNP sites I told you about earlier where you know that there's variation and the idea is that when this mutation appears it's gonna occur on some chromosome and that it's gonna at least initially be associated with specific sets of nucleotide polymorphisms nearby so if you can actually assay the sites you actually assay the vertical lines here you can measure the phenotype and the idea is if you see a phenotype that's associated with certain SNPs that it must also be associated with some gene that's nearby some mutation that's nearby so this is what she did and so she actually looked at a mutation in what's called the MC1R gene it's one of these genes that she knew from the house that the laboratory mouse might be responsible for this this MC1R protein sits on the membrane of the cell it's called the transmembrane G-coupled receptor and what happens is things like MSH will bind to the outside and when it binds to the outside of this it causes C-amp expression to be expressed or increased inside the cell and you get what's called a U-melanin being produced you get dark hair so if you look out, actually lots of people have lots of U-melanin in this class now if agudy is another protein that can bind to this protein, this MC1R and when it binds it shuts it down so when agudy is present it represses the activity of this MC1R gene you don't get a lot of C-amp and you get Phe-melanin being produced so I look out there and I see a few people with very light hair that have lots of Phe-melanin and you get light hair so this is a mouse that has lots of Phe-melanin being produced this U-melanin rather or Phe-melanin, this is an individual that has lots of U-melanin being produced dark hairs so this is the actual data from our paper and what we're seeing here is this is the two different copies of the gene from each mouse this is one particular mouse you can see, remember it has two copies this is the gene one from mom, one from dad so what you're seeing is copy one, copy two for this dark mouse and then you have this other dark mouse where you're seeing copy one, copy two and so forth these are all the dark mice and then here's all the light mice here's for instance, well let's go to the very bottom it's easier to see this line here represents the DNA sequence from mom or dad from one chromosome and here's the sequence from the other chromosome from this particular mouse and wherever you see a dot this is a shorthand notation that people use in these types of figures to mean that it's whatever is here is the same as at the top so it's gonna be a C wherever you see a dot this is the reference sequence at the top so wherever you see a dot it means you put in the reference sequence nucleotide at that position is that clear, is it clear how to read this and you see anything remarkable about this graph or about this figure I mean she sort of draws your attention to it by boxing it in but note this all these dark individuals have a T at this position a T at this position a T at this position and a C at that position in the gene, right and they have at least one copy of that right so up here all these individuals let's see all these individuals at the top from here on up they're all homozygous for having all four of those mutations that is to say both chromosomes have those four mutations these individuals from here down to here that are heterozygous, right and all the individuals down here are homozygous for not having those four mutations and so she used a subscript little D for having not having those four mutations big D for having those mutations so these are all the big D, big D individuals these are the big D, little D and these are the little D, little D what she's pointing out here is that there's a perfect association between the color of the coat of the mouse and these four mutations now the, so the inference is that these four mutations are responsible in the MC1R gene are responsible for the dark coloration you see in the mice now the best experiment she could do which she hasn't been able to manage to do for technical reasons is you take a white mouse and you basically through transgenic methods that you can learn about in advanced molecular biology courses you actually stick that gene into a mouse and see if it becomes a dark colored mouse that's the experiment that most molecular biologists would consider as definitive but this case the evidence is so overwhelming those four mutations are responsible for the coat coloration that's probably not necessary to even do the experiment so this is just to summarize what we have we have those four mutations if you're, if you've got it if you have at least one copy of those four mutations on at least one of the chromosomes you have dark coloration and this is just showing you where those mutations occurred in the MC1R gene two of them occurred on the part of the transmembrane protein that faces outside of the cell that's these up here and two of them occur on the part that faces inside the cell these are the, each one of these circles represents an amino acid that's coded for by the, by the MC1R DNA sequence so this is, this is the situation you have we're just showing you pictures of here I'm showing you a picture of the mice on their natural habit background here's a mouse that's mismatched it's actually a dark mouse on a white background here's a white mouse on a dark background and here's a dark mouse on a dark background on the lava flow so the thought of course is that if you're a predator you can actually see the mismatched individuals much more easily than you can see these individuals but of course when you make a statement like that it seems obvious but you have to remember that's well we're speaking right I look at that and I say it looks very obvious to me that these guys would be the ones that would be easy to find and pick off but you know I don't actually I'm not a predator on mice okay and so it turns out that maybe our intuition is wrong about this so you need to do experiments to actually determine whether the mismatched mice have an advantage so you might imagine I'm a bit of a Trek fan so this is one of the episodes where Kirk has to battle Spock and it's called the amic time it's the episode if you're interested in so what you need to do is some sort of tournament type of study right and so what this is a very difficult experiment to do now but it has been performed about 60 years ago by this fellow Dice and he basically studied alpidation and deer mice they're mice that are similar or closer related to the pocket mice that Dr. Hoekstra and Nockman studied and what he did was this just took a barn and on the floor he made two different arenas he put some dark colored rock on one and he put some light colored sand and rock on the other and he put dark and white mice out onto the deer mice out into these two different arenas and let them sort of scurry around that's what mice do and then he put some barn owls in the barn right and just let the barn owls do what they do which is they eat mice and so the barn owls would sort of swoop down and eat the mice and they just counted which mice were eaten and which survived and what they found in this experiment is that the deer mice that were mismatched to their substrate were much more frequently picked off by the owls you know owls have notoriously good eyesight but even they find it easier to find a mismatched mouse than a mouse that matches the substrate and then Dr. Hoekstra has done similar studies more recently where she was very difficult to get the permission to do experiments like that nowadays I guess people don't like to see the little mice being eaten by barn owls but you can take what she did she took made little clay models of mice so she has an entire lab graduate students in postdocs they have these fun little parties where they make little clay mice and they don't fire the mice okay so it's not like you make the clay mice and fire them you keep them so they're still soft clay and you paint them so you get to paint too it's like a craft thing so they paint the mice they paint them to look as much like they can like the dark colored or the light colored mice and then they go out in the field and in the evening they'll sort of stick these little mice all over the place and the next day they come in or a couple of days later they look around they pick up their little clay mice and they look for evidence of predation so if you see like a bite mark on them that's going to be preserved in the clay or scratch marks like some owl came by and then realize it's just clay it's half you know but they look for evidence of predation and they find in these experiments just like you'd expect the mice that mismatch their background in which they sort of place the clay mice or have show evidence of predation at a much higher rate than the mice that match the background okay just a little bit more on this so she's also studied the distribution of these MC1R alleles the case where you call the big D and little D alleles in what are called transects what you do is you make a line and you just walk along that line and you collect mice whenever you see them okay you trap them actually so you put traps along the transect and so this is the transect there's the lava flow so basically in this transect you have some parts where you have the regular light colored sand of the desert and then this is the dark colored part so this is a substrate color along the transect and as you'd expect the frequency of the MC1R gene allel those big D alleles sky rockets goes almost to one over the lava flows and then decreases on either side of it so this is you can actually if you want and it's useful to think about there being two populations you have the population of mice living on the lava flows and the population of mice not living on the lava flows and here's another example where you have migration of individuals from the lava flows out into the desert and from the desert into the lava flows as far as they can tell the mice don't know any better they don't know if they're mismatched or not okay they're mice they're not super smart and so you have natural migration of the mice that go out here but of course when the dark mice come out in the light sands they probably get eaten up pretty quickly and it's the same for the light mice that find themselves on the rocks and this is just a show that it's not what you do is this is the frequency of the MC1R big D allele over that transect once again noting that on the dark rocks the frequency of that big D allele is very high but it's very low in the white sands but here's another gene so you can say well what is that true for every single gene in the genome do they all show this pattern? because if they all show this pattern then clearly natural selection isn't acting on the MC1R gene and so this is some other gene that does nothing to do with coat coloration and you can see that it doesn't show any pattern going across that transect that its frequency is about the same on the light substrate as it is on the dark substrate so natural selection is clearly targeting just the MC1R gene okay and this is the last slide this is just to point out there's all sorts of interesting things you can do with coat coloration and mice and this is the type of work that Dr. Hoekstra's been doing more recently I should mention that she's now at Harvard that she's got faculty at Harvard and she's been turning her attention to mice that live in Nebraska on sand dunes and mice that live on the beaches along the Gulf of Mexico so I don't know if you know about the Gulf of Mexico but you go along these beaches and they have like pristine almost sugary in color of white sands along these strand beaches and then if you go inland if you go into like Florida just the mainland you see the normal dark dirt like we have everywhere the mice that live on these beaches on the white sand have very light colors coat colors like this guy right here this cute little guy here and here whereas the mice that live inland have more dark normal mousy type coat colors and she's been studying the mutations that are responsible for these light colored mice in the sands so that's it


Hi everyone, Joe for jaspyscasebreaks.com with a mosaic basketball cereal box spring number 36 last filler We got to do to unlock that hip-rate basketball jaspy exclusive We'll be more than sort of jaspyscasebreaks.com. Check it out. First. We got to do the break big. Thanks to this group here for making it happen Let's roll it and randomize it four and a six ten times or names and teams ten times for the teams two three four and a tenth and final time there we go We've got Brooklyn down to Atlanta. There's the first half of the list right there. No trade windows second half of the list right there Let's just print and rip now um We are booked for the night, but Jason jaspy may still have some some time available for some personal boxes for you if I want to take advantage of that All right, anything especial in here. These are the exclusives to this set Nothing too crazy here All right, nothing too wild there, but it's just a retail box. Who knows sometimes something happens sometimes nothing happens But a nice something here is who's going to win The last nine spots in the hip-rate basketball break That's a big hit in and of itself one and a four five times top nine after five one two three four and five Now sad times from ten on down Kenneth very close Thanks for giving me a go 10 through 30. Thank you very much But only the top nine can be happy because they advance to the next break and that's going to be nicholas walter steve Kenneth Kenneth you got in steve chad nicholas chad and steve birch There you go gang. I'll see you in the next video for the break itself jaspyscasebreaks.com. Bye. Bye


At the risk of stating the obvious, compliance can be hard. It's really tough to move fast and maintain a competitive edge while still taking into account all the necessary fail safes and standards imposed by third parties, whether those are regulatory bodies or internal or external customers. To manage that, you need consistency and automation. And to achieve that, you need some sort of infrastructure as code. Architecturally, there are a lot of different ways to approach that, and our speakers today have tried several of them. Janus Deenst and Jan Kolas from DB Cystal had to build a multi-tenant infrastructure that met both internal standards for process separation and external ISO 20000 service management requirements. And they did so by leveraging a couple of different architectures built on GitLab. So let's have a listen and see what they learned. Hello and welcome to our talk. We are here from Germany and I'm joined by my colleague Jan Kolas. I am Johannes Deenst and we are sharing the journey from Managed Cloud to GitOps. A journey we had over the last two years in our team and we want to deploy several client clusters per customer in HWS and how it all started and how it's going and where we are now. We are sharing in this talk today. So let's give some context where we're coming from. We are both employees in the same team at DB Cystal. DB Cystal is a subsidiary of Deutsche Bahn, the main German rail and logistics provider in Germany. We are operating worldwide with over 300,000 employees and DB Cystal is driving the digitalization of Deutsche Bahn in every aspect and in service station. And what we're doing specifically and why it's important that we are deploying several client clusters in the cloud, we will share in the next screen and I give over to Jan. Thank you. Yeah, we are running and building the DB Content Hub and in detail it's a headless content management system. We are providing it as a service in the whole firm and basically it's running on a whole infrastructure cluster. So it consists of several servers running on Linux, running our actual software, of course, running different other applications as EngineX, as a reverse proxy and so on for that later. And of course we are using the central services of the AWS cloud as the RDS relational database service, the Elasticsearch service and so on. And in front of all the customer clusters we are also running a centralized API to lighten the data access to control the request traffic and so on and of course to count the traffic because it's a small part of our business model. And the main requirement of this whole infrastructure was due to data privacy projection was to really separate each cluster from the other because we definitely were not allowed or it was definitely not allowed that the data of one customer cluster could reach the data of the other customer cluster. We had to ensure this. Therefore, this infrastructure we will present to you and more to that in a few minutes and in front of it, as I said, is the central API. It implements of course a routing mechanism based on an API key per customer so it interprets the key and routes the traffic to the correct load balancer in the background. And the data privacy protection was not the only requirement you had to fulfill. Deutsche Bahn is a big company. I always call it a corporate environment in a corporate environment there's a lot of certification, specification and requirements and constraints and one specific constraint is the ISO 20000. And this is over a few of the ISO 20000 and we are focusing specifically on the service management system requirements. So I can talk a few hours about ISO 20000 but we won't do that here because you want to learn something about infrastructure as code, for example and how we use it with GitLab. So the main things I want to talk about in short is the change management and release management and there are specific requirements to change management and release management if you are ISO 20000 certified. And one requirement, for example which is easily implemented with a version control system like GitLab is you need to have a request for change if you want something to get into production and this is a merge request or a pull request how you call it is not important but there are also other requirements for example you have to have release notes and release notes I remember doing it a few weeks earlier manually jotting down the commits what got changed in the software and with automation and pipelining you can automate release notes to a high degree or even 100%. What also is important in the pros of ISO 20000 which is common sense but also best practices testing and this is also aided by pipelines you can automatically test specific tests and automate all the tests if you are willing to do it. What we also have a requirement and I don't think it's very specific to ISO 20000 but I want to mention here we have to plan every change or every release in a centralized tool so every interface partner we have or every other team which uses our software knows about the change which is potentially breaking or interrupts the service and you can put this in a pipeline too if you are able to automate and put everything into Git for example so from the requirements to our first try and our first customer because the speciality of our team is we are not building a project but we are building a product which is sold to many customers and our first try was we were the first customer where we said okay we have a managed cloud and we want to deploy something into this managed cloud and what we did there was putting Ansible playbooks in a Git repository which is cool because then a developer from our team can check out a Git repository and deploy the software to or the infrastructure the configuration of the infrastructure specifically to the servers because managed cloud always means less automatization potential and you have to buy or configure the infrastructure beforehand a little bit and say okay we have a relation database for example and an easy two instance if you are in HWS and then you check out the Git repository as we did here on our local laptop and configured the provisioned infrastructure there and there is one big downside of that it's convenient but the big downside is you can't be sure that the provisioning of the infrastructure or the configuration of the infrastructure is the same as in the Git repository what you do want is that the configuration in the Git repository is the truth and is identical to the configuration on the server and one anecdote I want to share with you is that we had a plugin for our board software there and it turned out that the configuration of the plugin or the implementation of the plugin wasn't the same as in the Git repository in fact the one who deployed it changed it locally on his computer and we didn't know what he changed because it wasn't in the Git so we had to re-implement it to match the requirement when we updated the plugin so this was very tedious and there is also this compliance problem if you can't be sure what is on the server then you are not compliant you can't say what's the truth and that's a big problem with our company so if you get more customers and not just one we have three here and actually there were three we said ok that's not possible to provision the servers beforehand and then configure them we said ok we have to move to HWS to have a more cloudy approach and the cloudy approach means ok we provision our software from our Git repository and in fact what we did we used HWS cloud formation and put all the user customer configuration into the Git repository so there were three configuration directories and the cloud formation template this has not the problem with the compliance anymore because when you deploy over a pipeline and only deploy over a pipeline to the different customer clusters then you are automatically compliant only if someone goes into production and changes by hand but that's not recommended so the compliance is easy to achieve and the infrastructure is very viable over code so truth in the repository matches the truth in the cloud and this is very powerful one big disadvantage we noticed with our monorepo for though it was that somehow we were able to change the wrong customer configuration because the customer configuration was structured all the same way only the directory name was different and if you are somehow under stress or have to change something in a hurry then you were able to change the wrong customer and deploy it into production which was not so fun and that's the reason why we said ok it's doable that way but it's hard to maintain so we said ok we need a different approach which I give over to Jan people explain it in more detail yeah this is our third try and hopefully the final approach we are doing here it's a combination of Terraform and GitLab Git in general Terraform is a description or descriptive language for infrastructure in the cloud let it be Azure in our cloud in our case is the AWS cloud so in our case we have several assets we are maintaining and developing and enhancing for example as I said the different Docker containers the engine X Docker container our actual software the headless control management system is also bundled in a Docker container and all the Docker containers are running on the Linux instances of course our infrastructure description itself is an asset and all our assets are versionized more for that we have several other assets as our plugins we develop for the CMS system or of course the AMIs the Amazon machine images which are representation of the later Linux operating system running on the servers so when we develop or enhance a Docker container for example the container gets built by a pipeline and when the build is successful the internal registry we are using it's a JFrog and after that the change the new version is propagated in a central repository per customer so we have one repository per customer which covers basically a configuration of the later infrastructure which gets deployed into the AWS cloud now the new version of this Docker container or the new version of the infrastructure description by a merge request is created in the customer's repository this merge request is automatically accepted and the new version is merged into the configuration file of this customer it's just a simple JSON file so now when we want to deploy a new customer cluster or new customer infrastructure we just trigger a job or pipeline in this customer repository the job which is responsible for that takes the configuration file and translates it into GitLab variables and calls the infrastructure repository and passes the configuration as GitLab variables into this infrastructure pipeline in this job that is triggered in the infrastructure pipeline the GitLab variables are also passed into the terraform description of the later infrastructure and using this we can see here things like the AMI image the AMI, the Docker container but also things like how many nodes should be there in the Elasticsearch cluster or which instance, which database instance we want to use in the relational database service of AWS and afterwards just the infrastructure pipeline runs executes the whole terraform stack and the whole infrastructure is built or updated in this cloud and that is basically the whole magic behind our approach one side note is we are not allowed to use unfortunately to use GitLab variables for secrets and so on so we have set up our own secret management also we are using Mozilla SOPS to encrypt the files covering all our secrets these files are then automatically decrypted and stored in the AWS secrets manager all this one note, all this is possible because our GitLab runners have permission to access of course to access our account in the AWS cloud otherwise this approach is not possible with this approach the secrets are stored in the secrets manager and when the Linux instances comes up in Amazon language it is called cloud the server started in this cloud in a script the secrets are taken from the secrets manager and passed into the Docker containers perhaps to get access to the database to get access to the Elasticsearch servers and so on and a very important concept behind this for us is the GitFlow approach for now we are using three branches to develop so when we enhance or change a Docker container maybe the engine next Docker container we create a feature branch do some coding whatever change a Docker file then the usual refuel process is done and if everything is fine this feature branch is merged into the development branch now the build pipeline for the Docker container runs gets uploaded into Artifactory and the new version of this container is created this new version is propagated into the customer's repository and in the customer repository we have the same structure of these branches so this new version gets merged into the configuration file in the development branch of the customer and what we can do now is each branch is a representation of an environment in the AWS cloud so we are able to create a cluster a testing cluster, a development cluster for this customer or we are able to create a staging in our case it's a staging environment in the AWS or production environment we could enhance it with as many branches as we need for now these three branches are enough for us so when we did our tests in the development environment everything is cool we merge the Docker container into the main branch the same process starts from the beginning the main version is merged into the customer repository now we can pre-test for the production the Docker container on the staging environment and if really everything is fine on Monday 10 am we push the button and deploy everything on the production environment of this customer now you can see a real-life example of this pipelines on the top left you can see how we trigger this customer pipeline in this case we trigger a development environment in the AWS cloud so we push this button afterwards the infrastructure repository gets called or the infrastructure pipeline one side note here we have two AWS accounts one for testing purposes this is where the development environment is created and one production account where we create the production clusters of the customer the staging environment and the production environment in this case the development pipeline is triggered and one click later you'll see the Terraform representation of the the Terraform pipeline one click thank you in Terraform you do several steps at first you validate your Terraform description can we build this infrastructure and the second step is you plan what has to be done to create this infrastructure and finally the whole stuff is applied in our case to the AWS cloud we first create the environment of our main application because as you know the application needs a running database needs the Elasticsearch service and so on so you first apply the network storage this is the Elastic file system it's attached to each server running our software the database is created as I said the relational database service and the Elasticsearch service and afterwards our service setup is really deployed and really created and starts up okay we're already nearly at the end so let's wrap it up what we learned today we had the requirements of the O20,000 and also the data privacy protection requirements from the U but also from Deutsche Bahn where we say okay no customer threat should be able to get data from another threat so a real multi-tenant system we had to build there and we had to implement the change management release management as you could see hopefully we are able to do this except configuration as code because of a customer repository where we start a configuration but also with a GitLab flow that we can do release management and change management very easily but that's not the only advantages we got from this approach with Terraform and the configuration as code for each customer we also got a few more advantages and I give the word to Jan once more and we will go over them one by one using our current approach it's very easy to create or to set up new customer stacks in ancient times it was really really hard because we had to do several things by hand and manually and it takes time, the more customers you got the more time it takes of course and now we can just create a simple repository where we have scripts to initialize this new customer repository and then we can just push a button and create the whole production cluster of this customer and of course I would do the next what we also have and this is especially important for me Jan came a little bit later so he doesn't know these ancient times he only knows it from my history explanation but I really find it very satisfying to have a rolling updates now if you are not changing the relation database of course because this needs a downtime we are not doing anything by hand we just say pipeline run with a new configuration and a new infrastructure configuration it just rows into production usually without any problem and this is made possible by the pipeline approach we had chosen and of course several other things we could talk about here and one thing is we have a monitoring stack consisting of Prometheus, Grafana Elasticsearch, Kibana and so on and every machine every server that comes up automatically recognizes itself in this monitoring system and this is also possible because of this automatic approach we can also deploy new dashboards automatically into Grafana we can export the metrics the Linux system, the CPU load and so on into Prometheus and that's a cool feature that helps us very very much and the last one to emphasize the compliance and security approach with GitLab pipelines or with pipelines in general it's very easy and it's very easy with GitLab especially because it's a containerized build we can add stages for compliance checks and update security it's very easily if we need a new tool for example open source license checking we insert a new stage and this is usually just one little block and a little bit of configuration so we are easily compliant in that regard and if there are security updates with the rolling updates we are having no problem deploying them in under one day and very fast so that's it for our talk I hope you got something out of it I'm pretty sure it's a little bit high level because of the short time we could talk about it much much longer I think if you go into detail but we are there for question and answer so you can ask two questions or more if you like if you want to reach us there are our business emails I'm also reachable at Twitter so thank you and have a nice day thank you