Haematology of some marine and estuarine molluscs of commercial importance

It is well known that under certain conditions, populations of oysters and clams are susceptible to destructive epizootics caused by pathogenic micro-organisms. It has also been shown that exposure of mammals to certain heavy metals causes increased susceptibility to and severity of microbial infections (Koller, 1980). Consequently, pollutants that affect haemocyte viability or interfere with internal defence functions of the haemocytes which are considered as the major means of defence in moliuscs against invading foreign organisms and pathogens (Cheng, 1981) may have profound effect on long term survival of molluscan populations. All these justify the significance of the present study in the context of the current status on molluscan culture programme, and how the data on molluscan haematological studies .could be taken as the reliable criteria for pollution monitoring studies.

A facile and single pot strategy for the synthesis of novel naphthyridine derivatives under microwave irradiation conditions using ZnCl2 as catalyst, evaluation of AChE inhibitory activity, and molecular modeling studies

A series of novel naphthyridine derivatives 3 and 4 was prepared from substituted pyridine 2 and ketones using ZnCl2 as catalyst under microwave irradiation conditions. All the compounds were evaluated for AChE inhibitory activity and promising compounds 3d, 3e, 4b, and 4g was identified. Representative compounds 3d and 3e were found to show insignificant THLE-2 liver cell viability/toxicity. The binding mode between X-ray crystal structure of human AChE and compounds was studied using molecular docking method and fitness scores were found to be in good correlation with the activity data.

Multiple Antibiotic Resistances of Vibrio Isolates from Coastal and Brackish Water Areas

An experiment was designed to assess the occurrence of multiple antibiotic resistances in Vibrio sp from different (brackish and marine) environments. Water samples from nine marine landing sites and two coastal inland aquaculture farms were screened for the Vibrio spp and assessed their resistance to twenty-two different antibiotics, which are commonly encountered in the aquatic ecosystem. Tissue samples (shrimp, mussel and sepia) were tested from the sampling site with highest antibiotic resistance. Of the total 119 Vibrio isolates, 16. 8% were susceptible to all antibiotics. Of the resistant (83.19%) Vibrio strains, 30.3% were resistant against three antibiotics, 55.5% were resistant against 4-10 antibiotics, 14.14% were resistant against more than 10 antibiotics and 54% have shown multiple antibiotics resistance (MAR). Antibiotic resistance index was higher in Coastal 3, 6, Aqua farm 2 in isolates from water samples and all the tissues tested. Interestingly, incidence of antibiotic resistance in isolates from water samples was comparatively lower in aquaculture farms than that observed in coastal areas. Highest incidence of antibiotic resistance was evident against Amoxycillin, Ampicillin, Carbencillin and Cefuroxime followed by Rifampicin and Streptomycin and lowest against Chloramphenicol, Tetracycline, Chlortetracycline, Furazolidone, Nalidixic acid, Gentamycin Sulphafurazole, Trimethoprirn, Neomycin and Amikacin irrespective of the sampling sites. Results from various tissue samples collected from the sites of highest antibiotic resistance indicated that antibiotic resistance Vibrio spp collected from fish and tissue samples were higher than that of water samples. Overall results indicated that persistent use of antibiotics against diseases in human beings and other life forms may pollute the aquatic system and their impact on developing antibiotic resistant Vibrio sp may be a serious threat in addition to the use of antibiotics in aquaculture farms.

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

Understanding Subsystems in Biology through Dimensionality Reduction, Graph Partitioning and Analytical Modeling

Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. This set of subsystems was then used to predict functional relationships between genes, and this approach was shown to score significantly higher than conventional methods when benchmarking them against existing databases. Moreover, a mathematical treatment was developed to treat simple network subsystems based only on their topology (independent of particular parameter values). Application to a problem of experimental interest demonstrated the need for extentions to the conventional model to fully explain the experimental data. Finally, the notion of a subsystem was evaluated from a topological perspective. A number of different protein networks were examined to analyze their topological properties with respect to separability, seeking to find separable subsystems. These networks were shown to exhibit separability in a nonintuitive fashion, while the separable subsystems were of strong biological significance. It was demonstrated that the separability property found was not due to incomplete or biased data, but is likely to reflect biological structure.

Measuring experiential avoidance and psychological inflexibility : The Spanish version of the Acceptance and Action Questionnaire - II

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Tropospheric planetary wave dynamics and mixture modeling: Two preferred regimes and a regime shift

Investigation of preferred structures of planetary wave dynamics is addressed using multivariate Gaussian mixture models. The number of components in the mixture is obtained using order statistics of the mixing proportions, hence avoiding previous difficulties related to sample sizes and independence issues. The method is first applied to a few low-order stochastic dynamical systems and data from a general circulation model. The method is next applied to winter daily 500-hPa heights from 1949 to 2003 over the Northern Hemisphere. A spatial clustering algorithm is first applied to the leading two principal components (PCs) and shows significant clustering. The clustering is particularly robust for the first half of the record and less for the second half. The mixture model is then used to identify the clusters. Two highly significant extratropical planetary-scale preferred structures are obtained within the first two to four EOF state space. The first pattern shows a Pacific-North American (PNA) pattern and a negative North Atlantic Oscillation (NAO), and the second pattern is nearly opposite to the first one. It is also observed that some subspaces show multivariate Gaussianity, compatible with linearity, whereas others show multivariate non-Gaussianity. The same analysis is also applied to two subperiods, before and after 1978, and shows a similar regime behavior, with a slight stronger support for the first subperiod. In addition a significant regime shift is also observed between the two periods as well as a change in the shape of the distribution. The patterns associated with the regime shifts reflect essentially a PNA pattern and an NAO pattern consistent with the observed global warming effect on climate and the observed shift in sea surface temperature around the mid-1970s.

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes.