OpenLLM-France/wikimedia huggingface dataset:
# Thai/Tone

The Thai language has a total of five tones:
* High tone
* Mid tone ("default")
* Low tone
* Rising tone
* Falling tone
There are a total of three tone classes:
* Low class ("default")
* Mid class
* High class

## Tone classes

Thai Consonant Classes :
| Sound | Low class | Mid class | High class |
| k | ค, ฆ | | ข |
| g | | ก | |
| d | | ด, ฎ | |
| dt | | ต, ฏ | |
| j | | จ | |
| ch | ช, ฌ | | ฉ |
| s | ซ | | ส, ษ, ศ |
| y | ย, ญ | | |
| t | ท, ฑ, ฒ, ธ | | ถ, ฐ |
| p | พ, ภ | | ผ |
| f | ฟ, ภ | | ฝ |
| b | | บ | |
| bp | | ป | |
| m | ม | | |
| n | น | | |
| ng | ง | | |
| r | ร | | |
| l | ล, ฬ | | |
| w | ว | | |
| h | ฮ | | ห |
| (none) | | อ | |

## Tone rules

Note:
* Live syllables end in a long vowel or -m, -n, -ng, -y, -w. If it ends in a consonant, the middle vowel may be either short or long.
* Dead syllables end in a short vowel or -k, -t, -p. If it ends in a consonant, the middle vowel may be either short or long.
Thai Tone Rules :
| Syllable type / Tone mark | Low class | Mid and high classes |
| Live syllable | Mid tone (mid class also) | Rising tone (high class only) |
| Dead syllable (short vowel) | High tone | Low tone |
| Dead syllable (long vowel) | Falling tone | Low tone |
| -' | Falling tone | Low tone |
| -้ | High tone | Falling tone |
| -๊ | High tone | High tone |
| -๋ | Rising tone | Rising tone |
# Statistical Analysis: an Introduction using R/Contributors

This appendix collects some useful resources for people who want to contribute to this wikibook.

### Practical aids
#### /uploadToCommons.r

    An R script to automatically generate and upload graphical output (usually plots) to wikimedia commons.

### Useful URLs

What follows is a dumping ground for references that might be useful to colour (or even to incorporate into) the text. Fill up this list as necessary General
* resources for teaching stats
* Lindley on statistics as uncertainty
* Statistical method vs. scientific method
* More sci & stats
* Add 2 Chatfield refs: 2002 (good)
Bayes
* http://www3.interscience.wiley.com/cgi-bin/fulltext/119977436/PDFSTART
* Efron (1986)
* http://www.math.ualberta.ca/~irl/summer_school/lecture_notes/stats1.pdf
QQ plots
* http://www.analyse-it.com/blog/2008/11/normal-quantile-probability-plots.aspx
* http://books.google.co.uk/books?id=e9Qta2vGwboC&pg=PA9&lpg=PA9&dq=R+Normal+probability-probability+plot&source=web&ots=HomaTEowPu&sig=ShE0bO63---PeGc195EU4k3k4zM&hl=en&sa=X&oi=book_result&resnum=2&ct=result#PPA10,M1
Sig testing
* http://www.uv.es/sestio/TechRep/tr14-03.pdf
* http://tap.sagepub.com/cgi/reprint/18/1/69
* http://tap.sagepub.com/cgi/reprint/5/1/75
* http://www.informaworld.com/smpp/content~content=a785828317~db=all
* http://www.usq.edu.au/users/patrick/PAPERS/null%20hypothesis.pdf
* http://www.stat.auckland.ac.nz/~iase/publications/dissertations/07.Diaz.pdf
# Statistical Analysis: an Introduction using R/R/Factors

The factor function creates a factor and defines the available levels. By default the levels are taken from the ones in the vector***. Actually, you don't often need to use factor, because when reading data in from a file, R assumes by default that text should be converted to factors (see Statistical Analysis: an Introduction using R/R/R/Data frames). You may need to use as.factor. Internally, R stores the levels as numbers from 1 upwards, but it is not always obvious which number corresponds to which level, and it should not normally be necessary to know.
state.name #this is *NOT* a factor state.name[1] <- "Any text" #you can replace text in a character vector state.region[1] <- "Any text" #but you can't in a factor state.region[1] <- "South" #this is OK state.abb #this is not a factor, just a character vector
Coded <- factor(c(1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1)) Gender <- factor(Coded, labels=c("Female", "Male")) #we can then convert this to named levels
# Statistical Analysis: an Introduction using R/R
## Topics in R

All the R topics in the wikibook Statistical Analysis: an Introduction using R are listed here.
* R fundamentals (Reading these topics should give you a good grounding in R)
** R as a calculator
** Storing objects
** Functions
** Getting help
** Quitting
** Vectors
** Factors
** Missing data
** Matrices and arrays
** Lists
** Simple plotting
** Univariate plots
** Data frames
** Getting data into R
** Bivariate plots
* Programming R
** Blocks of code
** Your own functions
** if/ifelse

## The full list of topics is below

Pages with the prefix 'Statistical Analysis: an Introduction using R/R' in the '' and 'Talk' namespaces:
/All
# Statistical Analysis: an Introduction using R/Introduction

library(caTools) # external package providing write.gif function jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F",
m <- 1200 # define size C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ),
C <- matrix(C,m,m) # reshape as square matrix of complex numbers Z <- 0 # initialize Z to zero X <- array(0, c(m,m,20)) # initialize output 3D array for (k in 1:20) { # loop with 20 iterations
} write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=100)
# Statistical Analysis: an Introduction using R/R/Accessing elements of vectors

* A positive number or numbers, then this has the effect of picking those particular elements of the vector
* A negative number or numbers, then this has the effect of picking the whole vector except those elements
* A logical vector, then each element of the logical vector indicates whether to pick (if TRUE) or not (if FALSE) the equivalent element of the original vector.
[1] 1214 > which.min(state.area) #... this shows which element it is (the 39th as it happens) [1] 39 > state.name[39] #You can obtain individual elements by using square brackets [1] "Rhode Island" > state.name[39] <- "The smallest state" #You can replace elements using too > state.name #The 39th name ("Rhode Island") should now have been changed
[13] "Illinois" "Indiana" "Iowa" "Kansas"
[17] "Kentucky" "Louisiana" "Maine" "Maryland"
[21] "Massachusetts" "Michigan" "Minnesota" "Mississippi"
[25] "Missouri" "Montana" "Nebraska" "Nevada"
[29] "New Hampshire" "New Jersey" "New Mexico" "New York"
[33] "North Carolina" "North Dakota" "Ohio" "Oklahoma"
[37] "Oregon" "Pennsylvania" "THE SMALLEST STATE" "South Carolina"
[41] "South Dakota" "Tennessee" "Texas" "Utah"
[45] "Vermont" "Virginia" "Washington" "West Virginia"
[49] "Wisconsin" "Wyoming"
> state.name[1:10] #This returns a new vector consisting of only the first 10 states
> state.name[-(1:10)] #Using negative numbers gives everything but the first 10 states
[13] "Minnesota" "Mississippi" "Missouri" "Montana"
[17] "Nebraska" "Nevada" "New Hampshire" "New Jersey"
[21] "New Mexico" "New York" "North Carolina" "North Dakota"
[25] "Ohio" "Oklahoma" "Oregon" "Pennsylvania"
[29] "THE SMALLEST STATE" "South Carolina" "South Dakota" "Tennessee"
[33] "Texas" "Utah" "Vermont" "Virginia"
[37] "Washington" "West Virginia" "Wisconsin" "Wyoming"
> state.name[c(1,2,2,1)] #You can also obtain the same element multiple times [1] "Alabama" "Alaska" "Alaska" "Alabama" > ###Logical vectors are a little more complicated to get your head round > state.area < 10000 #A LOGICAL vector, identifying which states are small
[16] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE [31] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE [46] FALSE FALSE FALSE FALSE FALSE > state.name[state.area < 10000] #So this can be used to select the names of the small states [1] "Connecticut" "Delaware" "Hawaii" "Massachusetts"
[5] "New Hampshire" "New Jersey" "THE SMALLEST STATE" "Vermont"

## Notes

* ↑ if the logical vector is shorter than the original vector, then it is sequentially repeated until it is of the right length
# Statistical Analysis: an Introduction using R/R/Constructing vectors
#### Constructing vectors

* For a simple sequence of numbers from a to b, the easiest way is to use the notation a:b.
* Many functions return vectors. Two of the most useful are "seq" and "c". The function "seq" produces sequences of numbers, similar to a:b, but with more flexibility. The function "c" concatenates objects together into a vector, and so can be used to construct an arbitrary vector.
foo [1] 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 [26] 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 [51] 149 150 > c(2,3,6,6,6) #Creates a vector of 5 numbers [1] 2 3 6 6 6 > c(2,3,6,6,6) + 1 #N.B. you can do all the normal calculations on vectors [1] 3 4 7 7 7
# Statistical Analysis: an Introduction using R/Cover

An understanding of statistics is an invaluable tool in much of modern science. This is particularly true of fields such as the biosciences and social sciences in which data are “noisy”, that is where chance or uncontrollable effects have a substantial impact on the data recorded. This wikibook is aimed at the scientifically-minded reader who has not been exposed to much statistical thinking. As well as the Statistics wikibook, and guides on the RWiki, there are many introductory statistical texts on the market. This differs from most in that, although it quickly introduces “advanced” topics such as likelihood and Bayesian methods, it does not do so through formal mathematical proofs, but primarily via graphical methods and simulation. The aim is to produce a text that can be used as a “crash course” in statistics for basic-undergraduate-level readers who want to understand how to analyse their data using modern techniques, but who are not particularly mathematical.
The advent of computers has made an enormous impact on the way in which statistics is done in the real world. Although many introductory statistics courses start by describing simple t-tests and chi-squared tests, most scientists now use more advanced techniques to analyse their data. In particular, there is substantial focus on what are known as general and generalised linear models, as well as (more recently) a renewed interest in Bayesian techniques and methods such as bootstrapping. Instead of explaining overly simple t-tests and the like, leaving underlying theory until later, this book aims to do the opposite. The computer program ‘R’, and its flexible graphical capabilities, are used to give a feel for the underlying theory of statistics, notably the concepts of probability, likelihood, probability density functions, sampling distributions, and hypothesis testing. These principles can then be applied to understanding the various commonly used statistical tests, and easily extended to sophisticated statistics such as generalized linear modelling.
It is intended for the main text of the book to give a general understanding of statistical methodology, as independent as possible of the statistical package used. This text should be interspersed with relevant "R topics": sections of annotated R code, clearly distinguished from the main body of the book. It should be possible to use these R topics by themselves as a set of tutorials: an introduction to basic concepts in R.
Using this book, or any portions of it, in any other R or stats-related projects is encouraged: please feel free to copy and use any parts as you see fit. Books with partially overlapping aims to this are detailed at http://www.ling.uni-potsdam.de/~vasishth/SFLS.html and http://wiener.math.csi.cuny.edu/UsingR/. However, neither of these are editable or redistributable.
# Statistical Analysis: an Introduction using R/Datasets

Table 2.2: Number of deaths by horse-kick in the Prussian army (a classic dataset from von Bortkiewicz, 1898) :
| corp: | G | I | II | III | IV | IX | V | VI | VII | VIII | X | XI | XIV | XV | Total |
| year | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths | number of deaths |
| 1875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 3 |
| 1876 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 5 |
| 1877 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 2 | 0 | 7 |
| 1878 | 1 | 2 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 9 |
| 1879 | 0 | 0 | 0 | 1 | 1 | 0 | 2 | 2 | 0 | 1 | 0 | 2 | 1 | 0 | 10 |
| 1880 | 0 | 3 | 2 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 4 | 3 | 0 | 18 |
| 1881 | 1 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
| 1882 | 1 | 2 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 2 | 1 | 4 | 1 | 14 |
| 1883 | 0 | 0 | 1 | 2 | 0 | 1 | 1 | 2 | 1 | 0 | 0 | 3 | 0 | 0 | 11 |
| 1884 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 9 |
| 1885 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 5 |
| 1886 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 3 | 0 | 11 |
| 1887 | 1 | 1 | 2 | 1 | 0 | 1 | 0 | 3 | 2 | 1 | 0 | 1 | 2 | 0 | 15 |
| 1888 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 6 |
| 1889 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 2 | 0 | 2 | 11 |
| 1890 | 1 | 2 | 0 | 2 | 0 | 2 | 1 | 1 | 2 | 0 | 1 | 1 | 2 | 2 | 17 |
| 1891 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 3 | 3 | 1 | 0 | 12 |
| 1892 | 1 | 3 | 2 | 0 | 1 | 1 | 1 | 3 | 0 | 1 | 0 | 1 | 1 | 0 | 15 |
| 1893 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 1 | 3 | 0 | 0 | 8 |
| 1894 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 4 |
# Structural Biochemistry/Sublingual

Sublingual medications are administered by inserting them directly under the tongue. The medications dissolve rapidly and are absorbed through the mucous membranes of the mouth, where they enter into the bloodstream. The medications are compounded in the form of small, quick-dissolving tablets, sprays, lozenges, or liquid suspensions. The most common sublingual medication is the nitroglycerin tablet, which is a form of pain reliever that is absorbed the fastest when administered sublingually to the body. The sublingual administration method is not always appropriate. Sublingual medications should not be administered if the gums or mucous membranes have open sores or areas of irritation. The patient should not eat, drink, chew, or swallow until the medication has been absorbed. Swallowing the medication must be prevented, since it will decrease the drug's effectiveness as it is digested in the digestive system. Sublingual administration method is fat acting and the body will be able to sense its effect within 5-10 minutes after administration.

## Reference

http://www.enotes.com/sublingual-buccal-medication-administration-reference/sublingual-buccal-medication-administration
# General Information

Structural protein are essential for providing structure and rigidity to fluid biological cells and components. Structural proteins are fibrous proteins which provide support for the cells. Structural proteins are usually very large and are made up with up to thousands of amino acids. Insects and spiders use silk fibers to for various tasks such as making their cocoons and webs. Another example of a structural protein can be seeing in Keratin which is the protein of hair, feathers and horns among other things. Actin and collagen are specific examples that fall under this category of proteins. Collagen, recognized as one of the most abundant proteins in mammals, is the main component in connective tissue. Collagen can be found in the tendons, ligament and skin. Collagen can also be found abundant in cornea, cartilage, bone, and blood vessels. Collagen composes about 25-35% of the entire protein content in the human body, which illustrates the importance of structural proteins such as this in the body.

## Key Properties

Not only proteins serve an important role as the structural components, it also participates in all of the biological processes. For example, transport, storage, catalytic reactions, immunity, nervous system, growth, and etc. There are four general properties that allow proteins to function in such wide varieties:
* Protein is formed with monomers called “amino acids” and it is connected from one end to the other, becoming a linear polymer. The unique sequence of the amino acids causes the chain to fold into three dimensional shapes called “protein,” which the function is also determined by the shape. Because of the endless possible sequences of the amino acids, the folded protein world is also capable of intense diversity and varieties.
* Protein carries many kinds of functional groups such as, thiols, alcohols, carboxylic acid, and etc. Because these functional groups are reactive, it gives the protein wide range of reactive properties.
* Protein’s ability to interact with other macromolecules also increases the range of functions. Unlike other macromolecules, the ability to interact allows the proteins to develop into complex assembly with other molecules.
* The combination of rigidity and the flexibility of the protein is another reason for its usefulness in biological structures. Flexible proteins may work as a spring, hinge, and a lever while ridge proteins can play a role in cytoskeleton structures.

## References

* Berg, Jeremy Mark., John L. Tymoczko, and Lubert Stryer. Biochemistry. New York: W.H. Freeman, 2007. Print.
* 1 2 Biology, Eight Edition,Pearson, Benjamin Cummings, 2008.
# Structural Biochemistry/Structural Variation

A structural variation (SV) is a alteration of the genome in an organism's chromosome. These variations may be deletions, duplications, copy-number variants, inserts, inversions or translocation. Regions of alteration can range from as small as 1Kb to 3Mb in size.

## Duplication

In chromosomal duplication, extra copies of a region in the chromosome are formed. If these duplications are side by side, this is known as tandem duplication. If they are separated, however, they are said to be displaced.
If an organism has several copies of the same gene, its phenotype may be altered. As the gene segments are multiplied, so are the chances of that gene being transcripted, translated and expressed. As a result, people may produce more protein than others. This may play either a detrimental or beneficial role in the organism's physiology.
Organisms may also benefit from this anomaly in that it gives them extra genes that may mutate into other vital genes necessary for evolutionary fitness.

## Deletion

Deletion is a mutation in the chromosome that results in a loss of DNA sequences. Depending on the size of the deletion, the organism may experience various phenotypes. Similar to duplication, deletion may alter gene dosage.
Deletion can be extremely harmful to an organism, depending on the size and location. Some phenotypes require two genes to be present for normal expression. Haploinsufficiency is the term used to describe a mutant phenotype that only has one of the two genes.

## Sources

Amos-Landgraf, J. M., et al. Chromosome breakage in the Prader-Willi and Angelman syndromes involves recombination between large, transcribed repeats at proximal and distal breakpoints. American Journal of Human Genetics 65, 370–386 (1999)
Christian, S. L., et al. Large genomic duplicons map to sites of instability in Prader-Willi/Angelman syndrome chromosome region (15q11-q13). Human Molecular Genetics 8, 1025–1037 (1999)
# Structural Biochemistry/Subcutaneous
## Definition

A subcutaneous injection (SC, SQ, SubQ) is a method of drug administration in which the drug is injected into hypodermis, the fatty tissue layer directly beneath the dermis and epidermis. Because there is limited blood flow to the hypodermis, subcutaneous injection is usually used when slow absorption of medication is preferred. Drugs that are administered using this method must be soluble and potent in small concentrations. Common medications used with this procedure include heparin, insulin, growth hormones, and vaccines against MMR and varicella.

## Sites and Method of Injection

With subcutaneous injection, the drug is usually administered through loose interstitial tissues of the upper arm, the anterior surface of the thigh, the lower portion of the abdomen, or the lower back. When injecting, the skin is pinched up to prevent injection into muscle. It is recommended to use the same site for routine injections. The shot is given at a straight 90 degree angle if at least 2 inches of skin can be grasped. Otherwise, it is given at a 45 degree angle.

## Absorption Rate

Factors that may increase absorption rate are heat, massaging, co-administers vasodilators, or hyaluronidase at the site of injection. Epinephrine may decrease the absorption rate due to decreased blood flow.

## References

* ↑ Clinical Center - National Institutes of Health. "Patient Education: Giving a subcutaneous injection."
* ↑ "How To Give A Subcutaneous Injection." Drugs.com, n.d. Web. 28 Oct. 2012. http://www.drugs.com/cg/how-to-give-a-subcutaneous-injection.html.
# Star Sonata/Frequently Asked Questions

    Q: How do i find a good trading route?
    A: You have to find two AI or player bases which buy and sell goods for different profit margins. The bigger the better! Be aware that even AI bases can run out of stock, not just the players' whose bases extract resources or buy them from others. Often if you follow someone else's slave ships they will give away a good trading route too. Another way is to fly around to as meany bases as you can and after a while look at the comonotys trade window in profet mode with all stations selected, this will give you a view of where to buy and where to sell.
    Q: How do I build a base?
    A: See the base building section.
    Q: I am in the middle of nowhere; how do I find my way out?
    A: Press "Tab" button until either a wormhole or base appears in your target window. Then press "V" button to autopilot there.
    Q: How do these "Dg's" work? Are they totally random or what?
    A: Well its quite simple. See if a DG says "Dg 3.224" then it will have 3 levels of multiple Ai then a "boss". The boss will usually be much harder than the previous AI in the Dg with a bunch of little "helper" Ai. If the galaxy with the Dg says "Danger Factor 110" (or any number) then every level of the Dg will increase the danger factor by 10. So if you are in a DF 90 galaxy with a lvl 4 (4.33) Dg then the 1st level of it will be danger factor 100, then 110, then 120, then 130, then the boss will be in DF 140. Now get this: the decimal nimbers after the main one (#.234) don't mean anything, at all. Also the levels of a Dg scale down to better help you keep track of what evel you are in, for e.g. it will start out as level 5, then 4 all the way down to the boss (0). If you start out with a higher Dg number in a higher danger factor then the prize the boss drops will usually be better.