Now, we are going to look at the provisioning of mobile TV which is again a very specific kind of IP TV service over the well-known IEEE standard WiMAX and Wi-Fi. These two technologies are being considered here because these two technologies are basically IP centric. We look at these two technologies as the options and then we will see how WiMAX and Wi-Fi are suited for providing such service. Since IEEE basically provided lead to the IP-based service provisioning in 802.11 which is Wi-Fi and 802.16 which is Wi-MAX. It is important to look at the behavior adopted by both of these technologies in the domain of IEEE. So we are going to see how television broadcasting takes place in these two technologies. At the more higher layers, there are certain protocols which are common to both WiMAX and Wi-Fi because these technologies are the link layer technologies. So we have IP at the network layer, we have UDP at the transport layer and together we have the motion picture expert group MPEG standard which is basically a video encoding standard and the real time protocol family of protocols including RTP, RTSP, RTCP. So these protocols are at the upper layer but the main concern is how well suited are the two technologies for providing IPTV. As for Wi-MAX, since Wi-MAX provides a very high bandwidth and good data rate, it has the most important essential that it provides bandwidth. But does it provide quality of service? Yes, it does. In fact, we have already seen this. It has different classes of services. So for IPTV, the most important and relevant classes are the real time polling service and the extended or enhanced real time polling service. Now these two service types actually are well suited for IPTV because as you can see, the real time polling service actually is highly suited for periodic packet transmission. It means that the packet sizes may be varying, some packets may be large, some may be small but the periodicity is highly suited for real time polling service. IPTV is not the only service that is supported by real time polling service. In fact, voice over IP is also supported, which is bi-directional. In voice over IP, since it is mostly regarding audio visual conversation, so whenever the talkspot does not contain any audio, so the silence can be suppressed, saving certain bandwidth for usage by either video or for any other textual data that can be transmitted in line. Then we have the extended real time polling service. Again, it is suited for periodic traffic both with active and silent intervals. It is related to voice over IP. It doesn't have much concern with IPTV, but it supports both. But the extended real time polling service actually helps for video and multimedia streaming in the sense that it involves more negotiation features as compared to simple real time polling service. So extended real time polling service has more negotiation options which are available between two interacting parties. Ymax is highly suited for providing QS based IPTV service. Let's look at Wi-Fi. To begin with, we already know that Wi-Fi is highly deployed and it should be and preferably can be used for delivering the IPTV content down to the user, but there are certain constraints and limitations. Number one, it is not based on TDMA. It is contention based, which limits its application because any fluctuation in spectrum allocation in resource contention, the QS is going to drop at an unacceptable level. And then it is based on the ISM unlicensed band. So that also is again not much suited for delivery of Wi-Fi, but the only good thing about Wi-Fi is it's highly deployed and this is something which is part of every household. So QS can be compromised, but the wide deployment has to be and cashed and utilized. And this is something that people are comfortable with because it is only the last hop which is in your house that is carrying this IPTV traffic in your house. So quite likely within a certain geographical area like a room, like a floor or a building, there isn't much expectation of certain and unexpected service degradation because the number of competing sources are not there because a Wi-Fi access point is likely to interfere with another Wi-Fi access point in the downstream. If that does not happen, it becomes an acceptable choice. Now, does Wi-Fi offer quality of service? There is a variant, yes it does. It is IEEE 802.11e. It calls actually the class of service or type of service as the access categories. Now, the access category is something that we haven't come across, but it is almost the same as the type of service. So different classes of service or type of service can be mapped to different access categories and 802.11e provides differentiation in terms of traffic prioritization, admission, resource allocation and scheduling for these access categories.


Mayor Castro, I'm wondering if you can talk briefly about what you've done to build community colleges, especially some of the green energy programs and how you did it and what motivated you. Yeah, thank you very much for the question. What we've done is that we've actually embraced an entrepreneurial model of economic development through our local utility. San Antonio owns CPS Energy. So CPS has partnered up with the Alamo Colleges and they do training for companies that CPS has contracted with to provide solar power. For example, we signed a deal with OCI Solar. They're going to provide us up to 400 megawatts of solar power on a power purchase agreement, but they've also agreed to invest in a manufacturing facility in San Antonio. And all told, OCI Solar and their suppliers are going to provide 805 jobs for San Antonians. That's going to be run. A lot of the training will happen in the Alamo Colleges. We also have the Alamo Academies which high schools work with the Alamo Colleges to craft this apprenticeship model so students can both finish high school, get college credit and have employable skills. They can go and work at places like Boeing or Lockheed Martin and also if they want continue their education at one of our Alamo Colleges.


Okay, so this will be shorter of me just standing up here talking. Just about 15 slides. And what I'm going to talk about is how we can find rearrangements using these readalignment methods that we explored in the first part of the morning. So there's a lot of different categories of variations. So there are single nucleotide variants, like just single bases that have been changed in the reference. There are short indels, which we usually define as being indels that are less than the read length. So these are pieces of sequence that have been inserted into the reference or deleted from the reference. And then there are larger structural variations, like large insertions and deletions, pieces of the reference that have been inverted, and then translocations where whole arms of chromosomes could be swapped and also copy number variation where some sequence has been amplified or deleted. So in this module, we're just going to deal with structural variation and particular large insertions and deletions, inversions and translocations. And then in subsequent modules, like tomorrow, they'll cover single nucleotide variants and copy number variation. So there are two different ways of doing paradigm sequencing. So the method that I described in the morning was doing, which we refer to as paradigm sequencing, where we take fragments of DNA, which are fairly short from about 200 to 500 bases. We add sequencing adapters to each end of the DNA fragment, and then we sequence inwards from the ends. And we get this sequence where one read is mapped to the forward strand and one read is mapped to the reverse strand. We saw that this morning. Now, this gives us quite short-range information, and there's a method called mate pairs, which allows us to do much larger sequencing of pairs. So in this way, we take longer fragments of DNA, which are multiple kilobases, say 10 kilobases in length. We make a circle from the DNA, and then we shear the circle randomly, and we pull down the piece that has the adapter in it, and then sequence the ends of those fragments, and that gives us much longer-range information. So if we sequence the ends of this fragment here, where the adapter sequence, which we used for the pull-down, is in orange, that corresponds to the very far ends of this circle of DNA that we started with. Now instead of having local information, which is 200 to 500 bases, we have much further distance on our reference genome, and that makes it easier to map and easier to find very large structural variation. But this requires extra library prep. It requires extra sequencing. So the paired-end version, where we just sequence short fragments of length around 200 to 500, is much more common. So I'll be describing these rearrangement finding algorithms in terms of just normal aluminum paired-ends, where we have one read which is pointing forward, one read which is pointing reverse. So this just describes the expected orientation of our pairs. As I just said, one reads on the forward strand, one reads on the reverse strand. There's some sequence in the middle that we didn't sequence, we don't know about, and there's an expected insert size. So some people asked about BWA's flags of what a proper pair is in the last part of the module. So what BWA does is when it maps all of these pairs to the reference genome, it samples alignments to try to learn what the distribution of fragment sizes are. So usually this is a normal distribution, or at least it can be approximated with a normal distribution. So we'll try to figure out what the shape of this distribution is and what the mean insert size or fragment size is for the library. And it uses this information to determine whether the pairs are as expected when mapped to the reference genome. So if you imagine we've calculated this distribution, which has a mean of 300 bases and a standard deviation of say 30 base pairs, then any read that's outside of say 300 plus or minus three standard deviations of this insert size is mapping abnormally. And we call those discordant read pairs. And these discordant read pairs are the ones that give evidence of structural variation. So now what I'm going to do is just go through some different examples of structural variation that we can find and what the insert size and the read pair orientation tells us about this structural variation. So the easiest one to find are deletions. So I'm going to be using diagrams like this where we have the donor. This is the individual that we've sequenced. It might be the cancer genome. And then we have the reference on the bottom. So here the reference has this large red block, which is deleted and not in a donor. So I've denoted the deletion is just having these dashed lines here. And now if we sequence the donor, we're going to sample read pairs that span across this junction, the break point where this deletion is. And now if our paired end library is around 300 bases, we'd expect that on average the distance from this end of the pair to this end of the pair is about 300 bases. Now when we map pairs that have been sampled from the donor to the reference genome, because BWA has to account for the fact that there's this sequence that was deleted, it puts the pairs much further apart on the reference. So if you look at that insert size field in the SAM file, this will be 300 plus the size of this deletion on average. So what we can do is we can scan along the genome, looking for pairs that are mapped much further than expected. And this gives us a signal of a possible deletion. Now the opposite event is an insertion. So here this red box represents some sequence that has been inserted into the donor that's not in the reference. And here BWA is going to map the pairs much closer together, because it's not accounting for this insertion. So if the distance from here to here is 300 again, then the distance mapped to the reference is going to be 300 minus whatever this inserted sequence is. Now insertions are harder to find, because if you can imagine a 10,000 base pair insertion, you're not going to have reads that span from one end of the insertion to the other. So it's generally easier to find deletions using these sort of methods than it is to find insertions. So another type of structural variation would be a tandem duplication. So here there's a sequence in the donor that's copy 2, and it's been duplicated at the same location, so just the same sequence has been copied once. So here if we have pairs that are sampled from the end of the first copy that span the breakpoint and go to the start of the second copy, when we map these pairs to the reference genome, this pair is going to align here. But because there's no second copy in the reference, this pair has to be mapped back to the start of this duplicated sequence. And what happens is that now the orientation of the pairs are incorrect. Instead of the pairs pointing at each other, they point away from each other. So this is another signature of a structural variation that we can use to find these type of events. Likewise, if there's an inversion, we have a unique signature. Here in the donor, we have this blue segment that was flipped. So it's now the opposite sequencing strand. And if we sample the pair from the start of the inversion, when BWA goes to align it, it has to put this sequence here on the opposite strand of the reference, and now the pairs point in the same direction. So it's another signature of an inversion. So this is just a summary of the different types of structural variation and with the orientation we suspect. So for insertions and deletions, we expect the normal orientation of pairs, but the mapping distance is different. For inversions, we expect the pairs to point in the same direction, but the insert size will typically be quite different than expected. For TAMDN duplications, we expect the pairs to map away from each other. And again, the insert size will probably be quite different than expected. For translocations, we expect for interchromosome translocations, we expect one pair to be mapped to one chromosome and the other pair to be mapped to a different one. And the orientation can be really anything in these cases. So as I said before, insertions are particularly a tough case because we rarely will have pairs spanning across the insertion unless it's quite small, less than our fragment size. So what can happen is that we can only, we might only find pairs that are on, that were sampled from the inserted sequence that isn't part of the reference and these won't align to the reference so that the event might not be detected in this way. So here we'd probably want to use different methods like a de novo assembler, which would try to assemble the inserted sequence and then we could map like that. These are more experimental methods that are still in development. Another signature that doesn't use paired orientation is looking at split reads. So as I mentioned before, BWA will try to introduce small gaps when it aligns the reads. If these deletions are small enough, it might align one bit of the read to before the break point and then the other part of the read after the break point and we end up with a read with a long gap in it and this might also indicate that there's a deletion here. Most structural variation callers will use both split read signatures and paired end orientation and insert sizes to jointly look at for evidence of structural variation. So when we're looking for rearrangements in cancer, there's really two different approaches that we can take. Usually you've sequenced both the tumor and the individual's normal genome and we're not as interested in all the structural variation that's in the inherent germ line genome. We're more interested in the structural variation that's occurred in the tumor. So there are two approaches to doing this. First we can do structural variation calling in the tumor sample and the normal sample independently and then filter out all of the events that are in the tumor that are not in the normal. Or what we can do is just find structural variation in the somatic samples and remove any of them that have evidence like these discordant read pairs that are in the germ line sample. And most programs that you'll download and run for structural variation calling in cancer will be following approach two where it's jointly looking at both the tumor sample and the normal sample at the same time as a stronger way of filtering out germ line events. So now I think it's new for 2014 is that there's a gene fusion module. So this is going to be covered in the afternoon. So I'm just going to skip this, but this is a type of rearrangement where there's a translocation that's joined the exons of two different genes and then you get this chimeric protein that is possibly functionally relevant in cancer. Andrew McPherson will talk about these type of events in much more detail this afternoon. Now structural variation calling is quite difficult and the methods used and the software used is under constant development. In the practical session, which we're going to start in just a few minutes, I chose a tool named Hydra from Erin Quinlan's lab primarily because it's very easy to run and it gives you a good overview of the steps required to pre-process the data to run structural variation caller. This isn't an endorsement, it's the best structural variation caller and the state of the art in the field is changing quite rapidly. I'm going to take this chance just to plug a project that OICR is helping lead, which is a mutation calling challenge run through these dream competitions. So Paul Buchos' lab has developed a set of simulated genomes where they've spiked in structural variation events like deletions, insertions, inversions and they release this data to the community, anybody who's interested in developing software to call structural variation then people run their software on it, submit variant calls to Paul's group and then they score each one to see how well the different software is performing. And there's a lot of variability within structural variation calling methods and you can go to this website which is if you just Google ICG, CT, CGA, dream mutation calling challenge you can see sort of leaderboard of which tools are performing best and this changes quite rapidly so when you're looking to run different colors on your own data this is a great resource of figuring out what the state of the art is in the field. I also recommend that you don't just pick one tool and then trust its output, I suggest you run multiple different tools and cross-check against each other as a way of increasing both the power to find true events and removing ones that are possibly spurious that you might not trust. And within the practical session you'll be looking at different structural variations within IGV as a way of understanding how these patterns of pairs look when you look at their alignments. Okay, with that we'll start the next exercise.


Now, as gunshots echo across the windswept snow-covered reaches of the wild northwest, Quaker puffed wheat and Quaker puffed rice. The breakfast cereals shot from guns present the challenge of the Yukon. It's Yukon King, swiftness and strongest lead dog of the northwest, blazing the trail for Sergeant Preston of the northwest mounted police in his relentless pursuit of lawbreakers. And King, one new husky. Gold, gold discovered in the Yukon, a stampede to the Klondike in the wild race for riches. Back to the days of the gold rush, with Quaker puffed wheat and Quaker puffed rice bringing you the adventures of Sergeant Preston and his wonder dog Yukon King as they meet the challenge of the Yukon. Did you get that hurry up signal, fellas and girls? This is the last, the very last day on this program. We can offer you a sensational Sergeant Preston mounted police whistle. Have pencil and paper ready. Be sure to hear today how to get for yourself the official two-toned kind of police whistle that Sergeant Preston always carries. It's offered only by delicious nourishing Quaker puffed wheat and Quaker puffed rice. Stay glued to your radio for details. Young Corey Lane slammed the door of Matt Deacon's cabin angrily and started trudging down the main street toward the center of town. The wind-driven snow bit deep into his cheeks and he welcomed the sting. Ann Howard, who owned the Beaver City restaurant, lived next door to Matt and as Corey passed her door he thought of stopping. Then he rejected the idea. He wanted to be alone in his disappointment. Suddenly he heard his name called, he turned back. Deacon was standing in the open doorway of his cabin. A shot rang out. Matt clutched at the air and then slumped to the threshold. The door of Ann's cabin opened. Who's there? It's Corey, Ann. I thought I heard a shot. You did. Look. It's Matt. Did you shoot him? No, no. I don't know where the shot came from across the street. I didn't see anybody. Is he dead? I'll go see him. He's dead all right. Are you sure? Absolutely. Listen, listen. They've heard the shot downtown and they're coming from the cafes. Corey, you can't stay here. Why not? Because you'll be blamed. Come with me to my place. What's the sense? Don't argue. Just come on. All right. I'll put out the lamp. What's the idea, Ann? Matt Deacon has been murdered. I know that, but... And you'll be accused of it. Huh? I heard you talking with Hugh Mason, Matt's partner in the restaurant today. And a lot of other people must have heard you. Well? Deacon and Mason. You owe them money. Your claim is up to security and your notes due in a day or two. You came into town to get an extended and Hugh told you that Matt handed all such matters. That he'd be back from Dawson tonight and you could see him in his cabin. Matt refused to give you any more time. How do you know that? Oh, the nature of the beast. He wants your claim. Yeah, it's true. He did refuse. Too many people know that you're planning to see Matt tonight and when they find him dead, well, listen to me. I was outside your door when the shot was fired. And you saw me. I didn't have any gun. You can tell them. They'd say I was lying to protect you. Now, I would, too, Cory. Oh, and... But the best way is not to have to lie. Now, get out of here and go back to the palace to your room. And tonight that I was ever here, what's that but lying? Refuse to say anything. You can't do that. All right, then, lie. Say that you decided to see Matt in the morning instead of tonight. No. They're one of the men. I want to know if you saw anybody. I'll go out through the kitchen, out the back way. No, hurt. All right. And... Who is it? Kate Corby. Let me in. No, I haven't finished dressing. Oh, please, let me in. It's important. It's about Cory. Well, all right. Now, what about Cory? Matt Deacon has been murdered. Oh, so that's what the shot was. What does it have to do with Cory? Well, he came to see Matt tonight. I thought he might have stopped here. They're saying that... I don't care what they're saying. I haven't seen him. You're not lying yet, Anne. Not to Keg. Cory. Why haven't you gone? Keg's an old friend. You don't have to worry about him. Cory, you didn't do it. Of course not. The rest of the town will never believe that. I've been trying to persuade him to go back to his hotel room before it's too late. Then when they go after him, let him find him in bed. She's right. There are too many people. Not in back and you'll never be seen in this dorm. You can make it all right. Oh, please, Cory. Please, for my sake. All right, someone else.  All right, Cory. All right.  Vulking! Fire! Sergeant Preston! There's been a murder! Sergeant! Somebody shot Matt Deacon. Kill him! Come on, Jim. Ride with him. Inside the cabin, the body had been placed on a cot. Doc Forrest had finished his examination and was talking to Hugh Mason as the Sergeant and the Constable entered. Well, Sergeant Preston. Hello, Mason. How are you, Doc? Sergeant. This is Constable Downey. How do you do, Doctor? It's a sad business, Sergeant. How did it happen? We found him lying in the doorway. I was playing poke at the Al Dorado when we heard the shot. We all came running. Doc was the first one to get here. That's right, Sergeant. He was dead when I examined him. Bullet ended his left side and pierced the heart. And he wasn't able to say anything? No, Sergeant. Any idea who did it? Well, there was no one around, but with the darkness and the storm, it would have been a simple matter for the killer to take his getaway before I arrived. You saw no one? No, Sergeant. I'm offering a thousand-dollar reward. Are you? Matt was my partner. And no sneak and coyote's going to do a thing like this and live to brag about it. The money goes to you, Sergeant. The Northwest Mounder doesn't accept reward. Then it goes to anyone with information that'll lead to the killer's arrest. Maybe I can earn it myself. Huh? What do you mean? Corey Lane. He had a note coming through him. What about it? Well, we had supper together tonight. He wanted to talk to me about it, but I told him that Matt handled all our loans, that I expected him back from Dawson this evening, that Corey could discuss the matter with him here. At all? Corey was anxious to see him tonight. I wanted to head back for Wishbone Crick the first thing in the morning. You believe that Corey killed him? I don't say that, but I happen to know that Matt never renews a note. Corey stood to lose his claim. And you believe that he had a motive for killing him? It's up to you to decide, Sergeant. Thanks for the information. Is that desk locked? I don't know. No. If you don't mind, gentlemen, the constable and I would like to go through Matt's papers. Well, aren't you going to ask Corey any questions? Later. Now, if you don't mind... Sure, sure. Let's go, Doc. During the next half hour, the Sergeant and the constable made a thorough examination of the cabin and the contents of Matt Deacon's desk. When they left the cabin, there was still a crowd out in front, and Hugh Mason hurried to the Sergeant's side. Going after Corey, Sergeant. Where's he staying? The hotel, the palace. I'll show you. All right. All the men followed the Sergeant to the hotel, but he asked them to wait in the lobby, and he and the constable climbed the stairs alone. Corey was in room 204. Yes? Sergeant Preston and constable Downey, Northwest Mountain Police. Your Corey Lane. That's right. May we come in? Why, sure. Better leave the door open until I light the lamp. All right. What's on your mind, Sergeant? Did we wake you up? No, no, I... I had just gone to bed. You've been out tonight? I had supper at Ann Howard's place. I came back here afterwards. When was that? About seven. You stayed here? Well, yes. Take a look at these, Sergeant. Your mucklucks, Corey? Yeah. They're wet. There's a piece of ice here that hasn't melted yet. So you have been out since supper? Why, yes. Did you go to Matt Dickens' cabin? All right, I did. But I didn't kill him. Do you have a gun? I didn't when I was there. I've got one in my knapsack. I'd like to see it, Corey. Yeah, it's in here. It wasn't here. I never took it out at all. It's gone? Yeah. What was it? 38 revolver. Had my initial scratch on the butt. You think I used it and then threw it away? Why would I tell you I had a gun if I didn't think it was here? Sit down. Sergeant, I... Sit down. That's better. Does Matt refuse to extend your note? I still have two days. I was going to try and borrow the money. He refused? Yes. But I didn't kill him. I'd left. And Howard lives next door. I was in front of her cabin when I heard Matt call to me. They called to you by name? By name. Corey. Not Lane. Oh, Corey. I turned around and just I did. I heard a shot and Matt dropped.  She opened it as soon as she heard the shot. She'll tell you that I didn't have a gun.  How could I? But neither Constable Downey nor I believe you did it. We believe it was planned before tonight and that you were elected to take the blame for it. I was elected. By whom? Well, that we must find out.  Let the real criminals think they've succeeded in their plan. Are you arresting me? No, Corey. But I want you to take this whistle. Yes, sir. The Constable and I intend to stay close to you from now on. If for any reason you need our help, blow that whistle. We'll continue our adventure in just a moment.  Yes, today is the very last time on this program. You'll hear how to get a thrilling Sergeant Preston mounted police whistle. A whistle that's a 14-karat gold-plated copy of the very whistle that Sergeant Preston always carries. And that saved his life time and time again. You can't buy this sensational whistle anywhere in any store. It's offered only by delicious Quakerpuff rice and Quakerpuff wheat. The famous ready-to-serve breakfast cereal shot from guns. Wait till you see what a beauty of a whistle it is. Gleaming 14-karat gold-plated with a cord attached of gold-colored braided nylon.  Actually, over three-and-one-half inches long. It's not a toy by a long shot. It's of heavy-duty metal. It feels heavy. It is heavy. It's not an ordinary singletone whistle. It's an official two-toned police whistle. Now, this is the last day we can tell you how to get it. So listen carefully. Buy a package of Quakerpuff wheat or Quakerpuff rice. The bigger, better-tasting cereal that's shot from guns and full of bang-up nut-like flavor. Then send the box top with only 35 cents. That's 35 cents. And your name and address to UConn, Box L, Chicago 77, Illinois. And with this money-back guarantee, you can't lose. Hold this Sergeant Preston-mounted police whistle in your hand. Blow it just once. And if it isn't a better, far better whistle than you've ever heard or seen, you may return it and get every cent of your money back plus postage. Act now. This is absolutely the last day of this radio offer. Send your 35 cents and one box top from Quakerpuff wheat or Quakerpuff rice to UConn, Y-U-K-O-N. UConn, Box L, Chicago 77, Illinois. Now to continue. When the Sergeant and the Constable returned to the lobby it was still crowded and Hugh Mason was leading the discussion. Now this is a course I'll take. After all, Matt was my partner. The least that I can do to bring the criminal to justice. Talk to the clerk, Jim. Get a room as close as you can to Cory's. Right. Well, Sergeant, what'd he say? At first he said that he hadn't left his room this evening. That's a lie. He admitted it later. He went to see Matt. Did you arrest him? No. Well, why not? Because I don't have enough evidence for that. But if you don't lock him up or something you'll try to escape. Well, that would prove he was guilty, wouldn't it? He wouldn't get far, Mason, and sometimes it's a wise thing to give a man enough rope. Oh, enough rope to hang himself, huh? Maybe you're right. Gentlemen, I'd advise you all to go home. I intend to conduct a complete investigation tomorrow morning. Don't forget, fellas, a thousand dollars. What's vacant, Jim? A 205. The room next door. Good. There's only one caught, but I'll have another one put in. Fine. Oh, uh, and clerk? Yes, sir. If anyone should ask where we're staying. Oh, uh, I understand, sir. The constable warned me. Anyone ask? You're on the third floor. Nearly all the men in town adjourned to the El Dorado Cafe after they left the hotel. Keg was standing at the bar talking with Hank Travers when Hugh Mason walked up to them. We got things to talk over. I'll say we have. Follow me in the back room. You two, Hank. All right. What's the idea of making yourself so all fired important, Hugh? Why did you have to offer that reward? It was the natural thing to do. Sure, because it's the natural thing for a guy to think of his own skin first. I don't have to protest my innocence. I have 50 witnesses to prove that I was at the El Dorado when Matt was killed. Well, I was there, too. So if the case against Corey doesn't hold up and the sergeant starts looking for someone else, I'll be the one to press you. Keg, it's easy. Keg, if you were arrested, you'd be sure to implicate me. You can depend on it. We must make sure that Corey is convicted. He will be. All the evidence is against him. I don't know. There's Anne. The sergeant hasn't talked to her yet. Her story will be in Corey's favor. She might be able to convince the sergeant he had nothing to do with the shooting. Well, you won't give her the chance. You're not suggesting... No, no, no. Now, listen to me. Yeah, the sergeant has practically convinced that Corey is guilty. You heard what he said. The only reason he didn't arrest him is that he doesn't have enough evidence right now. He's given him enough rope. You heard him? Enough rope. Well, one false move. And Corey will be through. But he got mine. We'll persuade him to get out of town. That's impossible. You could do it. His good friend is pal, who's only thinking of his welfare. Oh, no, it isn't good enough. I haven't finished. I need an argument. Something will panic him. Make him want to run. There isn't anything. You're wrong. We still have his gun, haven't you? Yeah. No chance of getting it back in his room. That's a good thing there wasn't. That's it. Here. You haven't cleaned it or anything? No. One shot fired and his initials on the handle. Here's the story. Hank, you and a couple of other guys were looking around in the gully and back a man's cabin. You found this gun? It was long after midnight when Corey was awakened by a stealthy knocking at his door. Who is it? Hank. What's the matter? Let me in. There was someone out there with you. It's Hank Travers. He's found your gun. Where? In a gully back of Matt's cabin. My gun was stolen. Whatever you say, Corey, but it's going to look mighty bad if he turns it over to the police. He knew I was a friend of yours and he came to me first. It's a break. What does he want? He'll tell you, but let me handle it. All right. Come in, Hank. All right, Corey. So you found my gun? Yeah. I think maybe you stole it and used it to kill Matt. You're local. There were a couple of other guys with me when I found it. In the gully back of Matt's. One shot fired. Where is it? I haven't got it with me. So what do you want? Well, is this way, Corey? Matt was as mean as he come. He deserved what he got. I wouldn't like to see you hang for it. Thanks. But your initials are on the gun. If I give it to Sergeant Preston, it would just about sense a case against you. I'd collect $1,000 from Humason. So that's it. I'll sell it to you for 500. I'm not interested. Don't pay any attention to him, Hank. Of course he's interested. If I had $500, I'd have paid off my note. We'll get it, Hank. I'll go on home. We'll see you in the morning. You'd better make it early. Kegg, there's no way I can raise $500 my morning. And even if I could... No, I know. Besides... I'm calling. Only one thing left to do. What's that? Get out of here. Run away! You're sunk, if you don't. Lucky it's still snowing. There won't be any tracks to follow. I can take you to a place where you can hide out for a month if necessary. No one will ever find you. And after they stop looking, I'll see that you get across the border. I don't want to run away. Besides, there's no sense to it. No sense? You were at the cabin.  They find your gun with one shot fired from it in the gully and back of the cabin. With that kind of evidence against you? There isn't a jury in the world who wouldn't find you guilty. I didn't do it. Well, I believe that. No one else will. The sergeant believes I'm innocent too. Oh, at least he said... Listen, he came downstairs and told Mason he was giving you enough rope to hang yourself. What? That's what he believes. To hang myself. I heard him. I go on, get dressed.  For a moment, Corey's studio resolute. Enough rope to hang myself. Giving me that whistle in case I needed him. Swiftly, he began to dress. As he slipped into his parka, he saw the whistle lying on the table. And without understanding the impulse, put it in his pocket. Then he left the room. Keg was waiting in back of the hotel. Climb on, boy. Where are we going? We've cut through the forest to Galloway Creek. What's that? Your hideout. The old polar star mine. Let's go. Come on. The team had hardly disappeared in the storm when the sergeant and the constable stepped out the back entrance of the hotel and hurried to the run where King and the other members of the sergeant's team had been sleeping. Landon King, fast. It took only a few minutes to harness the team. The tracks aren't covered yet. No. There's the trail, King. Follow it. Quiet, boy. You'll have to keep the team quiet. All set, Jim? All set. King, on, boy. Keg drove his team hard over the drifted trail through the forest, up and over Windy Ridge, and then down the far side to Galloway Creek. At the head of the creek, they stopped and front the entrance to the old polar star mine. What's the matter? There's a sled. Those are dogs burrowed in the snow. There's somebody here already. We've got to find some other place. This'll do, Cory. Go on inside. Keg, what's the idea of the gun? It's your gun, Cory. Is that explanation enough? No. It should be. I'll get going. Light this lantern. Give it to me. Now march. With his own gun pressed into the small of his back, Cory was forced to walk into the mine. When the end of the main shaft was reached, Keg issued a command. To the left. Now Cory could see a faint glimmer of light ahead of him. As he neared it, he realized that it came from beneath the door. Don't bother to knock, Cory. We're expected. Go on, open the door. Beyond the door, there was a room, evidently used at one time to store tools. Inside were Hugh Mason and Hank Travers. Keg, what have you done? Why have you brought me here? Shall we tell him? Of course. We're helping Sergeant Preston. He told us that if you ran away, it would prove that you were giving. Why, Keg? I thought you were my friend. Anybody can make a mistake.  The only thing Keg likes about you is your girl. What are you going to do with me? We've been considering that, Cory, and I'm afraid there's only one answer. We can't just leave you here. You'd head straight back for town and tell the sergeants you'd been kidnapped.  The cold would finish you off, but the surest way is a bullet. Let him have it, Keg. Not me. I've done enough killing for one night. You?  You thought Matt called after you just before he was shot. He didn't. He didn't say Cory. He said Corby. He saw me. Why? That question is getting a little monotonous, especially since we have no intention of answering it. Shoot him, Hank. Not me. My cut isn't big enough to include money. All right. Then let me have his gun, Keg. May as well make it look like suicide just in case he's ever found. Here. Yes, Cory. Watch Keg hand the revolver to Hugh. His hand closed on the sergeant's whistle. Almost absentmindedly, he put it to his lips in blue. Hey, what's that? A whistle. Let me see that. Score. There's a name engraved on it. Let me see. Sergeant Preston. Where did you get this? He gave it to me. He told me my life was in danger. If I ever needed his help, I was to use it. I waited a little too long.  And that he didn't believe I killed Matt. Keg persuaded me he was lying. My friend. Now, there's a change of things. How does it? He suspects us. How could he? What good would it do him if he did? Cory's the only one who could prove anything, and he's through. Hey, what's the matter with this gun? He hit the empty chamber. Ah, now I've got it. Now. Now is right. Drop that gun, Mason. Sergeant Preston. Are you... Anyone else like to trade shots? Evidently not. Get their guns, Jim. I'll get them. Sergeant, how? When I blew that whistle, I had to... We were just entering the mine. We got here as fast as we could. But how did you know? The constable and I were in 205. There was a knot hole in the petition. We heard everything you said to Keg and Travis. It was Keg who shot Matt.  No, Cory. Not until he persuaded you to leave town. The man we suspected was Hugh Mason. Why? Hugh was Matt's partner. And used his position to steal money from him. Matt had all the proof, and he showed it to us in Dawson. I came here to arrest Mason for embezzlement. It better bandage his arm, Jim. I will, Sergeant. He must have known that Matt was on to him, and he stood to gain more than anyone else for Matt's death. Not only safety from prosecution, but according to their partnership agreement, all the firm's property went to the surviving partner in case of death. Safety and wealth. It was a great temptation. And you were right, Sergeant. Except that it was Keg who actually fired the shot. He hired Keg and Travis to help him. Well, your testimony, we can prove it. Now, let's get started back to town. Oh, uh... Here's your whistle, Sergeant. Are you handling as if it were worth a million dollars? Or whatever a man's life is worth. It saved mine. I'm glad, Cory. Come on, King. This case is closed. In just a moment, Sergeant Preston will give you a preview of Monday's adventure. A friend in need. Last chance, fellas and girls. This is your very last chance on this program to hear how to get a Sergeant Preston mounted police whistle of your very own. A genuine 14-karat gold-plated copy of Sergeant Preston's own mounted police whistle. And with it, you get a 12-inch gold-colored braided nylon cord. You can hang it from your buttonhole or belt for any emergency. To signal your pals when you're on a tight spot. To use hung patrol on dark nights. And it's perfect to call and train your dog. Remember, this is not a cheap toy whistle of tin or plastic. It's a big three-and-a-half-inch whistle of heavy-duty metal. So hurry, hurry, hurry. Send 35 cents. That's 35 cents with your name and address and one box top from a package of Quaker puffed rice or Quaker puffed wheat. The swell-tasting crisp fresh cereal shot from guns. Send to Yukon, Box L, Chicago 77, Illinois. That's Yukon. Y-U-K-O-N. Yukon. Now this is absolutely the last time on this program. We can tell you how to get a 14-karat gold-plated Sergeant Preston mounted police whistle. So listen and get this for sure. Send only 35 cents and one box top from Quaker puffed wheat or Quaker puffed rice to Yukon, Box L, Chicago 77, Illinois. That's Yukon, Box L, Chicago 77, Illinois. And now here's Sergeant Preston. Sergeant Preston reporting, sir. Oh, yes, Sergeant. A man named Kelma was arrested last week for embezzling money from the bank down in Whitehorse. The time of his arrest, he swore to get revenge on the bank manager for turning him into the police. Oh, yes, sir. I read the report on that case. Well, I've just had word that Kelma has escaped from the constable who was bringing him here to Dawson City. He may head back to Whitehorse and try to get the bank manager. I want you to track him down, Sergeant, before he gets a chance to carry out his threat. Right, sir. I'll start immediately. Let's go, King. Yes, it's up to Sergeant Preston to capture and escape criminal before he can get revenge on the man who had him arrested. One thing is certain. He won't be taken without a desperate fight. Be sure to hear this exciting adventure Monday. These radio dramas, a feature of the challenge of the Yukon Incorporated, are created by George W. Trendle, produced by Trendle Campbell Enterprises, directed by Fred Flower Day and edited by Fran Stryker. The part of Sergeant Preston is played by Paul Sutton. They are brought to you every Monday, Wednesday and Friday at the same time by Quaker Pop Wheat and Quaker Pop Rice. The breakfast cereals shot from guns. For a delicious hot breakfast, eat Quaker Oats. The giant of the cereals is Quaker Oats. Delicious, nutritious, makes you feel ambitious. The giant of the cereals is Quaker Oats. Say, boys and girls, do you want to be a star someday in sports and activities? Then start on Good Quaker Oats breakfast tomorrow because nourishing oatmeal gives you more growth and endurance than any other whole grain cereal. Remember, Quaker and Mother's Oats are the same. This is J. Michael wishing you good bye, good luck and good health from Quaker Pop Wheat and Quaker Pop Rice. So long. This is the Mutual Broadcasting System.


You guys signed me, not in the front. You've got a main bank. You guys are main bank. I'm listening to the back. I'm listening to the back. You guys are main bank. I'm listening to the back. You could say, why? I said, I can't. We teach you. I don't know. I'm listening to the back. We meant to tell you fake news, right? We don't know. We don't need you. It's one of those two categories. People are going to do this. It's fine. It's fine. It's fine. It's fine. It's fine. OK. Hello? It's fine. Great. It's really fine. It's fine. Check, check. Let us join together in centering silence for a moment. And now we'll join in our in-gathering hymn. It's numbers 379, but it's also written in the bulletin. Good afternoon and welcome to the First Unitarian Society of Madison. This is a community where curious seekers gather to explore spiritual, ethical and social issues in an accepting and nurturing environment. Unitarian Universalism supports the freedom of conscience of each individual as together we seek to be a force for good in the world. My name is Rosalind Woodward, and on behalf of the congregation, I'd like to extend a special welcome to visitors.  Newcomers are encouraged to stay for our fellowship hour after the service in the commons area of the Atrium Edition. Members of our staff and lay ministry will be on hand to welcome you. Just follow everybody, they'll lead you there. It's down there and down the hallway. If you're accompanied by a young child, please remember that if they need to talk or move around to the loger area, to the right, to my right, to your left, there's a good place to retire with them. And at this time, we ask you to turn off all beepers, cell phones and other electronic equipment that might cause a disturbance during the hour. I'd like now to acknowledge those individuals who help our services run smoothly. The sound operators, Steve is doing that, and we desperately need sound operators for the Saturday services, so if anybody, it's not that difficult, really. So if anybody is interested, please step up because there's been nobody the last few weeks. The lay minister is Bob Radford. Tammy Hofmeister was your greeter. The ushers are Carol Rohn and Paula Apfelbach. Susan Nicholl is down in the kitchen making coffee. And that's it. Please note the announcements in the red floors in your bulletin which describe upcoming events at the society and provide more information about today's activities. Again, welcome. We hope that today's service will stimulate your mind, touch your heart and stir your spirit. Thanks, Joe. These words are from Elizabeth Kubler-Ross. The most beautiful people we have known are those who have known defeat, known suffering, known struggle, known loss, and have found their way out of the depths. These persons have an appreciation, a sensitivity and an understanding of life that fills them with compassion, gentleness and a deep love of life and a deep loving concern. Such people do not just happen. And now please rise as you are able as Roz lights our chalice. The chalice lighting reading is responsive, but it's a bit difficult to see the difference between the parts. The lighter ones I'll read and the darker ones you all read together. And if no one's responding, I'll give you a cue. I'll say go. Okay. Let us not wish away the winter. It is a season to itself, not simply the way to spring. The clarity and brilliance of the winter sky delight. The loom of fog softens edges, lulls the eyes and ears of the quiet, awakens by risk the unquiet, a low dark sky can snow, emblem of the individuality of liberality and aggregate power. Snow invites to contemplation and to sport. Winter dark tends to warm light, fire and candle. Winter cold to hugs and huddles. Winter want to gifts and sharing. Winter danger to visions, plans and common endeavoring and the zest of narrow escapes. Winter tedium to marrymaking. Thank you. And now if you'll turn to your neighbor and give a friendly winter afternoon greeting. And I don't know whether you want to say thank you to Joe at the beginning of the reflections that Michael usually does. Please come down to the front. There are cushions right over there. Grab some, sit down, and we're going to double team you for this story. Ross is going to read it. Sharon Dennis Wyeth, the author of this story, says about her writing, it is based on remembrances of my own childhood, watching my own daughter grow up, my husband, the young people I meet in my travels. My desire to share what I have learned and to pass on something beautiful. The book is called Something Beautiful. When I look through my window, I see a brick wall. There's trash in the courtyard and a broken bottle that looks like fallen stars. There is writing on the halls of my building.  Where I walk, I pass a lady whose home is a big cardboard carton. She sleeps on the sidewalk wrapped in plastic. I run past a dark alley where mommy told me I must never stop. Behind a fence, there is a garden without any flowers. Mommy said that everyone should have something beautiful in their life. Where is my something beautiful? The teacher taught me the word in school. I wrote it in my book. B-E-A-U-T-I. Beautiful. I think it means something that when you have it, your heart is happy. I go to Miss Delfine's diner. Hi there, sugar pie, says Miss Delfine. What you up to? I'm looking for something beautiful, I tell her. Sit down a minute, she says, as she goes to the grill. She puts on a fish. The fish sizzles. Miss Delfine makes it into a sandwich. There's nothing more beautiful tasting than my fried fish sandwiches, she tells me. My teeth sink in. Hmm, is that good? When I go back outside, I see some of my friends. Do you have something beautiful, I ask them. I have my jump rope, says Sybil. I have my beads, said Rebecca. Check out my new shoes, says Jamal. My fruit store is a beautiful store, says Mr. Lee. You do have nice apples, I say. Thank you, says Mr. Lee, take one. Watch my move, says Mark, playing ball in the playground. Hear my sound, says Georgina, dancing on the sidewalk. Touch this smooth stone, says old Mr. Sims, sitting on his front steps. All these years, I've carried it in my pocket. Through the big window in the laundromat, I see Aunt Carolyn holding baby Carl.  I'm looking for something beautiful, I say. She hands me Carl and folds up the clothes. I tickle Carl and he giggles. He makes me giggle, too. My baby's laugh is something beautiful, says Aunt Carolyn. I go back home and sit down on my stoop.   I pick up the trash, I sweep up the glass. I scrub the door very hard. And when the word disappears, I feel powerful. Someday, I'll plant flowers in my courtyard. I'll invite all my friends to see. I will give a real home and a real bed to the lady who sleeps in a cardboard carton. She will sing and I will hear her song. Mommy comes home from work. She gives me a great big hug. Do you have something beautiful, I ask her? Of course, she said. I have you. And now if you will have him number 57 as the children go out for classes. Aesthetics are, by their very nature, subjective. One can set eyes upon a piece of art and decide it is pleasing or not. One can listen to music and either share the CD or burn it. The inexperienced martial artists may look at a technique and decide it is beautiful. The way the body moves, the precision and the speed can all be variables in creating something that is pleasing to the eye. The same is true of the veteran martial artist whose experience ensures they witness technique in their own way. They see the movement, the precision, the speed and they also understand the context of what they are seeing and that creates an entirely different set of expectations for what determines beauty. The subjectivity, I think, is important because your own experience determines your own enjoyment. Learning to enjoy the paradox of martial arts is, I think, paramount. The study of martial arts is inherently paradoxical. You're putting yourself through hardship, pain, violence and defeat so that in general you become stronger, more calm and more able to deal with threat. Most people who understand a bit about martial arts, I think, recognize this on an intellectual level. Few, however, have experience with actually appreciating beauty of graceful brutality in the same way that one can only really appreciate the virtue of great music after learning how difficult it can be to play an instrument. One can only appreciate what goes into responding to threat and the nature of that response when they've actually been threatened. So it is that martial arts have the capability to be beautiful because of their benefit despite their combative nature. So it is that when you see a martial artist respond to a horrific situation with grace, efficiency and speed and you understand just how difficult it is to do that you begin to appreciate the paradox of martial arts. Now from Mary Oliver, a poem called And Bob Dylan Too. Anything worth thinking about is worth singing about. That's Bob Dylan Par. Which is why we have songs of praise, songs of love, songs of sorrow, songs to the gods who have so many names, songs the shepherds sing on the lonely mountains while the sheep are honoring the grass by eating it. The dance song of the bees to tell where the flowers suddenly in the morning light have opened. A chorus of many shouting to heaven or at it or pleading or that greatest love affair, a violin and a human body and a composer may be hundreds of years dead. I think of Schubert scribbling on a cafe napkin. Thank you. Thank you. Our FUS theme for the month of December is beauty. Winter offers a grand symphony for all our senses and December marks the beginning with its celebratory holidays and changing landscapes. Scenes of families in holiday spirit and snow-covered landscapes with fir trees and cute baby animals are everywhere and we appreciate a new familiar artist's renderings of iconic secular pictures as well as sacred paintings and iconography. Festive decor is everywhere, sometimes tastefully rendered in garlands and flowering plants but often in pure holiday glitz and flashing lights including the lawns of those folks appearing to vie for the title of most gaudy display. All are beautiful in their own way. During December, music fills the air with jolly seasonal favorites and familiar Christmas carols and beloved ancient music and chants from chapel, temple and mosque. This December alone, and it does change through the years, this December alone, Americans will celebrate Advent, St. Nicholas Day, St. Lucia Day, Bode Day, Solstice, Ewell, Posadas Navidinas, Kwanzaa, Malid Adnabi, Christmas, Epiphany and that's not even an exhaustive list. Everyone who celebrates sees beauty in their own traditions yet juxtaposed with the scenes of celebration and holiday glitz can be the debt associated with maxed out credit cards. The charity drives for families who fundamentally need much more than a free meal and a few gifts and the glut of food and sweets that most of us regret having eaten soon afterward. This is certainly a contradictory season and it surely speaks to the nature of beauty. Now I ask you to just take a brief moment and close your eyes. What comes to you when I say beautiful? Francis Bacon wrote, the best part of beauty is that which no picture can express. Yet most of us saw something in our mind's eye when our physical eyes were closed. If Bacon was correct, beauty is both elusive and illusive meaning beauty can be both difficult to define and achieve and perhaps more to our point this afternoon, a bit deceptive, misleading or even unreal. The latter brings to mind, to me, yet another Mary Oliver poem. The poet visits the Museum of Fine Arts. For a long time I was not even in this world yet every summer every rose opened in perfect sweetness and lived in gracious repose in its own exotic fragrance. In its huge willingness to give something from its small self to the entirety of the world. I think of them, thousands upon thousands in many lands whenever summer came to them rising out of the patience of patients to leaf and bud and look up into the blue sky or with thanks into the rain that would feed their thirsty roots latched into the earth sandy or hard, Vermont or Arabia, what did it matter? The answer was simply to rise in joyfulness all their days. Have I found any better teaching? Not ever, not yet. Last week I saw my first Botticelli and almost fainted and if I could I would paint like that but I am shelved somewhere below with a few songs about roses teachers also of ways toward thanks and praise. In a recent workshop called mindfulness, healing and transformation John Kabat-Zinn said, sometimes we use poetry because the poets are doing the same work as the yogis. This feeling of breathlessness in the presence of the arts can indeed seem unreal, even transcendent. My daughter and I felt it more than 20 years ago when we looked up at the newly renovated ceiling of the Sistine Chapel we simultaneously grasped hands and one of us said let's remember this moment forever, so far we have and yet this chapel is in the Vatican which has been a source of many questionable and some say immoral pursuits including the crusades, the inquisition and countless missions to convert indigenous peoples to Catholicism effectively destroying their own cultures. Rutgers David Ehrenfeld said, dichotomies are most mischievous when they arbitrarily separate parts of a highly interrelated and complex system. Such is beauty. The first thing I do when I undertake this sort of assignment is to head for the dictionary. The succinct definitions pulled together by some patiently obsessive editor are a wonder to behold and I never fail to be grateful for such precision. Webster's dictionary defines beauty as the quality or aggregate of qualities in a person or thing that gives pleasure to the senses or pleasurably exalts the mind or spirit. An alternate suggestion was loveliness which in itself connotes something worthy of love. I liked that. When I was little we often picnicked on the Berkshire Downs in England. My mother would remark on the gorgeous view. I rolled my eyes at her enthusiasm. However, now when I hear Vaughan Williams' lark ascending I melt back into that landscape with an enjoyment she must have felt. So what is it that turns an incident into a moment of pleasure? And why might it be important? With the advent of recent brain scanning technology there's a new field of study called neuroesthetics. Researchers at NYU scanned the brains of volunteers while they looked at a variety of paintings. They were asked to rate the paintings on a scale from one to five. The findings revealed that even though the rating was on a linear scale at the most extreme ranges of approval part of the brain that is usually engaged when people turn inward in a mind wandering, self-referential or daydreaming state surprisingly became active. The contradictory network that normally engages during a focused task or action similar to that required by this assignment became deactivated. This suggests there may be a point at which an external stimulus triggers an inward focus and produces an awe-inspiring experience. But why? A huge metal tree now stands at the start of Willy Street. Serendipitously I was at the artist Erika's workshop soon after she received the city contract. Her workshop is piled high with bits of scrap metal salvaged from all sources. She and her husband, both welders, visit scrap yards and scour them for suitable pieces. They see potential beauty or use in discarded metal. Nature is her inspiration. Birds, animals and plants grow from cogwheels and bumpers. Prehistoric jewellery speaks of the splendor of gold, a gift from the gods to the earthbound. Initially panned from the surface of the earth and chipped from superficial rocks. Over centuries it became necessary to dig deeper. Slave labor entered the picture to fulfill an increasing demand for this precious metal. Sweat and toil is the currency. World markets still use gold as a monetary standard. The African elephant has been poached to near extinction for ivory. A courageous and imaginative PBS investigative journalist recently commissioned an electronics filled imitation husk. Some of you might have seen the program. And smuggled it through war-torn Rwanda toward a valuable Chinese market. The market, however, is now suppressed by international pressure and this particular tusk remains buried in a jungle until there is a demand or until it's forgotten. One has to wonder how many others are buried nearby. Monday I was in New York and I did something I'd wanted to do for some time. During a visit in 2002, I witnessed the gigantic scar left in the ground by the fall of the Twin Towers and I wanted to see the transformation. When the Parkham Museum was ready to launch my daughter, a film editor, was hired by the architects to make a video commenting on the design. The two fountain pools rest on the footprints of the original towers. In the museum, walls have been preserved and views created to record, demonstrate and provide healing space for those who visit to wonder at the enormity of the event. These men thought very carefully and deeply about the significance of what they had been asked to do. As I stepped out of the metro, the historic st. Paul's was to my left. It has survived the revolution and the great New York fire in 1776 and is an historic record of old New York. An ancient sycamore tree in the churchyard was destroyed in the blast, but the church survived unscathed and became a place of memorials and refuge for the workers and grievers for months. It is now dwarfed by reflective skyscrapers. New construction is everywhere. A skeleton of the unfinished transportation centre looked like huge beached whale ribs bleaching in the sun. Two giant ground-level waterfalls plunge into the ground. They are surrounded by chess-level black granite walls in which the names of those who have lost their lives are memorialised. Water flows into a deep square central cavern like endless tears shed for those who died there. Oak trees flank both fountains, rooted sentinels they bear witness to the tragedy. Benches are arrayed in protected spaces inviting contemplation and even the inevitable commemoration and even the inevitable commercial kiosks are relatively unobtrusive. What surprised me, however, was the way the surrounding buildings appear to accept and wrap around the park, seeming to make comment. They mirror the past and comment on the ongoing changes through their reflective glass. The new transport centre is no longer visible as dead bones but as a bird about to take flight. Brain areas involved in aesthetic response to artwork overlap those of the desirability of foods or attractiveness of a potential mate. Research suggests that as evolution has progressed this part of the brain has been co-opted for this task.  Why would the part of the brain known to be important for processing pain and disgust turn out to be the most important area for the appreciation of art? Does it have to do with transcendent beauty? The ability to transform pain and horror, destruction and waste into something life-promoting? In the 2010 winter issue of Parabola magazine Richard Whitaker wrote of what he calls distant beauty. Describing his drive through an area near the Bonneville flats where the distant vistas promise majestic hills and mountains so beautiful he wondered if they might be mirages he had a bit of an epiphany. It dawned on me he wrote that the closer I got to the snowy range the less connection I would have with the magic of this vision. Let me read that one more time. It occurred to me that the closer I got to the snowy range the less connection I would have with the magic of this vision. Eventually I realized the road would thread itself up and through the range itself. I would find the familiar trees and snow. It would be a familiar passage not lacking in beauty but by comparison nothing at all. I was left to ponder whether there's a kind of beauty that by its very nature cannot be grasped. A condition of beauty that dwells at some irreducible distance perhaps even part of our ontological condition. Personally I can't help but think that we see much of what we recognize as beauty within our world, our society. Human beauty is a great example. Compared to iconic beauties from Aphrodite, Adonis, and Nefertiti to Grace Kelly, Audrey Hepburn and even George Clooney who are close and personal they are all human or human like. And share with us, with the rest of us the familiar characteristics of joy sadness and even questioning of our personal worthiness. This does not even touch it doesn't go near the tremendous damage that is caused by seemingly immovable concepts of ideal beauty. Everything from ethnic cleansing to job discrimination can be based in these ideals of beauty. Beauty can be very, very cruel according to Joseph B. Worthlin The more often we see things around us even the beautiful and wonderful things the more they become invisible to us. That is why we often take for granted the beauty of this world the flowers, the trees, the birds the clouds, even those we love. Because we see these things so often we see them less and less. Let us all be more aware of our surroundings in a way which allows us to truly see the beautiful in our world, in others and in ourselves. And now please see all the need and give generously as we enter into our offering. Thank you so much. And now please rise as you are able for our closing hymn number 326 let all the beauty we have known. We seated our last words and made a postlude. Spiritually the more we see beauty in all things. The higher we ascend on the spiritual path the more divine our vision and we see beauty in all things. Why? God is in all. We start to see the light of God in all beings when we see God in everyone and everything all becomes beautiful. Thank you.


And this is a joint work with a number of colleagues. So most important, Friedrich Lenz fit his PhD with me exactly onto this project. So I'm going to tell you about his work. And Thomas Ings and Lars Cytgar are two experimental biologists, colleagues of mine at Queen Mary. So they did all the experiments on bumblebees. And Alexei Chechkin is an expert on stochastic theory. And my expertise is also from a theoretical point of view. So that was a joint collaboration. And as this workshop is quite cross-disciplinary, and I feel perhaps a little bit as an outsider, I thought I'd give you on one slide just a little bit of my scientific background that you know what to expect. So my background is statistical physics. Interfacing was applied mathematics. So I'm in a math department at Queen Mary. And my field is really non-linear dynamics, stochastic processes, and non-equilibrium statistical physics. And more recently, I like to apply this to nanosystems, and especially to biological dynamics. But here I have some pictures, so I'm interested in things like fractals, diffusion and dynamic systems, as well. And as I'm very interested in diffusion, there was a natural cross-link then to biological implications. And that's what I started to study about 10, 15 years ago, and I'm getting more and more into it. And in fact, it will come to exactly this picture to the end. You will see it again. And so you will see a bumblebee that performs a flight. And indeed, this is a trajectory generated from a model. And I will explain to you how we did that, at least on one slide. And now, as we're talking about biological dynamics, I may start with this cartoon to introduce the basic theme of this talk. Because what I'm interested in is really analyzing search patterns, biological search patterns. And as an example, this is from a nature paper by Shupur, Benishu, and others. You may think of an autumn that you're out in the woods searching for mushrooms. And as a certain physicist, I would like to model this problem. And now the most knife approach, well, we have already heard about it, is, OK, you put everything into a quadratic grid in the plane, quadratic grid. And now you put the mushrooms randomly on some nodes of the grid. And then you model the searcher as a point particle that moves with probability 1 quarter to the left to the right, up or down. Fine. OK, now you have a search problem. And that's what me, the physicists, like to play around with. And you can solve this or put it on a computer. Now, if you want to do better, then if you want to go to continuous time in space, you may model the search, say, by a groin in motion. So particles, say, moving in a fluid here in 2D. And again, looking for randomly distributed targets. So far, so good. But I mean, this workshop is on locomotion and navigation. And this has absolutely nothing to do with locomotion and navigation. So it's a point particle moving according to some rules, finding some point target. So this is nice because you can solve this analytically, but I'm not sure whether it tells you very much. And now, if you look at search problems, very quickly they can become very complicated. The problem is that then we can solve them anymore. But they are more real, they are closer to vitality. So to give a flavor, certainly one thing is whether you want to find all of them or only perhaps one of them or a few of them. Finding all of them, for example, was done exactly in this paper. It's called cover times. So you can develop then series of how to efficiently find all of the targets in a certain region. Finding, say, one of them is mathematically a problem of first passage or first arrival. Again, you can solve this analytically in certain situations. But now the question is also, you may wish to do efficient search in terms of optimality. But what do you mean with efficient? Next question. Efficient may mean you want to minimize the search time. And again, this is the problem that you can solve under certain circumstances. But this is not all because now they're coming into play, locomotion, navigation, and perception. So certainly, this is not just a point particle reality. Looking for some targets depends on your locomotion of whether you find them or not. If you move very quickly, you may easily miss them. Then sensory perception. You see this person has a certain range of perception. Of course, it depends on your perception of how you interact with the environment, whether you find targets easily or not. Another problem is, for example, search form depends on whether, here, if you pick a mushroom, it's gone. If you're punishing a target, you have a completely different sort of problem. OK, I stopped here. I could go on, of course. But you see, I'm covering the whole range from extremely abstract mathematical models that we are happy to solve up to models that are of interest to most of you, I suppose, as if you are experimental biologists. And so this is a problem here to bring this together. It's not easy. And one has to watch out here that one established a clear communication because, if you solve the search problem, it may not be what you are interested in. Now to summarize, and I may go through this picture. Four years ago at the Max Planck Institute in Dresden, we had an advanced study group, a very well-together statistic theorists, experimental biologists, and people doing statistical physics. And we stuck our heads together to make sense out of applying search theories to understand biological dynamics. And here you see the title, Statistical Physics Meets Movement Ecology. It's something where I may advertise Rantz talk later today, that you will talk about this in more detail, the discipline of movement ecology. So this is just a very small subset of it in view of search. So the pictures as follows. So this is the game you really need to play if you want to take things seriously. Now we have a frog moving in some environments. And what you can do and be afraid about is you put a sensor onto the frog and you measure the path of the frog of how it moves. And a certain index with the environment in a certain way. So according to locomotion, here the interaction also with respect to a certain perception and of how the environment is structured. And then certainly it also has a brain, so a certain memory. Now all what you record is then the movement ecology data meaning the path. And so this is now the experimental input. And as a theorist, now the statistical physics comes into play. What we want is to analyze the data and to construct a model by which you can reuse this data ideally. So you play the following game. Ideally, you want to capture as many details of the national environment as possible. So we are here. You may put this, for example, on the computer, do a computer simulation, and try to build a very complex model. Then you start here. But now if you want to do really theory, meaning if you want to cable it to something and obtain analytical solutions, you need to simplify. And meaning we go up here. So you see on the lowest level, we keep a lot of model complexity. But we pay a price because we can do very much apart from simulation. Now you may do what a statistical physics is called cross-graining, meaning you neglect details. You throw out information. For example, here you neglect many details of the real environment. You simplify it. And now you see we can go even further. Here we still have perception. But that's too complicated, so we kill it, we kick it out. Then we are here. We only keep, say, memory and interaction with the environment. And here, even further, you see now we have eliminated any memory. This is, in a way, a point particle moving in an environment. And if you rest the arrow, we are back to the random box I started with. Then this would be a point particle that moves according to certain dynamics in some environment. And this is something you can still solve for serious. But as an experimentalist, again, you may not be very happy. But you have this whole range of models. Now you take your model and you either solve it exactly or you do simulations. And you generate, again, synthetic trajectories. Then you analyze them according to some averaging. I don't go into detail here. And you calculate observables, which, again, then you can match to your data. So this is really, in our view, that's what we came up with a game you need to play if you really want to understand the biological search problem. But you see it's quite difficult. But I will exactly illustrate this now in the following. So the general framework is what one may call the physics of foraging. And there's a very nice book with the same title by a certain group of people where they studied this. And my interest is in identifying biologically relevant search strategies by mathematical modeling. And now what I'm going to tell you that was a lengthy introduction, but I just wanted to properly set the scene, is an experiment on bumblebee foraging in a lab on a predation list. And so I will explain to you the experiment, the statistical data analysis that we did, and to the end, the stochastic models we come up with. So that's the goal. OK, so now let's get into business. Bumblebees, if they try to find food, face two very practical problems. So A, if you're out in the wild, you have a very nice meadow. And so they need to find food in a very complex landscape. However, life may not always be easy. For example, there are these crab spiders here. You see them, and they can even camouflage, so they can change their color. So in order to survive, they need to avoid the predators. And that poses the question, OK, what type of motion should they perform in order to optimize their foraging and to avoid the predators? And my colleagues, Lars Schittke and Tom Ings, they did a very nice lab experiment as follows. And it's published here in this paper. So they let Bumblebees foraging in a cube of about 75 centimeter sight length. You see here a picture. And one wall of this cube was equipped with a grid of artificial yellow flowers, so 4 times 4 grid. And each flower was equipped with an artificial nectar source, and the Bumblebees were trained to feed on them. And now they took two cameras. And with the cameras, the position of the Bumblebees could be tracked with a very high frame rate. You see 50 frames per second. And here you see a trajectory of one Bumblebee. So only one was left into the box here at the front side and then performed the foraging. The advantage of this experiment is certainly it's in the lab, so you have full control about everything. And especially here you can vary the environment in a systematic way. This advantage is certainly there's no real free flight of a Bumblebee because I was certainly told by the biologists that they can always see the walls. You see it's a very small box. So we certainly cannot conclude on a free flight of a Bumblebee, but that was not the point. The point was rather here that we could vary the environment as follows. Now Tom put on four out of the 16 artificial flowers, artificial spider models. So you see one here. So it's this white plastic thing that he glued onto four randomly distributed artificial flowers. And now you see actually there is a mechanism here by which you can gently squeeze a Bumblebee. So no Bumblebee was harmed in the experiment I was told. So there are safe flowers without the spider images and dangerous flowers. And indeed there was a training phase where the Bumblebees learned to avoid the dangerous flowers, meaning when they approached the flower, then Tom was sitting there and then squeezing them gently and holding them for two seconds. And of course they didn't like it. So they were trained to avoid the flowers. In detail, this was a very complex experiment. It consisted of seven stages for our analysis. We were only interested in three of them. Spider-fee foraging, so no spider images. Then foraging on a predation list to one-quarter equipped with spider images and a memory test one day later. So after the night, then it was tested whether they still memorized to avoid the dangerous flowers. And about 30 Bumblebees were studied. And we had on average about 6,000 data points per Bumblebee for the data analysis. So this was the experiment. And now as a theorist, we wanted to reanalyze the data in view of the following two questions. First of all, I'm very interested in what type of motion the Bumblebees perform in terms of stochastic dynamics, meaning what I'm interested in is really to build a stochastic model that can reproduce this kind of not of spaghetti, or it looks like it a little bit. But secondly, in this context, especially are there then changes of the dynamics under variation of the environmental conditions? Certainly naturally, you would expect that if the Bumblebees move without spiders and the flowers, I mean, they are quite teppy because there is no risk. But if they perceive that there are spiders there, that they are getting nervous. And that they may change their type of motion. And the question is, how do they change their motion? Can you quantify this? So this was the question that we were posing. OK, so here's our data analysis. The first thing we did was to extract velocity distribution functions. So here you see actually a semi logarithmic plot. So this is the logarithmic axis. And this is linear. And these are the velocities of a Bumblebees parallel to the y-axis, perhaps just to go back. Sorry. So you see y is parallel to the wall, x is perpendicular to the wall, z is vertical. And here, as an example, I just show you the one parallel to the wall. And the thick line with the crosses, this is the data. And the other ones are with certain functions. And you see we try to fit this with a maximum likelihood with a mixture of two Gaussians. That's the red line, exponential, the blue one, power law, the green one, and the single Gaussian, which is the pink one. And you see, just by visual inspection, you can immediately draw two of them, which is not a single Gaussian. And it's not a power law either. So that's very clear. However, whether it's a mixture of two Gaussians or exponential is not so clear. And I mean, this is one of the notorious and difficult questions in this whole business. You extract a PDF from your data. What kind of function is that? It's very difficult to answer this question. But you can do something. And for example, a method that is very convenient is the quanti-quanti plot, meaning you plot regions of equal probability of your probability distribution that you have from the data against your perspective function that you speculate fits the data. And if it matches, you get a straight line. So it's very easy. And I can recommend it if you don't know about it already. So here, you plot the data PDF against your perspective candidate function. And again, you see here, it's very well a straight line. The red lines are actually surrogate data. So they give you the error bars. And the matching is pretty perfect for actually mixture of two Gaussians. And you see it here. Actually, if you take a close look, though we don't have much data here, but you see we have a little peak here, which is the little Gaussian with the smaller variance.  And so this is our finding that this is the probability distribution that we get. And in fact, this is the best fit. And I mean, we verified this by information criteria. You can do base analysis. And then there are so-called weights, acaika, information criteria, base criteria for the experts. And we really checked also quantitatively that double Gaussian is the best. And this certainly is a very simple biological explanation because there are two different flight modes, spatial flight modes. So one is if the bumblebee is far away from a flower, so then it flies around with a big Gaussian. So with the biggest use of velocities, if it approaches a flower, so then in fact, it reduces the speed. And then it approaches with a small Gaussian on top of it. And so there's really a spatial separation of the different flight modes. And we checked that by eliminating from the data near to the flower. And then we only got the big Gaussian. So it's a spatial variation of the flight. In a way, we physicists call this intermittent dynamics. Intermittent means you sample between two different stochastic processes. And here's a spatial sampling. Now that comes to surprise. We did this certainly for the different phases, especially for the spider phase and the non-spider phase. And I asked my PhD student, well, look at it. There should be a difference. That's what you would nicely expect. That if you have spiders there, you should get a different probability distribution. And I told my PhD student, yeah, I mean, do it. And he came to me and said, well, I don't find any difference. Sorry. After one year, we gave up. Then I told him, OK, I mean, we are not getting, making progress. Let's forget about it. We do something else. We couldn't find any difference. So they were meaning the velocity probability distribution for all the phases were exactly the same within error bars. That was the state of the art. So we couldn't see any difference. Then I told him, OK, look at correlation functions. That's the next best thing to do. And indeed, the answer was there. And that was a big surprise to us. So physicists call this velocity autocorrelation function. In statistics, it's called covariance. And if you don't know about it, I can only highly recommend it for statistical data analysis. OK, I don't have much time. No, a few minutes, right? But if you want, I can certainly explain to you what the correlation function is. The point is, it tells you about memory in the dynamics, meaning how much your dynamics is correlated in time. So meaning, this correlation function is optimal. I mean, the boundary starts with the velocity in a certain direction. And then you check how much your velocity at time t is correlated to this initial velocity. So meaning, if it's the parallel, the correlation function is maximal. You see here. That's why it starts from 1. It's normalized. But then, certainly, the velocity will vary. And then this is a scalar product if you do it via vectors. Meaning, if it's perpendicular, it's 0. If it's anti-parallel, it's negative. So this velocity correlation function gives you very nice information of how your trajectory looks like. Again, here, then first it flies parallel. Then it may mess around. And if it's negative, in fact, it has reversed its direction. And you see that exactly this is going on here. So this is the velocity autocollation function parallel, again, through the feeding plane for these stages. So this is a spider-free predation thread. In memory tests, you see that the two are quite identical. But this means, in the spider-free stage, the bumblebees fly quite persistently in the same direction. They are not worried. So they're approaching the flower very fast. That's what it means. But you see that for the infested stages, you have anti-correlations, meaning it starts here, but then it reverses the direction. So it flies anti-paralleled. So that means the bumblebee flies through the flower, but then spots, oh, there's a problem. There's a spider there, so it makes a turn. And this is exactly why this becomes negative. So they turn. That means the explanation is, observe no change in the PDFs. And if anyone of you has an explanation for that, I would be very happy to hear it. So it seems, perhaps for a biological reason, they don't want to or they can't change their velocity or speed distribution. It's the same. However, what they do change is the topology of their trajectories. So all the information about the interaction with the environment is in the topology, not in the speeds. So they perform more curved trajectories, which, of course, makes a lot of sense, because they are more careful. They're checking out the situation. So that's what we find. And that's what I've said. All the changes regarding the phases are in the velocity correlations, not in the PDFs. So if you do data analysis, do not always focus on the probability distributions. That may not be the whole story. And we did something else a bit more, because we wanted to check for the interaction with the environment as follows. We defined what one may call predator avoidance in a very simple way, because Friedrich looked at the position of probability distributions in front of a flower. So in fact, this is wrong. That should be a set. And why? Sorry for that. But you see, this is the plan exactly in front of a flower. And this is simply the difference of the probability distributions, the spatial ones, between the phase where there's no spider minus no spider. And you see, in fact, here, there's a peak. And here, above the peak, there's a minimum. So this means, in fact, the minimum says it's avoidance. So if there's a flower with a spider, there might be approaches, the flower, but then it sees, ah, there's a problem, so I fly back. So certainly it will not stay there. It will move away. And this means hovering. So this means, in fact, the bummer bee then stays in front of the flower, a little bit below, and checks out the situation. So we can quantify this. This is from the data. And I would say the bummer bee typically approaches the flower from the top, so it goes like this. And then it sees there's a problem. That's why there's a minimum.  And now we modeled this by a simple so-called Lange-Wang equation, but if you're not familiar with that, it's a stochastic differential equation where this is a kind of Newton law. So this is, in a way, the force, mass we have scaled away. It's on the right-hand side. And this is the following. This is a kind of friction. It's a friction coefficient. And this is a force that models the interaction with the flower. So this is a potential derivative of it. And this is Gaussian white noise. For physicists, this is very familiar. If you're an experimental biologist, perhaps not so much. But this, in a way, models what's called Brownian motion. But here, Brownian motion is an interaction. And this thing, if there is no spider, we switch it off. So we eliminate it. If there is a spider, we switch it on. And it's a repulsive force that models that the bummer bee wants to stay away from the spider. And these are simulation results where you can also solve this analytically. If you have no spider, you have some simple exponential decay. So you don't see that your velocity correlation function becomes negative. It remains positive. However, if there's a spider, and if you think as a physicist about it, it's very trivial effect, then you see that the correlation function becomes negative, as we have seen in the data here. So qualitatively, we use this one that this remains positive. I should say this is the error. So you see this is not an artifact. Really, this effect is beyond any experimental error. And now, if there's a spider, we reproduce qualitatively, at least, that this becomes negative. So we have an emergent, this emerges from the interaction between the bee and the flower. OK, this is a very simplistic model that doesn't tell you very much about the flights. I still have how much? Two minutes or so? OK, great. This is just an advertisement, this one slide, because we did better in terms of modeling the bummer bee flights. But here, I don't go into detail. We have heard already about correlated random walks. So what we did now, we wanted to model the free bummer bee flights, meaning flights far away from a flower, still in the box. And we started with a correlated random walk in 2D, which is a description of the dynamics in the co-moving frame. So meaning you have a picture here, you have the velocities, and you have the angle between two velocities at adjacent time steps. And standard correlated random walk means you assume the speed is constant, and then you simply sample the turning angle, the angle between the two velocities, from a non-uniform distribution. Say you may use a wrapped Gaussian distribution, for example, and this model's persistence, that your bummer bee flies with a certain probability in the same direction. This is a classical correlated random walk. Now we started from that, and we find it. And again, I don't go into detail. I'll just give you the result. We did quite a sophisticated data analysis, and we really extracted this model from the data. So it's not a talk. It's really extracted from real data analysis. And we found that the angle, in fact, is best drawn from a Gaussian noise, but it's correlated. Again, it's a correlation function, and in fact, the correlations are power law. So the angle is not random, but it persists in time with the power law correlations. And now you see here the velocity. We sampled it from a function g. So this is, again, friction. And if you have ever heard of active particles, another hot topic in the field of biophysical modeling, this model's activity of a biological organism. So it's a kind of activity term. And this is a Gaussian noise, but it's anti-correlated. So again, it's complicated correlation decay. So this may be called the generalized Lange-Wang equation again. So you put it on the computer, and what you get here is exactly the trajectory from our stochastic model. And it seems to reproduce the external data very well. We were surprised about that, because quite some assumptions go in. So if you're interested, this is around as a model for modeling more sophisticated trajectories of moving organisms. OK, so I summarize. The bumblebees are obviously quite clever. So they especially adjust their flight modes to the environment. And also, this is the main result. They have a temporal adjustment to the risk of infected or non-infected flowers. However, we only see it in the velocity autocollation functions when the spiders are there. And we have qualitatively have reproduced this from a simple stochastic model. OK, we have two papers about that. So what I've told you about is mainly published in this first letter. And if you're interested in the most sophisticated stochastic model, then this is in the second paper. And do I have one more minute? OK, then because I want to show you this one, this is something entirely different. But as we have seen some looping trajectories, I just included this. And it's a question to you. So this is another project that I am doing again with experimental biologists at Queen Mary. And they are tracking sea turtles. The logger had sea turtles at the coast of South Africa. So you see the islands kept ready. And this is the foraging dynamics. And you see it's not brown in motion, definitely not. So you see there are a lot of loops. This is looping over scales of hundreds of kilometers. And I've seen some loops in some talks. That's why I asked you. And so I'm just curious if anyone knows any other trajectories of animals that look like that. I mean, I would be very happy to discuss. So I'm just trying to learn whether this is special to the sea turtles or whether this is more general and animal foraging. OK, thank you very much.