APLICAÇÕES DA QUIMIOMETRIA EM DIFERENTES ÁREAS DA QUÍMICA

 Márcia M. C. Ferreira

Laboratório de Quimiometria Teórica e AplicadaInstituto de Química UNICAMP

Email: marcia@iqm.unicamp.brURL: http://lqta.iqm.unicamp.br

Quantitative analysis and classification of AFM images of human hair

 S. P. GURDEN, V. F. MONTEIRO, E. LONGO

J. Microsc., in press.

Root end

Face surface

Top surface

Central cortex

distal end

70 m

5 m

The central cortex of a hair fibre is surrounded by thin cellular sheets, known as cuticles, which overlap each other from root to tip.

These cuticles fulfil a number of useful roles including protection from physical and chemical insult and a tendency to maintain the hair in a clean and disentangled state

This work aims to build a computation algorithm capable of analysing an AFM image and calculating a set of parameters which describe as fully as possible the cuticular structure.

These descriptors are used for automatic classification of hair samples according to factors such as distance from the root-end and hair treatment.

AFM images of Caucasian hair

bleached hair near the distal end untreated hair near the root end

Two types of black, Caucasian human hair samples were used: (a) untreated and (a) bleached. . Each hair fibre had a thickness of approximately 65m and a length of approximately 20cm.

The AFM images were measured using a Digital Instruments NanoScope IIIa instrument under atmospheric conditions at 25C using a loading force of 3.6nN.

Background surface calculated during the planification step

Hair image after planification

Original hair image

A

B

C

step height

laye

r sp

acin

gtotal length

tilt

backtilt

cuticle surface

underlying cuticle

The quantitative analysis and classification of AFM hair images is carried out by characterizing the hair surface using descriptors which summarize the important characteristics of the cuticular structure.

Step height; Tilt; Backtilt; Layer spacing; Face distance(AB);Top distance(BC); Fit error; Roughness; Fitability; Cuticle density (#cuticles per mm). .

samples

descriptorsdummy

variables

0 11 11 0etc.

YX

R

T

R

descriptors

PLS loadings

PLS

P´

PLS scores

samples

samples

Mean Standard deviation

Step height (nm) 386.27 103.89

Tilt () 3.73 1.18

Backtilt () 19.83 6.55

Layer spacing (nm) 473.43 147.40

Face distance (nm) 1451.66 708.69

Top distance (nm) 5955.05 1471.19

Fit error (nm) 31.78 10.12

Cuticle density (mm-1) 129.43 20.92

Roughness 1.0153 0.0046

Fitability (%) 75.17 13.05

Cuticular descriptors calculated for the example image shown before.

Schematic of PLS-DA

The data was autoscaled before building the PLS model.

From 38 samples, the model was built using 36 (2 outliers).

Of the 36 samples, 31 were classified correctly, giving a success rate of 86%.

untreated/distal end; bleached/root end; untreated/root end; bleached/distal end.

The samples () are closely clustered. They are the most homogeneous in terms of cuticle structure, as the hair surface has not been greatly damaged by physical or chemical stress. They have similar descriptor values regardless of which particular image is analysed. The untreated/distal end () and bleached/root end () samples also form fairly well-defined groups (exception are the two untreated/distal end samples and the misclassified samples).

The bleached/distal end samples () are very scattered, indicating that representative sampling of this group is more difficult. As PC1 describes negative contributions from cuticle density and fitability, it is logical that the samples damaged by cosmetic treatment and long-term physical stress (far from the root end) have the highest PC1 scores and so, have low cuticle density and poor fitability.

Root and distal end samplesIn general, the root end samples ( and ) have negative component 2 scores and the distal end samples ( and ) have positive component 2 scores. This shows that at the root end of the hair, where the cuticles are more abundant, the degree of tilt and step height of the cuticles is higher as may be expected. At the distal end, where the cuticles are less abundant, the cuticles lie flatter and have a lower degree of backtilt due to physical wear. Distal end cuticles also have a less uniform pattern, also due to the effect of prolonged physical stress which chips away at the cuticle ends, leaving an irregular cuticle edge.

Effect of bleachingFor the samples measured at the root end, the bleached samples () lie further to the right that the untreated samples (). This indicates that one effect of bleaching is to remove the cuticles which protect the central hair cortex, thus making the hair less resistant to breakage or splitting. The bleached samples also have lower component 2 scores, symptomatic of cuticle detachment which also leaves the hair in a weaker condition, more vulnerable to subsequent damage.For the samples measured at the distal end (untreated, ; bleached, ), the removal of cuticle layers is even more pronounced, showing that bleaching of already vulnerable distal end hair can lead to complete removal of the cuticular layer in some cases, exposing the underlying central cortex.

Quantitative Determination of Epoxidized Soybean Oil Using Near-Infrared Spectroscopy

and Multivariate Calibration   

Thais F. Parreira

Henrique J. S. Sales, and Wanderson B. de Almeida 

Henkel S/A Indústrias Químicas

Appl. Spectrosc., 56, 1607-1614 (2002).

Soybean oil is a triglyceride which typically contains 14% stearic, 23% oleic, 55% linoleic and 8% linolenic acid. Three of them are unsaturated acids: oleic (18:1), linoleic (18:2) and linolenic (18:3).

Chemical modification on commercial available soybean oil such as epoxidation can enhance its properties (reactivity) for industrial applications.

The epoxidized soybean oil (ESO) is extensively used in plastic industry as plasticizer (at levels ranging from 0.1 to 27%), to increase flexibility and as stabilizer to minimize decomposition in polyvinyl chloride (PVC) products.

PVC degradation caused by sunlight eliminating HCl

Reaction of oxirane ring with HCl inhibiting the degradation process.

To follow the soybean oil epoxidation process, it is necessary to quantify some analytes related to the product’s quality:

•The epoxide index (E.I.) is directly related to the stabilizer feature of the product the higher the epoxide content, the more efficient the additive as thermal stabilizer.

• The iodine content (I.I.) is an indicator to the amount of unsaturations present in the epoxidized soybean oil.

• The percentage of water (% of Water), results from washing of the final product. Its concentration must be minimal since water can cause degradation of the epoxide group.

Degradation of epoxide groups by water

Iodine index determination halogenation of double bonds.

NIR absorbance spectra were recorded from 9300 cm-1 to 4500 cm-1 with a 2 cm-1 increment, using a BOMEM – MB160 – FTIR spectrophotometer.

Generic recorded spectrum

ORIGINAL DATA SET (2400 VARIABLES)

REDUCED DATA SET (160 VARIABLES)

Box car average

SELECTION C(Loadings & Regression vector)

SELECTION A(Spectral difference)

SELECTION BCORRELOGRAM

EXTERNAL VALIDATION(using the best model)

% WATER7050-7290, 5190-5310 cm-1 7110-7230, 5130-5310 cm-1

6930-7350, 4500-5910 cm-1

Method # Variables Latent Var. SECV PRESS

R

Boxcar averaging

160 3 0.011 0.006 0.992

Selection A 14 3 0.012 0.007 0.992

Selection Ba 12 3 0.013 0.008 0.990

Selection C 63 3 0.011 0.006 0.993

External pred.b 14 3 0.013c 0.002

PLS Models

a Variable selection by the correlogram applied to Selection B for cutoff = 0.80.b External prediction using PLS model with 14 variables selected in Selection A. c SEP (standard error of prediction).

FIGURA

Experimental, estimated, and residual values for water % for external set.PLS model from Selection A (3 LVs)

Sample Experimental Values

Predicted Values

Residuals

9 0.06 0.048 -0.012

15 0.07 0.060 -0.010

19 0.05 0.048 -0.002

27 0.08 0.083 0.003

44 0.19 0.219 0.029

48 0.13 0.144 0.014

50 0.13 0.121 -0.009

53 0.16 0.149 -0.011

56 0.06 0.047 -0.013

60 0.008 0.006 -0.002

MeanStd.

RangeRPDa

0.0940.0560.1824.00

0.0930.064

RERb

-0.0010.014

13.00aRPD = std(exp.)/std(residuals).bRER = range(exp.)/std(residuals).

The use of NIRS combined with multivariate regression is a feasible alternative to the widely established techniques, especially in industrial processes.

Using simple and intuitive variable selection methods, such as loadings/regression vector analysis and the correlogram, the number of variables can be significantly reduced without impairing the model quality.

The statistical parameters used, RPD and RER, indicated that NIRS determination was accurate for E.I. (RDP=26.0 and RER=80.7) and fairly good for water % and I.I.

From the results obtained, it can be concluded that the proposed methodology is appropriate for monitoring the epoxidation of soybean oil and to evaluate the additive’s quality in the industrial process, where time, effort and money are crucial.

CONCLUSIONS

Chemometric and Molecular Graphics and Modeling Study on Bacterial -Lactam Efflux Mechanism

by Multidrug Resistance AcrB Pump

Rudolf Kiralj

ABSTRACT

The primary purposes of this work

To establish relationships between activity expressed as log of minimal inhibitor concentration (pMIC) elevated by three strains of Salmonella typhimurium (HN891, SH7616, SH5014), and calculated descriptors for 16 penicillins and cephalosporins at neutral pH.

To visualize pump – drug molecular recognition mechanism, using crystal structure of AcrB transporter from Escherichia coli.

These results can aid in explaining bacterial drug efflux mechanism, and design of novel -lactams which would not be excreted from bacterial cells.

INTRODUCTION Antibiotics are characterized by their chemical composition and mode of action.  Penicillins and cephalosporins have the cell wall as target for their action.  -lactam antibiotics are the most used antibacterial inhibitors of the Penicillin- Binding-Proteins (PBPs), which are responsible for the construction and maintenance of bacterial cell wall.

There are different mechanisms by which bacteria exhibit resistance to antibiotics:  1-  Bacteria produce -lactamases which hydrolyze the -lactam antibiotic ring before their binding to PBPs.   2-  Bacteria change their permeability to the drug (passive membrane transport).  3-  Bacteria develop a structurally altered PBP that is still able to perform its metabolic function, but less affected by the drug.  4-  Bacteria change their express transport system that actively pump the drug to the outer cellular environment.

The major mechanism of MDR in bacteria is the pump drug efflux. In general

this is accomplished by the presence of AcrAB-TolC efflux systems, which

are responsible for the unidirectional pumping of a wide variety of lipophilic

and amphiphilic compounds out of the cell.

FACTORS THAT INFLUENCES THE MULTI DRUG EFFLUX RATE

Pumps number Substrate concentration

pH Highly charged residues

Substrate charged groups

MDR PUMPS consist of 3 components:

1- a resistance-nodulation-cell division transporter AcrB (trimeric)2- an outer membrane channel protein of the family TolC (trimeric)3- a membrane fusion lipoprotein AcrA (probably trimeric also)

AcrAB-TolC bacterial pump. S. Murakami et al., Nature, 419 (2002) 587.

General

METHODOLOGY

MICs for bacterial strains → Mass concentration MICs (from literature) for 16 -lactams effluxed by bacterial strains S. typhimurium SH5014 (parent strain), SH7616 (an acr mutant) and HN891 (an overproducer of the Acr pump).

Drugs Modeling → Molecular structures were refined or modeled by Spartan Pro using atomic coordinates from PPSD, CSD or 2D formula. Conformational search was done by Montecarlo method and the most stable conformers were optimized by the semiempirical method PM3.

Lipophilicity Parameters → logarithm of the octanol-water partition coefficient logKOW was from Nikaido et al and several others were calculated using

different approaches.

wC, Sf → are the number fraction and surface fraction of hydrophobic carbon

atoms, respectively.

Other molecular descriptors: geometrical, electronic and Hydrogen bond molecular properties were calculated using 2D or 3D geometry of the antibiotics.

16 antibiotics (penicillins and cephalosporins) as AcrB substrates

N

SHN CH3

O

HH

CH3

CO2

RO

O

H2C

CH3

1

2

34

56

7

8

NO

Cl

CH3

H2C

CCO2

H

CSO3

H

H2C

H2C

CH2

C

CO2

HNH3

RNo.

1

2

3

7

8

15

N

HN

O

HHR

O1

2

345

6

7

8 S

CO2

CH2

R1

9

H2C S

O

CH3

O

NH2

N

NC

SO3H

O

CH3

C

SN

NO

CH3

NH2

S

N

N

N

CH3

O

O

H2CS

H2CC

N S

N N

NN

CH3

S

SN

N

CH3

H2CN

N

NN

R R1

N

HN

O

HOO

1

2

345

6

7

8 O

CO2

CH2

9S

SN

N

CH3

H

O2C

No.

4

5

6

9

11

12

13

14

10

NNH

O

HHO

1

2

345

6

7

8 S

CO2

CH2

O9

O

CH3

H2C CH2

H2C

CO2

HNH3

16

H3C

OH

1: Nafcillin 2: Cloxacillin 3: Penicillin G 4: Cephalothin 5: Cefoxitin 6: Cephaloridine 7: Carbenicillin 8: Sulbenicillin

9: Cefsulodin10: Latamoxef11: Cefotaxime12: Ceftriaxone13: Cefmetazole14: Cefazolin15: Penicillin N16: Cephalosporin C

Molecules

Comparison among three pMICs: pHN891 and pSH5014 are highly correlated

(right). pSH7616 shows different trend (left). The three bacterial strains are not

distinguished when excreting highly charged antibiotics.

Correlation of pMICS

Chemometrics of pMICs

-Lactams were classified as good, moderately good to poor, and bad AcrB

substrates. Clustering of -lactams with respect to the number of charged

groups NCH and hydrophobic surface fraction Sf is visible.

PCA and HCA were performed using only pMIcs data.

PCA (left) and HCA (right) analysis of 9 lipophilicity descriptors: logarithm of

the octanol-water partition coefficient (logP) calculated by various methods,

surface fraction (Sf) and number fraction (wC) of hydrophobic carbons. Two

clusters and two isolated logPs are visible. The lipophilicity descriptors do not

contain the same information (82.8% of the variance contained in PC1 + PC2).

Chemometrics of lipophilicity descriptors

An example of lipophilicity – activity nonlinear relationship.

Log Kow was linearized by GlogKOW = exp[–(logKOW – 1.1)2];

Other transformations : SlogPs = (logPs)2; SlogKWIN = (logKWIN)2.

Lipophilicity – pMIC relationships

PLS regression models for pMICs

It is visible that the best PLS models are obtained when all types of parameters

are used: lipophilic, electronic and hydrogen bonding.

Nafcillin (1) 2.607 2.531

Cloxacillin (2) 2.930 3.036

Penicillin G (3) 4.621 4.297

Cephalothin (4) 4.996 5.074

Cefoxitin (5) 5.029 4.919

Cephaloridin (6) 4.715 4.925

Carbenicillin (7) 4.675 4.470

Sulbenicillin (8) 4.714 4.594

Cefsulodin (9) 3.919 4.538

Latamoxef (10) 6.637 6.724

Cefotaxime (11) 6.579 6.567

Ceftriaxone (12) 6.665 6.647

Cefmetazole (13) 5.975 5.752

Cefazolin (14) 5.357 5.308

Penicillin N (15) 4.652 4.734

Cephalosporin C (16) 4.414 4.369

Experimentala and Predicted pMICSH5014b

aH. Nikaido et al., J. Bacteriol., 180 (1998) 4686. bMIC are in mols per liter.

Except for sample 9, exp-cal differences are smaller than 10%.

S. Murakami et al., Nature, Nature 419 (2002) 587. Science 300 (2003) 976.

AcrB crystal structure

Crystal structure of the AcrB trimer determined by X-ray diffraction: protein

without (left) and with a ligand (right). Three distinctive units are visible: TolC

docking domains, Pore domains and Transmembrane domains. The system

of cavities and channels for drug efflux can be also noted: the three vestibules,

the large central cavity, the narrow pore, and the cone-like funnel.

Left: efflux mechanism by AcrAB-TolC pump. Right: AcrB trimer in two views.

Efflux mechanism and the inner structure of the AcrB protein.

The vestibule structure

The vestibule’s projection has functional surface through which the drug can pass without difficulty. This area is called BRAMLA, due to its resemblance with the map of Brazil (BRAzil Map-Like Area). The upper third of BRAMLA is surrounded by hydrophilic and the other two thirds by hydrophobic residues of the AcrB.

Left: Electrostatic potential of the pore anf the transmembrane domains.

The vestibule-drug interactions

Left: Schematic representation of drug-vestibule stereolectronic

complementarity that was deduced from similarity of the 16 antibiotic structures

and importance of lipophilic, electronic and hydrogen bonding molecular

parameters. Molecular recognition is obvious, and it can be weaken or

enhanced by the nature of R and R1 side chains. Right: 3D docking of nafcillin

(1) to the vestibule. Interactions between hidrophilic AcrB residues (in

rectangles) and nafcillin polar groups are visible.

2D docking of selected AcrB substrates to the BRAMLA area, using maximum

and minimum (right) stereoelectronic fit approach for some antibiotics. It can

be noticed that the antibiotic molecules differ in how well then can fit sterically

and electronically to the vestibule. These fittings correspond to biological

activities for the presented antibiotics.

The pore structure

The structure of the pore channell (left figures) and the pore recognition site (right

figures) viewed perpendicularly to or along the three-fold axis of the AcrB protein.

The pore channel consists of three short -helices and three random coils. The

pore recognition site contains highly hydrophobic (yellow) and hydrophilic (red or

pink) residues: these residues are selective with respect to drugs due to

hydrophobic, polar and hydrogen bond interactions.

The pore-drug interactions

Some drugs docked to the pore recognition site.

Lipophilic drugs enter the pore channel easier than hydrophilic ones due to:

1) weaker intermolecular interactions;

2) more favourable drug-pore recognition.

These conclusions, based on 3D docking of the presented drugs, are in agreement to chemometric results.

Substrate 14 bound to a portion of a pore from a protomer (left) and its electrostatic potential at molecular surface in free and bound state (right). There are four drug – pore hydrogen bonds involving residues Ala100, Ala103 and Gln104. This illustrates why hydrogen bonding and electronic descriptors are important in the PLS models.

CONCLUSIONS

PLS models of good quality were obtained using lipophilic, electronic and

hydrogen bond descriptors for 16 -lactams.

Proposed efflux mechanism based on chemometrics and molecular

graphics and modeling methods:

1) a drug molecule comes from periplasmic space and interacts with a vestibule

through a mechanism of molecular recognition large and highly hydrophilic

molecules hardly enter the vestibule and come to the central cavity of AcrB

protein.

2) a drug molecule from the central cavity comes to the pore recognition site and

through a mechanism of molecular recognition enters the pore channel again

large and highly hydrophilic molecules hardly enter the pore channel to be

excreted from the cell.

A METHODOLOGY FOR IDENTIFICATION OF ATMOSPHERIC POLLUTION SOURCES

      

Edilton de S. Barcellos

This work introduces a methodology to identify the principal emission pollution sources in the Região Metropolitana de São Paulo.

The analysis covered the primary pollutants CO, NO, NO2 and CH4, and the secondary one O3.

The data (kindly provided by the Sanitation Department of the State of São Paulo, CETESB), are time series consisting of concentrations measured hourly throughout the year of 1999 for each compound, in the site of P. D. Pedro II.

To capture the systematic variations for each compound, the data was firstly arranged as matrices 24 (hours of the day) 365 (days of the year) and submitted to a Principal Component Analysis (PCA).

To extract simultaneously the daily and weekly systematic variations, the data was rearranged in a multiway structure (24 hours of the day 7 days a week 52 weeks of the year) and the Tucker model was applied.

METHODOLOGYOutliers Outliers were identified from a visual inspection of the original data matrix and considered as missing values in the case their values were ten times larger than the mean value for that pollutant.

Missing Data The matrices containing missing data were subjected to the mdpca routine of the PLS-Toolbox (Eigenvector Research, Inc.) (MATLAB software version 5.1) was used.

Data Preprocessing For individual pollutants: no preprocessing was applied. In order to minimize the local (hourly) sudden variations, when the analysis was carried out for more than one pollutant simultaneously, the data set was standardized by the mean concentration of the pollutant in question, i.e., for a given pollutant, each entry value was divided by the mean value taken from all the data relative to that compound.

Constraints Non-negativity was the only constraint used.

The multiway analyses were performed by using the N-way Toolbox 1.02 for MATLAB (http://newton.foodsci.kvl.dk/Matlab/nwaytoolbox).

365

24

PCA

A

“loadings”

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 00

5

1 0

1 5

2 0

2 5

3 0

3 5

s s a

d o

s e

te

q aq i

s

s a

d os eteq a

q i

s

s a

d o

s eteq a

q is

s ad os e

te

q aq i

s s a

d o

s e

te

q a

q i

s s a

d o

s e

te

q a

q i

s

s ad os ete

q aq is s ad o

s e

teq a

q is

s a

d o

s e

te

q aq is s ad o

s e

te

q aq i

s s ad os e

teq aq i

s s ad os e

te

q a

q i

s s ad os ete

q aq is s a

d os eteq aq is

s ad o

s ete

q a

q is

s ad o

s e

te

q aq is s a

d o

s eteq a

q i

s

s a

d o

s e

te

q a

q i

s

s a

d o

s e

te

q a

q is s ad o

s e

teq a

q i

s s a

d o

s e

te

q aq i

s

s a

d os e

teq aq i

s

s ad o

s ete

q a

q i

s s a

d o

s e

te

q a

q is

s ad o

s ete

q a

q i

s

s a

d o

s e

te

q aq i

s s a

d os eteq a

q i

s s ad o

s e

te

q a

q i

s

s a

d o

s e

te

q a

q is

s a

d o

s e

te

q a

q i

s

s ad o

s e

teq a

q i

s

s a

d o

s e

te

q a

q is s a

d os ete

q aq is s ad os ete

q aq i

s

s a

d os e

te

q a

q i

s

s a

d o

s e

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q a

q i

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s a

d os e

te

q a

q is s ad os ete

q aq is s ad os e

teq a

q i

s

s a

d o

s eteq aq i

s

s a

d o

s ete

q aq i

s

s ad o

s eteq aq is s ad o

s e

te

q a

q i

s

s ad os ete

q a

q i

s

s ad o

s e

teq aq i

s

s a

d os e

teq aq is s ad o

s eteq aq i

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q a

q is s ad o

s ete

q aq i

D ia s d o An o

S c o re s p a ra C P 1 (8 2 . 95 0 5 % )

CO

BT

“scores”

0 5 1 0 1 5 2 0 2 50 .1

0 .1 5

0 .2

0 .2 5

0 .3

0 .3 5

H o ra s

L o a d in g s p a ra C P 1 (8 2 . 95 0 5 % )

CO

X

PARAFAC

24

7

52

X1 2 3 4 5 6 7

0 .2

0. 2 5

0 .3

0. 3 5

0 .4

0. 4 5

d ia s

Un

ida

de

s A

rbit

rár

ias

V a ria ç ã o S is te m á tic a S e m a na l ( 1 = Se x , 2= S á b, 3 =D o m , ... )

B

0 5 1 0 1 5 2 0 2 51 0

1 5

2 0

2 5

3 0

h o ra s

Un

ida

de

s A

rbit

rá

ria

s

V a ria ç ã o S i s te m á t ic a D iá ria (E x p . v a r. = 7 2 . 7 0 )

A

0 1 0 2 0 3 0 4 0 5 0 6 00 . 0 5

0 . 1

0 . 1 5

0 . 2

0 . 2 5

s e m a n a s

Un

ida

de

s A

rbit

rári

as

Va ri a ç ã o An u a l (5 2 s e m a n a s )

C

Threeway representation for CO (array X) and its decomposition by PARAFAC model into component matrices A, B and C.

Construção da matriz 365 (dias) 24 (horas) para o CO e sua decomposição em matrizes de “scores” e de “loadings”.

horizontal

...

...

...

vertical

perfil

i=1, ..., I

i=1, ..., I

j=1, ..., Jk=1, ..., K

j=1, ..., J

k=1, ..., K

∙ ∙∙

Horas

Dias

X1X2

XK

Seman

as

K I J

J K I

I J K

Construção da estrutura multimodo mostrando a disposição dos dias, horas e semanas nas matrizes.

0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 40 00

1 0

2 0

3 0

4 0

Scor

es

Days

(c)

Annual Variation of CO (PC1=82.9640%)

Daily Systematic Variation of CO (PC1=82.9640%)L

oadi

ngs

Hours

Annual Variation of CO (PC1=82.9640%)

Scor

es

Days

(a) (b)

0 5 10 15 20 250.1

0.15

0.2

0.25

0.3

0.35

10 0 12 0 14 0 16 0 18 0 20 0

0

2

4

6

8

1 0

1 2

1 4

1 6

1 8

2 0

S u

MoT u

W e

T hF

S aS u

Mo

T u

W eT hF S a

S u

MoT uW e

T h

F

S a

S u

Mo

T u

W e

T h

F

S a

S u

Mo

T u

W e

T hF S aS u

Mo

T u

W e

T h

F S a

S u

Mo

T u

W e

T h

F

S a

S u

Mo

T u

W e

T h

F

S a

S u

MoT u

W e

T h

F S a

S u

Mo

T u

W e

T h

F

S a

S u

MoT u

W e

T h

F

S a

S u

Mo

T u

W eT h

F S a

S u

MoT uW e

T h

F S aS u

Mo

T u

W e

T h

F

S a

S u

Mo

Carbon MonoxidePCA showing daily systematic variation throughout the year

CO, NO and NO2 have a profile that is not similar to the profile of CH4. This result indicates that there are at least two emission sources. One of them is a road traffic source. The profile for CH4 in this site may be due to industries that uses natural gas, and sludge treatment plants, among others less important.

0 5 10 15 20 250 .17

0 .18

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0.2

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0 .22

0 .23

0 .24

hours

loa

din

gs

S ou rc e E mis sion P ro file : C H4 (P C1 =94 .08 % )

Daily Systematic Variation of NO (PC1=65.2180%) Daily Systematic Variation of NO2 (PC1=91.7935%)L

oadi

ngs

Loa

ding

s

Hours Hours

0 5 10 15 20 250.1

0.15

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0.35

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0.14

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0.26

(a) (b)

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100

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S a

S u

S a

S u

S a

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S aS u

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S aS u

S a

S u

S a

S u

S aS u

S a

S u

S aS u

S a

S u

S a

S u

S a

S u

S a

S u

S a

S u

S aS u

S aS u

S aS u

S aS u

S aS u

S aS u

S a

S u

S aS u

S aS u

S aS u

S a

S u

S aS u

S a

S u

S aS u

S a

S u

S a

S u

S a

S u

S aS u

S a

S u

S aS u

S a

S u

S aS u

S aS u

S a

S u

S aS u

S a

S u

S a

S u

S a

S u

S a

S u

S aS u

S a

S u

S aS u

S a

S u

S a

S u

0 50 100 150 200 250 300 350 4000

100

200

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500

WeTh

We

Th

WeTh

WeTh

WeTh

We

Th

We

Th

WeTh

We

Th

We

Th

We

Th

WeTh

We

Th

We

Th

We

Th

We

Th

We

Th

We

Th

WeTh

WeTh

WeTh

We

Th

WeTh

We

Th

WeTh

We

Th

WeTh

WeTh

WeTh

We

Th

We

Th

We

Th

We

Th

WeTh

WeTh

We

Th

We

Th

WeTh

WeTh

We

Th

We

Th

We

Th

We

Th

We

Th

We

Th

WeTh

WeTh

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Th

0 5 10 15 20 250 .05

0.1

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Scor

es

Hours

(a)Days

(b)

Loa

ding

s

Daily Syst. Var. of O3 (PC1=87.81%)Annual Var. of O3 (PC1=87.81%)

The major sources of ozone in the troposphere are the photochemical reactions and the necessary conditions are: a source of carbon (CO and/or hydrocarbons), nitrogen oxides (NO, NO2), and sunlight .

Carbon Monoxide CycleThe chain of chemical reactions that occurs in the troposphere has the real starting point with the photochemical decomposition of ozone, resulting in molecules and oxygen atoms in the excited

O3 + h O2 + O O + H2O 2 HO

Depending on the concentration of nitrogen oxides (especially NO) in the environment, the reaction can follow two different paths.

Low NOx conc. High NOx conc.CO + HO CO2 + H CO + HO CO2 + H H + O2 + M HO2 + M H + O2 + M HO2 + MHO2 + O3 HO + 2 O2 HO2 + NO HO +NO2

Net: CO + O3 CO2 + O2 NO2 + h NO + OO + O2 + M O3 + M

Net: CO + 2 O2 CO2 + O3

0 5 10 15 20 250.05

0.1

0.15

0.2

0.25

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0.35

0.4

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hours

Arb

itrar

y U

nits

Daily Profile (Exp. var.=68.61%)

PC1PC2

PC3

0 10 20 30 40 50 600

0.05

0.1

0.15

0.2

0.25

0.3

0.35

weeks

Arb

itrar

y U

nits

Annual Variations (52 weeks)

PC1

PC2

1 2 3 4 5 6 70.2

0.25

0.3

0.35

0.4

0.45

0.5

days

Arb

itrar

y U

nits

Weekly Profile (1=Fri, 2=Sat,3=Sun,...)

PC1

PC2

PC3

(a) (b)

(c)(d)

(a) Daily emission source profile for the primary vehicular pollutants (PC1), for other primary pollutants (PC2), and for O3 (PC3); (b) Weekly systematic variations for the primary vehicular emissions (PC1), other primary sources (PC2) and O3 (PC3); (c) Annual variations for the primary pollutants (PC1) and for the secondary ones (PC2); (d) Traffic density profile in the RMSP.

TUCKER MODEL

CONCLUSIONS

The models were able to identify the different primary sources and distinguish the primary from secondary pollutants.

It was possible to explain the weekly source profiles, and the weekly profile for the secondary pollutant, O3 through reactions occurring in both atmospheric cycles namely the carbon monoxide cycle and methane cycle.

The proposed models have pointed out the importance in reducing not only NOx, but also CO and CH4 emissions from local anthropogenic sources for controlling local levels of ozone and global warming.

Lapachol e derivados de 1,4 naftoquinonas em carsinosarcoma W-256

 Subramanian S.,Trsic Milan (USP SC)

Structural Chemistry 9, 47 (1998).

Lapachol é uma naftoquinona extraída do caule de certas bignoniáceas da Ásia e América do Sul. Uma destas plantas: o nosso IPÊ ROXO. É um pó de cor amarela intensa.

O

O

OH1

2

34 1'

2'

3'

4'

O Lapachol e alguns de seus derivados foram testados com bons resultados experimentais em tumores como o walker 256 (W-256) carcinoma e várias pesquisas estão hoje sendo feitas. Neste exemplo, a estrutura do lapachol e vários derivados de 1,4 naftoquinonas são usados para investigar a relação entre parâmetros estruturais e a atividade biológica usando KNN e mais tarde usando SIMCA. O conjunto de dados consiste de 25 compostos extraídos da referência citada acima e são classificados como ativos e inativos. Os descritores (variáveis) são os coeficientes da função de onda, , do orbital molecular de mais alta energia, dos seguintes átomos de carbono b, c, m, n, o, p, q, s, t, u e foram obtidos de cálculos semiempíricos usando o método PM3.

O

O

O

H

H

H

a

C C C

C

Co

l

dc

b

e

fg

n

jk

i

h

m

p

r

q s

t

u

or C

or C

, C or Br , H, Cl or Br

, H, Cl or Br

, C, or N, or S, or Cl or H(a)

INATIVA (XXIII)

(b)ATIVA (III)

(d)ATIVA (III)

Representação 3D do HOMO Representação 3D do LUMO

(c)

INATIVA (XXIII)

A análise de componentes principais, nos dados autoescalados, mostra que PC1 discrimina os compostos ativos dos inativos. Do gráfico de escores e da tabela dos loadings, ve-se que os compostos ativos têm uma alta contribuição das variáveis p-u (loadings negativos) indicando uma alta densidade eletrônica na dupla ligação da cadeia lateral e grupos terminais. Os compostos inativos têm alta contribuição dos átomos b-n (loadings positivos). Provavelmente a quinona é capaz de participar de alguma reação de oxi-redução como agente redutor onde os elétrons da dupla ligação agem como doadores de elétrons.

LOADINGS

Variáveis PC1 PC2

b 0.365 0.212

c 0.382 0.0335

m 0.370 0.220

n 0.372 0.225

o 0.0888 -0.602

p -0.294 0.362

q -0.357 -0.270

s -0.358 0.102

t -0.184 0.424

u -0.249 0.312

Compound Class Number

XXVII 1 1

XXVIII 1 0

XXIX 1 1

XXX 1 1

XXXI 2 0

XXXII 2 2

XXXIII 2 2

XXXIV 2 0

XXXV 2 0

XXXVI 2 0

XXXVII 2 0

XXXVIII 1 1

XXXIX 1 1

XL 1 1

XLI 1 1

XLII 1 1

SIMCA2 PCs for each class

LQTALaboratório de Quimiometria

Teórica e Aplicada