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User talk:NCSAPS Kyle

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Good luck! --Panic (talk) 01:31, 12 March 2008 (UTC)

title: User talk:NCSAPS Kyle


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User talk:Tlaz

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Good luck! --Panic (talk) 01:31, 12 March 2008 (UTC)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

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User talk:JerEdit

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Good luck! --Panic (talk) 01:31, 12 March 2008 (UTC)

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User talk:Ken joram koen

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Good luck! --Panic (talk) 01:31, 12 March 2008 (UTC)

title: User talk:Ken joram koen


text:
Linear Algebra/Introduction

This book helps students to master the material of a standard undergraduate linear algebra course.

The material is standard in that the topics covered are Gaussian reduction, vector spaces, linear maps, determinants, and eigenvalues and eigenvectors. The audience is also standard: sophomores or juniors, usually with a background of at least one semester of calculus and perhaps with as many as three semesters.

The help that it gives to students comes from taking a developmental approach—this book's presentation emphasizes motivation and naturalness, driven home by a wide variety of examples and extensive, careful, exercises. The developmental approach is what sets this book apart, so some expansion of the term is appropriate here.

Courses in the beginning of most mathematics programs reward students less for understanding the theory and more for correctly applying formulas and algorithms. Later courses ask for mathematical maturity: the ability to follow different types of arguments, a familiarity with the themes that underlie many mathematical investigations like elementary set and function facts, and a capacity for some independent reading and thinking. Linear algebra is an ideal spot to work on the transition between the two kinds of courses. It comes early in a program so that progress made here pays off later, but also comes late enough that students are often majors and minors. The material is coherent, accessible, and elegant. There are a variety of argument styles—proofs by contradiction, if and only if statements, and proofs by induction, for instance—and examples are plentiful.

So, the aim of this book's exposition is to help students develop from being successful at their present level, in classes where a majority of the members are interested mainly in applications in science or engineering, to being successful at the next level, that of serious students of the subject of mathematics itself.

Helping students make this transition means taking the mathematics seriously, so all of the results in this book are proved. On the other hand, we cannot assume that students have already arrived, and so in contrast with more abstract texts, we give many examples and they are often quite detailed.

In the past, linear algebra texts commonly made this transition abruptly. They began with extensive computations of linear systems, matrix multiplications, and determinants. When the concepts—vector spaces and linear maps—finally appeared, and definitions and proofs started, often the change brought students to a stop. In this book, while we start with a computational topic, linear reduction, from the first we do more than compute. We do linear systems quickly but completely, including the proofs needed to justify what we are computing. Then, with the linear systems work as motivation and at a point where the study of linear combinations seems natural, the second chapter starts with the definition of a real vector space. This occurs by the end of the third week.

Another example of our emphasis on motivation and naturalness is that the third chapter on linear maps does not begin with the definition of homomorphism, but with that of isomorphism. That's because this definition is easily motivated by the observation that some spaces are "just like" others. After that, the next section takes the reasonable step of defining homomorphism by isolating the operation-preservation idea. This approach loses mathematical slickness, but it is a good trade because it comes in return for a large gain in sensibility to students.

One aim of a developmental approach is that students should feel throughout the presentation that they can see how the ideas arise, and perhaps picture themselves doing the same type of work.

The clearest example of the developmental approach taken here—and the feature that most recommends this book—is the exercises. A student progresses most while doing the exercises, so they have been selected with great care. Each problem set ranges from simple checks to reasonably involved proofs. Since an instructor usually assigns about a dozen exercises after each lecture, each section ends with about twice that many, thereby providing a selection. There are even a few problems that are challenging puzzles taken from various journals, competitions, or problems collections. (These are marked with a "?" and as part of the fun, the original wording has been retained as much as possible.) In total, the exercises are aimed to both build an ability at, and help students experience the pleasure of, doing mathematics.

Applications and Computers.
The point of view taken here, that linear algebra is about vector spaces and linear maps, is not taken to the complete exclusion of others. Applications and the role of the computer are important and vital aspects of the subject. Consequently, each of this book's chapters closes with a few application or computer-related topics. Some are: network flows, the speed and accuracy of computer linear reductions, Leontief Input/Output analysis, dimensional analysis, Markov chains, voting paradoxes, analytic projective geometry, and difference equations.

These topics are brief enough to be done in a day's class or to be given as independent projects for individuals or small groups. Most simply give the reader a taste of the subject, discuss how linear algebra comes in, point to some further reading, and give a few exercises. In short, these topics invite readers to see for themselves that linear algebra is a tool that a professional must have.

For people reading this book on their own.
This book's emphasis on motivation and development make it a good choice for self-study. But, while a professional instructor can judge what pace and topics suit a class, if you are an independent student then perhaps you would find some advice helpful.

Here are two timetables for a semester. The first focuses on core material. The second timetable is more ambitious (it supposes that you know One.II, the elements of vectors, usually covered in third semester calculus). See the table of contents for the titles of these subsections.

To help you make time trade-offs, in the table of contents I have marked subsections as optional if some instructors will pass over them in favor of spending more time elsewhere. You might also try picking one or two topics that appeal to you from the end of each chapter. You'll get more from these if you have access to computer software that can do the big calculations.

The most important advice is: do many exercises. The recommended exercises are labeled throughout. (The answers are available.) You should be aware, however, that few inexperienced people can write correct proofs. Try to find a knowledgeable person to work with you on this.

Finally, if I may, a caution for all students, independent or not: I cannot overemphasize how much the statement that I sometimes hear, "I understand the material, but it's only that I have trouble with the problems" reveals a lack of understanding of what we are up to. Being able to do things with the ideas is their point. The quotes below express this sentiment admirably. They state what I believe is the key to both the beauty and the power of mathematics and the sciences in general, and of linear algebra in particular (I took the liberty of formatting them as poems).

I know of no better tactic

 than the illustration of exciting principles

by well-chosen particulars.

--Stephen Jay Gould

If you really wish to learn

 then you must mount the machine

 and become acquainted with its tricks

by actual trial.

--Wilbur Wright

Jim Hefferon

Mathematics, Saint Michael's College

Colchester, Vermont USA 05439

http://joshua.smcvt.edu

2006-May-20

Author's Note. Inventing a good exercise, one that enlightens as well as tests, is a creative act, and hard work.

The inventor deserves recognition. But for some reason texts have traditionally not given attributions for questions. I have changed that here where I was sure of the source. I would greatly appreciate hearing from anyone who can help me to correctly attribute others of the questions.

線型代数学/序論

title: Linear Algebra/Introduction


text:
Linear Algebra/Notation

Lower case Greek alphabet
$ \begin{array}{ll

About the Cover. This is Cramer's Rule for the system $$x_1+2x_2=6$$, $$3x_1+x_2=8$$. The size of the first box is the determinant shown (the absolute value of the size is the area). The size of the second box is $$x_1$$ times that, and equals the size of the final box. Hence, $$x_1$$ is the final determinant divided by the first determinant.

title: Linear Algebra/Notation


text:
Linear Algebra/Appendix

Mathematics is made of arguments (reasoned discourse that is, not crockery-throwing). This section is a reference to the most used techniques. A reader having trouble with, say, proof by contradiction, can turn here for an outline of that method.

But this section gives only a sketch. For more, these are classics: Methods of Logic by Quine, Induction and Analogy in Mathematics by Pólya, and Naive Set Theory by Halmos. Reader can also read the wikibook Mathematical Proof.

title: Linear Algebra/Appendix


text:
Linear Algebra/Topic: Voting Paradoxes

Imagine that a Political Science class studying the American presidential process holds a mock election. Members of the class are asked to rank, from most preferred to least preferred, the nominees from the Democratic Party, the Republican Party, and the Third Party, and this is the result ($$>$$ means "is preferred to"). What is the preference of the group as a whole?

Overall, the group prefers the Democrat to the Republican by five votes; seventeen voters ranked the Democrat above the Republican versus twelve the other way. And, overall, the group prefers the Republican to the Third's nominee, fifteen to fourteen. But, strangely enough, the group also prefers the Third to the Democrat, eighteen to eleven.

This is an example of a voting paradox, specifically, a majority cycle.

Voting paradoxes are studied in part because of their implications for practical politics. For instance, the instructor can manipulate the class into choosing the Democrat as the overall winner by first asking the class to choose between the Republican and the Third,  and then asking the class to choose between the winner of that contest, the Republican, and the Democrat. By similar manipulations, any of the other two candidates can be made to come out as the winner. (In this Topic we will stick to three-candidate elections, but similar results apply to larger elections.)

Voting paradoxes are also studied simply because they are mathematically interesting. One interesting aspect is that the group's overall majority cycle occurs despite that each single voters's preference list is rational— in a straight-line order. That is, the majority cycle seems to arise in the aggregate, without being present in the elements of that aggregate, the preference lists. Recently, however, linear algebra has been used to argue that a tendency toward cyclic preference is actually present in each voter's list, and that it surfaces when there is more adding of the tendency than cancelling.

For this argument, abbreviating the choices as $$D$$, $$R$$, and $$T$$, we can describe how a voter with preference order $$D>R>T$$ contributes to the above cycle.

(The negative sign is here because the arrow describes $$T$$ as preferred to $$D$$, but this voter likes them the other way.) The descriptions for the other preference lists are in the Voting preferences table below.

Now, to conduct the election we linearly combine these descriptions; for instance, the Political Science mock election
 * $$5\cdot\,$$[[Image:Linalg_voting_diagram_43.png|100px]]$$\,+4\cdot\,$$[[Image:Linalg_voting_diagram_44.png|100px]]$$\,+\cdots+2\cdot\,$$[[Image:Linalg_voting_diagram_45.png|100px]]

yields the circular group preference shown earlier.

Of course, taking linear combinations is linear algebra. The above cycle notation is suggestive but inconvienent, so we temporarily switch to using column vectors by starting at the $$D$$ and taking the numbers from the cycle in counterclockwise order. Thus, the mock election and a single $$D>R>T$$ vote are represented in this way.

\begin{pmatrix} 7 \\ 1 \\ 5 \end{pmatrix} \quad\text{and}\quad \begin{pmatrix} -1 \\ 1 \\ 1 \end{pmatrix} $$

We will decompose vote vectors into two parts, one cyclic and the other acyclic. For the first part, we say that a vector is purely cyclic if it is in  this subspace of $$\mathbb{R}^3$$.

C =\{\begin{pmatrix} k \\ k \\ k \end{pmatrix} \,\big|\, k \in\mathbb{R}\} =\{k\cdot\begin{pmatrix} 1 \\ 1 \\ 1 \end{pmatrix} \,\big|\, k \in\mathbb{R}\} $$

For the second part, consider the subspace (see Problem 6) of vectors that are perpendicular to all of the vectors in $$C$$.

C^\perp =\{\begin{pmatrix} c_1 \\ c_2 \\ c_3 \end{pmatrix} \,\big|\, \begin{pmatrix} c_1 \\ c_2 \\ c_3 \end{pmatrix}\cdot\begin{pmatrix} k \\ k \\ k \end{pmatrix} =0 \text{ for all } k\in\mathbb{R}\} $$

=\{\begin{pmatrix} c_1 \\ c_2 \\ c_3 \end{pmatrix} \,\big|\, c_1+c_2+c_3=0\} =\{c_2\begin{pmatrix} -1 \\ 1 \\ 0 \end{pmatrix} +c_3\begin{pmatrix} -1 \\ 0 \\ 1 \end{pmatrix} \,\big|\, c_2,c_3\in\mathbb{R}\} $$

(Read that aloud as "$$C$$ perp".) So we are led to this basis for $$\mathbb{R}^3$$.

\langle \begin{pmatrix} 1 \\ 1 \\ 1 \end{pmatrix}, \begin{pmatrix} -1 \\ 1 \\ 0 \end{pmatrix}, \begin{pmatrix} -1 \\ 0 \\ 1 \end{pmatrix} \rangle $$

We can represent votes with respect to this basis, and thereby decompose them into a cyclic part and an acyclic part. (Note for readers who have covered the optional section in this chapter: that is, the space is the direct sum of $$C$$ and $$C^\perp$$.)

For example, consider the $$D>R>T$$ voter discussed above. The representation in terms of the basis is easily found,

 * $$\begin{array}{rcl}

\begin{array}{*{3}{rc}r} c_1 &-  &c_2  &-  &c_3  &=  &-1 \\ c_1 &+  &c_2  &   &     &=  &1  \\ c_1 &   &     &+  &c_3  &=  &1 \end{array} &\xrightarrow[-\rho_1+\rho_3]{-\rho_1+\rho_2}\; \xrightarrow[]{(-1/2)\rho_2+\rho_3} &\begin{array}{*{3}{rc}r} c_1 &-  &c_2  &-  &c_3      &=  &-1 \\ &  &2c_2 &+  &c_3      &=  &2  \\ &  &     &   &(3/2)c_3 &=  &1 \end{array} \end{array} $$

so that $$c_1=1/3$$, $$c_2=2/3$$, and $$c_3=2/3$$. Then

\begin{pmatrix} -1 \\ 1 \\ 1 \end{pmatrix} =\frac{1}{3}\cdot\begin{pmatrix} 1 \\ 1 \\ 1 \end{pmatrix} +\frac{2}{3}\cdot\begin{pmatrix} -1 \\ 1 \\ 0 \end{pmatrix} +\frac{2}{3}\cdot\begin{pmatrix} -1 \\ 0 \\ 1 \end{pmatrix} =\begin{pmatrix} 1/3 \\ 1/3 \\ 1/3 \end{pmatrix} +\begin{pmatrix} -4/3 \\ 2/3 \\ 2/3 \end{pmatrix} $$

gives the desired decomposition into a cyclic part and an acyclic part.

 * [[Image:Linalg_voting_diagram_3.png|x100px]]$$\,=\,$$[[Image:Linalg_voting_diagram_4.png|x100px]]$$\,+\,$$[[Image:Linalg_voting_diagram_5.png|x100px]]

Thus, this $$D>R>T$$ voter's rational preference list can indeed be seen to have a cyclic part.

The $$T>R>D$$ voter is opposite to the one just considered in that the "$$>$$" symbols are reversed. This voter's decomposition

$$\,=\,$$$$\,+\,$$

shows that these opposite preferences have decompositions that are opposite. We say that the first voter has positive spin since the cycle part is with the direction we have chosen for the arrows, while the second voter's spin is negative.

The fact that that these opposite voters cancel each other is reflected in the fact that their vote vectors add to zero. This suggests an alternate way to tally an election. We could first cancel as many opposite preference lists as possible, and then determine the outcome by adding the remaining lists.

The rows of the table below contain the three pairs of opposite preference lists. The columns group those pairs by spin. For instance, the first row contains the two voters just considered. If we conduct the election as just described then after the cancellation of as many opposite pairs of voters as possible, there will be left three sets of preference lists, one set from the first row, one set from the second row, and one set from the third row. We will finish by proving that a voting paradox can happen only if the spins of these three sets are in the same direction. That is, for a voting paradox to occur, the three remaining sets must all come from the left of the table or all come from the right (see Problem 3). This shows that there is some connection between the majority cycle and the decomposition that we are using---a voting paradox can happen only when the tendencies toward cyclic preference reinforce each other.

For the proof, assume that opposite preference orders have been cancelled, and we are left with one set of preference lists from each of the three rows. Consider the sum of these three (here, the numbers $$a$$, $$b$$, and $$c$$ could be positive, negative, or zero).

 * [[Image:Linalg_voting_diagram_27.png|x100px]]$$\,+\,$$[[Image:Linalg_voting_diagram_28.png|x100px]]$$\,+\,$$[[Image:Linalg_voting_diagram_29.png|x100px]]$$\,=\,$$[[Image:Linalg_voting_diagram_30.png|x100px]]

A voting paradox occurs when the three numbers on the right, $$a-b+c$$ and $$a+b-c$$ and $$-a+b+c$$, are all nonnegative or all nonpositive. On the left, at least two of the three numbers, $$a$$ and $$b$$ and $$c$$, are both nonnegative or both nonpositive. We can assume that they are $$a$$ and $$b$$. That makes four cases: the cycle is nonnegative and $$a$$ and $$b$$ are nonnegative, the cycle is nonpositive and $$a$$ and $$b$$ are nonpositive, etc. We will do only the first case, since the second is similar and the other two are also easy.

So assume that the cycle is nonnegative and that $$a$$ and $$b$$ are nonnegative. The conditions $$0\leq a-b+c$$ and $$0\leq -a+b+c$$ add to give that $$0\leq 2c$$, which implies that $$c$$ is also nonnegative, as desired. That ends the proof.

This result says only that having all three spin in the same direction is a necessary condition for a majority cycle. It is not sufficient; see Problem 4.

Voting theory and associated topics are the subject of current research. There are many intriguing results, most notably the one produced by K. Arrow, who won the Nobel Prize in part for this work, showing that no voting system is entirely fair (for a reasonable definition of "fair"). For more information, some good introductory articles are , ,, and. A quite readable recent book is. The long list of cases from recent American political history given in show that manipulation of these paradoxes is routine in practice (and the author proposes a solution).

This Topic is largely drawn from. (Author's Note: I would like to thank Professor Zwicker for his kind and illuminating discussions.)

Exercises
/Solutions/

title: Linear Algebra/Topic: Voting Paradoxes


text:
User talk:2tailakitsune

Welcome!
Welcome, !

Come introduce yourself at the new users page. If you have any questions, you can ask there or contact me personally.--Whiteknight (Page) (Talk) 02:16, 12 March 2008 (UTC)

title: User talk:2tailakitsune


text:
User:Joefaust

Http://joefaust.org

title: User:Joefaust


text: User talk:Joefaust

title: User talk:Joefaust


text:
Template:Linear Algebra/Book2/Index

[ Index ]

title: Template:Linear Algebra/Book2/Index


text:
User talk:Timetraveler3 14

Welcome!
Welcome, !

Come introduce yourself at the new users page. If you have any questions, you can ask there or contact me personally.--Whiteknight (Page) (Talk) 03:11, 12 March 2008 (UTC)

A Very warm welcome, and thanks!!!
I had been coming to wikibooks and editing content on inorganic chemistry since a few days To tell th truth, it was very difficult, given that I was alone and none of the work was of immediate use to anyone.

Your edits came in as a nice surprise. Your edits suggest me that you are a student, and if my unintelligent guess is somehow right - one pursuing an elementary course in Chemistry at the moment.

The two of us collaborating
Wikibooks is all about collaboration - (as are all other wikimedia projects) - I have my formal notes from two years back on chemical bonding and periodicity that might serve here well. I am quite willing to upload the scanned pages [they are not hasty jot downs or any other waste like that - they constitute a formal textbook ;)] of the notes - will you like to transcript those pages into text + diagarms?

I have already made up my Mind that I'll be uploading the scanned notes here on some wiki project, but if YOU are willing to collaborate, I will upload those notes NOW - so that wikification and editing by other contributors may start gaining momentum.

Waiting for your reply - and thanks for all the edits! I know you might not be able to get enough time for working on the project, because the same is here with me. But if you do, will you be able to collaborate? That's the question!

Reason for transcripting
The reason is very simple. How many people want to start writing a textbook from scratch? On the other hand - consider that you have a textbook with all the topics covered, but lacking in explanation, detail or has some minor flaws here and there. The latter will attract a lot more volunteers who are willing to make the book BETTER. I won't be surprised that if the textbooks uploaded here are helpful to the students, someone will be willing to re-format it, someone will be able to draw diagrams for us, somebody else will be willing to donate lab pphotographs for the book, thus making the book making process a LOT more simpler.

But for the rush of edits, we first need something that can be edited to be made better. If we have blank pages and scarce information like we have at the moment, how many people will be enthusiastic about the project? That's the sole reason of transcripting - providing an impetus to the project!

I hope i have conveyed all of my enthusiasm to you!

Reply
You can reply here iself, or if you wish you might come and chat on my talk page. --Thewinster (talk) 18:20, 14 March 2008 (UTC)

Thanks
Oh glad that you are willing to! Maybe we'll get to being somewhere. I have got my Entrance examination on the 13th of april - so i am afraid I wont be scanning/uploading my notes right now! Maybe ten-twenty pages before the month turns - but not the full set. :(

Contributing - what is needed?
As far as editing content goes - Well - It just depends on what sort of textbook we wish to create. Since you are a major in Physics, you'd be able to connect to the Physics textbook a little better.

The Physics textbook right now over there is more like an elaborate glossary - it doesnt really teach anything, but at the same time it is nearly comprehensive. Consider kinematics for example - you could emphasize the difference between distance and displacement; add a few problems on distinguishing them - like distance and displacement of a particle going round a semicircle, average speed and average velocity in such motions - then some graphs to analyze - and what not!

The possibilities are endless - but as students we would rather be confused about teaching a topic with a flow, nevertheless, we can always explain or elaborate what we have already understood. We could discuss this more, but for now - its just that not many textbooks here have any substantial self study content.

The chemistry text
I checked the Lewis Structures page - yeah thats perfect for the book right now. I'll upload a few pages right now for you to check out.

Proposed Editing Scheme
We cant concenterate on adding polished content at one go!! We just havent got the expertise or the proper guidance to. What we can do is

 * Add bare text on certain topics - which may or may not serve the purpose of selfstudying, but what people aquainted with the topic can understand.
 * While writing certain topics, we might explicitly assign some points for Ghost-Writing
 * As the text matures, we can add intext objective/descriptive questions which serve the purpose of teaching. Note that its always better for us to add such simple questions and explain each one of them rather than sit with a few textbooks and editing their content to create our own.
 * This doesnt complete the textbook - a textbook is more than just text; but we assume that further discussion on this topic is not needed unless we have a skeleton ready!

As time passes - I'm sure we can develop a modus operandi useful not only for Inorganic Chemistry but for every textbook over here.

I know you might not be able to contribute if you leave Chemistry as a subject of study, but lets just hope you'll contribute to Physics otherwise! ;) Cheers!

--Thewinster (talk) 17:50, 24 March 2008 (UTC)

Link To The Pages
Before we get any enthusiastic about our collabo - I'd like to confess that the scanning was very inferior. Also blame my sick handwriting some years back! You might not like the order of topics/type of discussion in the pages. In such a case - you might want to Tag the text with instructions or add some notes for future contributors and me on the discussion page. We cant expect to have the best text rightaway! Expect full support from me in any editing help required.

http://en.wikibooks.org/wiki/Inorganic_Chemistry/Chemical_Bonding/Ionic_Bond http://en.wikibooks.org/wiki/Inorganic_Chemistry/Chemical_Bonding/Fajan%27s_Rule

Second thoughts on wikipedia
And some major topics like Lewis Structures, the VSEPR theory etc. - We are better off copying the wikipedia text and reformatting it. Reformatting is necessary BECAUSE the text is toooooo lengthy on wikipedia - we are here to create a free textbook that can create interest in the topic! Too much black space (text) can freak readers! :D Discussions are welcome in this regard - but we'll copy the text first - you recommend which topics go up for copying from wikipedia - we can then request the admins here for transwkiying those articles here.

Presently, I am really excited about the book, because even if we fail in wikifying the scanned pages - somebody or the other will be able to reap benefits from the uploaded pages, lets just hope the admins dont have anyproblems with the scanned pages. I know the notes are not comprehensive, but then again - its the beginning! --Thewinster (talk) 20:19, 24 March 2008 (UTC)

Transwikied pages
Thanks!! And that will be one step more towards the text. I have some more days to go before i finish wid my entrance examinations. After that - i will transcribe the scanned pages myself and add a set target of 50-100 illustrations.

My only aim with chemical bonding is to make it into an ideal chapter which can be used as a template for writing other chapters, so that more contributors can put in their efforts.

Looking back at my chemical bonding notes - i do not think i should have scanned some of them, although some edits here and there will be just perfect!

Hope to have some news from your side...--Thewinster (talk) 16:53, 31 March 2008 (UTC)

To-Do List
I'm glad that you are taking so much interest in the project.

You can use http://en.wikibooks.org/w/index.php?title=Talk:Inorganic_Chemistry/Chemical_Bonding&action=edit for making a public To-Do list that everyone can view. You can use it for almost any kind of open discussion you want.

I can understand your confusion with regards to wikipedia. :)

For the moment - you can grab some textbook on Chemical bonding - check the order of topics and construct a module here on wikibooks using the same topics order, albeit using text from wikipedia.

Once everything is in place, you can edit as you want.

I'm really sorry I wont be able to contribute for a few more days, but if there's anything we can discuss - feel free to!!! --Thewinster (talk) 19:08, 31 March 2008 (UTC)

Re: Content from additional places
Well if you think the idea is useful you can surely carry on with it. I would modestly want to point out that getting in too much of reference material can be very confusing and more so if you are doing it to WRITE articles - the reason is you'll be confused how to differentiate our text from others.

Here's a little pathway I would suggest:
 * Prepare a list of topics to be written
 * Start with some text on wikipedia or bare skeleton text dealing with JUST the basics.
 * See which sections need editing and which need elaboration
 * After the book is nearly complete and we have an idea of what we want to create, we can mess around with all the other freely available content!

The purpose of this project is not just to present text - it is also to teach. I guess, apart from plain text (which is basically the SAME in every textbook) we also need illustrations and objective exercises placed at appropriate places.

I am really sorry i can not contribute for some tim. The reason being that the examinations I would be answering will require a 97 percentile for success!

Are you still around?
...and active on wikibooks? Remember we did a little collaboration on Inorganic Chemistry?

Would be glad to collaborate again! - Thewinster (talk) 05:27, 13 September 2010 (UTC)

title: User talk:Timetraveler3 14


text:
Inorganic Chemistry/Chemical Bonding/Lewis Dot Structures

Lewis structures, also called Lewis-dot diagrams, are diagrams that show the bonding between atoms of a molecule, and the lone pairs of electrons that may exist in the molecule. A Lewis structure can be drawn for any covalently-bonded molecule, as well as coordination compounds. The Lewis structure was named after Gilbert N. Lewis, who introduced it in his 1916 article The Atom and the Molecule. They are similar to electron dot diagrams in that the valence electrons in lone pairs are represented as dots, but they also contain lines to represent shared pairs in a chemical bond (single, double, triple, etc.).

Lewis's structures show each atom in the structure of the molecule using its chemical symbol. Lines are drawn between atoms that are bonded to one another (rarely, pairs of dots are used instead of lines). Excess electrons that form lone pairs are represented as pairs of dots, and are placed next to the atoms.

Choosing a central atom
For compounds containing multiple elements with only one atom in the formula, the central atom is the least electronegative single atom that is not hydrogen. For instance, in thionyl chloride (SOCl2), the sulfur atom is the central atom.

The initial Lewis structure should be drawn with each non-central atom connected by a single bond to the central atom, although not all atoms have to be connected to show bonds.

In some cases, the central atom is difficult to determine, generally when each element appears more than once. In these cases, the connectivity of the structure — that is, the determination of which atoms are connected to which others — must be determined either by trial and error or prior knowledge.

When the central atom is known or determined, the Lewis structure can be drawn in exactly the same fashion.

Counting electrons
The total number of electrons represented in a Lewis structure is equal to the sum of the numbers of valence electrons on each individual atom. Non-valence electrons are not represented in Lewis structures.

The octet rule states molecules with eight electrons in their valence shell will be stable, regardless of whether these electrons are bonding or nonbonding. The rule applies well to organic compounds. The 18-Electron rule is operative on atoms from period 4, which have to achieve 18 electrons to fill their orbitals and achieve a noble gas configuration. Similarly from period 6, the atoms have to achieve 32 electrons to fill their orbitals.

Placing electrons
Once the total number of available electrons has been determined, electrons must be placed into the structure. They should be placed initially as lone pairs: one pair of dots for each pair of electrons available. Lone pairs should initially be placed on outer atoms (other than hydrogen) until each outer atom has eight electrons in bonding pairs and lone pairs; extra lone pairs may then be placed on the central atom. When in doubt, lone pairs should be placed on more electronegative atoms first.

Once all lone pairs are placed, atoms, especially the central atoms, may not have an octet of electrons. In this case, the atoms must form a double bond; a lone pair of electrons is moved to form a second bond between the two atoms. As the bonding pair is shared between the two atoms, the atom that originally had the lone pair still has an octet; the other atom now has two more electrons in its valence shell.

Aside from organic compounds, only a minority of compounds have an octet of electrons in their valence shell. Incomplete octets are common for compounds of groups 2 and 13 such as beryllium, boron, and aluminum. Compounds with more than eight electrons in the Lewis representation of the valence shell of an atom are called hypervalent, and are common for elements of groups 15 to 18, such as phosphorus, sulfur, iodine, and xenon.

Lewis structures for polyatomic ions may be drawn by the same method. When counting electrons, negative ions should have extra electrons placed in their Lewis structures; positive ions should have fewer electrons than an uncharged molecule.

When the Lewis structure of an ion is written, the entire structure is placed in brackets, and the charge is written as a superscript on the upper right, outside the brackets.

A simpler method has been proposed for constructing Lewis structures eliminating the need for electron counting: the atoms are drawn showing the valence electrons, bonds are then formed by pairing up valence electrons of the atoms involved in the bond-making process and anions and cations are formed by adding or removing electrons to/from the appropriate atoms.

Formal Charge
In terms of Lewis structures, formal charge is used in the description, comparison and assessment of likely topological and resonance structures by determining the apparent electronic charge of each atom within, based upon its electron dot structure assuming exclusive covalency or non-polar bonding. It has uses in determining possible electron re-configuration when referring to reaction mechanisms, and often results in the same sign as the partial charge of the atom, with exceptions. In general, the formal charge of an atom can be calculated using the following formula, assuming non-standard definitions for the markup used:

$$C_f = N_v - U_e - B_n$$ Where:
 * $$C_f$$ is the Formal charge.
 * $$N_v$$ represents the number of valence electrons in a free atom of the element.
 * $$U_e$$ represents the number of unshared electrons on the atom.
 * $$B_n$$ represents the total number of bonds the atom has with another.

The formal charge of an atom is computed as the difference between the number of valence electrons that a neutral atom would have and the number of electrons that belong to it in the Lewis structure. Electrons in covalent bonds are split equally between the atoms involved in the bond. The total of the formal charges on an ion should be equal to the charge on the ion, and the total of the formal charges on a neutral molecule should be equal to zero.

Resonance
For some molecules and ions, it is difficult to determine which lone pairs should be moved to form double or triple bonds. This is sometimes the case when multiple atoms of the same type surround the central atom, and is especially common for polyatomic ions.

When this situation occurs, the molecule's Lewis structure is said to be a resonance structure, and the molecule exists as a resonance hybrid. Each of the different possibilities is superimposed on the others, and the molecule is considered to have a Lewis structure equivalent to an average of these states.

The nitrate ion (NO3-), for instance, must form a double bond between nitrogen and one of the oxygens to satisfy the octet rule for nitrogen. However, because the molecule is symmetrical, it does not matter which of the oxygens forms the double bond. In this case, there are three possible resonance structures. Expressing resonance when drawing Lewis structures may be done either by drawing each of the possible resonance forms and placing double-headed arrows between them or by using dashed lines to represent the partial bonds.

When comparing resonance structures for the same molecule, usually those with the fewest formal charges contribute more to the overall resonance hybrid. When formal charges are necessary, resonance structures that have negative charges on the more electronegative elements and positive charges on the less electronegative elements are favored.

Resonance hybridization
In Lewis resonance structures, the structure is written such that it appears the molecule may switch between multiple forms. However, the molecule itself exists as a hybrid of the forms.

In the case of the nitrate ion, there are two single bonds and one double bond in each resonance form. When the nitrate ion is examined, however, each bond appears as though it has a bond order of 1.333 — that is, each has properties as if it were composed of one and one-third total bonds. The length and bond energy of each is somewhere between that of a single bond and a double bond.

The resonance structure should not be interpreted to indicate that the molecule switches between forms, but that the molecule acts as the average of multiple forms.

Example: Lewis structure of the nitrite ion
The formula of the nitrite ion is NO2−.

'''Step one: Nitrogen is the most electronegative atom, so it is the central atom by multiple criteria.

Step two: Count valence electrons. Nitrogen has 5 valence electrons; each oxygen has 6, for a total of (6 × 2) + 5 = 17. The ion has a charge of −1, which indicates an extra electron, so the total number of electrons is 18.

Step three: Place ion pairs. Each oxygen must be bonded to the nitrogen, which uses four electrons — two in each bond. The 14 remaining electrons should initially be placed as 7 lone pairs. Each oxygen may take a maximum of 3 lone pairs, giving each oxygen 8 electrons including the bonding pair. The seventh lone pair must be placed on the nitrogen atom.

Step four: Satisfy the octet rule. Both oxygen atoms currently have 8 electrons assigned to them. The nitrogen atom has only 6 electrons assigned to it. One of the lone pairs on an oxygen atom must form a double bond, but either atom will work equally well. We therefore must have a resonance structure.

Step five: Tie up loose ends. Two Lewis structures must be drawn: one with each oxygen atom double-bonded to the nitrogen atom. The second oxygen atom in each structure will be single-bonded to the nitrogen atom. Place brackets around each structure, and add the charge (−) to the upper right outside the brackets. Draw a double-headed arrow between the two resonance forms.

Alternative formats

Chemical structures may be written in more compacted forms, particularly when showing organic molecules. In condensed structural formulas, many or even all of the covalent bonds may be left out, with subscripts indicating the number of identical groups attached to a particular atom. Another shorthand structural diagram is the skeletal formula (also known as a bond-line formula or carbon skeleton diagram). In skeletal formulae, carbon atoms are not signified by the symbol C but by the vertices of the lines. Hydrogen atoms bonded to carbon are not shown — they can be inferred by counting the number of bonds to a particular carbon atom — each carbon is assumed to have four bonds, so any bonds not shown are, by implication, to hydrogen atoms.

Other diagrams may be more complex than Lewis structures, showing bonds in 3D using various forms such as space filling diagrams.


text:
User talk:Australiansciences

Welcome!
Welcome, !

Come introduce yourself at the new users page. If you have any questions, you can ask there or contact me personally.--Whiteknight (Page) (Talk) 03:32, 12 March 2008 (UTC)

title: User talk:Australiansciences


text:
User talk:218.101.64.107

Welcome!
Welcome to Wikibooks, a collection of open-content textbooks! You don't have to log in to read or edit articles on Wikibooks, but creating an account is quick, free and non-intrusive, requires no personal information, and gives you many benefits, including:
 * The use of a username of your choice
 * The ability to view all your contributions via a "My contributions" link
 * Your own user page
 * Your own talk page which, if you choose, also allows users to send you messages without knowing your e-mail address
 * The use of your own personal watchlist to which you can add articles that interest you
 * The ability to rename pages.
 * The ability to upload images
 * The ability to customize the appearance and behavior of the website
 * The eligibility to become an administrator
 * The right to be heard in votes and elections
 * Your IP address will no longer be visible to other users.

We hope that you choose to become a Wikibookian and create an account. Feel free to ask me any questions you may have on my talk page. By the way, make sure to sign your posts and comments with four tildes (~~~~), which will let others know who left it.

Your edit to Entomology/Other Insects
Please use the discussion page to make suggestions about an article's contents, rather than inserting comments into the module itself. Thanks! Νεοπτόλεμος ( talk | email | contribs ) 03:45, 12 March 2008 (UTC)

title: User talk:218.101.64.107


text: User:Penubag

title: User:Penubag


text:
User talk:Penubag

" This talk page probably won't be read by me for a long time, please post responces on my other talk page. Thanks."

sig
Your signature should be put into the signature field of my preferences. That should work better, as ~ will work nicely then. – Mike.lifeguard  | talk 04:05, 16 March 2008 (UTC)
 * Yes, I know that, but I'm an interesting fellow and what I have in my signature field in my prefs is . That way when I type ~, my signature is different every time I sign. See: --penubag   (talk; w) --[[user:penubag|penubag ]] (talk; w) --[[user:penubag|penubag ]]''' ([[w:user talk:penubag|talk]]; [[w:user:penubag|w]]) 01:53, 18 March 2008 (UTC)
 * huh. Well I think that's probably a trivial server load, and very, very cool =D – Mike.lifeguard  | talk 01:59, 18 March 2008 (UTC)

title: User talk:Penubag


text:
Template talk:Lastedit

Typo in the template: editted should be edited (one t). 207.233.32.18 (talk) 04:28, 12 March 2008 (UTC)
 * [[Image:Yes check.svg|15px]] Fixed – Mike.lifeguard  | talk 15:01, 15 March 2008 (UTC)

title: Template talk:Lastedit


text:
User:Napzilla

Basic Information
Brian M Napoletano

Research Assistant

Department of Forestry and Natural Resources

Purdue University

Email: [mailto:brian@napoletano.net brian@napoletano.net]

Web:   http://napoletano.net

Research Themes

 * Variations in natural systems
 * Ecology of the soundscape

Biography
Brian Napoletano is presently a PhD Student at Purdue University in the Department of Forestry and Natural Resources. He is working with Dr. Bryan Pijanowski in the Human and Environment Modeling and Analysis (HEMA) laboratory on the collection, analysis and interpretation of acoustic signals as measurable aspects of ecosystem processes. His research foci include the investigation of anthropogenic sounds as biological stressors, the development of a species-richness sensor network through the automated recording, identification and logging of specific vocalizations and the measure of the changes in acoustic signal groups (e.g. biological signals) over multiple temporal scales. In addition to these novel research topics, Brian is being trained in remote sensing and GIS techniques, as well as ecological modelling approaches, through his work with Dr. Pijanowski and his academic coursework. Eventually, Brian hopes to develop the methods to tie ecological acoustics into landscape-level processes and use the high data density of sound studies to develop robust models of ecosystem functioning.

Prior to studying at Purdue University, Brian worked with Dr. Stuart Gage as both an Undergraduate and Master’s level research assistant on the development of a conceptual and methodological framework with which to interpret acoustic signals in the environment. Brian began these studies with a Bachelor of Arts in Telecommunication, and proceeded to integrate this knowledge of Information Technology Systems into the development of biological monitoring systems (centred on acoustics) as the central focus of his Master’s research in the Department of Zoology.

In addition to research experience, Brian has sought experience in education. He served as an instructor and coordinator for the Introduction to Earth Systems Science course at Michigan State University in the Fall Semester of 2004, and he is worked with Dr. John Dunning Jr. of Purdue University as a Teaching Assistant for the Global Environmental Issues course. Both courses target students with both science and non-science majors and challenge them to attempt to examine the Earth from a global interdisciplinary perspective. During the 2006--2007 Academic Year, Brian worked with Dr. Walter Mills as a Teaching Assistant for an introductory-level GIS course, and in 2007--2008 he worked with Dr. Guofan Shao as on a graduate-level course in Remote Sensing. Brian hopes to continue teaching and his research within a University community as a faculty member.

Current Degree Sought

 * Doctor of Philosophy
 * Institution: Purdue University
 * College: Agriculture
 * Department: Forestry and Natural Resources
 * Specialisation: Remote Sensing and GIS

Highest Completed Degree

 * Master in Science
 * Institution: Michigan State University
 * College: Natural Science
 * Department: Zoology
 * Specialisation: Remote Environmental Monitoring and Assessment
 * Date of Completion: August 2004

Previous Degree

 * Bachelor of Arts
 * Institution: Michigan State University
 * College: Communication Arts and Sciences
 * Department: Telecommunication
 * Specialisation: Information Technology Services, Residential Initiative to Increase Student Awareness of the Environment
 * Date of Completion: August 2001

Computing Profile

 * Operating System: Ubuntu Linux
 * Data Management and Analysis: MySQL, R, some Octave
 * GIS: ArcGIS, IDRISI, GRASS, Imagine
 * Markup Languages: XML (esp. DocBook), XHTML, LaTeX2e
 * Programming Languages: Python, bash, very little C++
 * Preferred Editor: Vim

title: User:Napzilla


text:
User talk:Napzilla

Welcome!
Welcome, !

Come introduce yourself at the new users page. If you have any questions, you can ask there or contact me personally.--Whiteknight (Page) (Talk) 04:46, 12 March 2008 (UTC)

title: User talk:Napzilla


text: User talk:68.96.48.121

title: User talk:68.96.48.121


text: Category:Book:A-level Physics (Advancing Physics)


text: User talk:87.247.162.3

title: User talk:87.247.162.3


text: User talk:217.219.171.2

title: User talk:217.219.171.2


text:
Talk:William Shakespeare's Works/The Life of William Shakespeare

conjecture "The evidence we have for WS birth, life and death is so scanty it really proves nothing"

Could the name "William Shakespeare" have been sold as a "cover name" for record purposes? An unmarried women wants to legitimize her children - uses the name of a dead child as her "husband" on parish records. A separate character in London, buys the use of the "cover name" (possibly including a background story and references) from a Merchant from Warwickshire, to use as a fake ID and escape debtors? Could the Parish records have been "amended" to show the baptism of a dead baby, or did the babe named William die soon after Baptism and the fact was then kept off records in order to sell the ID. It is a very unusual name, thus should have allowed easy identification, unlike John Smith say. Anon 12th March 2008.


text:
Qt/Lesson 1

Hello, World!
This is the Hello, World! program. It just displays a window:

To compile this, read ../Development Environment/ or just use.

Using Qt designer
This lesson will get you started using Qt designer for making your own simple application.

Open Qt Designer and select to make a new dialog without buttons. Drag a label on to it and set its title property to "Hello, World!", then go to Form > Preview to see your new dialog. Now, onto writing some code. We'll have to subclass the QDialog class to make the application run standalone on most modern operating systems.

Qt/Primeiro programa Programowanie_C%2B%2B_Qt4_w_systemie_Gnu-Linux/Helo_World_w_QT4

title: Qt/Lesson 1


text:
Chess Opening Theory/1. e4/1...e6/2. d4/2...d5/3. e5/3...c5/4. c3/4...Nc6/5. Nf3/5...Qb6/6. Bd3

=French Defence: Advance Variation=

White develops the light-squared bishop to d3. While he appears to have lost a pawn, 6... cxd4 7. cxd4 Nxd4? 8. Nxd4 Qxd4?? 9. Bb5+! will win Black's queen. Black usually plays 6...cxd4 7.cxd4 Bd7!, blocking the king from the Bb5+ and thus threatening to win the d4-pawn. White then usually plays a gambit. Note that 6...Bd7?! allows White the option of 7.dxc5! Bxc5 when Black does have to be careful - 8.O-O Ne7?? 9.b4!, trapping the bishop, shows that Black must be very careful. So 6...cxd4 is considered better, avoiding the tricks after 7.dxc5!

Theory table
.

'''1.e4 e6 2.d4 d5 3.e5 c5 4.c3 Nc6 5.Nf3 Qb6 6. Bd3'''


text:
Template:Linear Algebra/Book 2/Recommended

 * This exercise is recommended for all readers.


text: User talk:208.108.115.139

title: User talk:208.108.115.139


text:
Radiation Oncology/Toxicity/Dysphagia

Dysphagia
 * MDACC; 2012 PMID 20646872 -- "Candidate dosimetric predictors of long-term swallowing dysfunction after oropharyngeal intensity-modulated radiotherapy." (Schwartz DL, Int J Radiat Oncol Biol Phys. 2010 Dec 1;78(5):1356-65.)
 * Dose volume constraints for anterior oral cavity (V30 < 65% and V35 < 35%) and superior sub-region of the superior phayngeal constrictor (V55 < 80% and V65 < 30%) were predictive for swallowing dysfunction.

 * UC Davis; 2010 (2002-9) PMID 20231073 -- "Evaluating the role of prophylactic gastrostomy tube placement prior to definitive chemoradiotherapy for head and neck cancer." (Chen AM, Int J Radiat Oncol Biol Phys. 2010 Nov 15;78(4):1026-32.)
 * Retrospective. 70 pts with prophylactic G-tube prior to RT, 50 without. All treated with chemo/RT.
 * Median weight loss during RT: 19 lbs (GT) vs 43 lbs (no GT). 32% of pts without prophylactic G-tube required GT placement at median of 33 days from start of RT. At 6 months, median weight change -4% (GT) vs -7% (no GT). G-tube dependence at 6 months and 12 months 41% and 21% (GT) vs 8% and 0% (no GT).
 * Conclusion: prophylactic G-tube effective at preventing acute weight loss and need for IV hydration, but associated with significantly higher rates of late esophageal toxicity.

 * Alabama; 2010 PMID 19467801 -- "Dosimetric factors associated with long-term dysphagia after definitive radiotherapy for squamous cell carcinoma of the head and neck." (Caudell JJ, Int J Radiat Oncol Biol Phys. 2010 Feb 1;76(2):403-9. Epub 2009 May 19.)
 * Retrospective. 83 patients, definitive IMRT
 * Predictors for PEG tube dependence and aspiration: mean larynx dose >41 Gy, larynx V60 >24%, inferior pharyngeal constrictor V60 >12%
 * Predictors for pharyngoesophageal stricture: superior pharyngeal constrictor V65 >33%, middle pharyngeal constrictor V65 > 75%
 * Conclusion: Dose to larynx and pharyngeal constrictors predicted long-term swallowing complications

 * Harvard; 2008 (2004-2006) PMID 18468812 -- "Dose to larynx predicts for swallowing complications after intensity-modulated radiotherapy." (Caglar HB, Int J Radiat Oncol Biol Phys. 2008 Nov 15;72(4):1110-8. Epub 2008 May 28.)
 * Retrospective. 96 patients with H&N cancers, 85% definitive RT vs 15% postop RT. Induction chemo 29% vs. concurrent chemo 61%. Median F/U 10 months
 * Outcome: Clinically significant aspiration 32% and stricture 37%. No aspiration if mean larynx dose <48 Gy or mean inferior pharyngeal constrictor dose <54 Gy. No stricture if mean inferior pharyngeal constrictor dose <54 Gy; smoking correlated significantly.
 * Conclusion: Aspiration and stricture common side effects; dose limits established for larynx and inferior pharyngeal constrictors

 * Erasmus University Medical Center; 2007 (Netherlands)(2000-2005) PMID 17714815 -- "Dysphagia disorders in patients with cancer of the oropharynx are significantly affected by the radiation therapy dose to the superior and middle constrictor muscle: a dose-effect relationship." (Levendag PC, Radiother Oncol. 2007 Oct;85(1):64-73. Epub 2007 Aug 21.)
 * 81 patients with SCC of oropharynx. 23% severe late dysphagia. Mean F/U 1.5 years IMRT and 4 years 3D-CRT
 * Outcome: steep dose-effect relationship between mean dose to constrictor muscles and severe dysphagia. 70 Gy probability 40%, 50 Gy probability 20%, 22 Gy probability 2%. For each additional 10 Gy after 55 Gy, probability of dysphagia increases by 19%
 * Conclusion: Dose-effect between dose and swallowing complaints observed

 * U. Michigan; 2004 PMID 15590174 -- "Dysphagia and aspiration after chemoradiotherapy for head-and-neck cancer: which anatomic structures are affected and can they be spared by IMRT?" (Eisbruch A, Int J Radiat Oncol Biol Phys. 2004 Dec 1;60(5):1425-39.)
 * 26 pts treated with RT + gemcitabine. Prospective swallowing assessment with videofluoroscopy, endoscopy, and CT.
 * Dysphagia/Aspiration Related Structures (DARS) include: pharyngeal constrictor muscles, supraglottic and glottic larynx.
 * IMRT was able to reduce the V50(Gy) of the DARS.

title: Radiation Oncology/Toxicity/Dysphagia


text: User talk:24.24.6.50

title: User talk:24.24.6.50


text: Proteomics/Protein Primary Structure

title: Proteomics/Protein Primary Structure


text:
Proteomics/Post-translational Modification

Introduction
I. Definition A. Spontaneous or enzymatic alteration to one or more of a protein's amino acids B. Most often manifests as an addition or deletion to a side-chain C. Can occur at any point during or following full translation of a protein D. Often drastically effects overall structure and function of protein and associated complexes E. Are highly conserved among all living organisms II. Types of modifications A. Acetylation B. Amidation/Deamidation C. Glycosylation D. Oxidation i. S-Glutathionylation ii. S-Nitrosylation E. Phosphorylation i. Histidine ii. Serine iii. Theronine iv. Tyrosine F. Proteolysis G. Ubiquitinylation/SUMOylation H. Others? III. Manipulating in-vivo modifications A. Modifications can be prevented or induced in organism, tissue, and cell based model systems B. May allow for the detection of target proteins or dissection of related processes and pathways C. Exogenous introduction of stimuli i. endocrine/paracrine signals (aka hormones) ii. environmental (temp, UV, heavy-metals, peroxide, etc.) iii. antigens (virus, bacteria, allergens, lysates, etc.) iv. chemical/medicinal activators and inhibitors D. Genetic Approaches i. deletion, mutation, or reorganization of genetic elements (enhancers, promoters, genes, etc.) ii. gene inactivation or silencing by nucleic acid hybridization iii. transgenics (tansformations and transfections) E. Advantages and Draw-backs i. Qualitative but often hard to quantify ii. Often expolaratory in nature (observe and report) iii. Often very large scale in terms of available data IV. In vitro reconstitution strategies A. Often provide a more quantitative, in depth analysis of a particular post-translational modification B. The protein in question is added to a reaction with the appropriate reagents and/or enzymes C. Reactions can be followed in real-time more readily V. Methods of detection A. Most commonly used detection method of known modifications is through immuno/Western blotting B. Unexpected or novel modifications can be detected with a variety of analytical techniques, most notably mass spectrometry C. The best specific method of detection depends on many factors including the stability, frequency, and scale of the modification(s) D. In practice ease and cost dictate which methods are used first while the more exhaustive, cumbersome, or expensive methods follow as needed


text:
Proteomics/Protein Chips/Algorithms

Algorithms:

Introduction
The immense amount of data that is generated from the protein chip must be analyzed. Looking at each data point would be too time consuming, so statistical methods are applied to the data set. The analysis consists of using statistical algorithms to find the similarity between the protein expressions. The grouping together of similar proteins is called data clustering. Data clustering is a powerful method when identifying an individual within a group.

Distance
The clusters are made by the similarity of the distance between two data elements. The elements that are closest together will be grouped in the same cluster. Depending on the data set a different distance function may be used. The most common one in published research studies is the Euclidean distance function. Euclidean distance is the most straight forward method. It can be compared to measuring the straight line distance between two points with a ruler. The Euclidean distance equation can be used on a n-dimensional data set. This makes it very versatile for research studies.

The n-dimensional Euclidean equation is: :$$\sqrt{(p_1-q_1)^2 + (p_2-q_2)^2 + \cdots + (p_n-q_n)^2} = \sqrt{\sum_{i=1}^n (p_i-q_i)^2}.$$

pn = n-dimension of point p.

qn = n-dimension of point q.

Hierarchical Clustering
Hierarchical clustering is when previously defined clusters are used to determine new clusters. A hierarchical algorithm can be either an agglomerative algorithm or a divisive algorithm. An agglomerative algorithms starts with each data element as a separate cluster and then an algorithm used to sort them into larger clusters of the same similarity. A divisive algorithm works the exact opposite. It starts with the data set as one cluster and then divides the larger cluster into smaller ones.

Linkage
The term linkage refers distance between two points of two different clusters. The way that the distance is computed determines what type of linkage is used. All of the following types of linkages apply to hierarchical clustering.

Single linkage is the distance between the closet members of two different data clusters in hierarchical clustering.

Complete linkage is the opposite of single linkage in that the maximum distance between two members of two different clusters in calculated. This can be useful when compact clusters are needed.

Average linkage takes the average distance between the two clusters. It is also referred to as Unweighted Pair-Group Method using Arithmetic averages(UPGMA). This method is the most computational intense

Partitional Clustering
Partitional clustering finds the all the clusters at one time.

Self Organizing Maps
A self organizing map is (SOM) is best used when analyzing high dimensional data. A SOM can be used to reduce the dimensionality of the data so that it may be visualized. It consists of a training part and a mapping part. The training part builds the map using a sample input. The mapping part recognizes a new data vector and places it in the map according to the training algorithm. The SOM is made up of nodes that have a weight vector associated to them. The weight is assigned with small random values or it can be sampled using principal component eigenvectors. Using the eigenvectors will produce a better map because the weight would already have a good approximation. The weight vector has the same dimensionality of the input vector. A new data vector is placed by finding a node with the closest weight vector to it and assigning it the coordinates of that node to the vector. Euclidean distance is used to determine the closest neighbors. A SOM is best used for protein chip analysis if a particular protein is known and you want to see if there are any similar proteins to it.

An example of a self organizing map.

K-means Clustering
K-means clustering works by clustering n objects into k groups. The number of groups (k) has to be less than the number of objects(n) or else the algorithm will not work. The initial n values are divided into k sets using Lloyd’s algorithm, which iterates through the data set by sorting through common data points. The center of each of the newly formed cluster is then calculated and the algorithm is applied again. This process repeats until the centroids no longer switches clusters. Other types of algorithms can be used to iterate through the points besides Lloyd’s algorithm. Another type of clustering similar to K-means is fuzzy c-means. With a fuzzy c-mean cluster the points are based on the degree of likelihood that the point will be in a cluster. K-means clustering is appropriate to use if there is a specific number of protein groups you want to analyze on the protein chip.

An example of K-means clustering

Neural Network
A neural network can be used to determine relationships between data of finding patterns within the data set. It is called a neural network because it is related to the way the central nervous system in the human body works. Nodes or neurons are connected in the network. Different algorithms can be used to change the strength of the connections within the network. A cost function is employed depending on the data being studied. A neural network has to be trained in the same way a self organized map needs to be trained. This is called learning. There are set algorithms for different learning paradigms. Neural networks can be used in analyzing statistics, cognitive psychology, and artificial intelligence. The steps taken for a neural network is to first choose the model. The choice of model depends on the input data and what is being analyzed. Next a learning algorithm needs to be selected. The algorithm can be changed to reflect the data set. The robustness of the model can be tweaked based on the cost function and the learning algorithm. The neural network would be best used with protein chips when analyzing a new set of data without any prior information about it, a function can be inferred from the observed data. The high complexity of the data from the protein chips fits well into a neural network algorithm.

Reproducibility
Once the data from the protein chip has been gathered it needs to be analyzed. The most common method is to use a means of clustering to find relating expression patterns. These clustering methods will discard data points that do not fit within range of the algorithm. The problem is that by repeating analysis of the data, different data points may be discarded from each analysis. If a specific gene expression is being researched the data that most closely matches the expected results is kept.

A study was conducted on 37 microarray data sets and 8 simulated data sets. A low stability was achieved for all four clustering algorithms for the real data set. For the simulated datasets a larger data set was used. The results were same until samples were larger than n = 50. K-means and CLARA showed a better stability for large sample sizes (300-500).

Microarray Quality Control(MAQC) is an effort to establish a standard for microarray analysis.

title: Proteomics/Protein Chips/Algorithms


text:
Proteomics/Post-translational Modification/Amino Group Modification

This Section:

Amino Group Modification
Amino group modification involves the addition of a functional group at the N terminus of the amino acid.

A protein after its translation undergoes a chemical modification called Post Translational Modification. These modifications may alter the functions of the protein when attached to a biochemical functional group like acetate by changing the chemical nature of the amino acid or by structural changes like folding, conformation distribution, stability, activity etc.

Acetylation
Acetyaltion is an acylation (introduction of an acyl group to an organic compound) process which involves the substitution of an organic group of acetic acid for an active hydrogen atom at the N-terminus.

The most widespread modification in the eukaryotes is the Acetylation of the N-terminal α-amine group of proteins. About 50% of the proteins of yeast and approximately 90% of the proteins in humans are modified by this mechanism. The pattern of the modification is conserved all through the evolution. Even though it is such a common modification, not much information is available for about the biological function of N-α-terminal acetylation. N-α-acetyltransferase (NATs) is the enzyme responsible for the Acetylation. NATs belong to a family of the GNAT, which lies under the superfamily of the acetyltransferases. The acetylation and deacetylation takes place on lysine residues in the N-terminal tail in histone acetylation and deacetylation. These reactions take place in the presence of the enzymes histone acetyltransferase (HAT) or histone deacetylase (HDAC).

Pyroglutamate Formation

Pyroglutamate is formed through the cyclization (ring formation in the chemical compound) of glutamine. It is commonly observed in antibodies that contain glutamate or glutamine residues at their N-termini. The amino group and the glutamate or glutamine condenses to form a five-member ring called Pyroglutamate. This residue makes the protein more resistant to aminopeptidases and has many functional roles. It maintains structural integrity at the N-terminal α-helix and provides proper environment for the ionization of Histidine residues for catalysis and cytotoxicity against HeLa cells.

Myristoylation
N-myristoylation is also an acylation process found to be specific to N-terminal amino acid glycine in proteins where a myristoyl group (derived from myristic acid) is covalently attached via an amide bond to the alpha-amino group of N-terminal Glycine. Myristoylation plays vital role in the secondary cellular signaling, in the infectivity of retroviruses and oncogenesis in Eukaryotes. It also influences the physiological functions of calcium binding proteins. The cytosolic enzyme N-myristoyltransferase (NMT) catalyzes Myristoylation.

Methylation
Methylationof the protein is the most common form of post-translational modifications observed. Similar to other post-translational modifications, protein methylation is involved in regulating protein-protein interactions resulting in a plethora of effects during key cellular events, including regulation of transcription stress response, ageing and protein repair T-cell activation, nuclear transport , neuronal differentiation , ion channel function, and cytokine signaling. A protein is considered methylated when a methyl group is added at one or more nucleophilic side chains. Methylation on side chain nitrogens is considered largely irreversible while methylation of the carboxyl groups is potentially reversible. Protein residues methylated on nitrogen include the e-amine of lysine, the imidazole ring of histidine, the guanidino moiety of arginine, and the side chain amide nitrogens of glutamine and asparagine.

Methylation in the proteins negates the negative charge on it and increase the hydrophobicity of the protein. Methylation on carboxylate side chains cover up a negative charge and add hydrophobicity. N-Methylation of lysines does not alter the cationic charge but does increase hydrophobicity. In particular, dimethylation and trimethylation of lysine side chains in proteins increase both hydrophobicity and steric bulk and can affect protein–protein interactions if they are in an interacting surface.

Carbamylation
Carbamylation occurs when isocyanic acid (HCNO) reacts with the amino terminus residues, like lysine, of the protein. It is one of the common artifactual protein modifications recognized to Isoeletric focusing. The risk factor is urea (chaotrope) which exists in solution and is in equilibrium with ammonium cyanate. Isocyanic acid is the cyanate form that reacts with amino groups of proteins. For carbamylation to occur the amino acid groups of proteins like lysine, arginine side chains should be deprotonated which usually occurs at alkaline pH. Carbamylation occurs when proteins are left at room temperature in a solution of urea and where isocyanic acid can freely react with the protein.

Carbamylation by isocyanic acid is negative for next steps of protein characterization because isocyanic acid reacts with the amino terminus of proteins blocking the peptide or protein to N-terminal sequencing. Isocyanic acid attacks the side chains of lysine and arginine residues making a protein unsuitable for many enzymatic digests. Even if carbamylation does not prevent the enzymatic digest it will often confuse the results from mass spectroscopy experiments with peptides that have unexpected retention times and masses. Carbamylation of proteins in vivo were observed in several diseased states.

Formylation
Formylation is one of the posttranslational modifications of the protein, in which a protein is modified by the attachment of formyl group. The most commonly studied mechanisms is the N6-formylation of lysine which is associated with the histone and other nuclear proteins. The post translational modification of histone and other chromatin proteins play a role in the physiology of gene expression. "The N6-formyl-lysine residue appears to represent an endogenous histone secondary modification, one that bears chemical similarity to lysine N6-acetylation recognized as an important determinant of gene expression in mammalian cells." From the study it was concluded that N6-formyl modification of lysine is interfering with the signaling functions of acetylation and methylation, which play a role in the physiology of oxidative and nitrosative stress.


text: Talk:Animal Care/Bearded Dragon

title: Talk:Animal Care/Bearded Dragon


text: User talk:209.7.77.254

title: User talk:209.7.77.254


text: User talk:198.237.84.66

title: User talk:198.237.84.66


text:
Talk:X86 Disassembly/Loop Examples

For the first example, is the 'add ebx, 4' line a mistake? ebx is multiplied by 4 in the offset and compared to 100 for a 100 element array, so it seems only the inc is needed.

push ebp mov ebp, esp mov esi, [ebp + 8] mov ebx, 0 mov eax, 0 mov ecx, 0 _Label_1: mov ecx, [esi + ebx * 4] add eax, ecx add ebx, 4               ; remove? inc ebx cmp ebx, 100 jne _Label_1 mov esp, ebp pop ebp ret 4

title: Talk:X86 Disassembly/Loop Examples


text:
Proteomics/Protein Primary Structure/Sequencing Methods

This Section:

Protein sequencing denotes the process of finding the amino acid sequence, or primary structure of a protein. Sequencing plays a very vital role in Proteomics as the information obtained can be used to deduce function, structure, and location which in turn aids in identifying new or novel proteins as well as understanding of cellular processes. Better understanding of these processes allows for creation of drugs that target specific metabolic pathways among other things.

Though several methods exist to sequence proteins the two dominant methods are Mass Spectrometry and Edman Degradation. Other methods that are not as frequently used still can serve very specific roles, such as overcoming inadequacies or acting as a preliminary, that compliment the two predominant methods.

History
The advent of protein sequencing can be traced to two almost parallel discoveries by Frederick Sanger and  Pehr Edman.

Pehr Edman began his work in the Northrop-Kunitz laboratory at the Princeton branch of the Rockefeller Institute of Medical Research in 1947 where he attempted to find a method to decode the amino acid sequence of a protein using chemicals; specifically he had early success with fluorodinitrobenzene (FDNB) and phenylisothiocyanate (PITC). Throughout his year at Princeton, Edman was able to conduct enough experiments to understand that it was feasible to use reagents like FDNB and PITC to determine amino acid sequence. Edman returned to Sweden in 1947 and after two more years of work he was able to publish his paper that would describe the first successful method to sequence proteins .This groundbreaking paper described a method to determine the amino acid sequence of a protein and would come to be known as the Edman Degradation.

Five years earlier, Frederick Sanger had demonstrated a method to determine the amino acid residue located on the N-terminal end of a polypeptide chain by using the reagent fluorodinitrobenzene. While it was thought, that at most, this method could only provide the sequences found on the N-terminal, Sanger was able to take the method one step further. By using several proteolytic enzymes, partial hydrolysis and early version of chromatography, Sanger was able to cleave the protein into fragments and piece together the residues like a jigsaw puzzle. It wasn’t until 1955 that Sanger was able to present the complete sequence of insulin which led to him being awarded a Nobel Prize in Chemistry in 1958.

Mass Spectrometry, as a tool for the analysis of individual molecules, had been available many years before either Sanger or Edman began their work on protein sequencing. From its humble beginnings in the late 1800’s it has undergone many changes in its hardware and its software and has proven to be so critically important to the field of sequencing that several more Nobel Prizes were awarded to those that were able to improve upon this technology. Despite its importance today, it wasn’t until 1966 when K. Biemann, C. Cone, B.R. Webster, and G.P. Arsenault sequenced several oligopeptides containing glycine, alanine, serine, proline, and several other amino acids that the importance of mass spectrometry was fully realized. Further developments were to come in the late 80's as the Mass Spectrometer became a more robust piece of instrumentation in the laboratory. 1989 saw the first demonstration of fast atom bombardment ionization with Tandem Mass Spectroscopy as applied towards the identification of protein sequences. The work here was the early foundation for the Protein Mass Fingerprinting procedure that would come to use in the early 90's. With the arrival of MALDI and electrospray ionization as two new ionization methods the dynamic range of mass spectroscopy was greatly improved and paved the way for the mass spectrometer to be a dominant tool in the use of protein sequencing. The advent of Proteomics in 1996 saw many rapid developments such as increasing computational power, the growth of the world wide web and protein databases, and the advances in mass spectroscopy multi-quadrupole systems that enabled MALDI-MS/MS and other tandem mass spectrometer methods.

N-terminal Residue Identification
N-terminal residue identification encompasses a technique which chemically determines which amino acid forms the N-terminus of a peptide chain. This information can be used to aid in ordering of individual peptide sequences that were generated using other sequencing techniques that fragment the peptide chain. Frequently, the first round of Edman Degradation will also contain impurities that may make identification of the N-terminus residue difficult. The general process of N-terminal residue identification is described below :

 * 1) The free unprotonated α-amino groups are labeled using a reagent that will selectively label the terminal amino acid. Reagents that can accomplish this include 2,4-dinitrofluorobenzene (DFNB - Sanger's reagent), dansyl chloride, and phenylisothiocyanate (Edman's reagent).
 * 2) The labeled peptide is hydrolyzed with acid which yields the N-terminal residue and other free amino acids.
 * 3) Each of these derivative N-terminal residues can be separated and identified using chromatography.

These methods can be used to identify the N-terminal residue of the peptide. This is time consuming process which has decreased in usefulness now that more efficient sequencing techniques are now available. Further complicating the issue: certain reagents used in the process can also degrade amino acid residues to the point where they are unrecognizable. Of the reagents available to label the N-terminal residue dansyl chloride is about 100 times more sensitive than FDNB due to its highly fluorescent nature which makes it easily detectable in minute amounts. The use of Edman's reagent is also advantageous as it leaves the remaining residues in the peptide chain untouched as described in the next section.

Edman Degradation
The Edman Degradation method is based on the principal that single amino acid residues can be modified chemically such that they can be cleaved from the chain without disrupting the bonds between any other residues. The procedure can be achieved with very minute amounts of peptide, usually amounts on the order of 10-100 picomoles will allow for successful completion. Samples must contain only one protein component and should be free of any reagents that interfere with the degradation process such as glycine, glycerol, sucrose, guanidine, ethanolamine, ammonium sulfate, and ammonium salts. The general method is described below :

 * 1) The peptides to be sequenced must first be immobilized by being absorbed onto a chemically modified glass or by electroblotting onto a porous polyvinylidene fluoride (PVDF) membrane.
 * 2) Under mildly alkaline conditions phenylisothiocyanate (PITC) is reacted with an uncharged terminal group on the amino acid chain to form a phenylthiocarbamoyl derivative.
 * 3) This phenylthiocarbamoyl derivative is then cleaved using Trifluoroacetic acid producing its anilinothiazolinone derivative (ATZ-amino acid).  The next terminal amino acid is now exposed and ready for the same reactions to occur.
 * 4) A wash is performed to remove excess buffers and reagents and the ATZ amino acid is selectively extracted with ethyl acetate and converted to a more stable phenylthiohydantoin (PTH)- amino acid derivative.
 * 5) Identification of the PTH amino acid derivative is accomplished using chromatography or electrophoresis.
 * 6) The process can now be repeated for the remaining residues of the chain.

Automation of the Edman Degradation procedure was initiated in 1967 and continues to be a favorable sequencing method due to its sensitivity and rapid completion. Sequencers that can automate the Edman Degradation procedure include many models of the Applied Biosystems Procise or Protein Sequencer families.

The major drawback of the procedure remains the length of the peptide chain. If the chain exceeds a length of 50-60 residues (30 residues in practice) the procedure tends to fail due to the incompletion of the cyclical derivitization. This can be solved by taking the larger peptide chain and cleaving it into smaller fragments using cyanogen bromide, trypsin, chemotrypsin or any enzyme/chemical which can break peptide chains.

Mass spectrometry
Mass spectrometry is quickly becoming the gold standard by which to identify protein sequences due to its ease of automation and extreme accuracy. The use of mass spectroscopy now dominates the process of sequencing proteins. The two most popular methods to identify protein sequences using Mass Spectrometry are:

1. Peptide Mass Fingerprinting 2. Tandem Mass spectrometry

Peptide Mass Fingerprinting
This method, also known as Protein fingerprinting, was developed in 1993 by several groups. Cleaving an unknown protein into smaller fragments so that these smaller fragments can then be accurately measured with a mass spectrometer. A generalized procedure is shown below: 1. Protein samples are broken up into several smaller peptide fragments by proteolytic enzymes. 2. The resulting fragments are extracted using acetonitrile and dried by vacuum. The peptides are then dissolved in distilled water and ready for analysis. 3. The peptides are then inserted into the vacuum chamber of a mass spectrometer such as ESI-TOF. The mass spectrometer produces a peak list (i.e. a list of molecular weights), which is then compared against databases such as SwissProt or GeneBank to find close matches. Software is used to translate the retrieved genomic data into proteins which then undergo simulated cleavage by the same enzyme used to cleave the unknown protein. The mass is calculated of these fragments and then compared to that of the unknown protein.

Tandem Mass Spectrometry
Tandem Mass Spectrometry describes the partitioning of mass spectroscopy into separate steps where fragmentation occurs in between these steps. These separations can occur either physically in space, by separate chambers called quadrupoles, using either multiple mass spectrometers or in a single mass spectrometer by time. The generalized procedure is described below, 1. Enzymatic or chemical degradation of target protein to produce peptides. 2. Fractionation of peptides by high-performance liquid chromatography. 3. Resulting fragments fed into mass spectrometer for analysis.

Analysis of fragments by Tandem Mass Spectrometry occurs in two or more quadrupole systems with the first quadrupole filtering select ions that will undergo further analysis. These filtered ions are transferred to the second quadrupole which acts as a collision center to induce further fragmentation at amide linkages. A third quadrupole is then used to separate these fragments by mass. Tandem Mass Spectrometry mainly generates peptides of the N- and C- terminal types, which are represented in 2D via mass/charge vs. intensity graphs. The spectra produced by a mass spectrometer containing all the molecular weights of the fragments is called a peptide map and can serve as a means of identifying proteins analyzed by a mass spectrometer. Other approaches to identifying protein sequences include Protein Sequence tags and de novo methods.