 Thank you very much for coming. First thing that I would like to do is sharing my feelings because this is my first tech conference and I am really scared. But at the same time, I'm really excited to share with you what happened when a biologist met Python. This biologist is me. I did a PhD in molecular biology and after a while I was a science, if I stay in academia or go to industry. And in this time, I started to learn Python because I knew that it was useful for science. And it was kind of, in this point, I started to discover some models and packages that was kind of amazing. In that point, I started to, I don't know, to have a kind of relationship, wow, this is amazing. Why I didn't start to use it before this moment? Well, I decided to move to industry where I'm working as a product data manager, e-commerce platform that we sell products for scientists. And one of my roles was looking for new resources, in this case, publications. And it was in that moment when I realized and I discovered some amazing tools that I thought, why I cannot share with the world these new discoveries? And that is why I am here today. Well, this talk is not a conventional talk. We are going to have a biological adventure, but with Python. That means that we are going to cover some different biological topics, and how Python can have a role in this. As you can see, will be really different topics. My first story is about plants, because plants are amazing. They are there, and they cannot walk, they cannot speak, but even that, they can communicate one of each other. Imagine this situation. We are in our living room, we have a plant that is living there, really happy, and this plant perceive or see the light with two different kind of waves. But with our own, it's amazing situation, but maybe I can buy a new plant and put it there, because my plant is really happy there, I can have a new one near. And in this situation, you arrive to your home, to your living room, you left your plant, and this second plant also see the light to the same way. But this plant also reflect a kind of wave, saying that one plant can detect another, and they are started to be unhappy, because they are feeling that they are in danger, they need to survive, because they are in competition for light. Amazing topic, this was my topic of disease, and imagine that you want to know about this syndrome, that it's generated for this situation, that it's called shade avoidant syndrome. And this is where Python can help us. We can use this model called Biopython to look for more information. In this case, it's a simple example where I am looking in a database called PMC, as you can see in the line 5, and also I am looking for terms. I choose this database because contain open access publications, but there are many other databases that you can look a lot of kind of information related with different fields in science in general. In the second part of the script, we are doing a parsing of the information, and then what we are going to obtain in this case is title and URL. But again, we can obtain piece of text and other information. And we can see that this also has two different publications in open access journals. Sorry. Well, sorry, I give you a spoiler for the Magnetic Story, but I wanted to say the last thing. Remember, if you have to plant really close at home, give some space to them. They will be happier. Magnetic Story, it's also avocado. Because avocado, it's a fruit from other areas. I don't know if you know that, but this is the aspect that the primitive avocado has. The seed was huge, and this was a really problematic issue because it is really difficult for this person of this seed. But thanks to this animal, well, not this to the grand, grand, grand parent, but it was a giant slot around four meters of size that ate avocados. We have avocados nowadays, but you can think, yeah, but this is an animal around four meters. Only avocado, it's enough. Well, it was not enough for this animal. He needs to eat avocados and other fruits, and for that, he needs to move around a lot. And that was really useful for avocados because there was a huge distribution of this fruit and could survive. But at some point, this animal disappeared, but humanity appeared, and also we discovered that avocados were amazing. Nowadays, avocados are trendy foods, and for me, I am a bit worried about the prices of avocados because trendy foods, sometimes people increase the prices. Well, for that, I analyzed the prices. We wanted to visualize the prices of avocados in a range of years with bokeh. Bokeh is a model that allows us to produce interactive plots in a really easy way. Of course, this script is a bit summarized. I forget to tell you that I summarized a bit the scripts because it's too long, it's a lot of examples, but all the information is available in my GitHub that I'm going to give you later. Don't worry if you see that it's only some short pieces of the script. Well, as I was telling you, it's a really simple way to do it. We read the data, and then we choose the characteristics that we want to use in the style that we want to use in our plot, in this case, dots and lines. And, well, we're going to try to do a trial. Yeah, well, the dots are the distribution of the prices. In the lines is the average of the prices and the different colors. The blue is organic and the red is conventional. As you can see, we have different prices, but the tendency in time is similar. Well, this about avocados. My next topic or my next story, it's about virus. Because virus are amazing organisms, but do you know that still nowadays, scientists are not sure if they are alive or not? It's kind of an amazing topic, but sometimes it produces a lot of illness and a lot of problems. This is the case of virus Zika. The information that we have nowadays, it's transmitted by mosquito from the family Aedes. We don't have a vaccination to prevent the illness. And because the symptoms, if we are infected, we can have fever, rash, and pain. And the most serious and dangerous is microcephalia in newborns. Here is the distribution of the virus nowadays. And the most important thing when we are talking about viruses or illness is health fast. That's the spirit. For that, we can use networks that is a model that help us to visualize the network, to generate the network. In this case, we are going to analyze cases of illness in Brazil. The first, we generate a graph or plot with different nodes that represent different cities of Brazil. And we are going to see who was the evolution of the spirit of this virus. Let's see. Well, this is the difference, the different cities. We have different colors that represent the amount of cases. As you can see, it's in range. Every time that goes from one range to another, grows the size of the ball and also changes the color. I was preparing the talk. I was thinking, wow, it's amazing how Python can help biology. But I was studying and using a bit of machine learning and things to say, yeah, but I can see also biology in Python. How can this, how? And I start to say, yeah, I think that biology inspired computing. And I want to share with you this point of view. One example, there are many of these examples. Well, one example is the evolutionary adagone. Specifically in this case, this is one of the examples that I love it, for that I choose it. But it's ants colony optimization. In this case, it's based in ants. Ants can go to the nest to the food because they use pheromones. And they can communicate one to another using pheromone communication. And this is really useful because when they have some troubles in the way, they have a rock or something, they can say one to each other, hey, this is the easy way to arrive. Or this is a shorter way to do it. This is a kind of optimization process. And it's similar with the, or it's the base of this algorithm. But it's not the only one, of course. We have also the neural networks. Neural networks is based in our brain, specifically in neurons. Neurons communicate one or two to each other with electrical impulse and go from one to each other. Imagine that we have an input. You can see something. You receive information. You have input. And then this information is going from one neuron to another one. And then we can produce an output. In this case, it's, oh, or whatever. It's the same idea that it's applying in artificial neural network. But we need to have some things in mind that it's not exactly the same. But we are going to do an experiment. I think that it's moment to prepare yourself because I want that I'm going to show you a picture. And I need that you can, how many seconds do you need to recognize the object in this picture? Are you ready? Yeah? OK. Let's go. Three, two, one. Do you need one second? Maybe raise your hand if you need one second. More? Less. Less. Scientists, it's described that our brain needs 0.1 second to recognize an object that you see before. That means that if you know this object, you can use 0.1 second. It's really fast. It's really efficient, our brain. What our brain or what our body is doing is analyzing this picture. We are analyzing shapes. We are analyzing colors. We are analyzing by small parts. Similar with this, doing some machine learning. And here, I'm going to talk a bit about PyTorch model. I don't have a lot of information to say after the talk of yesterday. I don't know if you were here, but it was kind of amazing explaining all about PyTorch. But I would like to only indicate two different important treats for me. In this case, we are going to load a data set of flowers because we want to identify flowers. We are going to use a model called ResNet 50 that was pre-trained that this model has a specific characteristic. It's based in pyramidal cells. That means that these cells are not using layer by layer. These cells can send information from one layer to another far away. And this is what this model also do. In this case, we train and also evaluate and also test. And here, we have the results. We have image classification with different plant species. But sometimes, this is amazing and works good, but needs time and money. And sometimes, we don't have this time. This is the case for the next example, that it's about the snakes and parter. Imagine that you have a friend who is on holidays. So two different snakes take two pictures and send to you this picture and say, hey, I know that you know biology and also Python. Can you help me about if I am in danger or it's fine the situation, what I can do? And of course, this person sent to you a perfect pattern, clear, like real life, real data. All amazing. You can see all the pattern. And for that, we can use this model. Also, this model, we can analyze the pixels of the images. Or, well, we have another option, because maybe you can remember this poem that says, red torch yellow kills a fellow, red torch black, venom black. But maybe you have bad memory as me. And you don't remember if it was kills a fellow, kills a fellow, OK, no. Better use an script in Python. It's safe. It's safety. In this case, we're at an image. I generate three scales, only to simplify. I get a middle line of pixels. And then I translate to obtain the colors. And that is what we have. This is the image that your friend saw in the nature. And this is the patterns that you obtain. And after that, you can say, hey, you are safe. Go to the right, because the left, it's a venous one. The biological strategy of this snake, if this is not a venous snake, it's a snake that imitates only the colors to be safe for the predators. I'm sorry. Well, my last story is, how does happiness look like? Do you have an idea? Do you have an idea how does happiness look like? No? No? Well, first of all, a description of happiness, overall appreciation of one's life as a whole. Well, it's one definition of happiness. But after that, I want to know, what is the world happiness countries in this world? Based on the world happiness report of this year, these are the top five countries. If you are from one of these countries, please share with all of us what is the secret. And please, because we need to learn why and how arrived there. Well, imagine that you want to visualize the happiness. We can use this model that is called RD Kit. And really easy way, we can use this mind that it's a chemical formula. We can transform these formulas. And we can visualize the happiness. Here, we have the four hormones in humans that are related with happiness. It's that all now. We have a lot of models and packages related with biology and science. Only briefly, two words about Ecopy. Ecopy, it's a model used in ecology. And we can measure the diversity factors. My take home message are the four ones. Python helped to scientists in this specific case, in biology, as you see. But biology is helping to computing, or inspiring to computing, and also to Python. If we work together, these scientists start to collaborate more with tech people. If we normalize Python in science, we can increase the diversity in the community. And this enriches Python and enriches all of us. And of course, if you have some idea of model package, please do it, generate more tools. Because even you think that it's not so important. Really, there are a lot of people who are using these tools anonymously. Well, this is what happened, where some biologists met Python. And I finished only saying that all the information is available in my GitHub app, all. And thank you very much. And I hope that you enjoy the talk. Thank you. Finish your PhD and then you started working with Python? What happened? What introduced you to Python? When I was doing my PhD, I realized that there was a lot of manual tasks that has no sense to be in these years doing this task by hand, or not automatically. And even I tried to introduce some changes. You know, the changes needs time. And in academia, it's quite conservative. And when I finished, and I was with my own, I decided to explore all these interests that I had to do the things more automatically and more effective way. Thank you. Thank you.