hello everyone welcome back to dentistry and more so today we have a new topic that is fluoride toxicity so far we have covered under fluorides the history of fluorides the system mechanical fluorides the various mechanisms to prevent dental caries by fluorides and do defluration techniques and today we have fluoride toxicity so let's see what is fluoride toxicity in detail so fluoride is commonly known as doubly sword okay we know how a sword look like it has a handle and it has a blade most commonly the blade has only one cutting edge it serves its purpose whatever it is it has commonly one cutting edge but there are swords which has two cutting edge this it is a phrase commonly used in English literature doubly sword because if it goes to the both extremes it create problems if it's become very good this problem if it is becoming very bad that is also problem so in that sense we can say that fluoride is a doubly sword because if it goes very low creates problem if it goes very high if it creates problem fluoride should be at optimal level for to get an ultimate caries production that is one parts per million if it is going very down it gives a chance for creation of dental caries as the protective effect is lost and if it is goes very high around 4 to 5 ppm or 8 ppm it creates fluorosis in either dental or skeletal fluorosis so that's how it is becoming a doubly sword so to keep an optimal level at around one ppm will give us the maximum benefit so it should shouldn't be going very high going very low so that's why it's known as doubly sword so the so the toxicity is broadly classified as acute acute and chronic so don't forget the doubly sword concept of fluoride when it is low and when it is high it causes problem okay so the acute and chronic as we all know acute is sudden chronic is slow slow action acute is very past just like a single ingestion of very large amount of fluoride so that becomes acute you know acute pain and chronic pain acute is sudden pain and chronic is slow throbbing type it's so similarly the fluoride if we consume very large amount in very short of period in a single shot it's known as acute and chronic very slow amounts over a longer period of time so let's see what is acute toxicity so acute toxicity as we mentioned a large amount ingestion in a very short of period or a single shot so the speed and severity of response dependent on the amount of fluoride ingested and the weight and age of the individual so it depends the prognosis or the outcome of the patient dependent on how much fluoride we consumed and weight and age of the individual so most common adverse effect is nausea and the patient may have abdominal cramps in diarrhea and salivation dehydration and thirst so after two to four hours better it is possible if first aid is not administered so the golden hour is first two to four hours so we should do the treatment you improper treatment or the emergency treatment at the first two to four hours so usually death happens if the treatment is not given by cardiac failure or respiratory analysis so that is acute toxicity the symptoms and the golden hour two to four hours so if the death is not happening after 24 hours then the prognosis will be good so we need to learn the two doses here one is certainly lethal dose also known as cld and safely tolerated dose that is std so cld is 32 to 64 milligram per kilogram body weight and safely tolerable tolerated dose is 8 to 16 gram so that is almost one fourth of the cld so that is the easy way to study the cld and std cld is equal to four stds can just see multiply 8 32 with 4 16 64 so certainly the dose is per kilogram body weight if you multiply with 100 you get 3 grams 3.226.4 grams so on average if you consume 5 gram of fluoride that might cause your death so that is certainly the dose so this is one kilogram body weight so safely tolerated dose is 8 to 6 milligrams so on average if patient has 100 kg on average if patient has 70 kg or just take 100 kg so 100 kg body weight needs a 3.226.4 grams because we are converting milligram to gram okay so it becomes 3.226.4 gram of 100 kg body weight person so if a person consumes 3.226.4 gram of fluoride it might leads to death of the person so on an average you can take 5 gram as your lethal dose so if you consume 5 grams of fluorides you might die so that is certainly the dose and safely tolerated dose is one fourth of cld so we can say that if person has 100 kg safely tolerated dose of the body is 0.8 to 1.6 gram so this is milligram and I am talking about gram because I am talking about a person with body weight 100 kg for your easy comparison so we calculated 5 grams here and we can say it is one fourth the safely tolerated dose will be around 1.25 gram can be tolerated by the body so that is cld and std okay so this will be if we calculate we get 5 grams and this will be around 1.25 gram up to 1.25 gram we can tolerate it we can tolerate body can tolerate that amount and if it goes 4 times and it becomes 5 grams around 5 grams patient the person might die so what we do if we face a situation of fluoride toxicity in in our house house or in a clinic or anywhere anywhere around us where a person consumes a large amount of fluoride by mistake or any suicide attempt what we are supposed to do so first try to understand the difference that is we have three types of treatment that is if the amount consumed is less than 5 milligram for body weight and 5 to 15 and more than 15 okay so it is like if person has 100 kg weight okay so this is 0.5 gram becomes 0.5 gram and this is 0.5 to 1.5 gram and this is more than 1.5 gram so we need to understand the amount consumed this is just kilogram per body weight so I am explaining explaining about a person who is having 100 kg okay so you can you can calculate if the person has around 80 70 kg it will be somewhere here 0.4 grams or something like that so if it is very less just 0.5 gram we need to give calcium because calcium binds to this fluoride fluoride is highly electronegative the ion exist in highly electronegative state that is f minus so it will immediately react and bind to the calcium okay so calcium fluoride will be formed because it's very difficult fluoride to stay as an ion so it always exists compound so if we give calcium this fluoride will join with calcium and becomes calcium fluoride so it relieves GA symptoms and there is no need for induced vomiting if the person is consuming around 0.5 gram less than 5 milligram per body weight so what if the consumption is between 5 milligram to 15 milligram per body weight or 0.5 to 1.5 gram okay all calculation I am referring with a person of 100 kg so what we have to do is we have to induce vomiting using any ametic and we have to give the same oral calcium just like milk 5 person calcium gluconide or calcium lactate solution and we should rush him to the hospital for observing for few hours so vomiting oral calcium and taking him to hospital will be the treatment scenario if it is a moderate amount that is 0.5 to 1.5 gram so what if it is very high dose like 1.5 gram so we know lethal dose is 5 gram so we have to take him to the hospital very immediately induce vomiting and we should monitor the cardiac and we should keep IV not orally IV calcium gluconide because absorption absorption should be very fast and supporting measures and sometimes diuretics also should keep so if we don't know the amount consumed it's better to take him to hospital and can assess the symptoms if patient is very okay if person seems to be okay can assume that the amount consumed is mild or moderate person is not properly oriented and person has this um vomiting tendency and dizziness so all the symptoms you can rush him to hospital so these are the various protocols for emergency treatment of fluoride exposure so next is a chronic fluoride toxicity it is commonly there are two types that is one is dental and skater for for so chronic we know it is a chronic consumption or a very longer period of time not immediate consumption and it doesn't need immediate treatment because it happens for a very longer period so dental process we know the tooth optimal amount of fluoride in drinking water is 1 ppm if we are consuming water with more than 1 ppm for a very longer period especially during the tooth development that is the highlight point tooth development that is less than six years old because the second monar will be finishing its mineralization around six years so if person consumes water with fluoride more than 1 ppm after six years there are very less chances of fluorosis because most of the teeth completes its mineralization cycle so if person consumes water more than 1 ppm during the mineralization cycle there are chances of dental fluorosis so most commonly the fluorosis appears as white flecks and choque opaque areas on the enamel okay so most commonly it should be at 1 ppm and if it is going higher and higher the appearance will be changed if it is 2 ppm or 3 ppm that is two or three times greater it will be white flecks or choque opaque areas where if it is going high that is 4 to 5 ppm it become brown and pitted corroded appearance that is severe fluorosis that is about dental fluorosis that is in range 1 to 5 ppm we can say if it is going very high more than 6, 7, 8 ppm our bones also will be affected usually all mineralized structure will be affected but mostly it is visible on the teeth if it is a very milder or the up to 3 to 4 ppm the skeletal structures also will be affected but we cannot make it very obvious because it is bonds we can't see the bonds so the only calcified tissue we can see is our teeth so it is very visible on the teeth so the visible changes that what we can see in our bonds is crippling stage it is very very bad situation because the bonds will be crippled and its shape will be changed so that is the only state where we can see the bone or skeletal fluorosis not like dental fluorosis so it happens only when the fluoride amount is more than 8 ppm so symptoms will be severe pain in the back bones, joints, hips and stiffness and there will be a special syndrome known as knock knee syndrome because usually we can bend our legs and hands only in one side or backward but we can do outward bending of legs and hands that is knock knee syndrome it is very advanced stage of skeletal fluorosis so that's all about dental fluorosis and skeletal fluorosis which is seen in chronic fluoride toxicity and acute fluoride toxicity it requires treatment, imni treatment, various stages I mentioned and the chronic toxicity so fluoride is a double edged sword if it is used at a proper optimal level that is around 1 ppm it gives proper and a good protection against dental caries and if it is moves to the other side if it is very low and if it is very high it becomes a sword like double edged sword like function so fluoride is safe and effective if it is used with that's all about fluoride toxicity so we have completed the fluoride topics so I'll come up with a new topic on my next video thank you for watching


Hey there! Welcome to the Riffamonus channel here on YouTube. This is Code Club. What is Riffamonus, you ask? Well, thanks! It is a play on the word riff. And in music, a riff is kind of like a sample, right? Where you have this theme that is repeated over and over and perhaps in, you know, different musicians or different parts of a song or different parts of a musical. You hear that theme repeated in slightly different ways. And so my motivation when I teach is to largely riff, right? The way you'll see me teach is I'll take code from one context and bring it into a new context and show you can how you can adapt it to get something done that you that you want to achieve, right? And so we see that time and again. And that's ultimately how I and many other people have learned to code. That's in contrast to starting with a blank screen in our studio or wherever. That's not how we generally learn, you know, perhaps people in computer science who are just like special. That's how they learn. But for most of us biologists, that is not how we learn. And that's certainly not how I learned R. So I'm trying to teach you how I learn. Now, what does that have to do with today's episode? Well, there are many places that we can go and get code that we can then riff on, right? We could do a Google search, right? So you could Google, you know, how do I make a density plot in R? And invariably, you'll probably go to a page at Stack Overflow, you'll get someone else's solution, copy it into your instance of our studio, and then adapt it for your problem. Alternatively, another approach that I often use is if I look at the man page, the manual page for a command that I'm interested in, say, like, you know, GM histogram or GM density, at the very bottom are some examples of how to use that command. Well, I've learned a lot by taking that code, plopping it into an R script, and then working with it to achieve what I'm trying to get. Another approach is that perhaps you're reading a paper, and oh my goodness, these people actually posted their code. Well, you can go to that project, find the code, plop it again into your R studio window, and then go to work on it. Sometimes, you know, there's code that's been floating around a lab to make different types of plots or to do different types of statistical processes. So you could grab that code, and again, put it into our studio, and go to town with it. Sometimes, you've been working on a project, and you say, go away for a week or two, or longer, and you come back to your code, and you're thinking, I wish I knew what this code did. In all of these cases, we are left with the question, though, what does it mean, right? What does that code mean? Perhaps the person didn't write in a way that you're used to writing your code. Perhaps you're not sure what you wrote yourself, right? That is the topic of today's episode of Code Club. What are the strategies that you and I can use to better understand the code that we find out there in the wild and that we want to appropriate for our own analysis? In the past week, I've been doing some more work on this project we've been working on here in recent episodes of Code Club. As you recall, we've been developing L2 regularized logistic regression model using the relative abundances of microbial populations in a person's stool sample to predict whether or not they have a screen relevant neoplasia. So instead of screen relevant neoplasia, think SRN or maybe just colon cancer. So I developed these two models. I generated AUC values because we are doing 80-20 splits where we take 80% of the data to train the model and then test it on the held out 20. And so we iterate this say 100 times and for each iteration we get an AUC value, right? So I have 100 AUC values for the model with just the microbiome data and I have 100 AUC values for the microbiome data plus fit results, right? And so one question is, how do we know if the differences in the AUC values are significantly different? Now, this is an important question because I'd like to do some more fine tuning of my models, right? Perhaps I want to change how I do the pre-processing.  Perhaps I'd like to try other types of models, right? Perhaps I'd like to try L1, regularized logistic regression, or perhaps I'd like to try random forest or decision trees or any of these other types of models. Perhaps I'd like to try a different taxonomic level. But I don't have a tool to allow me to compare the distributions of these 100 AUCs from, again, those 80-20 splits to compare the distribution across these different modeling approaches or different sets of parameters or tunings and things like that. So, this is a challenge that I'm not really sure how to tackle. But I remembered that a former postdoc in my lab, Begum, wrote this great paper in Mbio, which you should all be reading. And in there, she took the same data set actually at the OTU level and generated models using six or seven different models for doing machine learning. And one of the goals that she had was to say, you know, we should try multiple models, but then use the model that is significantly better than the other, right? What approach gives us the best model, right? So, you know, perhaps it's random forest, but if logistic regression is better, then we should use that even if it's a simpler modeling approach. I noticed that on her pot, she had stars, and to me, stars mean significance. And so I figured her code must have code for testing significance. Because Begum was so well trained, I might have had something to do with that, I knew that the code for calculating the p-values to say that the comparison was significant or not. So I went to her GitHub repository for that paper, and wouldn't you know she's got nice directory structure that looks a lot like what we have for this project? Hmm, isn't that a coincidence? Anyway, code. That seems like a good place to put code, doesn't it? So, going into code, she's got a couple directories here that I'm not totally sure what they mean, learning old testing. Maybe I'll go with learning because I see something here about figures, and I see that she has a different R script for each of her figures. What an awesome practice! It's easy to figure out, then, where the code is to build each of the figures. Now, if I could only remember what figure it was, where she generated those p-values or showed those p-values. So I went to her paper and found figure two, where these five models were not significantly different from each other, but they were significantly different from the L1 regularized linear SVM and the decision tree. So I get to figure two.R. It is nicely written and laid out with all sorts of comments. Of course, all code could always have more comments. And as I kind of scan through here to look at what's going on, something catches my eye, which is this perm p-value. And if I look to the right here, I then see that she's got the p-values listed as a comment. And so this perm p-value, I'm pretty confident, is the function that I need for my own project, where I could give it the data and then the different conditions that I want to compare. You know, a model using just the genus level data or the model using the genus level data plus the fit result. So now I need to track down where is perm p-value. So if I come back to the top, so I can do a find on perm p-value, and this gets me those executions of it, but it doesn't seem like that function is defined in this script. So the next thing I do to try to find this code again, is to come back up to the top and look at anything that indicates that she's loading a library or some other exterior script. And lo and behold, she is sourcing a file in code learning called functions.R. And I bet it's there. So I'm going to go ahead to back to learning and look at functions.R. And again, I'm going to look for that function perm p-value. And here we go at line 150, down to 167 is this perm p-value function where you give it the data, the two different model names or conditions that we might want to compare, as well as the code to generate it. There's not a lot of comments in here telling me what's going on, but I do see it outputs a p-value. And so I think this looks pretty good. One thing I notice oddly is that there's install packages mosaic library mosaic and it's using a mosaic function in here. So I'm not familiar with the mosaic package. I'm not totally sure what's going on with that. It really would be nice to have some example data and output that I could use to run through perm p-value to understand what was going on. Since this project was finished, Begum has gone on and gotten an awesome bioinformatics job at Merck. See what kind of jobs you can get if you engage in reproducible research. Since Begum left, I have hired another postdoc named Dr. Courtney Armour and Courtney's kind of inherited projects similar to what Begum was working on. And I knew that she had code to compare different distributions of AUC values. And I thought maybe she could help walk me through this code. And what she told me was that Begum actually gave her a zipped directory. And in that directory was an RStudio project code example data and example output. That would be wonderful for helping me walk through what this trunk of code is doing. Now I have Begum's code. I have example data from Courtney and example output. I've got somebody that's actually used the code, right? So these are all great resources to helping me understand what the code is doing and what the output looks like. I can then take that hopefully and adapt it to my code to compare these two sets of AUC values. My goal for this episode is to show you the strategies that I use to better understand code that's new to me. And we're going to do that by going through this code that I got from Begum and Courtney. Go ahead down in the comments below. Let me know what kind of strategies do you use to try to better understand the code that other people have written, even if that other person maybe was used six months ago. Here I am in RStudio. I've got Courtney's project opened up that she got from Begum. You'll see that there are four files here. So there's plot AUC with stats, p-value condition, and the example data CSV. And so this is the type of output that we're getting from MECROPE ML. I see I've got the CV metric that's a cross validation and the AUC. So I'm most interested in the AUC. And then there's a condition column at the very end. And so as I kind of scroll through here, so there's condition A, I kind of get condition B. And let me just go to the bottom and condition C. So it looks like there's three different conditions. So I'm only interested in making two comparisons between A and B, say. So we'll hold on to that. But let's go ahead and look at these two R scripts and see if we can understand what exactly is going on. So they're using some packages in here like ggpubr that I had to install, as well as mosaic, right? And so as we kind of look through here, we can again read through and try to understand what's going on. And so it looks like we're reading in p-values by condition.csv. I don't see that file. I'm not quite sure where that's coming from. But there is a data read csv. But this comes after that file was generated. So that's a little bit odd. And so there's a there's a plotting step here where it seems to generate a couple of figures. So maybe this is actually run after, right? So this is plot AUC with stats. So maybe we need to get the stats first. So p-value condition.r, aha, there's our function, perm p-value, cond, right? And so then this from line five to 21 is a updated version with clearly more comments than what we had up on Begum's repository. And so now we see example input data, data read csv, that is that example csv file, the three different conditions. And then she's doing something here, I think making a table to fill with the different p-values comparing a to b, b to c, and a to c. So this is some stuff that I don't really care tremendously about. But for now, I'm going to go ahead and source this whole file, make sure that it works, and see what kind of output do I get from this p.table.condition file. So that took a few moments to run. One thing I know about this procedure is that is a permutation based test. And so as I kind of look back up here, I see that it's doing something 10,000 times, right? I think it's doing a shuffle. Yeah. So here we have shuffle of those conditions to kind of make a null model that it's then comparing a mean to. So anyway, we'll hold on to that. And so that's why it took a little bit of time to run. But again, if I look at p table.condition, I get out three comparisons between the three different conditions, as well as their p value. And so that all is interesting. Again, I'm only interested in one comparison. So why don't we go ahead and kind of trace the logic as we go through the data, right? And so we've got data. And so I'll go ahead and run this data. And so again, we see basically that TSV file that we had or CSV value that we had before, we've got these three conditions. And then as it comes through here, maybe I'll hold off on kind of running all this because I'm not sure I totally care about it all. I think up through this step, the select, I think it's making, if I do p dot table dot condition, it's making a table that's got two columns for each of the two conditions. And then it's doing a group buy and summarize to then get the p value for each comparison, right? And so then it's calling perm p value conned with condition one and condition two. So I'm going to come back up to the top. And I'm going to create a data filter for condition not equal to condition C. And so this then gives me a data frame with 200 rows that I can then feed into perm p value conned. I'll go ahead and make it simple and do a select on condition and AUC. And so now I get this two column data frame that's being fed in as data into perm p value conned. And so I'll go ahead and call this data. And my cond name one will be condition a condition a and then cond name two will be cond name B, right? So I'll go ahead and load both of those. And so these are the values then that I would use inside of this data frame, right? And so again, I guess this is doing what I had already given it up ahead here, right? So it's already it's doing a select and filter for those names. And so this test sub sample will probably be the same as what I had up in data. Okay, so that's fine. And again, we've got comments in here telling us a little bit about what's going on. And so what we're doing is we're quantifying now the absolute value of the difference in mean AUC between the two conditions. This is the observed difference, which is again, OBS. So again, what the test is going to do is it observes, it calculates the observed difference between the two means. So the mean AUC for condition a and the mean AUC for condition B. So that's the observed, right? And then we want to know how extreme is that difference. Then what it does down here in this AUC dot null, is it I think as it says, right, is it's shuffling the condition labels and calculating the difference in mean AUC 10,000 times. So what it's doing is it is taking this data frame, right, or these this AUC column, it's fixing it, but it's randomly changing the condition label in that column, and then calculating the mean for condition a and the mean for condition B, right, that would be the mean, if there was no difference between condition a and condition B, right. And so then it does that 10,000 times to get 10,000 differences in the mean when you shuffle a and B. Then I think what the question is going to be is how does that differ compared to the observed, right. So are those shuffled differences in mean AUC greater than the observed, right. And so if they're greater than or equal to the observed, then we want to count it. And then we can calculate a p value by looking at the fraction that are greater than or equal to the observed difference in mean, and then that returns the p value. Let's look at this AUC dot null line, because there's a few things going on in here that I'm not totally sure about. I'm going to go ahead and add a line break in here. So it doesn't all scroll off. I'm not totally familiar with this do function. I'm going to guess that it's going to do this 10,000 times. Again, that's not something I'm super familiar with. To learn more about it, of course, I could look at the help page, right. So do things repeatedly. So let's look at this. So this looks like it is coming from Mosaic and looking again at the example trying to figure out how do works, do something three times times our norm one. Okay, so it gets a bit confusing if do is coming from the player or mosaic. And so something you'll notice here for the mean function, so they grabbed it from mosaic. And so, you know, it perhaps would have been a little bit more clear to say mosaic colon colon do. So that's something to keep track of. So it's basically saying, do what's after the multiplication sign 10,000 times. So what's happening after the multiplication sign, because that's what's important, right? So then we have diff mosaic mean AUC tilde shuffle condition. So I guess we never quite covered what is mosaic mean doing that's different than a normal mean, right? And so what it seems is that it's taking mosaic mean on AUC tilde typically means as explained by condition and using test subsample. I'm going to go ahead and run part inside of the diff statement. And what we get is condition a condition B, and we get, I think the mean AUC for those two conditions. What I'm curious about though, is if I used dplyr tidy versus type stuff, what I get the same result test subsample, grouped by condition, summarize mean, yeah, on mean AUC. And I get the same values with a little bit of rounding, right? And so then what the difference does is it appears to then calculate the difference between condition a and condition B, abs then is the absolute value of that to give us 0.073. What's going on in the second version of it where we have the shuffle condition. So I'm curious what shuffle condition does, right? So if I look at test subsample, I see I've got condition a, and then probably at the bottom I have condition B. So if I were to do shuffle test subsample dollar sign condition, that is then going to randomize the order of that column, right? And so it is randomizing the label, and then calculating the difference in the mean with that shuffling. If we look at AUC null, it's going to take a moment or two because we're doing 10,000 iterations. And if I look at AUC dot null, I'm going to get all of these permuted values. And so then n equals length AUC null square bracket comma one. So the square bracket is a way to get a value out of a data frame. And so the square bracket comma one means get the first column out of AUC null, which is these values here. And it's actually saying what is the length of all this. And so that's going to be 10,000. And then what it says is what is the sum of the absolute value of AUC null greater than or equal to odds. So I'm not sure why we have the absolute value here, except that it wasn't used up here, right? So for some reason, they use the absolute value on row 19, but not here on row 21 when they did it. And so then if we look at the inside here, what we'll get back is a series of true falses. And I need to make sure I've got that loaded. And so again, we get those truths and falses. And it looks like a lot of falses. And so numerically, a false has a value of zero and a true has a value of one. So then if you sum those, you will get the number of shuffled differences. And there were zero, right? So there were zero cases where the shuffled difference in the mean AUC was equal to or greater than what we observed, right? That tells us that our value actually is pretty extreme. And so we can then say r plus one divided by n plus one, this is a bias correction, we get a p value, and I need to do that. And our p value then is 9.99 times 10 to the minus five, right? So basically, it's minuscule. So that then is our p value. So I think I have a pretty good handle now on how this function works. We give it a data frame that has column for the AUC and the condition. We also give it the conditions we're interested in, and then calculates the observed difference in the mean AUC between the two conditions does the same thing, but does it 10,000 times and each of those 10,000 times, it shuffles the labels. And that will allow us then down here is to determine the number of cases where we randomize the data that the randomize difference in means is greater than or equal to the observed. That then allows us to calculate the p value. So what I'd like to do, because I've been so heavy on dplyr and tidyverse in the series of episodes, so I think I'll try to do my best to change these lines to remove mosaic and make it in dplyr. Again, the advantage there isn't anything against mosaic, but that dplyr, tidyverse, those types of features are far more widely understood by me and people in my lab and people I'm teaching versus having to teach them. And when I say teach, it's also kind of update people or get people up to speed on using functions from the mosaic package. So why is the package if something's already available elsewhere? There are tradeoffs obviously. Before I put this into my own project, what I'd like to do is modify the code that I got from Courtney and Big Yume, using the data they gave me to make my modifications, and then make sure that those modifications don't break anything, right? So the results that I have here in p values by conditions, I should get something very similar to these, once I'm done messing with the code. So again, doing that within this setting with the code and the example data is a really nice testing framework. There's this other approach called test driven development that really encourage you to look into if you're developing functions, and you always want to make sure that as you modify those functions, you're not breaking the code. I'm kind of doing something like that here, but it's a little bit more messy. So before I go too far, I'm going to go ahead and delete this extra code that I used to make my own version of the test sub sample, and I'll use the data that's coming into the function. I will then look at what's going on here and again think about how I would change the code to make it do what I need it to do. So I'll go ahead and put just a line break in here in this comment so I don't have comments and text going off the right side of the screen. And the first thing that I want to take on is this line 15 the observed, right? I want to replace this with a tidy versus type code, right? And so what I could do is again, let's go ahead and make sure we've got test sub sample loaded, test sub sample. And I'll pipe that to a group by condition. And then we'll do a summarize. I'll do mean equals mean on AUC. And that gets us the mean for our two different conditions. And then I can do another summarize to do diff equals the diff of mean, and that gets us our negative 0.734. I can then go ahead and wrap that in ABS to get the absolute value that and to do our p value calculation down here. I'm going to want that to be a single number. Currently, it's actually a table right of row one columns one. So I'll do a poll on diff. So that gets me the same thing that I had, believe it or not, right here, this gives us a vector value. This is a named vector value, but I don't think that condition B matters. So we can test to make sure nothing broke by replacing line 15 by making that commented out and then putting obs here. I'll go ahead and save this and I'll reload the function. And I'll reload all this other stuff. And go about regenerating those p values and see if they've changed any with my modification. So there's the output 0.739 is the one I'm mostly looking at. And that looks pretty spot on. There's going to be some variation because again, we're using a random number generator, as we use a larger number of iterations. So currently we're using 10,000. The number will become more precise. We'll come back up. And the next thing I want to take on is this shuffling and creating the null distribution. I'm going to basically do exactly what I did up here on line 17 through 21. But I'm going to be shuffling the condition column. Okay, so let's go ahead and put this on separate lines. So it doesn't get all scrolly. And what I'd like to do perhaps is encapsulate this as a function, right, because then I could call that function down here with shuffling the labels. So what that'll look like is to have obs equal a function that I'll create called get mean diff. That'll give test sub sample to and that then will output my observed value. So now I need to create that that get mean diff function. So I'll do get mean diff equals a function. And again, in that body, we're going to put that code that I lifted. And I'm going to go ahead and call this x as the x being the data frame that's coming in, where we'll group by condition, all that. And then we will this will automatically be returned. So again, if I do get mean diff on that and I do get mean diff on test sub sample, we should get that 0.73. So we're in good shape there. So the next thing I want to do is generate this null distribution. And what I'm thinking I would do would be something like a map DBL one to 10,000. And so what this will do is this will output a double vector, so a numerical vector. And it's going to iterate over values from one to 10,000. I don't care about the individual values, I just want to repeat it 10,000 times. But I want to do is I want to have it run a function that would be like get rand mean diff. Right. And it would do that on test sub sample. Okay, so I don't have that function. But I'm going to make one, right. So we'll go ahead and comment this out for now. And so I need to make get rand mean diff. And I'll do that back up here again. And we will then do function. And so then with x, I'm going to take the condition column and random randomize it, right. So I'm going to use a mutate on the condition column. And I will then do sample condition. And so now we see that we do have a random sampling of those two conditions. And just to double check that we have the same number of condition a and b that we started with, we can do count on condition. And should we get 100 and 100. So that's good. Good. So now we have this, we can then pipe that to a summarize and call on our get mean diff. That then gives us the 0.01. I'll go ahead and say diff equals that, get that and then we can then pull diff, right. And so then we get the difference that we see. And again, we get different values each time, because we're doing this sampling, getting different permutations of that condition column. Good. So again, what we've done is I have encapsulated away the workflow that was happening right in here into its own function. And so it's more readable where I say get the mean difference. And so now what I have here is get the random mean difference on test sub sample, I think that should work. But we'll see. So I'll go ahead and do the function, I'll go ahead and load, get ran mean diff, and then rerun that. And I get, again, that small value. Good. So that works. So I can give get ran mean diff a data frame, a table, and it will then permute the condition column, and then output the mean difference. So now what I can do is AUC dot null equals all this. And maybe for testing purposes, I'll have it do one to 10. And now if I look at AUC dot null, I see my 10 values. So that's good. I can go ahead and then ramp this up to 10,000. So we get that AUC dot null, again, 10,000 values. So something that occurs to me is that this doesn't actually exist. I don't think right, so incorrect number of dimensions. So it's a vector. So there's no dimension. So now if we look at n 10,000. And here again, looking at our we don't need the absolute value because we already did that. So I can remove one set of parentheses and I can get rid of that column one. And so then I can look at r and see zero. And then our p value will again be very small. So I'll go ahead and save that. And let's go ahead and see if we can't get through without any error messages. So the p values that I got from my updated code look spot on with what I had previously. So I'm really happy with that. Again, the code works. One thing about it is that it's horribly slow. I did not time it. But I think all these, I think all the pipes from Deplier tend to slow things down quite a bit. That's something that I might come back and play with later. But I've got something that works that is readable that I understand. And that works well. The speed is another issue that again, I'll probably come back and try to clean up later to see if I can't make it a little bit faster. The last thing that I want to do for this episode is take this code that I've refactored and plop it into a new R script as part of my micro ML demo project. So I'll come back down here and grab all this code. I'll go ahead then and create a new R script in my micro ML demo. I'll paste this in there. I also need to blow the library tidy verse. And then go ahead and save this in code as get p values. And so that is good. And now I can come back to my compare genus AUCs and I'll need to do a source on code get p values dot r. Make sure that's loaded up. I misspelled tidy verse tidy verse and then resource it. And I can then come down and I should be able to use my function that I created called perm p value conned. And where I will then give it my AUC comparison, which just to see what that looks like is a condition and AUC double check what I've got here. If I do AUC comparison, pipe that to count on condition. And I've got 100 genus fit and 100 genus AUC values. Again, those are coming from that 80 20 split. We've got the 80 percent return in on and the 20 percent that we test on. And so then we will call these genus and genus fit. We'll run that and we'll see what kind of p value we get. So we got a really small p value. There were no randomized differences in mean that were as large or larger than what we observed. Looking at my values here, we see that the genus AUC average for those 100 splits was 0.642. For the genus plus fit was 0.721. We can now say that that is a significantly different quality of model. And it's interesting because fit alone, I believe had an AUC of about 0.7. And so the genus adds a little bit of information on top of the fit. It would be interesting to kind of go back and think about how would we test a determinant of genus plus fit is better than fit alone. I'm pretty sure that it is better. But again, practically speaking, is it that much better? I don't know. Clearly there's a lot of other permutations that we could do to the model. Things like how we pre-process the data, the taxonomic level we're looking at, the type of modeling approach we looked at, right? But now that we have the ability to calculate the p values, we can compare each of those models and therefore figure out which modeling approach is the best so we can come up with the best possible diagnostic. I want to recap the types of strategies that I used with you today. So first of all, we read through the code, right? We tried to kind of trace where the data would go through each of those steps. Second, we had example data, or, you know, perhaps we could create example data if you don't have it with the code, that we can then work through each step of the pipeline. What we also did was that we started to refactor the code to perhaps rewrite it in a language, so to speak, that I understand. So again, these are all the strategies that we used to modify the code to understand the code within that demo project that I had gotten from Courtney. And then I could plop it into my code here for this analysis I'm doing to compare the genus alone to the genus plus fit. In the end, I understand what's going on and I feel much better about the code. Something else to highlight is that I went down this path because micropml didn't have a function for comparing distributions like this. And I think that's pretty important. So I did go ahead and file an issue on the micropml package on GitHub, suggesting that we go ahead and add this feature. I think we'll probably use something perhaps a little bit more like Big Ooms code since it is so much faster than mine. But, you know, it's one of the nice things about having things on GitHub is that we can have this discussion of code. The really nice thing about having things on GitHub as you saw is that I could go get that code and work with it, right, that you could go take code from any of my projects, bring it to you and kind of go through the same process of trying to understand what's happening, and then adapt it to your project. Again, going back to the beginning of this episode, that is what Riffamonus is all about. And that's how I think you and I know I learn the best how to program. In light of all that, a question for you is what stumbling blocks do you still have to posting your code on GitHub for whatever project it is you're working on? When you write the paper up that you're working on right now, are you going to post the code to GitHub? I really hope you do, because I'm sure you've got some gems in there of code that I could borrow to use for my own analysis. And again, this social coding is going to make us all better. But even with all this effort to make the code better and better understandable by me, I can guarantee you that six months from now I might look back at the code and wonder, why did I do that? Or someone else might be watching the video and think, why did Pat do that, right? So it's a continual process learning how to code better and writing better code. Therefore, I really hope that you have liked this video, you've subscribed to the channel so you can be alerted when the next episode drops so we can all learn to program better.


It doesn't matter. Yeah, maybe they didn't want to take it. As long as I can hear it. Yeah, yeah. Hello. Welcome to Think Correct. And welcome to Understanding China. Here we are in Honolulu. And we have some friends from Beijing that we're going to be speaking to you live. Understanding China is about explaining the great ancient kingdom of China and the modern revolution that it has made of itself and for its people and how that revolution is affecting 1.4 billion people in China and by extension is affecting the whole world, all of us. So politically, economically, culturally in the arts this is all about understanding China from the point of view of the leaders of China. And today we have a return visit from a guest in the previous episode. And I asked a question to Lin Fanlin who is the executive director, the CEO of a large media company in Beijing. Beijing E-Mosa and I asked him Fanlin what effect will the One Belt One Road initiative that we've been discussing where China is expanding its view across the world what effect or what participation might America have in that strategy. So I'd like to replay just a little bit of that episode and then we translated Fanlin's answer which we missed at the very end of that episode and I thought that it was important for our listeners to hear the response that Fanlin had. Well this is actually very similar to the development philosophy, the international development philosophy of the United States which we've been trying to practice to share prosperity and technology and people with all the countries of the world especially since World War II. So I must say that it is really inspiring at this time to see China joining America in their role in international development and what America and China can do together could be very exciting. Yes, it is. China has become a second-largest economy and it is a very strong economy because from history China is also a great country in the East but apart from the cultural differences and the differences in China there are some differences in understanding so with China's economic power to increase the Chinese leaders we need to use more ways to eliminate China's misunderstandings so that we can respect different cultures and pursue the same values. So we'd like to ask for a moment for our translator in Beijing to explain what was just said there and then I want to ask a follow-up question to Lin Fanlin. In the past there have been a lot of misunderstandings in terms of what China is and what its economic revolution actually implies for the rest of the world and what I think Mr. Lin was trying to say is that now the Chinese is very much aware that it needs to do something to try and help people better understand China and its current endeavors in the world and that is exactly what it is doing with the One Belt One Road. It's not only a project of infrastructure building and economic development but it's also a project of mutual cultural understanding which is a very important part of it and this is basically what Mr. Lin wanted to express in his previous answer. So what I'd like to understand is the potential role that America and China as world development partners can have in implementing the One Belt One Road policy internationally across the whole world we understand the original concept of One Belt One Road and how far it goes from China to the west and now we're envisioning that belt coming all the way around the planet and with America as a potential partner. Mr. Lin said that even though the United States has been working on the United States and China for the past 15 to 16 years in terms of technology and energy I think that the next step will be based on infrastructure and the first collaboration between America and China Mr. Lin said that he believes that America and China have many common interests in terms of the One Belt One Road strategy and well America being the world's biggest economy and China being the second naturally they have a lot of economic interest for cooperation we can see from the situation in 2016 that America and China have cooperated a lot in fields like energy and technology and Mr. Lin believes that the next field for cooperation in the future will probably be the construction of more infrastructure across the globe. Okay and I'm curious to understand from a strategic point of view the resources that China is putting behind this effort are is China partnering government to government or are they bringing private sector people into play together with businesses in the other countries and are they working with NGOs and non-profit organizations also or is it mainly just government to government and large scale infrastructure Actually China and Belt One Road cooperation between these countries there are probably several forms one is government cooperation because Belt One Road and Belt One Road we have a brief freeze on the Beijing end and we'll try to reestablish that connection through audio sometimes Skype is a little bit irregular on the trans-specific connection so it takes us a few seconds to reestablish Hello I'm Crystal from Quok Talk I've got a new show here you've got to tune in, check out my topics on sensitive, provocative female issues so Tuesday mornings 10 o'clock don't miss it, it's going to be fun and dangerous Sure, what's your name? Does he continue speaking or does he work? Yes Beijing, we're back live again can you continue your response? Okay The second one is the market cooperation based on market cooperation this is the second layer of cooperation and the third one is from the social perspective we just talked about the popular capital this kind of open cooperation these several layers of cooperation to promote the Belt and Road and China and other countries including the United States Okay, well as Mr. Lin just said there are many different aspects to cooperation on the One Belt One Road strategy he has mentioned three and the first one is obviously to government cooperation as countries along the One Belt One Road line are mostly developing countries in terms of some policies we need the help of governments in order to establish and put into practice these policies the second level would be the markets themselves they play a huge guiding role in terms of their demand and the third aspect of it all would be a social aspect as you mentioned in terms of private capital and private investments sure, there are also a lot on that level to be said the most important thing to understand is that it's not necessarily working on three different levels but it's these three levels working together for the common promotion of One Belt One Road and these three levels also include America Okay I have a follow up question I've had a little experience in the international development field and what's been discovered over the years with US development strategy is that sometimes the bottom up is just as important as the top down in other words it's good to build railroads and ports and big hydroelectric dams but one of the most important things you can do with a developing economy for example and I think China has found this is if you educate women and you empower women to raise their children and give them understanding of how to raise their children in a healthy way with a profound effect on the economy and the future of a nation and I wonder if that kind of thinking is figuring in the strategy that China has with the nations that it's engaging with across the world Do you have any questions? Regarding this the women's independence and her own right is actually a very old topic I believe that in China in the whole world it's a very direct discussion but I believe in the modern society of China women have already surpassed our nation so I think what you just said I think maybe in the future including in China there will be more Mr. Lin just said that in terms of women's rights you're definitely very right it's a very old issue not only in China but as well as in the whole world as we all know in today's Chinese society though Mr. Lin has mentioned that the position of women is often even more is even superior to that of men now there are a lot of things that are being put into motion to make this even better not only in China obviously but along the One Belt One Road as well there's still a lot of work to be done obviously but our prospects in that matter are positive We'll be right back after this brief break it's the oldest overland trade route in the world a network dating back to the 5th century BC stretching from China through Central Asia to Europe's borders the Silk Road transported everything from spices to gunpowder and of course silk between East and West now China's President Xi Jinping is bringing it into the 21st century under the name One Belt One Road Goodbye Camels Hello Trains capable of transporting Chinese made goods like laptops 12,000 km to Germany that's faster than shipping by sea but maritime trade is also a big part of the One Belt One Road linking key ports in Asia Europe and Africa while the maritime route already carries much of the world's cargo its overland counterpart is much less developed and passes through some of the world's remote regions so the challenge for China and its partners create a 21st century superhighway while reducing prohibitive import and export tariffs so that East and West can do more business with each other Hello we're back for our second part of our program with Li Yanjun Li Yanjun is an old friend and someone who works, he's an economist and he works with the Ministry of Commerce to identify opportunities for China's businesses and for China's government agencies to make alliances across the world he's worked directly in Africa and in Europe and in Central Asia and he has a great deal of insight into the economic strategy behind the One Belt One Road policy understanding the Belt and Road initiative in one minute how the Belt and Road initiative will expand trade expanding global trade is one of the key priorities of the China proposed One Belt One Road initiative the grand design took concrete shape when China agreed to a 45 billion US dollar investment in Pakistan with most of the spending going to build the China-Pakistan Economic Corridor starting at Pakistan's Guardar Port on the Arabian Sea and ending at China's Xinjiang in the South Asian region and provide a shorter and cheaper route for trade with the Middle East and Africa statistics released by the China's Ministry of Commerce shows that during the first half of 2015 China has directly invested in 48 countries involved in the Belt and Road initiative with total investment volume reaching over 7 billion US dollars a year-on-year increase of 22% more trade potential will be tapped between countries along the route at this year's Boyle Forum for Asia in March President Xi Jinping said China's annual trade with countries along the One Belt One Road initiative could reach 2.5 trillion US dollars in 10 years meanwhile experts point out that to facilitate unimpeded trade steps will be taken to resolve investment in trade facilitation issues reduce investment in trade barriers lower trade investment costs as well as to promote regional economic integration so we're back now with Li Yanjun Li Yanjun from the Ministry of Commerce he heads an organization that works within the Ministry of Commerce to advise the Premier of China and other government agencies on the best strategy for China to have a positive impact across the world so Yanjun is very familiar with the One Belt One Road policy and its implementation and I have a first question for you Yanjun do you know the amount of capital that China has identified that will be deployed over the next 10 years for the One Belt One Road policy we've heard a number of estimates and I'd like to hear what is your understanding as to the amount of capital that will be coming from China to support the initiative ok this is the first day of the two meetings this year China's One Belt One Road China's One Belt One Road is also the one on the sea the one on the sea including the the one on the sea the one on the sea the one on the sea so on One Belt One Road like the 65 countries of the world we have the investment for China investment investment in the foreign company ah, a national open bank ah, ah, the and one more city is our foreign capital which is the Asia-Pacific Economic and Asia-Pacific Technology Investment Development Bank, and the gold bank, the gold bank of the national development bank. The third one is the commercial bank, the industrial bank, the Chinese industrial bank, the Chinese construction bank, and the Chinese agricultural bank. Okay, let's go ahead and translate some of this so far. Nicholas, can you translate it so far? Yes, so Mr Li said that today is the first day that the two sessions in China is going to start, so it's a very good topic to be talking about today. He says that China's One Belt One Road initiative, as we know, has a continental part and a maritime part to it, and that there are about 65 countries investing in it. In terms of China's investment, there are many different levels to it. Obviously, there's the China Development Bank as well as the Asian Infrastructure and Investment Bank that have been putting out a lot of capital and that will continue to do so. There are also many commercial banks in China that have been putting out capital for the strategy, such as China's Commercial Bank and China's Construction Bank and China's Agricultural Bank. This cooperation in terms of investment, China is obviously putting out a lot, but it's more something that will be done in cooperation with the other countries along the road under the guidance of the Chinese government and the governments of these countries as well. So it's a cooperative effort between the government of China and other governments. I'm particularly interested in the example of Pakistan, which was one of the first really big projects that China engaged with, building a huge hydroelectric power plant in Pakistan. Now, obviously, Pakistan is an extremely important country, very large population, and at a crossroads in Central Asia that involves China and India and Pakistan, the Islamic world. So I'm interested in Yanduan's assessment of that project and how it's working to bring hydroelectric power to the people of Pakistan and how it may work to bring stability to an area that needs a good deal of attention. Mr Lee says that China has been investing in the strategy of China to go out in the world and to implement its strategy has a lot to do with technology and environmental protection. In countries like Pakistan and Afghanistan and et cetera, there's a lot to do with technology and environmental protection. So I think it's really important for China to have a good deal of attention in terms of the strategy of China to go out in the world and to implement its strategy has a lot to do with technology and environmental protection. In countries like Pakistan and Afghanistan and et cetera, China has been investing in technology and infrastructure in order to solve the problem, the issue of providing electricity for these people. In addition to solving the problem of infrastructure building, transportation such as building roads, railways, in these fields, China has been a greatly increasing investment. There is an old Chinese sentence, an old Chinese saying that says that if you want to get rich, you must first build roads. And that is what China is trying to do for these countries, trying to build these roads that will connect these countries and its people together and then build the infrastructure, which includes this dam, which you've mentioned, that will be able to bring electricity to these people. That is how China is trying to help these countries connect these countries and invest in these countries. Mr Lee added that he hopes that the goal of China is to cooperate more with these countries in terms also of policies and to have a lot of bilateral preferential policies in terms of, for example, currency and payment in order to help enterprises in each country to go global and cooperate and invest and help each other. I have a follow-up question. I know that you have spent some time in Africa also looking into investments and projects there. Can you tell us of one example of a project in the continent of Africa in which China is involved? Mr Lee has said that in many countries like Africa, for example, he mentioned Morocco, China has been helping to build factories to improve the life of the people over there. He says that the way that China is currently supporting Africa has a lot to do with building infrastructure, obviously, but also with helping the country to industrialize and to build itself, to develop itself. Meanwhile, there are also a lot of organic resources that China can exploit for the African populations. This is a way that the continent of Africa and China have been able to realize win-win cooperation, win-win results, and win-win events. I think it will be fascinating in the coming years to see how the efforts of America and Europe to foster balanced development across the world start to mesh with the efforts of China to do the same thing. We certainly hope that that will turn into a series of partnerships that will be much greater than the sum of its parts, and that the insights that those three big developing groups have will be combined into something that's greater than the sum of its parts. So that's a theme that we'll keep exploring in Understanding China. And I want to thank Nicholas Bertiom, who is our translator for today. He comes to us from Beijing, Emoza, and we really thank Beijing, Emoza, and Lin Fan Lin for their support of this program. I want to acknowledge the efforts of Xiaofang Zhou in Beijing as the producer and the efforts of David Castellano in Beijing in making it possible for the bits to move across the Pacific. And it certainly worked a lot smoother this time, and we'll be back again soon with another episode of Understanding China.


Hey there, I'm Charlie Comstock model railroad hobbyist magazine. I'm here with Nick Muff Nick has got this Kansas City Southern layout downstairs in his house, and it's a very unusual layout in fact, we have to take quite a walk to get through it. He's got a Passenger car down there and we're gonna hike through that and he's got a bit of a station down here You can hear some of the sound effects in the background So you want to show us what you got here? Sure. I'd be glad to. Okay, let's go. All right Well, my real love is the passenger trains and passenger train travel So here in the basement I've recreated in full-scale and in an HO scale an experience in passenger train travel the lobby here simulates the Midway at Kansas City Human Station and To the left is the entrance to the Pullman car so the right here is what looks like part of the side of an F unit and the Numenate new the enigmatic door Operates and this represents the mock-up of a Pullman car People ask where I got it. I built it. I tell them that everything that's in this car I carried down those stairs. So come on in So there's a room at a museum restroom Come on in here and see the museum with Miss Southern Bell standing on the left and like all passenger trains It's cramped around the corner here and down the hall is Bafé lounge and again, this is all a mock-up Probably about one-third the length of a real passenger car and like all model railroaders do it's been selectively compressed So that it recreates the feeling of being in a passenger car. Even only it's only one-third as long as one And the table setting here. That's Kansas City Southerns Rocksbury pattern China and Kansas City Southern flatware and Holloware on the table And come on through this way through the door is the layout room Hobos jokingly referred to box cars as side door Pullmans This is a real side door Pullman and out here. We're in the layout room to the left here is the passenger car and Then the locomotive as opposed to the passenger car. The locomotive is not a mock-up. It's the real thing and then Across the room from right to left is Kansas City downtown in the late 40s early 50s So well, I like to have animations on the layout Okay visitors really are interested in those things And but I want really realistic animations So kite on the end of a piece of music while it goes around and around isn't going to work for me Okay, and so first animation on this end of the layout is this little Rock Island switcher number one. Aren't you afraid it's gonna come off the end of the trestle Well, we've arranged so it doesn't actually there is a diode in here That blocks this section of track so it can only run so far and it will stop automatically The same on the other end when he gets in behind the buildings where it's hidden. So next animation here is a hobo campsite Actually, the model was built by a friend of mine when he was just a teenager I've replaced the Fireplace electronics with something a little more modern. So it gives a very nice effect. One nice thing is to have Buttons that visitors can operate on the edge of the layout and so there is a button here that says here the lonesome hobo call So you push that and


Around the corner and down the hall from the cafe is a Newman Library multi-purpose room. This room is a customizable high-tech event space that is used for lectures, film screenings, cultural events, and poetry readings. It is not generally open for study or project use, but can be booked for a public event at no cost via the spaces section of the library's website. The Athenaeum is a suite of three spaces, a modular classroom, a collaborative boardroom, and a small studio called the Media Den. Athenaeum is dedicated to cultivating digital research skills in the liberal arts and human sciences through collaborative, hands-on experience pursuing digital research projects. You may have a class in the Athenaeum's classroom, utilize the space for a project, or attend a public event. For more information, visit the Spaces section of the library's website.


Welcome to the introductory video on postgraduate diploma program in business management that is PGDBM of Krishnakanta Hondiko State Open University. My name is Chainika Senapati and I am from the Maninam Devan School of Management Krishnakanta Hondiko State Open University. The postgraduate diploma in business management at Krishnakanta Hondiko State Open University comprises of learning at the theoretical knowledge and case studies covering different aspects and areas of management. The program is of one year duration with two semesters, each semester comprises four courses. All the courses are compulsory and each course carries four credits and the program has 32 credits in total. The program has four courses in each semester that I have already mentioned and they carry 100 marks that we divide this 100 marks into 80 and 20, 80 as end term examination and 20 for home assignments. Now let us discuss about the syllabus of the postgraduate diploma business management program. So here in the first semester, the learners will be able to know about four courses that is management process and organization behavior. The second course is about manager economics, then the third course is about accounting for managers and the fourth is about information technology in management. In the first course that is management process and organization behavior, the learners will be able to know about the various concepts of management like levels of management, functions of management, planning and forecasting, organizing, staffing, controlling, then the concepts of organization behavior like personality, perception, communication, attitude and emotions and etc. In the second course that is on manager economics, the learners will be able to know about the various concepts like demand forecasting, demand analysis, supply and market equilibrium, then consumer behavior, theory of production, cost analysis. So these are some of the concepts that will be covered in manager economics course. The third course is about accounting for managers. In this course, the learners will be able to know about the various aspects of financial accounting, the accounting concepts, principles, policies, double entry accounting, subsidiary books, ledger, trial balance and then ratio analysis. So these are some of the concepts that will be covered in accounting for managers. In the last course of the first semester that is on information technology in management, the learners will be able to know about the basic concepts related to computer, then data storage techniques and software and multimedia, then MS Word, MS Excel, MS PowerPoint, and they will also be able to learn about the working with the internet and management information systems and etc. In the second semester, the learners will be able to know about the various concepts related to the courses. The first course in the first second semester is about human resource management. The second course of the second semester is about marketing management. The third course is about financial management and the last course is about legal aspects of business. So in the first course of second semester that is on human resource management, the learners will be able to know about human resource planning, recruitment and selection, training and development, performance appraisal, compensation management, job evaluation, job analysis, etc. In the course marketing management, the learners will be able to know about the various concepts of marketing management like marketing environment, consumer buying behavior, then business buying behavior, segmentation, targeting and positioning, marketing mix, product and brand management and etc. In the third course of second semester that is on financial management, the learners will be able to learn about the various concepts like financial planning, sources of funds, cost of capital leverage, capital structure, capital budgeting decisions, and risk in capital budgeting, capital rationing. So these are the concepts that we covered in the course financial management. In the last course that is on legal aspects of business, here the learners will be know about the various laws related to business like law of contract, law of guarantee and guarantee, then contract of payment and pledge, then the learners will also know about law of partnership, law of sales of goods, law of negotiable instruments. So these are some of the concepts that will be covered in the course that is legal aspects of business. So dear learners, as I have already mentioned that there will be four courses in each semester and there will be eight courses in total in the program. So you will be know about the various aspects related to business management. And for more information about the program and the course you may visit our official website that is www.kkhsou.in and thank you so much for watching this introductory video on postgraduate diploma in business management of Krishna Kanto from DECO State of Kutty University. Thank you.