Journal of Life Sciences Volume 7 Number (7)

J LS

Journal of Life Sciences

Volume 7, Number 2, February 2013 (Serial Number 58)

Contents

Biotechnology and Molecular

97 Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

Anglv Shen, Chunyan Ma and Liu Shao

105 Automatic segmentation of Colon Cancer Cells Based on Active Contour Method: A New

Approach

Jamal Charara, Alaa Hilal, Ali Al Houseini, Walid Hassan and Mohamad Nassreddine

Development of Biotechnology for Microbial Synthesis of Gold and Silver Nanoparticles

Tamaz Levan Kalabegishvili, Ivane Giorgi Murusidze, Elena Ivan Kirkesali, Alexander Nikoloz Rcheulishvili, Eteri Nikoloz Ginturi, Eteri Simon Gelagutashvili, Nana Eremey Kuchava, Nanuli Vakhtang Bagdavadze, Dodo Trofim Pataraya, Manana Amiran Gurielidze, Hoi-Ying Holman, Marina Vladimirovna Frontasyeva, Inga Ivanovna Zinicovscaia, Sergey Sergeevich Pavlov and Vasiliy Timofeevich Gritsyna

Cloning and Expression of the Serine Carboxypeptidase Gene in Zea mays and Its Antifungal Activity against Rhizoctonia solani

Li Liu, Xiujing He, Zhiming Zhang, Maojun Zhao, Jing Wang, Haijian Lin, Ya’ou Shen and Guangtang Pan

131 Biosecurity Threats about the Combination of Aerobiology, Morphology and Genetic

Manipulation of Fungal Spores

Manousos E. Kambouris, Aristea Velegraki, George P. Patrinos and Konstantinos Poulas

Botany and Zoology

Stability of Cereal Crops to Drought and Saline Stress in Vivo and in Vitro

Nina Terletskaya, Nina Khailenko and Kabl Zhambakin

Diversity and Evolution of Inflorescences in Celastrales

Ivan A. Savinov

153 Morphogenesis of Oil Palm Fruit (Elaeis guineensis Jacq.) in Mesocarp and Endocarp

Development

Hermine Bille Ngalle, Joseph Martin Bell, Georges Franck Ngando Ebongue, Lambert Nyobe, Félix Chancelin Ngangnou and Godswill Ntsefong Ntsomboh

159 Effects of Aqueous Extracts of Seeds of Peganum harmala L. (zygophyllaceae) on 5th Stage Larvae Locusta migratoria cinerascens (Fabricius, 1781) (Orthoptera: Oedipodinae)

Abdelmadjid Benzara, Abdellah Ben Abdelkrim and Ouassila Khalfi-Habes

165 Genetical Crossbreeding Effect on the Zootechnical Performances of the Domestic Rabbit (Algeria) x Californian

Mefti Korteby Hakima, Kaidi Rachid, Sid Sihem, Boukhelifa Ahmed, Derradji Billel, Kenchache Youcef and Mareche Hachemi

Interdisciplinary Researches

171 Hyaluronidase Proof for Endothelial Glycocalyx as Partaker of Microcirculation Disturbances

Alexander Maksimenko, Askar Turashev, Andrey Fedorovich, Anatoly Rogoza and Elena Tischenko

Characterization of Fresh Cheese with Addition of Probiotics and Prebiotics

Natália Chinellato Azambuja, Patrícia Blumer Zacarchenco, Luciana Francisco Fleuri, Juliana Cunha Andrade, Izildinha Moreno, Ariene Gimenes Fernandes Van Dender and Darlila Aparecida Gallina

Salinity Risk and Management in Tunisian Semi Arid Area

Mohamed Hachicha, Sabri Kanzari, Mohsen Mansour, Omar Jouzdan and Awadis Arselan

Biochar for Soil Management: Effect on Soil Available N and Soil Water Storage

Yeboah Edward, Antwi Boasiako Ohene, Ekyem Seth Obosu, Tetteh Francis Marthy and Bonsu Kwasi Offei

Factors Associated with Physical-Activity Performance by Older Individuals in a Medium-Sized City in São Paulo State, Brazil

José Eduardo Corrente, Giovana Fumes and Tania Ruiz

Feb. 2013, Vol. 7, No. 2, pp. 97-104 Journal of Life Sciences, ISSN 1934-7391, USA

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

Anglv Shen 1 , Chunyan Ma 1 and Liu Shao 2

1. East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China 2. College of Fisheries and Life, Shanghai Ocean University, Shanghai 201306, China

Received: September 10, 2012 / Accepted: December 03, 2012 / Published: February 28, 2013

Abstract: The lethal and sublethal effects of oils on aquatic organisms have been widely investigated, but the potential molecular impacts of oils on aquatic organisms are remaining unclear now. In order to realize the effects of diesel oil on the Zhe oyster, the RAPD (random amplified polymorphic DNA) technique was used. RAPD is a useful assay procedure for the detection of genotoxin-induced DNA damage and mutations. In the present study, the Zhe oysters were exposed to diesel oil at different concentrations and for different exposure periods. The results showed that the DNA band change in RAPD profiles of oysters following diesel oil treatment included loss of normal DNA bands, the appearance of new DNA bands and variations in DNA intensity compared to oysters not exposed to diesel oil. The effects of changes to GTS (genome template stability) were time- and concentration-dependent, the GTS of 10 mg/L was 82.46%, 80.70% and 63.15% in the 8, 16 and 32 days, the GTS of 20 mg/L was 75.44%, 71.93% and 56.14% in the 8, 16 and 32 days, the GTS of 40 mg/L was 73.68%, 70.18% and 56.14% in the 8, 16 and 32 days, respectively. The DNA polymorphisms detected by RAPD analysis could be used as a useful biomarker assay for the detection of genotoxic effects in diesel oil pollution on the oysters, and may be useful for environmental contamination risk assessment.

Key words: Diesel oil, Zhe oyster, RAPD, GTS, DNA damage, biomarker.

1. Introduction  hydrocarbons have long been recognized as the most deleterious contaminants to biota in the world’s

Oysters, known as “milk of the sea”, are considered marine and estuarine waters, oysters have become delicious, nutritious and protein-rich, with a variety of good bio-indicators of environmental pollution in unsaturated fatty acids, amino acids, vitamins and coastal and estuarine ecosystems because they, as minerals. The Zhe oyster (Crassostrea plicatula) is an filter feeders, bioaccumulate contaminants [2]. important member of China’s coastal shellfish Over the past few decades, environmental population and has a high economic value. It is contamination of water by oil has increased drastically. considered a native species and is widely distributed Oils (crude and fuel) spilled into the environment have in China, with the production of this oyster reaching

multiple negative effects on aquatic organisms.

3.62 × 10 metric tons in 2002, accounting for 37.6% Marine pollution due to oil and oil exploration of the total annual marine molluscan yield [1]. pollution in ocean environments is an especially Bivalves (such as oysters) are widely used as sentinel critical environmental concern [3]. Most previous organisms for monitoring the concentration of selected studies have focused on the effects of oils on survival pollutants in coastal environments, such as heavy [4-11], morphological parameters [9, 11, 12], metals, organo-chlorine compounds and petroleum hematological physiological parameters [9, 12, 13],

biochemical parameters [14-21] and histological Corresponding author: Liu Shao, Ph.D., lecturer, research

field: marine environmental science. E-mail: parameters [13, 18, 19]. In addition, the molecular sl317500@163.com.

98 Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

level changes of oil on aquatic organisms, such as Waterborne Transportation Science, administered by micronucleus frequency, DNA repair, cytochrome

the Ministry of Transportation of the People’s P4501A, DNA adducts, DNA single strand breaks,

Republic of China.

microsatellite and AFLP loci were also studied The authors prepared mixtures of oil from fuel oil [22-27].

samples in a 2,000 mL beaker using 2,000 mg of

Recently, the RAPD (random amplified diesel oil per Liter of clean seawater. The beaker was polymorphic DNA) assay has been used to detect

stirred continuously at a uniform speed for 30 min and possible damage in the genomic DNA of organisms in

then emulsified with an ultrasonic cleaner (DL-720A, ecotoxicology as a good biomarker [28-34]. The

made in Shanghai, China) for 8 h. In this way, the RAPD assay presents some advantages: RAPD assays

authors simulated the effects of oil spilled in the sea. lie in the PCR-based technique being easy TPH (Total petroleum hydrocarbon) analysis was

identification of regions of amplification, deletion or performed using UV spectrophotometry [36]. The rearrangement without prior information about the

various concentrations were then diluted. genome [34]; RAPD assays require very little source

material and under certain circumstance and the

2.3 Experimental Design

analysis can also be performed non-destructively The oysters were exposed to four concentrations of which can be useful for the screening of rare or

mixtures of diesel oil (0, 10.0, 20.0 and 40.0 mg/L) for valuable samples; RAPD technique is the high level of

32 days. The oysters were fed with chlorella 200 mL a the overall sensitivity and relatively cheap and does

day, algae concentration was 3.5 × 10 6 cell/mL. There not require the use of specialized and expensive

were 100 oysters in the 100 L tank per experimental equipment; RAPD method has the potential to detect a

group, 50 L mixtures of oil in each tank, and the wide range of DNA damage [35]. However, there are

temperature of the test was 24 °C ± 2 °C. Test water no studies of oil contamination which use RAPD

changed daily, and 10 oysters were taken from each technology. In the present study, the principal

group at 2, 8, 16 and 32 days. The samples were objectives were to assess the impact of diesel oil

preserved with 95% ethanol and changed after three contamination on the Zhe oyster in terms of DNA

days. Each treatment was replicated three times. damage.

2.4 DNA Extraction and RAPD Analysis

2. Materials and Methods

Genomic DNA was isolated from adductor muscle

2.1 Animals tissue (approximately 100 mg) using the standard

The oysters used in this study come from phenol-chloroform method [37]. Subsequently, DNA Xiangshan county (29°21 ′16.50″ S, 121°58′46.72″ W),

was resuspended in a 50 μL TE buffer (2 mL 1 M Tris, Zhejiang province, in the People’s Republic of China.

40 mL 0.5 M EDTA, 158 mL ddH 2 O, pH 8.0) and The shell length was 4.03 cm ± 0.11 cm, and the shell

stored at -20 °C until use.

width was 2.65 cm ± 0.19 cm. The shell weight was The RAPD assays were performed on the

10.24 g ± 0.54 g, and the oysters have been GeneAmp PCR System 9700 (Applied biosystems, domesticated two weeks before testing.

Invitrogen, USA) in 25 μL of total volume containing approximately 2

2.2 Kinds of Fuel Oils and Preparation of Oil μL of genomic DNA (10

ng/L), 1 μL primers (10 μmol/L), 1 μL dNTP (2.5 In the present study, the fuel oil used was No. -20

mmol/L each), 0.2 μL Taq DNA polymerase (5 diesel oil. The oil was supplied by the Institute of

U/ μL), 10× reaction buffer (100 mmol/L Tris-HCl,

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

parameter, changes in this value were calculated as a mmol/L EDTA, 5 mmol/L DTT, 50% glycerol, 0.1%

pH 8.3, 15 mmol/L MgCl 2 , 500 mmol/L KCl, 0.1

percentage of their control (100%). In addition, as the Triton X-100), 18.3 μL distilled water. The 30

sampling time interval of 2 days is too short, there is primers used were 10 bp in length (S1-S30, Sangon

no calculation of the GTS and compared with others. Biotech, China). The RAPD protocol consisted of an

3. Results

initial denaturing step of 7 min at 94 °C, followed by

45 cycles at 94 °C for 1 min (denaturation), 37 °C for

3.1 TPH Concentrations

1 min (annealing) and 72 °C for 2 min (extension), Three different oil concentrations were set in this with an additional extension period of 10 min at test, 10 mg/L, 20 mg/L and 40 mg/L (the actual

72 °C. For each amplification, a negative control was concentration of oil), and with the corresponding TPH

run for each primer. Reaction mixtures were stored at were 1.05 × 10 -2 mg/L, 2.10 × 10 -2 mg/L and 4.20 ×

4 °C prior to use. 3 μL of each PCR product was

10 -2 mg/L, respectively.

electrophoresed in 1.5% agarose gels containing ethidium bromide for verifying the amplified

3.2 Effect of Diesel Oil Stress on RAPD Profile fragment length with a DNA marker DL2000

(Takara, China). Images were captured using a Aimed at verifying the genetic effect of diesel oil high-resolution scan and digitalized images were

contamination, the RAPD analysis was performed on counted directly for RAPD analysis.

DNA extracted from groups of 10 oysters from each replicate treated with diesel oil at concentrations of

2.5 Estimation of GTS (Genomic Template Stability) 0-40 mg/L. In total, 30 random 10-mer primers (Table 1) Genomic template stability (%) was calculated as

were used to amplify genomic DNA samples from the 100  (100a/n), where a represented RAPD diesel oil-treated and control groups, and only 18 polymorphic profiles detected in each sample treated

primers generated specific and stable results with a and n represented the number of total bands in the

total number of 57 bands.

control. Polymorphism observed in RAPD profiles The RAPD fingerprints showed virtual differences included disappearance of a normal band and between exposed oysters and control oysters. The appearance of a new band in comparison to control

changes included both loss and addition of bands RAPD profiles [38]. To compare the sensitivity of this

compared with the control groups (Fig. 1). For example,

Table 1 Sequences of 30 primers used in this experiment.

No. of primers

Sequences (5’ →3’) S1 GTTTCGCTCC S16 TTTGCCCGGA S2 TGATCCCTGG S17 AGGGAACGAG S3 CATCCCCCTG S18 CCACAGCAGT S4 GGACTGGAGT S19 ACCCCCGAAG S5 TGCGCCCTTC S20 GGACCCTTAC S6 TGCTCTGCCC S21 CAGGCCCTTC S7 GGTGACGCAG S22 TGCCGAGCTG S8 GTCCACACGG S23 AGTCAGCCAC S9 TGGGGGACTC S24 AATCGGGCTG S10 CTGCTGGGAC S25 AGGGGTCTTG S11 GTAGACCCGT S26 GGTCCCTGAC S12 CCTTGACGCA S27 GAAACGGGTG S13 TTCCCCCGCT S28 GTGACGTAGG S14 TCCGCTCTGG S29 GGGTAACGCC S15 GGAGGGTGTT S30 GTGATCGCAG

Sequences (5’ →3’)

No. of primers

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

Fig. 1 RAPD profiles of genomic DNA from adductor muscle of the Zhe oyster exposed to varying concentrations of diesel oil. The RAPD patterns were obtained using 10-mer primers (a) S8, (b) S11, (c) S21, (d) S26, (e) S27, (f) S28, (g) S29, and (h) S30. In (a), (b), (c), (d), (e), (f), (g) and (h), numbers 1-12: control, 20 mg/L diesel oil exposed for 2 days, 40 mg/L diesel oil exposed for 2 days, 10 mg/L diesel oil exposed for 8 days, 20 mg/L diesel oil exposed for 8 days, 40 mg/L diesel oil exposed for

8 days, 10 mg/L diesel oil exposed for 16 days, 20 mg/L diesel oil exposed for 16 days, 40 mg/L diesel oil exposed for 16 days,

10 mg/L diesel oil exposed for 32 days, 20 mg/L diesel oil exposed for 32 days, 40 mg/L diesel oil exposed for 32 days, respectively. M: molecular marker (2,000, 1,000, 750, 500, 250 and 100 bp from top to bottom).

the primer S8 showed the number of disappearing disappearing RAPD bands to be greater at several RAPD bands was greater at several concentrations of

concentrations of 10-40 mg/L for different exposed 10-40 mg/L for different days of exposure (Fig. 1a 8-1,

days (Fig. 1b 11-2, 11-3) as well. On the other hand, 8-2). The primer S11 showed the number of

some figures showed changes in the addition of bands

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

compared with the control groups, for instance, the were different, with the appearance of 12 new bands primer S11 showed the number of adding RAPD

in the 20 mg/L group, and the disappearance of 22 bands was greater at several concentrations of 40

normal bands in the 40 mg/L group (Table 3). This mg/L exposed for 2 days (Fig. 1a 11-1). Other figures

effect will be used to evaluate damage to oysters in showed the same phenomena, as shown in Fig. 1e, Fig.

cases of oil spillage.

1g 29-4 and Fig. 1h 30-2.

4. Discussion

3.3 The Correlation between GTS and the

4.1 Application of RAPD Technique in Ecotoxicology Concentration of Diesel Oil

In the field of ecotoxicology, RAPD studies In Table 2, the modifications in RAPD profiles are

describe the RAPD changes such as differences in shown as a percentage of their control [37]. Changes

band intensity as well as gain/loss of RAPD bands in the RAPD patterns were expressed as decreases in

[35]. The RAPD assay has been used to determine the GTS, a qualitative measure reflecting the obvious

mutagenic effects of heavy metal pollution on the change to the number of RAPD profiles generated by

model plant (Arabidopsis thaliana), DNA from plants the diesel oil-contaminated oysters. GTS values

exposed to heavy metals solution displayed calculated for 18 primers are presented. The GTS

polymorphic bands which were not detectable in DNA value was decreased with the increased time of

exposure. For instance, the GTS of 10 mg/L was of unexposed plants [29]. RAPD assay can be used to 82.46%, 80.70% and 63.15% in the 8, 16 and 32 days,

qualitatively detect the kinetics of B(a)P-induced respectively. There was significant linear relationship

DNA effects in the water flea (Daphnia magna) between GTS and the concentration of diesel oil. The

exposed to 25 µg/L and 50 µg/L the main changes

occurring in RAPD profiles produced by the 0.9734, y was GTS, and the x was concentration), y =

regression equations were y = 98.6x  0.228 (R² =

population of Daphnia magna was a decrease and 98.664x  0.266 (R² = 0.9769) and y = 94.299x 

increase in band intensity compared with the control 0.434 (R² = 0.9023) at 8, 16 and 32 days, respectively.

population [30, 31]. DNA changes in barley In addition, although the GTS of 20 mg/L and 40

(Hordeum vulgare) seedlings induced by cadmium mg/L were 56.14%, the changes of RAPD profiles

pollution using RAPD analysis and the results showed

Table 2 Changes of genomic template stability for all primers by diesel oil-contaminated oysters for different times (%).

No. -20 diesel oil concentration (mg/L)

Table 3 Changes of total bands in control, polymorphic bands and varied bands in diesel oil-contaminated oysters.

No. -20 diesel oil concentration (mg/L)

Exposure time (d)

16 57 5 6 10 6 9 8 32 57 11 10 12 13 3 22 a: appearance of new bands; b: disappearance of normal bands; a + b: polymorphic bands compared with control

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

that the changes occurring in RAPD profiles of root number of RAPD bands and polymorphic bands are tips following Cd treatment included variation in band

the key factors in the present study. There is a clear intensity, loss of normal bands and appearance of new

tendency of the concentration of pollutants to be bands compared with the normal seedlings and the

proportional to the number of RAPD polymorphic genomic template stability (a qualitative measure

bands. For instance, different polymorphic bands were reflecting changes in RAPD profiles) was detected at each concentration of Cd for different significantly affected at the above Cd concentration

primers, and the value of polymorphisms was p (%) = [32, 33]. The similar effects was found in rice (Oryza

34.5%, 40.9% and 44.5% for 30, 60 and 120 mg/L Cd, sativa ) contaminated by Cd [34]. Therefore, the

respectively [32]. In the present study, the authors comparison between “unexposed” and “exposed”

found that not only exposure concentrations but also genomes show that RAPD analysis can be used to

exposure times of pollutants were proportional to the evaluate how the environmental pollutants modify the

number and polymorphisms of bands (Table 3). The structure of DNA in living organisms.

number of RAPD polymorphic bands is exactly the number of loss and addition of bands compared with

4.2 RAPD Technique in Diesel Oil Pollution the control groups, and is the basis of the GTS The RAPD assay presents many advantages and the

calculation.

RAPD assay is very reliable after optimization [31].

5. Conclusion

Therefore, the RAPD method has the potential to RAPD (Random amplified polymorphic DNA) is a

detect DNA damage (e.g. DNA adducts DNA useful assay procedure for the detection of

breakage) as well as mutations (point mutations and large rearrangements) [35]. In the present study, the

genotoxin-induced DNA damage and mutations. This DNA damage in oysters stressed by the diesel oil was

study showed that the DNA band changes in RAPD very clear, for example, we selected changes in the

profiles of oysters following diesel oil treatment primers, as indicated by arrows, in comparison to a

included loss of normal DNA bands, the appearance control group, which showed changes including loss

of new DNA bands and variations in DNA intensity and addition of bands compared with the control

compared to oysters not exposed to diesel oil. This groups (Fig. 1). DNA damage leads to the instability

study also showed that the change of GTS showed a of the genomic template. In this way, previous studies

dose-dependent and time-dependent tendency to the demonstrated that the GTS parameter picked up

diesel oil. Therefore, a RAPD assay was quantitative significant effects of B(a)P and Cd exposure [32-34,

and could be used as an investigation tool for 38]. The RAPD assay was considered as a environmental toxicology, as well as a useful semi-quantitative assay [39] and another people use it

biomarker.

as a qualitative rather than a quantitative method [31].

Acknowledgments

One problem is that the number of primers and characteristics of bands of RAPD for statistical

This work was supported by the open fund of significance in GTS. At present, there are 2-14

Laboratory of Marine Spill Oil Identification and primers used in RAPD tests, obtaining a total number

Damage Assessment Technology (201104) and the of bands ranging from 51-180 [30-34, 38]. In the

Key Disciline Construction of Shanghai Education present study, we selected 18 primers in RAPD tests

Commission (No. J50701). The authors thank Yamei and got 57 bands (Table 3). Therefore, the number of

Bi and Yong Zhang for assistance with sample primers is not the most important factor, the total

collection and DNA extraction in this project.

Impact Assessment of Diesel Oil on the Zhe Oyster (Crassostrea plicatula) Using RAPD Analysis

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Feb. 2013, Vol. 7, No. 2, pp. 105-109 Journal of Life Sciences, ISSN 1934-7391, USA

Automatic segmentation of Colon Cancer Cells Based on Active Contour Method: A New Approach

Jamal Charara , Alaa Hilal, Ali Al Houseini, Walid Hassan and Mohamad Nassreddine. Department of Physics and Electronics, Faculty of Sciences I, Lebanese University, Beirut, Lebanon

Received: September 28, 2012 / Accepted: December 7, 2012 / Published: February 28, 2013.

Abstract: Automatic interpretation of the images of colon cell biopsies requires automatic segmentation of these cells in the image obtained. The active contour method for image segmentation is a well known method for automatic detection of the cell contour. However, the application of this method on colon cell images was not effective. In this paper, the authors have proposed a new technique to reduce the analysis time needed to detect cells in a given image. This technique is based on the active contour method but now using a progressive division of the dimensions of the image to achieve convergence. The model proposed succeeded in detecting cells whose boundaries are not necessarily defined by a gradient. The initial curve can be anywhere in the image, and interior contours can be automatically detected. The developed algorithm was successfully applied on textured multispectral images of three types of cells, including benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca) cells.

Key words: Active contours, multispectral image, texture, segmentation.

1. Introduction

cells in the image. It is based on color intensity and can be applied on sequences of objects in the image.

Medical images obtained microscopically are Nevertheless, segmentation of colon cancer cells important tools in medical diagnosis. Since colon possessing irregular shapes within a multispectral cancer, like any other cancer, is characterized by image was not efficient with the active contour

abnormal cellular proliferation, seeking abnormalities

[2-4].

inside microscopic images from biopsies, such as The objective of the present paper was to develop

elliptical non-regular cancer cells or non-natural contrast, is of paramount interest. However, diagnosis

a new approach aiming to reduce the time necessary based on observation of medical images would be

to detect cells in a given image. This approach was largely accelerated if the manual search of derived from the active contour method but now abnormalities can be replaced by an automatic

using a progressive division of the dimensions of the procedure. The majority of methods used to classify

image to achieve convergence. Three types of cells cancerous cells use morphological image processing,

were utilized to assess the efficiency of our particularly the shapes of the cells inside images. The

segmentation model, including benign hyperplasia effectiveness of an automatic search method is

(BH), intraepithelial neoplasia (IN) that is a generally assessed by its capacity to analyze and

precursor state for cancer, and carcinoma (Ca) that interpret a large number of images in a short time. The

corresponds to abnormal tissue proliferation (cancer). main drawback of these approaches is the long

2. Materials and Methods

analysis time [1].

2.1 Active Contour Model

The active contour (snake) method is a very effective technique for rapid segmentation of cancer

Active contour is a dynamic curve that tends, in an

Auto omatic Segme entation of C olon Cancer Cells Based d on Active Co ontour Metho od: A New Ap proach

iterative pro ocess, to m move toward and detect the contour of a an object obse erved in a cer rtain image. This curve consi sts of a set of points co onnected to e each other. An en nergy function n is generally y associated w with this curve (s snake) in such h a way:

Fig. . 1 Image re epresenting a single object with uniform m

(1) inte ensity U internal ,s separated by t the contour C 0 0 from its back k

where F inter rnal is an e nergy that depends on the

grou und with unifo orm intensity U U external .

physical pr operties of the contour r and F externa al is another ener rgy that depe ends on the properties of f the

image [3, 5, , 6]. The corr responding al lgorithm tend ds to search for a combinati on between different im mage

(b) and thus det tect the contou ur. The mode el have deve eloped was a able to detect t the contour of a an image wi ithout calcula ating its grad dient and/or detec cting its edges s [7].

points in ord der to minim mize the energ gy function F F snake

(a)

(d) Fig. 1 rep presents an im mage of a sing gle object hav ving

(c)

Fig. . 2 Four diffe erent cases of c contour detecti ion.

more intensi ity different t han that of th he backgroun d.

c 1 and c 2 are the aver rage intensitie es in the reg gions

2.2 Adaptation o of the Active C Contour Meth hod

inside and o outside C 0 , r respectively. The energy F is

The approach T h is derived from the ac ctive contour r defined as: mod del. It leads to effective and fast me edical image ∑ e | , |

segm mentation in n particular segmentati on of high ∑ h | , |

dim mensional im ages. Indeed d, it consists of a set of f According g to F, four r different c cases of con ntour eigh ht consecutiv ve segmentati ion steps. In t the first step, , detection ar e possible as s shown in F ig. 2. These four the dimension o f the image i is reduced to 12.5% of its s cases consid der all poss sible outcom mes of the g green orig ginal size. Th his means th at for an init tial image of f contour with h the real one e as follows [4 4]: 512 2 × 512 pixel ls, the dimen nsion of the image of the 0 e 0

first t step I 1 is red duced to 64 × × 64 pixels. S Segmentation 0 n 0 of the images begins with an initial a active square 0 e 0

0 0 con ntour of dimen nsion 42 × 42 2 pixels. C 1 i s the contour r It can also a add to F a reg gularization t term that lead ds to

resu ulting from the first se egmentation step. In the e the final exp pression of F: :

seco ond segmenta ation step, th he dimension of the image e .

is r reduced to 2 25% of its original size e. Thus, the e ∑

dim mension of the e image I 2 of f the second s step becomes s where   0 0 is a fixed parameter. C Consequently, , the

128 8 × 128 pixe ls and the co ontour C 1 , re esulting from m detection o of contours simplifies to become the

the first segme entation step p, is used a as an initial l solution of:

con ntour to segm ment the im mage I 2 . The contour C 2 2 inf

obta ained after th his segmentat tion is resized d and used to o ,, C segm ment the ima age I 3 of the th hird step.

Auto omatic Segme entation of C olon Cancer Cells Based d on Active Co ontour Metho od: A New Ap proach

Similarly, , it continues s with the re maining step ps to each h step can g give an idea about the or rder of the   finally segm ment the initia al 512 × 512 pixels image e. At

para ameter such a as:

each step, the initial image is reduced to the

) (6) correspondin ng dimension n (image I i ) and the con ntour

min

Results and 3. R d Discussio n

obtained by y the previou us segmentati ion step (C i- 1 ) is

F Fig. 4 shows the result aft ter 6 min of s segmentation n resized to I I i ’s size and d used as in itial contour r for

H, IN and Ca) ) segmentatio n. Fig. 3 shows th he segmenta ation usin ng the clas ssical active e contour s segmentation n procedure sc cheme. met thod. All of these contou urs are far fr from the real l This prog gressive seg gmentation o of the image e is

per image of the e three types of cells (BH

con ntour of each cell.

performed a automatically. . A condition n can be impo osed

F Fig. 5 represe ents the imag ges of the th hree types of f on the num mber of ite erations nece essary to re each

cell ls (BH, IN, a and Ca) resiz ed to the dim mension 64 × × convergence e. In this ca ase, a set of f 20 consecu utive

64 pixels. This s figure sho ows also tha at successful l iterations w as allowed f for each segm mentation step p. It segm mentations w were obtained d within 2-4 s. .

measures fo r each step th he vector D 0 that is the sp atial

H However, the e developed algorithm showed that t distance bet tween two co onsecutive co ontours of the e 20 bey yond a thresh hold number r of iteration ns, the active e iterations. In n addition, w we measure, a at the end of e each con ntour of th he model fluctuates around the e step, the ve ector elemen nt D si that i is the differe ence real l geometry in na repeated d manner (F Fig. 6). This

between the maximum an nd the minim mum of vector rD 0 .

The first ste ep (first twen nty iterations) ) is character rized to have D s1 s as the ma aximum valu ue of D si . This procedure c continues in performing 2 20 new itera ation steps until:

where  is a a parameter th hat depends o on the sensiti ivity of the requir red segmenta ation and is in ntroduced by y the

Fig. . 4 Segmenta tion results of three images o of dimension

user. Howev ver, an analy ysis of the c urve obtaine ed at

512 × 512 pixels u sing classical a active contour model.

Initi al image I o 512 × 512

Initialization n

Segmenta ation

Step p 1: Image I 1 64 × 64

Square cont our

Contour C C 1

Step p 2: Image I 2 128 × 128

C 1 Resized t to 128 × 128

Contour C C 2

Step p 3: Image I 3 192 × 192

C 2 Resized t to 192 × 192

Contour C C 3

Step p 4: Image I 4 256 × 256

C 3 Resized t to 256 × 256

Contour C C 4

Step p 5: Image I 5 320 × 320

C 4 Resized t to 320 × 320

Contour C C 5

Step p 6: Image I 6 384 × 384

C 5 Resized t to 384 × 384

Contour C C 6

Step p 7: Image I 7 448 × 448

C 6 Resized t to 448 × 448

Contour C C 7

Step p 8: Image I 8 512 × 512

C 7 Resized to o 512 × 512

Final con ntour

Fig. 3 The e eight steps of th he segmentatio on process.

Auto omatic Segme entation of C olon Cancer Cells Based d on Active Co ontour Metho od: A New Ap proach

dec reases.

F Fig. 8 shows s complete s segmentation of full size e ima ages (512 × 5 512 pixels) of f Fig 4 with   = 5%. The e ave erage process sing time wa as 147.1969 s(  2.4533 3 min n). This figure e shows the e efficiency of the model in n

dete ecting active contours of irregular obj jects such as s

(a)

can ncerous cells o of types IN an nd Ca.

Conclusion 4. C n

The effectiven T ness of the te echnique was based on the e pro gressive segm mentation of the image. T he model led d to tw wo major con nsequences. F First time con nsumption of f the segmentation n process was s shortened to o less than 3

(b)

Tab le 1

 values s for the eight s segmentation s steps.

Fig. 5 (a) Th he three types of colon cells a are reduced to

Max (%) 64 pixels; (b) ) Segmentation n results of th he three image es of

Ste ep

Image Size

Min

size 64 × 64 p 0.6 pixels.

Fig. 6 Con nstant fluctua ation of the contour after r 75

iterations. X: 200

50 Y: 7.031

20 00 250 300 350 400 Saturation with hin 200 iterations. Com mputation time : 2.457

fluctuation i increases the processing t time without any

77 secondes.

improvemen 28 x 128 nt of the detec cted contour. Table 1 s shows the va alues of  parameter for p r the

eight steps o of segmentati ion of the im mage illustrate ed in

Y: 2508

Fig.5. The v value of  va aries between n 0.2% and 1. .7%.

This value, that ensures convergence , must be un nique

for the eigh ht segmentat tion steps. F Fig. 7 shows the

variations of f  for segme entation steps s 1 and 2. Al so it

shows that a 400 as the resoluti ion of the im mage increases s the Saturation w within 100 iterations. C Computation time : 4.89 91 secondes. number of it terations nece essary to achi ieve converge ence

Fig. . 7 Analysis o of  for segmen ntation steps 1 and 2.

Automatic Segmentation of Colon Cancer Cells Based on Active Contour Method: A New Approach

Costa, Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria, Pattern Recognition 40 (2007) 1899-1910.

[2] T.E. Schneider, Automated classification of analysis and reference cells for cancer diagnostics in microscopic images of epithelial cells from the oral mucosa, Acta Polytechnica 47 (2007) 86-90.

[3] G.D. Giannoglou, Y.S. Chatzizisis, V. Koutkias, I. (a) (b) (c)

Kompatsiaris, M. Papadogiorgaki, V. Mezaris, et al., A

Fig. 8 Time consumption and segmentation results for the

novel active contour model for fully automated

three types of colon cells: (a) BH: 164.74 s; (b) IN: 136.98 s

segmentation of intravascular ultrasound images: In vivo

and (c) Ca: 139.87 s.

validation in human coronary arteries, Computers in Biology and Medicine 37 (2007) 1292-1302.

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cancer cells has become fully automated. Therefore, Convergence analysis of active contours, Image and the proposed model allows accurate and efficient

Vision Computing 26 (2008) 1118-1128. segmentation of images containing distinct objects in

[5] R.M. Haralick, K. Shanmugam, I. Dinstein, Textural

a very short time. The approach is very simple and features for image classification, IEEE Transactionson Systems, Man and Cybernetics 3 (1973) 610-621.

exhibits attractive results. This method is useful in the [6] T. Chan, L. Vese, Active contours without edges, IEEE

automatic segmentation between different Transactions on Image Processing 10 (2001) 266-277. histopathological images and thus allowing a faster

[7] D. Mayumi, U. Sabino, L.F. Costa, E.G. Rizatti, M.A. segmentation of microscopic bio-images.

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Acknowledgments

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prostate cancer diagnosis, in: Proceedings of the 7th Doctoral School of Sciences & Technology at the

International Symposium on Signal Processing and its Lebanese University. Applications, 2003, pp. 37-40. [9] M.A. Garcia, D. Puig, Supervised texture classification

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Feb. 2013, Vol. 7, No. 2, pp. 110-122 Journal of Life Sciences, ISSN 1934-7391, USA

Development of Biotechnology for Microbial Synthesis of Gold and Silver Nanoparticles

Tamaz Levan Kalabegishvili 1, 2 , Ivane Giorgi Murusidze 2 , Elena Ivan Kirkesali 1 , Alexander Nikoloz

1 1 1 Rcheulishvili 1 , Eteri Nikoloz Ginturi , Eteri Simon Gelagutashvili , Nana Eremey Kuchava , Nanuli Vakhtang Bagdavadze 1 , Dodo Trofim Pataraya 3 , Manana Amiran Gurielidze 3 , Hoi-Ying Holman 4 , Marina Vladimirovna

Frontasyeva 5 , Inga Ivanovna Zinicovscaia 5 , Sergey Sergeevich Pavlov 5 and Vasiliy Timofeevich Gritsyna 6

1. Department of Biological System Physics, E. Andronikashvili Institute of Physics, Javakhishvili State University, Tbilisi 0177, Georgia

2 . Ilia State University, Institute of Applied Physics, Tbilisi 0162, Georgia

3. S. Durmishidze Institute of Biochemistry and Biotechnology of N.L.E. Georgian Agrarian University, Tbilisi 0159, Georgia

4. Department of Ecology, Center for Environmental Biotechnology, Lawrence Berkeley National Laboratory, Berkeley 94720, United States 5. Department of Nuclear Physics, Joint Institute for Nuclear Research, Dubna 141980, Russia 6. Departement of Solid State Physics, V.N. Karazin Kharkiv National University, Kharkiv 01077, Ukraine

Received: September 28, 2012 / Accepted: December 07, 2012 / Published: February 28, 2013.

Abstract: Several bacterial strains of Actinomycetes belonging to Streptomyces and Arthrobacter genera for the first time were used to study the biotechnology of synthesis of gold and silver nanoparticles. The experimental conditions of gold and silver nanoparticles

production by the cells of studied strains in aqueous chloroauric acid (HAuCl 4 ) and in silver nitrate (AgNO 3 ) solutions, respectively, were determined. Concentration and time-dependences of nanoparticle formation were investigated. The complex of optical and analytical methods was used for testing the gold and silver nanoparticles in the bacterial biomass. The TEM (Transmission Electron Microscopy) and XRD (X-ray Diffraction) data in all cases demonstrated the presence of crystals with fcc (face centered cubic) structure. The results obtained show that the Actinomycetes are capable of producing gold and silver nanoparticles of spherical shape extracellularly when exposed to suitable compounds. The particle size distribution shows that the sizes of nanoparticles are in the range of 5 nm to 80 nm. The biomass obtained may be used for industrial as well as medical and pharmaceutical purposes.

Key words: Microbial synthesis, nanoparticles, gold, silver, biotechnology.

1. Introduction  defense mechanisms that contribute to their survival in aggressive environments containing harmful metallic

In recent years, the microbial technologies of metal compounds. The same mechanism must be nanoparticles production have received great attention responsible for their ability to produce metallic in materials science and industry [1-3]. A large nanoparticles when exposed to such environments [4]. number of microorganisms are characterized by their Nanoparticles produced in the microbial biomass affinity with metal ions and their tolerance to high have ultra small size, high surface area to mass ratio metal concentrations. Microorganisms often exhibit and reactivity, which determines their unique physical

and chemical properties [5]. Microbial cells have  Corresponding author: Marina Vladimirovna Frontasyeva, developed specific mechanisms for surface functional Ph.D., research field: application of NAA for life sciences and

material science. E-mail: marina@nf.jinr.ru. groups (peptides, proteins, nucleic acids) interacting

Development of Biotechnology for Microbial Synthesis of Gold and Silver Nanoparticles

with metal ions in the aqueous solutions which result Gram-positive bacteria. Among microorganisms, in extracellular inorganic precipitation [6, 7]. Anionic

actinomycetes (actinobacteria) appear to be a very bacterial surfaces interact with metal cations, which

specific taxonomic group responsible for the produce a negative charge density throughout the wall

biosynthesis of a number of biologically active and then intracellular binding of metals [8].