I was introduced to LinQ in 2008, by then it just came out and I was actually initially suspicious about why LinQ C sharp is a statically type language and why all this happening. But later I realized that it is really useful and today I will show you some examples about how LinQ we are using, I mean I am using LinQ in several scenarios. Before that little bit about myself, so I have written three books, first one was on data structure and then on dotnet generics, basically I love data collections and manipulations and my latest book that is coming up is on LinQ and it is available in next month and you can actually go and get a discount coupon, buy it if you will. But that I will leave up to you to decide whether you will buy it or not after the session. So this talk is about, I will show you some examples that I have used in the book and I hope you like it and after that we will break for Q&A. But how many people here use a C sharp and LinQ, only a few, but you have a general notion of composition everybody, right? So yesterday Venkat and I think in court Jughalbandi they showed some kind of composition, but that was on the surface, but we will show some examples which are really deep. So this example is basically we all have been programming for quite a while and we have these loops, traditional loops that we have and we can actually, we do so many things inside a loop. We have some initialization statements, some variable declaration. I want to show you a video that is basically a general strategy to convert a loop to a LinQ statement. Let us say you have a list in a loop implementation like this, you have some numbers and then you loop through that number and if those numbers are greater than a threshold then you put them in some other data structure, in this case a list. So as a first step is to identify the range across which you have to run the operate on which range that LinQ query will run. In this case our range is the entire number length range. So what we do is we will, so that is the range and then what is the condition, what is the condition that is if this number is greater than threshold that is the condition. So this part is the condition part and this is the range part and this is the action that we are taking when this condition is becoming true. So if I write this for loop in a for each loop then it will become for each element in numbers. First step to convert your for loops to a LinQ statement is to understand that every for loop can essentially be transformed into a for each loop. Then once you do that then your query because LinQ actually work on strongly type collections. So unless you have a strongly type collections you cannot you know use. So if numbers element greater than threshold then good numbers dot add element. So as you can see we have replaced this looping operate in looping variable key and now we have much cleaner syntax and we are operating in an object on each element level. So if I this is the first step if you think as a for starters this is the first step and then you can next step is just to replace. So this is our this part will be replaced with where clause and this is the range state and this is the action. So what I can do is so if instead we can just reverse this and say good numbers is equal to numbers dot where n greater than first. So you can make the connection just name it same element greater than threshold. So if you can see this condition and this condition are the same thing only thing is it just wrapped it in a for clause. So by virtue of using LinQ we have actually flattened the loop but do not get mistaken link is not magic so the complexity remains the same. So even though you are flattening it is not so in order to find the good numbers you have to still traverse the inter so big O notations and all that complexity stop stays the same it is just that we are being able to tell what we want get done in a very concise manner. So basically this part here and this part here and this part here is identical. So what I will do is I will comment out this section for now. If I run it again coming to this section this line this length line so we are using the for each link to see that we get the same thing 11, 15, 18, 17. Now I will comment out this for each part everything is commented except I will just come into the link part and then see. So we get the same thing back. Another important thing to notice is that at the end we had two lists but if you drop that two list that link statement does not get executed it creates a query which you can get the results. So it is basically being lazy as long as there is no operator such that the evaluation is absolutely necessary in this case a two list. So when you get write a two list it actually had to create a list and put all the elements evaluate it and put it in that list and give it back to you. But if you drop the two list until the point where you actually need it it will be great. I hope you understood this pattern any question on this? I will get back to showing how we can remove a nested loop. So for this demo I am using a tool called link pad which is really cool utility. So let us say I have a for loop let us say I have a dictionary of string and a list of string and I am keeping country capital map CC map for example I will say country tourist spot let us say and we will add a couple of entries in there and let us say I want to print all the tourist spots. So what I have to do is I have to look through this dictionary and then I have to create a collection on which I want to hold on to this thing and then I say psps.addrange and then I will dump it. So dump is a method in link pad extension method so you can actually dump any result. So this shows like that but instead of all this I could have just wrote I will comment it out. So this basically is a nested loop for each of this element we are going somewhere here we had only two we can go beyond that and so I will comment that and what we can say ccmap.selectmany and I am saying m.value and that is what I want to give to. So this guy as you see add range takes an i enumerable of string and selectmany also returns an i enumerable of result in this case value is also result of string. So I will I do not need to actually convert it to a list. So the result is the same so that is how you flatten a nested loop let us go back to demo and talk yes it does iterates through all the nodes that you have it iterates through all the possibilities all the loop that you are doing manually to the first step but I will show some examples then it will be more useful later. So here this examples are of from the book it is human computer interactions and we all know about T9 before T9 I think the latest one is swipe people who have android phone probably use this. This is Michael Orwell for value but I could manually select something else since it is an a I am just going to go to the next word test message and see that the a is there and test is there as well. Now if you notice what I just did on the s I so although it seems really cool but the idea behind this is not new it is very old and this is working on a algorithm called the longest subsequence. So we will get to that so before that so here are some amusing examples of subsequence. So as anybody knows what is subsequence is a subsequence is a string of another string where the characters of the first string occurs in monotonically increasing indices that might sound too difficult to understand. So I have given example let us say ornamental is a word so all these words rental, mental, oral, metal these things are if you see the characters of the word rental R E N T A L appear in monotonically increasing indices in the word ornamental. So rental is a subsequence of ornamental but the longest subsequence of ornamental is ornament because that is the longest here is a funny example world is not enough and wine is a subsequence world is not enough. So I will show you the code for achieving this so this is an example from the book. So this is a swipe simulation can all of you see this properly is it too big or does it fit properly everybody can see right this. So let us say I have a touch keyboard and I traverse through the characters these characters U J N B B N by way of moving my finger and so this is a simulation of that. Now what I am doing here is so I have a dictionary P 9 P X T is a dictionary that has lot of words in it and in this what I want to achieve is I want to provide suggestions that like the demo that other guy was showing here first step is I am creating a list of this words. So if I dump this query you will see that so query I query has some words in it. So at this point query has just the words now what I am doing is I have created a method called longest subsequence of please do not think about that right now. Let us say longest subsequence is a built in method that if you give two inputs to it and it will return you whether what is the longest subsequence. So for each of those words I look through and I filter them using a where clause and if the longest subsequence is the word itself then it is must be a potential candidate for suggestion. Let us say if I then if I go back here you see U is here N is here D is here E are under S is here T A N D S. So if I find the longest subsequence of the word subsequence understands is a longest subsequence that I can get back. So and that matches with the word understands in itself so that is a potential match for this. Now if I word I if I then after that I sort the match you know in the descending order of the length so the most probable word tops the list and then by I sort them by alphabetically and then take the first four elements and drop them as suggestions. So I let it fly and see what it produces. So here is the output it thinks that I wanted to mean understand I mean I do not know what is that mean but second one onwards but that is how it is. So do you like the example? Let us move on to the another one a spell checking this says one of the key areas that people have Peter Norvig he is I do not know how many people know Peter Norvig you know. So can you tell others who is here. So Peter Norvig was travelling on a plane and he only had some Shakespeare stories in his computer and he wrote a small spell checker using that content of that story as a dictionary words. So this is his code in python which is sheer genius and I try to clone it using C sharp in link you and just for the statistics his code is about 25 lines and mine is about 59 lines. So let us go there. Can you see this code properly? What about now? One second. I am sorry one second I probably have to go to his site. Here it is. What about now? Can you see this? Yes. So see this what he has done. He has created few methods called known edits and this is a very fancy example of list comprehension. So he has taken a range and broken the word into two pieces and that he is calling splits. The first part of that split takes for 0 to the ith character and second part is from the ith location till the end. And deletes like deletes is a suppose sometimes we misplace one of the characters that is deletes and transposes is we transpose one or later for the other maybe in apple we translate you know p and a actually should come in reverse order and but they come in reverse order that is a problem and replacements and inserts. So python is one of those languages which I can read but can write. But so I read his code and translated it. So I will show equivalent statements that I wrote in link. So here is my version. So what I did is I also created array and take the first element. I also created that length array but I have been unfortunately C sharp is not as expressive as python in this case. So I had to project that collection into splits which is first and last. First and second are the two parts of the splits and then I created all the transposes. And you are asking about select many. So here is an example. So let us see what select many does here is. So in this I have let us say I have a word called apple and if I use this paradigm then I will have A as the first element P P A E as the second one. Then another split will be A P and P A E like that and what select many is doing here is it is joining all this together to create a you know replacements for those characters. And at the end which we are doing is we are concatenating everything together so that we get a list of the possible mistakes. And here this is the training part we are loading all the words in the dictionary which is dictionary of how many times this occurs in the mistakes dictionary. And then we can so I mistook you know mistyped one word mystery and if I run this it will show mystery I will run it. So this is a really large code but I do not expect you to understand and all these things in this demo but I will upload it in the slide. You can take a look but any questions general questions you have so far. I hope you are familiar with all this link operators where select many select projections and all that. This is one of my favorite example this is Fibonacci series example. So how many times you have tried to calculate a Fibonacci number and then you run out of memory actually so the tail recursions are yesterday Venkat was talking about memoization and which is a really nice concept and I will show you. So memoization basically means caching calculate cache and for faster execution. So you have calculated if you calculated once in software engineering we have a principle called dry right do not repeat yourself. I think memoization actually fits into that block like if you have already calculated something do not do and here I will show you what can be done to lazily you will have Fibonacci series and such things. So if you will Fibonacci series is basically starts every series such series starts with a seed values and so what I am doing here I have created a sequence exam X static class and I have created two static methods and that works takes a seed values and I am converting them to an enumerable which might seem not useful at all but you will see in a minute that it is really useful. And then there is a so what is recurring relationship does is they have seed value and then they follow a rule to calculate the next value. For Fibonacci series we have the last two values if you sum them then we get the next value. So here what I have done is so basically you can encapsulate that in a funk. So if you take 2 t t if you say funk of int and in and the return is also an int but it does not have to be an int in case of Fibonacci it is an integer in case of some other series it can be a other data types. So what we do is we create take as long as it is true so this is also very interesting that as long as it is true we are looping through and then we are creating taking the first element last but one element and the last element and then concatenating the evaluated result back to the same result sequence and returning the last value. So this is now let us come to how we can use so Fibonacci rule let us say long, long, long and then x and y, x plus y this means take two element give me the sum of those two. Now how it will work here see if you want to find the first 5 Fibonacci numbers you say sequence x or start with long, 1, 1 these are my seed values. So I am giving it two seed values and then I am saying then follow a Fibonacci rule now what this does is it goes in there and finds 1, 1 and then it puts it in this sequence and if you notice that this sequence is the sequence that was passed to it. So actually we are not creating another structure and we are returning yield return so we can take as many elements as we want because nothing is that is the beauty of deferred execution. So you do not have to really wait till the end so instead of 5 I can now say 40 very confidently and it will work. So you see how fast it calculated first 40 Fibonacci series because age at each instance it had to calculate only one addition because everything else is pre-calculated and another thing that is very interesting is this looks like a plain English rules like you say start with 1, 1 then follow Fibonacci rule then take first 40 as soon as you say take 40 the execution starts. If you do not say take 40 this will create a query that can give you the results but not yet. So this is a example of a embedded DSL embedded domain specific language that you can do inside a host language in this case C sharp. Any question on this? No questions either I am boring people or they are not in the honor of my presence they are not leaving the room or something that is of the track. Now this is another favorite thing that is external domain specific language. So if you see all these things are doing the same thing. So all this code are doing the same thing they are trying to find Armstrong number everybody knows I am assuming that what is the Armstrong number. An Armstrong number is a number whose if you sum the cube of all the digits they sum up to the same number. For example 1 if you cube 1 it is 1 and sum of 1 is 1. So 1 is Armstrong number and 153 is also an Armstrong number. If you cube 1 1 5 cube plus 3 cube sums up to 153. So that is an Armstrong number. So at the left end you have Armstrong number coded in an imperative style which we have been doing since ages and on the right hand side you have an embedded DSL which is kind of similar like what you saw right now for Fibonacci and at the bottom is a domain specific language for the mathematicians. They do not want to know how to write code and they say as I told sum of the cube of the digits of the number is the number itself. Can this be a language? So that is a example of an external DSL where you do not declutter all the take out all the programming aspect of it and just give it a free flowing view. So I will show you this last one as a demo. So I call this I created this language and I am working on to post it on github and that language I am naming as Armstrong in favor of the number Armstrong number. So here is a small demo showing the external DSL Armstrong number in action. In this video I will demonstrate Armstrong which is a domain specific language for finding numbers that are interesting like Armstrong numbers, some product numbers and factorial numbers. Unlike embedded DSLs we have here in this example I will show the demonstration of the language that we built in the book. So if I type the expression like I am typing it here if you can see sum of the cube of the digits of the number is the number itself. So this is the definition of the Armstrong number. So what will happen is once I enter this expression it will be translated to a link cube statement and then that link cube statement will be evaluated and passed and then the results will be showing Armstrong numbers within 1 to 10,000 range. So let us say it entered. So from that I generated this query ran it input is a predefined range of 1 to 10,000 and when fired it provided this thing. So in the interest I will show you the example here. So I call it Armstrong console and so unfortunately it is it cannot be this font size cannot be increased. So if you say sum of the sum of the cube and you can do odd digits even digits all these things odd digits. These are the key words or the phrases of the language some of the odd digits of the number is 10. So I entered some of the so how it is interpreting is we are putting it into a stack and then reverse the stack and then taking one element at a time and figuring out what could have been the meaning. That itself is a syntactic sugar you can omit it. So here you say sum of the odd digits. So and this is an example of an example of query that you can do and you can do all kinds of bracketing some division and all that. You say sum of another important interesting number is called factorial numbers. So you can say sum of the factorial of the digits of the number is the number itself. So there are three such numbers in 1 to 10,000 range and this query so enough of demo. Now let us show you some code. So how this is done? So this is an example from the book and so everything is created as an so whatever every domain every language how languages are there is computer language or human languages are not different. They are like we have set of vocabularies and set of rules to glue those words together to say something to express ourselves. So domain specific languages are also not different. So we have something like here in this domain we have something like cubes where these are the domain specific language for this elements and so as you can see everything is written. So the digits if you see so digits takes a signature of digits is like this. So when I say digits of the number this method gets called. So it is an extension method on integer that takes an integer and returns the digits as an enumerable of integers and so on and so forth and then some amount of plumbing here for the parsing and then essentially you get what you just saw. So the idea is this is an example of a domain which is universally understood mathematics and you can translate it to so any domain. So you can do file systems you know FTP what not. Wave development, testing all kinds of things can be done. Hope you enjoyed this thing and as much as I did while creating this here, here is it. It is a map, it is a plain map and some kind of. So here you see so basically whatever I am typing is a plain map. So if I say times it will be star if I say star sometimes what we say 5 times 4 but what we actually mean is 4 5 into 4. So and then we say ease or proper divisor or even. So what I did essentially is a mapping between English like phrases to my extension methods and then glued those together to you know create a query. So if you see here in this in this output see even though you see this some of the I given the command the some of the odd digits of the number is 10 and my stack contents whatever I wrote. So some off is ignored since it is not a keyword then dies also ignored. These are just like stop words reduction things and then odd digits is a valid armstrong phrase. So it is it is registered because odd digits has a mapping in the extension method and then the number is also pulled because the number gives me the object on which I can call that method. So as I know if I do not have the object integer then I cannot call digits on it. That is why I am always typing number digits of the number that of the is just a source secret source but I that if I do not give the number then it will not work. So it seems may seem like free flowing which is free flowing for the context of demo but it can be full-fledged using a lecture odd digits some no you have to say odd some of the you have to say odd digits of the number that or you can minimalistic. So you can say odd digits first you say some then say odd digits then say number then say is 10 this will also work because we are doing a minimalistic we are not using any stop words but you do not say English like that. No we do not want to stick to the syntax it can be but you when you when you when you say some when you explain a rule you follow a syntax even without knowing it. So you do not say we normally say some of the cube of the digits we do not say cube of the digits cube the digits and then do the some we do not say like that. So even though we do not actually mean it it is our we are already programmed to do like that. Actually this does not allow debugging then I have to so as soon as the input this statement comes in we there is a method called generate armstrong statement and that does the conversions. Now for each of this token if there is a matching mapping then we create a builder and then we sanitize the brace because we do not want the brace to be. Yeah I think this part is fine I was interested in the link compiler that you showed where so you have a string which represents the link query. Oh how did you how did I execute any link query. Okay for that there is a link compiler called eval evaluate link compiler is there I will show you the page. So that is a open source tool. Yes. So if you go to codeplex and there are actually another one called text to delegate by some Japanese programmer. So here so if you I use this once I generated my link queue I pass it on to evaluate to do the operation. So this can be done on a free flowing user interface. So where users can come in and say in voice okay give me this customer's data and then you translate it to your query and then pump it. Let us go back to the talk. Okay another important interesting thing is metaprogramming. I do not know whether you are familiar or not. Microsoft is releasing Roslin. Roslin is so far compilers has been had remain like a black box. You write source code something happens we do not know exactly what is happening and at the other end we get some code generated and it is executing on some machines architecture. We do not know what happens inside. But the bad thing about this is by virtue of parsing your code compiler has lot of lot of information about the code but it is lost as soon as you come out of the execution and we are not able to tap into that power because writing a parser is not a trivial task and with the changing you know language structure every now and then it is very difficult to maintain a decent parser which parse. So Microsoft has released Roslin as a CTP. So you can go in this link and download it and you need Visual Studio 2012 to install it. And once you install it it will it has several APIs. So you can build your own refactoring tools and say when you right click and say rename as method rename method or extract method some same similar stuff you can do you on your own. But I will not show refactoring today I will show some examples like how many times you have seen like a method is taking some five arguments and may be using only two of them. So this type of methods are actually potential candidates for refactoring. So before you refactor you have to know which methods to refactor. So we will show some example. So here you see Roslin gives you a very clean API to work with. So there is something called a syntax tree. So you can say syntax tree has a static method called parse text and you can parse in raw C sharp code or I think you can also parse BV code. And so here I parse you know C sharp code here. In fact this can be any code actually looking at don't judge it by this but it is a C sharp code let's assume it is compile level in C as well. So here you say get root I want to find out there are several tokens and method declaration token is one token. So get root gives you the root of the syntax tree and then you say descendant and then do a filter operation on that and say give me all the methods that has been declared. See how clean this API is I mean I think this is very hard to beat and come up with something like this and then you say okay this gives me what this gives me an I enumerable of syntax node and then I cast it to a method declaration syntax node. So that gives me a method and I projected it to a list and so I now have a method declaration syntax list. Now is the fun is we are now going now this is all list now we can use all our link queries now we project it to a and then I say parameters Z as this point is a method info method declarations syntax and that has parameter list which is a parameter list syntax and then you say parameters that gives you a list of parameters and then you say p.identifier. So you can see how deep this API is. So it identifies not only the name of the parameter but the type everything it is beyond reflection okay and then say identifier.value text will give you the name of the parameter. Now you get the method name as jet.identifier which is value text and if it is using all the parameter then all of then this condition must be true right body.getText and two string contents x. So if it is using all the parameter this should be true because jet.body gives me the body of the method and then if I say two string I get it as a string okay and then I am filtering all those that are not using all the parameters and projecting their names and the statement. So as you can see here I had two methods two dummy methods one called fun that has a parameter called Z which is not used in the body. So it will print out the it printed out the name of the method. So if you have really long you know which ones to target for your refactoring and this is really super easy to build because API is very clean. Similar stuff another one is if they say that if you have lot of local variables it is not good and maybe you can store it somewhere and so this gives you a number of the local variables. So similar first like the last time we created a method declarations syntax node and then find the local declaration statement and so syntax kind I just put a dot here and show you what kind see such a big list. So everything that you can have you know imagine of or maybe even do not know of is here you do not have to tell anything you just put the code. It is not cross language it is C sharp no there are two DLs that you can use one is one two references one for C sharp one for VB. So if you if you give the language it has some inbuilt mechanism to identify which language it is and then essentially use some factory method is there depending on that it uses that other syntax tree if it is you parse it VB code it will still parse it. There is a fantastic video by Anders Heiselberg in C sharp build event. You just type and Anders Heiselberg C sharp Roslin then build and you will get the video. So there he shows what is the potential what is the path forward for this. So here it shows the number of local variables it has. So all why I am showing all this is because I want to you know say that before and after refactoring you want to see that your results are appropriate and for now you there are some tools that you can buy like N depends which is costly. So N depends gives you support for finding all this information and but you can do it on your own using Roslin. I think enough of Roslin I will just have another last. I have two topics you guys decide data mining machine learning which one you want machine learning. So machine learning so this is a flower called Iris there are three types three species Iris versicolor Iris virginica and Iris etosa. And some botanist actually created a long file with all the sepal length and petal length and what not. And the task is to identify the flower. Suppose I am not a botanist I tell you the measurements of that sepal length petal length everything you have to tell me the machine learning algorithm has to tell me whether this is a Iris which flavor you know which species sorry. So for this there is a algorithm called KNN KN nearest neighbor. How many of you are familiar with that algorithm? For the rest so if you look at this you know picture here. So in two dimension if I plot a point then it will be somewhere here and if I say if you just imagine the origin and 0 1 is here and 1 1 is here. So if I say 0.5 is in between if I say 0.5 is in between so maybe if 0 has a class 0 1 has a class and 0.5 might be having the same class. So if you project your you know records and post it into two dimension it will look something like this. And let us say these are the records of cancer patients and these rates are you know malignant cases and blue are like benign cases. And we have a new patient record which is green and we have to identify with the you know what kind be what could be the case for this person. As you can see the circle of confidence inside we have two red and only one blue. So in the neighborhood we see a popularity of malignant cases. So we might want to say this new person is unfortunately will be a malignant case. So you are what your nearest neighbor are neighbors are. So you are known by the company you keep that is the broader sense if you say. So let us see this example in coded. So what I will show you the file also. So this is the iris file you can download it just say iris csv and you will get this file. So these are the sepal lane sepal weed petlain and the name is the class iris setosa has such and things versicolor and then virginica.  So what I do did here is I loaded this iris versicolor selected the rows keep the first one because that is the header we do not want that and then created a projection of sepal length and name and random subset is a method available from a API called molding. So that gives you a random subset. So you do not have a sampling issue. So you get a random subset of 100 and then you create the euclidean distance function. So you have 8 elements because there are 4 elements. So we have 8 elements and then we create a normal square root euclidean distance functions that we are using you can use other distance functions. And then at the end we just sort the records by you know minimum distance from the minimum distance from the record and then create a look up if I just I will just put a dump here so that you see what the look up is. Now you see here all the elements 5 elements that are closest to it is things that it is a versicolor. So these are all the closest 5 closest elements that are versicolor. So may be the test data that I gave it is test sepal length, petal length may be this cloud is also a versicolor. I think we have to end here and if you have question and please buy the book please it will buy me a coffee that is all. Any questions? Data mining and how do you say it is a machine supervised machine learning algorithm. So supervised machine learning so suppose how you teach your children a b c d. So you take a picture of a and show tell your son or daughter this is a this is a this is a you do it for say 4 or 5 months at the end that person you know your kid learns this is a. Now we know that is black but from childhood if you are taught that is blue you will tell that is blue similarly this is called supervised learning. So we we are telling these things are tagged with these things so you try to learn the matching. So if I give you 5 kinds of apples you will still recognize they are all apples. You will say this is a green apple this is a shimla apple and this is a fuji apple but they are all apples but you know how to classify them you will not be saying these are pumpkins that is how supervised learning works. That is all thank you.


Okay. All right. Dan, you want to come up? Dan Kastner is the scientific director for the Division of Intramural Research at NHGRI, and he's with us today to give you a presentation about the division. Council has no direct oversight or responsibility to the Intramural Research Program, but we think it's very appropriate that you get a full range and understanding of all of the kind of science that goes on under the NHGRI. And so Dan's going to fill you in about the Intramural Program. And we do this just, again, for New Council, we do this every couple to a few years. We have a presentation by the scientific director to just sort of let you know what's happening on the other part of the Institute, which as, and I'm sure Dan will describe, has its own external advisory group, the Board of Scientific Counselors. So again, this is as much as anything just for your orientation to other things going on with Institute funds. Dan. All right. Well, thank you very much, Eric and Rudy, and thank you all for the opportunity to talk to you this morning. I realize that I have just half an hour, so I won't perseverate here. But in any case, really the Intramural Program is something that is near and dear to my heart. And over the course of the next little bit, we will talk about seven different things. First of all, I'll give you a little bit of background with regard to intramural research at the NIH in general, just in terms of how it operates. Secondly, I'll give you a big picture view of the NHGRI Intramural Research Program. Thirdly, I'll give you a sampler of some recent scientific accomplishments beyond what Eric mentioned just a couple minutes ago. Fourthly, I'll talk a little bit about the NHGRI Intramural Research Program as a genomic catalyst, as basically an organization that was developed, actually, at least in part, with the idea of Francis Collins back in 1993, that really the Intramural Program of NHGRI should be the engine that would drive the infusion of genomic thinking and technology into the broader intramural program of the NIH. Fifthly, we'll talk a little bit about scientific review of the NHGRI Intramural Research Program. Sixth, I'll give you a little bit of an update with regard to our budget. And then finally, we'll talk a bit about opportunities and challenges for the future, all in 30 minutes. So in any case, first of all, the distinctive features of intramural NIH, and these are things that I usually talk to the site visitors about when we begin our orientation for site visits. And so those of you who have been to site visits, perhaps will recognize at least this slide. So the first thing is that in the intramural NIH, there is really an institutional commitment to researchers over projects. And so it's a little bit more like the Hughes Institute than a grant-driven kind of environment. There is a quadrennial, heavily retrospective review process, and it varies from institute to institute as to what percentage of the review actually is retrospective versus prospective. In NHGRI, it's about 50-50 in terms of retrospective and prospective. In some of the other institutes, it's as much as 80-20 retrospective to prospective. Thirdly, because of the fact that we have this commitment to researchers and a quadrennial retrospective type of review, the intramural program perhaps lends itself to long-term studies that require stable funding. And it's a place where, at least to some extent, one can conduct high-risk, high-reward projects that would be difficult to do with typical R01 funding mechanisms. There are also specialized resources in the intramural program, and certainly the one that probably stands out the most is the clinical center of the NIH, and we'll turn to that in just a moment. Next, there's a critical mass in certain areas, and particularly in genomic medicine, I would say. In immunology, in structural biology, and in vaccine research, there is very much a critical mass in terms of really excellence in the intramural program. One of the things that the intramural program was specifically designed to do was to be able to turn on a dime. But when there are public health emergencies like the Ebola emergency, or back in the 1980s, the HIV crisis, that substantial resources can be marshaled to study those areas and to make progress relatively rapidly. Next, it is the intellectual home for institute directors and extramural program staff, and we really want to have a vibrant environment where these people can have an interest in science. And then finally, it is, of course, close to the seat of government, and it's not unusual for senators or Congress people to come to the intramural program to see what's going on, and we certainly want to have the best possible environment to show off to them. This is just a picture of a clinical center of the NIH. It's a 234-bed hospital where patients can be admitted at no cost to themselves or to the investigator who is admitting them. And because of that, it really does open the possibility for people to do research studies that are driven by the science. Of course, all of the patients have to come in on a IRB approved protocol, but still it really does open the doors, then, for a lot of intellectual freedom with regard to the clinical research. Within the NHGRI intramural program, just a brief overview of what it's all about. We have right now 22 tenured investigators and three tenure-track investigators, and actually we are in the process of converting one of our physician scientist development program members into a tenure-track position, and so this will soon become four tenure-track investigators. Three of our tenured investigators are members of the National Academy of Medicine and two are members of the National Academy of Sciences. We have 14 associate investigators. The associate investigators are like the research track at academic medical centers. We have nine adjunct investigators whose primary appointment is with other institutes but who collaborate with the NHGRI. As I mentioned, we have one member of the physician scientist development program, which is a program that Les B. Sacker developed a number of years ago to help individuals who have finished their clinical training get sufficient research background that they can compete for tenure-track positions. We currently have 513 personnel within the intramural program. This doesn't include contractors. With contractors, it's a little above 600. And after a reorganization that we did in late 2013, we now have nine branches and I will illustrate the organizational structure in the next slide. We have eight cores, the NIH intramural sequencing program, CIDR, which Eric alluded to in his talk, and then the Undiagnosed Diseases Program. The fiscal year 2016 budget for the intramural program is $105.4 million. So it represents about 20% of the overall funding of the NHGRI. And it's a far-flung operation with seven buildings on campus and two off campus. And that's just in part a product of the fact that the NHGRI intramural program is young relative to some of the other intramural programs. And so we've basically gotten space wherever we can as we've grown. This is just the organizational chart of the intramural program. A number of individuals are relatively new in their positions. So Paul Liu, our deputy scientific director, who Eric alluded to at the end of his talk, is the at least relatively new deputy scientific director. He replaced Andy Baxavanas, who took a NIH-wide intramural role with regard to computational biology. Paul replaced him. We have three new branch chiefs who were appointed at the time of the reorganization. And they are Charles Rotimi, Julie Segre, and Pam Schwartzberg. And then Laura Kaley is a relatively new acting chief of social and behavioral research. The intramural program of the NHGRI does tend to focus more on the dimensions of the density plot from the iconic 2011 figure in nature in basic understanding the biology of disease and advancing the science of medicine are the two areas that really are the major focus of the intramural program. And that's at least in part due to the accessibility of the clinical center. So there is more of a clinical bent to what's going on in the NHGRI intramural program than perhaps extramurally, although as Eric has said, certainly the intramural program has sort of blazed the way in terms of this and the extramural program I think is now very much orienting in this direction. Just highlighting a few of the things that have gone on in the intramural program over the last several years, one of them that really does highlight the ability to focus on a particular disease for a long time and to learn something about it and to make great headway is Les Bissecker's project on Proteus Syndrome. And Les actually began studying Proteus Syndrome 20 years ago back in 1996 with a natural history protocol. That protocol allowed for the delineation of a number of sub-phenotypes. Eventually, with the advent of next-gen sequencing, Les was able to demonstrate that in fact Proteus Syndrome is caused by activating mutations, Mosaic activating mutations in AKT-1. And at this point, Les actually has begun a targeted treatment protocol for Proteus Syndrome. So this is really an example of where over the course of a 20-year period of time with persistence and intensive study, we've gone from the delineation of clinical phenotypes on to a treatment protocol. Another example of this sort of thing is the work of Chuck Venditti, who was a member of, initially, a member of the Physician-Scientist Development Program. And during his tenure track, he became one of the world's experts in organic acidurias and in fact accrued over the course of time a very large cohort of patients with different organic acidurias. And this is just an excerpt from a paper that he published in Nature Genetics back in 2011 describing a new gene that's the cause of combined malonic and methylmalonic aciduria. Moving on to the social and behavioral research branch, Philip Shaw, who is one of our new tenure track or relatively new tenure track investigators, has actually at this point accrued the largest cohort of patients with ADHD who have undergone serial MRI scans. And so basically, what Philip is trying to do is to define a biologic phenotype rather than just going based on clinical phenotyping of patients. And so this actually illustrates some of the data that Philip has garnered, basically showing the connectome tracks within the brain that turn out to be highly heritable and in fact that correlate with ADHD. So just as we've learned recently that schizophrenia is a disorder in which there's excessive pruning of certain synapses, in the case of ADHD there's a deficiency in terms of pruning of certain synapses over the course of adolescent development. Another example of a focused examination of a particular group of patients that has led to great insight is Ellen Sudranski's work on gauchay disease and the observation that in fact mutations in glucocerebrosidase predispose to Parkinson's disease. And she has gone on, as illustrated in this slide, to make IPS cell lines from patients that have glucocerebrosidase mutations and have developed Parkinson's disease and has screened these cell lines with a compound from the NCATS, from the National Center for... But anyway the NCGC and chemical genomics and has identified a compound that actually has an effect in terms of the trafficking of glucocerebrosidase in the cells and thereby the accumulation of alpha-synuclein. And so this actually does represent then a possible therapeutic modality for Parkinson's disease going forward. This slide illustrates yet another interaction between one of our intramural investigators, this being Joan Bailey Wilson, with a very productive collaboration with Steve Wank, who is a gastroenterologist in NIDDK, identifying a gene that predisposes to small intestinal carcinoid tumors. And then on this slide, from Pam Schwartzberg's group, the identification of germline mutations in the PI3 kinase catalytic subunit as leading to an immunodeficiency and basically what happens in this condition is that because of activating mutations in PI3 kinase, there's actually an exhaustion of certain subsets of T cells leading to an immunodeficiency. From my own group, we described a condition a couple of years ago that we call DA-DA-2, which is deficiency of adenosine deaminase type 2. Now, some of you may know adenosine deaminase type 1 or just ADA is the enzyme that's deficient in many patients with severe combined immunodeficiency disease. In this particular case, the phenotype was one in which patients were referred to us with recurrent fevers and strokes. And these patients, in fact, had multiple lacunar strokes in the deep nuclei of the brain as illustrated here on these MRIs. These patients have mutations in adenosine deaminase 2. This figure here actually shows polyarteritis nodosa, which is another phenotype that can be associated with this condition. And in any case, having this cohort of patients and having some detailed phenotypic information about these patients and linking up with another group in Israel that had a cohort of patients with the PAN form of DA-DA-2 allowed us to at least start thinking about treatment for this condition. So in the cohort of patients that we have, they had cumulatively 44 strokes over 1,064 patient months, so roughly one stroke every two years. Because of the fact that we saw tumor necrosis factor in the perivascular areas in these patients, we started on our protocol TNF inhibition in these patients. And so in 383 follow-up patient months, same patients, 12 patients, zero strokes. So really, this is an example of how the intensive study of the clinical center does allow one to learn something that can do some good for patients. And then finally, another paper that we had just come out last week in the New England Journal deals with vibratory urticaria.  And this is illustrated here in a control individual before challenge and after challenge, no difference in the appearance of the arm. Whereas in this patient, you can see the development of a hive induced by stimulation. And this is caused actually by mutations in a gene, ADGRE2, which encodes a novel protein, which is actually a mechanosensor on the surfaces of mass cells that actually can detect vibration. And these particular patients happen to have a mutation that leads to increased sensitivity to vibratory stimuli. So in any case, another interesting development. This slide simply illustrates some of the work that Francis Collins group is doing with regard to the epigenome in type 2 diabetes. And one of the things that they have found is that in islet cells, actually, there is a chromatin state that is specific for the islet cell and a stretch enhancer, as Francis calls it, which actually turns on the glucokinase gene in islet cells. Another interesting development, which is coming out, I believe, this week is a paper from Laura Elnitsky's group, which shows that there's hypermethylation of the ZNF-154 promoter in solid tumors, and that this actually could be the basis for a blood test for certain solid tumors. Here, turning to the canine side of things is actually something from Elaine Ostrander's group that deals with a venereal tumor that can be transmitted between dogs. And basically, this image just shows the chromosomal translocations that occur in these venereal tumors. And basically, in these tumors, there are a number of rearrangements, a number of mutations that actually are in immune-related genes that render these tumors undetectable, or at least very difficult to detect by the immune system. Work from Bill Paven's group, looking at SOX-10, actually shows a difference in terms of the activation mode of SOX-10 in pigmentation, where it is activated versus Alenoma, where it is inactivated. And then, work from my own group, looking at Pyron, the protein that is mutated in familial Mediterranean fever. This is something that we have under review at Nature, basically showing that, in fact, Pyron is controlled by roe-GTP aces. And in fact, there are certain bacterial toxins which poison Roe-A, one of the Roe-GTP aces, and that this is what actually is being sensed and what regulates the Pyron inflammatory. This mechanism, which is known as the guard mechanism for regulating innate immunity, is something that, at least up until now, has only been seen in plants. And so, it's at least the first example of where this guard mechanism is. So anyway, then turning to the impact of the NHGRI IRP on the NIH campus and beyond, just a few things that I will briefly mention. First of all, many of our investigators, our experts in particular diseases, that then can be referral centers for investigators other places. This is just an illustration of Max Munke's work with MUNCA syndrome, which is a form of craniosynostosis, and essentially just illustrating a recent review of his group on this topic. But there are many intramural investigators that essentially serve as experts in these diseases. Next gen sequencing at NISC is another area where the NHGRI intramural program has really had a major impact, and this graph simply shows that over the course of the last five years, that the number of samples that NISC has sequenced has gone up by an order of magnitude, and that if one looks at the samples, at least the investigators who have submitted the samples, because this is something that is available to investigators in other institutes, that in fact, the majority of samples are from other institutes. And so this really does serve a very important function at the NIH. Recently, Les Beesecker and Jim Mulliken spearheaded the Clinical Center Genomics Opportunity Project, which is a project that allows investigators from other institutes to do whole exome sequencing for medically important diseases. So this is another area where NHGRI is taking the lead in terms of genomic analysis in the intramural program. Of course, Les Beesecker's ClinSeq program is another example of this, in which essentially the basis of genomic medicine has really been defined. And just some examples here of some of the papers that have resulted from that. And then, of course, as Eric alluded to, the Undiagnosed Diseases Network, which is really an outgrowth of the Undiagnosed Diseases Program, which Bill Gaul established in the intramural program back in 2008, which is now really grown into something that has had major impact across the research community. And in connection with that, our Social and Behavioral Research Branch has really spearheaded the analysis of a lot of the secondary findings in the Undiagnosed Disease Program and in the ClinSeq program. And so that also has software has been made available through a number of our intramural investigators. And just for lack of time, I won't go through all of those. We have the current topics in genome analysis series, which now gone on for 12 years. And actually, Andy Baxivanis and Eric have had a leadership role in that course. And you can see here that testing to its impact, there have been 740,000 YouTube views of posted lectures to date. Then moving to other areas of impact, I'll just mention Charles Rotimi's role in terms of the H3Africa work, Julie Segre's work, defining the landscape of bacterial and fungal organization of humans, her pioneering work with regard to tracing hospital acquired in a recent paper from a set of papers from Sean Burgess, basically laying out a strain NIH one for zebrafish, CRISPR-Cas9 mutagenesis, targeted mutagenesis of 162 different loci. And then finally, the all of the CRISPR-Cas mutations that science in the intramural program is evaluated by board of scientific counselors, a set of outside experts. John Atkinson is currently the chair of our board of scientific counselors. The three members who are highlighted in yellow are our newest members, Lucille Adams Campbell, an epidemiologist from Georgetown, Jeff Murray, many of you know AIDS Foundation, and Tim Towns. The investigators in the program are rated on a scale of outstanding, excellent, and very good. And at least over the last four years, 27 have been rated as fully outstanding out of 39, which is approximately. Just to turn to the budget, and I see that we're nearly out of time. The budget this year, as I mentioned, is $105 million a year. It has been relatively flat over the last several years. And so when I took the position as a scientific director in 2010, it was $104 million. It did go down and it's now beginning to go up. On average, our investigators have had a 16% reduction in overall budgets over this period of time. We have recruited two new tenure track investigators that I will just mention. Adam Philopipe, a bioinformatician from the National Biodefense Analysis and Countermeasures Center, that's a part of the Department of Homeland Security. And Adam was actually in the part of the Department of Homeland Security that would screen the white powder that people would receive in the mail, or might receive in the mail, to determine whether it was a pathogen. He's an expert in algorithm development, single molecule sequencing. And then finally, Peter McGuire, who's being promoted from our physician scientist development program to a tenure track position. And this image just shows the metabolomics work that he's doing, basically showing that in a principal component analysis of metabolism, metabolites that in point of fact, one can separate subsets of T lymphocytes. So in terms of challenges for the future, continued faculty recruitment is one of them, expanded engagement with other institutes, a seamless integration of bioinformatics with bench science, or right now, renovating space in one of our buildings to bring together Adam Philippi, with two other individuals, Julie Segre and Laura Elnitsky, towards a molecular taxonomy of human disease, so that basically we're looking to do more of what I've described up until now, that is to say, identify monogenic disease genes, and then a 10,000 exome recall cohort is basically developing a cohort of patients at the clinical center, who will be fully genotype, and where we can call them back if they have interesting genotypes for deep phenotype. So I think with that, I'll call it to a close. I did actually make it in the allotted time. And if you have any questions, I'll try to answer. Thanks. You can certainly take some time for questions or comments. I guess I had a couple of budgetary questions. So the 105 million, I think you mentioned that investigators could have patients evaluated, yes, internally for no cost. So the cost of that actually, I didn't have time to go through that aspect of the budget. But we are actually charged each of the institutes that sees patients, we are charged a so called school tax, which is 14% of our intramural budget. So that since we have a approximately $100 million budget, that means that in essence, we spend about $14 million a year. So it's, it's quote unquote, no cost, but we're paying $14 million a year to have that privilege of being able to do that. Yeah, so the that 14 million is part of the 105 or in addition. That's right. And so that you pay that based on your budget, not based on the number of patients that you evaluate. That's right. And so we do have any idea of whether you're losing money or you're making money in the based on the number of patients that you're seeing. Yeah, well, right now, NHGRI is a little bit on the side of losing money. So that we actually could be good if we could see more patients. What about with the NISC? What's the finances of that? So the NISC, it's also part of the $105 million. And we spend approximately $7 million a year on NISC. And that basically allows us to maintain the sequencers, maintain at least some bioinformatic analysis of the sequence. We do have a program within the intramural NHGRI where people can apply for competitive grants, basically, or competitive funding of sequencing, where people can either have large flagship projects or smaller pilot projects that if they compete well in this process, they will be able to do. And then as part of this clinical center genomics opportunity, we do provide funding for 1000 exomes worth for other investigators. That's actually a joint project with Michael Goddusman's office, the deputy director for internal research, who basically is paying half. Yeah, so you might not be responsible for the answer to this, but so that $7 million is also part of the 105. That's correct. And you know how competitive you are with the commercial sequencing price wise? Well, yes, we, we are relatively competitive right now. To charge, if we charge internal investigators within NHGRI, the cost is about $500 an exome. Very good. Thanks. The other thing, Val, the other subtle thing worth pointing out about that is, since a lot of what they do is fee for service, I mean, and so people will vote for their feet.  And so the value added is, is the is the analysis by other members of the faculty. Thanks. Yeah. The first, thank you for the overview. Could you say something about the 10,000 individual recall cohort? I was not aware of that activity, how you were doing the recruitment, what the goal is. Well, you mentioned also the diversity within that group. Yeah. So right now, it's just in its early stages. Oh, sorry. Right now, it's, it's in its early stages, it's sort of clean seek on steroids, you could say. So clean seek is certainly at least the core of that. But patients from other institutes are being incorporated into this and probably will be something that will take a year or two to accrue all of the patients for this. And the idea is that if we have a cohort of individuals where we have deep genomic information, then we can, if there are investigators at the NIH or elsewhere who are interested in the phenotype that would be associated with a given genotype, that then we could call them back to the clinical center for deep phenotyping at the clinical center. Anything about the ethnic diversity in the group? And you answered part of my question. So these are patients ascertained through a condition from another investigator. That's correct. Somehow you roll them in this. So right now, at least in clean seek, there are a number of patients who are African American, less has been specifically trying to expand the diversity of the clinical cohort on because of that. But it's a work in progress in terms of that. Just want to follow up on that comment, because when you said clean seek on steroids, because clean seek is volunteers, right? I mean, whereas the clinic, whereas the clinical center, and I just conferred with my colleague. So it's probably totally, yeah, but but but it seemed to be very, very different set of parameters that you're first, you know, characterizing the population. And in fact, maybe I mean, the people who come there are more selective for rare conditions, but they won't have the same selection bias that you'd have from a volunteer cohort. That's true. And so one of the things that it's is at least in the works, although it has not been formalized yet, is that Lass and Richard Siegel in an I am s are establishing a relationship with a Nova Fairfax. And they actually have been doing. Okay, that's a. Okay, that is a hospital in Northern Virginia. And they actually have been doing whole genome sequencing on just individuals who come through that hospital. And a lot of cases, it's through the obstetrics department. And so it's just normal deliveries. And they have actually 8000 individuals who have had all genome sequencing, where in fact that might shift the distribution more towards the type of individual that you're talking about of people that are just from the population. Yeah, so thank you. Thank you for that. Summary that I just have a have a two questions and it just shows my naivete about the about the intramural program. What is tenure? I know what tenure means that my place, but I'm not sure what tenure means here. That was one question. And then the other was the sort of the introduction is sort of high risk high reward, a long term investment in programs that couldn't be done elsewhere. And you showed some beautiful examples of science. I'm not sure. I agree that a lot of it couldn't be done elsewhere. And so can you talk a little bit about the things that intramural program does that, you know, that are examples of things that that really are, you know, such long term high risk high reward investments, or the turning on a dime. Is there anything going on in not Ebola last year? Yes, Zika, for example, that that, you know, if you tried to do it through ordinary grant mechanisms would take a year and a half before anybody got a dollar, start to work on it. So can you talk a little bit? Yeah. So with regard to tenure, first of all, so tenure is something that as opposed to in most academic institutions in which one can be tenured. And if you don't have grant funding, then it's going to be very difficult to maintain your, your research program. In fact, in the intramural program, tenure does involve a long term commitment to an individual, as long as they maintain their productivity as judged by reviews by the board of scientific counselors. And that's that's really the crux of the matter. The key is that people have to maintain their productivity in order to maintain their funding. But if one does maintain that, then it is a commitment in a way that one doesn't usually see in a university or academic health center. So that's with regard to tenure. Now, with regard to programs that people do in the intramural program that are high risk, high reward or things that can't be done other places. Well, I suppose that, you know, one can say that nearly anything, if one focuses enough on it, that one could do it in another place. I do think that the examples of some of the cohorts of patients with rare diseases, where in point of fact, it would be very difficult to maintain those cohorts over a long period of time. As I mentioned with lesbysackers work, for example, on proteas syndrome is something that sure, one could do it somewhere else. It's not as if it couldn't be done anywhere else. But certainly those kinds of projects are projects that would be hard to maintain anyway, in another in another setting. As far as turning on a dime is concerned, certainly, right now, there is an initiative in NIAID to start tackling Zika virus. And so there are such things as that certainly. Last year, Ebola, as you may know, there were Ebola patients that were at the clinical center of the NIH. And certainly in the 1980s, with the beginning of the HIV epidemic, there was a lot of work that was being done in the intramural program. But, you know, by and large, I would have to say that the most important thing with regard to intramural science is excellence. And that certainly is the thing that in our review process is really, really critical. Anything else? Okay. So thank you, Dan. Great. And Rudy, you're going to instruct us. So why don't we try to be back by 1.15 to resume the open session. There's a cafeteria one floor up that I recommend for lunch. You can bring your food back down here if you like, if there aren't tables available. You can also go upstairs on the fourth floor and visit the staff. You can leave your computers. Someone will be in the room all the time. I would advise you to take purses and wallets. Okay, see you at 1.15.


was to have been... make thy name be remembered in all generations. But I think I'm going to depart from my prepared sermon. While I was listening to the sergeant's solo, I kept looking up at our service flag. I was thinking of the men in service. I see some of them here this morning. Sergeant Jackson, Lieutenant Carter, and, uh... Private Park, first class. First class is right. I was over at the U.S.O. the other night, and I met somebody I hadn't seen quite a long time. The last time I saw this man, well, one of my members treated me to a ticket to Yankee Stadium to see Joe Lewis versus Max Smaley. 49 seconds. An American fist won a victory. But it wasn't the final victory. No, that victory's going to take a little longer and a whole lot more American fist. Now those two men that were matched in the ring that night are matched again. This time in a far greater arena and for much greater stakes. Max Smaley, a paratrooper in the Nazi army, men turned into machines challenging the world. Joe Lewis, training for the fight of his life. This time it's a fight not between man and man, but between nation and nation. It's a fight for the real championship of the world to determine which way of life shall survive, their way or our way. And this time we must see to it that there is no return engagement. For the stakes this time are the greatest men have ever fought for. And what are the stakes? The American stakes. The German stakes. The Bible of the Nazis. The Gospel according to Hitler. I'm not going to read all of this, but there are one or two things in this book that will interest you. I quote, What is denied to us the German fist must take. If our forefathers had made their decisions by the same fascist nonsense as the present day does, we would possess for a third of our existing territory. Further, he says, from time to time the illustrated papers show how a Negro has become a lawyer, a teacher, perhaps even a minister. It never dawns on the degenerate middle class America that this is truly a sin against all reasons, that it is criminal madness to train a born half-ape until one believes one has made a lawyer of him. This book was written 20 years ago. The plan which it foreshadowed has become a reality. And the Nazis now instruct their disciples in terms such as these. We must strive by any means to conquer the world. Any methods are permissible.  Kill the Slavs, the Russians, the Czechs. Don't stop whether you have an old man, a woman, a girl or boy, kill. If we want to create our great German empire, we must exterminate everybody who stands against us. The liberty of the whole earth depends on the outcome of this contest. Americans have always guarded liberty. The sea took root in Boston. In that city is the granary burial ground. Sixteen within this ground are buried the victims of the Boston massacre, March 5th, 1770. The first to die in the Boston massacre was Crispus Attucks. During this freedom's cause the wise contend, dear to our country shall your fame extend, while to the world the lettered stone shall tell where Caldwell, Attucks, Gray and Maverick fell. At Concert Bridge, Sam Crass and John Ferris helped to fire the shopper around the world. At Punker Hill on June 17, 1775, John belonged to Peter Salem, a colored man who carried it at Nextington, Concert and Bunker Hill and with it shot Major Pitcair. And on Christmas Day, 1776, when all but the bravest hearts had lost hope, Prince Whipple took his place alongside those who pushed on. In the winter of 78 at Valley Forge, Washington wrote, Our soldiers have been a week without food. They are naked and starved. We cannot enough admire their unshakable patience and loyalty. Here's Samuel Haynes, Salem 4, thousands of others left their bleeding footprints in the snow. In this war the people of the new world won their independence. They joined hands and 13 colonies became the United States. Then the people of the new republic began to fill it. Together they pioneered and together they made territories into states. By 1812, a wilderness was becoming a great nation. Lord Admiral Ferris immortal words, We have met the enemy and they are ours. They shall not sleep on our soil. Thomas Wilson was there. The government of the people, by the people, all the people shall not perish from this earth. With malice toward none, with charity for all. Then America began to rebuild. Jim fought in the Spanish-American war. At Santiago, Cuba, the 9th and 10th Cavalry, the 24th and 25th Institution, added to their long records new metals, courage and gallant. I'm Jim. After we cleaned up in Cuba, we went on with building factories, cities, everything. As for me, I went to Panama, to help on a little job. A little job, connecting the Atlantic and Pacific. A little job. What his sons were, came with the 813th Pioneer Regiment near Marseille, the 332nd Labor Battalion, and the 808th Pioneer Regiment near Verdun, with the 8th Illinois on the Swanson Front, 372nd on the Plains of Montau, 371st at Bucie Falls in the Argonne, a line of fire for 191 days, not a man ever captured, not a foot of ground ever lost. The American troops received the part of Gaire, the 369th, and for action above and beyond the call of duty, many received honored metal. When they cleaned up in France, the boys came marching home to 28 German, for which the French nation decorated them for exceptional bravery. And there were those among the honored at Arlington, Samuel Washington, Walter Waters, William Fox, John Sims, Young, Charles Young, Colonel, United States Army, and to their memory, sermons and stone and bronze, tributes of a grateful nation, to commemorate their heroism and sacrifice of all college soldiers who served in the various wars engaged in by the United States of America, that a lasting record shall be made of their unselfish devotion to duty. And in France's Bucie Falls, the French people erected a memorial to the 371st Infantry. This stone and the ground on which it stands are dedicated to the Negro troops who fought and died here on April 21, 1918. Twenty-three years later, on June 15, 1941, an invading German army Yes, the Nazis destroyed our monuments in France, but our monuments here stand and will always stand. The founder of Tuskegee Institute, Brooker T. Washington, 1856-1915, he lifted the veil of ignorance from his people and pointed the way to progress through education and industry. The late George Washington Carver, honored in the chemistry of agriculture, and the men and women building the monuments of tomorrow, law, elected judge of New York City courts, serving his second 10-year term, explored the only other American with Admiral Peary when our flag was planted on the North Pole, medicine, leading New York City surgeon, father of the blues, financier and publisher, contributing to the war bond drive, education, principle of a New York City public school, Schoenberg Collection of Literature, international prize-winning sculpture, singer, all trained and educated in schools like... Power, the sign of life, action, the sign, the TV, the sign of life, all of us are friends in the sign of life, open the sign. Welcome to Berlin, and 51 nations are represented here today. The fastest spinters of the world are getting ready. America, Westmeyer, Germany, and Metcalf of America. Mancats, Germany, America, Japan, three meters high at six feet eight inches, and Johnson of America. It's a mighty tree in the ground of America. Its fame has spread to the four corners of the earth. Men of every faith have helped to nourish it and to sow its seed in new ground. All men stand in admiration of it, except the Nazis, the fascists, and the Japanese militaries. The fills of the new order. There is not a man alive who does not know of the crimes of these adventurers. In Europe, the German Army High Command, thousands of innocent men, women, and children have been shot and hanged. In Asia, the Japanese Army is the key of the colored races suitable for these crimes. In America's Army, in every branch of the service, master, engineer, yes? My boy is in the infantry. He's up there, turned from the left in the bottom row. I got a letter from him last night. He's an officer. Robert? Did he get to be an officer? Yes. He says so right here. Dear Mama, at last it's happened. I'm an officer. Lieutenant Robert A. Bronson, infantry. Don't it sound good? Of course I haven't got my bars yet. But I'm on my way to officer's candidate school for four months, and if I pass the examination and graduate and get my commission, I'll be an officer. Now I can do it, Mama. If I could go through those first 13 weeks the way I did, I can go through anything. That morning at the railroad depot, I didn't know which side was up, but misery sure loves couple. When I said goodbye to you, I was laughing, but I wasn't tickled. And the first thing I heard when I stopped at some place in the middle of no place. After the United States Army, the reception center here at this camp, we're glad to welcome you here today and into the United States Army. We're glad to see all of your happy, smiling faces. Our fellows, during the few days that you'll be with us here at this camp, you'll be converted from a civilian into a full-fledged soldier. You'll be viewed by someone who'll inquire into your past life before you came into the Army. You'll be assisted by the machineers on the construction of guns and gun parts. That's right. What machines did you operate? Operate boring mills, tool bindings, drill presses, planes, and... Forward! Forward! Quick! Up! Up! Up! Pick it up! Pick it up! Company! Hold! But I don't want to give you the impression that it's only for religious services and advice that you can come to the chaplain. You can come to him at any time. For example, if you get in trouble with your girlfriend, she doesn't write to you anymore, you want to know how to propose, why, just come see the chaplain and he'll give you some advice. Or if you get in trouble with your first sergeant, you want to know how to handle him, just come around and see the chaplain and he'll give you some advice.  Now, chaplain Sherman has just told you how to get along in this man's army. It's my painful duty to tell you what'll happen to you if you don't get along. Regarding this salute, man, it isn't a form of civility. It's merely a form of recognition used between members of the military service. The average man, when he first learns the salute, he's awkward. It's a strange gesture to him and he feels shy. He usually sneaks his hand up in this manner and gives a broken-handed salute.  When you salute an officer, stand erect like a stout. Bring your hand up to your forehead in a military manner. Tip of the forefinger above the center of the right eye. Now, make sure your thumb is alongside your hand because if it's out here, they may misconstrue it. Then cut the hand garbage at the side. Don't let it drop down like a dead fish. Pull in! Pull out! Sit down. Put your feet in the scene. One L on a ten and a half stages. Stand up now. Pick up both weights. Put the weights back. Sit down. Step down.  Pull out! All right, men. Strip down completely. Put every piece of civilian clothing that you have into that very thing. Now, when you're completely stripping and have everything into that bag, put on a set of summer underwear, a pair of shorts, a pair of white woolen socks. All right, now you try on your green herring bones for TIG hats first. That's the green hat with a brim on it. Try it on. If it fits, place it into your barracks bag. Now, don't mind if it's a little loose. But I made it. All of us made it. They put us through so many twists and turns we didn't know whether we were coming or going. But Uncle Sam did. We were going. He put us on the train that same afternoon for the replacement center. We kept passing troop trains going back and forth all over the country. We finally arrived at a place called Military Secret. But this much, I can tell you, it was cold. And even before we had a chance to throw out, they had us in the school of the soldier. The sergeant gave it to us straight. You sleep in that bed, you make it. You wear them clothes, you wash them. You walk on that floor, you clean it. There's no service here. You understand that? And I bet right now, Mama, I can make your bed better than you can. But before I had a chance to get the bed warm, there I was. In the beginning, the new hands were all feet. Right? Hey! A word? Country marching. Vivoics, tent pitching, general orders. The tougher the day, the shorter the night. But you get used to it. And if you can't take it, they got doctors. Some of the best in the country. Specialists in every field. And in our dental clinic, we have a wedge. But even so, this is one kind of drilling no soldier like. But after all the making beds and peeling potatoes and drilling and marching, they give you a rifle and teach you to shoot it. In no time at all, I could hit the broad side of a bomb. It was fun. It's not all worth it. There's football, baseball, boxing, and oh yes, ping pong. If you want to read, there's a library. Can you imagine me listening to poetry? But it came in good. The very next day, I got a chance to use it. We were sent on a detail to a nearby camp. I saw some soldiering that was beautiful to behold. Tell Uncle Everett to quit joking about the wax. Because these girls have forgotten more about jeeps and trucks than he'll ever know. Corporal or no corporal. They will saturday night. Real apple pie girls. Just the right size and everything. Just the time you like. But in the army, man proposes and GI disposes. And the next morning, I was out there like the rest of them. Getting tough. In this army, they want you to be tough. Tough. And I mean tough. The army means a good fighter who can stand up against a strong enemy and beat him to the draw. And after a hard week, a soldier welcomes a Sunday. I have a GI report. I think you'll all be interested in. This is an official statement from the war department. In the present world war, they are more than five times as many men with high school education in our armed forces than were in World War I. This has meant an increase in officer candidates. Official figures tell us in the present war they are three times as many colored officers as in World War I. From a score of different schools, many have earned their commissions and taken their place alongside those who come from any one of the hundreds of ROTC schools and the greatest of all military institutions. West Point. This time you see me, I'll be wearing an officer's uniform ready to get in there and get this war over with. That's a promise. Your loving son, Robert. Thank you, Mrs. Bronson. That's a promise millions of Americans have made. Thousands have put it into action. In every camp, men are getting their final work out. At Tuskegee, more pilots are earning their wings. Short while, these young officers will be full-fledged combat flyers taking their place at the controls of our fighter planes. Above their native land they fly. Tomorrow, what a surprise the Nazis will get from black, brown, yellow and white men all American land on the airfields of Berlin and Tokyo. In the far north, seasoned soldiers are toughening themselves for they know that in a fight against all that is evil, truth must be ready. Ready to strike hard and often. No waiting for weather or temperature. Any weather can be good weather. Any temperature can be right. When men know the meaning of their jobs and are determined to get it done, come what may. And the men we knew as entertainers, carpenters, bellboys, schoolteachers, farmers are today soldiers in a modern army. Tankmen, gunners, radio operators and motor mechanics. Every man's schooled in the meaning of teamwork. Every man qualified to replace any of his teammates at any time. Every man ready to do his share. Ready to build bridges, highways, railroads, ports and landing fields who provides to food. The men who supply the means of communication. Telegraphs, telephones and radios. The cavalry. Then decisive action. Ready to knock zero in Messerschmitts out of the skies. Backbone of the army. A lot of defeat hangs over the Axis partners. As the allies liberate town after town. Only the beginning. To win the final victory over Germany and Japan will be dealt harder and faster. And with all our strength. There can be no let up in the floor of supplies. More and more food, equipment and ammunition must reach our troops in the battle area. Our troops must build them through the swamps of the South Pacific. The far north and across the rivers of Europe. Build them in record time and under enemy fire.