So thanks, Patricia, for this nice introduction and the invitation to be part of this wonderful workshop today. So, microbe is developing computational methods for drug discovery applications. And obviously, as you can imagine, deep learning plays an increasingly more important role in this domain. However, our domain has quite some issues if you compared, for example, to image recognition or to large language models. And the issue is that we have a much smaller set of data, usually in the amount of hundreds or thousands of data points. And that's why training a deep neural network model on such a small data set is quite challenging. And that's why our effort in our group is to try to incorporate as much as possible of physical chemical knowledge into deep neural networks. So to say, combine the best of two worlds, the knowledge we have gathered over the last decades on physics, chemistry, biology, and the other side, learning from the data which is existing. And the hope is that with such an approach, we can learn models using a much smaller training set size and to generate model which generalize much better to unseen data. And this, I think, is a huge challenge. And if you look at a lot of papers which are coming up, I think those, a lot of these models are actually very poorly validated about channelizability. So the type of data which we try to include in our neural networks is our knowledge about physical interactions between molecules, effects like salvation, or even aspects on protein ligand dynamics. And I will show on a few examples how we try to incorporate this knowledge into deep neural network models. So as a summary, I will start with a short introduction about the fundamental questions we actually have in computer data track design. Talk a little bit about the type of neural networks which we are working with in our group. And then I will show you on two examples how we can actually combine this physical world with the data science world. We'll talk about high content screening and the novel design. And then about protein ligand talking in order to predict how drugs actually interact with the target protein. So drug discovery is actually a very time and cost intensive process, starting from the identification of the mechanism of a disease to the identification of the target protein, then finding hit and bleed structures which might be able to target this target protein. And then we typically have a very long cyclic optimization process where we design modifications, we synthesize the modification and then evaluate them biologically. And then with the optimized lead compound, we go into preclinical and clinical studies. And hopefully after an average 10 to 15 years we can go into the approval phase of our track candidate. Now AI plays a more and more important role along this whole trajectory starting from the identification of target proteins, for example, based on omics data, AlphaFold, which we have heard a lot to actually generate a structural model for our target protein. And then aspects which I will particular talk about today to then identify hit and bleed structures, for example, by screening billions of molecules to use docking to identify how those compounds actually interact with the protein. And then we use more and more AI models to actually assist in the synthesis planning of those compounds. We have also now working on methods to estimate efficiently the binding affinity of compounds. But that goes even further into preclinical studies where we have no models to predict so-called pharmacokinetic properties, so how the drug is distributed in our body to tox prediction, which is a very important hindrance of compound development and even to the selection of candidates for clinical trials. As you see that in all the aspects, AI machine learning plays a more and more critical role. Now the question which we are particularly in our group interested is in the so-called drug design part, so which ranges from the hit identification up to the optimization of lead compounds as well as the earlier toxicity prediction. So let's imagine you have this 3D structure of your target protein available. Then the first question which we have to address is where's actually the binding site on this protein? So if you have an alpha four structure, for example, a crystal structure, where can the ligands actually dock and bind and interact with the protein? Then you have typically a large library of nowadays billions of potentially compounds which you can synthesize relatively rapidly. So the question is which of those ligands is actually able to bind? And then how does one of those compounds for example bind to the protein? So which means what's this orientation? It's position in the conformation. And this is very critical if you then want to make for example, rational modification to optimize the interaction of those compounds with your target protein. And then finally we want to be able also to predict what kind of modifications, chemical modifications need to an improvement or also deterioration of the binding infinity in order to optimize the interaction strengths. Now as I said, there are a lot of classical computation methods which try to address these questions but what we are trying to do is to combine AI models, deep learning methods with physical chemical modeling. And the type of networks which we're using, I will just summarize them here in four different slides. I will use them throughout the different sub projects I will discuss with you. convolutional network which you have already seen before. So it's a principle usually used for processes, processing images where in principle run a filter like a magnifying glass over parts of a text here in order to analyze the whole phrase here. And you can do the same with images where you have so-called kernels of filters which are running over the image to generate more and more abstract features of the original image which you can see here down at the bottom. And then we can use this combination of features to make for example a classification if the input image is a female or male face here on the bottom. Another network which we are quite frequently using in the context of chemistry are graph neural networks. So graph neural networks are sharing quite some similarities with convolutional neural networks where convolutional network in principle use information of neighboring pixels to get some more characteristic futureization of the pixel of interest. And you can do the same with graphs where you have of course a much more heterogeneous structure. You can for example learn about the environment of this node by neighboring nodes which are connected by edges. And this node edge, so this graph representation is of course very feasible to handle chemicals where the nodes would be atoms and the chemical bonds would be your edges. And using these graph neural networks you can actually obtain a very sophisticated futureization of the environment of a certain function or the whole molecule itself which can then be used for a downward tasks in the modeling. Now one model which we are quite heavily using are transformer neural networks which you have heard already in the context of AlphaFort. Obviously, chat GPT is based on transform models or also Google translate. And they in principle are working on the so-called attention principle. So for example, if you want to translate a sentence here in English to a German language then what you do is in multiple encoding stages using the attention principle you learn how for example the word it is connected with other words in the sentence or in the whole paragraph if you think about chat GPT. And if it goes from multiple this encoding layers you get an abstract representation of your paragraph in reduced embedding which we call also the latent space. And from this latent space you can then go through a decoding stage by translating word from word for example the generated German word. So for example you start with the start codon and then you translate based on the embedding you have generated the translated sentence word by word backwards. And for example chat GPT is actually using only the decoder stage where instead of using the start codon you use in principle the prompt you have generated and then chat GPT is generating word by word as a response to the prompt which you have provided to chat GPT. Now one other model which has become very popular in the last two years are diffusion models. They are typically used for image generation. So if you want to generate images with Dali or stable diffusion they use this type of generative models. And the idea is the following that you start with an image and you add stepwise more and more noise to it until in principle you have a completely noise image. And then you learn in your network which in principle denoises the image one by one. So it learns in principle what amount of noise it should remove to get back to the original image. And if you condition this network with the text prompt you can then actually generate it completely new images based on the text provided. So here's just a few examples. So if you give for example here impressionist landscape of a Japanese garden in autumn this bridge over a coy pond the leaves of the trees vibrant colors you get this wonderful picture. Or for example here if you want to have some cats eating eyes in front of a Japanese temple then you would for example get this beautiful image here. And this is just learning by the text prompt and the diffusion model to reproduce in principle to generate images. And we will see how you can use all these models now in a biological or biochemical context. So we're using these different type of techniques to come up with a completely new putated drug discovery platform. Again, trying to combine physical chemical knowledge with deep learning. And this network in principle goes through all the whole stage of drug design generating the novel design a library of compounds which then undergoes virtual screening to find some initial hit compounds which bind to the target protein of interest. Then you run them through some post prediction and post ranking to actually get the complex structure of your protein ligand complex. And then you can even want to go to affinity prediction to get an optimized lead. Today I will talk more about the first part of the presentation. So let me start with the first example and this is how we can use these type of architecture for high content screening and do novel design. So what we typically do in a high content screening so this means screening of billions of compounds which can hopefully identify some compounds which target our protein of interest. What we typically do is something called virtual screening for example here similarity based virtual screening. And that starts typically with one known active compound null which functions as a query molecule. And then we have our huge library of molecules and what we do is we take compound by compound from this library and measure the chemical similarity with our query molecule. And the idea is that compounds which are similar in chemistry to our query molecule should also have similar biological activity. So what we do is we then rank our library of billions of compound based on the similarity to the query molecule and the top ranked molecules are then hopefully additional active compounds which we then can further optimize in our drug design process. And the question is how do you measure this similarity? How can you represent chemistry in the computer in order to compare to compounds how similar they are from a chemical standpoint? And I just want to give you two widely used examples. One is fingerprints. As in sprints of what you do is you analyze the topology of your compound and translate them in something like a barcode which you use in a supermarket. And then you just compare the similarity and differences of the barcodes in order to say which compounds are similar and which compounds are different from each other. Or you can do this based on shapes or pharmacophores. And this is important. I will show you an example for this in a second. Sometimes compounds which are chemically very different, they actually have a different, similar interaction with the protein of interest. You see two crystal structure of these two compounds and here you use the superimposition of the two compounds. And then you see they share their shape and they share their interaction with the protein of interest. And also sometimes you need to go beyond this simple 2D representation of chemistry to a more 3D representation of chemistry. Now what I also should say is that this process is relatively slow. In particular if you think about 3D similarity which means you have to align in 3D space first the ligand onto the other ligand and then measure in 3D the similarity of the compounds. So this is quite a long process if you think about that you have to screen or you want to screen billions and multiple billions of compounds. So we had the idea to do this in a completely different way. And our idea is to try to translate this very high dimensional chemical space into a low dimensional latent space. And the idea is to model this latent space in such a way that the chemical similarity is conserved. And so that means that if compounds are similar in chemistry they are close by and molecules which are different are more distant away from for example these compounds of interest. And what this then allows us is to just use Euclidean space distances. So which means you just for example take from your query molecule all the atoms which are close by and then you directly know all the compounds which have a high potential to be also active against the target of interest. And that will be an extremely fast process which can also work then in three dimensional space. So how do we do this? How do we do this translation from the high dimensional space to the low dimensional space? Well, we use what we also use for a natural language processing. We do an out encoder based on transformer models. And here the language is not the natural language of words and sentences, but it's a chemical language which is called smiles here for example. So this is a consequence of letters which completely describes the chemistry of your molecule. And what we then do is we use again as I mentioned a transformer model to translate this language into a latent space or reduced embedding. And with this embedding we want can also then decode back to the smiles the compounds which we put into this out encoder model. And what we developed is an additional loss function which allows to generate such an embedding that it conserves actually the chemical similarity but again in this reduced latent representation. And then as I said before we can just look in the neighborhood and identify all the existing compounds in the neighborhood. And that would be our prediction of potentially new active compounds. Or we can also use it for the noble design. So we can use the same volume but now just randomly generate points of compounds which have not been generated before. And that allows us to generate completely new chemistry which nobody has seen before but which might have a high likelihood to bind to our target protein. So this is really true generative modeling of chemistry and targeting specific target proteins. So does it work in practice? So here's an example where we actually screened 1.5 billion compounds against one reference compounds. We did this for 10 very different systems. And the aim was to identify the 10 most similar compounds for which we know the ground truth. And what you see in the results without our additional loss function to form the latent space based on similarity we are not be able even up to a very large number to identify all the similar compounds. While with our methods in the top 15,000 which means 0.001% of the full data bias we can actually identify all similar compounds to a reference structure. So this gives you in principle an enrichment of a factor of 10,000 to identify active molecules. And we now went actually to 3D space. I showed you this slide before where we was actually shaped in pharmacophore similarity. We trained a model similar way as I showed you before. And as you can see here in the first tests and this is relatively fresh from my graded student Manuel Selner that if you start, for example, with this molecule Rallivixifin which is actually a drug binding the aspiration receptor and anti-cancer drug then within the top 10 structures using our transformer model you could identify compounds which are completely different in their chemical scaffold. But from these three compounds, for example we actually know that they also bind to the same receptor sometimes even with different pharmacology which is actually quite interesting. Now in the second part of my talk I will talk about protein ligand docking. So just to give the context to the previous part of my presentation so what this high content screening typically is doing it reuses the large chemical space to maybe only 10 or 100,000 of molecules. Then we can use more sophisticated techniques like protein ligand docking to reduce this chemical space even further before you actually start to testing them in an experiment. So what is protein ligand docking? So protein ligand docking is principle D method so if you don't have experimental crystallography or cryoAM data available to generate the structure of protein ligand complex. As AlphaFold right now can only predict protein structures when protein ligand structures is currently still a very hot topic on the investigation. So classically how we do this is that we take the protein structure of interest with a certain shape and certain physical chemical properties and then we take the ligand with a similar shape and physical chemical properties and then we generate millions and billions of different configurations and each of these configurations which we also call poses is then evaluated with a scoring function which principle measures how good the ligand fits to the binding side of the protein. And then the pose with the most negative score is then the predicted protein ligand conformation. And as you can imagine by generating millions or billions of these configurations this is a quite intensive process if you think that you want to dock hundreds of thousands or millions of compounds. And typically what you have to do is you have to find a balance between the number of configurations or poses you have generated and how you evaluate the protein ligand interaction strengths using our scoring function. And typically this is tilted towards the generation of poses on the cost of a precise evaluation of those complexes. And what you typically also do is you keep the protein rigid because otherwise the conformation space would just explode and you would not be able to correctly sample at all protein ligand complexes. So we asked the question if we can overcome this limitation of classical protein ligand docking methods. And the first approach which we developed this is my student Matt, my senior scientist Amir in our group is to come up with a completely different idea to do protein ligand complex prediction. And the idea is that you take a training set of protein ligand structures from the PDB you abstract the protein structure by its C alpha atoms and that allows you to model the side chains in principle implicitly. So you include actually some protein flexibility into this context. And then from this protein known protein ligand complex you can calculate the so-called Euclidean distance matrix. So this is all the distances between all the atoms of your ligand with all the C alpha atoms of your residues in the binding side of the protein. So this fully characterizes how the ligand interacts with the protein. And then what you do is you represent also the ligand as a 2D representation because you don't know typically what the confirmation of the ligand is. You feed this protein and ligand information into your network and train it so it can reproduce this Euclidean distance matrix. And once you have trained such a model you can just take the protein structure you abstract it by the C alpha atoms. You take the ligand in its 2D representation you run it through the neural network it generates automatically the Euclidean distance matrix. And this allows you in one shot to actually regenerate how the ligand interacts in the binding side of the protein. There's no sampling just in one runs through the neural network you get the protein ligand complex you then of course need to regenerate your side chains into some refinement but in principle it's no longer sequential it's actually a very fast process. It's just a little bit more details how we actually do this. So we take the ligand as a graph, 2D graph the protein as a three-dimensional graph we feed them to these graph neural networks and then use this advanced featureization or embedding of each molecule and each node of your binding sites put them together, run them through another neural network to get this protein ligand distances which you then can use together with some optimization to reconstruct the protein ligand complex. And this actually works relatively well in practice we compared this for example with some standard techniques which are used for example the auto dogmina which is a very commonly used technique and we see that we can improve our the performance of the model compared to this classical models and which is also important to a significant reduced cost compared to classical dogging methods. Here you also see that we can actually incorporate flexibility relatively nicely in our model here just for example if you look here at androgen receptor so this is the known complex of the ligand bound protein. Here in red you see actually the unbound state so this is the crystal structure without ligand you actually see that it would actually overlap with the ligand so if you don't include this flexibility you will never be able to bind this ligand correctly but what you see is that in our methods the side chains here the thionine and here the slicing were flipped over from this red to the quake line and then allows the ligand to perfectly interact with the binding side of the protein so which means the method is also not just fast but it also allows to incorporate necessary protein flexibility. Now this method right now was only being possible if we actually know the binding side but often we do not know the binding side and this comes into the problem of so-called blind docking and the question what can we do in this context where we don't know the binding side. Now what I also show you here is that very often we do not only have one binding side so this is here for example at GPCR and you can see that we actually have multiple binding side also steric binding side, multiple allosteric binding sides and obviously the binding side is dependent on the ligand of interest so some ligands bind only to a specific binding side and we of course do not only want to identify one binding side but we actually want to identify for a given ligand what binding side is actually relevant for this ligand. So the method which we've developed is called a pocket net and this is a method which again tries to incorporate physical information with deep learning. So what we do is we use some classical pocket prediction methods which are based for example on physical chemical information which generates an ensemble of pockets and then we run this information into a neural network to get with the conditioning on the ligand and then get a re-ranked ensemble of the binding pockets. So how does this work in practice? So what we get from these tools are in principle multiple pockets. From the pocket, from each pocket we generate again a graph of the binding side of the residue surrounding this binding pocket which generate graph of the ligand, some additional features for example the score which we obtain from the different methods but also some physical chemical features of ligand and binding side and then feed this into a simple neural network which again gives us a score about the principle which can be then translated into a rank of each binding pocket. And that actually works quite nicely. You see that our model outperforms quite significantly existing methods to identify binding pockets and in contrast to these methods we can actually also identify the specific binding side for a given ligand which these other methods cannot. There you still need to figure out which binding side actually each ligand would bind to. However, there's a new kit in town. These are diffusion models and diffusion models have been in the last year become very popular in the biology or pharmaceutical area and I just want to show you one example for this. This is DiffDoc from MIT and I showed you previously how you do a diffusion model for images where you in principle noise an image and then you denoise the image. You can do the same with protein ligand complexes. You can start with the known protein ligand complex from the PDB and then for example the ligand would be binding here and then you know noise this protein ligand complex by adding translation rotational and torsional noise to each ligand which means gives you then the ligand in any place around the protein surface. And then if you learn a model which can actually go the backward direction so which means you start from many different configurations of the ligand and then principle denoise it back to hopefully the correct protein ligand complex. And this is a completely new way how you can predict protein ligand complexes and in the initial paper very good results were shown but on a very small test set there was no generalization tested. So and we tested it then actually on a much more chilling data set and the method completely failed which means this is also an indication that shows you that these type of models which are trained on a relatively limited amount of data cannot generalize to your protein or protein ligand complexes. And our idea now is to integrate physics into these diffusion models. And we do this in three different on three different levels. First we calculate salvation information in form of hydration sites. We'll talk about this later in more detail. This is a critical component if you want to understand protein ligand interactions. We also predict interaction sites so-called pharmacophore. So that tells you in principle which kind of chemical entity of a molecule binds at a specific place in the binding site. And then we also add physical interactions throughout the diffusion process. For example, repulsive forces that the ligand cannot collide with the protein. And if we do this, then so as again we tested this on a much more realistic data set where we do this three to one split. And in particular that the test set is as diverse as possible to the training set. So because you don't want to test it on a system for which you already have data that's not interesting for drug discovery applications. And what you see here is if you do this on a more challenging data set the success rate of DiffDoc which was originally published to be around 40% drops to below 10%. But we can actually rescue the performance of these diffusion models by actually integrating physics where we reach now a success rate of around 40% at least in the top one position. It's still not 100% and we are still heavily working on that but you can see that by integrating physics you can actually much more significantly improve the generalizability of these neural network models. Now the last few minutes I'm going to talk about the second part which is actually the scoring problem. So far I talked about how we can more efficiently sample more precisely sample protein ligand complex. How can we now improve the quality of scoring? So which means estimating the strengths of interaction between protein and ligand. And for that we use classical convolutional neural networks which are usually used for using pixelated images with different colors. We use RGB colors to represent the colors so these would be multiple input layers and which represents for example, different birds, cats, dogs, and ships. We do them, run them through multiple convolution layer to get abstract features which you run then through fully connected neural network to classify for example, that this image here is an image of a boat and not a bird, cat, or a dog. And we can do a similar approach now for protein ligand complexes. But in this case we are not talking about 2D images but three-dimensional images. So what we do in practice is we take known and protein ligand complexes but we take the ligand out and we dock it into the protein structure. We generate 25 poses at least one of them is the correct configuration. And then what we do is we represent the protein by different layers of a three-dimensional image. For example, one layer would be the density of carbon atoms. One would be the density of oxygen atoms, et cetera, et cetera. We do the same on the ligand side where we have different densities carbon-dense oxygen densities of each of these different poses. And then we combine these layers in three dimensions from protein and ligand into the convolution network and that convolution network then learns to classify which of those poses is the correct one and which is the incorrect one. And if you apply this, so what we did is we actually trained this only on 600 protein ligand complexes with very small training set because we really want to see that we can generalize to very new protein ligand systems. Here we use a test set of 2000 protein ligand complexes. And what you see here is by using this re-scoring with the convolution network we can approve the quality by about 10% compared to classical M-scoring methods which is good, but not what we actually would have wished for. So we asked the question, what is missing in our model? So far in our model, we only have studied the protein ligand, direct protein ligand interaction because we have only used images of the protein and images of the ligand. However, we know in protein ligand binding that the free energy of binding which actually determines if a ligand binds, how the ligand binds is actually the free energy difference between the bound complex and the unbound structure. And in the unbound structure, protein ligand are not in vacuum but they're surrounding by a lot of water molecules. And before the ligand can actually interact with the protein, these water molecules have to remove from the binding side and around the ligand which we call desolvation. And this desolvation is actually very critical for protein ligand binding. It's actually very often the driving force for protein ligand binding as well as for protein-protein interactions and other association events in biochemistry. But also sometimes water mediating interactions between protein and ligand. So how can we now incorporate this information into our neural network? So how can we incorporate more of the physics into our neural network? So what we actually did is we predict now this hydration information. So we run, for example, MD simulation with a tool which we developed a long time ago, what site to measure the density of water which gives us then this high density clusters. And from the trajectory, we can then also predict entropy and the free energy of binding for each of those water molecules. Then gives us a clear picture. For example, all these red water molecules are water molecules which are not happy. So if you replace them, that would be actually beneficiary for the binding affinity. And in blue, you would have waters which are actually happier. So you don't want to actually replace them. And based on this profile, you can actually estimate which binding pose is more fably than other binding poses together with the protein ligand interactions. So how do we incorporate this? We use this information, generate now again additional layers. For example, water occupancy or the free energy of deservation and add them to the input layers of protein and ligand. And again, learn a convolution network. I will talk about this in a second. If we applied this on the same training and test set, we saw actually now suddenly a significant improvement in the docking performance where we can reduce the error from about 40 to close to 10%. And that shows you that if you integrate really your knowledge into this neural network, here, for example, the importance of salvation and deservation, you can significantly improve those models even with a very small training set. What we also did with this model is a lot of people think neural networks are actually black boxes, but this is actually no longer true. You can do a lot of interpretation of the neural network. We used here something which is called layer-wise relevance back propagation. So in principle, you learn what of the input features are most relevant for the output of the neural network. And from that, you can actually obtain a lot of valuable information. For example, you can identify the importance of the different parts of the ligand, the most important residues. But what we are particularly interested is actually the deservation contributions. So for example, we identified water molecules coinciding with water-mediated interactions, which you see here, for example, in the crystal structure. We saw water molecules with positive enthalpy. So this is actually not surprising because this are typically in hydrophobic regions where water molecules don't like to be and that has to be typically occupied by hydrophobic portions of your ligands, nothing else than the hydrophobic effect, which we are quite familiar with. But what we also identified, that was a little bit surprising. We found water molecules with negative enthalpy, which are important for estimating the correctness of a protein ligand complex. We call this anthropically favorable first shell water layers. That has not been recognized until this study in much detail or even a very few studies before. And for example, by Geyhard Kleben, Marburg, and what he had, for example, then identified in crystal structures is that, for example, this ligand here is more of this ligand. It has nothing to do with direct interactions between protein and ligand or deservation. It has something to do that this ligand destroys this anthropically favorable water network, which is something like a cap you put on something though that the water cannot go out. And here it's principle the same principle, or if you want to say it in simple words in principle, if you want to break out out of the prison or the binding site, then here you have to actually pay a higher price because you in principle have to destroy this water network. And this brings me to the end of my presentation. I hope I could demonstrate that if you actually integrate your physical knowledge into neural networks, you can significantly improve in particular the generalizability of this model. And this is actually a huge challenge, not just in our field, and sometimes it's a little bit underappreciated because you really want to apply this new network on your stuff, not just the interpretation of stuff which you already know. And with this, I'd like to thank the people actually who did the work. I showed you results from Manuel, from Amre, and from Matt predominantly today. And with this, I'd like to thank you for your attention. And thanks again for the kind invitation to this workshop.


This is going to be a picture of you, so who will volunteer to help us? Won't someone stand up? Ah, that's fine.  Just stand still a minute, friend. There now. You can sit down, too. And now, let's get on with our story.  Now, you have already learned to protect yourself from many of your enemies. For instance, you've learned to wear clothes to protect your body from heat and cold. You've learned to grow fine crops. And you raise pigs and chickens. These things all protect you from the enemy hunger. And if a storm should come, you find shelter in your house. Or you've learned to protect yourself from the weather, too.  No, no, no, wait. It's no use running away. No one can run away from disease. Yes, that's right. It's disease that is your worst enemy. For disease can be anywhere. In the air you breathe. In the ground you walk on. Or, hey, wait, don't drink that water. Disease can be in water, too. Yes, I know, it looks clear and pure. But you see, disease is caused by tiny living creatures. A creature so small you couldn't possibly see them. This water may be full of them. But here, let's call on a man who knows all about these little creatures, the doctor. He studies disease every day by using a very wonderful instrument, the microscope. Now there's nothing mysterious about a microscope.  But suppose we show you how it works. Fine, doctor, we'll borrow your glasses. Thank you. And, oh, just a minute, Mr. Fly, we're going to look at you through the doctor's glasses. See, these glasses will magnify. That means they will make the fly look bigger than it really is. But let's try a stronger magnifying glass. You see, now the fly looks even bigger. If we use two strong glasses, the fly grows larger again. And if we use three or even four glasses, the fly begins to look like a monster. Here, let's look at his leg. Why, his leg is covered with hair. And so, through our magnifying glasses, we're seeing something we've never seen before. Now, if we take these four glasses, put a tube around them, we have a microscope. Simple, isn't it? And yet, by looking through this instrument, we can actually see the tiny creatures that cause disease. All right? Let's try our microscope on some of that water you were going to drink. This little piece of glass has a drop of that water on it, and we're going to look at it through the microscope. Come on now, everybody, watch closely. There, see?   Well, just look at this giant tree, tall and sturdy and strong. You'd think it would take something mighty big and powerful to hurt this great tree, wouldn't you? But let's get real close. Do you see this little beetle on the trunk? Just watch him. See? He's boring a hole in the bark. Well, one tiny beetle certainly can't hurt a big tree like this. But suppose we go inside and see what it's doing. Look at that. The beetles drill deep into the wood and laid its eggs. And now look, the eggs are hatched, and the young beetles are boring more holes and laying more eggs. Then there are more beetles, and still more, until, finally, there are thousands of beetles all eating away at the life of the great tree. Now the tree becomes sick, and before long, the tree dies, killed by tiny enemies much smaller than itself.  If we could look inside you, we'd see that the microbes are carried down into your intestines, like this. And now we can watch the little enemies begin their deadly work. At first, there may only be a few of them, but see what is happening.  And these grow and split again and become two more. Now the microbes multiply by the thousands, just like the beetles in the tree.  But remember, it isn't only in water that we find these little killers. No. With our microscope, we can find microbes almost anywhere. Suppose we take a look around your farm. What's this? You have no latrine? You use the cornfield? Well, remember we said there were microbes in the soil? You see? Here they are. The vicious, deadly killers that live in soil foul with human waste. But these microbes are not only in the field. They may be right inside your house. Let's go inside and see. Well, there are some flies on the table. Now you may think that flies are harmless, but let's look at the fly's leg through our microscope. See? It's covered with filth. If we look closer, we see the filth is full of microbes from the field where you left your waste. And when the fly walks across your food, he'll leave thousands of these deadly killers behind. But microbes can be found in other places, too. We see that many people sleep in this room. And one of them is sick. This is where we find microbes in the air. Microbes that came from the sick person's lung. And now, as the others breathe, they draw into their bodies the disease that is in the air. If we look around, we may find other microbes in this room. This is the anotheles mosquito. Our microscope would show that this anotheles mosquito is full of malaria microbes. And when the mosquito bites you, it will leave many of these microbes in your blood. Then you will have the disease the mosquito carries. Yes, it's true that disease can be anywhere, and you must protect yourself against it. You must fight it. How? By attacking disease before it attacks you. First of all, build yourself a latrine. That way you'll destroy the breeding place of disease and the flies that carry it. Cover your food so the flies can't get at it. Cover your bed at night. Then the mosquito can't reach you, and you'll be safe from the disease the mosquito carries. So we have seen that there are many kinds of microbes. But remember this, they are all living creatures. Therefore, the microbes themselves can be killed. For instance, the microbes we saw in the water can be killed by boiling the water before you drink it. The microbes that live in dirt and filth can be killed with soap and water. So wash yourself regularly.  Fresh air is your friend too. So let's put windows in your house. And let the sun and fresh air come in and help you. Now that you know what disease is, you must fight it wherever you find it. Protect yourself against it, and you will make your life healthy, happy and prosperous.


What's going on guys?  Now not too long ago a reddit user dropped some news on the Destiny community and that being info on the next DLC expansion. He stated it was called Rise of Iron. Now obviously people were sceptic as they should be but within the reddit post was this image which you guys can see on the screen now. It clearly shows what looks to be a very real looking poster of the next Destiny expansion. Yes of course this could be fake no doubt about it but let me tell you guys something I have worked with Photoshop for many years. There isn't much I don't know about it also studying art my whole life which led to me studying graphic design among many other things. Creating a poster like this would be a great great difficulty although not impossible. It's a little bit of too much work in my opinion just to be a prank under Destiny community which is why I think this image could be 100% real unless it's some leaks concept art from Bungie which it could actually indeed be. At this stage we truly don't know but let's take a closer look at this image anyway. It depicts no doubt a titan wearing that also sexy iron banner gear also surrounded by wolves and the rumoured expansion this poster claims to represent being called Rise of Iron. It's clear that whatever this poster represents is 100% iron banner related so how right went our thoughts which enters one's head. DLC based on a monthly PVP event that don't quite cut it for me nor do I think would it sell it. What we could be seeing here is the story of the iron banner how it came about, what it represents and why it is what it is. Could this iron guy be or was going to say man but that don't fit right? Could this iron guy be the true iron lord? Makes more sense to me. Now I've done a little research into the iron banner lore and there isn't much info out there so let's take a closer look at the poster.  He also seems to be wearing a cloak but I wouldn't fall too deep into that. We are also seeing wolves and snow. No doubt this dude's just probably feeling that breeze and don't want to get his balls cold. The buildings do kind of look reminiscent to those on the cosmodrome but I could be wrong and that's basically all we have. I mean it's easy for one to jump to the conclusion that this could be a new planet or another add-on 2A planet we already have. The cold side of the earth where wolves roam wild. There could be a million explanations for this poster. I guess we won't be finding out any time soon. Well not until E3 anyway. Hopefully at least by that event we will know if this poster is legit or fake.   Dude must be as bored as hell and on that note I'm gonna end the video. Tell me your thoughts in the comment section on this poster. Real or not let me know. Thanks for stopping by as always. Hope you enjoyed the video and I'll catch you guys on that next one.


Okay, so we all are aware of the basic data models, which are available in DHIs too. So we have two broad models in which we collect data, just to refresh people on the data models and the people who are relatively new to DHIs too. So district health information system that this DHI has to allows two kinds of broad data types to be captured through its framework. One is the aggregate model, where you kind of enter pre-summarized information in terms of numbers on a fixed frequency, which could start from daily and then can range from weekly, six monthly, quarterly, annual and different frequencies which are available, depending upon the kind of data you're collecting and at what frequency you would like to collect this data, based on your program data and monitoring evaluation needs. Then we have the data sets, which are basically the source through which you're collecting your aggregate information. So you define your data elements, which are your placeholders for the information which you want to capture and you combine your data elements into a data set which basically becomes your reporting form for your data collection. Then we have tracker model, which has two components. Most of us are aware of this terminology, the event programs and the tracker programs. The key difference between the event and the tracker programs is the kind of data which you're capturing. So when we talk about event programs, basically these are line listing registers where we are making note or making a record of one specific event and we are not tracking the event program. We're not tracking the specific information that we're capturing in the event program, but we are creating instances of an event which are happening in our program. So the most basic examples are, for example, a birth registration program where you're registering each birth or a death registration or any vital event that happens, birth, death, marriages, you can kind of make a record of it and store it as an electronic information in your system. More relevant examples could be many organizations conduct these behavioral change events or SBCC events where they just want to make note of when the event was conducted, where they were conducted, how many participants it came up, how many male, how many female. Just basic information for that specific event, which happened at one point of time and you want to make a note of it. Then the other thing is tracker programs. As the name says tracker, it means that you're tracking individuals or tracking a respective entity, which could be anything from a person to a household, to a village, to a lab sample, to a commodity. So given the data model and the nature of DHS2 is very generic and flexible, you could define the entity which you want to track. So the more we go ahead with the tracker concepts, we will see that in our use cases, what use cases are we going to cover and how we have modified the DHS2 data model to capture data for use cases. So tracker programs would allow you to track a person over a period of time through different services, which you provide to that person, which could be either based on a clinical protocol, as they've seen for HIV and TB programs, where you have fixed follow up visits to be done at certain point of time to kind of maintain the continuum of care. Or you could have ad hoc reporting on the basis of the visits the person makes to the clinic to get services from a physician or a doctor. So you can apply the tracker programs to different use cases. We will be covering two broad use cases for COVID surveillance and COVID vaccination, which were taken up hugely on the DHS2 platform over the last two years over the course of the pandemic. In case you have a specific use case, which you would like to discuss, please feel free to put that use case on the Slack channel. And we would be happy to discuss those use cases with you individually, or if you have time during the academy, then you could also take up some interesting examples and discuss those. So as I just mentioned, so even programs collect individual data, but there are no registrations involved. That means you're not tracking an individual person, item or a substance. So these are useful when you need granular data, but you don't want to track and perform any individual level follow up. So as I mentioned that you want to capture data for births, for deaths, for maternal deaths, but then you're not tracking that specific event. You just want to make note of that event and the data which is associated to it. So any sort of service, a valence line is which you're creating. They could be your event programs. You need to have a good foundation of the event programs and the event data model before you start configuring your event programs for your area of work or your organization. There is a DHS2 event fundamental course available online. We will share the link of that on the Slack channel as well. You could go and join that course, which will take you through to the basics of the events model, which covers both events and trackers. So that would be a useful start for people who are looking to adapt trackers in their respective areas, tracker plus events both. Now as I mentioned, tracker programs, they will collect individual data. They will involve registration of a person or an item or a substance. So this would involve the registration and subsequent follow-ups for that specific track entity, which you're trying to track over the period of time. So here, since we're talking about individuals, we were talking about persons or lab samples or households or villages. We always need some unique identifiers to identify the person or the entity across the programs where a person could be enrolled into multiple programs. So I have programs in my system for HIV, TB and non-communicable diseases. So as a patient, I could be part of multiple disease registries. But in order to identify that specific patient in the system, you need to have some set of identifiers available, which could be your national ID, which could be your health ID or individual program ID, depending upon what unique identifiers does the program considers when it comes to data collection and use moving ahead. As we have seen already that the tracker programs will have more consideration in terms of identifying such unique attributes and personal identifiable information, they're more complex as compared to the event status model. And during the course, we will be spending time to understand this model in more detail and we will see how we can use the tracker capture in terms of data collection, in terms of data analysis, both on web and Android, so that the foundations of the use of tracker capture in specific use cases is easily understandable to you so that you can adapt the learnings into your specific programs aspects. So our target audiences for this academy are people who want to understand DHS to tracker and want to know that how they can use tracker for data entry and the associated analytics. In addition to this course, this will just give you an overview of the track to use. But we have several other courses which are which are being planned and will run online over the course of next year as well. So you have tracker configuration, we are planning to have a course in December this year for configuration. So this would take you through the more detailed concepts of DHS to events and tracker data model and it will take you to the details of how you actually configure your events and tracker programs. Then we have a specific course on Android implementation. So Android implementations will have their own considerations in terms of choosing your devices, rolling out your devices in the field, using mobile device management systems in terms of the technical functionalities. In terms of technical functionalities, how does Android differ from the web and what kind of considerations you want to keep in mind if you are doing the Android implementation. So that's a specific course that has been developed to help organizations and the community who are looking to use DHS to Android app for their program implementations. Then we started a new course last year or earlier this year on tracker implementation management. So basically the first two parts are really kind of talking about even this course talks about the technical aspects, but then the implementation is the is the crucial factor in success of any IT, digital IT implementation. So we have a specific course on tracker implementation management where we share experiences with different partners and use cases which we've already done in the past and what has been their experience in terms of the implementation of tracker system, what learnings they've learned, what challenges they faced and how the system can enhance the sexist criteria of your implementation if you take into account all the considerations which are beneficial for the tracker implementation. So I just request everyone to put your mics on mute, please. Yeah, so the course material has a basic assumption that you have taken the event fundamentals online or you had similar experience of working with events model in DHS too. So in case there are some terms that are not clear because of your lack of experience with DHS to before, then please do make notes and let the facilitators know on Slack, they will try to make you understand as much as possible. So that you can keep up the pace with the course content and the base to follow. So the learning goals of the Academy are to kind of increase the level of understanding of the features which are available within DHS to tracker, increase the ability to enter tracker data, utilize all the features which are there as part of data entry both on web and Android and increase the ability to create outputs from the tracker data. So we'll have a look at how you can aggregate the case-based information collecting tracker or the line listing information collecting events and how you can associate and triangulate different data sources in your DHS to instance when you're using aggregate and tracker systems together. So we will have dedicated sessions on those. The performance objectives are to explain what DHS tracker is and what different methods you can capture the tracker data. How you can utilize the different data to capture methods to enter the data for your specific programs. How you can review the tracker data and generate outputs using the in-built analysis tools in the DHS to and how over the course of a few past years, how we've got supplementary applications we've seen that can enhance the use of DHS to ability to create tracker data outputs. So we have examples of a couple of custom web applications which were developed during the pandemic for enhancing the capabilities of the tracker related outputs. So we'll have some examples of those and we'll show you how things can be extended apart from the default core functionalities which are available as part of the analytics package DHS to. So we'll go through the latest tracker features and we'll show you how to use them. We'll have sessions on the tracker data module later today. We'll have a look at both the web and the Android application on how we can capture data for the tracker data model. We'll identify the applications which are used to analyze the tracker data, both in terms of plan list and aggregate numbers. And we'll have a look at the different types of visualizations that we can do using tracker data, which are tables, charts and maps and how the dashboards could be utilized to plot the tracker data and use those dashboards as a mechanism to visualize the information, analyze information and disseminate the information with your team members across the organization. So just quickly taking you through to the course overview. We're at day one today. We were discussing the introduction to the academy. We will be using a model as our learning platform. So we will introduce you to Moodle and DHS to instances, which you will be using over the course of the next five days for performing different graded and ungraded exercises. We'll be covering two use cases in this academy, one related to covid surveillance, one related to covid vaccination. So we'll give an overview of both the use cases, how the program have been set up and what is the workflow that the program envisages. Then we'll have a look at the tracker data model, the various components and the key terminologies, which we will be using through next five days. Day two will focus upon the tracker capture web and tracker capture, Android, where we'll see how both the platforms could be used for tracker data collection. Day three will focus on the analysis of tracker data on Android. So in recent developments, which have happened in the Android application, offline analytics and in-device analytics have been developed as part of the new functionalities and enhancements. So we'll have a look at those and then we'll also have a look at the event reports app in the web version on how you can generate the data from the event supports app. Therefore, we'll have a look at three applications, event visualizer, which is another app available to visualize tracker data, then you have the maps app to to visualize a tracker and events data and how we can use program indicators to aggregate our tracker and events data and how that those indicators can be used in the analytics apps such as maps and data visualizer to plot various visualization items and use them on our dashboards. Day five, we'll have a small session on the custom apps for tracker data, which have been recently developed and were intensively used in the COVID surveillance programs and the COVID vaccination program as well. We have an exam, which would be basically multiple choice questions based on the content that were covered during the last four days in the academy. And we will be having a smaller closing session to gather feedback from you and tell you the plans for the next academies that are in the pipelines moving ahead. So in terms of the academy certification process, the course has your ungraded exercises, which are just for building your understanding. There's no marking them for these specific ungraded exercises. There are assignments, which will cover the grades of the course, the minimum and a final exam, which also contributes to your overall score. We have set a overall grade of 70 percent for between the grade assignment and the final exam for you to pass the course, be kind of not kind of judging you on the basis of the assignments and the exams. The questions would be from the material, which is shared with you on daily sessions and recordings. And if you attend or do the sessions later on as well, view the sessions later on as well. If you're not able to join during the academies, you should be fine. You will be more than be able to do the assignments as well as the exam. So this is the weightage, which has been given to different components. You have your daily attendance, so that contributes to 10 percent to your overall grade. There are assignments related to each topic, which are the grade assignments for data entry, web and Android, Android analysis, event visualizer and reports, maps, programmatic analysis and exam. So each of them has more or less equal weighted. So make sure that you submit all your grade assignments and do appear for the final examination so that it contributes to your overall rate of 70 percent, which is the passing criteria. So the exercise and assignments are basically based on learning by doing the ungraded exercises are there for every session. The assignments are there for some of the sessions. You will have ample amount of time to do both the ungraded exercises and the grade exercises during the course. The major input or submission that are required from you are the grade assignments, which are to be done after the sessions. And you could reach out to us via the Slack channel if you have any questions or clarifications on the assignments which are given to you. You will have some buffer time to complete all your assignments by Friday, November 5th. So in case you're not able to finish these assignments during the course of this week, you'll have a week more to submit your grade assignments, knowing that we all are doing the scores in between our work commitments. So we understand we might not be able to do everything at once. So you'll have a buffer of one week to submit a grade assignment so that your scores are added to the exam and your certificates could be generated accordingly. So the exam would be on day five. That is fixed. That cannot be extended. But your grade assignments will be extended by a week so that you can cover those during the course of the next week, proceeding the academy. So if there are any questions, please feel free to ask or you can put them on the chat box or use the Slack channel to ask questions. We prefer using Slack because we lose history of the chats after the meeting ends today. So please put your questions on the Slack channel. And there is a team of facilitators who will be answering your questions as early as possible. So that was all for the first presentation. And I think we've done the mentee session already for the attendance. We will share a word of the day in Zoom chat every day. You need to add this word of the day in the daily attendance quizzes and you'll have three attempts in case you misspell or copy the word incorrectly. So if you enter the word of the day, that means you have gone through the sessions and we will also put the word of the day in the course recordings as well so that if you're not able to attend the sessions live, you can review the recordings and identify the word of the day and put it into the modal platform so that attendance is counted for that specific day. So please ensure that you report the word of the day for each day so as to get your attendance counted for that specific day. So that was my last slide. I'll check the Slack channel and the chat if there are any questions. I think before we move on to the next session, we can take a quick break. So we meet after 15 minutes. So it's one o'clock India time. So we started 1.15 so we'll start the next session 15 minutes and we can take a break for now. So thank you so much and we look forward towards the next session.


Oh, it's always an exciting day for a new review. Hey guys, what is up? Welcome to my channel. For today's video, I'm going to be reviewing the brand new Tom Ford Shade and Illuminate Concealer and the Shade and Illuminate Blushes. So if you want to see my thoughts on them, then just keep watching. Hi guys, if you're new here, my name is Morgan. I am a product knowledge enthusiast. I just love knowing anything and everything about all of the new makeup on the market and sharing my thoughts with you guys. And I took a gander over to Nordstrom today. I actually did the online order pickup, which if you haven't done that, it's a godsend. It's amazing. So the new Shade and Illuminate items launched online at Nordstrom and I saw that they were available for pickup yesterday. So I added them and it could have been same day pickup. We had what was Hurricane Ida yesterday over us and my area flooded. So I couldn't go. We went today though. So I was able to pick up what I wanted more than what I wanted, honestly. I will have timestamps if you're interested in specific products. We're going to start off with the Shade and Illuminate Concealer. I did also pick up two colors of the blushes. Let's start off with the concealer so I can look not quite as crazy. Let's address the elephant in the room with these, the price, oh my God. This was a major, I bought these so you didn't have to. We all know Tom Ford's pricing is always crazy, but I feel like he's starting to lose his mind. Okay? So we're going to start off with the concealer. This is the Shade and Illuminate Concealer that just launched. It says it's going to be a Nordstrom exclusive. So the only place you can purchase this at least right now is Nordstrom. So I will have that linked down below for you guys. This is $90. I cannot believe I spent $90 in that concealer. This concealer is going to have to be amazing for me to recommend it to you guys. I will say it right now for my base, I'm wearing the Tom Ford foundation stick and I love it. It's worth every penny so I don't know, I wouldn't be surprised if I like this. This is described as a Nordstrom exclusive crease-proof concealer that helps boost skins radiance and even skin tone. It's supposed to have a medium coverage, be buildable and waterproof and merges three-dimensional skincare with three-dimensional radiance, has hyaluronic acid. Okay, anyways, let's just get into it. It comes in the typical Tom Ford box here. This is made in Italy, has a six-month shelf life, not very long and has 0.18 ounces of product. Here is what the packaging looks like. Honestly, really gorgeous. It fits in perfectly with the Tom Ford makeup line. It's very, very heavy. It definitely feels luxe. So you're not getting skimped out on the packaging here. It's very, very heavy. So I ended up with the shade 2N0 cream. At my Nordstrom, they didn't have testers of these so I couldn't see what my perfect shade was going to be so me and the SA were kind of looking through the shades and we thought by looking at this, this would be my best color. So we will see. I haven't even, haven't even opened it or tested it. I'm going to get in a little bit closer here. Let's see what she can do. Here's what the applicator looks like. Pretty standard. I'm just going to do a little bit and let's just see. I'm using an alf sponge right now. Oh, this is a good color for me. OK, so a little bit goes a long way. You don't need too much of it. That's spread out beautifully and it gave me pretty good coverage and I'm not going to lie to you guys. It looks pretty smooth under there. Let's just do a dab more. I know this is the trendy thing to do. Then let's get the leftovers on the lid. I'm not going to lie to y'all. This is just the first time I'm drying it, but it feels really good. The consistency of it is a bit thin in a good way, but it has a little bit of thickness in there that I think gives it the coverage. It feels very soft. It feels really nice. I'm not going to lie. It doesn't feel too liquidy, but it still feels thin. So you can see it's not a water kind of consistency, but it feels really lightweight and it doesn't feel thick at all. Listen, I'm not saying it's worth $90, but it looks really good right now. I'll take a close-up of my under eyes. I'm going to set one eye and then leave the other eye on set just to see, I don't know, that makes a difference. I'm going to get the red spots. So if you're around my skin tone, cream is a great color. I don't want to say this, but so far I like it. We're still at the very, very beginning stages, though. Because this is a review, I'm only going to set one of my under eyes. So I'm going to use my Gucci beauty powder. I'm just going to set under this eye. I'm still going to set the rest of my face, but not this under eye. Side note, this powder just smells delightful to apply. So here's what it looks like set. It looks really good. I would definitely say it is a medium coverage. It's not a crazy full coverage, but it's just enough. I think if I put a bit of a color corrector underneath, it would all be good. I wouldn't have any darkness showing through. So that first application looks really good.  So give me one second. I did my eyes. I used the Tom Ford Quad in Seuss Le Sabre. I know I just butchered that, but how pretty is this for everyday rose tones? Really love it. You really just have some amazing quality. Price is expensive, but the quality is so bomb. And then thanks to you guys, I used my new favorite eyeliner pen, the eye defining pen from Tom Ford. It really is also the best. I just wanted to take a quick look at the concealer now that it's set down and kind of settled into the skin. Something that I did forget to mention, there are 20 shades. The range looks pretty good. A bit heavy in the lighter tones though, but we do have 20 shades. The finish of this concealer has a slight radiance to it. So it's not going to be too matte or drying. I think it's a gorgeous, natural finish. And it looks good on both the set and unset. I would say maybe a little bit of product has sunk into the fine lines, but like very, very small for not being set. I'm not mad at it. You can just kind of take your finger and push it and it will go back to looking perfect. And the side that has been set literally looks perfect. We're early on, but I have very, very good feelings about this concealer. Unfortunately, I hate to bring it to you. But let's move on to the shade and illuminate blush. I was expecting his blush to be around $60 to $70. For reference, his cheek duo is our $84, but I thought, I don't know, I just thought $60 to $70 for this. $90 is a lot. He's upping the prices of his makeup. So just, you're paying a lot for these. I did pick up two shades. I immediately regretted it. I wish I had only picked up one, but we will see. So these are not a Nortrum exclusive. So they are available right now on the Beautylish website. The Beautylish site actually has more shades than the Nortrum website. So on Nortrum, there's four shades. And then on Beautylish, there are six shades. So definitely take a look on Beautylish. I just thought Nortrum was more convenient because you could get it today. And on the Beautylish website, it did say it would take a few days to ship. So pros and cons, you have to weigh them out. Here is the box that they are going to come in. Then as per usual, they're going to come in their velvet slip. Here's what the packaging looks like. And then it's this kind. See how it opens just like this. No annoying brush or anything above the mirror. It stands up on its own and the cheek color. These are made in Italy. They are 0.22 ounces. Now these are a skin perfecting blush featuring one matte and one frost shade to deliver sheer to medium buildable coverage. So right here in my hand, I have this shade Sundrunk. And I have not tried these on myself, but I just tried this on my mom. She has a medium skin tone. She's about an NC40 for reference. And this shade looked beautiful on her. If you're worried about this peachy shade, she actually preferred this shade on her skin tone. So they looked beautiful on her. So let's try them on me though so that we can see them in action. I'm using a refer number four brush. It looks dirty, but I swear to you, it is clean. So let me swatch these first. So the first one we'll do is Sundrunk. And you can definitely tell they are not overly pigmented, which I prefer. It looks like the peachy shade looks a little bit more matte. It almost makes the skin look airbrushed underneath. And then the pinky shade, I guess, has a little bit more sheen, but they're quite flat. Honestly, I don't know. It says online that you can blend the shades together for warmth or you can use separately to sculpt your cheekbones and highlight your complexion. You can't necessarily highlight with a glow. The other shade that I got is explicit flush. This is number two and Sundrunk was number five, by the way. So this one's a little bit more pinky. It's less warm. I haven't tried this one yet. So there seems to be a lighter shade and then a more of a pop shade. And you can see the lighter shade really does seem to kind of airbrush the skin underneath. It's very interesting. So let's try explicit flush first, which is gonna be the bottom colors that I swatched. I wanna see how the lighter shade looks first. So we'll put that down first. I get way more color from it than I was expecting. And one thing that I did notice from applying this on my mom is that it really did give a smoothing effect to the skin. They claim that this almost perfects the skin and I 100% agree with that. I saw that happening. It makes the skin look smoother underneath. And then let's go in with the pop shade right here. Or at least that's what I'm calling it. You see these carry more pigment. I agree that they're more sheer to buildable, but they give you a lot of color. They're very thin and smooth on the cheek. I'm not telling you guys to spend $90 for a blush. Absolutely not. But as far as the quality, it's gorgeous. It gives not a complete matte finish on the cheek, but it's very skin-like. It has a realistic radiance to it. Beautiful. Okay, let me try the other color now. So we're gonna do sun drunk. This is the one that I tried on my mom in both shades looked good on her skin tone.  So I definitely see this, especially if you get the darker colors carrying well over to other skin tones. They have pigment to them, but they're so easy to blend out. And this shade is the one that kind of airbrushes the skin and makes the cheek look really smooth. So if you have a lot of texture on your cheek, I think you will enjoy this formula because it does give that perfected airbrushed look to the skin. Let's use this shade, which is a little bit more mauve. The texture of the powder is so soft, you guys. And it makes the skin look soft as well. It carries that onto the skin. This review was a little bit more positive than I wanted it to be, but they look beautiful. Thank you for $90. I definitely did not need both, but I'm happy with them, I will say that. Gonna kind of use my sponge to blend out the edges, make sure it blends into the skin great. I don't have any bronzer or any highlight on right now, and here is how they're looking. If you buy one, you of course have the option to use one shade on each cheek, but I like the look of them blended together as well.  You can mix them together as well. I'm surprised that they're both blushes. I would have thought at least one would have been more so of a blush topper, as opposed to them pretty much having virtually the same finish. What I'm trying to understand here, it says one finish is matte and one finish is a frost, but the differences are minuscule. They both look like a, not quite a satin. They both look like a hybrid between a matte and a satin, not completely, completely flat. I mean, you can see that finish on the cheek. Beautiful formula though, I must say.  All right, guys, here are final thoughts. I will be doing a wear test, and hopefully everything stays consistent to right now, because I really like everything that I tried today. So let's start off with the concealer. For me, I think the concealer is the standout so far of my purchases. I didn't wanna love this, $90 for a concealer is outrageous. And I do think it's important to acknowledge the price. However, as a standalone product, as far as the quality, this is one of the most beautiful concealers that I've tried recently. I mean, I love it. It looks so skin-like and natural. It seems to set down really well. I do prefer the side right now that has a little bit of powder over it, but I do think the concealer is doing a very nice job being self-set. And if you do find that you don't like the look of powder on your under eyes, I mean, it's not really sinking into my fine lines. Obviously I'm 25, so I don't have mature eyes. But where the fine lines are, I find that if you just lightly pat in, it's all good and it doesn't seem to sink in too much after. You can get away with using very little product with this. It does give pretty good coverage. Feels really light on the skin. Looks gorgeous and radiant. Seems to stay put so far. So hopefully this stays consistent because I'm really loving this. As far as the blushes, I also am very happy with these. What I'm most impressed with about these is how airbrushed it makes the skin look. It just gets the most perfected, poreless look on the skin. They still give quite a lot of color while still blending out to be quite sheer and they work out completely effortlessly. So if you're more on the fair side, these will still work on you. When it comes to the darker shade, you might need to use a lighter hand just to be careful because they did pat quite a punch as you saw on the demo. But if you're looking for a nice, smoothing blush that works out effortlessly, has pretty colors, I do like these. I don't think you need to buy more than one color. If you are similar to me and that you like the brand, Tom Ford, you enjoy the luxury experience and what luxury makeup has to offer, I do recommend picking up one of these. However, if you're like, I'm not paying $90 for a blush, you'll be okay. You don't need to spend $90 on a blush. But so far, I mean, I think Tom Ford quality-wise did a fantastic job with this launch. All right, you guys, that is all I have for today's video. I hope you guys enjoyed it. If you aren't subscribed to my channel already, I would love it if you would consider taking the time to do so and I will see you all in the next one. I guys have a good one.