Investigations on the biology of Indian Mackerel Rastrelliger kanagurta (Cuvier) along the Central Kerala coast with special reference to maturation, feeding and lipid dynamics

Dept. of Marine Biology, Microbiology & Biochemistry, Cochin University of Science and Technology

Studies on food and feeding of marine demersal finfishes with special reference to trophic interactions

Food and feeding habits of fourteen demersal finfishes exploited off the Karnataka coast were studied to investigate trophic interactions within the marine food web. Index of Relative Importance (lRI),Ontogenetic, seasonal (pre-monsoon, monsoon and post-monsoon) variation in feeding and prey-predator relationship studies were conducted.The results of prey-predator trophic interaction studies identified four major trophic guilds based on the predators feeding similarity.Trophic guild I is 'copepod and detritus fceders'with an average group similarity of 61.4%. The second trophic guild, 'prawn and crab feeders'with an average similarity of 52.7%. 'Acetes feeders', the largest trophic guild with an average group similarity of 62.5%, composed of six demersal finfish species.The guild 'piscivores' is constituted by C. limba/us and P. arsius with an average similarity of 45%.For each predator, ontogenetic diet shift is common and is characterized by prey of low to high trophic level.Strong selection of certain prey types was observed in some predators while most of them avoided abundant prey.In addition to Acetes spp, strong predation impact was observed for penaeid prawns, epibenthic crabs and detritus.This information on trophic guilds and prey-predator interactions can be used to construct trophic model on the benthic ecosystem off Karnataka and to investigate fishery induced changes as well as predation impact of different animals on commercially important demersals

Effect of Feeding Rate in Polymer Extrusion ”

Starve feeding of single screw extruder was described as an important means of improving the performance characteristics of the extruder. In addition to such improvement with versatility, the starve feeding technique also may affect the mechanical properties of the extrudate since the heat transfer an(l mixing characteristics in the starve fed and Hood fed extruders are not the same. Since the material is more loosely packed in the channels of the starve fed extruder, there may be greater bed mobility and uniformity. Further, the. thermal an(l shear induced degradation are also less since possibilities of developing local high temperatures are less compared to a densely compacted extruder bed. This study has been undertaken mainly to explore the effect of feeding rate on the mechanical properties of rubber and plastic extrudates since the effect of feeding rate has not been analysed from this angle so far.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

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.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Habitat ecology and food and feeding of the herring bow crab Varuna litterata (Fabricius, 1798) of Cochin backwaters, Kerala, India

Habitat ecology and food and feeding of the herring bow crab, Varuna litterata of Cochin Backwaters, Kerala, India were investigated for a period of one year (April 2011-March 2012). Among the 15 stations surveyed, the crabs were found to occur only in 4 stations, which had a close proximity to the sea. Sediment analysis of the stations revealed that the substratum of these stations is sandy in nature and is rich in organic carbon content (0.79% to 1.07%). These estuarine crabs is euryhaline and are found to be distributed in areas with a sandy substratum, higher organic carbon content and more tidal influx. The stomach contents analysis of crabs examined showed that their diet included crustacean remains, plants, sand and debris, fishes, miscellaneous group and unidentified matter. In adults and sub-adults, crustaceans formed the dominant food group, while in juveniles, sand and debris formed the dominant group. From the present study, V. litterata was found to be a predatory omnivore capable of ingesting both animal and plant tissues

Effects of Different Feeding Regimes on the Digestibility and Faecal Excretion of Nitrogen, Soluble Carbohydrates and Fibre Fractions in Water Buffaloes kept under Subtropical Conditions

In the course of the ‘Livestock Revolution’, extension and intensification of, among others, ruminant livestock production systems are current phenomena, with all their positive and negative side effects. Manure, one of the inevitable secondary products of livestock rearing, is a valuable source of plant nutrients and its skillful recycling to the soil-plant interface is essential for soil fertility, nutrient - and especially phosphorus - uses efficiency and the preservation or re-establishment of environmentally sustainable farming systems, for which organic farming systems are exemplarily. Against this background, the PhD research project presented here, which was embedded in the DFG-funded Research Training Group 1397 ‘Regulation of soil organic matter and nutrient turnover in organic agriculture ’ investigated possibilities to manipulate the diets of water buffalo (Bubalus bubalis L.) so as to produce manure of desired quality for organic vegetable production, without affecting the productivity of the animals used. Consisting of two major parts, the first study (chapter 2) tested the effects of diets differing in their ratios of carbon (C) to nitrogen (N) and of structural to non-structural carbohydrates on the quality of buffalo manure under subtropical conditions in Sohar, Sultanate of Oman. To this end, two trials were conducted with twelve water buffalo heifers each, using a full Latin Square design. One control and four tests diets were examined during three subsequent 7 day experimental periods preceded each by 21 days adaptation. Diets consisted of varying proportions of Rhodes grass hay, soybean meal, wheat bran, maize, dates, and a commercial concentrate to achieve a (1) high C/N and high NDF (neutral detergent fibre)/SC (soluble carbohydrate) ratio (HH), (2) low C/N and low NDF/SC ratio (LL); (3) high C/N and low NDF/SC ratio (HL) and (4) low C/N and high NDF/SC (LH) ratio. Effects of these diets, which were offered at 1.45 times maintenance requirements of metabolizable energy, and of individual diet characteristics, respectively, on the amount and quality of faeces excreted were determined and statistically analysed. The faeces produced from diets HH and LL were further tested in a companion PhD study (Mr. K. Siegfried) concerning their nutrient release in field experiments with radish and cabbage. The second study (chapter 3) focused on the effects of the above-described experimental diets on the rate of passage of feed particles through the gastrointestinal tract of four randomly chosen animals per treatment. To this end, an oral pulse dose of 683 mg fibre particles per kg live weight marked with Ytterbium (Yb; 14.5 mg Yb g-1 organic matter) was dosed at the start of the 7 day experimental period which followed 21 days of adaptation. During the first two days a sample for Yb determination was kept from each faecal excretion, during days 3 – 7 faecal samples were kept from the first morning and the first evening defecation only. Particle passage was modelled using a one-compartment age-dependent Gamma-2 model. In both studies individual feed intake and faecal excretion were quantified throughout the experimental periods and representative samples of feeds and faeces were subjected to proximate analysis following standard protocols. In the first study the organic matter (OM) intake and excretion of LL and LH buffaloes were significantly lower than of HH and HL animals, respectively. Digestibility of N was highest in LH (88%) and lowest in HH (74%). While NDF digestibility was also highest in LH (85%) it was lowest in LL (78%). Faecal N concentration was positively correlated (P≤0.001) with N intake, and was significantly higher in faeces excreted by LL than by HH animals. Concentrations of fibre and starch in faecal OM were positively affected by the respective dietary concentrations, with NDF being highest in HH (77%) and lowest in LL (63%). The faecal C/N ratio was positively related (P≤0.001) to NDF intake; C/N ratios were 12 and 7 for HH and LL (P≤0.001), while values for HL and LH were 11.5 and 10.6 (P>0.05). The results from the second study showed that dietary N concentration was positively affecting faecal N concentration (P≤0.001), while there was a negative correlation with the faecal concentration of NDF (P≤0.05) and the faecal ratios of NDF/N and C/N (P≤0.001). Particle passage through the mixing compartment was lower (P≤0.05) for HL (0.033 h-1) than for LL (0.043 h-1) animals, while values of 0.034 h-1 and 0.038 h-1 were obtained for groups LH and HH. At 55.4 h, total tract mean retention time was significantly (P≤0.05) lower in group LL that in all other groups where these values varied between 71 h (HH) and 79 h (HL); this was probably due to the high dietary N concentration of diet LL which was negatively correlated with time of first marker appearance in faeces (r= 0.84, P≤0.001), while the dietary C concentration was negatively correlated with particle passage through the mixing compartment (r= 0.57, P≤0.05). The results suggest that manure quality of river buffalo heifers can be considerably influenced by diet composition. Despite the reportedly high fibre digestion capacity of buffalo, digestive processes did not suppress the expression of diet characteristics in the faeces. This is important when aiming at producing a specific manure quality for fertilization purposes in (organic) crop cultivation. Although there was a strong correlation between the ingestion and the faecal excretion of nitrogen, the correlation between diet and faecal C/N ratio was weak. To impact on manure mineralization, the dietary NDF and N concentrations seem to be the key control points, but modulating effects are achieved by the inclusion of starch into the diet. Within the boundaries defined by the animals’ metabolic and (re)productive requirements for energy and nutrients, diet formulation may thus take into account the abiotically and biotically determined manure turnover processes in the soil and the nutrient requirements of the crops to which the manure is applied, so as to increase nutrient use efficiency along the continuum of the feed, the animal, the soil and the crop in (organic) farming systems.

Water for Food: Feeding what?

Key Findings: “Water for food” is not necessarily synonymous with “water for food security. Countries with similar geological conditions face very different geopolitical realities; national water policies and agricultural motivations reflect this. Water policies and research should seek to understand “water for food” and its connections with economics, the environment, and securities more broadly

KAM: Automatic Planning and Interpretation of Numerical Experiments Using Geometrical Methods

KAM is a computer program that can automatically plan, monitor, and interpret numerical experiments with Hamiltonian systems with two degrees of freedom. The program has recently helped solve an open problem in hydrodynamics. Unlike other approaches to qualitative reasoning about physical system dynamics, KAM embodies a significant amount of knowledge about nonlinear dynamics. KAM's ability to control numerical experiments arises from the fact that it not only produces pictures for us to see, but also looks at (sic---in its mind's eye) the pictures it draws to guide its own actions. KAM is organized in three semantic levels: orbit recognition, phase space searching, and parameter space searching. Within each level spatial properties and relationships that are not explicitly represented in the initial representation are extracted by applying three operations ---(1) aggregation, (2) partition, and (3) classification--- iteratively.

Automatic Analysis and Synthesis of Controllers for Dynamical Systems Based On P

I present a novel design methodology for the synthesis of automatic controllers, together with a computational environment---the Control Engineer's Workbench---integrating a suite of programs that automatically analyze and design controllers for high-performance, global control of nonlinear systems. This work demonstrates that difficult control synthesis tasks can be automated, using programs that actively exploit and efficiently represent knowledge of nonlinear dynamics and phase space and effectively use the representation to guide and perform the control design. The Control Engineer's Workbench combines powerful numerical and symbolic computations with artificial intelligence reasoning techniques. As a demonstration, the Workbench automatically designed a high-quality maglev controller that outperforms a previous linear design by a factor of 20.