Hey everybody, Jay here. I am going to review Marvel Legacy number one. I picked this up last week. Went to the comic book shop and was actually looking for it. I read a few things about it online and I figured I'd give it a shot. I haven't really touched a Marvel comic in a while, but this one kind of drew me back in. I guess you could say. So when we start with it, it starts with Loki and some Frost Giants breaking into a facility to steal something. It doesn't really say what kind of have an idea because it's Loki. So we kind of get a little feeling of what he's wanting. Then it cuts to Sam Wilson, Lady Thor, trying to stop them there with a couple shield agents. I learned that shield is dismembering. Okay. Then they meet up with Loki and the Frost Giants and they start to fight. That's when we learn that since Steve Rogers is back and his doppelgangers gone, Sam Wilson is going to give up their role of cap and give it completely back to Steve. Then we learn Lady Thor, which who is Jane Foster is dying of cancer and the powers aren't really helping her out as much as they was anymore. Beyond that, they cut to a scene of Mary Jane with scientists and they learn Tony's body is gone. He was in a coma for a while and now he's just gone. No other explanation. It's just vanished.  We then cut to Steve and a diner where they're talking about Captain America. He has his shield. He's kind of worried about coming back, I guess ever since everything with Captain Hydra went down. Then they cut back to Lady Thor and Sam Wilson's Captain America fighting. That's when Iron Heart comes in and they're still battling Loki and the Frost Giants. It's not bad they have Iron Heart joke around about the Avengers, she keeps saying. Avengers, what is that? Is Avengers a Venge? No, I don't know. It's got a few things about Sam and Lady Thor kissing. Okay, but that's just me. Then it cuts to one of my favorite characters, which is Deadpool. Deadpool's on the run for killing Agent Colson. He's setting in a gas station bathroom store praying, knowing that he's messed up, hoping that things can be resolved. He's confessing his sins, which was pretty cool because you know Deadpool's praying, things are serious. Then it cuts back to Sam Wilson and Lady Thor making out. There's still a Frost Giants around in there after a package. Enter a mysterious guy. We find out who is actually Wolverine. He gets the package and it turned out to be an infinity stone. Okay, not really understanding. If you remember Wolverine died, I think it was back in 2014, late, maybe early 15. He's back. That's pretty much all you can say about it.  And you're just kind of like, what's Wolverine gonna do with that? So it was actually pretty cool. And then it cuts to the scene with a young Jean Geary standing next to the adamantium that was actually poured on Wolverine when he died and it hardened. It shows it's been cut down the middle. It's actually a pretty cool scene. Then this is when the comic kind of takes a turn to where I'm like, what? Why? It shows Johnny Storm and the thing and apparently they're gonna be a duo now. And Johnny's is kind of like, I don't even know why we're doing this. They don't even need us and things like, yeah, they do. They definitely need us. Not really. With all the heroes I have right now, I don't believe they actually need them. But at the same time, I'm kind of like, okay, I don't mind seeing where this goes. I guess we'll just have to find out what's going on. And then comes toward the end, we see the children of Reed and Sue exploring universes and Reed's with them and they want to come home. I thought they died, but apparently not. The Fantastic Four, I feel like they don't, they're not really needed. With all the heroes that we have now in the Marvel Universe, it's just kind of, they're from what I can tell and what I've seen. They're pretty much just rebooting by starting over. They're getting rid of heroes that were reboots of other heroes and they're going back to their originals, which is what I've been saying they need to do for a long time because it's just there who can beat the originals. We also learned that the Hulk is still alive. Bruce Banner is still alive. They, he's on a different planet and somebody is trying to communicate with him to tell him to come home. Awesome. The Hulk. Gotta love the Hulk. My rating on the comic is, honestly, I give it at least 8.5 out of 10. It jumped around a lot, but it's one shot. So it kind of has to. I enjoyed it. It was very cool. It was very awesome. Glad to see Wolverine back. Glad to know the Hulk's back. Nice to see Tony's coming out of the coma. I didn't touch anything on Spider-Man though, which was, I don't know, maybe they have something else to install for him, but it was a good comic. Definitely 8.5 out of 10, I give it. Definitely going to have to check out a few of these comics that they got going on. Definitely want to see what they do with Deadpool. Definitely want to see what they're going to do with Wolverine, but yeah, I mean, it was good. Check it out. Check out some of the other stories they got going on.  Go to your local comic book shop. Buy it. Get it going. Let's see what's going on. Well, thank you for your time. I appreciate it. Y'all have a good day and all season.


It seems like your kids are old.  Yes.  Like, these are my adults. No, no. Can we talk about that a little bit? Have you recorded it? I'm sure. Okay, cool. Sweet. Okay. Because the kids have a short attention spot. Okay, so like I said, five minutes. Anything you want to talk about? I'm Ty, by the way. Hi, Sandy. Sandy, nice to meet you. Nice to meet you. How long have you been teaching? Okay, so I do volunteer teaching. Okay. So my regular job is as a biochemist. This is with a junior achievement. Have you ever heard of that before? Yes. Okay, so every semester they ask people to volunteer and I volunteer and I help them out. I do these talks and then what I do is I cut up the talks and I show the kids, hey, look, this is like an old man who looked mean. But when we talk to him, he's like talks about his favorite dog and stuff like that.  Anything like that? Those tend to be the more fun talks. Well, my children. Okay. I have a 20-year-old at UK. Okay. And a 17-year-old at Henry Clay. You want to talk about like maybe how it seems like your kids are old. You gave me two old ages. Yet you call them your children. Yes.  Like these are my adults. No. Can we talk about that real quick? Because my dad always calls me his little girl. Okay, okay, okay. Yeah. What is it that keeps... So when you see them, do you see them for the kids that they used to be or like the adults that they grew up to be? I see them as the kids that they used to be. Kids that they used to be? Yeah. What is that? Why is that? I guess because I'm still providing for them and taking care of them. Oh, even... Yeah, they're still in the house. Okay, okay, okay. You're still the mama bear? Yes. Okay, at that point you can call them whatever you want. That's right. At what point would you be more willing to be like, you know what?  But you're a man now and I respect that. Probably when he graduates college. Okay, okay. What are they studying by the way? Computer science engineering. Okay. You need to get a job. Yeah. Can I ask you a question? Mm-hmm. In the event that they were to graduate today, they did a piece of paper and they showed it to you. Would that make you more willing to see them more as the grown adults that they are? Are they adult? Sure. Really? Yeah. Okay, so that is it. Yeah. They just need to graduate and that's it. Probably. Okay. They're going to show some responsibility I guess. So it's time to move on. Yeah.  It's like, I got to graduate so I can be a man. No. Because they don't know that. I haven't told them that yet. Okay. But they do have pressure to graduate. Okay. Because we kind of put, we set high standards for them. I guess. I can tell. And they're very intelligent. But that's good. Yeah. And you know, sometimes I feel like I've been too hard around them maybe, but it seems to be working out for them. Why do you feel that way? Or I don't have kids myself. I got a cat at best. Well, we were just talking about it on the way here that especially my oldest, I think I was more strict as far as make sure this gets turned in. If this doesn't get turned in, you're losing your computer.  I feel like kids tend to respond well to that in the long term. But in the short term, perhaps they don't see it for the effort that it takes on your part to be like, I wish I could love you and follow you. But I have to discipline you. Oh, excuse me. Bless you. Bless me. Bless me. Well, I noticed my youngest, they both have good worth ethics, but my youngest just got his first job. Nice. In March. And he is like, they were so impressed with him. And he's all impressed with himself saying how good he is and how proud they are of his work. So I'm like, where do you think you learned that from? You learned that from your mom and dad. And your rivalry between the two brothers? Not a lot. Are they both in computer science? Well, the youngest is still in high school, but that's what he's going to go into. He wants to do gaming. Really? I love gaming. Okay. Okay. I think that's a great market to actually be in. Making gaming and stuff. Yeah. So he's actually at the Votex school for half a day. What does that mean? The vocational school on Leicester Road.  It used to be like shop and stuff like that. People would go to. They would get bus to that. So he does his required courses at school. And the other half a day, he's over there taking. Okay. Has he made a game before? Like has he worked with Flash or anything like that? I don't know. He may have. Okay, okay. I don't know that. It's a cool thing. I think you're doing good jobs. He can do a lot of stuff on computers more than I can. Okay. What would you say is the hardest thing about being a parent just before the last question? The hardest thing. I guess is having that good balance. Of letting them learn with their own mistakes. And not trying to shield them too much. I see. You got to let them kind of get hurt. Yeah. You got to let them learn a few things on their own. That's hard. That is hard. Yeah. That letting them learn to drive in Lexington. Thank you so much for the chat. Oh, thank you. I'm sweaty. Now you're right. Sandy was wonderful. Thank you so much. Kids would love this. Hi kids. See you later. Oh, they liked it. Yeah. We're streaming on Discord right now too. Yeah. Very good job. Oh, I asked you for a core. Were you not okay with that? Oh, okay. Okay. Okay. Okay.


Classic Radio from M.B.N. Ranger Bill! It's the sugarcrate gang. Fables of Faith. Radio Drama, a hallmark of moody broadcasting for over 60 years. Join us now as we take you back to another classic in M.B.N. Radio History. Ranger Bill, Warrior of the Woodland, struggling against extreme odds, traveling dangerous trails, fighting the many enemies of nature. This is the job of the Guardian of the Forest, Ranger Bill, pouring rain, freezing cold, blistering heat, snow, floods, bears, rattlesnakes, mountain lions. Yes, all this in exchange for the satisfaction and pride of a job well done. Every year at this time, Bill goes to the State University to lecture. This lecture is given to the student body as a whole, but it's directed toward those studying geology and anthropology. The lecture is on the science of staying alive in the mountains and forests. You see many of the students go into the mountains and forests to gather material first hand under the leadership of competent guides and with the Rangers constantly checking on their well-being. What Bill is trying to do is prevent the isolated adventurers from going out into the wild country and becoming casualties because they haven't the knowledge of survival. Say, we'd better perhaps get into our story. This one is called Fossil Canyon. We're ready to begin the second hour of your lecture, Bill. That's fine, Dean Sands. How's your voice holding up? As long as this pitcher of water holds out, I'll be able to keep my windmill running all right. Have you ever heard of a windmill run with water? Yeah, you're looking at one, sir. I hardly think that, but I'm enjoying your sense of humor. I never tire of hearing you talk about survival in the outdoors. I'll never forget your first lecture three years ago. My little did I realize how much knowledge and experience a man must have to stay alive when meeting nature face to face. But, well, we're not here to listen to me. I'll make a few introductory remarks and turn the rostrum over to you. I'll be fine with me. Perhaps I have some questions first. Attention, please. Thank you. Mr. Jefferson suggested that you may have some questions before he begins this second half of the lecture. If you have, then you can ask them now. Yeah, I have a question, Dean Sands. I'll yield the rostrum to Mr. Jefferson Bob. Then he can answer you direct. Bill, if you please. Thank you, Dean. Now, Bob, why are your questions? Your first hour lecture was interesting, but I object to the inference you made that we're not able to take care of ourselves. Are you making that inference, Mr. Jefferson? Yes, Bob, I am. Mr. Jefferson, I think you're underestimating our ability. We're adults. It's been my experience that college students are the hardest to convince that they should keep out of the wild country. Also, it's a statistical fact that college students are highest on the casualty list. And I might add on the mortality list. I can't swallow that, Mr. Jefferson. Why should we be the highest on the casualty list?  You wander into places that even experienced guides and rangers avoid if at all possible. You mean to tell me that I couldn't go out into the boondocks and survive? You fit the nail right on the head, Bob. Let me illustrate what I'm talking about. Perhaps a true story will convince you. I'd have to be a good one to convince me that I or any student couldn't go out into the wild country and come back alive. I'm sure you'll be convinced, Bob. Members of the faculty and student body, the story I'm about to tell you is true. It illustrates to perfection what I've been telling you. Dean Sands remembers this tragic story very well, even though it happened several years ago. Virgil Adams and Stanley Grimshaw, both State University students, decided to go into fossil canyon. Virgil and Stanley didn't know that it's 115 degrees at the bottom of fossil canyon, and there were a lot of other things they didn't know about that death trap. I'd sure like to find a good fossil down here, Stan. So would I. They'd been found here before. Only last year is a matter of fact. Professor Glomkin would give us a good boost toward our degree if we made a real fire. I'll say it with Virgil. I'm going to make as good a collection of all the fossils I can find, and so am I. I'd rather find a good specimen prehistoric age. Hey, we're only halfway down to the bottom of the canyon. Let's step it up and get down there before it begins to get dark. Then we can do some work. That's what I was thinking. Let's go. What's the matter, Stan? I guess it's heat, Virgil. I didn't think it was this hot down here. I didn't either. I'm beginning to feel the heat myself. Look at my shirt. It's wringing wet. Let's stop and rest a while. Yeah, sure. We could use a rest, too. Boy, there isn't that breath of air moving down here at all. You said it. We've still got another third of the way to go. Let's stop. I'm really getting sick. Yeah, well, let's try to make the bottom. Then we'll rest a long time. It isn't fire. I can make it. I'll try. It's miserably hot down here. I'm beginning to feel lightheaded. Let's stop and leave our packs and gear here. Maybe that'll help. Feel any better, Stan? No. You? No. Hey, Stan, let's try to cross the canyon and get in the shade. This sun is terrific. Go ahead. I'll follow you if I can. Hey, Burge, do you hear me? I hear you, Stan. You've got to make your shade. I can't help you because I can hardly stand on my feet myself. I can't see you, Burge. Burge, I can't make it. Help me. Don't leave me out here. The heat and sun. Stan, try to get up. Can you hear me? I'll try to help you. Where are you, Stan? Stan, I can't see you. Water. I've got to get water. Virgil and Stanley made many mistakes on their trip to Fossil Canyon. The reason Virgil couldn't see Stanley was that Stanley had fallen behind a large rock that also fallen into quicksand. Real quicksand. He was never seen again. Bill, will you point out the mistakes that the two young men made? I'll be glad to, Dean Sands. First of all, Virgil and Stanley never should have entered the canyon in the heat of the day. They should have waited until sunset or early in the morning is even a better time. Dean, I think continuing the story will drive home the mistakes more strongly than if I were to talk on them now. Perhaps you're right, Bill. Do as you wish. My rangers and I were at our headquarters when we found out about this whole thing. Do you want me to get the phone, Bill? Thanks, pal, but I'll take it. Okay, it's probably for you anyhow. Hello, ranger headquarters. Bill Jefferson speaking. A little versatile. Oh, yes, Dean. How are you? Is that right? What's on your mind? When was he supposed to be back, Dean? Well, perhaps he'd been delayed somewhere and couldn't get word to you. What do you mean? Fossil Canyon. What's the matter, Mr. Jefferson? We just brought out the remains of a human being from Fossil Canyon three days ago. The week before that, we lugged out another. We most certainly will. I'll call you as soon as we know something definite.  I get scared when you get that gleam in your eye like you've got now. I am angry, pal. I'm going to get a letter off to Colonel Landers when we get back. A good, strong letter. Fossil Canyon? Yeah. Something's got to be done about that place. What? I don't know. Stumpy, how many times have you been down there? The miserable hole is to bring out green horn tender feed laced to the back of a horse. Mine would go in there. Canyon rim. Let's hold it up, fellas. Oh, darn. Oh, boy. Now scan the canyon floor with your glasses. You might see Virgil and save time. We've got plenty of good ideas. Yeah, there's lots of rocks and boulders down there that could hide him. You said a mouthful there, sonny. But as big as the canyon is, we might spot something that'll tell us where he's at. Well, I don't see anything. How about the rest of it? I can't see anything but rocks and more rocks. That goes for me, too. Lots of rocks, but no human being. You spotted something, Gray Wolf? There. He's flying over top of Canyon. Yes, I see him, too. Look to your rifles, fellas. We've got to keep these terrible birds off the ground. You see him dive, then open fire. Oh, boy, you said it, Bill. Those birds give me the shivers. They only fly around the dead. Let's go. Come on, let's go. Let's take it easy going down, fellas. When we get to the bottom, we'll stop and rest before going on. It's 115 degrees down there. Tell the student body what preparations you made before going down into that natural oven. Gladly, Dean Sands. First of all, each man had four canteens of water, two for himself and two for his horse. If needed, a third one could also be used for his horse. The horse is doing all the work and needs the water. If the horse collapses, the man's in a bad way. We keep a close watch on the animals to be sure they perspire freely. That's a healthy condition and means that their bodies are cooling. On the trip into the canyon, we stopped frequently and rested the horses. Also, each man swabbed his animal's mouth to keep it moist. We kept our sun helmets pulled down, our sunglasses on, and our shirts buttoned up to the neck to keep the sun's rays off our bodies, especially the backs of our necks. After about two hours, we reached the bottom of the canyon. His heat is murderous. That's putting it mildly, pal. Let's hold up, fellas. Okay, easy boy. Oh, boy. Loosen the cinch straps and give the horses a chance to relax a bit before we go on.  Okay, Bill. Water, Henry, and I'll hold this here bucket. All right, Stumpy. A little bit now, Henry. You only need enough to wet their mouths and throats. We'll give them a good drink after dark. How's that? Yeah, just dandy. I'll give them each a mouthful and we can go on. Bill, I not see any signs of man here yet. I know. I've been looking around, too. Actually, we're not sure if the virgil's down here. Dean said it's only supposition. But we'll have to look thoroughly anyhow. Ah, I'm not right. All right, let's get ready to ride, fellas. Henry and I'll take the west end of the canyon and you two take the east end. Give a yodel if you find anything. We do, right? Drew is hot down here. I'm really beginning to feel it. Take a drink of water, pal. Not too much. You'll get a waterlog feeling. That's enough to keep your body from dehydrating. Oh, okay, Bill. Let's ride along way to the west end. There's another trail leading into here. Virgil may have come down it. Ah, okay. How do you feel now? Oh, much better. That's fine. Keep a sharp eye out for anything that belongs to a man. We find nothing all the time here. I'm glad for that. Me, too, young fella. I'm so hot down here that I could fry an egg on my saddle horn. Bill and Henry don't find anything that means there's Virgil, fella. He ain't down here. Not make me happy. We talk too soon, Grey Wolf. Not what I'm afraid of.  Oh, say he is. Another hour. Let's get him into the shade and give him first aid, fellas.  Yeah, sure, Bill. Right away. All right, let's lift him together, fellas. Carry him over in the shade. Okay, Bill. I ready. Say when. You all set, Stumpy? You sure am. On the count of three. Okay. One, two, quicksand. Let's go, fellas. Henry, don't move. You'll go down faster. Grey Wolf, Stumpy, hold my feet. I'm gonna stretch myself out and grab him. Okay, make it fast, Bill. Not real quicksand. Keep your arms up so I can grab him, Henry. He sink plenty quick. I'll start to sink myself soon. When I grab Henry's hands, you fellas pull away. I'll have to hold my breath because my face will be in the sand. We do that. Cool, Stumpy, pull. Come on, fellas. Pull harder. I'm free. You have it plenty far into quicksand. It feels okay, Grey Wolf. Let my lungs would burst before Henry Hollardy was free. It was quite a scare out there, St. Cole, young fella. I suddenly got off your light-headed. Must have staggered into it. Well, that was close. That's real quicksand. Well, thank the Lord that you're safe. I know somebody that wasn't as fortunate as Henry was. What are you talking about? I'm not talking about Virgil, fellas. You mean there was another man and he fell into the quicksand? Yeah, that's what I mean, pal. Cool, that's cool. Where would you find this out, young fella? Yeah, how come he didn't say something about it before? Well, I wasn't sure for a while. Both men wear the same size shoes. I thought that Virgil might have been doubling back on his own trail because he was delirious from the heat. Then I found definite trail signs that there were two men. Oh, not very tragic. One of them died in quicksand. Yes, it is, Trenching Grey Wolf. He must have wandered into the death pit. But we can't worry about him right now. We've got to get Virgil out of here and get him to Naughty Pine Hospital. Boy, am I glad to get out of fossil canyon. That place gives me the creeps. It even smells like death. You can say that again, young sir. That there hole in the ground reminds me of an open grave. Hold it up here, fellas. Whoa, whoa, whoa, star. Oh, Matilde! Henry, unpack the radio. I'll help Grey Wolf from Stumpy lift Virgil off his storm. You not take Virgil out by horseback, Bill. Well, Grey Wolf, I'm afraid the long ride might do him in. I'll have him picked up by helicopter. Dean, this is Bill Jefferson. Yes, Mr. Jefferson. Have you found him? Yes. He's in Naughty Pine Hospital right now. We flew him back by helicopter. The doctor says we got to him just in time. Uh, are you going to be at your office for an hour, Dean? Will you please? It's extremely important. While you're waiting for us, will you find out if anybody else went to Fossil Canyon with Virgil? To you in an hour. Right. Goodbye. Let's pile into the car, fellas. We're going to college. That's the story up to date, Dean Sands. And gruesome as it is. Oh, it's terrible. Why can't these young men learn by other people's experiences? We didn't take time to check for more evidence as to the identity of the other man because I was primarily concerned with saving Virgil's life. Yes, I understand, Bill. I began checking carefully after you called and I find that Stanley Grimshaw is missing. Whether or not he went with Virgil can't be determined. Thanks, Dean. We'll check with the hospital and let you know what we find. Perhaps we can talk with Virgil. I'd like to talk with Virgil, Doctor. I'm sorry, Bill. I can't allow it. But I thought you said we got to Virgil just in time. I'm sorry I made a misleading statement, Bill. What I meant was that life was just about gone and you found him before it was too late. He's suffering from shock, exposure, heat exhaustion and sunstroke. I'm sorry, but you can't talk to him. Well, this is extremely important, Doctor. We're sure that another man lost his life in the canyon, but we don't know who this man is. I'd like to help you, Bill, but I've got to think of my patient first. Perhaps in a day or so you can question him. Okay, Doc. I know you would. You thought it was all right. Let's go out and talk to the students, fellas. Say, young fella, can you tell me if Stanley Grimshaw hung around with Virgil Thompson? Well, they're a friendly ranger. I'd say they paled around together. Did Stanley say anything to you about going to Fossil Canyon? No, he didn't. He's my roommate, too. However, I went home over the weekend. Professor, you hear anything about Stanley and Virgil going to Fossil Canyon together? I don't recall hearing anything about that ranger. If I did, I would have warned them against going through with it. We're up a tree, Bill. Nobody knows a thing about those two. What now? Well, let's go back to the hospital, Henry. We're as we can talk to Virgil now. I'm sorry, Bill. Virgil's not responding the way he should. I'm afraid it'll be three days before he's out of the woods. Okay, Doc. But we can't wait three days. Fellas, let's saddle up and go out to the canyon and do our own sleuthing. Virgil and Person X had horses when they got here, fellas. Yeah, that's right, sonny. But to let the horses get away, those two fellers are real tender feet. Two men get off horses and walk down into canyon. I wonder why they did that. They should have ridden down into the canyon. Well, perhaps they weren't experienced enough to handle the horses on a steep downgrade. Let's ride down and watch for some of their gear along the way. There's plenty of trail sign along here. Shouldn't be hard to find some of the gear. You plenty right, Stumpty. Hey, look up ahead there. There's some gear alongside the trail. Get him, bro. Get him. Come on, Virginia.  I'll take this pack. Stumpty, you take the other. Henry, Grey Wolf, look through the other gear. Yeah, okay, we'll look through it. You hold it. I found something, I think. What is it, Bill? Here's a notebook with a name in it. Stanley Grimshaw. Bob and your fellow students are now convinced that it takes knowledge and experience to survive out in the wild country. You're right, Mr. Jefferson. I'm thoroughly convinced. I'm glad to hear that I've driven my point home. That's the sole purpose of this lecture.  One more question, please. Go right ahead, Bob. How'd you close off the canyon? Well, we've put up a fence and the gate around both entrances to the canyon. On each gate is a sign. It reads, Keep out of Fossil Canyon unless you want to become a fossil yourself. That's really sound advice, Bill. Certainly the students of State U should learn from their classmates' tragic experiences. I know that I wouldn't go into Fossil Canyon if I was paid to do it. See you next week for more adventure with... Hi there, boys and girls. This is Ranger Bill back again for just a third of a minute with an extra word of thanks to you for joining us today. Hope you'll team up with the Rangers every week at this time when your local station gives us this chance to get together. See you then.


Clioblastoma, GBM, is a common and highly malignant primary tumor of the central nervous system in adults. Recent research has focused on understanding how the tumor microenvironment, TME, influences tumor genesis and prognosis. This study examined the role of macrophages in the TME in relation to patient outcomes for recurrent GBM. Macrophages were found to play a critical role in enhancing tumor progression and promoting resistance to radiotherapy and immunosuppression. Additionally, M1 macrophages were shown to produce pro-inflammatory cytokines, while M2 macrophages were associated with immunosuppression and tumor progression. The study concluded that macrophages in the TME could potentially serve as a therapeutic target for improved patient outcomes in recurrent GBM. This article was authored by Nicola Montemuro, Bavya Pawa, Anish Tael, and others.


Hello, my name is Shinji Nakagawa. Today, I'd like to tell you about our new method on log-response ratio and some standard deviations are missing. First, I'd like to acknowledge my lab members at UNSW Sydney, Australia, and my co-authors, Vex, Russia, Dan Alistar, and Wolfgang the Cat. He sometimes takes human form. It looks like this. OK, I think some of you may be familiar with log-response ratio, but everybody probably knows. Co-NZD, HGZG, this is just a small sample-sized collection version of the Co-NZD. More generally, they are known as the standardized mean difference. And for the reason I'm going to show, so d equal mean minus another mean divided by full standard deviation. Log-response ratio is a ratio of two means, and it's long. And it turns out to be the most standardized or a unique less defect size in ecology and evolution. So it's important defect size for evolutionary biologists like myself. OK, so the missing data everywhere and the meta-nike data are no exception. And generally, it looks like this. When we try to collect descriptive statistics from papers, so you get mean, standard deviation, sample size for the control, and the same thing for the treatment group or experimental group, and quite often, standard deviation missing. Sometimes, occasionally, and I'm missing, but that's very rare. So when that happens, what we usually do is just delete those rows or cases because you can't calculate log-response ratio sampling variance, which we will see the reason why a bit later on. So when we delete these cases, it's called the complete cases approach because we just use the cases where SD are not missing. But this will lead to some biases overall mean estimate or other meta-nike parameters. And this paper points out this. And this paper says we should use multiple invitations otherwise or the meta-nike results are biased often. I guess. And another interesting thing is a survey included in this study showed 75% of meta-nike data in a collogen evolution have missing SD. So it's a very common problem. And multiple invitations are certainly a solution, but problem is when we run the complex meta-analysis, multiple invitations are very difficult to implement. For example, when you have effects size, multiple effects size from the same studies or a collogen evolution, we often have multiple species on the top of multiple effects size per study. Then we have to account for what we call phylogenetic relatedness. That gets very complex model. In this case, multiple invitations technique is very hard to implement. So I was kind of looking for the easier solution compared to multiple invitations. And I couldn't come up with tell. I came across this paper by Donkastan Spake and Rebecca Spake-Zabeks. You saw in the second acknowledgement slide. In this study, they conducted simulation study. And they proposed this formula there with CB. And I'd like to explain this. So this is log-response ratio. You've seen this before. Important thing is meta-analysis. You need the sampling variance. Inverse of this is going to be a weight for each case or each study. And this has a SD mean sample size. And you can see the SD divided mean can be written as a CB coefficient of variation. And they propose rather than using the CB specific to study, we can calculate overall CB, average CB, across all available study. And you can use that. And that would lead to less biased or more accurate overall meta-analytic mean. And you are wondering why is that? And the reason being, they showed this in the simulation is when n's are small, and quite often many studies in the ecology revolution, lots of study has a small n's or sample sizes. SD are estimates so inaccurately. Yes, those are estimate, not true SD. This will result some bias or an inaccuracy over all mean. So that's quite cool. But Fenaro reading this paper toward the end of this paper, they said, actually, this CB, average CB can be calculated. There's missing data because average CB can be calculated without or excluding those missing cases. And you can use average CB for those missing cases. And I thought, oh, this will solve everything. This is why we conducted this new study and the proposed four different new methods, which I'm going to tell you about. So as you have already seen, this is a Doncaster spec estimator. And they use, for sampling variance or log response ratio, they use average CB. We made two improvements for this. We actually used, it looks scary, but it's just a weighted average CB, giving more weights on the study with higher sample size. Another one is that those two latter terms, it's a small sample size correction. It's a bit like a HGG to the Cohen's D. So what's the study? So we had a four method, but I want to just share with you the two method, the missing cases, all cases. So you've seen this before, grades are missing data. And we use this sampling variance. Let's call it the V-Calder. That's our estimator. Those missing a standard deviation, we replace with our V-Calder or all cases method. We use all, for all regardless it's missing SB or not, use this V-Calder for all the studies or all cases, I guess. So we conducted simulation. And this is our simulation. Actually, we vary the degree of missingness from 5% to up to 30%. It's surprising thing is bias. Logical response ratio here is a bias in the overall mean, overall meta-analytic mean. And all cases performs really well, close to zero, zero bias. Next are missing cases. What really surprising, so full data, one is the family simulated, that data set didn't have any missing data. That's really counterintuitive because that should, this should perform least bias, with least bias. But you may remember this Don Castan's fake paper. What they showed is, sometimes each study has a two few sample size, and that affects the accuracy of the meta-analytic mean estimate. So that's what's happening. So actually turns out to be using that new estimator, our estimators for the sampling variance would perform the best. And we don't show this here. It performs in like estimating heterogeneity, coverage, everything is better for all cases. And the missing cases compared to full data, which is surprising. So conclusion, all cases mess with the best one, but we should also probably run the missing cases as a sort of sensitivity analysis. See, you get consistent result. And what we can say, the biggest conclusion is all future meta-analysis can use our method because regardless of how complex or how complex your meta-analysis models are, you can use this method easily because only you need to get weighted average of CV, then you can plug in. One thing I didn't tell you about is assumptions and normality, log response ratio is passed and proposed by hedges. That's the same hedges for the hedges D. And this formulas seem to fail when there's a lot of counter data or non-normal. Yes, so lots of counter data in ecological evolution. So we need to be careful. In such case, we propose several different, couple of different solutions for this all in this webpage, it's connected to paper. You can find this in the webpage for the paper or it's here and thank you.


So, good morning everyone and welcome back to this course. So, yesterday we had covered query processing and we ran out of time at the end and I am sure many of you had questions about query processing. So, let us start off today by taking a few questions on query processing after which we will move on to query optimization. So, I will set aside about 5 minutes now for any questions. If you have questions at this point please indicate it on a view or send it by chat. So, let us start by seeing if anybody out there has a question. I see that Varangal has a question Varangal you are on now please go ahead. My question is related to the previous slide I mean in chapter 12 in the query processing a quiz was given in which random IO was involved in both class disk and hard disk and we were asked to just find out which of the following query processing methodology will be working fine. Could you please explain that sir again. So, the question was we had 4 different join algorithms nested loop index nested loop sort merge and hash join and the question was supposing you switched from a magnetic disk to a flash or a solid state disk which of those 4 algorithms will benefit the most that is we will see the maximum increase in speed that is not to say it will be the most efficient always, but which will benefit the most and the answer logic is as follows. First of all as we discussed hard disk have a mechanical device where arm moves to a position on the disk the disk spins and eventually to read a particular piece of information from the disk takes of the order of 10 milliseconds. In contrast flash disk can read it within a microsecond also. So, this is the basic difference between flash and hard disk that is random axis if I say go to some random place and read one block it is much faster on flash.  So, when I say go somewhere and read 5 megabytes that is sequential axis and the question is which of these algorithms benefits and to answer that you have to understand which of those algorithms has the maximum amount of random IO because that is the one which will have a most reduction in cost. As we discussed nested loops join has two loops one loop goes over the outer relation one loop goes over the inner relation repeatedly. So, now if you see the access to that relation is sequential occasionally you go back to the beginning of the relation and then come down again in the inner loop, but most of the IO is sequential. So, nested loop will not benefit as much it will benefit somewhat, but not that much. Similarly merge join the actual merge phase is entirely sequential it is not going to benefit the sorting phase has some amount of seeks it will have some benefit, but again the sorting is also predominantly sequential. So, its benefit will also be limited similarly hash join the phase where you read the original relations and partition them the input is basically sequential the output is not fully sequential, but it is not fully random either you are going to write out pieces of the partitions in fairly large blocks multi megabyte blocks typically. So, the number of seeks required for a hash join is again not that high whereas, for an index nested loops join every index look up has at least one seek assuming the inner relation is big if the inner relation is in memory it would not benefit at all, but if the inner relation is too big to fit in memory then it is going to have at least one and usually several IOs each for each outer tuple you look up the index fetch the tuple. So, that is multiple IOs operations or multiple seeks are involved. So, now if you have so many seeks if you have 1000 tuples outside and even 2 or 36 it is like 2006 for a fairly small result and can be much larger and in that case you are going to see a very big benefit I hope that answered your question. Let us see if anybody else has a question PSG Coimbatore has their question flag up let us see if you are on PSG you are on please go ahead and ask whatever you wanted to ask. Hello can you hear me sir. Yes, we can please go ahead yeah there are situations the main table has only one primary key that has null values and what is that situation the table accept null values sir. So, first of all if something has to is declared as a primary key you cannot store a null value in SQL that is a constraint which all database systems enforce currently. There were earlier versions of SQL which allowed null values in a primary key column, but the current standard since at least 92 if not earlier disallows null value. So, you cannot do that now the question I interpreted as there is a situation where most records have an identifier which you want to use as the primary key, but there are a few records for which you do not have that identifier, but you still want to record that information in the database. One way to do that is to simply declare it as a unique key not as a primary key if you declare it as a unique key it will make sure that two records cannot have the same value unless they are null and depending on the implement I believe unique allows multiple records with null, but I have to cross check this. So, you could try using the unique key if you need it in this particular situation. Let us go back to you to see if that was the correct interpretation of your question or you have something more to say back to you. Thank you sir. Thank you PSG. SGS, ITS, Indoor you are on now. Indoor please go ahead with your question. Sir, I have a practical question. If we are working in a proxy server, then there are many rules sometimes there are 10,000 or more than 10,000 games and the fields like a priority like source address, destination address and many more fields there. Then how we perform indexing on these rules? If we try to arrange the rules according to priority then these are simple top to down approach then which is appropriate for indexing. Thank you sir. Okay. So, let me first elaborate on that question. The question is in a proxy server there are often many rules on what can be allowed, what should be blocked, who is allowed to access what. Those are the kinds of rules you are talking about right. Please confirm that that is what you are talking about. Back to you briefly to confirm that my interpretation is correct and the goal is to index these rules right. Okay. So, the question is if you have rules like this with basically pattern matching rules and any request that comes through you have to index into these rules to see which rules apply to it and amongst those rules which apply you may have multiple things and then there is a priority mechanism to say usually the first of those rules which apply will override the remaining ones. So, the question is you have rules which involve many fields and then you have request coming in which has several attributes and you have to index to see which of these rules apply. That is actually a very interesting question. So, normally when we store relational data in a database the number of fields is fixed indices are on fixed fields. So, what we do is given a value or a range of values for a particular attribute or for a list of attributes. The index lets us retrieve all the tuples satisfying that equality or that range. The question here is actually inverted. So, you can think of it the other way. You can think of these rules as kind of queries. They have a predicate. The predicate says if A equal to 5 or B equal to 10 then do something. More realistically in this proxy example, it says if site equal to star dot xxx that is the pornographic domain then block. Don't allow it. If site equal to some other such site, block it. Now, there is one country in the world which has a huge number of such rules. Guess which one? Our neighbor China. They put a lot of restrictions on what sites people from inside China can view going out. So, this technology I am sure has been developed vastly in China because they actually block outgoing requests for the entire country. They have a bunch of servers apparently which take you know we usually think of proxy as within the institute and each institute here is a tiny fraction of the country. These guys have the same kind of restriction going on for I don't know how many terabits of pipeline that they have throughout the nation going out. So, the question is how can you efficiently implement these rules? So, the first thing is it is an indexing problem, but it is usually not an external memory indexing problem like B trees are designed for hard disk or flash. Now, if you had to go to hard disk checking one request is going to take at least 10 milliseconds for one random IO that is way too slow. So, all of these are in memory indexing. So, next question is how do you index predicates? And this problem incidentally has been studied not only by Chinese government security agencies, but also by many other people and in a different context. The context in which it has primarily been studied in the database area is if you have people subscribing to something.  You can register a query with Google say that I am interested in results for this set of keywords and whenever the result changes something significant comes up Google will send you an email saying that here are some new results for your query which is actually quite nice to track what is going on. One of the uses for this kind of tracking are people who own copyright and something like music or books so on who would set up a Google query which says free download of XYZ and any time Google finds a new site with free download of XYZ it is going to send you an email saying here are these new sites which offer free download of XYZ. You can guess why I know about this. You can substitute some three words for XYZ which are very close to home. So, in fact we found a few sites which were offering free downloads of some earlier editions of a book and of course that was completely illegal. So, the question is given a large number of such requests which people have registered with a company like Google how does Google efficiently find out which of these requests are satisfied by a set of new pages. They have a set of new sites which they have found and they need to match it with the queries that people have registered. So, this is a reverse indexing. The query is indexed the data is used to look up the query to see which queries match the data which is coming in and I cannot go into all the techniques. It is not that hard for keyword queries actually. You can easily reverse the role of data and query in keyword search engine like Google but it is a little bit harder for database queries. Even in the database context people have done a lot of work on published subscribe systems. What are these published subscribe? You can publish data on in a database and you can have people subscribing to data. Now, Google queries are similar to published subscribe but here the published subscribe is more specific. Your subscription could say give me all updates on relation x or it can say give me all relation updates on relation x where the city equals Mumbai or the institute equal to IIT Bombay or department equal to CS or whatever. So, you have conditions conditional subscription which subscribes to just those updates. So, now you have the same indexing problem where an update comes in you need to know which all subscriptions are affected by the update. So, how do you do this? If there are fixed number of attributes for any relation which is usually the case. So, the issues here you have a bunch of queries which look up different attributes. So, for each query there are multiple attributes which it may refer to. So, whenever an update comes which affects where it insert or in general an update or a delete satisfies that attribute that query is a candidate. Now, there are all kinds of interesting issues here. There may be certain attributes with where the query you know basically every data that comes in satisfies the query. But there may be some other attribute where the updates which satisfy the query are relatively rare and this in fact can be turned into its head which says given the query what is the best index to use for it. And the best index will be the one which returns the fewest tuples for it you have query which says this and this. Now, inverting the problem we select the best index for the query, but what we are doing with that index is not indexing the data, but we are indexing the query which says if you have an and condition this and this the query is indexed on one of those things. And whenever that is satisfied by any new thing which comes in or anything which changes at all you can check that query and check its other conditions and then see if it has a result. So, there is a lot of interesting issues here. If you send me email later through Moodle send me a note. I will send you pointers to some work on indexing queries like this. In fact, one of the well-known papers in this area came from person from Oracle who also spent a fair amount of time at Amrita in Kolam I think few about 5-6 years ago a person called Jagannathan Srinivasan. Then there are other papers of course in this area. I will be happy to send you pointers. So, that was for the database area. Coming to a proxy server now there is no question of disk base indexing and such like everything is on the fly. So, the specific set of techniques is usually not driven by database issues but by data structure issues. Although they are very similar, the problems are similar, but the solutions will look a little bit different because everything is in memory. Everything has to run super fast in microseconds at most. So, there is a lot of data structure hacking and so on including hacking at the firmware level and other levels on routers. So, the same kind of thing which you are talking about for proxies also happens on internet routers. There are a lot of rules in routers which have to be matched very fast. So, they have specialized hardware in Cisco for example is well known for this. They have highly parallel specialized hardware to do this kind of matching on the fly. So, I hope that answers your question. If anybody else has a question, we can take that. Otherwise, let us switch over to today's topic. Ok, good. Looks like there are no further questions.


Hi everyone, I'm Joe for jasperjasecasebreaks.com coming at you with a pack of 2019 Panini Prism Football, a hobby pack. This is Ram Team Break number one, so there's three different things happening here. First of all, we're giving away five spots within this prism pack break, which is coming from a fresh box, from this prism pack break. Then the second dice roll will be the actual pack break itself, names and teams, blah, blah, blah, blah. And the third and final dice roll is where we're giving away five of those Tom Brady Mixer spots. That's going to be a lot of fun. So big thanks to this group for getting into the action. So we only sold 27 spots outright, and we're giving away these five spots here. We've got to do two more of these packs to knock out the break, and we're going to do the same deal, the five spot giveaway within the break. So let's gather everybody's names here. First dice roll, top five after six, four and a two, six times. Two, three, three, four, five and once more, six and final time, top five after six. So Nolan, David, Stephen, Barry, and Stephen again, extra spots for you.  Let's gather everybody's names again for the second dice roll, which is where we're going to assign you a team for this one pack break. Let's roll it and randomize it. Four and a four, eighth of the hard way, names and teams. One, two, three, four, five, six, and eighth and final time. Four and a four, hard eight for the teams, one, two, three, four, and eighth and final time. After eight, we've got Houston down to Washington. All right, here's the first half of the list right here and the second half of the list right over here. All right, no trade windows in these little filler packs, it's alphabetized by team, and let's just print and rip. Sure, Brandon, just hit us up at, click the support button that's on the website, and they can help you out. Here's the final printout.  All right, so what do we got, we've got 12 packs in here. So from front to back, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, we're going to go back to random.org and we're going to generate a number between 1 and 12 and that's the pack we're going to do. So let's flip screens really quick, there it is, 1 through 12, we're going to generate a number right here, and it's going to be 1, so it's the pack right up front. The autograph, there's Tom Brady, that's the mixer that we're trying to get into. We've got a Jordan Agans to 249, a Devin Singletary rookie card, and a Travis Fulham rookie card. Bad, Devin Singletary, not so bad. That goes to the Bills, that's for Stephen Flatt, Jordan Agans for the Texans, that's going to go to Anthony and the Texans, and we got Tom Brady right here as well, still Patriots edition here back in 2019, Nolan with the Pats. If I told you, if I told Patriots fan in 2019 that he'd be a buccaneer and win a Super Bowl with them, I think people would think I was crazy. All right, let's flip back over here, let's grab everyone's names from spot 1 through 32, here's the third and final dice roll, and this is where the top five will get into that Tom Brady mixer, that's where we're giving away a Tom Brady autograph number to five. So let's roll it, randomized third and final time, three and a five, eight times top five after eight, good luck, one, two, three, four, five, six, seven, and eighth and final time, after eight. So from six on down, there's going to be a lot of sad faces here, six through 32, sorry Michael, very close but not quite, but thanks everybody for giving this a shot, appreciate it. We do have two more filler packs we've got to do, so first you don't succeed, maybe give it another shot, thank you though for taking the risk. Top five, you're happy because you're in the mixer, chance at a Tom Brady as well, Steven Flatt is happy, so is Nolan, Nolan again with the extra spot, Steven Flatt again with the extra spot, and Steven Flatt again, wow, hey scared money, don't make money. Steven with three of the five and Nolan with a couple as well, so thanks guys, thanks everybody, I'm Joe for jaspeaskasebrakes.com and I'll see you next time for the next break, bye bye.


I am Jefferson Lerma. I am very happy. Who's slotting it home, eh? Terry's back in front. The first half of the first six minutes has been in the commentary, half of the field. So, thank you on the penalty spot. Back to Jefferson Lerma. Oh, it's a fizzer. It's a Colombian rocket from Jefferson Lerma to light up St Andrews. He's already on two for the season. Much like his previous goal, a layback to him on the edge of the penalty area. And no mistake with a right-footed bullet. Seventh minute, it's Lerma for the Terries 1-0. Wilshire has it on this near side. Is it going to be Pearson for Wilshire, I'm wondering? Midfield, it's Billing, plays it in for Dan Drew. And that's a nice link up between those two. And Billing now looking up for opportunities in the middle. Rolls it in towards the penalty spot. Will it run for Lerma? Yes, it will! As Jefferson Lerma's effort from the edge of the penalty area took a touch-off for Defender. Completely from Expetta Nelly. And Volman edge back in. It's Lerma, tries to play 1-2. Solanke in the penalty area. It's saved by Nichols out towards the edge of the box. The 18-yard box was just on the edge of it. Left footed from Tavernier towards the back post. Looking for Lloyd Kelly. Wins then, back out to Lerma! The Terries here. Solanke trying to take on Reem. He's got support arriving. Comes back to the ball. And Jefferson Lerma back from Solanke. Lerma with the finish, full on one, form of two. Good run from Zimora from the halfway line. All the way to the edge of the box. Brings Anthony in to play 1-2. Back to Zimora on the ball. And across the base of goal, drops to Lerma! Against Manchester City. They spoil Edison's 100th clean sheet. Kelly's long diagonal down the middle for Billing. He wins the header. Lays it off for Dom Solanke. Tries to go around the outside. This man's still going Dom Solanke. Looks to pull it back to Field Billing. Here comes Lerma on his right foot. Another step towards the safety line. Provided by Jefferson Lerma's brilliant finish. Runners coming from deep. In from Christie. Solanke was coming in. He's Lerma again!