Hello, everybody, and thank you for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled, Machine Learning with Vertica, Data Preparation and Model Management. My name is Sue Leclerc, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Wakas Dylan, part of the Vertica Product Management Team at Vertica. Before we could begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address will do our best to answer offline. Alternately, you can visit Vertica forums to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also, a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to view on demand later this week. We'll send you a notification as soon as it's ready. So let's get started. Wakas, over to you. Thank you, Sue. Hi, everyone. My name is Wakas Dylan, and I'm a product manager here at Vertica. So today we're going to go through data preparation and model management in Vertica. And the session would essentially be starting with some introduction and going through some of the machine learning considerations when you're doing machine learning at scale. Remember that we have two major sections here. The first one is on data preparation. So we'd go through what data preparation is, what are the Vertica functions for data exploration and data preparation, and then share an example with you. Similarly, in the second part of this talk, we'll go through important export models using PMML and how that works with Vertica. And we'll share examples for that as well. So yeah, let's dive right in. So Vertica essentially is an open architecture with a rich ecosystem. So you have a lot of options for data transformation and ingesting data from different tools. And then you also have options for connecting through ODBC, JDBC, and some other connectors to BI and visualization tools. There's a whole lot of them that Vertica connects to. And in the middle sits Vertica, which you can have on external tables, or you can have in-place analytics on ARC, on cloud, or on-prem. So that choice is yours. But essentially what it does is it offers you a lot of options for performing your data analytics on scale. And within that, data analytics, machine learning is also a core component. And then you have a lot of options and functions for that. Now, machine learning in Vertica is actually built on top of the architecture that the distributed analytic database offers. So it offers a lot of those capabilities and builds on top of them. So you eliminate the overhead of data transfer when you're working with Vertica machine learning. You keep your data secure, storing and managing the models. That's really easy and much more efficient. You can serve a lot of concurrent users all at the same time. And then it's really scalable and avoids the maintenance cost of a separate system. So essentially a lot of benefits here. But one important thing to mention here is that all of the algorithms that you see, whether they are analytics functions, advanced analytics functions, or machine learning functions, they are distributed not just across the cluster on different nodes. So each node gets a distributed workload. On each node, there might be multiple threads and multiple processes that are running with each of these functions. So highly distributed solution and sort of one of its kind in this space. So when we talk about Vertica machine learning, it essentially covers whole machine learning process. And we see it as something starting with data ingestion and doing data analysis and understanding, going through the steps of data preparation, modeling, evaluation, and finally deployment as well. So when you're using Vertica, you're using Vertica for machine learning, it takes care of all these steps. And you can do all of that inside of the Vertica database. But when we look at three main pillars that Vertica machine learning aims to build on, the first one is to have Vertica as a platform for high performance machine learning. We have a lot of functions for data exploration and preparation. We'll go through some of them here. We have distributed in-database algorithms for model training and prediction. We have scalable functions for model evaluation. And finally, we have distributed scoring functions as well. But doing all of the stuff in the database, that's a really good thing. But we don't want to isolate it in this space. We understand that a lot of our customers, our users, they like to work with other tools and work with Vertica as well. So they might use Vertica for data prep with another tool for model training or use Vertica for model training and take those models out to other tools and do prediction there. So integration is really important, part of our overall offering. So it's a pretty flexible system. We have been offering UDXs in four languages, a lot of development there over the past few years. But the new capability of importing PMM models for in-database scoring and exporting Vertica native models for external scoring is something that we have recently added. And another talk would actually go through the TensorFlow integration, so a really exciting and important milestone that we have where you can bring TensorFlow models into Vertica for in-database scoring. For this talk, we'll focus on data exploration and preparation, importing PMML and exporting PMML models. And finally, since Vertica is not just a QE engine, but also a data store, so we have a lot of really good capability for model storage and management as well. So yeah, let's dive into the first part on machine learning at scale. So when we say machine learning at scale, we're actually having a few really important considerations and they have their own implications. The first one is that we want to have speed, but also want it to come at a reasonable cost. So it's really important for us to pick the right scaling architecture. Secondly, it's not easy to move big data around. It might be easy to do that on a smaller data set, on an Excel sheet or something of the like, but once you're talking about big data and data analytics at really big scale, it's really not easy to move that data around from one tool to another. So what you'd want to do is bring models to the data instead of having to move this data to the tools. And the third thing here is that some sub-sampling, it can actually compromise your accuracy and a lot of tools that are out there, they actually force you to take smaller samples of your data because they can only handle so much data. But that can impact your accuracy and the need here is that you should be able to work with all of your data. We'll just go through each of these really quickly. So the first aspect here is scalability. Now, if you want to scale your architecture, you have two main options. The first is vertical scaling. Let's say you have a machine, a server, essentially, and you can keep on adding resources like RAM and CPU and keep on increasing the performance as well as the capacity of that system. But there's a limit to what you can do here and the limit, you can hit that in terms of cost as well as in terms of technology. Beyond a certain point, you would not be able to scale more. So the right solution to follow here is actually horizontal scaling in which you can keep on adding more instances to have more computing power and more capacity. So essentially, what you get with this sort of an architecture is a supercomputer which stitches together several nodes and the workload is distributed on each of those nodes for massively parallel processing and really fast speeds as well. The second aspect of having big data and the difficulty around moving it around is actually can be clarified with this example. So what usually happens is that you, and this is a simplified version, you have a lot of applications and tools from which you might be collecting the data and this data then goes into an analytics database. That database then in turn might be connected to some BI tools, dashboards and applications, some ad hoc queries being done on the database. When you want to do machine learning in this architecture, what usually happens is that you have your machine learning tools and the data that is coming into the analytics database is actually being exported out to the machine learning tools. You're training your models there and afterwards when you have new incoming data that data again goes out to the machine learning tools for prediction, but those results that you get from those tools usually end up back in the distributed database because you want to put it on dashboard or you want to power up some applications with that. So there's essentially a lot of data overhead that's involved here. There are problems with that, including data governance, data movement and other complications that you need to resolve here. One of the possible solutions to overcome that difficulty is that you have machine learning as part of the distributed analytical database as well. So you get the benefits of having it applied on all of the data that's inside the database and not having to care about all the data movement there. If there are some use cases where it still makes sense to at least train the models outside, that's where you can do your data preparation inside the database and then take the data out, the prepared data, build your model and then bring the model back to the analytics database. In this case, we'll talk about Vertica. So the model would be archived hosted by Vertica and then you can keep on applying predictions on the new data that's incoming into the database. So the third consideration here for machine learning on scale is sampling versus full data set. As I mentioned, a lot of tools, they cannot handle big data and you are forced to sub-sample. But what happens here, as you can see in the figure on the leftmost figure A, is that if you have a single data point, essentially any model can explain that. But if you have more data points as in figure B, there would be a smaller number of models that could be able to explain that. And in figure C, even more data points, lesser number of models explain. But lesser also means here that these models would probably be more accurate and the objective for building machine learning models is mostly to have prediction capability and generalization capability essentially on unseen data. So if you build a model that's accurate on one data point, it would not have very good generalization capability. The conventional wisdom with machine learning is that the more data points that you have and for learning, the better and more accurate models that you'll get out of your machine learning models. So you need to pick a tool which can handle all of your data and does not force you to sub-sample that. And doing that, even a simpler model might be much better than a more complex model here. So yeah, let's go to data exploration and data preparation part. So Vertica is a really powerful tool and it offers a lot of capabilities in this space. And as I mentioned, we support the whole process. You can define the problem and you can gather your data and construct your data set inside Vertica. And then comes the data prep part, training, modeling, deployment, and managing the model. But this is a really critical step in the overall machine learning process by some estimates that it takes between 60 to 80% of the overall effort of a machine learning process. So a lot of functions here, you can use part of Vertica to data exploration, need to application, applied detection, balancing, normalization, and essentially a lot more. You can actually go to Vertica's documentation and find them there. Within Vertica, we actually divide them into two parts. Within data prep, one is exploration functions, the second is transformation functions. Now, within exploration, you have a rich set of functions that you can use in DB. And then if you want to build your own, you can use the UDEX to do that. Similarly, for transformation, there's a lot of functions around time, so you use pattern matching, applied detection that you can use to transform that data. And it's just a snapshot of some of those functions that are available in Vertica right now. And again, the good thing about these functions is not just their presence in the database, the good thing is actually their ability to scale on really, really large data sets and be able to compute those data for you on that data set in an acceptable amount of time which makes your machine learning processes really practical. So let's go to an example and see how we can use some of these functions. As I mentioned, there's a whole lot of them and we'd not be able to go through all of them, but just for our understanding, we can go through some of them and see how they work. So we have here a sample data set of network flows. It's a simulated attack from some source nodes and then there are some victim nodes on which these attacks are happening. So yeah, let's just look at the data here real quick. We'll load the data, we'll browse the data, compute some statistics around it, ask some questions, make plots and then clean the data. The objective here is not to make a prediction per se, which is what we mostly do in machine learning algorithms, but to just go through the data prep process and see how easy it is to do that with Vertica and what kind of options might be there to help you through that process. So the first step is loading the data. Since in this case, we know the structure of the data so we create a table and create different column names and data types, but let's say you have a data set for which you do not already know the structure. There's a really cool feature in Vertica called FlagsTables and you can use that to initially import the data into the database and then go through all of the variables and then assign them variable types. And you can also use that if your data is dynamic and it's changing to import the data first and then create these definitions. So once we've done that, we load the data into the database. It's for one week of data out of the whole data set right now. But once you've done that, we'd like to look at the flows, just to look at the data, how it looks. And once we do select start from flows and just have a limit here, we see that there's already some data duplication. And by duplication, I mean rows, which have the exact same data for each of the columns. So as part of the cleaning process, the first thing we'd want to do is probably to remove that duplication. So we create a table with distinct flows. And you can see here, we have about a million flows here, which are unique. So moving on, the next step we want to do here is we want to, this is essentially time series data and they're seven days in a week. So we want to look at the trends of this data. So the network traffic that's there, you can call it flows. So based on hours of the day, how does the traffic move and how does it differ from one day to another? So it's part of an exploration process. There might be a lot of further explorations that you want to do, but we can start with this one and see how it goes. And you can see in the graph here that we have seven days of data and the weekend traffic, which isn't pink and purple here, seems a little different from the rest of the days, pretty close to each other. But yeah, definitely something we can look into and see if there's some real difference and if there's something we want to explore further here. But the thing is that this is just data for one week, as I mentioned, but if we load data for 70 days, you'd have a longer graph probably, but a lot of lines and would not be able to really make sense out of that data. It would be a really crowded plot for that matter. So we have to come up with a better way of being able to explore that and we'll come back to that in a little bit. So what are some other things that we can do? We can get some statistics. We can take one sample flow and look at some of the values here. We see that the forward column here and TUS column here, they have zero values. And when we explore further, we see that there's a lot of values here or records here for which these columns are essentially zero. So probably not really helpful for our use case. Then we can look at the flow end. So flow end is the end time when the last packet in a flow was sent. And you can do a select main flow and max flow end to see the data when it started and when it ended. And you can see it's about one week of data from the first till 8th. Now we also want to look at the data, whether it's balanced or not, because balanced data is really important for a lot of classification use cases that we want to try this with this. And you can see that source address, destination address, source port and destination port. And you can see it's highly in balanced data in source versus destination address space. Probably something that we need to do. Really powerful Vertica balancing functions that you can use with min, under sampling, over sampling or hybrid sampling here. And that can be really useful here. Another thing we can look at is, the summary statistics of these columns. So of the unique flows table that we created, we just use the summarized num call function in Vertica. And it gives us a lot of pretty cool count means standard and percentile information on that. Now if we look at the duration, which is the last record here, we can see that the mean is around 4.6 seconds. But when we look at the percentile information, we see that the median is around 0.27. So there's a lot of short flows that have duration less than 0.27 seconds. Yes, there would be more and they would probably be bringing the mean to the 4.6 value. But then the number of short flows is probably pretty high. We can ask some other questions from the data about the features. We can look at the protocols here and look at the count. So we see that most of the traffic that we have is for TCP and UDP, which is sort of expected for a data set like this. And then we want to look at, you know, what are the most popular network services here? So again, simple QE here, select destination port count and other information here. We get the destination port and count for each. So we can see that most of the traffic here is web traffic, HTTP and HTTPS, followed by domain name resolution. So let's explore some more. We can look at the label distributions. We see that the labels that are given with that because this is essentially data for which we already know whether something was an anomaly or not, a record was anomaly or not and we can create our algorithm based on it. So we see that there's this background label, a lot of records there and then anomaly spam seems to be really high. There are anomaly UDP scans and SSH scans as well. So another question we can ask is like among the SMTP flows, how labels are distributed? And we can say that anomaly spam is highest and then comes the background spam. So can we say out of this that SMTP flows, they're their spams and maybe we can build a model that actually answers that question for us. That can be one machine learning model that you can build out of this data set. Again, we can also verify the destination port of flows that were labeled as spam. So you can expect port 25 for SMTP service here and we can see that SMTP with destination port 25, you have a lot of accounts here but then there are some other destination ports for which the count is really low. And essentially when we are doing an analysis of this scale these data points might not really be needed. So as part of the data prep slash data cleaning we might want to get rid of these records here. So now what we can do is going back to the graph that I showed earlier, we can try and plot the daily trends by aggregating them. Again, we take the unique flows and convert it into a flow count, into a manageable number that we can then feed into one of the algorithms. Now PCA, principal component analysis, it's a really powerful algorithm in Vertica. And what it essentially does is a lot of times when you have a high number of columns which might be highly correlated with each other you can feed them into the BC algorithm and it would get for you a list of principal components which would be linearly independent from each other. Now, each of these components would explain a certain extent of the variance of the overall data set that you have. So you can see here, component one explains about 73.9% of the variance and component two explains about 16% of the variance. So if you combine that, those two components alone would get for around 90% of the variance. Now, you can use PCA for a lot of different purposes but in this specific example, we want to see if we combine all the data points that we have together and we do that by day of the week. What sort of information can we get out of it? Is there any sort of insight that this provides? Because once you have two data points, it's really easy to plot them. So we just apply the PCA, we first train it and then we apply it on our data set and this is the graph we get as a result. Now, you can see component one is on the access here, component two on the y-axis and each of these points represents a day of the week. Now, with just two points, it's easy to plot that and compare this to the graph that we saw earlier which had a lot of lines and the more weeks that we added or more days we added, the more lines we'd have versus this graph in which you can clearly tell that five days traffic starting from Monday till Friday, that's closely clustered together so probably pretty similar to each other and then Saturday traffic is pretty much apart from all of these days and it's also farther away from Sunday. So these two days of traffic is different from other days of the traffic and we can always dive deeper into this and look at what exactly is happening here and see how this traffic is actually different but with just a few functions and some pretty simple SQL queries, we were already able to get a pretty good insight from the data set that we had. Now, let's move on to our next part of this talk on importing and exporting PMML models to and from Vertica. So current common practice is that when you're putting your machine learning models into production, you'd have a dev or a test environment and in that you might be using a lot of different tools, Cycler and Spark are and once you want to deploy these models into production, you'd put them into containers and there would be a pool of containers in the production environment which would be talking to your database, could be your analytical database and all of the new data that's incoming would be coming into the database itself. So as I mentioned in one of the slides earlier, there is a lot of data transfer that's happening between that pool of containers hosting your machine learning models versus the database which you'd be getting data for scoring and then sending the scores back to the database. So why would you really need to transfer your models? The thing is that no single machine learning platform provides everything. There might be some really cool algorithms that Python provides, but then Spark might have its own benefits in terms of some additional algorithms or some other stuff that you're looking at. And that's the reason why a lot of these tools would be used in the same company at the same time. And then there might be some functional considerations as well. You might want to isolate your data between Data Science Team and your production environment and you might want to score your pre-trained models on some edge nodes where you cannot host probably a big solution. So there's a whole lot of use cases where model movement or model transfer from one tool to another makes sense. One of the common methods for transferring models from one tool to another is the PMML standard. It's an XML-based model exchange format, sort of a standard way to define statistical and data mining models and helps you share models between the different applications that are PMML compliant. Really popular tool and that's the tool of choice that we have for moving models to and from Vertica. Now, with this model movement capability, there's a lot of model management capabilities that Vertica offers. So models are essentially first-class citizens of Vertica. What that means is that each model is associated with the DB schema. So the user that initially creates a model, that's the owner of it, but he can transfer the ownership to other users. He can work with the ownership rights in any way that you would work with any other relation in a database would be. So the same command that you use for granting access to a model, changing its owner, changing its name or dropping it, you can use similar commands for models as well. There are a lot of functions for exploring the contents of models and that really helps in putting these models into production. The metadata of these models is also available for model management and governance. And finally, the import-export part enables you to apply all of these operations to the models that you have imported or you might want to export while they're in the database. And I think it would be nice to actually go through an example to showcase some of these capabilities in our model management, including the PMML model export and import. So the workflow for export here would be that we'd train some data. We'll train a logistic regression model and we'd save it as an IndieBee Vertica model. Then we'll explore the summary and attributes of the model, look at what's inside the model, what the training parameters are, some coefficients and stuff. And then we can export the model as PMML and an external tool can import that model from PMML. And similarly, we'll go through an example for export. We'll have an external PMML model trained outside of Vertica. We'll import that PMML model and from there on, essentially, we treat it as an IndieBee PMML model. We'll explore the summary and attributes of the model in much the same way as an IndieBee model. We'll apply the model for IndieBee scoring and get the prediction results. And finally, we'll bring some test data. We'll use that on test data for which the scoring needs to be done. So first, we want to create a connection with the database. In this case, we are using a Python Jupyter notebook. We have the Vertica Python connector here that you can use. Really powerful connector allows you to do a lot of cool stuff with the database using the Jupyter front end. But essentially, you can use any other SQL front end too or for that matter, any other Python ID which lets you connect to the database. So exporting model. First, we'll create a logistic regression model here. Select logistic regression. We'll give it a model name. The input relation which might be a table, time table or view. The response column and the predictor columns. So we get a logistic regression model as a result. Now we look at the models table and see that the model has been created. This is a table in Vertica that contains a list of all the models that are there in the database. So we can see here that my model that we just created, it's created with Vertica models as a category. Model type is logistic regression and we have some other metadata around this model as well. So now we can look at some of the summary statistics of the model. We can look at the details. So it gives us the predictor, coefficient, standard error, z value and t value. We can look at the regularization parameters. We didn't use any, so it would be a value of fun but if you had used it, it would show it up here. The call string and also additional information regarding iteration count, rejected row count and accepted row count. Now we can also look at the list of attributes of the model. So select get model attribute using parameters, model name is my model. So for this particular model that we just created, it would give us the name of all the attributes that are there. Similarly, you can look at the coefficients of the model in a columnar format. So using parameter name my model and in this case we add attribute name is equal details because we want all the details for that particular model and we get the predictor name, coefficient, standard error, z value and p value here. So now what we can do is we can export this model. So we use the fillet export models and we give it a path to where we want the model to be exported to. We give it the name of the model that needs to be exported that because essentially you might have a lot of models that you have created. And you give it the category here which in our examples is PMML. And you get a status message here that export models has been successful. So now let's move on to the importing models example. In much the same way that we created a model in Vertica and exported it out you might want to create a model outside of Vertica in another tool and then bring that to Vertica for scoring because Vertica contains all of the hard data and it might make sense to host that model in Vertica because scoring happens a lot more frequently than model training. So in this particular case we select import models and we are importing a logistic regression model that was created in Spark. The category here is again PMML. So we get the status message that import was successful. Now let's look at the attributes, look at the models table and see that the model is really present there. Now previously when we ran this query because we had only my model there so that was the only entry you saw but now once this model is imported you can see that as line item number two here, Spark logistic regression, it's a public schema. The category here however is different because it's not an indivigated model rather an imported model. So you get PMML here and then other metadata regarding the model as well. Now let's do some of the same operations that we did with the indivib models. We can look at the summary of the imported PMML model. So you can see the function name, data fields, predictors and some additional information here. Moving on, let's look at the attributes of the PMML model. Let's look at model attribute, essentially the same query that we applied earlier with just the difference here is only the model name. So you get the attribute names, attribute fields and number of rows. We can also look at the coefficients of the PMML model name, exponent and coefficient here. So yeah, pretty much similar to what you can do with an indivib model. You can also perform all the operations on an important model. And one additional thing we'd want to do here is to use this important model for prediction. So in this case, we do a select predict PMML and give it some values using parameters model name and logistic location and match by position. It's a really cool feature. This is true in this case, set to true. So if you have model being imported from another platform in which let's say you have 50 columns, now the name of the columns in that environment in which you're training the model might be slightly different than the names of the column that you have set up for Vertica. But as long as the order is the same, Vertica can actually match those columns by position and you don't need to have the exact same names for those columns. So in this case, we have set that to true and we see that predict PMML gives a status of one. Now, using the important model, in this case, we had certain values that we had given it but you can also use it on a table as well. So in that case, you also get the prediction here and you can look at the compute matrix to see how well you did. Now, just a prep, just sort of wrapping this up. It's really important to know the important distinction between using your models in any tool, any single node solution tool that you might already be using like Python or R versus Vertica. What happens is let's say you build a model in Python. It might be a single node solution. Now, after building that model, let's say you want to do prediction on really large amounts of data and you don't want to go through that overhead of keeping to move that data out of the database to do prediction every time you want to do it. So what you can do is you can import that model into Vertica but what Vertica does differently than Python is that the PMML model would actually be distributed across each node in the cluster. So it would be applying on the data segments in each of those nodes and there might be different threads running for that prediction. So the speed that you get here for model prediction would be much, much faster. Similarly, once you build a model for machine learning in Vertica, the objective mostly is that you want to use up all of your data and build a model that's accurate and is not just using a sample of the data but using all the data that's available to it, essentially. So you can build that model. The model building process would again go through the same technique. It would actually be distributed across all the nodes in the cluster and it would be using up all the threads and processes available to it within those nodes. So really fast model training, but let's say you wanted to deploy it on an edge node and maybe do prediction closer to where the data was being generated. So you can export that model in a PMML format and always deploy it on the edge node. So it's really helpful for a lot of use cases. So just summarizing the takeaways from our discussion today. So Vertica is a really powerful tool for machine learning, for data preparation, model training, prediction, and deployment. You might want to use Vertica for all of these steps or some of these steps. Either way, Vertica supports both approaches. In the upcoming releases, we are planning to have the model import and export capability through PMML models. Initially we're supporting K-means, linear and logistic regression, but we keep on adding more algorithms and the plan is to actually move to supporting custom models. If we want to do that with the upcoming release, TensorFlow integration is always there, which you can use, but with PMML, this is the starting point for us and we keep on improving that. Vertica models can be exported in PMML format for scoring on other platforms. And similarly, models that you have built in other tools can be imported for IndieB machine learning and IndieB scoring with Vertica. There are a lot of critical model management tools that are provided in Vertica and there are a lot of them on the roadmap as well, which we keep on developing. Many ML functions and algorithms, they are already part of the IndieB library and we keep on adding to that as well. So thank you so much for joining the discussion today and if you have any questions, so we'd love to take them now. Back to you soon.


There for a time for member statements the member from Oxford. Thank you very much Mr. Speaker. Mr. Speaker today many Canadians of Chinese Vietnamese and Korean heritage will be celebrating the Mid-Autumn Festival with their family and friends. On behalf of the people of Oxford and the PC caucus I'm pleased to offer best wishes to everyone celebrating the Mid-Autumn Festival or Moon Festival as it's often known. This is an ancient festival during which gratitude is expressed for good harvest and abundance and togetherness with family as celebrated. I am looking forward to upcoming events with our friends in the Taiwanese community later this week as we celebrate the Mid-Autumn Festival. Oxford is proud of the strong relationships we've had with Taiwan since George Leslie Mackay traveled there from Embro in late 1800s and became one of the most well-known Canadians in Taiwan. In fact Mr. Speaker on March the 21st 2018 Kai the Biberian the George Leslie Mackay story will celebrate its opening night and world premiere at the Embro Town Hall on George's 174th birthday. I would also like to wish a happy belated birthday to George's granddaughter Margaret Mackay who recently celebrated her 98th birthday. I'm sure families from many communities around Ontario will be coming together to celebrate the Mid-Autumn Festival. Some will bring mooncakes, some will be sharing fruit and gifts and others will be lighting and hanging lanterns. It is in the spirit of celebration and togetherness that we wish them all the best for a happy Mid-Autumn Festival. Thank you very much Mr. Speaker. Thank you Speaker. People across the province are wondering about the impending visit of American Secretary of Education Betsy DeVos and why this government would welcome someone whose platform is to publicly, I used to undermine publicly funded education and funnel public dollars into private pockets. Betsy DeVos is part of an administration that dropped protections for trans students. She shields private schools that discriminate against LGBTQ kids and children with special needs. She has cut work-study opportunities, cuts billions out of the education budget and attacks civil rights on post-secondary campuses. I understand we want to build trade relationships. I understand she's the Secretary of Education in the United States so have your meetings. It doesn't mean we have to welcome her with open arms. We need to make it clear to her that strong public schools are inclusive spaces where all children should feel safe and able to learn. The message we send to our students should trump the message we send to Betsy DeVos. Inviting her into our classrooms says we condone her exclusionary views. I will always defend accessible inclusive public education which ensures better futures for all our children. Your liberal government under funds our students and their futures.  You aren't fairly funding our special needs students or strengthening public systems and you can't say you do when you sell off our public assets for parts. Our schools deserve better. Ontario educators and students are amazing. I don't worry about the fabulous impression we will make on the Secretary of Education. I worry about the influence she could have on this government. Public education should not be for sale. Thank you very much speaker and in my member statement today I'd like to honour two members of my community. David Hollinger and Doug Gibbons. This past weekend at the Kitchener Waterloo Sertoma Club David was awarded the Service to Mankind Award. David has lived his life as someone always willing to give to other people. Often performing work to help inspire youth. Serving on the board of governors for Conestoga College and fundraising for local charities and community projects. David is now retired and he still volunteers for One Roof and Kitchener. This is a support centre for youth in crisis. He also helps to run the volunteer coordinated festival of lights in our community called the Wonders of Winter. He spends at least 600 hours a year volunteering at the Wonders of Winter alone. The other recipient Doug Gibbons was also honoured this weekend as the Serto Man of the Year. Doug has been involved in many activities but primarily with our local speed skating club for over 20 years and coaching for 10 years. Doug has been a bingo chairman for the last 15 years and a minor soccer team coach for 30 years. Speakers it is community builders like Doug and David who make Kitchener Waterloo such a great place to live. I congratulate them and it is my pleasure to praise them here today. Thank you. Thank you for the member statements. The member from Carlton, Mississippi Mills. Thank you Mr. Speaker. This statement is on marijuana selling. At the Trillium Party we understand that small business is the backbone of Ontario's economy. We know that small business creates 75 to 80% of all the jobs in Ontario. If small business thrives Ontario thrives. We are strongly opposed to the government's plan to sell legal marijuana through a large single desk LCBO type government monopoly agency. We strongly support selling legal marijuana through regulated independent small business outlets. This will be competitive and it will be effective, efficient and the lowest cost service to consumers. And it will be safe because it will be regulated. The competition that comes from a free market from a free market economy will encourage consumers to use safe regulated marijuana because it will be at a competitive price. This lowest price will provide competition for illegal sellers of unregulated and unsafe marijuana. It will be safer for consumers. Thank you. Thank you for the member's statements. The member from Niagara West Gladwell. Thank you Mr. Speaker. I rise today to speak to a tragic reality occurring in the island in the country of Iceland and to draw attention to a concerning rise in lack of respect for human value, dignity and worth for those individuals and families impacted by Down syndrome. Earlier this year the American broadcaster CBS reported that Iceland is eliminating Down syndrome through the use of abortion. CBS reported that almost 100% of unborn children diagnosed with Down syndrome are aborted in Iceland. Mr. Speaker, the reality is that Iceland is not eliminating Down syndrome, they are eliminating people. I had the chance to meet with the Niagara Down syndrome society last month and we talked about the enormous social, economic and cultural contributions individuals with Down syndrome have brought to the Niagara region and all of Canada. I was proud to meet with them and stand with the families and individuals who live with the struggles that Down syndrome can entail. The article that CBS released which details what is occurring in Iceland bears a headline which asks the question, what kind of society do you want to live in? I know what my answer is Mr. Speaker. I want to live in a society where individuals with Down syndrome are cherished, loved and respected. I want us all in this legislature to work hard to make Ontario a society where individuals with Down syndrome are recognized for their unique gifts, valued for their rich perspective and treated as equals by a society that esteems them. Join me in that work. Thank you. Thank you Speaker. I rise today in support of education workers and families in Ontario who are extremely concerned and disappointed that this liberal government has agreed to host President Donald Trump's Education Secretary Betsy DeVos. Ontario Secondary School Teachers Federation President Harvey Bischoff stated that Betsy DeVos' visit is alarming and an affront to our members that Ontario would allow someone who openly promotes a corporate assault on public education to visit schools in our province. This visit is insulting to education workers in this province. We know that Ms. DeVos believes in publicly funded private schools which is something new Democrats will never stand for. Worse than that, she has targeted transgender students and rolled back policies for addressing sexual assault on post-secondary campuses. Why should Ontario schools, education workers and students entertain a guest who is so out of touch with the rights and values that we hold dear? As a parent of a child in one of our high schools, I am appalled by this liberal government's decision. Why would we expose students to someone who doesn't support the very things that we try to instill in our children? Things like acceptance and equality. This liberal government must listen to the teachers and education workers of this province and revoke their invitation to Ms. DeVos. Thank you. Mr. Speaker, I'm honoured to rise in observance of Cyprus National Day and recognise the 57th anniversary of the independence of the Republic of Cyprus. Cyprus is a small Mediterranean country with a population of almost 800,000 people. The friendship between Canada and the Republic of Cyprus goes back over 50 years when Cyprus asked UN to create a peacekeeping force. Canada's peacekeeping operation in Cyprus from 1964 to today is one of Canada's longest and best known overseas military commitments. As we celebrate Cyprus National Day, we also need to remember the sacrifice of the 28 fallen Canadian peacekeepers that paid the ultimate price in our country's efforts to bring peace in Cyprus.  To celebrate this occasion, Mr. Speaker, the legislature will be raising the Cyprus flag at Queen's Park tomorrow, October 5th, at 12 noon. There will be many special guests in attendance, including the High Commissioner of the Republic of Cyprus in Ottawa, His Excellency Pavlos and His Thadias. I would like to thank my constituent, Christina Emma Galitis, the President of the Cyprus Federation of Canada, for organising tomorrow's Cyprus National Day celebration at Queen's Park, and for the 50 years of friendship between Canada and the Republic of Cyprus. And finally, Mr. Speaker, I'd like to extend best wishes to everyone celebrating Cyprus National Day. Thank you. Thank you for the member statements. The member from Renfrew University, Pembroke. In 1967, Algonquin College in Pembroke welcomed its first 16 students, primarily in evening courses taught at what was then Champlain High School. The following year, full-time programs began with 49 students in classes that included business administration, general technology and architectural drawing. Later, a permanent location was found at the site of the former Maple Leaf Dairy, which, after modifications, would serve as home to the campus until 2012. The campus would grow and continue to offer more and more programs and opportunities for post-secondary students. Today, it offers 21 full-time programs at its waterfront campus, the most magnificent college location in the province, in my unbiased opinion. Its growth continues this fall as registration topped 1,000 students for the first time. An amazing accomplishment from its humble beginnings some 50 years ago. Its Renaissance Square waterfront campus, which opened in 2012, was indeed a game changer and has helped turn Algonquin Pembroke into a destination college. Since opening, it has encouraged other economic development, including the building of three independently owned student residences providing 170 spaces. These are much needed because approximately 50% of Algonquin students come from outside Renfrew County. Yes, Speaker, Algonquin has come a long way. I would like to thank the visionaries, such as Bill Kutchke, who saw the potential, the opportunities and benefits of establishing a college in Pembroke. I also want to thank the leadership teams, faculty and staff, both past and present, for continuing to make Algonquin a special place and one of the true jewels of the city of Pembroke and the county of Renfrew. Thank you very much. Thank you for their member statements, the member from Davenport. Thank you, Mr. Speaker. October is Hispanic Heritage Month in the province of Ontario. For the third year in a row, Hispanic Heritage Month will honour the more than 400,000 Ontarians of Hispanic and Latino descent and serve as a chance to remember, elevate and educate future generations about the achievements of our Hispanic Latino community. Today, the Hispanic Latino community is one of the fastest growing and most diverse groups in our province, and I'm truly humbled personally to represent 10,000 members of the Hispanic Latino community in my riding of Davenport. The Hispanic Latino community continues to contribute to our province's prosperity. They start and build businesses, volunteer, contribute to Ontario's cultural and artistic landscape. In May of 2015, my first bill as an MPP, Bill 28, an act to proclaim the month of October as Hispanic Heritage Month, was passed in the Ontario Legislature, and by proclaiming the month of October as Hispanic Heritage Month in Ontario, our province recognises the rich contributions of Hispanic and Latino Canadians to our social, economic and political, multicultural fabric. Mr. Speaker, I want to take this opportunity to urge all members to join me this afternoon, later today, for a reception in the Government Caucus Room to enjoy an exhibit showcasing art from the Hispanic Latino community and recognise and celebrate the third Hispanic Heritage Month in Ontario. I'm truly grateful that I was able to make Hispanic Heritage Month a reality in Ontario and welcome all members to participate in the festivities across Ontario. Thank you, Mr. Speaker. I rise today to celebrate the Mid-Autumn Moon Festival. The Mid-Autumn Moon Festival is celebrated on the 15th day of the 8th lunar month. The festival tradition goes back over 3,000 years when it coincided with the harvest. The Mid-Autumn Festival is celebrated by the people of China, Korea, Japan, Indonesia, Singapore, Taiwan and Vietnam. In the Chinese culture, it is believed that a full moon is a symbol of peace, prosperity and family reunion. To celebrate Mid-Autumn Moon Festival, Koreans visit their ancestral hometowns and share a feast of Korean traditional foods such as Songpyeon, which is rice cake and Yaju, which is rice wine. Here in Ontario, eating moon cakes, other sweet treats is just one of the many traditions associated with this festival. Mr. Speaker, I urge all members from both sides of this house to join me in celebrating this important festival. Once again, let's unite to send a collective message that in Ontario, cultural diverse is our strength. Thank you, Mr. Speaker. I thank all members for their statements.


Bubblesocker, yep, bubblesocker. And here I thought I was making my NBA debut, well it's probably for the best. This looks like it'll be a little bit more my speed. Every Sunday from 3 to 6, 18 teams or roughly 200 BU students come out to FitRec and play. Despite the fact that students filled the sign up sheet to capacity in just over an hour, I still wasn't quite sure what it was. Bubblesocker is a fairly new sport and people wear literally a bubble around them. So they'll have a big sphere that's inflated with air, it's made of PVC, clear see-through plastic and you play soccer 4 on 4 while you're wearing this bubble and you bounce off other people, you bounce off the floor, you bounce off the walls and it's a little bit more contact oriented than regular soccer but it's a little safer because you have a big bubble around you so it's a pretty fun game. And lots of contact there was, I mean lots and lots and lots and lots of contact. People just kept pinballing into one another and well then there was this, ouch. Most BU Bubblesocker players are former football players but as you can see from all the hits they're taking, this more closely resembles American football. Trust me though, nobody's getting concussions in these. And naturally, as with all sports, there is a limit to what you can do. In terms of like contact, just if you're going to hit someone, you have to make sure that they can see you so you can't like come up behind someone and just like nail them. Also, you can't like push people into the boards because we don't want the bubbles to pop. So armed with some game knowledge and some safety reassurance, it was time for my Bubblesocker debut. Let me just state for the record that climbing into one of these contraptions was not easy. Playing in one of them, as it turns out, was much harder. Let's just say I wasn't very good, like at all. In fact, I was probably the reason why my team lost.  Still smiling though, I guess I could snag an award for Team Spirit. It wasn't minor improvement as time wore on, but there's no saving that kicking form, particularly not with that bubble around me. Successful game of Bubblesocker, we lost, but we had a good time. Which, as it turns out, was really the main goal of the game. I think my favorite part about playing was just that all of us suck at it. So like, there's no, you're not feeling self-conscious. Like, even if you get knocked over, it's just kind of like part of the game, so it makes it more fun. It's the sort of fun that the Boston University Intramural Sports Department is always on the lookout for. We actually don't offer a lot of those trendy new things, like you'd consider Bubblesocker. Bubblesocker is more of an aberration for us. So you never know. It could be popular this semester, next semester, maybe next year. And then after a while, people, you know, they're on to the next thing, whether it's maybe glow-in-the-dark football, which exists, or any of these things that tend to pop up, pique people's interest, and then, you know, they're on to the next thing. So we'll see, and we'll try and adapt and continue to offer the students here at BU stuff that are going to keep them active and interested.


Hey guys, so this is going to be an extremely ghetto review. Mark and I are here. We got this great view right behind us here. We're going to have Dana here do the review of our Friday the 13th part 7 review. The new blood! Hey guys, so we thought we, because of where we are right now, we thought we'd do one local apologies for the audio. I forgot to bring my microphone as well as my tripod head. So we had to recruit another one of my PAs to film this video for us while we're waiting to start our night shoot at Cypress Mountain in Vancouver. So part 7, this movie is definitely a victim of certain things. Oh yeah. A victim of the MPAA for sure, but also a victim of terrible acting. This is like the worst acting I think in the entire series. It's pretty terrible. It's got absolutely terrible editing for the first half of the movie. And then it also has no direction with any of the characters. It sets up all these tiny little things with each of the victims. I honestly feel like John Fugler's cut is like three and a half hours long. Like it feels like each one had a character. Each there's like this whole arc that we just never get told because everything is so truncated in this movie. Oh yeah. All the deaths.  Yeah. A saw blade hedge trimmer. Which is apparently a real thing that I've never seen before. Well that's why I said man, this movie just cements the fact that really Jason wanted to be a gardener. Like he at one point puts a decapitated head in a planter. In a planter, yeah. That's great. Yeah.   And Jeremy brought up a good point is that five and seven were both super cut back on the gore but had nudity. Five way more than this movie. Six had almost no nudity. No nudity.  And it had way more gore. So as I said maybe gore over titties. It was fun. It had moments. You could tell there was potential for a lot of this movie. True. It's not a good film. No. It's not just a victim of the producer and the studio choices. It's a victim of a not a great movie. Oh we should talk about the fact that this movie fucks the timeline up even more. Oh yeah. It only takes less than 2000. 2001 according to IMU which makes sense because like whatever we were in in the future previously because the first four movies are four days but then we jump ahead like 10 years.  Wait one second. Jesus. It is recording right. It's recording. Okay good. You should leave that and that should stay. There's a little girl in this one who's it takes place sometime after Jason's gone in and no daddy no who like kills her dad. Because she has psychic powers. She has psychic powers. I'll get into that in a second. But she kills her dad and then it flashed forward like 10 years so 2001 and like it's funny watching this movie like this movie was released in 88 I think. Yeah. So we're 13 years in the future and like it's literally it doesn't matter. No it doesn't. Like it's just everyone wears the same clothes. It's all 80s contemporary. It's terrible. Like that car is like oh look at that new 2000s ambulance car. I make a jokes about how they should have used their cell phones but not that cell phones are great in 2001 either. No but probably wouldn't work out. Actually cell phones wouldn't work out there. If that movie was made in 2001 there would be no cell reception. I had a lot of odd similarities between this and what was what was Nightmare 4. Nightmare 4 was the one yeah I guess. Yeah there's a lot of the dream master where it's the powers and things. The powers and whatnot. Yeah that there's a lot of similarities because the main female she has psychic powers. But no one said Motherfucker like 13 times like Black Eyed in that movie. Yeah but also she's a terrible actress. Her arc in this movie is terrible. And much like the nunchuck thing there's a terrible stunt double when the chick gets thrown out the window and it's obvious he had to do with very late.   The stunt team on this film didn't seem to be that safe with the main Jason. Yeah this Cain Hauteur has a whole bunch of stories. Well first Cain Hauteur Jason appearance. Yeah that's big. He's the guy. He's a stocky dude. He's imposing. He is the best Jason because of I mean he's the guy you've got to do it the most. He does it 7, 8, 9, 10. He's great in this movie but you can just tell some of the stunts are like really sketchy and you're like oh you guys did that. A roof fell on him. Yeah they dropped the roof on him and it's like still obviously him where it really didn't need to be. No it didn't. It could have been a good cut or something. But he looks to what we're saying before. This Jason is the coolest looking Jason. He's got like bones showing through his legs. He's been underwater for 10 years. Fish have eaten parts of them. After he gets blown up he's even more broken down. Yeah. He's more corpse like. They do a cool effect where she's psychic powers his mask off and we get to spend a lot of time with Jason. It looks like he has a broken jaw. Yeah it's like a super cool prosthetic.  Yeah they were like it's it's an this movie has a lot of cool stunts that might be been a little dangerous. That were dangerous. That were like there shouldn't have happened. But it's still it's it's not the greatest.  Yeah I know it's not the best of movies for sure and it's definitely there's some cool moments but it definitely is hindered by the fact there is no gore.  You don't really see much. It's got a cool prosthetic piece that you kind of see half Howard Howard. And then he pops her over the TV which I gotta I gotta say I don't know how to talk there's this whole thing I was watching the camp Crystal Lake documentary the Crystal Lake memoirs they talked about that actress being dead repeatedly and it seems by a little bit of research that she is not dead so I don't think she's dead but it's weird because like that's kind of strange. It's not on like any mention of anything and it just seems like the researchers for that documentary came out. It must have been after 09 because that's when they said she died. Well you thought someone would have like called the family.  I guess you kind of don't want to call hey we just wanted to make sure she's dead right I guess you couldn't really do that.   Circular saw hedge trimmers like that's even more bizarre where did he find that that's hilarious I'd say the Jason part is definitely a solid part.  Some of the effects with the whole psychic stuff is decent it's terrible at first and that's the other thing too we talked about the editing the jumps in the cuts are just awful and sometimes they will cut back to things and then cut away it's like what was the point of that it seems that the first part was done by a very inexperienced editor and then it actually starts to meld together a little bit better but it's a very interesting movie in terms of all the issues that happened with it the how it's put together how terrible all the acting is and the prosthetics the prosthetics are really good. But John the John Buickler guy like he's a special effects guy. Okay well I'm gonna give this a two. Yeah I think two is about right it's not as terrible as five no but it's a big step down from six. It's a huge step down from six it's unfortunate but the Jason is at least a step up and well he goes that CJ Graham was fucking phenomenal but Kane took what CJ Graham started with the motions and all that and he kind of made it his own and he made it great so I think Jason is the only reason this movie gets a two. Was Jason nine about him becoming a gardener?  Anyway that's our review for Jason versus Kerry. Hope you guys enjoyed our little scenic review and we'll see you guys for number eight. Eight yeah you're right eight.


All right. Well, thanks first of all for being so dedicated to TubeCon. It's 5.30 on a beautiful day. And you're here and not at the bars. So thank you so much. I also want to say thank you to Oleg. He was supposed to give this talk, but unfortunately he had to travel and leave early. So I'll do my best to give him justice. But the great slides that you see are actually from him. So if you ever see Oleg Nanchev, then give him some kudos. Talk today. Thank you, Captain Obvious, making observability actionable with Prometheus and Captain. Obviously, I try to make it obvious. I really like Captain. My name is Andy Grapper. I work for Nanchev during the day, but I'm also a captain maintainer and a contributor. And I really like to show you what we are doing with Captain. If you think Captain, if you've heard about Captain, you think Captain is yet another CICD tool. And it's a, if this stands with this tool, then hopefully I prove you wrong because I want to give you a little different perspective on Captain. Captain has also changed over the last couple of years. But if you want to get shirts or hats, we all have a booth at the pavilion. So check us out tomorrow. But let me first get into my talk. I talk about observability, making observability actionable. And when I talk about and think about observability, I think about SREs. SREs are using observability data to keep systems reliable and resilient. But you may say, well, one doesn't simply do a SRE. Well, I counter. I think actually Captain makes it pretty simple. This is a post from Tarash. He is a performance engineer at Facebook. And when he saw that Captain two years ago, when we were still in the early stages, you already saw, we are trying to bring the SRE core principles to the cloud-mated community, letting observability that you pull out from Prometheus or any other observability frame that she had, using this to drive those actions that actually keep the system reliable. So first of all, let me get started on what problem we really want to solve. We all know that modern cloud-mated systems are no longer simple. You have a lot of different tools and you need to connect them in one way or the other, which means you have to build a lot of point-to-point integrations and try to put in also your possibility to make better decisions. I'm pretty sure some of these tools are familiar. The question is, how can you make better informed decisions if you don't have visibility? Well, fortunately, we always talk a lot about observability in this conference. I also want to give a shout-out to Henry, in case you have not seen his phenomenal YouTube channel. It's called Isn't Observable? Check it out. He is looking at new frameworks in the space all the time, and it gives his perspective. He also creates tutorials, so check it out. The point is, observability is a must, because if we don't have observability, we cannot make good decisions. We don't even know if a system is broken. The problem is that observability is not easy if you know that you have a big landscape of tools and you don't really know how you actually get data out of this. Well, fortunately, again, we have movements where we are trying to figure out how to standardize open observability. I don't need to remind you about some of these great products or frameworks and projects here that are really trying to make it easier for us to get insights. Prometheus is one of them, right? I think we don't. And again, if you like these slides, all that is the one to put them together. I really like it, right? Sauron's eye, Prometheus' eye, we give you all the visibility and what happens in your environment. What we want to bring to the table, if you have data, then we as captain make it actionable. If you have a desired state based on the data that a system should be helping, should be up and running, it should be performing in a certain way, we can act on this data to always keep the system in that state that you expect from your system-based observability data. So in other words, captain is an observability-driven orchestration engine for your cloud-native apps. What this means is, again, another cool slide from Oleg, one platform to orchestrate them all. I hope I have some of the ring fans in here at least. Captain has been around for a while. We are a CNC at Sandbox project right now, very close to getting to reaching intubation. What's really important is that orchestration and observability-driven orchestration is at the core. And what this means is, if I come back to the slide that I had earlier, the click-off works as expected. Somebody needs to observe the signal here. There we go. One option that you have, and you can build orchestration yourself, you can connect all of these tools, but what we try to do is, I just click here, instead of you having to connect all of these tools and making decisions on what to do next, captain's control plane reaches out to your observability platform and based on how the state of your system is, they call the right tools to actually bring the system back to its healthy state. So, at the core of captain, we have SLO, Service Level Objectives. You define the desired state of what your system, you expect your system to be, and then we orchestrate the tool to bring the system always back to the desired state. Captain has a really cool REST API. We are standardizing on cloud events, and we are in the CDF events spec, and I see amount of time. What I would like you to know is, if you have an app, it's instrumented with open telemetry, you get some data, we can act on it to bring the system always back to its desired state based on your observability. Captain is extensible. There's a lot of stuff that we have out here. Check out Nana's video. If you know Nana, she did a video on captain. Learn more about Captain at Q-Con, which means please meet us at the pavilion. I am there and some others tomorrow, Thursday and Friday. Thank you.


A lot of us absolutely loved and still love Linkin Park because they have these deep lyrics that a lot of us can connect to and in this video where we break down Chester Bennington's lyrics for the song Heavy I want to try something just a little bit different. What is up everybody this is Chris from the Rewired Soul where we talk about the problem but focus on the solution and if you're new to my channel my channel is for anybody who is looking to improve their mental and emotional well-being so if you're into that stuff make sure you subscribe and ring that notification bell. So yeah I've been on a Linkin Park kick lately and just listening and listening like every now and then I just go to like Linkin Park's greatest hits on Spotify and just start listening like crazy and I always get the song Heavy stuck in my head like for some reason it's one of the most relatable songs for me so that's one of the reasons why I wanted to start out with this breakdown doing this I know a lot of you have asked me to do some lyrical breakdowns on it so anyways a couple things the first one is I'm hoping like heck that genius doesn't take this video down I I spent like two hours the other night trying to figure out a way to like upload like the music video and use little clips without it getting taken down and every single test failed it just got taken down like nope nobody can watch this so hopefully hopefully this works if it didn't work you're not watching this but anyways so what I want to do with this video I want to break down these lyrics I want to talk about how I can relate to them and throughout the video I want you to ask yourself how you can personally relate to these right these lyrics but something I mentioned in a previous video about Billie Eilish like I want to talk about solutions like so many of us can relate to these lyrics about about being in that dark place but like what's the solution what's the solution so you know I am not a licensed therapist but I'm somebody who can relate to that dark place that that pain that internal struggle that Chester was in and I'm somebody who is actively working on my mental health and I'm miles away from where I used to be in that dark place so what I want to do with this video is offer some solutions as we break down these lyrics but if you're somebody who's struggling like feel free to try some of these suggestions if you would like if you're really struggling make sure you reach out and ask for help all right talk to your doctor talk to a therapist talk to anybody I usually have some resources down below in the description so how's your day going whatever and he goes he's all fine and then a few minutes later he's like you know what I'm not fine let me tell you what's going on in my life and he just like then it was like probably like dude yeah you know like this is what I'm dealing with and that was the type of conversation that started off most of these sessions I think we all need that I think we all absolutely need that like so many of us I think about just going to the gas station grocery store wherever it is somebody's like hey how's your day going you're like oh I'm good right and you're like dying inside right like this is something that so many of us do out of automation now I'm not saying like go to your local like grocery store like spill your guts to the cashier I'm not saying that but in my opinion we need somebody at least one person in our life where we can tell them how we're really doing you know what I mean like that's super important something somebody told me a long time ago when I first started on my mental health journey journey of getting out of that dark place was you don't got to dance with everybody but you got to dance with somebody I don't like my mind right now stacking up problems that are so unnecessary wish that I could slow things down I want to let go but there's comfort in the panic I can absolutely relate to this like Chester talks about like kind of these thought traps that we get into right like this this is something that so many of us struggle with and something that I work on with my therapist is recognizing these cognitive distortions right recognizing what my brain is doing the other thing that really helps me out a lot is meditation right when I can sit back and just meditate and meditation is about it's not about stopping thoughts it's about watching your thoughts right so I can watch my thoughts just kind of go by like these clouds in the sky right and then I can say okay that one that one is a bad thought right that one is maybe a not a true thought whether it's like you know some negative thinking talking bad about myself maybe it's like talking negatively and pessimistic about the future or whatever it is meditation helps me acknowledge that and then I can start getting into the solution so when he talks about there's comfort in the panic that's something so many of us can relate to what I've realized like just through my own mental health journey as well as working with other people at the treatment center I was working out and just talking to others is like that comfort in the panic that's something that's very real so like for example I was diagnosed with depression a generalized anxiety disorder and some other stuff and and like I remember when my life started getting better especially when I got clean and sober over six years ago like I remember when everything would be fine my brain would start losing it right it would start freaking out and I'm like what the heck is happening like nothing is going on and the reason why from my experiences is that there's comfort in that panic like I find this to be very common with children who grew up in a toxic household right it's what we're used to that's one of the reasons why a lot of us get into toxic relationships when we get older because there's comfort in that right and what's helped me out is a lot of self-love self-compassion and realizing like this is not okay like this is not the type of situation that I want to be in and it takes time for me it took time for for that like my new comfort level is not being in a state of panic and I drive myself crazy thinking everything's about me yeah crazy cuz I can't escape the gravity one of my favorite lines one of my favorite lines is right there and like where he talks about like and I drive myself crazy thinking everything's about me like oh my god like this was such a liberating thing for me and it's something I'm not gonna lie something I still struggle with every single day but we think everything is about this like about us and if you check down below there's a full there's a full link to the genius video that I'm using clips from but Chester talks about how like a lot of our own problems are based on our ego right like for example for example have you ever been out in public and you hear people like laughing and you think it's about you here's another example I like to use like have you ever been driving and like stuck in traffic and there's dozens of cars around dozens and dozens of cars around somebody honks their horn you're like what the heck right we think they're honking at us when like there's a good chance that they're honking at somebody else and we just happen to hear it we constantly think everything's about us something that I find even even more helpful that I realize is when somebody lashes out on me I think it's about me but like I have to realize like how many how many people have like a rough start to the morning right maybe they got into an argument with a significant other or a family member or whatever and then later on I encounter them and they lash out at me but it's not about me it's about that situation right like have you ever had a rough day at work and lashed out at the people you live with whether it's a significant other a child or like maybe your parents called you and you freaked out on them it wasn't about them it was about the other thing so something that that helps me is realizing not everything is about me and as a youtuber who gets a bunch of hate comments I get some nice comments but a bunch of hate comments I I have to look at that and say you know what maybe this person struggling maybe the person on the other side is going through a difficult time now that doesn't justify their behavior towards you but it helps me not take things so personal so right there like I keep dragging around what's bringing me down and what if I would just let go I'd be set free like we know like so many of us like we know we know what the issue is we know what we need to do so there's something I heard a long time ago which is common sense versus common action right like it's common sense for a lot of us to do the things that are good for our mental health whether it's therapy or meditation or having a support group or whatever it is we know we know and if we would just let go and quit trying to do things our way we'd be set free right like how long how long do we sit in our own misery before we get help like in my act of addiction I sat in that forever forever knowing that it was destroying my life and when I got clean and sober and my depression and anxiety like I knew I knew what I had to do I knew I had to reach out to other people I knew how to talk to other people I knew there were things that I had to do but I didn't want to and part of it goes back to there being comfort in the panic some people what I've realized is we latch our our mental state our negative mental state whether we're sad or lonely or whatever like we turn that into a part of who we are and we're afraid to get better because without this thing who are we you see what I mean and I think that holds a lot of us back from getting better I know I'm not the center of the universe but you keep spinning around me just the same when he says you know I know I'm not the center of the universe but you keep spinning around me just the same what Chester talks about in that genius video is like codependent relationships right like it's it's very difficult because a lot of mental health issues from my experience are ego driven right and we think everything's about us but when you have people in your life who are enabling you or codependent like it feeds into that and codependence do this all the time like for those in addiction but not even addiction but like with other other mental health struggles like people will be there all the time and they they they can keep us in a state of being sick right and then that feeds into our own sense of self-importance but anyways like I know a lot of us miss Chester just like watching that interview with him it's like right but I want to do more lyrical breakdowns all link in park and again I want you to let me know down in the comments below like what you can relate to how you can relate if you have solutions that you have found helpful for any of the things that we discussed let everybody know down in the comments below all right but if you like this video please give it a thumbs up and if you're new make sure you subscribe and ring that notification bell and if it's huge huge thank you to everybody support the channel over on patreon you are all amazing and if you would like to become a patron you can click the top right there all right thanks so much for watching I'll see you next time


I would like to thank the organizers for giving me the opportunity to present today is a pity I cannot be in London in the south of Sweden in the tropical south of Sweden. But anyway, we're having a very nice day here in Stockholm as well. So the title of my talk is hemicellulosis molecular structure assembly in plant cell walls and food applications. And I also wanted to highlight the contribution of of Cetil and you must see the beauty student who has basically introduced our group into the wonders of x-ray and neutron scattering so she's a very, very big contributor of this of this presentation. So the starting point of the research that we do in my group is actually understanding the molecular structure and super molecular assembly of plant cell walls. And from a biological point of view, plant cell walls is the tissue that is the tissue that surrounds plant cells and provides them with different functions such as structural support and other regulatory functions. And also a plant cell walls are the main renewable resource from plant biomass that we can use for bioenergy and also for material applications. But also in this context, in the context of the Nordic light cell food, a plant cell walls are very important components in many plant based foods, for example, in cereals and fruits and vegetables, etc. So basically understanding them, the structure is very important, not only to utilize the different components that are present in this in this plant based foods, but also to understand the viability and all the other other properties. So from a molecular perspective, plant cell walls are very complex and hierarchical structures composed mainly by polysaccharides and here you can see a sketch of the composition and assembly of cereal plant cell walls. Where we have cellulose microfibries embedded in a matrix of different matrix polysaccharides, for example, Arabinox islands and beta-glucans. We have also pectins, proteins, phenolics, lignin, etc. So you can see this is a very complex and rich composition of different molecules. All of them have different molecular structure depending on the tissue, depending on the variety, depending on the developmental stage. And also what is important to understand is that what contributes to plant cell wall function and recalcitrance is what are the different interactions between the different components. We have covalent cross-linking, we have hydrogen bonding non-polar interactions. So basically understanding the complexity of this system is what we want to achieve. We are mainly interested in hemicelluloses in my group, which is, as I said, an important component in plant cell walls. In some tissues, for example, in cereal brands is actually up to 50% of the total composition. And these hemicelluloses are, as I said, matrix polysaccharides that interlock cellulose microfibries. And they have a wide diversity of structures. They have a similar backbone as cellulose, meaning that they have a backbone of beta-1-4 linkages. But differently from cellulose, we can find different sugars in the backbone, glucose, mannose, and xylose. We can find different decorations by both neutral sugars, for example galactose or arabinox, but also acidic sugars like glucuronic acid. And also we find acetylation, different chemical modifications. So all of these basically complex structures have a role, and that's what we are actually very interested in deciphering. So for a long time, people thought that this pattern of sugars was random, so there was no specific ordering in the way that the different backbone sugars, the sugar modifications and the acetylations were placed in the polymer. But actually from very recent years, there has been a lot of progress by us and other researchers in the field using glycomics or mass-petrometric based glycomics to try to decipher if there is actually a sequence in the order of the sugars in hemicellulose. And we have actually found that there are actually regular structures, and now we can start thinking what is actually the implications of these sugars, of these sequences. So we know now that the different sequences, they are able to modulate macromolecular conformation and chain flexibility. They also fine-tune the hydration or aggregation possibilities of these hemicelluloses. They tailor interactions with the other wood component, with the other components in plant cell walls, with the cellulose and with the lignin. And also from an application point of view, they also prevent enzymatic deconstruction and influence different macroscopic functional properties, like for example, rheological properties when we are making different applications from these hemicelluloses. And I just wanted to highlight just two important contributions that we did recently about, you know, how these sequences might influence plant cell wall assembly. So this is a sketch, a molecular model that we made for the assembly of plant cell walls, where we actually could find that manans, for example, with high acetylation are much more flexible. But the presence of a specific regular features in Xiland actually tailors the interaction with cellulose. So you can actually find, as you can see here, Xiland, which is an important hemicellulose, co-crystallizing on the surface of cellulose. On the other hand, you can find also other motives with a high clustering of glucuronic acid, which actually interact with the phenolic component. So it has a big implication on the assembly of plant cell walls. But also when we're trying to make different cell wall components, we have also found that this molecular structure influence the way these hemicelluloses assemble. So we can find a rigid layers of hemicelluloses closely absorbed onto Xiland, onto cellulose surfaces, and also flexible Xiland components. And this we discovered like that is very important to change the properties of these cellulose fibular networks. So having Xiland drastically increases the ductility of these fibular networks and having manan drastically increase the compressibility of this cellular network. So what we found is that last year in collaboration with the University of Queensland in Australia, and it has also implications for food applications because we can actually now combining hemicelluloses and cellulose, we could maybe find you in different gels materials that can be used in dietary fibre applications. So now jumping onto the main topic of the talk today, I wanted to show you a project that we've been driving for for some years now, trying to valorize by products from serial production, and how X-ray scattering techniques have really helped us to understand the properties of the different food products that we are creating from this, from this serial by products. So at the starting point of this project, we were collaborating together with landmen and novizimes was to valorize the outer layer of the serial grain, the bran which constitutes approximately 20% of the overall serial kernel. And I put here the same picture again we were very interested in valorizing particularly the Arabian Xiland, the hemicelluloses in serial bran, because they constitute approximately again 50% of the total composition. In order to do that, we designed a process using just water in high pressure and temperature what we call subcritical water extraction. And this allowed us to isolate and fractionate these feruloy-related Arabian Xiland, these wonderful orange strings here with high molecular weight at around 100,000 Dalton, and most importantly keeping these phenolic acids covalently bound to the hemicellulose structure. And you will see now why this is so important to us. On one side keeping this phenolic acid actually keeps and gives this hemicellulose some antioxidant properties that are quite important in different applications. So, and this is actually part of the PST work. What we wanted to see if we could use this feruloy-related Arabian Xiland making films that could be used as barriers combining not only the oxygen properties of the of the of the films but also the antioxidant properties of this ferulic acid covalently bound. We did prove that so we could see that we could make self-tanding films that were favored by the molecular composition of these Arabian Xiland. And we found that ferulic acid in covalent form provided higher antioxidant activity to these films rather than have, you know, free ferulic acid added to the film. So in some part of the project we wanted to make gels of this feruloy-related Arabian Xiland. And in order to do that we basically mimicked what CDL cells do when they're creating their cell walls. So what we did was using the presence of this ferulic acid we use lacquist oxidation to create radicals and be able to recreate these covalent bonds in these in these gels. Remember that when we do this enzymatic reaction we decrease the amount of ferulic acid and we increase the amount of these the ferulic bridges that we can find. Also by size exclusion chromatography we confirmed that these covalent bound structures on one half have higher molecular weight than the free feruloy-related Arabian Xiland but also smaller sizes. So basically we're creating a more compact polymeric topology but with higher molecular weight. Then we teamed up with Patricia Lopez-Sante at Chalmers and she helped us doing a rheological analysis and we prepared different gels. First of all, you can see here the appearance of the gels by enzymatic crosslinking and here after we did some regeneration of these gels by freeze drying and resuspending at different pHs. And you can see here how the viscosity drastically increases when we have the crosslink but also even more when we regenerated the films in after freeze drying. And here you can see in cryo-SEM the porous structure of these films. But at this stage we had no idea about how the ultra-structure of these gels looked like. And this is when I have to again give the credit to Cicilli, she was really pushy, I really want to understand, I really want to get to know better synchrotron and scattering techniques. So it was her idea to apply to this Vinova project to increase the knowledge base of in scattering techniques. And we were very happy also to couple with Thomas Privilege at Maxfort Laboratory. We've been trying to collaborate for some years and Vinova gave us the opportunity to do that. So we're very thankful to do that and it has actually highlighted so much the importance to go beyond molecular level and understand supramolecular organization. So first of all, we did some wide angle x-ray scattering from both the polymeric and the crosslink films. And we could see basically that the local crystallinity increased both upon enzymatic crosslinking, but it was not so much influenced by regeneration. You can see here that it almost the crystallinity degree doubles when we are crosslinking enzymatically. Then we moved forward and we did some preliminary lab scale x small angle x-ray scattering from both the crosslink and the regenerated films. Here you can see the preliminary results that we obtained also in the lab source at Chalmers. So now we decided to feed the results from x from sax to the correlation length equation with these two terms, the N exponent, the power law for larger aggregates, and then the Lorent exponent and the correlation length for the polymeric range. This has really helped us to really understand what happens to our gels, and we're now in the discussion on trying to propose a model of how does this supramolecular organization of our films, of our gels look like. So we've seen that a regeneration causes a larger power law exponent so we're thinking that this creates the higher density of the clusters. We're also finding a larger correlation length upon regeneration, and then we can see that basically we have a junctions of the polymeric chains where we have higher order, but we also have a more density and larger clusters at larger orders of size. And we can see that these films combine both chemical crosslinking and physical effects that contribute to both hydrogen bonding, sorry, I contribute both to network formation and to the rheological properties. And now we're very excited because next week, we will be running the first analysis at the Cossack beam line in Lund, and hopefully we'll be able to go in September to Lund to basically further down the study of this complex of these complex gels. So I just wanted to finish. I mean, I think I'm preaching for the converted here but it's very important for all of us in the in the food science area to understand structure because it correlates basically to the raw materials processing products and health. And also, from my standpoint what I have learned a lot in this in basically this last year is the need to integrate the biochemical analysis that we're mainly experts in my lab at KTH but also with advanced bio physical characterization and that's what exactly a neutral and x-ray scattering can provide is a further understanding of other orders of magnitude. So in this presentation I just wanted to highlight that as an example, the complex composition of molecular structure and architecture of plant-based materials and how hemicellulosis basically contribute to disassembly at a molecular level and how we can use these hemicellulosis as valuable matrices. These are packaging applications, as I can show you with antioxidant properties, but also as food gels that can also contribute to texture but also hopefully adding some nutritional benefits in terms of prebiotic activity and also antioxidant properties. So I wanted to again thank my group, especially to Sechil, who will present her thesis on the 4th of November. So if you're interested in basically a bit more of the detail that I have provided today, please contact me and I will provide you the link for her presentation. And finally, of course, I wanted to thank the support in this particular case from Formas to the overall project and specifically to Vinova for actually giving us the possibility not only to Sechil but also for PIs that are completely unaware of x-ray scattering techniques of the potential of this platform. So thank you very much. Thank you very much for a very interesting, very fast presentation.