bedtime stOry byk gler.....

Perghh..mentang2 la pas Maghrib pulun tdO cam ape je kan (b'jam2 kut)..dh bgn 2 smgt kut nk study...huh,xde maknenye..! duk b'cter ngn rum8 smp xigt dunia..xpsl2 rum8 aku xtdO2 gak smp Subuh..

Duk cter lak scary2 stories time schOOl dulu....bkn men lg smp xigt ari ni de klas.. pastu mula la bukak cter self-xperience..huishh,xsangka jgk byknye..bley r tahan.

Rupe2nye..time alam p'sekOlahan lar y plng byk cter2 ni..histeria la..nmpk benda2 y xptt nmpk la..dgr la..ishh..seram sejuk jgk rasenye. Apa2 pun finally dh xrasa ape2 pun coz cOnclude ngn cter psl lab...ntah apekejadah ntah tbe2 kuar cte psl lab katak..xphm jgk dr mane dtgnye..

sO, smbg study skit n pasni g klas...hOpe ari ni everything runs smOOthly..daa=)

...EXCEL...

Today, I've learnt how to make linear regression and also quadratic regression using Microsoft Office Excel 2007.

LINEAR REGRESSION

In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. Models of the unknown parameters are estimated from the data using linear functions. Such models are called linear models.


Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.

Linear regression has many practical uses. Most applications of linear regression fall into one of the following two broad categories:

• If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y.
• Given a variable y and a number of variables X₁, ..., X_p that may be related to y, then linear regression analysis can be applied to quantify the strength of the relationship between y and the X_j, to assess which X_j may have no relationship with y at all, and to identify which subsets of the X_j contain redundant information about y, thus once one of them is known, the others are no longer informative.

Application of linear regression

Trend line

A trend line represents a trend, the long-term movement in time series data after other components have been accounted for. It tells whether a particular data set (say GDP, oil prices or stock prices) have increased or decreased over the period of time. A trend line could simply be drawn by eye through a set of data points, but more properly their position and slope is calculated using statistical techniques like linear regression. Trend lines typically are straight lines, although some variations use higher degree polynomials depending on the degree of curvature desired in the line.

Trend lines are sometimes used in business analytics to show changes in data over time. This has the advantage of being simple. Trend lines are often used to argue that a particular action or event (such as training, or an advertising campaign) caused observed changes at a point in time. This is a simple technique, and does not require a control group, experimental design, or a sophisticated analysis technique. However, it suffers from a lack of scientific validity in cases where other potential changes can affect the data.

QUADRATIC REGRESSION

In mathematics, a quadratic equation is a polynomial equation of the second degree. The general form is 

where x represents a variable, and a, b, and c, constants, with a ≠ 0. (If a = 0, the equation becomes a linear equation.The constants a, b, and c, are called respectively, the quadratic coefficient, the linear coefficient and the constant term or free term. The term "quadratic" comes from quadratus, which is the Latin word for "square".

In statistics, to build a second order (quadratic) model, linear regression is used, sometimes iteratively, to obtain results. 

These are the graphs that i've made using M.Excel..

....SMILES...

SMILES stand for Simplified Molecular Input Line Entry Specification

Introduction of SMILES

• is a line notation (a typographical method using printable characters) for entering and representing molecules and reactions.
• The primary reason SMILES is more useful than a connection table is that it is a linguistic construct, rather than a computer data structure. 
• SMILES is a true language, albeit with a simple vocabulary (atom and bond symbols) and only a few grammar rules.
• SMILES representations of structure can in turn be used as "words" in the vocabulary of other languages designed for storage of chemical information (information about chemicals) and chemical intelligence (information about chemistry). 

 A) Canonicalization

SMILES denotes a molecular structure as a graph with optional chiral indications. This is essentially the two-dimensional picture chemists draw to describe a molecule.

• "unique SMILES" -  A canonicalization algorithm exists to generate one special generic SMILES among all valid possibilitie.
• "isomeric SMILES" - SMILES written with isotopic and chiral specifications.

Examples:

Input SMILES	Unique SMILES
OCC	CCO
[CH3][CH2][OH]	CCO
OC(=O)C(Br)(Cl)N	NC(Cl)(Br)C(=O)O
ClC(Br)(N)C(=O)O	NC(Cl)(Br)C(=O)O

B) Atoms

Atoms are represented by their atomic symbols. Each non-hydrogen atom is specified independently by its atomic symbol enclosed in square brackets, [ ].Elements in the "organic subset" B, C, N, O, P, S, F, Cl, Br, and I may be written without brackets if the number of attached hydrogens conforms to the lowest normal valence consistent with explicit bonds.

• In aromatic rings - specified by lower case letters, e.g., aliphatic carbon is represented by the capital letter C.
• In aromatic carbon - specified by lower case c.

Following atomic symbols are valid SMILES notations

Examples:

C	methane	CH4
N	ammonia	NH3

Atoms with valences other than "normal" and elements not in the "organic subset" must be described in brackets [ ].

Examples:

[S]	ELEMENT SULPHUR
[Au]	ELEMENT GOLD

C) Bonds

Single, double, triple, and aromatic bonds are represented by the symbols -, =, #, and :, respectively.

Examples:

CC	ethane	CH3CH3
C=O	formaldehyde	CH2O
O=C=O	carbon dioxide	CO2

D) Branches

Branches are specified by enclosing them in parentheses, and can be nested or stacked. In all cases, the implicit connection to a parenthesized expression (a "branch") is to the left.

Example: 

 

CCN(CC)CC

Triethylamine

 

These are the examples of SMILES that I have 

made using ACD/ChemSketch  

SMILES is not only comprehensive but also well documented!!!

iNtrOductiOn of Protein Data Bank (PDB)

<a href="http://www.rcsb.org/pdb/home/home.do">Visit rscb</a>

An Information Portal to Biological Macromolecular Structures

The PDB archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

1) Crystal structure of Bacillus subtilis Lon N-terminal domain

Duman, R.E.,   Lowe, J.

Journal: (2010) J.Mol.Biol. 401: 653-670 
PubMed: 20600124    
DOI: 10.1016/j.jmb.2010.06.030    
Search Related Articles in PubMed

PubMed Abstract:

Lon ATP-dependent proteases are key components of the protein quality control systems of bacterial cells and eukaryotic organelles. Eubacterial Lon proteases contain an N-terminal domain, an ATPase domain, and a protease domain, all in one polypeptide chain. The N-terminal domain is thought to be involved in substrate recognition, the ATPase domain in substrate unfolding and translocation into the protease chamber, and the protease domain in the hydrolysis of polypeptides into small peptide fragments.  Here, we present crystal structures of truncated versions of Lon protease from Bacillus subtilis (BsLon), which reveal previously unknown architectural features of Lon complexes. Our analytical ultracentrifugation and electron microscopy show different oligomerisation of Lon proteases from two different bacterial species, Aquifex aeolicus and B. subtilis.

Molecular Description

Classification	hydrolase
Structure Weight	48027.40
Molecule	ATP-dependent protease La 1
Polymer	1
Type	polypeptide(L)
Chains	A,B

2) Crystal Structure of Dipeptidyl Aminopeptidase IV from Stenotrophomonas maltophilia 

 


Dipeptidyl aminopeptidase IV from Stenotrophomonas maltophilia exhibits activity against a substrate containing a 4-hydroxyproline residue.

Journal: (2008) J.Bacteriol. 190: 7819-7829

PubMed: 18820015  
PubMedCentral: PMC2583625  
DOI: 10.1128/JB.02010-07  
Search Related Articles in PubMed  

PubMed Abstract:

The crystal structure of dipeptidyl aminopeptidase IV from Stenotrophomonas maltophilia was determined at 2.8-A resolution by the multiple isomorphous replacement method, using platinum and selenomethionine derivatives. The crystals belong to space group P4(3)2(1)2, with unit cell parameters a = b... [ Read More & Search PubMed Abstracts ]

The crystal structure of dipeptidyl aminopeptidase IV from Stenotrophomonas maltophilia was determined at 2.8-A resolution by the multiple isomorphous replacement method, using platinum and selenomethionine derivatives. The crystals belong to space group P4(3)2(1)2, with unit cell parameters a = b = 105.9 A and c = 161.9 A. Dipeptidyl aminopeptidase IV is a homodimer, and the subunit structure is composed of two domains, namely, N-terminal beta-propeller and C-terminal catalytic domains. At the active site, a hydrophobic pocket to accommodate a proline residue of the substrate is conserved as well as those of mammalian enzymes. Stenotrophomonas dipeptidyl aminopeptidase IV exhibited activity toward a substrate containing a 4-hydroxyproline residue at the second position from the N terminus.One of the residues at the active sites is changed to Asn611. Accordingly, it was considered that Asn611 would be one of the major factors involved in the recognition of substrates containing 4-hydroxyproline.

Molecular Description

Classification	hydrolase
Structure Weight	82181.10
Molecule	Dipeptidyl peptidase IV
Polymer	1
Type	polypeptide(L)
Chains	A

3) Human START domain of Acyl-coenzyme A thioesterase 11 (ACOT1)

Journal : to be published

Molecular Description

Classification	lipid transport
Structure Weight	60191.69
Molecule	Thioesterase, adipose associated, isoform BFIT2
Polymer	1
Type	polypeptide(L)
Chains	A,B
Fragment	STARTdomain, UNP residues 339-594

ChEmSkEtCH....29122010

<a href="http://www.acdlabs.com/products/draw_nom/draw/chemsketch/">visit ACD/ChemSketch</a>

ACD/ChemSketch
This software is really useful and convenient especially for those who want to draw :-

• chemical structures and its nomenclature.
• graphical images.

In creating the graphical objects, there are so many things that we can do such as:-

• drawing a a diagram(curve)
• drawing the labaratory apparatus
• creating a table
• simple structures-atoms

Example of table : -

Name of alcohol	Chemical formula of alcohol
methanol	CH3OH
ethanol	CH3CH2OH or C2H5OH
propan-1-ol	CH3CH2CH2OH or C3H7OH
propan-2-ol	CH3CH(OH)CH3

Drawing using ChemSketch have never been easier...try it!!