Can you hear me? Thank you very much for the invitation to be here. Today I will be telling you about our unpublished work, so you can give me feedback. In the lab, we do three things. I will always say I do three things, because if I say more, we get lost. So I will always try to say three things for everything. So one of the things we do is to model behavior. And I will be talking about modeling collective behavior. We are also neuroscientists, so we are looking at brain activity while the animals are in groups or interacting with other animals. There's another path in the lab that is exploring new approaches to machine learning or AI. I will be talking about modeling collective behavior, but I will tell you one minute about our work in brains by just showing you a video. So here what we have is an animal, a zebrafish. This one might be nine days old or 10 days old. It's embedded in agarose. Agarose is transparent. The animal is intact. We don't open the animal, but it's trapped in agarose. We can actually remove a bit of agarose, so it can move the tail. We can also remove agarose from the eyes, and it can move them, the eyes, and the tail. In this particular video, we didn't remove anything, so the image is a bit more clear. And what you're going to see is the brain activity. We are expressing genetically calcium indicators, in this case, in all the brain. So you will see calcium changes that are coming from the brain activity, and in red, there will be a tracking of another fish around this one. Because the brain turns out to respond in the contralateral side to the fish, I flipped the trajectory, so it's easier to see the other animal close to brain activity on this side. So I'm going to run the video. Let's see what the image is. This is raw data. It's not treated data. So you can maybe see, you can already imagine the correlations between the position of the other animal and where in the brain gets active. This is normal epifluorescence. We can do better, but this is just to show to you. If you treat the data in a very simple way and you ask yourself which part of the brain is responding to what position of the other animal, let's say that these are the positions of the other animal in different colors. And for example, this part of the brain is responding to this position of the animal, this one to that. So that's our reason to choose zebrafish. We can do both behavior in collectives in the lab. We can manipulate the brain. We can record brain activity. So it's quite powerful to use this system. As I explained to you before, one other part of the lab is trying to explore new approaches to AI. In particular, we are trying to see how far we can get doing some kind of machine learning without function minimization. So if you are curious about what we are doing, this is an archive paper about our approach. It's based on modern algebra, and it doesn't use functions. Anyway, today I will stick to modeling behavior. And in the past, we've done it in different ways. Our philosophy has always been similar to many of you to use very simple models that could give insight about what the animals are doing. And I want to explain a bit of that philosophy to basically make myself the enemy of myself and say, OK, there is a limit to what you can do with very simple models. And today I will explore with you what happens if we go into very complicated models and how best maybe to do it. Anyway, so the type of things we've been doing, for example, was to start in my case, because I was a physicist in two lives before, in my previous life maybe a neuroscientist. I tend to produce these models based on ideas of neuroscience. For example, one we've been playing with is the idea that what an animal is doing is to estimate the probability that there is something out in the world, given what it's seen, and giving us the behaviors of other agents. So we've been working with that idea using very simple models. And it has been quite successful for very simple experiments. That doesn't give you the whole picture. We've been trying to model other parts of collective behavior. One of them, how an animal approaches another, using control theory for coordination, basically using the idea of minimal time in a burst and glide fish. For animal fights, we've been using game theory. We've been using heuristic rules. But in other cases, very simple models. Let me illustrate one of the more complicated ones, but it's still very simple, from the idea that what brains are doing is to estimate something out in the world, but not only using your private information, but also the information of what other agents are doing. You can come up with some assumptions with very simple predictions for experiments. In this particular case, we have this experiment in which our focal animal is choosing between two options, X and Y, with different animals in X and different animals in Y. So this is the formula we get from that idea that animals are estimating. And in this particular case, it has two variables, number of animals at X and number of animals at Y, and three parameters, K, S, and A. And here, I'm plotting N, X, and Y, and my formula here that gives me this surface that depends on the values of the parameters. And I can change in the experiment the number of animals in each of the two options in X and Y. So imagine I change the animals in X, and I have two, and one in Y. That would be the prediction to do the experiment you compare. So you see, it's only two variables and three parameters. And for me, it's already a bit of a nightmare model. I mean, it's from the theory you need to simplify to go this simple, but it still has a few parameters. So why I'm not happy with this and why I'm going to spend 30 minutes talking about something else. The reason is that, as you can see, the experiment is very simple, and the theory has been matched to the simplicity of the experiment. And that's why it can be very simple. I think that's a very fruitful approach, but it's limited to always being doing very simple experiments. So what happens if we now do a more complicated experiment? Can we use these type of models? But in general, no, we cannot. It captures some part of the behavior, but not the totality. So you now imagine that you want to apply this previous model to an animals moving in a group. Let's say I put my zebra fish in a tank, and I let them move. And I see how accurate is the, imagine I try to predict at each point in time, whether the animal is going to move to the right or to the left, depending on the configuration of animals. And I plot what is the accuracy of my model. I mean, 50% is a chance. So 70% is something, but you manage to capture that maybe with one or two or three parameters, but you are far from very good prediction. So what can you do? You can try. This is fake data, by the way, but just to illustrate the point. You can try to do models with more variables and with more parameters. Now this is, again, a bit trivial, because at some point you're going to have enough variables and parameters to really explain your training data, because that what happens. I mean, with enough parameters, you can explain whatever you want. So one requirement is going to be that we are not going to use training data, except to fit the models, but we are going to fit them in a way that they should be tested in new data. We are not happy fitting the data, but we are happier if we can predict what the animal is going to do in new data. Now I will go all the way to a large number of variables, but I will argue also that we should play with systems to have less and less number of variables so we can have some insight. So today I'm not going to make the point that you should have full insight with very few variables. I'm not going to say that you should have these type of systems, but I will argue in general that it should not be there. So you are better off always being in that curve, either getting lots of insight with very simple models or more ability to predict with larger models, but you should never be in the stupid region of modeling. For today, I'm going to forget. I'm not going to do a talk on the work we have published, but only on how far we can get using deep nets. There are reasons to use deep nets. One is that for many problems is the most general function approximator we have. So it's very good at approximating data. In the last few years, let's say 2017, there were many practical advances to use deep nets. They are coming out all the time. There are reasons not to use them, and probably you have those reasons. One is that there is a bit of black magic in how to use them, and also they can act as black boxes. So during the talk, I am a bit divided. I believe that there is some value in using them, but you will by yourself decide whether you are one of these people that want to use them or not. Because it's true that there is some black magic. There's actually some black magic in all modeling. And to some extent, there are black boxes, but we will play with them to see how far we can get. I will try to talk about three things. Maybe I don't have time for the last one. One is to give you an update on our tracking system using deep nets. Another one is to, from data, obtain in this tracking system how we can build models using deep nets. The last one I have, if I have time, I will tell you our efforts in using reinforcement learning in modeling collective behavior. Our idea for tracking a few years ago was that, as Stefania was saying, that when 20 months go into a region of occlusion, our idea was that to know who was who afterwards, you would look at the images. There would be a way of looking at the images that would give you a fingerprint of the animal. So this is George, and this is Tom. And just by analyzing the images afterwards, you will see that that was Tom and that was George. And if you do that, we had a way of doing it using a kind of texture analysis that was invariant under rotations and translations. You can see that in a video in 2D and here time, there is a trajectory here for eight animals and in color where the tracking system agrees with a human validator and in black, which you might not be able to see, the little regions in which they disagree. Now we thought, OK, this system, some people use it. It has the limitation, probably Guy has been the one trying to push it to more animals. I think maybe they are trying to use like 20 or 40. How far did you go? How many? 20. Yeah, so that's about the limit of this system. After 20, you need to be very patient to get the results. It's very slow and it might crash. So we thought a few years ago, why not to use convolutional neural networks? These are deep networks that instead of the typical classification all-to-all connections network, it has some convolutional layers. These are filters that are looking at the image in little patches and they are designed to be a translational invariant. And these are well known. I mean, everyone is using them. And OK, let's test the idea of whether convolutional neural networks can distinguish animals. So in general, we don't have a way for all animal species to find the nose, let's say, or the head. We can do it in zero fish. But we thought in general cannot be done. So all we do is to put the elongated region of the animal in this diagonal. It can be this way or that way, because we are not distinguishing head from tail. So what we do is we train the network with all these images, irrespective of their orientation. And we try to see by giving these inputs to the network whether it can learn some identities of some individuals. To test the idea, to obtain some ground truth, we obtain videos. The image is not very good. Of 184 animals, they are in independent little tanks. So we know who is who. As our ground truth, we have all these images for their 184 animals. It's 10,000 of them. So this is our library to do a test of any network. So how do we test it? We take 3,000 images to train the network, 2,700 for training, 300 images to stop the training when it can still give good predictions of who is who in a new data set, in this validation data set. And then we test in another data set of 300 images. For that test data set, we obtain for a single image the following accuracy. So this is single image accuracy. And that's the group size, the number of individuals that need to be identified. 2, 10, 30, 60, 80, 100, 150. ID tracker is somewhere here. And the new system with CNNs is much better. This is not to say that ID tracker was really bad here, because it is a single image. Between two crossings, you have many images. So you can estimate better who is who. But still, this means that for the new system, you need very few images between two crossings to know who is who. And as you can see, magically, it doesn't degrade that much for very large groups. OK, so this is a little proof that the system can cope in distinguishing 150 of my fish. Now, this is not to say that it would work in a video. And the reason for that is that we are training the system with 3,000 images. In a video, I'm not going to have a straight away 3,000 images for each individual. So I run into a huge problem. I need to obtain my 3,000 images per individual. I will explain how we do it, but this is for you to see the complexity of the problem. The animals are crossing very often. So it would be unlikely that I have a portion of the video in which I have 3,000 images per individual. By the way, this is the 30 day old zebrafish. OK, so the core of our tracking system is really here. It's a quality check procedure to gather together many training images. And I will run you through it so you can modify it by yourself for your needs. So after the preprocessing, I told you we have a crossing detector. What is a crossing detector is another network that what it does is to find in the video when 20 months are crossing. So we have some heuristics to find some of these crossings basically based on the number of pixels in the blob, whether they're going to form two blobs afterwards or they come from two blobs in the past. And then we have these as training data sets for crossings. And then we have also data set from the video obtained as individuals and another one for crossings. So we have another network. It's a bit simpler. But we train the network only on two outputs, whether it's a crossing or it's an individual. We use the training images that we obtain with our heuristics. And then the network will tell us in the video which images correspond to crossings and which two individuals. It's our test set that in our case it will be the whole video. And this is what you find. I'm here plotting in the y-axis my 100 animals in the video. And this is frame number. I will put in black the little fragments of the video in which my network is telling me that animals are crossings and in white when they are individuals. So this is the result of that crossing network. Okay, now what we need is to identify all the animals in between. And as I was telling you before, the way we do it is by carefully adding images for a training dataset. Let's start with the first part, which already will work for simple videos. For more complex videos it jumps into a new protocol for the super complicated ones that I never want to be in to protocol three. Okay, so the first thing to do is to find the portions of the video in which animals are not crossing. Typically in a video of our fish we have for 10 minutes 100 of those, some of them very short, but still 100. For example, by eye I could see here three. So I ask the system to find one of those and I ask the system to find the one in which the animal traveling the shortest travels the farthest in the video. So I find these global fragments and all I do is to train my network on those, on that data. So I have images for animal number one to up to 100. There are very few, but that's how I start. This is my training dataset. I keep something for validation so I don't over train. I train my network and I train it to identify all the individuals because they are not crossing. And then what I do is to ask how the network will assign the rest of the video. This is my test set.  So I want a quality check. My quality check, I don't go into the details. It has an internal way of finding whether it's certain about the assignment. So it's a checking other thing that there are no two blobs identify the same animal. And out of that quality check I only retain those global fragments that have high quality. In this case are these ones here and my quality check also says, oh, are they covering a lot of the video? No, okay, I'm done. I don't like protocol one. You can still use it. The accuracy will not be close to 100%. Maybe it's like a 90% but you want fast tracking. This might be a way to do it but you will need to go into the code and change a bit one parameter in the quality check because in our case we don't consider it valid. It jumps into protocol two which is the one I really use for most of my videos. In that one all that happens is that I now use these global fragments as training data set for my network again. I train the network and I assign again the rest of the video. Now I have more global fragments. I've passed the quality check. They're still not covering the video as much as I want. So I ask the system to do it again and again. It does step three, four, five, and step nine. It has covered most of the video. The internal quality checks says, okay, it's enough of the video and we are done with the protocols. In the difficult cases we jump to protocol three but here for fish I never had that situation. It always was with protocol two. Okay, so we are here at step nine. Protocol three, I will mention only that sometimes the network cannot just move along the video and I have to force the convolutional part of the network to learn about the whole video and then only the classification part in a second step retrain it. So I force the network to see the whole video but most of the time we don't use it. Okay, so if I zoom in my video you will see that there are some little parts I didn't identify and also the crossings are still there. So we have another part of the algorithm that finds the assignation of those little fragments and also estimates accuracy. It's a conservative method to estimate accuracy. You don't see the histogram here very well. The mean value of the estimation is 99.95% accuracy in the identification of all the animals and when you have a human validator it goes up because it's a conservative measure that we are using. It goes to 99.997 so out of 10,000 frames you will have three problems with the identification and typically it's animals that are close and it's for one frame. Okay, so we learned that it works quite well but then we need to also solve the crossings so we have another algorithm to solve the crossings and once you go through that algorithm in the end you have oops, sorry, you have your trajectory for the tracking system. I'm going to show you two cases. The one I was showing to you before, our fish and the ants. In the case of ants it's a difficult case. It jumps actually to protocol three and the reason for that is that as you can see at the beginning of the video many of the ants are still so the training system only has basically one image and it doesn't have enough variability to learn more of the ants so I need to force the system to learn a bit but you will see at some point they start to move and for ID tracker this situation previous ID tracker was an impossible one with the new one is possible. Okay, so let me quickly tell you that the code is open source and has a nice GUI and documentation. Let me quickly move to a way of doing models using AI in a data driven way and what we want to do now is from our videos is to see how we can for example in this case predict the position of this animal given the positions and velocities are possibly accelerations of the other animals. So imagine that I want to predict the future of this animal and the animal is going to be here in one second and I want to predict the position and my prediction is going to be there. If you now ask a deep net to learn this problem it's actually a very difficult problem to learn and use a naive deep net it will not learn it but in 2017 there were improvements on deep nets that make it to learn so you can see the real and the predicted one. I'm going to tell you very quickly how this works. So the ideas are in this paper is a method to what's used to mimic physics and we are going to use it for modeling fish. So on the left you have the physics implementation on the right the deep net implementation and you can see that they work closely. Okay, so it works quite well if you look at the turning angle of the animal one second in the future against the accuracy of the system already works quite well with no neighbors because already by knowing that the animal is accelerating to the left you're going to know a bit of the future but if you add five 10 or 15 neighbors this network has a very high accuracy are predicting whether it's going to turn right or left close to 90% and that's the maximum we have achieved with any network. You can use this network for data analysis you can have to the right the probability of going the focal to the right you can find configurations in which the animal will go to the right but you can also because the model is an interpolation you can go there say okay I want George to have a different velocity a different position and I want to find out whether the focal animal is going to move to the right or to the left you can play with the model and get some intuition or what are the variables to consider but let me go through the theory so you can have a sense of what we are doing. So the network can be represented in this way now it looks like a little monster but I'm going to introduce it more slowly. So it has one object here which is a little network that is going to describe the interaction of my focal animal and another animal. I'm going to use a coordinate system center at the focal so the focal is at zero zero and with the velocity pointing in the y-axis. So the variables I'm going to consider for it are going to be the speed and the acceleration in the perpendicular direction to the speed. For the focal we've been trying different variables but the ones that seem to work quite well is the position of the focal respect sorry the position of the neighbor respect to the focal and the velocity as well respect to the focal velocity. Now the network we are using here in the simulation you just saw is a very rich one that has six variables as input but it will have 128 outputs and the reason to have many of those outputs is that this is just describing the interaction between two animals and we need some other part of the network to do that aggregation of all these interactions and for that we use this rich output from this network. So what we have after these little networks G is a sum of those little networks for my 15 individuals and then that has 128 outputs and I have another network that takes the 128 outputs and transforms it into a future position of my focal animal. Okay so that's a nightmare black book problem because basically you have 16 variables per neighbor that we were using 15 neighbors and there were two variables for the focal that's 92 variables and lots of parameters. Can we do better at getting some insight of what a model of the fish might be? We can but let's go slowly. One insight we are getting at this network working is that we are using first, it has some structure. It's not a network of all neurons, it has some structure. The beginning of the structure is that I'm describing the interaction between a pair of animals, one focal and another neighbor and the rest of the network is an aggregation of that. So let's try to mimic that network by using a simpler one that we can understand. The interaction one we can understand a bit. It has six variables but it's not super complicated. Instead of having 128 outputs I'm going to have a single one and I'm going to train that to be the probability that the focal animal goes to the right given the variables for the focal and the variables for that neighbor. So basically now I'm transforming six variables, these six inputs into a single output instead of 128. Let's plot that. So this is the result of the network. It has six variables so we needed to look at the six dimensional plot and I'm going to do it in the following way. The reason is not that difficult to look at six variables in this case because they mean different things. So one is the space, so the position of the neighbor in X, the position of the neighbor in Y. This is my focal, it has two variables. One is the speed, so if it has higher speed I have a large arrow and if it has acceleration to the right I will have an arrow to the right. I don't have an arrow to the right so the acceleration to the right is zero. And now I have these little diagrams and basically they are plotting the velocity of the neighbor. Here we have the angle of the velocity vector of the neighbor respect to the velocity vector of the focal and here is the speed. So these plots are giving you the probability that the focal animal goes to the right in red and to the left, to the north right in blue. Okay, so what does this mean? It means that if a neighbor is in this position of my plot, the neighbor is moving to the right and what this animal is going to do is just to move to the right. That's what this plot says. An animal here in a space with that velocity vector so high speed and moving to the right makes my focal animal to move to the right. If it's on the blue part, it means that the neighbor is moving to the left and it's again here moving a high speed so my animal moves to the left according to the plot. Now it gets more complicated and more interesting so there are some portions of the space in which my neighbor is not moving a high speed independently of what is moving to the left or to the right, my focal animal is moving to the right. So there are parts of the space in which the other animal, the neighbor, is not moving fast, it's only an attraction part. Here it's going to align but here it's only going to attract. There are regions, you cannot say it in this plot because the quality of the projector is not that good. So this part here was blue. You find it being white by it's blue which means that there is a repulsion of the neighbor to the focal when it's going at low velocity, the high velocity is not producing repulsions but a low velocity is producing repulsion. Now another variable was the velocity of my focal animal if I plot the same thing than before but for my focal going at low velocity you can see that it's only this region that is more important, there's still repulsion in some regions, repulsion is more important than before and it's more restricted in the space. So how it reacts to a neighbor depends also on the velocity of the focal and as well on the acceleration normal to the velocity. The animal is turning to the right. The evidence to move in the opposite side is to be at very high velocities. Only a neighbor at very high velocity pointing to the other side will make it change his direction but all the plot turns more red. Okay, so so far we've been looking at the simple interaction between focal and one neighbor before we had another function afterwards another network to take that into account. It was very complicated so now we are moving to a different network which is called attention network. I was studied recently. The idea is to keep the network we were talking about before by has now some weight so it's weighting the animals with this other function that needs to learn to produce a good probability of moving to the right or to the left. We found that this really depends mostly on four variables, the speed of the two animals and the position of the network. So I'm going to plot to you when the system learns to produce these weights which are relative weights, they are normalized weights. One obtains for the exponential of this new function something like this. So it's the same thing than before but now there is no dependency on the angle but it finds these regions of interest to weight the animal that are on those regions. So animals at high speed on these particular regions would be important for a focal animal at high speed. The way you read this is that if you have for example two of the 15 animals up there, their score is very high and if the other ones are lower it's very likely that these two win out and my focal is going to follow the direction of those two but depending on where the others are that might not be the case. If the others are also having lower but higher scores to move to the right in the end those will win out and my focal will move to the right because all these are relative weights. And again for low velocity the way you aggregate information is different you are doing it more locally and not with the far individual. Okay, so I have one more thing but probably I'm late, no? I'm late, do I have some time or four minutes? So we very quickly, yes, we don't have very good results for the next thing so I can be quick. So we are using the deep nets as I showed to you for our tracking system to build models in a data driven way but also in a top down way as I'm going to describe quickly here basically we are using agents that have a little brain and we are using reinforcement learning which is a method to change the parameters of the brain of the little network in a way that obeys our score we give to the system. So what we have in reinforcement learning is the idea that the world is in a particular state that is learned by the agent and the agent takes some actions that modify the environment and then the agent reacts again to the environment. So basically in this scenario you will have a world in which you have other agents. I produce a retina for my agent that has 160 values on a sphere in this particular case a very little brain of three layers and the actions you can take in 3D space are very simple here, the speed and two angles and afterwards there is some physics that describes the movement of the animal given the actions it has taken and the animal needs to learn to modify these nets to maximize our score we give to the system and this has like it was a nightmare to really use this agent type of agents before but now there are little tricks developed recently at the end of last year that makes this much faster. So the type of questions you can ask for example is does exist a sensory motor transformation so a particular set of parameter values that produces behavior X so now we are basically have a rich model that can be many types of models and we want the animal to do behavior X we say okay do behavior X and I'll find out whether it's possible to actually find a particular brain sensory motor transformation that produces behavior X as an example I'm going to show you not so good calculations on to the question of do we need to add a blind angle to this particular agent to have a milling pattern in behavior and the answer is well I don't really know but this is the answer numerically so numerically you can manage having a score that asks the animal gives a higher score if he's going to produce milling basically a high angular momentum and some attraction and some repulsion they learn the animals to be together and you have this I'm not sure I should call milling but they are rotating so they are still learning as we speak they are learning so they are still in their way to produce milling and I believe this type of modeling will be very important in the near future because there will be tricks that make this faster in better and better computers and now it's getting fairly easy to produce a world for them that is reasonable and with this I want to just finish by saying yes there is black magic there's black magic in all the modeling I do there's a bit more maybe in these systems because the trick is to add some structure to them that makes sense for the problem so they can learn from the data sometimes it will be black boxes we don't mind about black boxes to some extent for example for a tracking system we do mind for modeling behavior and in that case I propose that instead of using the best predictive networks we reduce their complexity in a way that we can still plot and understand them so we can keep knowing how far a network can go we can reduce the number of variables to be able to plot it and we can also go back to our models of one or two or three parameters and with that we finish just to acknowledge the people that did the work so there are really four people so Francisco Romero and Mattia Bergomi coded the tracking system Francisco Eras did the modeling work using interaction and attention networks and Robert Hins did the experiments with this and I'll finish thank you


So, this talk is about our recent work on cryptanalysis of Simon and Symec with block size 32 and key size 64 bit. So let me start with a disclaimer in mathematical terms. So Simon implies NSA, does NSA implies backdoors. So a corollary of this disclaimer, backdoor in Simon implies backdoor in Symec. The reason both the ciphers have a similar structure. So if we look at the best known results on these two ciphers till now, they can reach a maximum of 23 rounds out of 32 rounds. So this means the security margin is still 28 percent. So how to improve it? So we introduce new property of block ciphers called correlated sequences. So consider n bit block cipher with mn bit master key. So for t rounds of cipher, we can consider it as a keyed sequence of length t. So if you consider the round, it is basically a composition of two functions, one is non-linear and another is linear and this requires majority of the computations. So the goal is given a keyed sequence and another key which is not equal to k obtain another sequence of the same length by computing the non-linear function at most t times. So more formally we say two sequences are sigma t correlated, if one can be obtained from another by computing the non-linear function exactly sigma times. So this is our main result on these two block ciphers. So we can theoretically construct one eight correlated sequences for both these two ciphers. So that means if we want output of these two ciphers after six rounds, we need to compute this f only this many times compared to the naive approach which requires this. So we use these sequences for six forward rounds, six backward rounds and do partial encryption just for 12 rounds. So the overall time complexity is limited by this term for 25 rounds. For 27 round attack, we use the property of key scheduling algorithm and one round differential simultaneously and we can push the attack from 25 to 27 rounds with the same complexities. So what's next? So we saw the security margin from 28 percent to 16 percent. So now if you see the attack technique is generic and it can be applied to any block ciphers. So the only problem here is how to construct such sequences for other ciphers. So thank you.


What's up everybody Jason here for Jasper's case breaks Tom 2021 Panini flawless baseball just sold out This is a one box brief case break random teams number six 30 total spots everybody gets around on team in the MLB. Here we go guys dice roller customer names From Dusty down to Steve bottom straight up Danny down to Danny one goes in a pack filler and The backs of the Washington Nationals roll Two and a four six times. Good luck one Two three four five Six Two and four six Michael down a Michael six times one Two three four five six Dodgers out of the Royals. All right, so six times six times. Here we go Michael you have the Dodgers Dusty with the Yankees Rockies Cardinals and Tigers and AIDS Michael the Blue Jays there you go Michael Nicholas with the Angels damn look at that brother doing well twins Dusty with the Cubs Pirates Orioles Danny of the Guardians Dusty with the White Sox Steve with the Braves very nice Dusty with the Rangers Danny with the Reds Michael with the Tampa Bay Rays Steve with the Astros Dusty with the Brewers D-Vax Michael with the Giants Dusty with the Marlins Steve with the Nationals Dusty with the Mariners Nicholas with the Phillies Dusty with the Red Sox Steve with the Padres Dusty with the Mets and Michael with the Kansas City Royals I'll pause the video for trades If we don't have any trades, we'll come back and we'll break open flawless All right, welcome back guys. No trades. No trades Stick into what they got. I know it's early not a lot of people watching right now But hey, you got a break sold out And we about to do it flawless RT6 this feels heavier than normal, but I Could just be tripping. Oh There's a middle card. Wow, there we go There's a middle card guys. We got a middle card I guess judging by the first briefcase that was probably gonna happen, right? All right, we go guys good luck first one is a jumbo patch Suspended for the Chicago White Sox. I heard someone say last week. Yeah, like two home runs Something crazy 17 out of 25. That's dusty a one out of five Ramon Lariano. Look at that Little majestic tag right there three color patch at the top one out of five Definitely Oakland Dusty with Oakland gas Cameron two color patch an autograph to 25 for Detroit Detroit Dusty another majestic tag two out of five Brandon Rogers That is for the Colorado Rockies another one for Dusty Back to back for the Colorado Rockies one out of five honored Inc. Gold Andres Galaraga Have yourself a break Dusty Xander Bogarts for Boston eight out of ten Boston that's also Dusty Jesus and last one here is a dual Rookie patch autographs Casey Mies and Nate Pearson That's two color patches and autographs 18 out of 20 That's Detroit, which is Dusty Toronto, which is Michael So that'll be a randomizer right there. That was some nice cards though. I must say very nice low-numbered cards Good luck. There is three hits in here though. So they're still the big stuff I want to say that this should be the Relic or this should be the diamond. I want to say or maybe not No, that looks like a patch. I don't know Mervis maybe this is the diamond. Oh, this is a diamond. Okay, George Bradford Kansas City 15 out of 20 Kansas City going to Michael Okay, so this is gonna be your patch autograph Christian paché for Atlanta four out of 25 nice rookie RPA and he was just traded I think to Oakland, right? Good good prospect right there, but that's still gonna go to Atlanta here Steve Burch last spot mojo. There you go, buddy Very nice RPA here comes Looks like a cleat It's a one-of-one cleat. Oh My god, it's actually not a cleat. Wow, it's Bill Terry. That's a bat barrel Bat barrel one-of-one Bill Terry for New York, and I want to say that is New York Giants, I don't want to actually flip this card. I think it's gonna get damaged in this, but then again, it's slathered I don't want to mess with it. Ah Okay, hold on. Let me double check it out really quick. I just don't know why they don't just actually just Put them in a normal slider box or something Yep, this is card number one bat barrel set Bill Terry, New York Giants New York Giants So that'll be going to the Giants and Michael. Well, look at that And just in case of course, you know, not many of us know who Bill Terry is He is a first baseman slash manager I was in the 1920s as a player 23 to 36 as a manager for the Yankees after actually he was a manager for the Yankees while he was actually playing as a Yankee Damn, that's crazy Three-time All-Star World Series champion and all batting champion and number three retired for San Francisco Whoo, and then he was inducted into the baseball Hall of Fame in 1954 In his 14th ballot. So I mean regardless this dude's a Hall of Famer Is that a picture? Who's that with? That's pretty nice There you go. Very nice. Is that a cell sheet hit?  Guess it might be. Oh, yeah, it's right there. Actually, that's exactly the card right there. There you go, man Well, congrats man. That is a Bill Terry bat barrel. I don't know Michael if you're super Into leaving it as is sealed, but I I'm just saying I I'm afraid to like the what I'm saying is that like if I move this and I Though shipping team might have to pack this if it flips it's gonna move and I don't want the car to get damaged If you're okay with it I don't if you don't mind me actually ripping it open and actually putting it into a reel or actually if anything putting more Fillers onto this so that way it doesn't move because I don't want the car to get damaged But I'm letting you know now it's gonna get damaged like this So I mean if you're cool with this thing sealed, let me know I we won't touch it like that and leave it as is but I feel like it's probably better to Unseal it put some fillers in there. So it doesn't move at all But it's up to you man I know sometimes people hate to open this because then it might damage the value of it But the car's gonna get damaged in here rattling. So Let me know But pretty sure you guys that was flawless baseball a very nice RPA Nice diamond and some very nice hits in here. We do have a randomizer to do though Really quick before we end up here. Casey my snake Pearson Yeah, I'm just saying is that I think it's best to keep the card in better condition than it is to keep it sealed And obviously you have it on video and I'll do it on video so people can see it But um, yeah, if not the cards gonna move as it flips And I wouldn't want the car to get more damaged if it already is or you know, try to preserve it But like I said that I'll leave that up to you and you let me know man I'm just saying for preserving the car. So it doesn't get as damaged For the cleat we don't do it because the cleat kind of stops the card from moving But with this it's a little different. There's no cleat to stop the the card from moving out or the spike, you know So we got blue jays Tigers blue jays Tigers Roll it Four and a five nine times Good luck one two three four five six seven eight nine Boom stays with the Tigers four to five nine times. So there you go Tigers. I believe that is Dusty there you go, man You can pretty much wipe out the whole Loose box loose cards in there Appreciate it guys. Jasper's case breaks.com look out for the next one


The present meta-analysis suggests that superparamagnetic iron oxide nanoparticles are a viable alternative to standard methods in detecting sentinel lymph nodes in breast cancer patients. Further studies with larger populations should be conducted to confirm these results.


Great building blocks for Hermann. Let's hear from him. What do you think has allowed you to get better and better each outing? I mean, you know, a lot of work, you know, a lot of work put into getting back, becoming a better player, you know. When you have an opportunity to play, you know, you have to take advantage of it, you know, so really focus on the work and just trying to execute out there. You mentioned being a better player. Is there something specifically you've been focusing on to get to another level? I mean, in what you mentioned, being a better player, how to get better. Is there something specific that helps you get better, whether it's mechanics or attack plans? How can you get better? That's what I mean by attack plans. I mean, the strikers are very smart, they know a lot about sequence and that's based on how you work on each striker, on each INI, on how you develop under the counter and always keep focused on that, taking the sequence well and trying to make the team as less charged as possible.  And for me, it's just, yeah.


Creo-electron microscopy structures reveal that the apo form of human SLC-15A4 is in an outward-facing conformation, while the dimeric form involves four cholesterol molecules. The TASL bound complex shows an unprecedented interaction mode with solute carriers and undergoes a conformational change to form a binding pocket for TASL insertion. These findings provide insights into the molecular basis of regulatory switch involving a human solute carrier and offer an important framework for structure-guided drug discovery targeting SLC-15A4-TASL-related human autoimmune diseases. This article was authored by Su Dong Chen, Min It-Sie, Sen Sen Jong, and others.


You know, he's he's kind of picked up he's been that way sort of I feel like for the last couple of months You know, he's had a couple of pickups here and there But for the most part he's he's really started to lock it in Again the split finger right from the get-go is a factor. The slider was good I thought he did a good job of mixing in his fastball again.  You mentioned the commander you impressed by the fact ten strikeout zero walks tonight Yeah, that's what he's been and even there I think the his final inning there ran two three ball counts and gets right back in though and You know just the ability to to flip any pitch in there for a strike But also still have the swing and miss stuff that he has He was terrific You mentioned the two three ball counts in that eighth inning but did get out of it Was there any temptation at all to put him back out there in the ninth? No, no, that was it. We're we're we're pretty Pretty sure we're we're gonna Be the end of the night there You know after having a tough inning in the seventh where he really had to grind through make some pitches And then just you know getting a little behind there in the eighth We were we were good that being it and I think masa was when we told him he was he knew it was coming Labor with the home run now has a hundred hits the youngest Yankee to have a hundred hits since Mickey Mantle when you hear that With the amount of talent that's come through that organization. Is that pretty unbelievable? It is it is and it's really been good to see him the last couple few weeks now You know after going through a little bump there where he was struggling a little bit at the plate He's he's found it. He's having good at bats Obviously a big one to get us going tonight You know some really good plays in the field for us tonight so You know good the long ball Early kind of gave us that cushion and then masa took it from there Yeah Yeah proud of him for bouncing back, you know, that's one of the things I talked to him even within that game is like Keep going, you know, this is fight compete and you know I thought he did a good job of Turning the page from that first inning even in the game in Oakland the other night I thought the rest of the night he was solid and I thought he was really good tonight and really on a good page with With masa where they worked really well together for the second time in a row and and there's no question.  You know, I think the abats have been good from him You know, we've seen him. We've seen him obviously Take some walks and that's been something he's done really all year really throughout his career You know, I felt like his first at bat tonight where he flew out on that first pitch I thought he got a good pitch to hit and put a good swing on it I actually kind of looked at him after when he came in the dugout like that was close I felt like even even on some strikeouts in Oakland I felt like he was he was on some pitches and closer and then You know to get us to get a big one tonight for us to tell pad the score was nice Yeah, it was partially that but a little unique obviously in that I wanted to get DD in the game kind of a unique situation where you're kind of rehabbing him back but in the middle of a So to get You know to get DD, you know, three and a half innings and then at bat I felt like it was a good first step for him and and also with the lead there to you know Just just to keep etch-a-very out on the field, you know, it felt like is something we wanted to do there


The signal oil program the whistler that whistle is your signal for the signal oil program The whistler I am the whistler and I know many things for I walked by night I know many strange tales hidden in the hearts of men and women who have stepped into the shadows Yes, I know the nameless terrors of which they dare not speak Yes friends, it's time for the signal oil program the whistler Rated tops in popularity for a longer period of time than any other West Coast program in radio history And signal gasoline is tops too, tops in quality It takes extra quality you know to give you extra mileage And signal is the famous go farther gasoline So look for the signal circle sign in yellow and black That identifies friendly dealer owned signal stations from Canada to Mexico And now the whistler strange story Silent partner for 20 out of every 24 hours Saunders Junction is a sleepy little western town The railhead of a dairy cattle empire called the Robertson Ranch But twice a day at 6 o'clock in the morning and evening It comes to life It's three blocks of stores and supply houses suddenly active As the trains pull into the station And Robertson trucks line the platform The drivers and swampers hustling hundreds of gleaming cans of Robertson milk Into the waiting cars to be taken south to the city In the wintertime a stranger in Saunders Junction would have trouble distinguishing morning from evening The same trains the same shouting men The same darkness morning and evening But Matt Robertson was no stranger As he stood on the platform watching the train pull out He was thinking a little bitterly now of the day 30 years ago When his father passed the Robertson Ranch over to him And as he looked down the line of trucks with Robertson painted on their sides He couldn't help reflecting on the changes those 30 years had brought Okay boys, let's roll Barry, hold Barry, just a minute Did you check that shipment on Barry? For sure, what's the matter Matt? Let's look at the sheets, I didn't see them Oh, I didn't think you wanted them I've got a right to know how much milk I'm shipping off my own ranch, haven't I Barry? Come on, give me the sheets Oh sure Matt It's just that Wilk said...  Yeah, sure do And 3,000 ain't nothing to what we're going to do when Santa Susanna gets going We decided, Jant, whether we're going to buy Santa Susanna ranch Oh, it's all bought, yesterday Where did you hear that? Well Wilk changed himself Yeah, we're going to double milk production, he says He went ahead and bought it? Why, I thought you knew Wilk, went ahead Went right ahead and bought it Oh, funny, he didn't say nothing to you You're his partner and everything Oh, I can't figure why... Just forget it Barry I'll take it up with him directly Sure Matt, sure I better get back, I gotta get these sheets to the boss The boss, eh? Look Barry, before you get in that truck Take a look at the name on the side of it Huh? It says Robertson Ranch, you see it?  I want to see young Charlie Brack Charlie? What's the matter, what's he been... Oh, don't get worked up Matt, it's nothing serious You know he's been on probation after that shooting scrape up north He just ain't reported to me this month like he's supposed to that's all Oh, I see Can't say I blame him for putting that fella in the hospital But the book says he checks with me once a month And that's what he better do Well look Sheriff, I'll send him in tomorrow Tomorrow he'll be working stock all day if I know his boss Will Caines will have ever hand on the place Moving cattle over to Santa Susana place he just bought Oh, you heard about that too, huh? Sure, yesterday, it was all over town What's the matter Matt, something wrong?  Yeah Alright Sheriff, let's go It's ten miles from town to the main ranch house You know every bump, every rabbit on the winding dirt road Matt And as you guide the car through the night You can see in your mind the vast stretches of pasture land on either side The cottonwood thickets, the oak covered hills All part of an empire that once was yours All of it, 27,000 acres of the best land in the state As usual there's a light on in Wilk's office down by the gate when you arrive For an hour after train time, morning or night Will Caines always works alone over his reports And then as you reach the door and Sheriff grabs your arm Sounds like Charlie, maybe we better wait Please, Wilk, listen to me You listen to me, I don't give a hang what you think Sir Dorothy thinks either for that matter You gotta think of her, Wilk If you're not kid anymore, she's a grown woman with a mind of her own It's her business to do with Well, I'm making it my business, get that?  Just a little private argument, that's all Sheriff, he's been seeing my daughter, Dorothy And I don't intend to let it go on I think we can settle that without the Sheriff, Wilk Whether you know it or not, sonny, it's settled already Um, come on, Charlie I'd like to talk to you for a minute Oh, excuse us, will you boys? I'll, uh, see you later about that, Wilk Well, what do you want?  I got Santa Susanna at a good price I'm gonna make money with it There was no reason to dilly-dally any further That's all I see You know what's funny, I thought we had a partnership You can look at that any way you want I'm running the ranch the way I want to run it The, uh, Robertson Ranch I'll change that, too, one of these days Well, you low-down double-crossing skunk I didn't know when you were gonna... Wait a minute, Matt I'm glad you mentioned that point You seem to have forgotten now that when you took me in the whole spread was two jumps from receivership If it hadn't been for me, there'd be nothing left 14,000 acres, they were only 1,500 head poorest kind of stock the Lord ever made We've come a long way since then, Matt and we're going a long ways further If you want to come along, that's fine If you want to sell out, that's better yet You can take it over on your way to Santa Susana tonight Santa Susana Removing all our dry stock over there tomorrow to put on that rich pasture, they'll need you Is that all, boss? Yeah, that's all Charlie Hello, Matt You, you waiting for me?  The ranch Oh, now wait a minute, Charlie I've had about all I can take, Matt I don't want any more I know what you mean It's making it hard for her, too Dorothy? Yeah I'd be forcing her to make a choice, Matt It's not right to make a choose between me and her father I'm not going to tell anyone who's going to climb on the six o'clock train tomorrow night and get out of here He thinks I'm a criminal, a killer Or maybe they're right If I...  I could kill him, Matt He could kill him, Matt Yes, he feels the same way about Wilcanes as you do He could kill Wilcanes, too, and everybody knows it Even the sheriff after their quarrel tonight Suddenly, you see your special partnership agreement with Wilcanes The bold black letters of the survivorship clause that will make Robertson ranch all yours again in the event of his death If Wilc were found dead If Charlie Preck were picked up fleeing the scene on the six o'clock southbound just after the crime Then, Matt, the yellow lettering on the trucks Robertson ranch would mean again exactly what it said Charlie, you... You say you aren't telling anyone you're leaving? I can't The sheriff would keep me here because of my probation Still three months to go What about Dorothy? See her tomorrow Where is she, Charlie? Up at North Ranch You, uh... You're going to tell her? No I'll write her a letter from New Orleans or somewhere, that's all Aren't you afraid if you see her, she'll guess something's wrong? No, I told you I won't tell her anything I just want to see her once more, that's all Charlie, I... I think you're right No use making a big fuss about it I've been thinking about it for a long time now, Matt It's best for her She'll forget me in time That... That Bill Ashford over there is my nice fellow Tell you what, Charlie I'm going over to Santa Susana tonight I'll stop at North Ranch in the way I'll tell her you'll be over Oh, gee, thanks, Matt Charlie Yeah? If you decided to keep this under your hat Why did you tell me? Well, I wanted someone to know I guess I just trust you, Matt I figured you wouldn't say anything You're right, Charlie I won't say a word to anyone With the prologue of Silent Partner The signal oil company is bringing you another strange story By the Whistler Recognize that noise? That was the breaking of a perfectly good New Year's resolution Made only two weeks ago There's one resolution, however, that I do hope you won't break The resolution to try, just try, signal gasoline in your car Because seriously, friends There are real advantages for the driver who powers his car with signal Not only because signal is the famous go farther gasoline But also because of what makes signals good mileage possible I'm talking about the extra efficiency Today's signal gasoline gets from your motor Which naturally gives you quicker starting, faster pickup smoother, knock free power The superior kind of performance you expect of a superior quality gasoline Yes, it's a fact The same features a gasoline must have for extra driving pleasure Also give you extra mileage The thing signal gasoline is famous for That's why we say to be sure of the tops in gasoline quality There are just two things to remember One, in gasoline it takes extra quality to go farther And two, signal is the famous go farther gasoline And now back to the Whistler The plan is clear now, isn't it, Matt? Will Keynes is going to be out of your way for good And young Charlie Brecht will be blamed Driving over to the Santa Susana, the whole thing runs through your mind You can almost see it working out Here are the sheriff's questions after Willk has been found dead Kind of a sudden the way you decided to leave town, wasn't it, Charlie? Wasn't as sudden as it seemed I told Matt I was going I'm sorry, Charlie, but you're mistaken about that Well, of course I told you that day after my argument with Willk That wasn't your last argument with him, was it, Charlie?   Well, for one thing, I promised Charlie I'd deliver a message to you, Dorothy Oh, how is he? Oh, he's all right Kind of a hot-headed young fella, but... You know, if people would just make an effort to understand, Charlie... We're talking about Dad, aren't you? Well, I... Please, Matt He and Charlie have quarreled again, haven't they? Dorothy, you know I don't like to go mixing into things Tell me, Matt Well, yes, they did Sort of a showdown, I guess Each calling the other names and all They stopped when Sheriff Walker and I came in on him I see Matt, do you mind if I ride back with you? Oh, I... I wouldn't do that But I want to see Charlie, I want to talk to him But he's going to meet you tomorrow, Dorothy That's what he wanted me to tell you He's coming over here? No, uh, not here He wants to meet you in town About one o'clock In town? But he usually comes here Well, I... I guess he figured it would be best that way Because of Dad? I suppose Well, I'll have to get someone to drive me And maybe Bill Ashford will do it Oh, good I'll tell Charlie when I get back Thanks, Matt I... I wish Dad could be as understanding as you are You have to lie, Matt To send Dorothy into town When Charlie will be looking for her at the North Ranch Above all, you can't take the chance of having them meet Of course, you know there'll be questions later on And Sheriff Walker is investigating But you can always say you lied to prevent more trouble That's a good excuse, Matt And all through the night as you're giving orders to the hands You're thinking it through But in the morning when you meet Charlie back at your place You find there's something else to think about Matt, I... I know you'll think I'm screw a change in my mind But I've got to tell Dorothy about leaving It don't seem right to just run off That won't make it any easier on her, Charlie Oh, but I didn't sleep last night thinking about it I feel that I should tell her I think your first hunch was best I didn't ask what you think I'm telling her and it's none of your business Oh, I'm sorry, Matt, forget it I... I'm all mixed up Sure, kid I understand But you should know I'm gonna have one of the boys tell her to meet me in town Just before train time tonight Why don't you ride over and tell her yourself, Charlie As long as she's expecting you I got everybody over at Santa Susana moving stock I'm holding down the fort here alone Does it matter if you're quitting? You know, Wilk, my work isn't done He'll start wondering about it and maybe get suspicious Oh, sure, the sheriff Yeah, not that Wilk wouldn't be glad to see me leave But the sheriff won't I want things to look usual right up to the time I climb under that six o'clock train Hmm, seems a shame You'll just get to see Dorothy for a minute Hey, look Maybe I can help out You don't mean you'll work for me Why not? Well, after being up all night handling your own shift While that's no good, you're worn out already Oh, well, I'm not so tired I can't do a friend a favor, Charlie Yeah, but, Matt... Go on, get over to North Ranch and talk to it Oh, gee, I sure appreciate this, Matt Forget it, forget it Oh, uh, I wouldn't take the station wagon Forming my check on you Oh, I won't, I'll just saddle up I can be there this afternoon Well, I'll leave right away if you want it, Charlie And don't worry I'll be taking care of things for you here And it's a deadly eight hours you spend Taking Charlie's shift at the main ranch After that long night of work at Santa Susanna By two in the afternoon There's a weary throb at the base of your brain An ache in your back A gritty heaviness in your eyelids But you manage to stick it out somehow And late that afternoon You drag up the stairs of the ranch house Down the hall to Charlie's room He's angrily throwing his things into a suitcase When you open the door Hey, say what's wrong, Charlie? Huh? You and Dorothy have words or something? Oh, hello, Matt No, I didn't see you But I thought you... Look, Matt, I appreciate you taking my place and all But I'll just soon not talk about today Whatever you say, Charlie I'm sorry, Matt You might as well know She, uh... She wasn't at the ranch when I got there Wasn't there? That's right I hadn't left a message or anything When I got inquiring around One of the boys said he saw a drive off for town With Bill Ashford What? Why, but that isn't like Dorothy I'm sure she... I'm sure she'd been listening to her father Probably swung over to see him things his way Anyway, it don't make no difference I'm leaving On the six o'clock, just like I said Well... Good luck to you, Charlie Thanks, Matt You would swell It's one thing I won't forget about Saunders' junction Listen, if...   I suppose you know Charlie Brecht left Yeah, good riddance By the known in time And I sent a brass band down to the depot I don't think Dorothy would agree with you Dorothy will forget him in a week I'm not so sure I didn't come in here to discuss that Look, I'm busy trying to hurry through this stuff If you don't mind... I mind everything you've done I don't want to get on that again I've told you what you can do Sure Just like you told Charlie Only it's different with me, Wilk I'm not running out I'm staying What are you talking about? I invited you to stay or go Or do whatever you please I think that last is more to my liking, Wilk I want to do whatever I please With the Robertson ranch Just like in the old days That's why I brought this along Matt, are you crazy? Don't point that thing at me Sit down, Wilk You're not going anywhere What are you thinking of, Matt? You'll never get away with this There's a survivorship clause In our partnership agreement, Wilk I think it'll make things pretty simple Matt, you fool You'll hang Oh, no, Wilk, no I'll give you the satisfaction of knowing That you even took care of that for me When you drove Charlie Brecht to the breaking point He killed you and ran out No, Matt It's that clear cut, Wilk No, Matt, no Sheriff Walker You, Matt? Yes, I'm calling from the ranch Will Keynes has been murdered What did you say? My partner, Will Keynes He and Charlie Brecht had another fight I got here too late to stop him Charlie Brecht? He shot Wilk? Yes, that's right Look, Sheriff, I'm going after Charlie I'll contact you as soon as I catch up to him Good, but be careful, Matt And call in the minute you're locating Yes, Sheriff Twenty minutes later, you're in the station wagon The headlights cutting through the darkness The clock on the dash shows almost six As you reach the depot Just as the train is pulling out Just a minute, wait Don't close that window Huh? Oh, Matt Oh, say You don't want to take it out of here, too No, no, no, no I'm trying to catch Charlie Brecht Charlie?  Wondered if I could use the phone I want to call the sheriff The whistler will return in just a moment With a strange ending to tonight's story Meantime, for you drivers Who are not already enjoying the convenience Of a signal credit card I'd like to mention just a few of the ways In which this simplified modern method Of taking care of your driving needs Can add to your driving pleasure With a signal credit card Instead of paying cash each time You buy signal gasoline or lubricants You just say charge it Then at the end of the month You merely send signal oil company a check To cover your month's purchases Just as you now take care of your telephone, Electric or department store bills This gives you a handy record Of what it costs to run your car And on trips, it eliminates the necessity Of carrying large sums of money For driving expenses And possible emergencies For signal credit cards are honored At all signal service stations Almost 2,000 of them Throughout the Pacific Coast states From Canada to Mexico To add to this convenience to your driving Here's all you do Just stop by your nearest signal dealer And tell him you want to fill out An application blank For a signal credit card And now back to the whistler It was made to order, Matt This opportunity thrust in your hands When Charlie suddenly decided to leave secretly On the six o'clock southbound He left you free to kill Wilk Without fear of the consequences To once again look on Robertson Ranch Is yours Only a few minutes after your call To the sheriff's office He's there with you at the station And the questions you knew were coming Are on his lips Now, wait a minute, Matt You say you heard Charlie and Wilk In his office Ran across the yard in time to see Charlie Run off after the shot That's right, Sharon When was that? About an hour ago, I think What were you doing just before that? Well, I came in kind of tired And laid down for a few minutes When was that? This afternoon What's the matter with that? Just beginning to wonder, Matt You really wanted To get back, didn't you?  What is all this? Well, that man's a killer Wait a minute, Matt Charlie couldn't have done it He was 200 miles from here When it happened What do you mean? Charlie got on the six o'clock train tonight And then laid down on that bed Tired the way you were That was a big mistake, Matt I don't know what you're talking about Take a look out that window there The east window See that funny light out there That's dawn coming up, Matt You forgot, this is the dead of winter And the nights are long It's six o'clock in the morning now Morning? You stretched out on that bed yesterday Passed out like a baby and didn't open your eyes 12 hours 12 hours I slept the clock around Yeah, now Let's go up to my office and wrap this up All nice and formal Let that whistle be your signal For the signal oil program The Whistler Each Wednesday night at this same time Brought to you by the Signal Oil Company Marketers of signal gasoline and motor oil And fine quality automotive accessories Signal has asked me to remind you To get the most driving pleasure Drive at sensible speeds Be courteous and obey traffic regulations It may save a life Possibly your own Featured in tonight's story were Bill Boucher and David Ellis The Whistler was produced by George W. Allen With story by Joel Malone and Harold Swanton And music by Wilbur Hatch And was transmitted to our troops overseas By the Armed Forces Radio Service Next Wednesday For a full hour of mystery Over most of these stations Tune in a half hour earlier Enjoy the Saint As well as the Whistler This is Marvin Miller speaking This is CBS, the Columbia Broadcasting System


What's up everyone? Take down here. Welcome back to another hockey card video. You guys already know today we're gonna be opening up some more of the 2020-21 SP hockey packs. Today we're gonna be doing 24 packs. I'm getting these at Walmart, three packs for $15 or something like that, but they've had a lot of great deals at Walmart, so I recommend checking them out. And as you guys know, we're gonna hopefully try to get a complete base sets, a complete rookie sets, and a lot of rookie jersey patches, which I am hoping to get more last year for the 1920 set. And I was going for it. I did get a lot of those jersey patches, have only gotten two. So hopefully we get some more today. Let's get started here. And as always, I'm gonna skip through the base and just focus on the inserts, because those are the highlights for the video. So we have base, base. Ooh, we have a Sydney Crosby authentic profiles. I've yet to get this one. So that is a nice one. And there's no way. We have the regular authentic profiles. And then right behind, we have the Sydney Crosby foil one. That is incredible. I didn't even know about these. This is the first time pulling the foil one. So that's really cool. And then base. Open up the next pack here. Base, base. We have a Nether rookie card, which I already have this one, a blue base, and then a regular base. I'm getting close to the blue base set as well. I just, obviously you get more regular base than you do blue base. Sometimes you get one per pack. Sometimes you get none. Whereas these, you get two to three per pack. So base, blue base. We have a rookie that I don't think I have yet. It's author Kalyev. Don't remember him. And then two more base. Hoping for the Jersey rookie patch. That'd be nice. Derek Stahl, huge fan of another base. We have Connor Michael, which we already have his rookie card. Blue base and regular base. Something good. Brad Marchand base, base. We have Sutter rookie. And we do have a Kravsov numbered rookie card. Numbered 934 out of 2299. So that's really interesting, that one. Then a regular base. Last pack of that stack. Base, blue base. We have a rookie card for Bowden, which I already have. And then base and base. Base, base, blue base. Besser, Brock Besser authentic profiles. I don't think I have that one yet. Regular base. That's really cool. At least we're getting things that I don't already have. I don't know how many are in the set for that, but could be getting close to that as well, because I did get a few in the last video. Base, Sutter rookie. I think I already got one, at least in this video. Blue base and then two more base. Eric Stahl, we just picked his base up a minute ago. I don't know why it's not focusing today. We do have another authentic profiles for Connor Hellbuck. Blue base and two more regular base. Comment below what you guys think. Am I going too fast in these videos? Should I focus a little bit more on the base cards like I used to, or just do how I'm doing it, which is base, Valradi, rookie. I don't think I have that one. Ooh, we did get a patch. We did get one. We did get Shamevsky, butchers name. I already have his rookie card, but now we have his patch, which is awesome. I thought I already had this one. Something tells me I just mistaken it. So we did get a Jersey patch out of this, and another base. That is really awesome. I wanted to get more. I wanted to get Norris, Stussel, because I'm an Ottawa Sanders fan, of course, but obviously other ones as well. So we did get one in this video, which I was not actually thinking we were going to. Base, base, McDavid, blue base. We have a Ollie Juvy rookie. Of course it's not focusing today. Ovechkin, that's the one that I am missing for the base set of 1920. So if you guys have an Ovechkin, which is card number 100, I'm not sure what this one is, card 12. So if you guys have an Ovechkin of the last year's set, I definitely need that to complete it. We do have a Kravazkov SP authentic. This is a foil back, not numbered, just foil. That's really cool. I didn't even know about those ones. We have another rookie for bellows. Two more base, next pack here. Base, base, a Hiskonen authentic profiles. Blue base, regular base. Getting a lot of doubles in the base now. So hopefully we have a complete set. I'm going to find out after this video and see how many more packs I need to buy or if I want to try to buy a hobby box again. We do have a Kravazkov numbered rookie one. Oh, wow, that's crazy. So we got a numbered rookie one. So the first one here, we have, it's numbered 934. This one here is numbered 950. So that's the first time we got a numbered double. So there's only, oh, this is different too. I'm not sure what the difference is between the two. Oh, I can tell now. This is regular foil. This is rainbow foil. So they are different after all. So this one here is numbered out of 2299. This one's out of 1299. So one's regular and one is foil. That is interesting. I wouldn't have picked that one up. We have a rookie and two base. Very interesting. I'm glad I pointed that out. So I thought I had a double and I was going to use that in a video. Now I get to keep both. Base, blue base, Sutter rookie, which I have a few of now and two more base. Patrick Kane, base, base. Thomas Harley, rookie. Blue base and a regular base. Base, base. Hoglander, don't have this rookie yet. Blue base, regular base. Base, base. Stutzel rookie, I already have that one. Blue base, regular base. Base, base. Liligren, I think I have that one. I'm going to have to double check. Blue base, regular base. Base, abanac rookie, which I don't know if I have his already. Blue base, base, base. I feel like I'm going to have another avalanche soon here. Base, blue base. Mitch Marner, authentic profiles. I do not have that one yet. I've got a few in this video, two more base. So I'll see how many of those I need to have a complete set as well. Base, Karmic Michael, two in the same video. Rookie, blue base, two more base. Base, Nick Robertson. So we have both the Robertsons in this video. Rookie, base, blue base. In the final pack, did good in this video compared to the hobby box at least. Base, blue base. Connor McDavid, authentic profiles. That is awesome. Two more base. So that was a lot of base cards, a lot of the blue base cards as well. But we did get a lot of great stuff. We do have the Crosby Ed McDavid authentic profiles. We do have Crosby's foil authentic profiles as well. We do have a foil rookie card for, oh, I didn't even realize this. Not only do we have the foil rookie card, we have the regular rookie card. We have the regular numbered rookie card numbered out of 2299. And then we have the foil rookie card for the same player numbered out of 1299. I didn't realize we had all four in the same video. Regular rookie, regular numbered, foil and numbered foil, which that is crazy. And we did get a patch in this video as well. So hopefully we do get a few more of these. And comment down below what you guys think. Should I get a hobby box or should I just stick with these packs here because I am getting a lot of great stuff out of them. Hope you guys enjoyed. I'll see you guys in the next video. Please take care. Peace.


So the boat obviously designed to be in the water. It is inevitable You're gonna have water in your bilge at one point or another We've designed the stringer grid for all of our boxes that don't drain overboard the drain through PVC pipe and Eventually work its way to the bilge now once it's in the bilge We'll have a bilge pump and it will kick on it's got an automatic float whenever water reaches a certain level It will kick the pump on alleviating all that water and sending it overboard This is a critical part on the boat just in case any hose any through hole anything were to break in time or leak That water will go to the bilge and like I said, it will be immediately pumped out by the bilge pump Now as I said, it's got an automatic float. You can also come up to the dash and work them manually via the switch