All right. So throughout all these unprecedented times that we're in, I'm trying to think of the most relevant, relevant sermons and relevant lessons from the Bible that we can find in times like this. So this morning, of course, we're in Job chapter one. It's quite a day for this man named Job in the Bible. He literally in this story, he literally in one day lost all of his possessions and all of his family in one day. And it's quite a story and we're going to learn a little bit about Job this morning. So what I want to talk about, the subject I want to talk about this morning, is I want to talk about the subject of perseverance and persevering in your life. And you say, you know, what is perseverance? The actual definition of the word perseverance is this. It's persistence in doing something despite difficulty. All right. So that persistence in doing something despite difficulty, it means if you're someone that perseveres, that means that when things get difficult, you keep going and you don't stop and you keep going through whatever that is. You don't let things get in your way and stop you from doing things. Now, there's many examples in the Bible. We just saw one. There's many examples in the Bible of perseverance and persistence through difficulty. If you look at Job, he's maybe one of the best examples in the Bible. You look at Job chapter one. Just look down at verse number 20. So you look at all these things that have happened to Job. He's lost his children. He's lost his possessions. The Bible says that Job, he had a lot of earthly possessions. He had many children. And the Bible says that he lost them all. And in verse number 20, the most amazing part of Job chapter one is in verse number 20, the Bible says, and Job arose and rent his mantle and shaved his head and fell down upon the ground and worshiped and said, naked came I out of my mother's womb, naked shall I return thither. The Lord gave and the Lord hath taken away. Blessed be the name of the Lord. And in all this, Job sinned not nor charged God foolishly. So the Bible says that through all this, Job just recognized that, hey, you know, those things were just given to me by God. You know, praise God that I had those things while I had them. The Lord gave me those things. It's up to the Lord if he takes them from me. I mean, now, look, for someone who has just lost his own children, that is an amazing testimony of faith right there to not charge God foolishly. Because the Bible implies here that if Job would have, you know, been angry at the Lord for this, that that would have been foolish on his part. All right? Even in Job chapter two, look, Job hasn't lost his wife yet. He hasn't lost his wife. And I don't say yet because he didn't lose his wife. But in Job chapter two, look at verse number seven of Job chapter two. So in Job chapter two, you know, God in Job chapter one says, all right, you can take away everything from Job, but don't touch him. Don't touch his health.  And in Job chapter two, in verse number seven, the Bible says, so went Satan forth from the presence of the Lord and smoked Job with sore boils from the soul of his foot unto his crown. Meaning from, he had boils on his skin from his feet to his head. And he took him a poster to scrape himself with all. And he sat down among the ashes.  I mean, nice lady, right? Look, I mean, just as a side note, you know, when you're going through, you know, part of the benefits of marriage is when you're going through, like, look, there will be times in your marriage where one of you is having a good time and the other is not. Where maybe someone, the wife is down or the husband is down. And look, I mean, in this lady's defense, she just lost her children too. But she's obviously not taking it in the same faithful stride that Job is. Okay? So, you know, encourage each other in your marriage. All right? When one of you is down, pick up the other, whether it be husband or wife or whatever. But anyway, she's obviously not taking this whole situation with the same perseverance that Job was. Okay? She was more, she got in the flesh, right? Look at verse number 10 and see how Job responds to her. But he said unto her, Thou speakest as one of the foolish women speaketh. What? Shall we receive good at the hand of God and shall we not receive evil? In all this did not Job sin with his lips. So he's basically once again saying, look, all the good that we had, I mean, it's just beautiful. All the good that we had was from the Lord. And he's just like, what did we just expect it to just be good all the time? I mean, so he rebukes his wife here. Then, of course, in the story, you know, Job's friends show up and, you know, it gets really interesting from there. We're not going to go any further into the story of Job. But I just want to show you that Job is a great example of perseverance in the Bible. He never lost his faith. And he kept going through it. Even when his friends show up and just beat him down. And, you know, that's a good example in Job, by the way, of how to not be a friend of someone who's going through hard times. Okay? Let's look at another example. Turn to 2 Corinthians chapter 11. Another great example of perseverance in the Bible, maybe the most complete example of perseverance in the Bible is the Apostle Paul. Turn to 2 Corinthians chapter 11 and look at verse number 23. And the Bible says this. It says, are they ministers of Christ?  I am more in labor's more abundant in stripes above measure in prisons more frequent in deaths off. Then he goes into all these other details of all the things that he has suffered for the cause of Christ. Okay? But basically, he's saying, he's worked harder in labor's more abundant. He's been beaten more. And he's been imprisoned more than anybody for the cause of Christ. Paul suffered terribly his whole life for the cause of Christ, you know, by the, you know, first by the Jews, and then eventually at the end of his life, because I believe, you know, it's not in the Bible, but I believe that, you know, Paul was beheaded by the Romans by Nero. And, you know, secular history will say that we don't know for sure, of course, because it's not in the Bible. But look, he led a very difficult life all the way to the end. There's no doubt about it. But he never stopped. Paul never stopped. Turn to second Timothy chapter four. Paul, look, especially for me, Paul is a is this rare example of somebody who gets saved later in life. And then from that point, completely finishes the race. Perfectly. Now that I mean, that's a great example for somebody like me, for people who got saved, you know, later in life. But you know, it's a good example for anybody. Paul finished the race.  You know, you see a lot of people do that. You know, they're in it for six months or a year or whatever. And then they're done. That was not Paul. Paul is a perfect example of what we need to be as saved Christians. Look at second Timothy chapter four and verse number six. And this is another kind of, you know, biblical evidence that I think Paul was executed right here just the way he worded this right here. He says, for I am now ready to be offered. And the time of my departure is at hand. I have fought a good fight. I have finished my course. I have kept the faith. He went all the way to the end. He never stopped. It's a great example for us to persevere to the end through difficulty. I mean, that's the example, right? I mean, that's the definition of perseverance is persistence through difficulty. Paul's whole life was difficult. I mean, think about it. He had this, he had this great life as this high, you know, Pharisee, right? He was Pharisee of the Pharisees, the Bible says. He had everybody's respect. I'm sure he had plenty of money. I'm sure he had all this preeminence amongst everyone. And he goes into this thing where, you know, Jesus stops him on the road to Damascus. And he not only, you know, gets saved, but he just drives the gospel for his whole life at the expense of his body, of his health, of everything. And eventually his life, eventually his life. Now look, that is, that's the definition of perseverance right there. Okay? Now, here's one that you may not have thought of before. And I'm going to get some mileage out of this story in the Bible tonight, but turn to Luke chapter 18 because you're like, I've heard of Joe before and I've heard of Paul before. What else you got? Turn to Luke 18. I want to really focus on this story in the Bible tonight. So we're going to read this parable in the Bible. Let's read this parable. It's the parable of the widow and the unjust judge. Luke 18, we're going to start reading at verse number one. The Bible says, and he spake a parable unto them, to this end, that men ought always to pray and not to faint, saying, there was in a city a judge which feared not God, neither regarded man. So this guy, this judge, he didn't care about God. He didn't care about people. He just, he didn't care. He didn't care about the people that were coming to him and he didn't fear God. All right? And there was a widow in that city and she came unto him saying, avenge me of mine, adversary. And he would not for a while, but afterward he said within himself, though I fear not God, no regard man, yet because this widow trouble with me, I will avenge her. Lest by her continual coming, she weary me. And the Lord said, hear what the unjust judge sayeth. And shall not God avenge his own elect, which cry day and night unto him, though he bear long with them? I tell you that he will avenge them speedily. Nevertheless, when the Son of Man cometh, shall he find faith on the earth. And he spake this parable unto certain which trusted in themselves that they were righteous. Who were those? Those were the Pharisees and despised others. Right? So look, this is very similar to Luke chapter 11. Turn to Luke chapter 11. I'll give you the point of the parable here. In Luke chapter 11, look at verse number 11. I'm going to give you the point of the parable and then I'm going to kind of apply it a little bit different. Okay? Look at Luke chapter 11 verse number 11 where the Bible says, if a son asked bread of any of you that it is his father, will he give him a stone? Or if he asked a fish, will he, for a fish, give him a serpent? Or if he shall ask an egg, will he offer him a scorpion? If ye then being evil know how to give good gifts under your children, how much more shall your heavenly father give the Holy Spirit to them that ask him? So in Luke chapter 11, we see this idea comparing, you know, your heavenly father to you as a father to your child. Right? He's saying, look, if you would give good gifts to your children, you know, how much more would your heavenly father give you gifts or answer your prayers? That's what the Bible is saying. And very similar in Luke chapter 18, the story of the unjust judge, God is using that, Jesus is using that to explain that, look, this guy was unjust. He didn't fear God, he didn't fear man, he didn't care about man, anything, but he answered this widow, he gave this widow what she wanted. If this unjust judge would give this widow, would avenge this widow, how much more do you think God will avenge you? Is what the Bible is saying, what Jesus was trying to say. Right? Turn to Hebrews chapter 12, because he's comparing the unjust judge to God, the perfect judge. Just like he's comparing in Luke 11, you know, you as a father to God as the perfect father. Right? It's the same type of application. Hebrews chapter 12, look at verse 23. Let's look at what the Bible says about God being a judge. In verse number 23, of Hebrews chapter 12, the Bible says, to the general assembly and church of the firstborn, which are written in heaven, and to God the judge of all, and to the spirits of just men made perfect. So look, God is the judge, he is the judge. So that's what Jesus is talking about, he's comparing this unjust judge to God, saying, look, if this guy would avenge this lady, your heavenly father will avenge you. Alright? So now, let's look at some applications.  I mean, that's what you all are thinking right now. I can see it in your eyes, right? But look, let me ask you this. If it's so Christianity 101 to preach a sermon on perseverance, let me ask you this, why doesn't anyone do it? Because I don't see it happening. I don't see it happening. So turn to James chapter 1. Before I make application on this tonight, turn to James chapter 1. Let me just yell at you about a couple things here. Alright?  I love seeing you all every week and I love doing this and it's great. But look, I mean, I don't really want to waste my time.   I don't know if you've noticed that about me, but I don't waste time. I don't have time to waste in my life. Alright? Look at James chapter 1 in verse number 22.  I want to give you some specific tools. Alright? I want to give you some, I don't want to just say, hey, persevere! And then just leave. I want to give you some specific tools on how you can persevere. Because look, if you don't do it, you need to learn how to do it. Okay? So that's what I want to show you. Look at James chapter 1 in verse 22. Here's step one. But be ye doers of the word and not hearers only, deceiving your own selves. For if any be a hearer of the word and not a doer, he is like unto a man beholding his natural face in a glass. For he beholdeth himself and goeth his way and straightway forgeteth what manner of man he was. So here's a guy that he goes and he looks at himself in the mirror.  He forgets who he was. This is the person who hears preaching and forgets what the sermon was about before they walk out the door. Right? I mean, this is the person who hears the preaching and applies it, you know, for a day and then just forgets about it. Right? I mean, look, look, don't let Bible preaching turn into like your TV replacement. I mean, can't that happen? Can't that happen? Can't that happen from, you know, I mean, look, for those of you that used to YouTube church this thing. Right? You go and you look at the, I used to do it.  Right?   I want that one. Right? But look, don't let this turn into some cheap entertainment that isn't, isn't sticking to you. Otherwise, you know what? You know, I'm not wasting my time here. You are too. You know, you are too. If you just let this stuff not, if you become a hearer and not a doer, we are all wasting our time here. Okay? So don't be that guy. All right, look, I mean, this perseverance thing, here's how I can, here's how I know, you know, my dad used to always say this to me. If it was easy, everybody would do it. What did he mean by that? He's, it's simple. Look, the gospel is simple. It's very, it's, it's a good analogy. The gospel is simple, yet everyone's going to Hell. Perseverance, the idea of perseverance, very simple. But why does no one do it? Because it's not easy. You know, the gospel is easy, obviously. But look, it's not easy to persevere things. I mean, you may know what to do, but it's not easy. You're going to hit hard things.  All right, so tonight, let me just give you some areas, some specific areas. I just picked a few. We'll just have a popcorn sermon. I just picked a few areas where it's hard to persevere today. Okay, but as a Christian, these are areas that as a Christian, if you don't persevere in these areas, the cost will be heavy. The cost will be heavy for you. Okay, so that's why I picked these areas. Because look, there's, you should make, you should make perseverance a culture in your life. But I'm going to give you some specific areas tonight where if you don't persevere, that there will be serious consequences. All right, the first one is this. Raising godly children. Raising godly children. Look, this takes perseverance. You cannot listen to a couple sermons on raising godly children.  You cannot do that. Apply it for a week and be done. It does not work that way. You have to be consistent and not stop being consistent. Oh, I heard a sermon on discipline, so I'm gonna, I'm gonna be vigilant for a week. And then after that, my emotions and my wife's emotions will take over and that's it. Well, that's not how it works. You're wasting your time listening to those sermons if that's how you take those sermons. Look, you need to make it a culture. You need to make a culture. Turn to Deuteronomy chapter 6. Look, folks, you do not want to know what failure looks like in this area. You do not want to know. Look, Job, Job lost his, he lost, he had a terrible day. He lost his children. But look, if you fail in the area of raising your children amongst this world that we live in here, that is a disaster that you will have to watch every day for the rest of your life. You could argue that it might be worse in some cases than losing your children. Unfortunately, there is wicked people out there doing wicked things and they're recruiting people into their wickedness. And if you don't get this right, it will be a heavy cost. Look, your children, I mean, think about it. If you don't discipline your small children, it could mean that they turn away from the Christian life. That's what that small mistake could turn into. They could turn away, I mean, they could reject the Lord because you didn't discipline them when they were three or four. That's what that small mistake could turn into later. Are you at Deuteronomy chapter six? Look, there's much danger. There's much danger. It cannot be overstated. Look at Deuteronomy chapter six and verse number six where the Bible says, And these words which I command thee this day shall be in thine heart. And he's talking about the law here. He's preaching about the law. He's talking about the law of God. And thou shalt teach them diligently what? The laws of God unto thy children and shall talk of them when thou sitest in thine house, when thou walkest by the way, and when thou liest down, and when thou riseeth up. And he continues through the chapter and he talks about this in generational type of language. Meaning this isn't when they're four. This is all the time. This is throughout their whole childhood. And this is what they need to be doing when they have children. And this is the only way that it will work. And look, we know from reading the Old Testament that it failed over and over again because they didn't do this. They didn't teach the law to their children, period. That's where they all failed. That's where they all went wrong. So look, it's an area in your life where you have to persevere. You have to.  In this area, you are headed for blessing or disaster. One of the two. It's like the bottles. Right? It's going to be one or the other. I mean, it should scare you a little bit. I've told you that before. Let's look at another one. Let's look at another one. This applies to a lot of people today. Some people even in here, finding a job, finding employment is hard. It's something that needs to be persevered after. Look, for many of you, many of you, maybe it's not because you know the things that I'm about to say. But look, let's talk about finding a job. There are some things that I didn't see coming out of this whole thing that we're in right now. And one of those things is that there's a lot of opportunity out there right now. There's a lot of opportunity right now from this. You say why?  The economy's crashing. Why is there opportunity? Well, the reason that there's opportunity is because we live in a socialist state where there's a lot of people who just don't want to work. And look, people can make nearly as much money right now, sitting at home, then they can't actually working.  Instead of having to buy another car. So I figured I'll just do this this week and I just love talking to people. Let me tell you some stories about the two guys I've been talking to in the last week and a half. And look, I didn't see this coming. I didn't see this coming, but there's great opportunity here. I was telling a brother talking to Soul Winning today. I was like, look, I just love seeing people succeed. And there's a lot of opportunity right here. These guys at this minus shop that I was going to, he's telling me how just people just walked off the job. They're like, they just walked off the job just so they could just go sit home. Because unemployment went up 600 bucks a month. And then he's talking to me about how they're going to, you know, they're going to make another $2,000 a month income for everybody for no reason. He's like, it's going to be worse. He's like, more people are going to walk off the job then. But he's like, hey, you know, he's like, the owner pulled us aside. And he's like, thank you for being loyal. And he's like, we're getting raises and I'm going to be the manager and all this kind of look, there's a lot of opportunity for people.  He's like, but the guy says to me, this guy's not saved. Here's what he says to me. He's like, it just doesn't seem right to just sit at home and not do anything. And I'm like, praise God, man. I'm like, you know, I'm like, don't you ever like question mark that thing that you just said?  What they're doing there. Right. So look, turn back to Luke 18. I'm going to give you some practical advice on how to get a job from this parable in Luke chapter 18. I mean, this guy's not even saved. He's not even saved. And he knew this. Look, let's look at Luke 18. Now look, let me ask you something about this lady that's talking to this judge. Did it work for her? Did it work for her what she was asking for? She got what she wanted, didn't she? So I want to give you just a few steps that this lady did that, that it worked. Look at Luke 18. Start reading at verse number two, saying there was in a city a judge which feared not God near the regarded man. And there was in that there was a widow in that city and she came onto him saying avenge me of mine adversary. So she wanted something and he would not for a while but afterward he said within himself though I fear God, fear not God nor regard man yet because this widow troubled with me I will avenge her lest by her continual coming she weary me. So look, here's the step one. Step one is this, find the decision makers. If you're looking for a job somewhere you need to find the decision maker. Who did this lady go to? She wanted something done. She was wronged by somebody and she wanted something done. She didn't go to her neighbor and complain about it. She didn't go to her friends and complain about it. She went to the judge. And she's like I'm going to this guy because he can make it happen. So she went to him. Go, you have to find the decision maker. She found the judge. So that's step one. And I'll get more specific on each of these points by giving you an example when I'm done. But the second thing is this. You need to go to them. You need to go to the decision makers.  Did she write this judge letters? Did she send him emails? You know, haha. But my point is she went to him. She went to this man. Personally. All day, every day. Look, sending applications on the look, listen to me. Whether you're watching on online or whatever, sending applications on the internet will get you nowhere. Nowhere. No one in the history of hiring anybody has ever hired a resume. Ever. Never. You have to be relentless and you have to go to them. Find that decision maker, go to them. And the third thing is this. If and when, I say, let's just say when because these things will come up. If and when obstacles are met, remove them. There's perseverance right there. You must because you will hit obstacles. Look, if it was easy, everybody would do it. Right? If it was easy to go just get a good job, everybody would do it. If it was easy, but people hit obstacles and then they stop. That's the problem. Look, now I have followed this three step process several times and it works every single time. Let me give you an example of my move to Sacramento. I hunt down. Let me just get specific with you. I hunt down hiring managers on LinkedIn. That's what I do. I hunt down. I find somebody who's hiring. I go to their company website. I find them on LinkedIn. I hunt down the people and then I set up an appointment. I make my own fate. You set up an appointment to get face to face with the judge. See, you find the judge. You set up an appointment to get face to face with the judge. Because no one's going to hire a resume. I don't care how great your resume is. There's a lot of great resumes out there because people just lie. People just make stuff up. I mean, I can't tell you how many times I see this guy.  This guy invented oxygen.   Because people just lie. They just make stuff up. You have to go to people. All right. So look, a simpler version of this.  Just go to places. Just go to, just go. Just show up to places.  You know how many friends I have that own businesses that have just stopped looking for people because they can't find anybody? They can't find anybody that's willing to show up every day, work hard, do a good job. Go to places. Look a man in the eye. Shake his hand. I don't know. Give him an air, elbow, bump, or whatever they do right now. But you have to go there. You have to show these people your face, right? So when you hit those obstacles, you remove them. Look, the people that I found in Sacramento that I applied for a job and I got an interview, they needed me to take a test. It was a very difficult test. I had never taken this test before. So I just, I dropped everything for a week. I studied for the test. I took the test. And my job was dependent on me passing that test. My wife told me we moved to Sacramento. I'm working this job. I don't know if I passed the test. She's like, does the results of that test matter? I'm like, it matters. My job depended on it. And I'm like, nah, it's not a big deal. Every night, I can't even sleep until we get the results back. And I'm like, it's fine. She's like, isn't it important? I'm like, no, it's not important. She's like, doesn't everyone fail that test? I'm like, yeah, it's no big deal. It's no problem. Anyway, look, the Lord will bless these efforts. Okay?  All right? So, but my point is this, the whole thing takes very specific action Look at this lady. She found the guy. She found the guy that could do it. She went to him and just like, he's like by her continual coming to me. I just, fine. That's how it works. People meet you. They see you. They're like, man, this guy came all the way down here. He drove here. He walked into my place of work.  And this guy's coming in wanting to work. I mean, that's how it works. That's who you need to be. That's what you need to do. And it will work for you. And look, there's a ton of opportunity out there right now for these people. You know, I wrote an article in 2005 for some conservative website.  I mean, I think I put it more eloquently than that. But there's just a lot of opportunity right now because a lot of people went home to play video games and get drunk. So take advantage of that. Take advantage of that because that's not us, right? That's not who we are. Take advantage of that. Look, there's guys here that have already done that, already taken advantage of that. I mean, they go out, just hunt those jobs down, just get them, and they just drive through in those jobs, and they're just doing great. It's awesome. I love seeing it. Nothing makes me happier than seeing people succeed, especially my fellow brothers and sisters. All right, I love it. It keeps me going. Help me. You know, just, there's a lot of opportunity. I'm here to just announce that to you. I mean, these guys, these guys at this auto shop, one of them was 23. He's going to be running that auto shop in two years. You mark my words.  I can't, I can't preach this hard enough. I mean, it's just, it's great. There's a lot of opportunity, even today, even with messed up situations that are out there today. All right. And look, look, you don't believe me? You say, turn to Matthew chapter four. The Lord, look, the Lord will reward perseverance and labor. Okay. Think, think of this. Think of this. My wife brought this up to me. What were the disciples all doing when Jesus called them? They weren't playing video games. What were they doing? Turn to Matthew four. Look at verse 18. The Bible says this, And Jesus walking by the sea of Galilee saw two brethren, Simon called Peter and Andrew his brother, casting a net into the sea, for they were fishers. And he said unto them, follow me and I will make you fishers of men. In a straight way, they left their nets and followed him. And going on from thence, he saw other two brethren, James the son of Zebedee and John his brother, the sons of thunder, in a ship with Zebedee their father doing what? Mending their nets and he called them. They're all just out there just working. And that's when Jesus said, Hey, I got something. I got the next level for you. I got the next step for you. Let's go. But they're out there and they're working and they're doing those things and they're getting it done. And Jesus calls them out of that. So look, the Lord will reward perseverance and labor. My friends, think of Elisha. Turn to 1 Kings chapter 19. Look, I could go on all day long about this one.  Elisha. Look at 1 Kings chapter 19. Look at verse number 19. This is right after Elijah, right? Elijah had just done his great work and he slayed the prophets of Baal and then God, he was depressed and he just wanted to die because everyone was trying to kill him. Jezebel was trying to kill him. Look at verse 19. So he was told to go call Elisha. In verse 19 of 1 Kings 19, the Bible says, So he departed thence and found Elisha, the son of Shaffet, who was plowing with 12 yoke of oxen before him. And he with the 12th and the Elijah passed by him and cast his mantle upon him. And he left the oxen and ran after Elijah and said, Let me, I pray thee, kiss my father and my mother and then I will follow thee.  And he returned back from him and took a yoke of oxen and slew them and boiled their flesh with the instruments of the oxen and gave unto the people and they did eat. Then he arose and went after Elijah and he ministered unto him. Look, the guy was farming. The guy was plowing. You know, he was plowing with a yoke of oxen. He was working when he was called, you know, to be a prophet of the Lord. So like, look, so many areas of life that there's so many other areas of life that perseverance could be applied. Here's another one for you. Here's another one for you, health. I mean health. I mean health is important in our life, right? We had a sermon about this a couple of weeks ago. Getting healthy takes more than five minutes of effort. Living a healthy lifestyle takes more than five minutes of effort.  It's more fun to eat candy all the time. It's more fun to do all these things. So to get healthier, you have to live consistently. You have to persevere in that area of your life. It's going to take some cultural life changes, right? It takes more than two days of discipline, just like with your kids, right? And by the way, you want to teach your kids to live healthy lifestyles as well? Look, this is a big one for homeschoolers too. Look, if we take our kids out of the public school system that has all these sports and FIED programs and all these different things, look, we should replace it with something. Look, I don't think that we should take them to public school and have them be involved in all the sports like some people do at the public school. That should not be the answer. That's not separation. You will have problems there. But look, you should replace it with something. You should do healthy things, like taking your family on an eight-mile hike.  Your kids should know how to exercise.  They should know how to do these things. Because when they get to be adults, they need to, they should continue to be healthy adults. No matter what kind of job that they end up having. They should know how to eat healthy, how to exercise, how to take care of themselves. They should, you know, it's not just sin.  All right? But look, you should teach them to live a healthy lifestyle, even if they're homeschooled. You need to replace that with something. You know, it could be, there's all kinds of physical activity things out there to do. All right? Now look, here's another one. Living, turn to Job chapter two. Living the Christian life in general. Just living the Christian life in general will take perseverance because it's not, it's not always just going to be easy. There's going to be obstacles in your Christian life. Look at Job chapter two and verse number seven again. So Satan went forth from the presence of the Lord and smote Job with sore boils from the soul of his foot unto his crown.   But he said unto her, Thou speakest as one of the foolish women speaketh. What shall we receive good at the hand of God and shall we not receive evil? And all this did not Job sin with his lips. Look, he wouldn't stop. He wouldn't stop his Christian life. He wouldn't stop what he believed. He wouldn't stop exercising his faith. Nothing would stop Job and he would not become disloyal to God.  Like Bible reading. If you have extra time now, now is probably a good time to either catch up on Bible reading or get a Bible reading program going. Well look, there's a lot of distractions out there. Distractions are going to be obstacles to your Bible reading.  You don't think Christians do those things? I mean, it's sad, but I'm sure it does. I mean, look, I believe right now that sin is creeping back into people's lives. That's one of the things that bothers me the most about what's going on right now is because I feel like, you know, if people were struggling with things and you know, then they get out of church, it's just not going to be good for people. It's just not going to be good for people. You know, they're going to get, but look, you should take it the other way. If you have more time, you should be like, you know what? I'm reading my Bible for an hour and a half a day. If I have more time, I'm going to read my Bible more. I'm going to read, I'm going to do things, like I'm going to put some time aside for prayer as we've been talking about.  Like a real one.    So look, folks, I hate to break it to you, but anything worth doing, anything worth doing is going to take perseverance in your life. And by that very definition of what perseverance means, that means that anything worth doing, whether it's finding a job or, you know, reading your Bible or raising spiritual and godly children, whether, you know, whatever it is, you're going to hit obstacles. You might hit big obstacles. You might hit small obstacles. But here's the nice thing, though. Here's the nice thing.   All you have to do, like, it's really easy. You just set a goal. You set a goal.  That's my goal. That's a big goal. But then you got to break that goal down and look, you're going to hit obstacles along the way. But as long as you see the goal, it's like Brother Trevor plowing the field, right? As long as you're focused on that point, you're going to get there. You're going to get there. You set the goal. I'm going to get that job. I'm going to get a job. You know, I'm going to take advantage of this opportunity.  You set that goal and whatever obstacle you hit, you just drive through it. That's it. There's no obstacle that can't be overcome. There's none. You can, with faith in God and perseverance, you can push through these obstacles. If you have a godly goal, you can get through them. If your goal is godly. If your goal is biblical. You just keep going. And look, it's not going to be easy. Let me just get that out of the way for you. It's not going to be easy. There's going to be things that are going to be hard. They're going to be difficult. You might be depressed. You might fail. You might fail twice. You might fail five times. You know, I had, I have a saying when it comes to, to ideas that I've had throughout, you know, my career and my life. And my, my ideas, my idea factor is this, about 10 percent. You know, I've had some good ideas, but about 10 percent of my, you know what, that means if I have 10 ideas, nine of them don't work out. But you just keep going. You just keep going. You just keep pushing through, pushing through. And then you get that one. See? Then you get that one. You got to make it a culture and just never stop. Look, like Job, like a man who lost everything, he lost his own health. You know, how many times have you heard people say, well, at least you have your health. He lost his health too. You know, that's what, that's what Satan between chapter one and chapter two told God. He said, oh yeah, well at least he has his health. That's why he still is faithful to you. Then he took his health away and he still never lost his faith. He still never became disloyal to God. Like Paul, a man who kept going, you know, through everything, no matter what, even to the point where he was killed. And still he's like, I finished my race. Like the woman with the judge. She just persisted until it worked out. So look, make it a culture in your life. And it's not going to be easy. It's going to be hard, especially if you're not used to it. Especially if you're not used to it. If you're used to just hitting hard things and just being like, it's too hard. No, it's not. Look, if it's in the Bible and you're supposed to do it, there is nothing that you can't overcome. Guaranteed. Guaranteed. If it's a biblical goal, you can get there. That's the beauty of it. So you always know, if you're focused on that side of the field and that side of the field is where God wants you, you will get there. But not if you don't persevere through it. See? All right. Perseverance. Let's bow our heads and have a word of prayer. Dear Heavenly Father, we thank you for these men in the Bible. Lord, we thank you for all these great examples and this woman in this parable, Lord. We thank you for all these great examples in the Bible that you give us, Lord, to help us through hard times in our life, Lord. I pray that anyone that's persevering, Lord, that's pushing through difficult times right now, Lord, I pray that you just give them that faith and that encouragement and to just help them keep going and push through any obstacles that may come, Lord. And just thank you for this church. Thank you for the fellowship that we have here, Lord. It's such a blessing to, at a time like this, to still be able to meet here and just preach your word and listen to your word, Lord, and sing to you. We thank you for everything. In Jesus' name we pray. Amen.


おはようございます。私のプレゼンテーションに感謝します。始めましょう。私はまさきまつした。NTTコミュニケーションのオープンスタックR&Dチームのメンバーです。日本で大きなテレコンコンパニーです。ネットワークスポンサーのインターネットアクセスです。オープンスタックはキロサイコルを開始しました。リパティサイコルは16パッチをトロップ、オスロロー、コンフィッグをコミュニケーションに読めました。私はコミュニケーションについてコメントをしています。まず、ロックコレクターを読みましょう。ロックコレクターはユニファイトロッキングレイヤーで読みましょう。ユニファイトロッキングレイヤーはコミュニケーションについてコメントをしています。マネットフロッキングレイヤーはアドホックスクリプトをマネットフロッキングレイヤーに読みましょう。コメントを読みましょう。ロックコレクターです。ロックコレクターは3パッチを読みましょう。インプット、フィルター、ココロを説明しないようにエンジンでドームの間に説明を進めているのですインプットの説明はコレンティングデータに適用するこのフィギュアはコナッツプラグインがログファイスを読みますサイスログプラグインはオープンスタックプロセスをしていますフィッタープラグインはそして、多くのフィルターを追加することができます。このフィルターの出発プラグインを各デスネーションに送り、フレンディはローグコレクターの一つです。このコレクターはC Rubyを作り、Googleのフレンディとしても知らないでしょう。フレンディはクバネティスのスタンダードローグコレクターです。フレンディはTREASURE DETAINCで保管しています。このプラグインはアンライテックスインフラストラクチャーのサービスです。ローグスタッシュはローグコレクターのインフリメンテーションです。このプラグインはJ Rubyで、イラスティックコールで保管しています。このプラグインはイラスティックサーチを作り、この2つのローグコレクターはとても似ています。このプラグインはとても似ています。今日、このプラグインはとても似ています。このプラグインはアウトラインです。まず、この2つのローグコレクターはコンフィグレーション、プラグイン、パフォーマンス、トランスポットプロットコールです。次に、フレンディとローグスタッシュはフレンディとローグスタッシュをインフリメントしています。まず、コンフィグレーションの1つのプラグインです。フレンディとローグスタッシュは違うサイズでコンフィグレーションを使用しています。フレンディとローグスタッシュはタッグで、ローグスタッシュはタッグで、このフィグレーションはノバAPIローグスタッシュとオープンスタッグとノバ、ノバAPIローグスタッシュはタッグで、オープンスタッグとノバ。ノバAPIローグスタッシュはインフラッシュdbです。これをこの部分にしています。ノバAPIローグスタッシュはイラスティックスタッシュです。ここはインフリグレーションのフレンディです。不安定です。この頃、フレンディがノバAPI.xのコンフィグレーションをNOVAAPI.LOCK and the inputs will be tagged as OpenStack.NOVA.Every input will be tagged with tag attribute like this.Then, tagged inputs are in much section.In this case, the input in the previous slide will be matched at the first sectionbecause it's tagged as NOVESTAT.NOVA.You can also use wildcards like the second section.As a whole, NOVA related tags will be routed to Elasticsearchat the first section, and all other OpenStack related logs will be routed to InfluxDB.If you want to output logs to multiple destinations, you can use copy program.In this example, FriendD sends same logs simultaneously to two destinationsInfluxDB and Elasticsearch.Let's take a look at LogStash configuration.Differently from FriendD, there is no tags in LogStash.All inputs will be aggregated and scattered to all outputs.This figure shows that inputs from NOVA API and CINDER API are aggregated and scattered to outputs.This is the configuration corresponding to the previous slide.Let me compare FriendD and LogStash in configuration.In order to compare, let's assume that we have three inputs corresponding one to oneto each output separately.In this situation, FriendD can route different streams separately in simple tag matching.On the other hand, you have to split logs to do the same thing with LogStashbecause all inputs are aggregated in LogStash.You need to define conditions like this to split aggregated logs.As you can see, these conditions might be complicated.For the second case, let's assume that we want to aggregate logs from all inputsand send aggregated logs to several destinations.In this case, LogStash can be configured quite simplyfor unconditional scattering.To make the equivalent configuration with FriendD, you have to use copy plugin.Let me summarize this part.FriendD and LogStash have different styles of configurations.Generally speaking, FriendD is suited to handle multiple streams separately.By contrast, LogStash is good at handling logs in gather scatter style.Next topic is about plugins.Briefly, they both provide number of plugins.There are more than 300 plugins for FriendDand more than 200 plugins for LogStash.Popular plugins are bundled with LogStashSo you will not need another plugins in many cases.These bundled plugins are maintained by LogStash project.That's a great point of LogStash.FriendD is packaged only with minimal plugins.So you will need to install some plugins.Most FriendD plugins are developed by individuals.So some plugins may not be maintained well.Plugins can be installed easily by one commandwith both FriendD and LogStash.Next thing is performance.To be honest, I can say which one is fasterbecause that depends on circumstances.What I can say is that both FriendD and LogStash are more than enough faster to use for OpenStack.They can handle more than 10,000 logs per second.Then filters greatly affect performance.It's not a good idea to apply heavy filters on log collectors.Heavy workloads should be performed on data processing structures.Next, I have heard many times that CRuby is slowbecause it's a GVL and only one thread can run at the same time.That is not true, especially for IO bound loads.This diagram shows two threads attempt to perform IO operations at the same time.When thread1 is executing Ruby code, thread2 has to wait GVL.However, GVL is released just before actual read or write in this section.Then thread2 acquires GVL to execute Ruby code.Actual IO operations can be performed in parallel.Let me move on next topic, transport protocol.FriendD and LogStash have their own transport protocol.FriendD has forward protocol and LogStash has Lamborgac protocol.They both support failure detection and fallback.Here is an example of active standby configuration in LogStash.They are primary node and secondary node.In this configuration, secondary node is used only when primary node fails.On the other hand, FriendD runs in active active mode by default.In this case, Desk1 and Desk2 are configured as destinations.SourceFriendD instance will send logs to Desk1 and Desk2 simultaneously.Outputs are equally balanced.Please note that it's client-side load balancing.You can configure active standby mode by standby attribute.When you specify standby attribute on the server, it is used as secondary node.In this case, as a node named secondary, we've used when primary node fails.FriendD has some additional features.You can configure weighted load balancing.This configuration means that 60% of outputs are sent to Desk1and 40% are sent to Desk2.If it's Desk1 or Desk2 fails, of course, all outputs are sent to the active server.The another feature is at least one semantics.When require arc response is specified,SourceFriendD weights acknowledgement from the destination.SourceFriendD instance attempts to retransmit until acknowledgement is received.This feature enables more reliable output, but it may affect performance.Let me summarize.Both transport protocols are capable of high availability in active standby mode.However, as we have seen, FriendD's forward protocol has some great features.Active active mode and at least one semantics and weighted load balancing.Next thing is forwarders.Folders are lightweight implementation in order to tailor log files and send logs to log collectors.Folders have only limited features and not plugable, but they use less memory.They are built as one binary.This means less dependency and easy installation.These two folders have little different behavior when multiple destinations are specified.FriendFolder will send duplicated log to all servers.On the other hand, LogStashFolder will send only one server marked as active.I'd like to move on the main topic, how to integrate log collectors with OpenStack.I will introduce three ways to send logs to log collectors.First, you can tailor log files by LocaFriendD or LogStash.In this way, you have to pass many form of log files.The second way is AssistLog.AssistLog is installed by default in most Linux distributions, so you don't need to install agents to servers.The third way is DirectOutput from OsloLog.OsloLog is a logging library used by OpenStack components.We can receive logs without any passing in this way.For the first way, we can tailor log files.This is a logging architecture with localFriendD on each OpenStack nodesand two aggregators to receive logs from them.You can configure high availability in active standby or active active mode, as we have seen.As you know, there are many many log files related to OpenStack.It's troublesome to configure to handle all log files, so wildcards are useful to specify log files for components.Here is an example of tailing log files related to NOVA.Another problem is passing.You have to pass many forms of logs for analysis.Here is an example of passing NOVA APIs log.It's a regular expression here, so this example is a configuration only for NOVA API.log.The second way is RCSlog.In this way, OpenStack components will send logs to localRCSlog.Then localRCSlog will send logs to aggregators by CISlog protocol in TCP or UDP.Logging.conf is a file used to configure logging in detail.Here is an example of logging.conf to send logs to RCSlog in JSON format.Output to RCSlog is configured in the handler section.CISlogHandler is specified here, and CISlogFacility is set to local1 here.In Formatter section, this JSONFormatter will format logs in JSON.It's an example output from JSONFormatter.The CISlog facility is useful to distinguish where does the log come from.In CISlog, you can assign user-specified facilities as from local0 to local7.Logs are tags like CISlog.local0 in friendly.For example, you might assign these facilities, as you can see.Here is an example of RCSlog.conf at OpenStack nodes.CISlog supports high availability in Active Standby mode.Both Flentys and Logstarch have bundled CISlog plugin.There is a difference between them in using protocol.In Flentys CISlog plugin, you need to choose either TCP or UDP,and UDP is used by default.If you want to receive logs reliably, you can specify to use TCP with protocol type attribute.On the other hand, Logstarch will listen on same port with both TCP and UDP.To pass logs in JSON format,you can set the format or codec option like this.The third way is direct output from Oslo log by using FlentHandler.FlentHandler sends logs directly from OpenStack components by forward protocol.Logstarch also can receive forward protocol from FlentHandler.Currently, FlentHandler can send logs only to one host.That's why I introduced localFlentty in this figure.This localFlentty is responsible for buffering and load balancing.Here is the login.conf to use FlentHandler.The FlentHandler is included in FlentLogger package.To handle logs with FlentHandler, logs must be type of dictionary in Python.The FlentHarmatter provides logs as dictionary.It is proposed by us as a blueprint for Oslo log.By using FlentHarmatter,Flentty and Logstarch can receive logs without any passing.This is an example output from FlentHarmatter.You can get logs in this form without any passing.Finally, let me summarize this presentation.In the aspect of configuration,Flentty is suited to handle logs separately.Meanwhile, Logstarch is suited to handle logs aggregately.To achieve highly available log collection,both Flentty and Logstarch are capable of failure detection and fallback.However, Flentty is some great features.Fly-side load balancing,at least one semantics,and weighted load balancing.The last topic was integration with OpenStack.FlentHarmatter enables to get logs as type of dictionary without any passing.We need more reviews for our blueprint,FlentHarmatter of Oslo log.So, that's it.Thank you for your time.We are running our booth over there.I hope you will enjoy robot racing over WebRTC in our booth.Thank you.


And okay, module six, we are going to focus on more metabolomics specific data analysis. And so we're going to cover three topics. One is basic steps in LC-MS spectral processing. And then we are going to focus on functional analysis. So one is for quantitative metabolomics. And this is basically the concentrations we collected yesterday. How we move from the peak tables, from tables of component concentrations, a list of significant compounds to their functions. And the last one is how we actually do the functional analysis for the entire metabolomics direct from LC-MS peaks. So overall that the module five, we teach the statistics and machine learning, which is neutral. You can mostly apply to metabolomics and to adoromics. Now we are going to focus on more metabolomics specific. And although metabolomics have some unique things like on target and the targeted. So my emphasis here is that raw data processing, like is different. So LC-MS spectral processing you are going to learn is unique and we try to make it as smooth automated as easy as possible. But yesterday you used the GC-LC out of the magnet to do that. That's really unique. Now after that, we are going to do the functional analysis. This regarding, I'm having two different topics about quantitative metabolomics and LC-MS on targeted metabolomics, you should feel some similarity. And the concept of the enrichment analysis is actually very similar. And we try to override that. We want to make you feel there's a consistent patterns and you can almost leverage to across. So don't need to always think of this and that, but there's consistent framework. This we got in a data input. So metabolomics here shows some metabolomic experiment. I just mentioned about we cannot do studies at one samples or one replicates. We usually need to do a lot of replicates and cohort. So a lot of samples give you a confidence because at the representative of a population, so you can extrapolate using the P values. And metabolomics also sometimes have the technical replicates just because at her early days and the stability of the machine is not that so high. And we need to have technical replicates. So we need to have like a lot of times early days we talk about replicates and have an average. Now this is seems the last comment, but it's still a lot of times we do have biological replicates and technical replicates, okay. And you see the slides yesterday and we do have targeted quantitative metabolomics and on target sometime we call the global metabolomics. So data generation after data generation on target metabolomics using a calibration curve and internal standard actually get absolute quantification. And you use a constituent table and do the pathway analysis, a statistical analysis you get biomarkers and pathways are probably quite similar to what we have done with the gene experiment data. For on target metabolomics there's some slight difference from the peaks and we want to directly using them to find the patterns. Then we try to annotate only those significant peaks because annotating peaks and identify them it takes in some time as it is how to say this is one of the bottlenecks. But today we want to more or less share the similar workflow. So we can almost from peaks directly to the functions without doing an extra step. So you will feel it with this after we finish this module. So metabolism analysis designed to work for almost the most common type of data matrices, okay. And for targeting metabolomics you generate a constituent table you can direct upload. And sometime you need to add in some labels if you don't have label for MR and it's that's an on target on MR you can do a spectral binning. You can actually use them using some algorithm to chop into individual things. And for LCMS or LCMS metabolomics data and you can upload either peak list you use your vendor provided software or some proprietary software or what software you would like to do to use and upload the peak list and you can sometime manually annotate some of them. So it's done to really matter. The next one is you can also upload a raw structure. So this is a really on target and the format must be in a common format or open public format. MZML, MZXML or MZ data. So we don't support in the proprietary just because they are proprietary. So now we are starting LCMS raw structure with data processing. And yesterday David already mentioned about what the LCMS structure looks like. It is very large and noisy. So if you do use a high-revolution ones you actually can get a gig bytes of size for the LC high-revolution ones. So it's quite unnecessary if you need to upload the data like this and do the processing. And what we need to do for LCMS structure is actually we need to find the MS features. And this feature is usually defined by their M2Z results and the retention time and intensity. And you can see on the left side you do see each peak actually have this two dimensional coordinate which is their tentative ID or tag. You have this return time, have this M2Z ratio and this is really the unit for our untarget data analysis. But keep in mind that each component can give rise to multiple mass signals. So David mentioned about neutral loss and edX and isotopes. So they are actually all very informative. We can use these signals actually to do the data compound annotation. And if hope for next year we are going to give you the MS2 and how do we leverage this to do a compound annotation. So overall that all of this is called MS features. They are not noises. They are useful for doing a compound annotation and the functional analysis. So the purpose of processing a raw MS data is to identify, quantify and align all these MS features and across all the samples. And we would like to get a table of these features and index by their retention time and Z value with their quantitative information for subsequent statistical analysis. The quantitative information here we're talking about the peak height or peak area. We do understand they are not absolute quantification but within the same equipment, within the same batch, they are actually comparable. So they do suffer from some matrix effect but overall that if we talk about they are similar matrix, same type of samples running through the same machine, they are comparable and the statistics can be performed robustly. So it's just another compare across different studies across different locations, across different machines. That's a limitation but overall that within the same machine, within the same matrix running through the, like same day, it should be very stable. Nowadays the machine is actually quite robust and now you with some internal standard that you should be able to achieve a stability of the spectrum. If that's not and you need to calibrate your machine. So a profile centroid. So I think David mentioned it, you all will probably going to cover a bit on this is we don't upload a raw spectra to the metabolism list. Also the raw spectra generated from machine is called a profile and we need to centroid. So centroid is really convert the MS data into a centroid mode to condense the Gaussian profile into a centroid. And how do we do that? We use a protein with that. So you must, if you deal with the raw data, you need to install it and do the conversion. So this is a very versatile, you knew why they use in proteomics and metabolomics. So we are not going to ask you to do that because our data is all open public format. So if you really need that you should download that, which is quite commonly used. Now what are the options to do that raw spectra processing? And here's several most popular one, all three, XMS and probably all this one. And it was first released 2006 and then there's new ones, but MZ mine now is version three and MS style and open MS. Open MS is originally developed for the proteomics, but now it is adapt to metabolomics also. So all of this can process the raw spectra. We are going to focus on XMS. Why is that metabolomics? Actually the underlying algorithm is based on XMS. We also do a lot of the evaluations on different tools. We do feel XMS is very robust and efficient and across a different platforms both high-revolution and low-revolution is okay. And we also closely follow the development of this tools. We have a lot of optimizations and parameters. So we are very comfortable to offer this just because we use it internally a lot. So also XMS have a largest community, have a lot of the helps and you can use for LCMS and MS and MS multi-reaction monitoring isotope labeling. So it's a very, very versatile. And the R package is well-documented and yeah, it's quite handy and you can modify to a different purpose. So if you go to XMS and see how they design originally and this is a overall high-level flow, you're reading the raw MS data. As we are talking about the MZML or CDF and you do the peak detection and peak detection is for individual samples. Now we need to do a peak matching and across the different samples because this could be a shift in the retention time. After that, you need to do some retention time correction and alignment. So make sure that peaks generated by same compounds will be compared across, right? If you miss that, then basically you're comparing apple versus orange. So peak alignment is quite a peak alignment and peak feeling. So after you align the peaks, you'll notice some peaks actually presented across a lot of samples but missing other samples, so you should guess. That's peaks probably also present. So you can actually ask the algorithm to zoom in to that region, try to extract that peaks. So overall, that this peak detection, peak alignment and peak feeling is iterative a few times to get a good result. Overall, that it performed well and after that you get a peak intensity table and we can do peak statistical analysis and directed from that. Of course, we can also do an annotation from that result. So the most important algorithms in XMS is called the center wave. There's some other algorithms for peak detection called the matched filter, which is designed for low resolution spectrum. But the center wave is really at least at the moment when I think 2010, the design is for the high resolution. I think nowadays our resolution much higher than that time. I think that's for top probably in mind designed for that. So the center wave algorithm actually working very well for at the time and they consider a lot of the factors and the peak height shape and how to define signals. And overall, they have many, I think, 30 or 40 different parameters overall to tune to find the best performing combinations. And this part is actually scared a lot of people away. And at the early days, we're doing this metabolomic workshop and this part is actually most, most challenging. And just because I myself, I can write all the code, understand it, but I just don't understand why you choose that parameter because it's dependent on data and dependent on the machine. And the different machine, different data and have different, even people have different preferences. So the issue for this is the XMS group from the scripts, they actually have a default for different type of machine like a tough orbit trap, they have different default. But we also found that even default doesn't work across same type of machine because the sample also important and parameter column also important. So overall, the best way to do is actually make it automated. We automated the whole thing based on your data, based on the specific starting from your sample instrument default, then we optimize based on data. And this is actually you are going to use as we want to be auto optimization workflow. So you upload the data and they have a very high level of defined what's instrument type or what's positive naked mode or stuff. And we will do optimization and get the best parameter to get it out rather than you rather than ask you to specify parameter, you have no idea about, actually most people will find it hard to do and you need to test multi times. But as I mentioned, when you talk about a try and error and repeat and machine can do it very well. And so how do we actually do that? We need to find that the signals that we believe is more likely to be true. And we want to pick up the signals and pick up these peaks and try to evaluate whether this kind of set of parameters get the peaks that looks like a real, right? And we also do not want to use the whole spectrum because LCMS high-revolution ones, you see most places don't have signal, they are flat, they basically have a lot of noises. What we would like to do is just using some regions randomly but we focus on the regions that contain real signals and use this signal to change the algorithm to get the parameters. So you can see here, we show that we found out the regions that contain signals and we just extract them. And you can see at a high level they contain signal but in between is both high and low signals will be there. So that's sufficient. We don't use the whole spectrum because you are including a lot of the baseline noises. So we just choose the regions that contain the real signals. Then we start to train. How do we find the best combination of the values for these parameters? So there's a lot of the parameters to train and I think we have choose the most important eight. And you can see it's peak width, peak height and the difference and signal to noise threshold and a lot of several other things we found the most meaningful. And we try to update the different values from the default. Then we computed the quality scores. You can see this quality score, the QS, which looks scary but what they try to do is that make sure this value is being maximized when you choose the values. What they try to maximize is one is a relative reliable peak ratio which is identified by the isotopes. So there's a lot of false positives or noise peaks. David mentioned about if we really try to clean the untaggined tableon peaks from like 12,000 all the way we can get to 2,500 or even a few thousand. So almost the majority of peaks will be removed. So they are really the noise. So that's because one of the things that's a lot of region contains that just based on noise.  One is they have isotopes. They have isotopic peaks. That means they are more likely to be true. This is very clear. The other one is Gaussian peak ratio. So the peak real peaks have the Gaussian peak Gaussian shape. So it's a very tried and we know as a if you run this that you know that's that's reliable in identifier. The other one is the coefficient that you should be able to have a group of features. So if we found this is a true statement they should be have a stable group features. So more or less we give different weight and we want to make sure it's a reliable not a random features that appear across different acoustic samples or broad samples and a Gaussian shape have this isotopic. So emphasize this one. Once they have a higher value then we think that's the best parameter is set. Okay. So we use this just empirical. Approaches basically we select the most meaningful regions. We select most the set of the parameters and we select the most meaningful criteria and we optimize it. We just want to see how good they could be. And we compared with every other available optimization algorithms. There's definitely a lot to try to do the same thing. And there's OVET, there's IPO, there's Opti-LCMS which is the metabolism part of metabolism. This is called auto tuner. So we can see that so I can tell you IPO is the one when we developed, we use it it could run for weeks and without give you result because you use the whole structure and try one and one and really they want to give you the best result but practically it just cannot finish because the high-revolution takes a lot of time and so they just stuck. So we want to have a reasonable solution and still get the best result. Overall, that we did the optimization we tried our implementer algorithms. We tested on a simple mixture and this is the standard. This is IBD Realsyme, where they are from this collecting using obitrap. So we can see our approach actually quite similar to just not, you see that you should notice here's days and what we can do is a minutes, okay? We can just a few minutes, we can get things done and we can see the result using XMS without auto tuning and with optimizers. So we can get actually better high-quality peaks with more isotopes, with better more attacks with more formula assigned. So overall that we found out the captures better signals and the more real peaks. So we actually doing this multiple times most likely we get the same result like this. Sometime it like 10% better, we can get sometime 20% better. So it's feels as working very well. So we are very confident and to put it through our website, of course you can do the default and you can do the manual tuning if you know what you're doing. But if you don't know and like most people just let algorithm do the best and they are really good at it. So today you are going to the lab and try to upload the not upload try to use a building example data and to run through the process and a few other result. So the overall that we want to get a result directly into like a PCA, you can see the patterns. So and you can see some signal peaks and you actually see the each features if you click and what's the TIC looks like and what the peaks actually look raw peak at that across different groups. And this is a box plot summarizing different groups.  So this is a really if you are running an instrument and you know this one is make you feel comfortable but for most of us I don't think we need to get very detailed under the future level but the thing that what we try to design a type of analysis is that we hide the complexity but we don't but if you want to see it, you can see it overall that you can just get a result and we do a lot of the quality checking from behind but everything is there and you can see this you can see the visualization you can see the R code you can see the pyramid. So once we get MS features across samples what we can do is basically you open a new door and suddenly you can do almost everything. Okay, you have that data table contain peaks you can do statistical analysis which is independent about the concentrations of all the peaks you can actually do some biomarker analysis of course this biomarker won't be compound it will be related to some peaks features you need to spend more time to identify what it is and you can do a functional analysis and actually you can do a pathway analysis because nowadays if you high-resolution peaks you can directly map into the pathways and actually with good accuracy and so basically up to this step you will have a lot to do and without spending years to identify all the compounds you're almost very close to writing your papers you do need to do some slightly more like MS-MS to identify a few more compounds but the global or un-typing metabolomics with the current tools and functions you're getting very smooth and streamlined. So what I would like to just mention is that there's a new generation of the spectrum processing called ASSARI this is based on Python and we are actually evaluating this and this tool seems 10 times faster I think and the result is quite similar to XMS and probably we are going to put it there in the future but we still need to understand how we make sure they are performed very well across different platforms. So far it's designed for the OBJAP so the feeling is that if you're doing an RSIK and you know there's algorithm called Clisto before that if you're doing RSIK you're running in a supercomputer or put a lot of memories but if you use Clisto you can run it on a laptop and quickly and it's amazing and hard to believe you can do it but the result is almost the same as using a heavyweight higher the algorithm called a high set or top hat and the thing is just all about how you program the algorithm and get some redundancy memory intensive gone. So I think that ASSARI will be published soon and so far it's in bio archive so if you're really eager and hard to call and you are welcome to read it the same as them they are using a lot of the high-resolution features to simplify the analysis. We don't have a question. Okay, good. So no more questions, let's move on. So we have a peak and we have compounds and we want to understand the functions. So functions and what is functions? So this is something here we show as a tag map because yesterday we showed SMPDB small common databases. So we talk about functions. We always think about pathways, talk about biology. So this is almost equivalent. So how do we usually get there is that we need to link our individual peaks and the compounds to their groups. The groups is about metabolite set or gene set which in our pathways or joint pathways or networks. So once we see they're connected they are working together doing something we know they are doing a function. And so how do we testing whether this group's active and we use a process called enrichment analysis. So enrichment analysis is really tried very intuitive. It just test whether these groups biological and meaningful groups or metabolites or genes that has significantly enriched or significantly reduced in your data, okay? This is exactly what we would like them to do but we could give the name called the enrichment analysis. And what do we mean by biology meaningful? And we'll be mentioned about pathways but as David mentioned we have like a quarter million metabolites but we only have much less number of pathways. So we need to really expand the functions beyond pathways. So what are the possibilities? We can have some signatures that are associated with disease signature database. We also have some SNF associated the metabolites. So we know if you change mutate some SNFs or associated with a lot of other compounds changes so that means they are related. So these libraries they are biological meaningful so they are not as clear as how pathways exactly but they are grouped we know they are correlated so they are a meaningful. So that's why one of the reasons why metabolites was used quite a lot is that we define a lot of a lot of the functions manually and curated from HMDB, from SMPDB, from CAG, from drug bank, from blood, urine, CSF, fecal. And we also collect from a genetic studies they found a lot of the metabolites associated with SNPs and we also know some of the chemical related subclass, main class. So and all of this is meaningful. They help you refine your hypothesis to a more narrow scope and to further doing tests. So this part here we continue this update. So we need to define the functions. The defined function cannot be do it manually. So this is a library is quite taken a lot of our time. I know they were doing this database maintenance update quite a effort. So we just make it more computable. We direct the quality from the underlying database to do the testing. So you can see below there's options that only use metabolite sets continue at least two inches. And this is more or less quite unique to a metabolomics. Why is that? We talk about a metabolite set. We talk about functions. We talk about groups. So we usually don't talk about single individual compounds. So the more groups that you have and the more confidence this is function rather individual compounds. Unfortunately metabolomics is still a coverage of the issue and a lot of the metabolites set that contains very few metabolites. So that's the things you need to pay attention to is that the functions when you define you need to see how many hits. If you have more and it means that functions more likely to be real. If you have very few, that means you don't know because metabolite can involve the multifunction. If these functions really have one or two at the definition the boundary is not that clear. So this is not compared to gene set. Gene set is much more have high coverage. They have 20, but metabolites. So most of them have less number. So here that the figures just shows how we think about functions. So functions are coordinated changes. So this is intuitively we think functions are carried out by a group of metabolites, a group of genes. If you do that, they must talk to each other. Let's go out and watch movie together and play soccer. So being able to do that, they must do it and have a patterns not random. So they can be both on the top and left up-regulated or down-regulated, all significant changes. So here I get a lot of questions people ask here should all the metabolites be up-regulated and all down-regulated? My answer is it depends. I also think a lot of metabolites it could be product and a substitute. So it's significantly changed. So direction in metabolomics, I don't think it's that meaningful. So you just use, if you use the t-test score, try to use the absolute value. p-value is fine, p-value is not direction. So on gene expression, sometimes it's meaningful both up and down for metabolomics. I think directionality is not that quite meaningful. So just put it on hand. So here is that we show actually from metabolomics you can see the changes sometimes so strong and the features especially some of the peaks and general from same compounds, that one must be similarly changed high. So if multiple peaks correspond in the same compounds so they must be higher or lower together because that's the same compound and that's sometimes how passed to annotated peaks they must change together. Now we're talking about functions and we talk about metabolites set. We talk about pathways and the defined functions. Now we need to evaluate how these groups of functions of this enriched. So I mentioned this about how they do the enrichment and hopefully in one direction but we don't talk about direct and we talk about they change it, okay? And compared to the random as long as it's not random they both either up and bottom it's all fine metabolomics. And if you want to do the enrichment analysis in the statistics you already have two widely used algorithm. One is called overrepresentation analysis and like a fish exact test or hypergeometric test. So if you don't know, you can Google it. So it's a very simple stats but it's very robust. So overrepresentation analysis and they start from the significant compounds and using a cutoff. So because if you want to be tested first you need to define what are the significant compounds? And you can use 0.05, you can use 0.1 as long as you think that the meaningful groups to test. And also I can let you know you can always test the enrichment for any patterns not just significant ones. You want to test a group of co-regulated genes or genes of metabolites change similarly. So you can just manually select that and define what functions enriched, okay? I'm just saying here most people are testing a significant compound based on P values but you can test any compounds. If you see this group of 10 compounds as changes together both higher both low. This you can test what function involved, what function enriched. It's not related to statistical test, it's related to what you want to test for. If you want to find out this group of genes or compounds you can always use this group to do the enrichment analysis. So you define it based on T test, you can define it based on classroom, okay? The other one is called a gene set enrichment analysis. This is quite a common genomics or transportomics. So this one is you use a complete ranked compound list and it's cattle free. So in metabolomics you can talk about the metabolite set enrichment analysis because we use metabolite set but input will be ranked compound list as not a significant ones. What they try to test whether the top or bottom is highly enriched for certain functions. Why did we do this is because they think when we do a significant cattle whatever we are doing, that's arbitrary, okay? And you use 0.1, you use 0.05, you 0.01, you can potentially get different results. What they use as gene set or just yes, they try to avoid this, you use a complete rank, just want to see the top or bottom whether there's some themes enriched. So it's cattle free and it seems to be more in, more policy, less subjective but in reality they are complementary, okay? I think both of them is useful, okay? Cattle free and rely on cattle is all useful. So no need to just choose, I have to do this, not doing that. So functional analysis metabolomics, we use a lot what we learned from transcriptomics and if you're doing a compound concentration you're almost doing some similar like a gene expression profiling and you can directly upload the compound concentration table and do enrichment analysis against the pathways or gene metabolocet and to test whether the conditions are significantly changed under the conditions. So it's really smooth and streamlined. Slightly more challenges on target metabolomics. How can we do that? One reason that we cannot direct the mapping peaks to the pathways, right? This is because we can do the genes because gene can map into the gene set then based on names and targeted metabolomics can match the metabolite set based on names but we cannot match in peaks to that pathways because the pathways defined by metabolites and pathway not defined peaks, can we do that? So this is part is puzzling a lot of people and they think that is the main challenge and we will tell you we can actually get around that easily with high-revolution mass spec result. And here we just focus on targeted metabolomics for this session and next one we focus on targeted how we do that. So in metabolite analyst we have actually three different modes to do the enrichment analysis or... And so we follow the one is if you give a list of metabolite names we just do the regular over-representation analysis. So this list of names could be generated from the T test and over could be generated from your cluster in a result you want how these compounds are involved co-regulated. The other one is called SSP it's a list of metabolite names plus concentration data and this one is unique to targeted metabolomics because you can compare your concentration against the reference standard which is like a clinical chemistry defined by the textbook you can compare to that. So this is really make it how do you diagnose it using glucose diagnose diabetes because there's a reference standard if your glucose is married absolute concentration is the stuff you can do this diagnosis it's not possible with on target, okay? And then the last one is called the quantitative enrichment analysis QEA which is similar to GSEA we want to directly upload the table and you don't need to do any cut-off we just use global test algorithms which is similarly designed GSEA but is more sensitive and so here's the stuff how you go to metabolomics one is over-representation analysis single sum of profiling and quantitative enrichment analysis. So the over-representation analysis is you need to do some results you select the interest compounds and it's going to compare to the metabolized libraries and do an enrichment analysis using a hypergeometric test and you get the ranked pathways and if you're doing a single sample profiling you're going to compare with the normal references and this is from HMDB and you can get some normal ranges so if you're really high and you think it's abnormal and if it's a signature way in a path with several common way in a path of change and you will detect the pathways so this is the same thing QGA is a very streamlined you upload a compound-concentrated table it will assess the changes and test with a metabolized library and give you results so overall that we put all the different input and we have algorithms and they will do the comparison with the metabolized library and give you a report. So you can upload a compound list and you will need to also upload a compound this is for over-reference analysis if you do a single sample profiling you need to list plus the concentration because the reference standard is you have a unit so you need to make sure the unit is the same and here it shows this microurine normalized to creatinine so you really need to have a unit which is absolute concentration with the standard unit so you can compare. So here's that shows if you upload a compound with a concentration and you can compare with what's reported range so you can see here is a lot of literature reported range and some of them you see oh this is high it's low compared to the literature and why it's can this is all the result based on the textbook from the literature mining and the collect by the HMDB and you see that said oh this is high and why it's high is because we have 10 studies they are talking about this range each individual study different regions different cohort they have different converters interval have different mean values so there are a lot of difference but yours if you marry like this you're very sure it's very high so whether there's a measurement around or actually disease markers it's all clear here but what I'm saying is if you want to do such things you need to really have a higher extreme values to see this a lot of times you will see you're actually part of this probably over life with something with one some studies and different from other studies so it really require every study reporting and using a similar technology and all absolute quantification so it really require a lot of the community effort so from the original analysis we do the overall quantification analysis of global GSEA test and we can get the pathways and you can see the pathways going to be ranked from the top to the bottom basically the top is the most significant and they also have the color based on probably most the significant photo change average photo change and you just let you to choose but you need to understand that a lot of pathways they share the same compounds so some compounds oh these two pathways both significant and report them but you can see that the underlying compound the same set of underlying compounds it's same almost you're talking the same thing but you reported it twice one for Partway A, one for Partway B so this is quite a common another visual addition we show is called enrichment map what they try to do is put different pathways links to each other if the pathway share a lot of common compounds they will be linked so you know overall that this to give you a separate the first one to claim but it could hide some information and the second one is slightly more informative because they tell you which pathway is more correlated you can actually click on a particular pathway to show that who is the common underlying compounds so if you choose the biomarkers they will help you prioritize some so people keep asking what's the difference between a metabolite setting rich analysis and pathway analysis so the enrichment analysis part is the same but pathway have actual information it's called structures so if we know the structures we actually can get actual confidence about what the pathway changed so for example if this is a graphics and we see the structure like this and we know if the compound is changing at this edge and versus the change at the center at here and the red or blue nodes they have different consequence so if you change this one and it's not called this metabolized enzymes or genes stuff you have a big influence on the whole pathway so this is the more things we feel intuitive they're supposed to give you some feelings about whether this pathway is enriched if the same compound one pathway have this compound changed one pathway have this compound changed and you should really know that this one is more likely to change the whole pathway this will probably downstream and not affect the whole pathway so this is really intuitive information we combine how do we measure them? that graph theory they have a degree centrality they have between a centrality so the high degree and this means a hub and the high between us they mean the bottleneck so they are all important measures so what we want to do is that we didn't give you a combined scores together just to tell you what to do what we have done is make it transparent just give you a new dimension here is your key values we just rank them from top to bottom it's most significant it will be ranked on the top but we'll give you an actual dimension the pathway impact so if the change in the metabolites is more likely in the most significant the strategically important locations we will be spreading out to this group so if you see the metabolite both significant and they have a lot of changes this signal change is also important locations you will find out the compounds this pathway is more likely to be changed so here if you click this compound click this node and you can see a lot of compounds significant and a lot of the compounds actually in the location that seems upstream and in the center so more likely this pathway is affected so the part is that we pathway give you actual information to let you feel whether this is based on structure and you can click it to see how many compounds actually involved but this one visualizing help you know not only as a group you also know the location upstream downstream center or peripheral so this is a pathway information give you more more confidence about biology because you know the structure and the final is that report generation so a lot of people use the tools and they want to have saved the whole process and we do have PDF report generation and embedded results so if you get any results and you want to go you go to the last page download you can see general report or general for you but the thing that once a general report is static you cannot label it and you it's not interactive interactive it's online okay once a general it's become static this is unfortunate but overall that you have to accept the offline is more static how you do it you actually put this in a PowerPoint start manually label them before you forget so in online you mouse over you'll know what name is up so you want to put a publication you put it in a PowerPoint or a game and label this a few pathways we can actually label everything in the community but you won't be able to read it so the ways which one you want to advertise it to label it it's not too hard to do that now we've covered how we do it for the target metabolomics and can we do the same way to untarget metabolomics so the answer is yes so we try the hard and for the community almost 10 years I think nowadays communities start accepting we can actually do the same things using for untarget metabolomics assumptions that you need to use high-revolution LCMS so if we don't do it what we did earlier days before we just do the functions using untarget metabolomics your collecting spectra you do the cleaning we do the peaking alignment and yeah if you do a lot of things it takes quite a long and large data and even peak annotation it takes weeks to month if you really want to using standard to get it it takes years okay up to identify most of them you can actually understand the functions so this part is actually the most time-consuming and it requires a lot of deep knowledge about mass spec so it really prevents people from using LCMS based metabolomics it really slow down things and if we talk about genomics RNA-seq everybody understand ATGC but nobody understand peaks and from peaks to compounds it really take a lot of analytical commission knowledge but how can we accelerate that and make it easier so what we think is with high relative peak we can actually do a putative annotation directly in the peak a high relative MS potentially use some retention time to direct the mapping to the pathways and potential compounds we can do that with errors yes so so what we realize this is published by my collaborative Dr. Shu Zhao Li from Now It's in Jackson Laboratory what they found the concept is like GSE if we do see a function changes the function must be coordinated so if we map in the peaks to that to that compounds in the pathways we must have a pattern okay it's not a random so such pattern will jointly point out to that pathways we are looking for so individual compound if you imagine peaks to that compounds and to that pathways you do have as long as the area is random and if you have high relative peaks majority still point to the right direction so this is you can do a simulation and we can see clearly that's true and actually we see we did a recently we did a simulation study we found out as long as 13% 25 to 30% of the peaks you annotated right remaining is random we can almost 100% we call that pathways no problem at all so we do have a certain percentage of accurate peak to compound stuff even like 13% and we can find that pathway accurately so this is quite quite quite as comforting because we use the tools and we try to do simulation and we found out exactly what we thought it should work and I scheduled it about 30% okay and all the remaining is random so this is how MamiChop works so the algorithm developed by Shujali is called MamiChop and in the table analyst we call it the peak setting original analysis called PSEA because exactly we're using the peak set to define all pathways but if you respect the original it's called MamiChop and so what we try to do is like similarly we have done with empirical p-value calculating so we just use this raw original data mapping to the possible pathways and say which pathways are enriched okay now we actually randomly draw from the whole peak list whole peak list same size okay same size not just using a significant one here we just see if we use a cutoff like six peaks like eight peaks here and we test which pathways are enriched now we don't do that this is the original one we test and do the pathway enrichment analysis again and again so just randomly do it again and again and eventually we'll see the original one how robust it is so we get a p-value overall data the first one is better you use a high rate of peak you'll be able to identify that enriched pathway no problem what will the remaining ones actually give you a confidence how randomly you can get that one okay so the first one already gave the answer the remaining ones overall it requires higher resolution mass spectrometry so we're talking about the whole peaks probably 3,000 2500 higher if you have peaks very low and the confidence won't be that good and we also talk about if we talk about 3000 peaks how much peaks to identify the pathway we talk about 10% we talk about 300 peaks you don't need to care about p-values you just talk about ranking based on top 10% we'll try to identify what's the most likely peaks pathways involved you cannot select like here we show it's 8 it's not 8 peaks it had to be meaningful number of the whole higher resolution peaks like 3000 you have significant ones need to be several hundred to identify them using on target metabolomics all we need is multiple groups multiple information higher resolution ones so this is the last part shows that the things we just validated MamiChalk against the several other tools one called GSEA we just basically adapting GSEA approach see how good they are compared MamiChalk actually MamiChalk is much more sensitive just really a lot of the things designed specifically from metabolomics make it very powerful and sensitive results so we have about like 30% 95% 98% all the parts we perturbed we know it's changed because we did a simulation we know the ground choose we can identify them just about 30% we get it so this is quite satisfying so we know actually that using high resolution on target metabolomics we can get functions very accurately but we can get the function accurately so this is a functional analysis page and modules you are going to click here and upload you are picking test the tables or rank the peak list okay it's completely the peak list not just significant ones and so one of the things we always get people's questions that they try and they didn't read the tutorials they didn't attend workshops they didn't get a good result and they tell us to help them and actually I'm just repeating again again high resolution MS is really critical and you get a very accurate result and need to complete the peak list and the people you have total just a few hundred peaks and we assume that's just only significant peaks and the function is groups behavior so we need multiple peaks talking about the same peaks same pathways to get that confidence so this is not a big deal if you're really using all the chat bell to get there and so get a result you actually see the potential hits what's the what's the result look like you can see the result quite similar to the target so we actually need to do it to be consistent okay and in between there's some possibilities fuzzy mapping but it's all hidden under the interface you don't need to know what we would like you to know is the result is robust and it's accurate you can definitely find where it's mapped but it's we do a lot of the simulation mapping so here's some interpretation of the result so p-values we have to be Fisher and Gamma Gamma is more from the distribution from permutation what I would like to see is the function ranking is quite accurate it's just p-values which are more accurate and fishing you should trust Gamma you can trust but because of public server we're only testing 100 times probably better so that's sometimes the Gamma distribution suppose more robust but it's dependent on the permutation time server only give 100 or 200 so this is GSE GSE actually can see both up and down you get a result you have more hits you see like this so you can actually see from here what's underlying compounds from the from the match how many annotations like isotopes and add-ups and see this so if you are really understanding LCMS and this one also makes you happy because you know that's likely to be real so here's another one is that you can actually see your peaks as hit map people and this hit map people allow you actually high rocker classroom we'll show up on the center this is your focus view it's the overall view and if you see the real whole peak it will be much longer but here just show how you can see like from these patterns and show up in the center it's a focus now you can test using MamiChalk not this here is not significant peaks it's just peaks with interesting pattern you would like to know and you will test which path is enriched so we'll just let you know that all of this very free really to do any of the patterns from the peaks and get the functions so the annotation a lot of this you see the annotation if you click these peaks you'll see some annotations right beside this individual peaks and adax isotopes and stuff it will tell you this is most likely to be it's not 100% but it will try the best what it most likely to be once you see this one hit by so many different things yes individual could be wrong they are correct so this is the instance so the next one we are going to ready for the lab almost so the lab session going to be in 2 for 2 hours we will right so this is we are on a break now


Good morning everyone, Basel Chapman. This is the 9 o'clock Tiger Financial News Network Market Update and we're looking at this on Thursday, the 23rd of March, $32,300 on the Dow futures, up 42 S&P futures. Let me just go to this at the moment. I'm going to go to the continuous contract. It's up 18 at $39.88. The reason why I'm talking about this, because you can see these different, look at the weekly chart of the E-mini, it's above the inside track repellent zone. It's at a 38.2, 382.0 extension to the downside support, but it needs a lot more. It needs to get by Monday or Tuesday of next week. It needs to be trading closer to the 4,015, to the 4,030 area to really say, hey, yesterday was just an emotional aberration to the downside. We're looking at the NQ, which is the E-mini, NASDAQ continuous contract, up 117,000, 824. This is a way better chart, and I was just mentioning a moment ago, I did my show, recorded a little at 8 to 9, it'll be replayed at 10. If the futures are able to hold for about an hour above 12,960 in the next two days, by husband by Friday, there's a real good chance it could test the high of yesterday. You do not want to see a close below yesterday's low of 12,000 and 678. The other thing is the IWM, that's the Russell 2000 trading up 22 cents, 171.86. Not a good looking chart. I'd like everything in sync, and we're getting a lot of things out of sync, because the XLF, the financials, still struggling like crazy, which is really helping gold. Gold is up sharp, it's up 25 bucks up in the higher range, doing very nicely. The dollar is struggling, the dollar is down 27 ticks at 103.29, looking at the bonds. This is going to be also fascinating because the bonds are saying down 730 seconds at 132 and 2430 seconds. We're in a range. We haven't broken up or down right now. We sort of, yields are stuck in a range. And if you're looking at Crude Oil trying very hard to break to the upside down 53 cents at 70.39, I just think it's stuck in the lower range until Crude Oil can actually trade for a day or two above 72.50. It's just kind of stuck. So as I'm looking at it right now, I want to see for a positive reaction going into Friday's close, the down must be 80 or 90 points higher after two o'clock, the S&P and the Nasdaq need to be up sharply. I don't want to see down 100.