The demographic date of the pregnant women included in the study.
\r\n\tThe need to regulate human activities and reduce risks to achieve sustainable development is of paramount importance. Although there is some perception of imbalances and vulnerabilities, there is not still a real awareness of the negative effects of human-induced activities on such fundamental environments for the survival of humanity in the medium and long term.
\r\n\tThe efforts made to reduce the causes and mitigate the effects of ongoing global climate change continue to be critical in coastal and marine environments. Indeed, the efforts to mitigate impacts on the oceans and many of the adaptation strategies implemented in coastal areas remain ineffective or inadequate.
\r\n\tThe development of novel solutions to address coastal processes as an alternative to traditional coastal protection structures (seawalls, groynes and breakwaters) is becoming increasingly important. The innovative approaches seek to strengthen the benefits of traditional natural defences, such as beaches and natural dunes. They are typically marketed as being characterised by lower environmental impacts, lower costs and easier implementation, and include: artificial reefs, beach dewatering, artificial dunes and vegetation.
\r\n\tThis book aims to present concepts, methods and techniques to assess and control a wide range of coastal and marine processes (e.g., littoral dynamics - numerical and physical, coastal morphodynamics - numerical and physical, climate change, global warming, sea-level rise, coastal management, coastal protection, extreme events, storms, waves, tides, currents, coastal flooding).
\r\n\tThis book also aims to assist coastal communities in carrying out operational coastal management by presenting and discussing management tools and primary options that should be considered in any adaptation programme that is to be implemented.
The fetal foot is one of the first structures identifiable early in the human embryos. At the end of the fourth week of embryonic development, the limb buds appear as outpouchings from the ventrolateral body wall. At 6 weeks, the terminal portion of the limb buds flattens to form the hand- and footplates and becomes separated from the proximal segment by a circular constriction. It is known that the development of the lower limbs is similar to the upper limbs and lags by only 1–2 days. By 8 weeks (or 56 days), the digital separation is already complete. The fingers and the toes are distinct and separated in the hands and feet [1, 2]. In another word, the fetal hands and feet would be recognizable as distinct formed structures by 8 weeks of embryological development or 10 weeks by the last menstrual period (LMP) according to a 28 day cycle.
About a century ago, Streeter reported a linear correlation between gestational age and foot length in 704 human fetal specimens from around 50 days post-conception until birth [3]. This linear correlation has been confirmed by studies on live fetuses in utero on transabdominal [4, 5, 6] or transvaginal scans [7] or on dead fetuses at abortion [8, 9, 10] or stillbirth [11, 12], and nomograms have been developed for assessment of fetal gestational age with foot length (FT) from the first trimester to later gestation. Hence, fetal foot length could by itself stand as a proxy for gestational age even in early pregnancy.
Conventionally, crown-rump length (CRL) is used as the reference parameter for assessment of fetal gestational age in the first trimester ultrasound scan [13, 14]. It has been suggested that the ultrasound measurement of the crown-rump length in the embryo or fetus is the most accurate method to establish or confirm gestational age in the first trimester up to 13 + 6 weeks [14]. The use of routine first trimester ultrasound scan has been shown to be associated with a reduction in induction of labor for post-term pregnancy [15]. However, there is little information on the comparison of other fetal parameters including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), and foot length to CRL in the assessment of gestational age in early gestation [16]. This information may be important for the assessment of fetal gestational age in the first trimester and subsequent management of pregnancy.
In order to ascertain the performance of various parameters in assessment of gestational age in the first trimester, in this chapter, the correlation between FT, CRL, and gestational age assessed by date (GA) will be compared from 10 to 14 weeks gestation. The correlation of the other fetal parameters (BPD, HC, AC, and FL) will also be compared to GA, CRL, and FT. Moreover, the ratio of some of these parameters will also be calculated and presented, as the availability of such ratios may be helpful when fetal abnormality is suspected on ultrasound examination in early pregnancy [17, 18, 19].
Transabdominal ultrasound examination was performed as a part of routine antenatal assessment for women attending an obstetric clinic at a gestation of 10–14 + 6 weeks from March 7, 2014, to September 7, 2016 (Accuvix V20 Prestige, Medison with 4–8 MHz volumetric transducer or EPIQ 7, Philips with X6–1 matrix transducer). The following fetal measurements were taken prospectively: crown-rump length (CRL), biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), and foot length (FT). Only pregnancies with normal outcomes were included in the analysis and excluded if the entire foot could not be clearly seen during the ultrasound examination. The fetal foot length was measured from the most posterior point of the foot in its long axis to the tip of the first or the second toe whichever was longer (Figure 1). The estimated gestational age in weeks (GA) was calculated either from the last normal menstrual period (LMP) or from the first trimester dating scan if there was a discrepancy of more than a week [14]. This was a retrospective analysis involving minimal risk, conforming to the standards established by the NHMRC not requiring ethical review; ethics approval was therefore not sought within the institution [20].
Fetal foot on first trimester ultrasound scan.
Results for 35 ultrasound scans were analyzed with SPSS statistical package version 20 (SPSS Inc., Chicago, IL, USA). A two-sided probability (p) value of <0.05 was considered statistically significant. The regression models for the fetal measurements were obtained and would be presented in the relevant sections.
The mean age, gravidity, and parity were 32.0 years, 2.3, and 0.7, respectively (Table 1). A total of 32 out of the 35 women were Asians (91.4%) and 3 were Caucasians (8.6%).
Mean ± S.D. | Range | |
---|---|---|
Age (years) | 32.0 ± 4.6 | 21–44 |
Gravidity | 2.3 ± 1.5 | 1–8 |
Parity | 0.7 ± 0.7 | 0–3 |
The demographic date of the pregnant women included in the study.
SD, standard deviation.
The correlation of foot length, crown-rump length, and the gestational age assessed by date are shown in Figures 2–4 and tabulated in Table 2. FT, CRL, and GA all showed positive correlation with one another in a linear fashion (p < 0.001) (Figures 2–4). The coefficient of determination of regression (R2) was the highest between FT and CRL (0.804), lower between FL and GA (0.675), and the lowest between CRL and GA (0.608) (Table 2).
The graph of fetal foot length against gestational age assessed by date. FT = GA × 2.472−19.44. R2 = 0.675, p < 0.001. FT, foot length (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
The graph of fetal crown-rump length against gestational age assessed by date. CRL = GA × 10.464−64.682. R2 = 0.608, p < 0.001. CRL, crown-rump length (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
The graph of fetal foot length against crown-rump length. FT = CRL × 0.187−1.074. R2 = 0.804, p < 0.001. FT, foot length (mm); CRL, crown-rump length (mm); R2, coefficient of determination of regression; p, probability.
Parameters | FT | CRL | GA | |||
---|---|---|---|---|---|---|
R2 | p† | R2 | p† | R2 | p† | |
FT | – | – | 0.804 | <0.001* | 0.675 | <0.001* |
CRL | 0.804 | <0.001* | – | – | 0.608 | <0.001* |
GA | 0.675 | <0.001* | 0.608 | <0.001* | – | – |
The correlation between fetal foot length, crown-rump length, and gestational age assessed by date.
FT, foot length (mm); CRL, crown rump length (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of linear regression; p, probability; †, ANOVA; *, statistically significant.
The correlation of BPD, HC, AC, and FL with FT, CRL, and GA is shown in Figures 5–7,8–10,11–13, and14–16, respectively, and summarized in Table 3. Correlation in a linear fashion is seen for all (p < 0.001). Overall, the correlation of BPD, HC, AC, and FL with FT was the highest, lower with CRL, and the lowest with GA (Table 3).
The correlation of fetal biparietal diameter with foot length. BPD = FT × 1.131 + 8.748. R2 = 0.884, p < 0.001. BPD, biparietal diameter (mm); FT, foot length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal biparietal diameter with crown-rump length. BPD = CRL × 0.239 + 5.78. R2 = 0.793, p < 0.001. BPD, biparietal diameter (mm); CRL, crown-rump length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal biparietal diameter with gestational age assessed by date. BPD = GA × 3.01−15.967. R2 = 0.693, p < 0.001. BPD, biparietal diameter (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
The correlation of fetal head circumference with foot length. HC = FT × 3.932 + 36.257. R2 = 0.897, p < 0.001. HC, head circumference (mm); FT, foot length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal head circumference with crown-rump length. HC = CRL × 0.869 + 23.646. R2 = 0.834, p < 0.001. BPD, biparietal diameter (mm); CRL, crown-rump length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal head circumference with gestational age assessed by date. HC = GA × 10.488−49.951. R2 = 0.706, p < 0.001. HC, head circumference (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
The correlation of fetal abdominal circumference with foot length. AC = FT × 3.639 + 24.905. R2 = 0.903, p < 0.001. HC, head circumference (mm); FT, foot length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal abdominal circumference with crown-rump length. AC = CRL × 0.717 + 18.686. R2 = 0.811, p < 0.001. AC, abdominal circumference (mm); CRL, crown-rump length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal abdominal circumference with gestational age assessed by date. AC = GA × 10.16−60.453. R2 = 0.772, p < 0.001. AC, abdominal circumference (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length with foot length. FL = FT × 0.922−2.707. R2 = 0.878, p < 0.001. FL, femur length (mm); FT, foot length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length with crown-rump length. FL = CRL × 0.209−6.159. R2 = 0.843, p < 0.001. FL, femur length (mm); CRL, crown-rump length (mm); R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length with gestational age assessed by date. FL = GA × 2.56−24.255. R2 = 0.698, p < 0.001. FL, femur length (mm); GA, gestational age assessed by date (weeks); R2, coefficient of determination of regression; p, probability.
FT | CRL | GA | ||||
---|---|---|---|---|---|---|
R2 | p† | R2 | p† | R2 | p† | |
BPD | 0.884 | <0.001* | 0.793 | <0.001* | 0.693 | <0.001* |
HC | 0.897 | <0.001* | 0.834 | <0.001* | 0.706 | <0.001* |
AC | 0.903 | <0.001* | 0.811 | <0.001* | 0.772 | <0.001* |
FL | 0.878 | <0.001* | 0.843 | <0.001* | 0.698 | <0.001* |
FT | – | – | 0.804 | <0.001* | 0.675 | <0.001* |
CRL | 0.804 | <0.001* | – | – | 0.608 | <0.001* |
The correlation of fetal biparietal diameter, head circumference, abdominal circumference, and femur length to foot length, crown-rump length, and gestational age assessed by date.
FT, foot length (mm); CRL, crown rump length (mm); GA, gestational age assessed by date (weeks); BPD, biparietal diameter (mm), HC, head circumference (mm); AC, abdominal circumference; FL, femur length (mm); R2, coefficient of determination of regression; p, probability; †, ANOVA; *, statistically significant.
The correlation of combinations of fetal parameters to FT, CRL, and GA is shown in Table 4. The highest correlation was seen between the combination of [BPD, HC, AC, and FL] and FT (R2 = 0.881, p < 0.001), followed by correlation to CRL (R2 = 0.795, p < 0.001), and the least with GA (R2 = 0.685, p < 0.001). The addition of CRL to the combination yielded a lower R2 value of 0.859. However, the correlation of the combination, with or without FT, to CRL yielded the same R2 of 0.795 (p < 0.001).
FT | CRL | GA | ||||
---|---|---|---|---|---|---|
R2 | p† | R2 | p† | R2 | p† | |
BPD, HC, AC, FL, FT, CRL | – | – | – | – | 0.560 | <0.001* |
BPD, HC, AC, FL, CRL | 0.859 | <0.001* | – | – | 0.601 | <0.001* |
BPD, HC, AC, FL, FT | – | – | 0.795 | <0.001* | 0.685 | <0.001* |
BPD, HC, AC, FL | 0.881 | <0.001* | 0.795 | <0.001* | 0.685 | <0.001* |
The correlation of multiple fetal parameters to foot length, crown-rump length, and gestational age.
BPD, biparietal diameter (mm); HC, head circumference (mm); AC, abdominal circumference (mm); FL, femur length (mm): FT, fetal foot length (mm); CRL, crown rump length (mm); R2, coefficient of correlation of regression; p, probability; †, ANOVA; *, statistically significant.
The combination, in comparison to FT or CRL alone, gave a higher correlation to GA (compare to Table 2). However, the correlation of the combination of [BPD, HC, AC, and FL] or FT alone to CRL yielded a similar R2 (0.795 vs. 0.804, compare Table 4 to Table 2).
The ratios of fetal parameters FL/FT and FL/AC to FT, CRL, GA, BPD, and HC are shown in Figures 17–21 and 22–26, respectively. The correlation followed an inverse relationship, and the R2 was higher with HC or BPD or CRL or FT than GA in general (Table 5).
The correlation of fetal femur length/foot length ratio to foot length. R2 = 0.283, p = 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/foot length ratio to crown-rump length. R2 = 0.458, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/foot length ratio to gestational age assessed by date. R2 = 0.309, p = 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/foot length ratio to biparietal diameter. R2 = 0.522, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/foot length ratio to head circumference. R2 = 0.477, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/abdominal circumference ratio to foot length. R2 = 0.684, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/abdominal circumference ratio to crown-rump length. R2 = 0.686, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/abdominal circumference ratio to gestational age assessed by date. R2 = 0.495, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/abdominal circumference ratio to biparietal diameter. R2 = 0.765, p < 0.001. R2, coefficient of determination of regression; p, probability.
The correlation of fetal femur length/abdominal circumference ratio to head circumference. R2 = 0.773, p < 0.001. R2, coefficient of determination of regression; p, probability.
FL/AC | FL/FT | |||
---|---|---|---|---|
R2 | p† | R2 | p† | |
FT | 0.684 | <0.001* | 0.283 | 0.001* |
CRL | 0.686 | <0.001* | 0.458 | <0.001* |
GA | 0.495 | <0.001* | 0.309 | 0.001* |
BPD | 0.765 | <0.001* | 0.522 | <0.001* |
HC | 0.773 | <0.001* | 0.477 | <0.001* |
The correlation of foot length/abdominal circumference ratio to foot length, crown-rump length, and gestational age assessed by date, biparietal diameter, and abdominal circumference in a reverse relationship.
FL, femur length (mm); AC, abdominal circumference (mm); FT, foot length (mm); CRL, crown rump length (mm); GA, gestational age assessed by date (weeks); BPD, biparietal diameter (mm); HC, head circumference (mm); R2, coefficient of determination of regression; p, probability; †, ANOVA; *, statistically significant.
The Pearson coefficient for intra-observer correlation was 0.992 (p < 0.001) and for inter-observer correlation was 0.990 (p < 0.001) in the measurement of fetal foot length.
An accurate estimation of fetal gestational age in early pregnancy is important for the assessment of the due date [14, 15] and fetal growth [21], the assignment of risk scores for the pregnancy [22], the prediction of fetal abnormality [23], and in the management of twin pregnancies [24]. CRL has been recommended as the standard parameter for assessment of fetal gestational age in the first trimester [14]. It was deduced that CRL gave a better estimation of fetal gestational age than the dates by the observation that it gave a better estimation of the date of delivery [14]. However, it is known that the measurement of CRL could be affected by the fetal posture. Variations in the estimation of fetal gestational age by a few days could be observed for the same gestation with different reference charts derived for CRL [4, 13, 25, 26, 27, 28]. Since fetal foot length has been established as an accurate estimate for gestational age [3], it could be used as a proxy for the later. In this study, it could be seen that CRL correlates better with FT than GA. It can therefore be concluded that CRL is a better estimate of fetal gestational age than the date (Table 2), consistent with the previous observations [15]. However, in comparison to FT in the correlation to the other fetal parameters such as BPD, HC, AC, and FL, CRL performs less well in this study. The addition of CRL to the combination also lowers the R2 (Table 4). Therefore, the use of the combination of BPD, HC, AC, and FL could well be applied from 10 to beyond 14 weeks for the estimation of fetal gestational age rather than using CRL below 14 weeks and the combination of BPD, HC, AC, and FL thereafter as in the current obstetrical practice [14]. Similarly when we use ratio of fetal parameters in the assessment of suspected fetal abnormalities, it may be better to use the ratio against fetal parameters such as FT, BPD, HC, or even CRL than against the gestational age by date, as long as the particular reference fetal parameter being used is not significantly affected by the abnormality in question (Table 5) [23, 29]. Of note, these ratios may not follow a linear correlation but rather an inverse relationship and vary according to the gestational age in the first trimester as alluded in a previous publication (Figures 17–21,22–26, Table 5) [19].
The major limitation of the study is the sample size. The population studied comprised mainly of Asians, and hence there could be a question on generalizability. However, it has already been shown that ethnicity of the population is not an issue in sonographic estimation of fetal gestational age using crown-rump length [26]. Moreover, it has also been shown that less than 3.5% of the total variability of fetal skeletal growth was due to differences between populations when the mothers were adequately nourished [30].
With the advancement of ultrasound technology, small structures could be measured with high accuracy. Rather than relying on CRL, a parameter that could be markedly affected by fetal posture, it is perhaps time to review our ultrasound practice at 10–14 weeks in the first trimester.
In the sonographic assessment of fetal gestational age in the first trimester, the use of a combination of fetal parameters such as BPD, HC, AC, and FL is more accurate than CRL or GA at 10–14 weeks gestation in normal pregnancies. The use of these parameters as references for comparison may also be helpful when fetal abnormality is suspected in early pregnancy.
I would like to thank Dr. Hoi Yin Mary Tang of the Department of Obstetrics and Gynecology, The University of Hong Kong, for her great kindness in checking the data.
Nil to declare.
Microalgae are the major producers of biomass and organic compounds in the ocean. More than 5000 species of marine microalgae are known to date and are separated into six major divisions: Chlorophyta (green algae), Ochrophyta (yellow algae, golden brown and diatoms), Haptophyta (coccolithophorids), Pyrrhophyta (dinoflagellates), Euglenophyta and Cyanophyta (blue-green algae) [1]. Among the 5000 species about 300 can proliferate in high numbers to form the so-called red tide and brown tide phenomena [1, 2].
\nMany planktonic organisms can form mass occurrences in the water column. When the cell densities reach values considerably higher than their general background distribution, they are called blooms [3]. Blooms can be almost mono specific, others are formed by a combination of species [4, 5]. Many prominent blooms can be traced back to high nutrient loads [6, 7, 8], but they can occur whenever a species is able to outgrow its competitors while partially reducing grazer pressure [9].
\nMicroscopic marine algae can be vectors of microbial communities because they are universally associated in the ocean. In nature, most microbial communities are found adhered to microalgae, organism and inanimate surfaces. These interactions are dynamic and are important factors in microbial proliferation and survival. Aquatic algae in situ as well as in laboratory culture condition are often found to be associated with a variety of bacterial strains [10]. Bacteria community can be defined as multi – species of bacteria assemblages in which organisms live together in a contiguous environment (host) and interact with other [11]. Bacteria reproduce asexually, are sized between 0.1 to 20 μm, and can be rod, cocci or comma shaped.
\nFor marine microorganisms (bacterioplankton), there are approximately 106 bacterial cells per ml of surface seawater throughout the world’s oceans [12]. While this number has been known for at least 30 years, how many bacterial species are actually present in the bacterioplankton are still unknown. Bacterioplankton commonly found in marine environment are mainly from bacteria group of Proteobacteria, Cytophaga-Flexibactar-Bacteroides (CFB), marine Archaea and other groups of bacteria, where bacteria group from Proteobacteria is the largest. Proteobacteria group are divided to some class, which are Alpha (α-), Beta (β-), Delta (δ-), Epsilon (ε-) and Gamma (γ-) Proteobacteria [13]. Up until now, the estimated abundance and genetic diversity of bacterioplankton are based on the data in the GenBank database. Hagström et al. [14] had analyzed on all of the 16S rDNA sequences sent to GenBank to get the estimation of marine bacterioplankton species that were available in the GenBank database. Their studies show that the richness of marine bacterioplankton species in the GenBank database was low relatively.
\nThe ecology of bacterioplankton and phytoplankton is widely recognized to be tightly coupled. Interactions between bacteria and phytoplankton such as dinoflagellates may play an important role in regulating dinoflagellate toxin production. Previous studies on the interactions between bacteria and dinoflagellates have been shown to be highly variable and are sometimes specific. Effects of bacteria on toxic dinoflagellates include negative effects such as cell lysis and death [15] and positive effects such as growth enhancement with an addition of bacteria to cultures [16]. Examples of factors which may cause stimulation or inhibition by bacteria include production of co-factors and secretion of signaling molecules controlling cellular processes of dinoflagellates [17]. In addition, bacterial influences on nutrient availability may result in stimulation or inhibition of the growth or toxin production of dinoflagellates. Both toxin production by dinoflagellates and bacteria associated with toxic or non-toxic dinoflagellates have been documented. For example, Gallacher et al. [18] described evidence of paralytic shellfish toxin (PST) production by bacteria associated with dinoflagellates cultures.
\nCultures of dinoflagellates contain a considerable amount of bacteria which probably accompanied the dinoflagellates in the original sample. Bacterial assemblage found in the phycosphere of dinoflagellates may play an important role in regulating dinoflagellate toxin production. While several studies have suggested that bacteria-phytoplankton interactions have the potential to dramatically influence harmful algal bloom dynamics, little is known about how bacteria and phytoplankton communities interact at the species composition level. Other studies have indicated that inside a phytoplankton bloom, α-Proteobacteria overwhelm the free-living bacterioplankton, while microorganisms connected to phytoplankton are basically distinguished as fitting in with (CFB), γ-Proteobacteria, and Planctomycetes groups [19, 20].
\nAt present the precise association of bacteria with cultured dinoflagellates is still not well understood. Moreover, current estimates indicate that more than 99% of the microorganisms present in many natural environments are not readily culturable and therefore not accessible for biotechnology or basic research [21]. Technology to access the genomic DNA or RNA of microorganisms, directly from environmental samples without prior cultivation, has opened new ways of understanding microbial diversity and functions. Thus, this present study is an important step towards understanding bacteria-dinoflagellate interactions.
\nMetagenomics has become a powerful tool to investigate the biodiversity of complex microbial communities and for studying its metabolic pathways. This technique can be considered as a revolutionary approach to study the microbial community that is unapproachable by available conventional methods and this approach also can capture the total genomes that present in a community of interest. According to Schloss and Handelsman [22], metagenomics was builds on advances in microbial genomics and in the polymerase chain reaction (PCR) amplification and cloning of genes. The field of metagenomics has played a pivotal role for significant progress in microbial ecology, evolution, and diversity over the past years. This approach has allowed researchers to elucidate some possible mechanisms governing ecosystem function and diversity.
\nDinoflagellate Culture. Clonal culture of Alexandrium tamiyavanichii were obtained from UKM Microalgae Culture Collections and maintained in ES-DK medium [23] and growth in a light–dark cycle (14:10 hour) incubator at 26°C (model 2015 Shelab, USA).
\nDNA Extraction. Bulk genomic DNA were directly extracted from a 2.0 L of mid exponential growth phase dinoflagellate [24]. Firstly, culture medium was filtered through 0.2 μm nitrate cellulose membrane (Whatmann, England). Cell pellets were then concentrated and resuspended in buffer (100 mM EDTA, 10 mM Tris–HCl [pH 8.0]) and treated with proteinase K (0.5 mg/mL)-1% sodium dodecyl sulfate (SDS) for 1 h at 37°C. Lysates were further treated by CTAB extraction (0.5 M NaCl, 1% CTAB) for 10 min at 65°C. Then, DNA was extracted once with equal volume of chloroform-isoamyl alcohol (24:1) and phenol-chloroform-isoamyl alcohol (25:4:1) and spin at 21000 x g for 5 min at 4°C. After that, DNA was precipitated with 0.6 volume of isopropanol by centrifugation at 21000 x g for 15 min. The DNA pellet was then washed with one volume of 70% ethanol and spin at 21000 x g for 10 min. Finally, DNA was resuspended in 50 μL of ddH2O and stored at −20°C.
\nMetagenomic Library Construction and End-sequences Analysis. Sheared DNA with sizes ranged from 30 kilo bases to 40 kilo bases were used to construct the metagenomic library. Metagenomic library was constructed using CopyControl pCC1FOS library construction kit (Epicenter, USA) following the manufacturer’s protocol and fosmids were then purified using Millipore 96-well prep BAC purification kit (Millipore, USA) following the manufacturer’s protocol and end-sequenced using T7 universal primer (5′- TAATACGACTCACTATAGGG-3′). End-sequences were edited by using Staden Package software [25]. Low quality DNA sequences were identified and trimmed using Pregap4. The resulting high-quality sequences were assembled into contigs by using Gap4. All dataset was then analyzed by BLASTX [26]. The taxonomical analysis of sequence matches was performed using MEGAN version 4.0 [27] and gene ontology analysis was carried out using Blast2GO suite [28].
\nAbout 4–6 x 1030 microbes exist on this earth [29]. They form the foundation of the biosphere, regulate the biogeochemical cycle and influence geology, hydrology, local and global climate. Furthermore, microorganisms have the potential to produce beneficial products to humans such as bioactive compound products, enzymes and polymers. Research on microbial interactions in a natural environment allows us to better understand complex global issues such as greenhouse gases, biodegradation of harmful compounds and enable us to discover new natural products such as antibiotics. However, it is estimated that 99% of the microbes are “viable but nonculturable” [21, 30]. In the meantime, the function and role of the majority of microbes present in the natural environment are still not well understood. Furthermore, they are a valuable resource in biotechnology applications and new product discoveries. The design of metagenomic techniques has allowed us to study in-depth interactions and the role of microbial communities in a natural environment without the need for culturing [31].
\nThe metagenomic approach to studying the bacterial community has begun about more than 20 years ago. Since then, the analysis of bacterial communities using this technique has been widely reported. However, most metagenomic studies have been carried out on bacterial communities from seawater samples, sediments, freshwater. Some metagenomic studies on bacterial communities associated with other organisms were also reported such as from the marine sponges [32], beetle [33], polychetes [34] and tubeworm [35]. However, the analysis of bacterial communities associated with HAB by using metagenomic methods is still poorly reported.
\nA total of 1501 fosmid clones with insert sizes of 30 kbp to 40 kbp were selected for amplification. Sequences of 80 bp to 550 bp in length were obtained from 238 clones. BLASTX results showed that 23% of the sequences had no match with GenBank data at e-value >10−4, 11% were functionally unknown and 11% were putative (Figure 1). Figure 2 shows the functional classification of significant sequences. Most of the sequences could be functionally categorized into a metabolism cluster (37%). There were approximately 14% sequences with no classification and could potentially represent novel genes. Analysis of these partial sequences also revealed some promising enzymes that possess various potential industrial applications such as chitinases, kinases, agarases and oxygenases.
\nDistribution of fosmid end sequences based on BLASTX results.
Classification of significant sequences into the functional categories.
The results also showed that the bacterial flora of the Alexandrium tamiyavanichii culture was dominated by the Alpha-proteobacteria, followed by Bacteroidetes and Gamma-proteobacteria (Figure 3). This is similar to the findings of Hold et al. [36] and Green et al. [37]. Alpha-proteobacteria is the largest group in the proteobacteria clade and many members under these taxa are as yet uncultivated bacteria. In this study some of the partial sequences matched those of as yet uncultivable bacteria species. These results suggested that bacteria associated with dinoflagellates are a valuable source for metagenomic studies. Such studies could yield products useful for environmental monitoring, bioremediation and biodegradation [38].
\nClassification of sequences into bacteria divisions.
Fosmid end-sequencing has been done to assess the diversity of gene reservoirs in the constructed metagenomic fosmid library. The nucleotide sequence analysis obtained from 238 fosmid clones showed that most of the sequences are still functionally unknown and are believed to represent most of the undiscovered proteins and potentially to provide important new information or pathways if analyzed more deeply. The analysis of the fosmid end sequences also revealed that the majority of the sequences have similarities with the sequence of the Proteobacteria phylum. Some studies also showed that microflora around the marine dinoflagelate phycosphere was dominated by the Proteobacteria phylum [36, 37, 39]. We also found that part of the sequences was matched with genes derived from Roseobacter-Sulfitobacter-Silicibacter clade. Many Roseobacter species have been shown to utilize dimethyl sulfoniopropionate (DMSP) as both a carbon source and a sulfur source, and it is likely DMSP metabolism is important in Roseobacter-phytoplankton interactions [40].
\nAnalysis of these partial sequences also revealed some genes that might be important in bacteria-algal interactions. One of the contigs was similar to the response regulator of the LuxR family protein from Roseovarius sp. This protein is known to be responsible for a variety of biological processes in the natural environment, including the quorum sensing and production of toxins [41]. In a complex community like during an algal bloom, this protein may play a significant role in determining the population structure and function through signaling or inducing the production of certain proteins [42]. Thus, it is believed that bacteria use this type of protein to adapt to the changing conditions around the phycosphere of dinoflagellate such as changes in nutrients, cell densities, and increasing concentration of PSP toxins.
\nThe end-sequences obtained can not be used to describe the metabolic activity of each bacterial taxon involved but the analysis of the nucleotide sequences has shown that the constructed metagenomic library has great potential as a source to study the physiology and function of the bacterial community involved.
\nThe advantage of metagenomic method is that it allows us to study the genome of the bacterial community directly from the natural environment in which the function and role of certain bacteria on the environment can be determined. Studies have shown that metagenomics are a very useful technology in finding genes that can be applied in industrial, biotechnology, pharmaceutical and medical fields such as esterase, lipase, agarase, polymerase, polyketide synthases, chitinase and so on. Some potentially uncultured microbes and new genes were discovered through this study. In this study, metagenomic libraries using fosmid vectors were constructed from the bacterial community associated with A. tamiyavanichii. A total of 1501 fosmid clones ranging from 30 to 40 Kbp have been obtained and this is equivalent to 13 bacterial genomes. Finally, to our knowledge, the metagenomic library in this study was the first being constructed from the bacterial communities associated with toxic marine dinoflagellate. This collection of libraries can be used as a major source for finding new genes or pathways for biosynthesis and to study the interactions of dinoflagelates more profoundly, especially in the production of the saxitoxins as there is a hypothesis that the toxins produced by dinoflagelates are derived from bacteria.
\nWe thank all the people involved directly or indirectly in this study especially staffs from the School of Fisheries and Aquaculture Sciences, Universiti Malaysia Terengganu and Bachok Marine Research Station, University of Malaya.
\nThe authors declare no conflict of interest.
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",metaTitle:"Copyright Policy",metaDescription:"Copyright is the term used to describe the rights related to the publication and distribution of original works. Most importantly from a publisher's perspective, copyright governs how authors, publishers and the general public can use, publish and distribute publications.",metaKeywords:null,canonicalURL:"/page/copyright-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
\n\nIntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
\n\nHOW COPYRIGHT WORKS WITH OPEN ACCESS LICENSES?
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\n\nWork - a Chapter, including Conference Papers, and any and all text, graphics, images and/or other materials forming part of or accompanying the Chapter/Conference Paper.
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The CC BY 3.0 license permits Works to be freely shared in any medium or format, as well as the reuse and adaptation of the original contents of Works (e.g. figures and tables created by the Authors), as long as the source Work is cited and its Authors are acknowledged in the following manner:
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:null},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. 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