Hello, I'm Chancellor Ronnie Green. Thank you for joining me for today's Nebraska lecture. This distinguished lecture series features some of the University of Nebraska-Lincoln's most notable scholars, researchers, artists, and thinkers. At Nebraska, we believe in the power of every person. For about two decades, the Nebraska lectures have showcased some of Nebraska's finest scholars, people who embody the spirit of this institution and are committed to sharing their knowledge with the public. Our speakers are renowned experts in their fields. They are scholars who strive to collaborate, breaking down the barriers between disciplines. They are educators who are committed to mentoring and shaping the next generation. They are problem solvers who have spent their careers addressing some of society's most pressing challenges. I am so proud of their accomplishments and dedication to our University and the state of Nebraska. Thank you to the Office of Research and Economic Development, the University's Research Council, the Osher Lifelong Learning Institute, and other partners for making this lecture series possible. I hope you enjoy today's Nebraska lecture. I'm pleased to introduce Harkam Awalia, who will be giving today's Nebraska lecture titled Changing Climate, Warmer Nights, and Crop Yields. Harkam Awalia is an associate professor of agronomy and a faculty fellow with the Daugherty Water for Food Global Institute. His research focuses on crop-abidic stress tolerance, phenomics, and functional genomics. In 2019, he became Nebraska's human chair of agronomy. A live question and answer session will follow his lecture and viewers may email questions for him at unrsearchatunl.edu. Researchers around the world agree that rising global temperatures combined with erratic precipitation are posing an unprecedented challenge for human health and nutrition. Harkam Awalia's lecture will present a perspective on how genetic improvements and technological advances can help address this challenge for agriculture and global food security. His major research activities include functional characterization of heat-regulated genes during reproductive development and genetic and physiological characterization of roots and wheat during drought stress. He also conducts comparative transcriptome analyses of cereal crops in response to a suite of abiotic stresses, elucidating the gene networks regulating early seed development and seed size and cereals. Harkam Awalia earned a bachelor's degree in plant breeding and genetics from Punjab Agricultural University in India in 2000 and a PhD in 2005 from the University of California at Riverside in plant biology. We're grateful to have Harkam Awalia at UNL and I'm particularly grateful for his initial ideas around plant phenomics work that resulted in our greenhouse innovation center and I'm really looking forward to learning more from him today and he and his perspective on these grand challenges of climate change and food security. Without further ado, I present to you Dr. Harkam Awalia. Hello, everyone. My name is Harkam Awalia. I am a professor in the Department of Agronomy and Horticulture and today I have the pleasure of presenting the Nebraska lecture for the spring 2021. I would like to start by thanking Chancellor Green, Vice Chancellor Wilhelm and the Research Council for accepting the nomination from my colleague Steve Benzinger for giving me this great opportunity to present this lecture. It is truly an honor and I hope that my lecture can help stimulate some discussion and spur some ideas and is generally useful for you to read more and learn more about it. So with this, I will get going. I really think that the idea of having a, celebrating the the research fair this week is very good. I also think that it's a great time, one of my favorite times of the year to be in Lincoln as we see a lot of flower bloom on trees. The city looks at its best this week and next week. So it was kind of interesting from that perspective that there was a news clip that caught my attention that the Cherry Blossom, which was being recorded in Japan or Kyoto for a very, very long time, so 1200 years. There was a trend that was seen in terms of earlier and earlier blossoms. So if you look at the, at 2021, it is at March 26. So this been for the last 200 years a drop in the, or earlier and earlier peak blooms. And that sort of caught my attention because I think that's what we, one of the main points about my talk today is the flowering time and the, and the greens that develop from those flowers for some of the major crops and how temperature impacts them. So for the, for today's talk, here's my outline. The first we would talk about some of the climatic factors that are impacting crop productivity, basically looking at those factors and what the predictions are and how that could affect, you know, the yields. Then we will sort of zone in on, on high temperature and learn a little bit more about what the temperature distributions look like. And then finally, we would, you know, further kind of focus on some of the work that our team has been doing on high night temperature stress or warmer nights. Hence the title, you know, having warmer nights. So why, so what we would, what I would do there is like give you an overview of why we are beginning to work on warmer night and why it's important. And then some of the new technologies, both generic and, you know, imaging related technologies that we have developed and how we are trying to link those to see if we can understand the crop response to warmer nights or high night temperature stress in better detail. So as many of you are aware that, you know, climate is central to many of the discussions, news and so on. And so also in the arena of science. What we do know from, you know, the data and the modeling is that the, that the, we are getting more heat waves, we are getting more flooding and more drought events. So basically, the, the frequency of the heat and precipitation extreme events is increasing. Also increasing are the, are the duration as well as the intensity. So not only do we get more of these events, but we, we have them last longer in many instances. And then also they are more intense.  What is also predicted, you know, what's known is that these events are already showing a trend where for many, many decades now where they're more frequent, but it's also predicted that in the future some of these extreme events are going to continue to show that trend. That means that they're going to continue to have occur more frequently. They're going to have more intensity and cause more losses, both in terms of food production and other aspects, you know, in terms of the economy and, and social aspects. What's less known is how do we take these global trends and decide. So, so if you're a farmer and you're wanting to know, okay, you say, all right, you know, I understand that there's going to be more events of rain and flooding or I'm going to have to see more drought and I need to prepare for it. So is it going to rain more next year or is it going to be more dry next year? So those are things that are less tractable. However, there are instances, for instance, we do know that some parts of South Asia, for instance, and Sub-Saharan Africa are going to be more dry and more hot. They have shown that trend and it seems like that's going to continue. Generally speaking, it's at a local level. It is very difficult to at least, you know, very reliably predict what would be the, you know, the frequency of drought or flooding event or heat waves in the next few months or years. So with this, let's kind of zone in on some of the, you know, of the factors that are impacting crop productivity and climate influences those.  So we'll, we'll, you know, so keep this in context when we, when I discuss these points.  Plants that produce nearly all of our food directly or indirectly are about 90% 80 to 90, 95% water. So water is very essential for life and for cellular processes. And the process of photosynthesis, which is the capturing of carbon dioxide from, from the air into strings of carbon that would form sugars that eventually serve as food source for us are possible because of the presence of water. There's also this relationship between carbon dioxide and water where a typical plant, when it opens is it has these openings on the, on its surface, on the leaf surface. When it opens because of the difference in concentrations inside the leaf and outside in the environment for every one carbon dioxide that a typical plant would import into inside its leaf. There's an inevitable loss of somewhere from 300 to 400 water molecules. It's just inherent from the differences in size of these two molecules. So however, with water, the challenge is that it's highly variable. So it's not very easy. As I mentioned to predict, you know, whether it's going to rain heavily for the next five years or is going to be a drought period for the next 15 years. The next component is carbon dioxide. So plants, when you dry them up are about 40% carbon and that carbon's derived from the environment. And the, therefore, in, you know, changes in concentration of carbon dioxide in the atmosphere are inevitably going to impact the, you know, how much carbon, less or more that the, the plants would take up. And then finally, you know, finally, among the A by T factors that are very important is temperature, which is both again linked also to carbon dioxide as I would elaborate shortly. But also, you know, if you have higher temperatures in the air temperature, it inherently increases the capacity of air to hold water, which means that you're going to have a bigger drive to get the water from the soil as well as from the plants. So in, you know, resulting in more drought related stresses. So these are some of the A biotic stresses and I'll, that's kind of an area for my research, which is to look at the A biotic factors in their impact crop, productivity trying to understand what the mechanisms are. There are also biotic factors that I won't touch on, but clearly changes in temperature, moisture impact the incidence and the frequency of crop diseases and pests. And that clearly is an important aspect again with lots of variability in terms of changes in domain of these biotic factors. So what we do know for sure in terms of when I was talking about, you know, long term trends, what we do know is that the carbon dioxide concentration is going up and over the years. And this results in, you know, many, many benefits because if plants are trying to capture this carbon dioxide, the more carbon dioxide there is in atmosphere, it should be able to take advantage of that. Which many plants also do some, you know, there are plants such as wheat and rice, which we predict could benefit from higher CO2 concentration, whereas others that are such as maize and sorghum, which already have a mechanism of concentrating carbon dioxide are likely to benefit, but to a lesser extent than, you know, some of the other like rice and soybean and wheat. So, however, with rising CO2 concentration, there is a challenge. And that challenge is that CO2 is a greenhouse gas. So if you think of Earth's a blanket of atmosphere around Earth's surface, most of the atmosphere is made up of oxygen and nitrogen, but about 1% of the, 1% of the atmosphere is made up of greenhouse gases. Carbon dioxide is the major greenhouse gas. And it's an advantage for life on this planet because carbon dioxide along with other greenhouse gases would trap the long-wave radiations emitted by a surface and reflects them back, keeping the atmosphere warm enough for life to be possible, and also sending some of those, the heat into the outer space. So the problem is as carbon dioxide concentration increases, as you are seeing, that's very evident. And we know that for a fact that it is, the temperature also start to increase. So here's a graph that shows the, uses the baseline of 20th century. So the average is at zero. And it should, it tracks the, the anomalies in global temperature from 1880 all the way to 2000. And you could see that the trend in the last 20, 30, 40 years is that most of the temperatures are above, so above the average. So because of the increasing carbon dioxide concentration, one of the major drivers for this anomaly or increasing in temperatures. So this complicates things a little bit for plants and plant processes. And, you know, it also does for, you know, not only for plant physiology, but for human physiology or any other life form. So I wanted to show you, you know, this animation, which shows from the, essentially the point that I made, where it usually the 30 year average as a baseline to, to show the changes in temperature. So the fits, if the parts of the globe that are blue are below that average and parts that are, will turn red are above that average. So if you look at this, these are five year averages. And the baseline is derived from 1951 to 1980. So as you see that there's, you know, a gradual increase in, in more red.  So this kind of shows you the, you know, how much the temperature has, you know, increase and what kind of distribution it has both over water and land mass. With, you know, so what does that really mean in terms of, you know, if the average temperature changes by I say 0.6 or 0.8, does that make a difference?  But when you think in terms of averages, you also need to see how the, there's a shift in this graph, you know, that increases the, you know, the mean. So if you look at the Y axis, which is the probability of occurrence, you would see the shift in graph which will result in less cold weather events, which is really good for crops because if you have extreme cold weather such as we had one in, in February, there's a pretty big loss for crops that are out there in during that time. For instance, winter wheat crops have been hurting because of that very cold window. However, what is more worrisome is that the shift brings not only more hot weather, but also turns these into, you know, events that become more, you know, record hot weather. And that really is devastating for crops, especially when it, these occur during flowering, which is when in terms of cereals, a lot of grain formation takes place. So in terms of what really is happening at the physiological level and at the molecular level with rising temperature in plants, there are many, many aspects, but I could think of these three or four as the main ideas. First is that the rate of respiration increases. So if we, let's say if you are in a really comfortable environment listening to me, you know, 65, 70, if we were to raise the temperature of the room by another 10 degree or 15 degree, you would clearly be respiring at a higher rate to meet the metabolic needs of your, of your body. So similarly, increased respiration due to rising temperature results in carbon loss. So when, when organisms respire, they take in oxygen and they release carbon dioxide. And so that higher level of carbon dioxide loss from plants, especially when they are in the form, in the stage of grain development is, is quite detrimental because the furoscences, which is the process that fixes these carbon molecules from carbon, you know, from carbon dioxide from the air into these sugar molecules that are stored in many cases, either as sugar or as starch and oils is something that we consume and as, you know, plant products for our food. So losing some of that carbon simply because of higher respiration rate that plants have to now maintain due to higher temperature is very detrimental to, to the crops. And further, as the temperature increases, as I mentioned, the capacity of the air to around the, on the plants to hold water increases. So it draws more water out through water loss from the surface of the soil, as well as from the, the openings in the plant surface. The third thing is that is the developmental acceleration. So what plants, many of those crops are annual when they experience stress, they shift, they accelerate, they accelerate and their developmental events. And when they do that during grain development, which is the time when the plant is actively bringing in carbon from the air and trying to push as much carbon as it can into the seeds or tubers, the, that acceleration compresses that window because the plant has this escape mode in many cases. Instead of, you know, just delaying it, it would actually try to escape. And that results in smaller grain filling periods. So those are windows or time frames when the grain is actively being filled with starch and proteins and lipids and oils. So that really is detrimental. And then also higher temperatures bring in more cellular maintenance cost. So what does that really mean? So I could, you know, it means there's many, many aspects to it, but the aspect that's more, most important that I think is to do with this remarkable enzyme called Rubisco. So Rubisco is the, is the enzyme that's central to doing the magic that plants do in terms of drawing in carbon dioxide from the air and turning it into, you know, food for itself and also what we use for consumption. So Rubisco as the, if you look at the first graph, there, you know, there's the temperature on the, on the, on the X axis. The, as the temperature increases, the, the rate of Rubisco turnover increases. So that's the green line on the, on your left graph. As the turnover rate increases, what that means is that more and more Rubisco gets degraded more quickly, so needs to be made more quickly. And that's kind of an impact of temperature. So it slows down the whole photosynthetic process. But you also see is a broken orange line that's going downwards. That's called, that is called Rubisco specificity. And what that means is that Rubisco, which is, you know, probably a fundamental protein, probably also the most abundant protein on this planet, as far as we know, is the, is also able to react with oxygen. So like very, you know, like many important things, Rubisco has some issues. So as the temperature increases, Rubisco starts to like interacting and, and, you know, accelerating the experiment, the, the, the, the reaction for oxygen to, to bind to the molecule that is the carbon chain, rather than the carbon. So if we don't add an extra carbon, that's a carbon that opportunity that's lost. So the plant ends up spending a lot of energy and going through various cycles to recover at partially some of the carbon that that's lost because the oxygen won over in that particular reaction compared to carbon dioxide. So as the temperatures rise, the Rubisco is, you know, likeability for promoting the carbon dioxide reaction so that, you know, we could get an extra carbon dioxide, carbon added to a carbon string that towards its way to make sugars is decreased. So that really hurts the plant and is energetically inefficient. And it also impacts, you know, basically, it's a temperature impact. So what it also does, if you look at the graph on the, on the, on the, on the right, it shows that as the temperature increases, you know, around 3035 degree, the, the rate of photosynthesis drops, primarily, because of what I mentioned about Rubisco's turnover rate and specificity, but the rate of respiration continues to increase. So this combined effect of, of higher temperature on dropping rates of fixing carbon and increasing rates processes such as respiration that are in the business of losing carbon, you know, that that converge and that results in a physiologically challenging situation for, for plants as the temperatures rise. So what does that mean in terms of global crop yields? So I have summarized the, you know, results from several models from this paper, where they predict that for every one degree increase, what's the percentage of yield loss? So if, if the, if the global temperatures increase by one degree, it's expected that, you know, the losses for wheat would range from around minus six, you know, sort of by 6% drop, and rice would be somewhere around three, and maize would be around seven, and soybean would be around 3% again. So what we do know is that, and what's very concerning is that the, these four crops combined together to provide about 66 or two-thirds of the calories that we consume as humans. So the, so it's a really big deal, every one degree increase, if the smalls are, you know, somewhat accurate, and this is, this data is derived from multiple models that were published by this paper. It can have a really big impact on global food availability, food security, and other things that follow, which are more social and economic when there's not enough food. So what my research has been interested in is, is these two major cereal crops, wheat and rice, which wheat is the most widely grown crop in the world, and rice is perhaps the most important crop for food security as many nations and regions in the world that are not that developed and not rich depend on rice for the, for sustenance. So to give you an impact of how much temperature can impact, you know, affect the rice yield. So one of the components of yield is how big is the seed. And if you look at these, the seeds on the top are seeds of rice that we pluck out when it has only developed for 24 hours. And the next one is the one that we pluck out when the, you know, it's, it's 48 hours and then there's 72 hours and 96. So what is very noticeable here is that the seed size is increasing, you know, fairly strongly over each day. And that is increasing seed size is really important during this stage because if you do, if you disturb the stage, the seeds not going to become as big and then it's not going to have the potential to store as much as much carbon in form of starch and proteins and so on. So the, what we did do is that when we had the exact same collection, but after 24 hours, we put some of the plants at 42 degrees centigrade, which is fairly high temperature, but it's not uncommon to see that in many parts of the world where rice grows. So what we do see is, and these are comparable images, so you could see how big a difference there is in terms of a 96 hour rice growing in very good temperature and the one that's being heat stressed. So that was kind of very striking for us. And, and also when we look at these seeds or what are the outcomes of these seeds under, at maturity, which is when you let them develop after removing them from stress. What we found was that even a 35 degree moderate stress, if you look at the top panel, which is not stressed, and the second panel is the one that has 24 hours of heat stress and 48 hours of heat stress and 72. And then they're kept in good condition. So these are one, two or three days worth of stress at 35 degrees centigrade, which is very, very common in many of the rice growing regions in South Asia. For instance, so you see that there's a dramatic decrease in size and also the quality. And of course, if you have a stress as severe as this, you get a lot of deformity and pretty much no seed at all. So, so what do we do when we see that a crop that is so important for food security and is very, very sensitive during some stages of its grain development? How do we address this? And what are some of the solutions? So when you're thinking of solutions to kind of laid out broadly or somewhat broadly, you know, as temperatures rise, there's clearly adaptations that farmers have been doing in terms of planting times and your breeders have been also incorporating better genetics. But you know, there's, but there are decisions that the farmers will have to make in terms of regionally will have to make in terms of what they grow and at what time of the year they grow and, you know, whether they and where they grow. So, you know, whether they have to move some crops north and other crops south, you know, that that would really be, you know, one aspect of trying to address this rising temperature. The other aspect would be use of genetics. The genetics is very powerful because we do know that not all rice growing optimal conditions like 28 or 30, there are rice and wheat and corn that grow in very, you know, hot environments. So if we can, they may not be as productive, but if we can discover what helps them maintain, what are the genes and the proteins that help them maintain and survive in those hot environments, we could use generic technologies to discover that and then maybe even try to incorporate some of that. And that's modern plant breeding is all about incorporating those, you know, those traits into the crops. And so there's also technological improvements that can help with accelerating the genetics as well as independently promoting, you know, better mitigation of rising temperature on crop use. Besides that, you know, something that I won't discuss is also the, you know, as I said that it's difficult to, even though the global temperature is rising, it's difficult to address that. The, you know, at a global scale because at regional level, the farmer's going to know, am I going to get a heat wave? Or what do I do in this situation, which may be different from a farmer that's, you know, 2000 miles away from this particular region. So there's got to be some discussion and I'm sure there are discussions being held about building resilience at local scale that still, you know, takes advantage of the things and the trends that we know at global scale. So what I and my colleagues have been focusing on in terms of the genetics and rising temperature is the idea of high night temperatures. So the reason that we want to try and understand the plant responses to high night temperature a little bit better is because of this. So if you look at the, at the distribution of temperature anomalies for 112 years globally. So here are the anomalies for the daytime temperatures. So blue means cooler temperatures compared to the average and red means the higher temperatures. So the more brighter red it is, the higher the temperature is. And if you look at the nighttime temperatures of the, of these, you know, of the global landmass, the, what we find are two key differences. One is that the, there's a lot more of red on the nighttime. And that indicates that the, that the nighttime temperatures are more widespread. And the other aspect is that it is also very uniformly distributed. So there's not as many patchy patchwork of red as there is for the daytime temperature. So what that led us to think is, and you know, and it's spurred by evidence and discussion in the literature among the scientists is that the, that the, that the higher nighttime temperature might be a better trade to to try and understand and improve. And because it could actually be having a bigger impact rather than day temperature, it can go up and down and are very spotty. So this is the temperature distribution or extremes for, for the US. And you could see that the, that on the average maximum temperature would be the, the, would be on their, on your left and the average minimum temperature, which is the nigh temperature are on your right. And you can see that a lot of the red, which is the higher than average anomalous temperatures are, are, you know, for the nighttime and also if you look the, the number of years that have had higher average minimum temperature are a lot more since 2000. So that plus the fact that the, you know, when we think we're thinking of rice, there was a several papers that suggested that rice yields are very sensitive to nighttime temperatures. And that for every one degree increase in the nighttime temperature during the growing season, you could get up to 10% drop in yield. Not only is yield important for rice and many other crops, but so is also quality. In terms of rice, the milling quality is, is quite important. And for because it impacts the cooking and where and what price the farmers get for, for, for their crop. So further as hybrid rice takes off, which is basically about 90% of what us farmers grow for rice, you know, it is even more susceptible to, to heat stress in general. So what we did was we, we put together a team of scientists that was as diverse as I can possibly imagine in terms of people from computer science, quantum genetics, metabolomics, image analysis, people who like me who do a lot of her work in the greenhouses, as opposed to people who do a lot of their work in, in the field, agronomists as well as educators to, we put this team together and we seek funding from NSF, which is the science foundation to build a center for wheat and rice, for heat resilience. And this is a multi state team. And our goal was to, to try and search for rice varieties and the genes that underlying those rice varieties that may be more tolerant to higher nighttime temperature. So the way we started doing that is that we built a set of infrastructure, which is as far as we know is very unique. It's in the country and probably globally of these large tents. So this is a wheat winter wheat tents set in Kansas state, where we keep these tents on top of the crop and we grow a large variety of winter wheat that are available publicly. And we test them by closing these tents during nighttime and increasing the temperature by three to four degrees. What we found was that on average, winter wheat, you know, this includes about 30 of the varieties that are from the Nebraska's wheat breeding program. We find that about 5% decline in yield for every one degree is in temperature, which is only imposed during the flowering. So a similar experiment and set of tents are also being run in the, in Arkansas state University team. And we are also finding a lot of interesting rice lines that seem to either be very sensitive or very tolerant. And what that helps us do is helps us take our next step in terms of trying to figure out why they are tolerant or sensitive. So the way we are doing this for rice, which is going to be the focus of what I will talk now, is that we are using about 400 varieties of rice from all over the world. As I said that not all many parts of the world grow rice, but not all of them have optimal temperatures. So we're hoping that from studying these rice varieties, we can discover new insights that could lead to genetic discoveries of high night temperature tolerance in rice. So we take these varieties. These varieties also have a lot of genetic resources. So basically DNA sequencing information available for these lines. And we tested them in our greenhouse conditions in Nebraska. So we heated a greenhouse at night time and kept a parallel set of plants without heating. You know, so basically good temperature. And what we wanted to see was the flower of rice kind of going back to the theme of, you know, rice flowering. What we find, so for that, we needed to develop a set of imaging systems so we can very, you know, in detail manner, very minutely pick up changes that we otherwise could not pick up from our, you know, naked eyes. So for this we worked with our team members from computer science to develop this system for imaging just the rice flower, which is called the panicle because it's the panicle is the seed bearing cluster or flower, you know, of organ of rice. So this imaging system will go around and take images and give us ideas on how the rice is developing under high night temperature. So we did this for several hundred rice lines and so which eventually turns out to be, you know, several thousand plants. You only see the panicle here because the rest of the plants underneath where it's alive and we image it again and again over the grain development. So doing this, we, using these multiple images from all angles, we can 3D reconstruct the panicle which is shown here so that we can look at the surface area and other features. So using this information we were able to derive about 30, 35 digital traits. These are traits that are not easy to describe but the software that we developed can extract them and we should see that they are different among different varieties. So we use these two map and here's an instance of how we map. So it's basically a genetic association map. So it shows the 12 chromosomes of rice and each of the chromosome has many, many thousands of mile markers. So accumulatively we have about 1.2 million sequence, DNA sequence based mile markers and we ask the question, our software stops at each of those markers and asks the question whether, you know, is if you have this marker instead of that alternate marker does it make a difference in this trait? And, you know, using that we were able to discover, for instance, one gene that has been known to, you know, to determine how tight this cluster is. So if you look at the one, if this gene is deficient you get this very tight cluster otherwise you get this very branchy. So that shows that our method is working and so at the, at moment we are falling up on three or four of these genes to see what their role is in, in heat tolerance. In addition we also looked at the grain size and grain size of course is a contributing factor to yield and what we discovered with a similar approach but just, you know, looking at just single grains instead of the whole grain cluster on the flower is the, that the genes that we see under control conditions that control the grain size are not always the genes that can do the same for under under heat stress. So one of the genes that we discovered from this is called the, the phi 1. The phi 1 gene shown here as a marker here is not seen when there's no stress. So essentially it's on chromosome 8 and what we next saw was that there's two genetic variations of this gene.  What we found very striking was that the, when you expose these different variants to heat stress the varieties that have the type 1 variant seem to activate this gene whereas the varieties that are having the type 2 variant deactivate this gene. So that was very striking so what we did next was we used the CRISPR technology to make mutations in this gene and what we find is quite striking so I'll show you the, this is your control which has no edits and it looks, the starch looks quite crystalline and it's got this very nice structure but if you heat stresses you see how the starch turns into these small globular things with lots of air, you know, air packets in there which kind of shows up as browning when you look under a light box. However if you have this gene mutated even under control conditions you see this very disrupted packaging of starch which means it's not fully packed in so it's not as nutritious so even though it may look big it may, the seed may look regular size but inside it's not got the nutrition in the right packaging as well as in the right amount. So this is one instance of how we are using both the imaging technologies and also the genetic technologies in terms of trying to discover what makes rice you know more sensitive and also tolerant to high night temperature. So as to summarize you know we've talked about carbon dioxide and higher temperatures we also talked about imaging and discovery of genetic variants using imaging and more importantly what we do know is that the chances of breeding for higher temperatures needs to be done in higher temperatures not under optimal conditions and of course you know the story that I briefly mentioned about the phi 1 gene with this I want to thank the tremendous amount of work and that's been put in I have the good fortune of working with you know these talented lab members this is our team from the NSF funded project from Kansas, Arkansas and Nebraska and also you know what makes the truly a pleasure to work in Nebraska is you know many of these people listed here who have had the great opportunity of learning and learning from and working with and I really have enjoyed that experience with that thank you for your attention and I'll be happy for any questions. Well thank you Harkamall for sharing this research with us we should all be concerned about how these temperature changes could impact our global food security and we should be proud as a university to be conducting world leading research in this area that are part of two of our grand challenges in our in 2025 plan because it Nebraska is such a strong agricultural state these shifts have the potential to affect so much about our future here thank you to all our viewers who joined us today to learn about changing climate warmer nights and crop yields you're certainly welcome to submit questions using the Q&A button at the bottom of your zoom screen or by emailing unlresearch at unl.edu so Harkamall great lecture wonderful wonderful presentation of what is a major issue and and challenge for us ahead in global food security and you know certainly your work in the in the major cereal crop arena is a big part of that you know I want I wanted to start maybe to with a few questions that might help our our viewers understand this the way the science has developed in recent years you talked about working in rice principally also working in wheat in your program these these crop species so if you take rice for example or wheat what what has the technology looked like in terms of the ability to do the kinds of genetics work that you do how has that developed so for for example you know rice and when we help how well annotated are those genomes you know what what's the platform really that you have to work yeah I think the that's a very good question Chancellor Green I think the the fact that rice is is such an important crop but also the good fortune that when the genomic technologies but sequencing technologies were just taking off rice also has a very small genome so it was the first in 2004 it was the first crop genome to be sequenced and that really that combination of genome sequence and not only just having the sequence but knowing where what what is basically the genetic landmarks on the long strands of chromosomes was very important so from that perspective rice today has more than four thousand genomes that have been re-sequenced which which is the biggest resource among all the crop plants that we know you know similar progress to you know the genomic progress has also advanced in the last few years for wheat which has several times bigger genome even a bigger genome than what human genome is so they create that creates a lot of challenges however there's a good good conservation of the order of genes that occurs in many of these crops so it's not a perfect order but it generally gives us a sense of if we discover things in rice that can itself have a very tremendous effect but we can with some level of certainty can go to and look for genes in the corresponding region in wheat so just to clarify it's not something that you know you could go okay I know this so I definitely know this but what you could get is that if this kind of traits in this neighborhood in the rice genome you kind of have you know a broader zip code if not the exact address in many cases so it really and you know the with the sequencing technology almost becoming cheaper and better and more accurate nearly every year your things are just getting going to get better and better if what we do really need in terms of research such especially in terms of temperature would be better infrastructure because in for instance in case of drought you could hold or you know withhold or impose or irrigate and you can do the treatment whereas in case of heat stress it's gonna be very difficult so that's something that we are you know the genomics is there but the the technologies on the infrastructure side would be where the the gap is in all all transparency in her come on those this it's hard for me sometimes to relate to our students today that it was when they were being born you know it's not that much time is hard to believe it's a lapse already when the human genome was sequenced in the early two thousands and all of the platform development that that enabled us to lead to to get you know a higher density information these genomes of rice you're right was was the first big one in the plant arena I remember really well here at Nebraska in 2010 I think it was the International Wheat Genome Sequencing Consortium that your colleague Steve Banzier was involved in helping you work with as well so it's great to see these technologies continue to develop in that way so you you've chosen to work in rice very heavily in rice and you are working in that before you came to Nebraska as I remember in your initial time here why you talked a little bit about the importance of rice and the importance of it as a staple in the world food diets you know talk a little bit more about why you initially focused on on working in rice yeah so a large part of my research program is in rice and that's because of several reasons we've discussed the increased access to the genomic resources but what's also quite powerful in rice as a crop and then there's other crops to that do that but you know is the ability to do genetic transformation so basically it's very very easy for most labs to make gene edits which basically would change DNA sequence and of course it requires transgenics but you would you can remove the trans genes it'd be a more of a gene edited technology so that in rice is quite fast and you could do it in many genetic backgrounds which really is not something that is as tractable for instance in wheat because of just the genome characteristics it's called polyploidy where you have multiple genomes in the same nucleus which really you know creates some complication in terms of it's doable but it's complication in terms of targeting specific changes. My plant breeding friends for years have told me how easy it is to work in a diploid as compared to a hexaploid right just as you mentioned. So you talked in your presentation about your work on nighttime temperatures as you know the increase in nighttime temperatures being a stressor that is what you're really evaluating against and you talked about rice work do you do we know much about that in maize given the importance of maize here in Nebraska? Well there's not been as far as I know there's not been a very detailed study but what we do know is through several modeling studies that had taken historic data for maize what we know is that the years such as 2009 had cooler nights and similar rainfall to the years such as 2010 in Nebraska Iowa in the region and the however the July and August temperature of 2010 was on average about five times warmer at night night and that was one of the reasons they think that the the yields of maize in the Midwest or in Nebraska Iowa were lower even though we got the same amount of precipitation just the nights were warmer besides that there is some evidence from historic data where they've shown that the for instance the counties in Nebraska Iowa and Indiana for Illinois the the counties that had are hotter were showing in general a less degree of yield increase from growing similar maize varieties so the counties that had lower temperatures showed greater yield gains even though the yield gains were there where they were not uniform and the counties in the southern part of for instance Nebraska had lower yield gains and they think it's because of overall higher temperature the that study did not separate the daytime from the nighttime right right you talked you mentioned a few months ago and you mentioned it in your talk gene editing technology CRISPR it was the the mechanism for that do you do you see a lot of promise in that technology relative to being able to to develop you know crops and crop varieties that will be able to respond better to these council stressors yeah I think the the gene editing technology in this form or in a in a future more targeted form is going to play an important role the the challenge would of course be that you can make a change in one variety background it's not guaranteed it's going to have the same effect in an in another background so that's something that you know needs to be addressed because it's called the genomic context so you can make a change but may not have the similar you know impact in terms of let's say in terms of high night temperature tolerance so that's something that would need to be addressed but I definitely think that unless something dramatically change improves in in with a new technology which we don't know we may right be happening you know as we speak but one form or the other of better targeted gene editing without any trust gene is going to you know be part of our future agriculture and then you had one I was struck by one of your your graphics where you talked about the four or five ways that we could address this issue right adaptability being one of those genetic improvement they wanted amongst others so it how have we have we really understood yet fully what the adaptability part will mean by adaptability there I mean adaptability of the environment in which crops are being produced you know it's been striking in recent years the growth in the world and in hardiness zones if you want to think about it that growing season conditions and corn being grown in parts of Canada that corn was never grown in you know historically and that kind of adaptation so there's a combination of all of these things right that you see how that will happen is do you have any thoughts on what the relative importance of those might be where we grow and how we grow versus you know just the genetic improvement itself yeah I think that that will be one of the major players of in terms of how we react to these changes farmers you know farming is one of the first organized operation of you know our civilization that's how we built these civilizations around you know river valleys and you know where water was there so I think the adaptation definitely in the in terms of the the temperates zone not as much in the tropical would be quite possible in fact I think that the based on what I've read you know I'm not a climatologist but from what I've read the the the latitudes around 40s is where the maximum gain may be from even increasing higher you know CO2 fertilization basically if carbons what's limiting you know having more carbon in not as warmer temperatures is going to benefit agriculture and of course you know as we push you know a kilometer up north in terms of a range of a crop across the US or Canada is a fairly large you know band of land and for productivity what I think from my understanding of literature is that the tropics where the species are not as custom to large variation in temperature like what we are and this you know plant species in Nebraska that's where maybe there's a lot more challenges so so yes I think the the primary front since it's farming the farmers would be the prime you know the the main front and the center of this adaptation backed by better technology predictions and genetics also was struck and if we've had a couple of questions coming in during your talk about there's a lot of discussion currently in our society about how well understood this problem is climate change in general right climate change and variability and the change in that over time and causes of that over time and adaptation to that is is not one of the better understood than things culturally but as an observation you know I've certainly noticed in the last 10 years in Nebraska the production agriculture community embracing this at a much higher level and understanding this issue and the way they have understood it is by experiencing it right there you know I've thought about Keith Keith Hearman the chair that you you hold and great honor that you have to hold the hearman chair of agronomy longtime farmer in Nebraska who understands this and who sees this I think that's very very very positive as well so harkam all wonderful lecture congratulations to you on your your work and your continuing work and addressing these challenges we're so proud to have you on our faculty at the university in Nebraska Lincoln and at work that you're doing and on a wonderful lecture so I'll I'll ask everyone virtually to give you a big round of applause for a wonderful Nebraska lecture and thank you for being with us thank you Chancellor thank you


Teaching English Abroad, What's Next, How to Advance a NuriFL Career Many people start teaching English as a means to fund their world travels. Once they have started teaching, they find it to be an immensely rewarding experience and would like to make a career out of it. They like to progress within the field but are unaware of the options available to them. In fact, there are many opportunities in TEFL slash TEES all beyond the classroom and below we show some of the places a career as an English teacher can take you. Here's how to advance a NuriFL career. Teacher Trainer After teaching for a couple of years, many teachers move on to this position. A teacher trainer usually works for a TEFL or TEES all language center and teaches the modules part of a teacher training course such as grammar, phonology, and lesson planning. Teacher trainer jobs can be found advertised online and it's also a great idea to keep in contact with the training center where you completed your training to see if and when they need new training staff. Director of Studies The director of studies, also referred to as DOS, is a senior managerial position in a school. A DOS in an English language school oversees the academic programs, manages the teaching team, provides support and training for teachers, and also helps the school to grow. School Manager A school manager, also known as center manager, manages an entire school or language center. The responsibilities for this position include managing anything from staff, students, courses, budgets and more. Usually, you can become a school manager as part of an English language center franchise program available abroad. Teacher Recruitment Once you have spent a few years working as an EFL teacher abroad and have developed a detailed understanding of the local EFL job market, you can move on to become a teacher recruiter. This is really a great way to take your EFL career in a different direction. Recruiters work for schools to provide suitably qualified teachers when needed. Recruitment agencies operate both abroad and back home, so you can often choose where you would prefer to be. Coursebook Sales Language schools around the world are always looking for new teaching materials on the market. That's why coursebook sales can be another great way to move beyond the classroom while remaining in the EFL industry. Networking in your host country and the ability to speak the local language will be tremendous advantages. Coursebook Writing Fact is, a lot of writers of popular EFL coursebooks actually started out as English teachers themselves. Once you have a couple of years of teaching experience, you'll have the necessary insight into what would make a quality coursebook for both students and teachers alike. Then, you can get started in writing your own and either self-publish the book or go through a publisher. TOEFL Slash Alts Examiner Alts and TOEFL are the two biggest examinations for English learners around the world. Once you're an experienced English teacher and maybe even taught prep classes for one of the tests, you'll be able to become a test examiner. If this is something you are interested in, you will need at least an undergraduate degree and a TEFL qualification. Check out the official TOEFL or ELTS websites for more details and the exact requirements. Opening your own school While it might seem like a daunting task, opening your own language school abroad is something a lot of English teachers are successful at. You will need to know the local regulations for setting up a business as a foreign citizen in order to open your own language school abroad so we recommend getting in touch with the local embassy or Chamber of Commerce if this is something you are interested in.  Without a doubt, there are plenty of options when it comes to taking your teaching career abroad to the next level. We hope that the ideas above are helpful in finding out how to advance in your EFL career. Speak with an ITTT advisor today to put together your personal plan for teaching English abroad. Send us an email or call us toll free at 1-800-490-0531 to speak with an ITTT advisor today.


Oh, that's blinding. OK, I guess we are at time. So hello, everyone. Thanks for joining here today. My name is Andrea Frittoli. I work for Yulet Packard Enterprise. I'm QA tech lead for the helium distribution we make there. And I'm a member of the core QA team for OpenStack. So today I will be talking about stable interfaces in Tempest and how to use them for OpenStack integration testing. One logistic question. I may say Neutron or Newton or understand either of them. There was an email actually in the distribution list, April 1st, about renaming Neutron to Neutron to Quantum. But maybe it wasn't such a bad idea. Does this work? Right. So how many of you in the room are familiar with QA for OpenStack at Tempest? Well, quite a few, but probably half of you. OK, well, I'll provide some more details. Basically, what we do in the QA team, our official mission statement is to provide tools and initiate projects and initiative to ensure that OpenStack quality is maintained at any time during the release cycle. So our job is to make sure that you can go and take OpenStack at any point and you can find the quality you would expect. If I learn how to use this. Right, so we have a number of QA projects that we maintain to achieve that. Kind of group them by topic. So the first three are related to kind of syntax checks that you may want to use in your project to check your syntax for your JSON code, of your Bash code, or of your Python code, whatever technology you use in your project. Then Tempest, Tempest, Leib and Grenade are really about tests and test frameworks. So they contain the framework and the test to verify your deployed cloud and upgrade in the cloud in case of Grenade. Dev stack and related are projects which allow you to deploy a cloud, not a production cloud, maybe, but a cloud which is good enough for testing purposes. Then down towards the bottom stack, bits and OpenStack health dashboard are related to actually the collection and visualization of test results. Plus, we have other tools like OS Teststar, which is a wrapper around Teststar. To make Teststar nicer for our needs, basically. Some cookie cutter that you can use to develop start development of plugins. And the rather new OS performance tools, which is a combination of Python tools to collect performance data from the cloud. So what is Tempest? So Tempest is the integration framework, test integration framework for OpenStack. So it's strictly black box testing, so API driven testing. And it includes the framework and the test which are executed thousands of times every day in the gate. So whenever you submit a patch to some project in OpenStack, it's quite likely that some DevStack cloud will be started and then Tempest test will be executed against it. So when Tempest started, the scope was relatively little. So a handful, a little more of projects at the time. Then more and more projects started to be incubated and then integrated into OpenStack. So we started having more and more tests to include in Tempest. And more knowledge also needed, required in the QA team to manage those tests. So we already started at that time to realize that the model didn't scale very well. But then when the governance model changed to the big tent, I think at Liberty Times, then, well, we realized it really didn't work like that. We couldn't provide enough QA coverage for all the projects that were coming into OpenStack at that point in time. So we had to change model. So we decided that we should use a more distributed approach of QA and, as a QA team, only maintain the tests for a core, small core of services and let all the other projects care for their own QA using the tool that we provide. So the first initiative that we started was Tempest Lib. So it was initially Tempest underscore Lib, as Matt can tell you. So the idea was to create a stable interface within Tempest. So a part of the interface that we guarantee is backward compatible that other projects can consume and use to create their own test. And we wanted to do that within Tempest. However, the Tempest namespace was taken on PyPy, so someone else was having some Tempest project. So we couldn't do that. So Tempest Lib, as a separate repo, was born at that time. Recently, Matt managed to get the Tempest namespace back because it's not used anymore. So the project that used that name is actually not alive anymore. So we moved back to a namespace within the Tempest repo. Later, so that was towards the end of Liberty, the plug-ins interface was introduced in Tempest as well, which allows you to define a set of tests and configuration items that can be loaded then in Tempest. So what are the projects that we support? So Kiston, Nova, Glanz, Sinden, Neutron, and Swift. These tests are part of the Tempest suite, and they're executed as part of the integrated gate, which means that every change which runs integrated gate runs all the tests for all these projects. So where do the other tests leave? So they're a combination of things. So there are several Tempest plug-ins out there in different repositories. There are CLI tests for several Python clients for the different services, and there is a combination of other functional and integration tests that they use different parts of Tempest stable APIs. Those tests, they are not executed against changes in Tempest. They are only executed against changes to the repositories that own those tests. And because of that, they should only use the stable interface of Tempest because otherwise, they have no guarantee that they will break at some point. So six months ago, we presented Tempest plug-ins, and this was the situation in Tokyo. The interface was pretty new. So there were only four plug-ins out there. But now, it's a bit different. So we are almost at 30 plug-ins available, which is a good number. I don't know if you can read the names there. Maybe a bit small. But something that I wanted to note is that you see the name of the repositories there in the graph, or how does this work? Anyways, you see the name of the repositories there in the graph where the Tempest plug-in is actually hosted. And I would say probably 90% of the cases, the plug-in is hosted within the repository of the service they want to test. So there are some exceptions. Sahara test, Intel, NFV, CI test. But those are probably not even plug-ins. Oh, no, they are plug-ins. But yeah, those are the two only exceptions that I can see. So while there is a certain degree of convenience in hosting the plug-in in the same repository as the service because it allows you to change the service API and the plug-in. So the test at the same time with one change. There are also disadvantages in doing that. One is that in terms of dependencies, it is not possible while the plug-in is hosted in a repo for a service to declare the right requirements for the plug-in. So it means that anytime you want to go and install a plug-in, you actually have to install all the dependency for the service that the plug-in is testing. So this may not be a problem if you're just a single node DevStack. But if you start to consider more different deployments where you have a cloud, an actual cloud, and a test driver, then you end up installing a lot of dependencies on your test drivers that you don't need to install. And the other thing is that Tempest is branchless. So that means that we run the same version of Tempest against all stable branches that are supported in OpenStack. And if you write a Tempest plug-in in the repository of a service, which is branched, it means that your plug-in will be branched as well, which is not consistent with the model we have in Tempest. So you will run into problems eventually when the next release comes out. Same graph type of graph for CLI tests. So over time, you see that different Python clients have started to introduce CLI tests based on the stable interface. Other tests that I could find. So there are a couple of Tempest plug-ins which are almost ready, not used yet, as far as I could tell from Kingbird and Vitrage. There are several Tempest tests in different repositories which could become plug-ins eventually. I know, for instance, the designate team is already working on creating a plug-in, proper Tempest plug-in in a dedicated repo. So other type of tests. So there are some test suits which only use part of the available stable API, like the REST client and a little more, which is good to see because the idea of having a stable interface was exactly that to have something out there that people can pick and choose and use what they want, what actually best suited for the type of testing that they need to do. So I've seen examples where the REST client is used and maybe other interfaces are mocked to provide more efficient testing for specific projects. So what are these stable interfaces, in fact? So this is the full list, I hope. There are clients and authentication providers and many more. Rather than going through all of them one by one in this list, I wanted to show how they are used across different repositories. So what I'm showing here is the number of repositories that use a certain interface at least once via an explicit import. So it was interesting to do this graph because basically I could verify that pretty much most of the interfaces that we expose in Tempest Sleep are being used by projects. So things like the REST client. It works somehow, I don't know how. Well, things like the REST client is used by a good number of projects. Decorators, exceptions are used as well a lot. The second most used, actually, it's interesting, is utils. It's a simple thing that we expose that is a method basically to generate test data, things like random name or random password. Seems to be pretty popular. Down towards the bottom of the graph, I don't know if it's readable, but you can see for instance things like auth. You would expect auth to be used by most projects, but in fact it is not explicitly imported. It is used but indirectly. There are some other interfaces within Tempest that basically allow you to hide that and avoid the need to call that interface directly. There are other interfaces that are not yet stable, but they are potential candidates to become so. So some of these service clients, they are not yet in the stable area. We're working on that to make them stable. Credentials providers, those are tools that we'll describe more later, but basically about getting interface credentials that you can use in your tests. Client managers to collect all your clients in a single object. And actually the plugin interface itself, which is not stable yet. It's not in the stable area, but it is in fact stable. So it's pretty much just a matter of moving it over. And while the attribute decorator is pretty much used everywhere, so it may be worth moving it over. It's very small. It's probably five or six line of code, I don't know. And the same graph here for internal APIs. There are a lot of them that are being used. So the config one goes along with the plugins one. There is a little utility again, three or four lines of code in the config module that allows you to register config options. And that is used by pretty much old plugins. That's why that's pike on the config. And then test is the attribute decorator again. It's creating that spike over there. There are other interfaces which are used that are not stable, like the manager for the scenario tests and so forth. So we may consider moving them to stable in future. And if you are writing integration test and there is an area of tempest that you're using that you think should be in the stable area, please let us know. And also you're welcome to contribute to making that a stable interface. Basically the requirement we have for interface to be stable is that, first of all, it should not depend on configuration because we don't want consumers of the stable interface to depend on tempest configuration. And secondly, once we move an interface into the stable area, we guarantee backward compatibility so we should provide a decent level of documentation and good interface that will not change in future. So to provide some more details about the different interfaces, I wanted to go through an example of how to write a tempest plugin and where these different interfaces come into play. So the first is the tempest plugin interface itself. That's an interface that allows you to specify to tempest where to find a group of tests that are external to tempest and to provide a set of configuration items which are specific for those tests and no native to tempest. We plan in future to do some extension to these which will allow to integrate custom service clients. So if you have your service which comes with its own specific service client, then you can integrate it via the plugin interface. Plugins are discovered, oops. Plugins are discovered via Steve Door which means that as long as they are installed, and visible to tempest, they will be discovered and the test and the configuration option will be loaded and become available. Normally we install tempest in a dedicated virtual environment and there is one environment specific environment in a tempest, Toxini which installs, creates a virtual environment with a site enabled which allows tempest to discover plugins which are installed system wide. So externally from the virtual environment where tempest is installed. There is also something that I wanted to note that once the plugin is installed it will automatically become visible to tempest and if you don't want to run the test you should not install the plugin because otherwise there is no way that you can basically install the plugin and configure tempest not to run that plugin unless you specify then a regular expression to filter those tests off. So this is an example from Manila of how it may look like. So basically in the top line you can see that all you need to import is plugins from tempest.test-discover. If you want to use the register-opt-group method then you can import it from tempest config as well. Okay once you have your plugin structure in place one thing that you may need to do is to write your own service client. That's not necessarily the case for all plugins so you may want to write a plugin where you just bundle together a bunch of neutron tests that you don't want to run in the integrated gate and put them in a plugin. In that case you don't need an extra service client but in most of the cases the plugin will be associated to a dedicated service and then you will need an extra service client. So to do that you can use the rest client interface which provides method to run all the different HTTP methods to decorate them using the auth providers. It provides you with validation of HTTP codes and also for with handling of non-200 return codes. In terms of service clients, as you've seen before a number of the service clients for the core services are already part of this stable interface. The remaining ones are being moved over and they provide a single method for specific APIs. So for each API available from each of the core services you have a dedicated method and they allow you normally to pass any parameter to the API code so in case you want to do fast testing or negative testing or play with the parameters that you pass into the API you can do that. This is an example, the telemetric client that is now in a plugin and shows you basically importing from Tempest sleep come on the rest client, you can define your own rest client. What they do in this specific client plugin is also to define an extra manager and you can see below that in the manager object the telemetric client is instantiated by passing the off-provider in plus a set of parameters. So where do you get the off-provider from? The off-provider comes from the authentication layer in Tempest so the authentication layers provides you tools to basically encapsulate your credentials for your test accounts into credential objects, provides facilities to select the end points from the catalog based on different filters like regions and end point type and so forth. It allows you to decorate requests with all information for V2 or V3 and also to do things like injecting alternative data if you want to play in your test with the off data and mix proper data, proper tokens within valid data. So to verify things like a tenant cannot access other tenants project, I should say, resources. This is an example of using the off-layer so it's in Tempest's leap of an interface which is not yet part of the stable interface but we are working on making it stable interface is the client managers. The client managers provide you one object which is bound to a set of credentials and for that object you can get access to all the service clients which are registered. It's nice to have in the sense that it hides the complexity of the off-layer so if you have a client manager object you can initialize it with a set of credentials and it will provide you with all the clients, initialize with the right off-provider and everything that you need so you don't have to worry about anything else. So what we're working on is a way to register service clients from plugins into the existing managers from Tempest so you can get them from there and also lazy loading of clients which means that if you get a client manager object not all the clients will be initialized which makes sense because in some cases you only want to test your own service so maybe your service plus Kiston or plus Nova and you don't want to have all the config parameters that are required to initialize all of the other clients. Again, an example, the managers are in Tempest.manager and in Tempest.clients as well there is a more sophisticated version and finally credential providers. Credential providers are also not yet in the stable area of Tempest but we are working on moving them there. They allow you to get set of credentials for your tests. When you want to run multiple tests in parallel then if you want to keep them isolated you may need several credentials maybe one or two credentials for each parallel stream that you're running. So these credential providers give you a way to have access to this set of credentials. They also manage network resources associated to those credentials. There are two of them. Actually one is the dynamic credential provider formerly known as tenant isolation but now tenant is forbidden. So as long as you have access to admin credentials and you are allowed to inject your admin credentials into your Tempest config file then you can use the dynamic credential providers which will create credentials on the fly for your test. So each test class when it starts it will get one or two sets of credentials. The other alternative if you don't have access to admin credentials at test runtime or simply if you just want to reuse test accounts multiple times which is a valid use case as well is to use the pre-provision credentials provider. In that case you create the credentials that you want to run to use for testing before running the test. You store them into a YAML file and you feed the YAML file to Tempest and the pre-provision credential provider will allocate them taking care of the locking and make sure the credentials are not used by two different tests at the same time. Finally if you're working on a new service or an existing service it may well be that your API supports micro versions and so if you're writing tests for micro versions you can use a set of utilities from Tempest Lib to deal with that. They allow you basically to define the acceptable range of micro versions for the test that you're running so you can say this test is only valid between version X and Y. They allow you to match that against the configurable range of micro versions as well and they select accordingly the micro versions to be sent via the API when you make requests. This is an example actually from Tempest Nova tests of using the micro version framework. So yeah, there are several other Michelinius utilities in the Lib area. So one for to generate random test data which is pretty popular. You have an SSH client you can use if you want to do validation on VMs. We have skip decorators that you can use to skip tests based on different conditions like service not being available and test attributes, well that's not yet stable but as I was saying it's just a few lines of code and it's widely used. The same for CLI tests. So what kind of stable interfaces are available to you if you want to write tests for your Python clients. So there are four pieces that you can use. One is the execute command that give you access to running basically an external command like Nova or Newton or so. You have a CLI client class which is a wrapper around that which exposes, it wraps around the execute method basically it gives you a nicer interface. We have an output parser which allows you to parse the output, the console output of clients that like in tabular format for instance and the client test base which is the base test class that you need to implement to run the tests and that provides you with the link to the former free already in. This is an example from Mril CLI test. The only thing they need to import in there is actually base because once you get the base class then you get everything else from it. Right and that's all basically. So this is a list of more references. The documentation from the developer documentation of Tempest, the plugin documentation, the link to the source code for this presentation and yeah, you can find us on OpenStack QA on FreeNode and I don't have an idea what the time is. There is yeah, there is still some time for questions. So if you have any question, you can ask them here now or just find me later as well. There are two microphones in the two lanes. So if you want to ask any question please use the microphones. Just one quick question. Are there minimum requirements for the cloud that you're running Tempest against as far as memory, CPU and where you're running Tempest from as well for it to operate properly? Right, so in terms of sizing of your cloud it depends very much on the concurrency, the level of concurrency that you are using in your test driver. So if you want to run multiple tests in parallel you may need more resources in your cloud of course. But we found that if you go beyond I think the level of the four parallel streams you don't gain very much in terms of the time it takes to run your tests. You only gain in the amount of stress you may put on your cloud just because there are some of the tests which are long running. So then the rest of the tests take less than the single test to execute. In terms of connectivity it really depends on how your cloud is configured. And so the minimum requirement that you need to have is that you need to have connectivity to the endpoints as they are specified in your catalog. So as long as you have access to the core endpoint for authorization then you can download the catalog and if the URLs in there are reachable by Tempest then you should be able to test. There are some other requirements that you may incur into if you want to do things like validating your VMs. So if you, for instance, start up servers and you attach floating APIs to them then you need to make sure that your test driver is somehow connected to the network which is providing floating or public APIs to your VMs. But otherwise that's all you need. So the tests are strictly black box so you only need access to the APIs and optionally to the VMs. Does this compare against Rally which is another popular testing framework for OpenStack? Well, they're separated, so to say. So Rally is part of its own program. It's not part of the QA program in terms of governance. There are some parts that overlap between what we provide here and what is provided in Rally. I'm not an expert in Rally, to be honest. I don't want to comment too much on that. I know Rally provides tools for benchmarking that are not covered in the current Tempest tree. And yeah, so if there are no more questions, I think it was a long day for our first day in the summit. So thanks everyone.


Good morning friends. I am Poorva and today we will work out the following question. State which of the following are not the probability distributions of a random variable. Give reasons for your answer. Now in a probability distribution, some of all probabilities is 1. So this is the key idea behind our question. Let us begin with the solution now. Now this is the probability distribution table in the first part. Now in this table we are given that when x is equal to 0 probability of x is equal to 0.4. When x is equal to 1 probability of x is equal to 0.4. And when x is equal to 2 probability of x is equal to 0.2. Now by key idea we know that in a probability distribution some of all probabilities is 1. So here we will find the sum of probabilities. Now the sum of probabilities that is px is equal to 0.4 plus 0.4 plus 0.2. And this comes out to be equal to 1. Now since the sum of probabilities is equal to 1. So we have thus this is a probability distribution of a random variable. Now this is the table given to us in the second part. Here we have when x is equal to 0 probability of x is equal to 0.1. When x is equal to 1 probability of x is equal to 0.5. When x is equal to 2 probability of x is equal to 0.2. When x is equal to 3 probability of x is equal to minus 0.1. And when x is equal to 4 probability of x is equal to 0.3. Now sum of the probabilities that is px is equal to 0.1 plus 0.5 plus 0.2 plus of minus 0.1 plus 0.3. And we get this is equal to 1. Now here we have when x is equal to 3 probability of x is equal to minus 0.1. But probability cannot be negative. Therefore this is not a probability distribution of a random variable. This is our third part. Here we have when y is equal to minus 1 probability of y is equal to 0.6. When y is equal to 0 probability of y is equal to 0.1. And when y is equal to 1 probability of y is equal to 0.2. Now sum of the probabilities that is probability of y is equal to 0.6 plus 0.1 plus 0.2 And we get this is equal to 0.9. Now by key idea we know that in a probability distribution sum of all probabilities is 1. So here we have since the sum of the probabilities is equal to 0.9. So this is not a probability distribution of a random variable. Now this is our fourth part. Here we have when z is equal to 3 probability of z is equal to 0.3. When z is 2 probability of z is 0.2. When z is 1 probability of z is 0.4. When z is 0 probability of z is 0.1. And when z is equal to minus 1 probability of z is equal to 0.05. Now the sum of the probabilities that is probability of z is equal to 0.3 plus 0.2 plus 0.4 plus 0.1 plus 0.05. And this is equal to 1.05. So again by key idea since the sum of the probabilities is not equal to 1. So we have this is not a probability distribution of a random variable. So we have got our answer as second, third and fourth. Hope you have understood the solution. Bye and take care.