Generation of Variability for Salt Tolerance in Rice Using Tissue Culture Techniques

The study deals with the generation of variability for salt tolerance in rice using tissue culture techniques. Rice is the staple food of more than half of the world’s population. The management of drought, salinity and acidity in soils are all energy intensive agricultural practices. The Genetic variability is the basis of crop improvement. Somaclonal and androclonal variation can be effectively used for this purpose. In the present study, eight isozymes were studied and esterase and isocitric dehydrogenase was found to have varietal specific, developmental stage specific and stress specific banding pattern in rice. Under salt stress thickness of bands and enzyme activity showed changes. Pokkali, a moderately salt tolerant variety, had a specific band 7, which was present only in this variety and showed slight changes under stress. This band was faint in tillering and flowering stage .Based on the results obtained in the present study it is suggested that esterase could possibly be used as an isozyme marker for salt tolerance in rice. Varietal differences and stage specific variations could be detected using esterase and isocitric dehydrogenase . Moreover somaclonal and androclonal variation could be effectively detected using isozyme markers.

Development of plant tissue culture of Plumbago rosea Linn. for enhanced production of secondary metabolites

P rosea syn. Indica belong to the family of plumbaginaceae, is an important medicinal plant, cultivated widely in India. The roots of these plant are generally used for medicinal purposes mainly as diuretic, germicidal, vessicant, and abortifacient. It is also used for anaemia, diarrhea, leprosy and common wart. The bark of the root contains orange yellow pigment named plumbagin, a crystalline substance, belongs to the class of naphthoquinone. Its chemical structure is 5-hydroxy 2-methyl 1,4naphthoquinone. Apart from P rosea, P zeylanica, P europea, Drosera and Drosophyllum also contains plumbagin. The most exploited source of plumbagin is, of course, P. rosea roots. The roots contain O.9mg/ g D.Wt. of plumbagin in the roots. These plants grow very slowly and the roots suitable for plumbagin extraction can be obtained only after several years of growth. The productivity of the plant is also rather poor. The focus of the present study was to develop alternative strategies to obtain plumbagin. The tissue culture of P rosea for micropropagation has been studied

Development of a cell culture system from the ovarian tissue of African catfish Clarias gariepinus

A growth medium with Leibovitz-15 L-15.as the base, supplemented with foetal bovine serum 10% vrv., fish muscle extract 10% vrv., prawn muscle extract 10% vrv., lectin concanavalin A. 0.02 mg mly1., lipopolysaccharide 0.02 mg mly1., glucose D 0.2 mg mly1., ovary extract 0.5% vrv.and prawn haemolymph 0.5%. has been formulated with 354"10 mOsm for the development and maintenance of a cell culture system from the ovarian tissue of African catfish, Clarias gariepinus. For its subculturing, a cell dissociationrextracting solution, composed of equal portions of trypsin phosphate versene glucose TPVG. containing 0.0125% wrv.trypsin and 25% vrv.non-enzymatic cell dissociation solution 1 and 2, has also been developed with which the cell culture can be passaged 15 times after which they cease to multiply and consequently perish. The cell cultures can be maintained for 12–15 days without fluid change between the passages. This is the first report of a cell culture system from the ovarian tissues of African catfish

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Area and Volume Calculation of Necrotic Tissue regions of heart using Interpolation

This paper attempts to develop an improved tool, which would read two dimensional(2D) cardiac MRI images and compute areas and volume of the scar tissue. Here the computation would be done on the cardiac MR images to quantify the extent of damage inflicted by myocardial infarction on the cardiac muscle (myocardium) using Interpolation

Study of chemical properties and evaluation of collagen in mantle, epidermal connective tissue and tentacle of Indian Squid, Loligo duvauceli Orbigny

The chemical composition and evaluation of Indian squid (Loligo duvauceli) mantle, epidermal connective tissue and tentacle is investigated in this current study. It is observed that squid mantle contains 22.2% total protein; 63.5% of the total protein is myofibrillar protein. The unique property of squid myofibrillar protein is its water solubility. Squid mantle contains 12.0% total collagen. Epidermal connective tissue has highest amounts of total collagen (17.8%). SDS-PAGE of total collagen identified high molecular weight α-, β- and γ- sub-chains. Amino acid profile analysis indicates that mantle and tentacle contain essential amino acids. Arginine forms a major portion of mantle collagen (272.5 g/100 g N). Isoleucine, glutamic acid and lysine are other amino acids that are found in significantly high amounts in the mantle. Sulphur containing cystine is deficit in mantle collagen. Papain digest of mantle and epidermal connective tissue is rich in uronic acid, while papain digest, collagenase digest and urea digest of epidermal connective tissue has significant amounts of sialic acid (25.2, 33.2 and 99.8 μmol /100 g, respectively). PAS staining of papain digest, collagenase digest and urea digest also identify the association of hexoses with low molecular weight collagen fragments. Histochemical sectioning also emphasized the localized distribution of collagen in epidermal and dermal region and very sparse fibres traverse the myotome bundles

Not the outside but the inside matters : functional analysis of Capricious and Tartan during Drosophila tracheal morphogenesis

The tubular structures, which transport essential gases, liquids, or cells from one site to another, are shared among various divergent organisms. These highly organized tubular networks include lung, kidney, vasculature and mammary gland in mammals as well as trachea and salivary gland in Drosophila melanogaster. Many questions regarding the tubular morphogenesis cannot be addressed sufficiently by investigating the mammalian organs because their structures are extremely complex and therefore, systematic analyses of genetic and cellular programs guiding the development is not possible. In contrast, the Drosophila tracheal development provides an excellent model system since many molecular markers and powerful tools for genetic manipulations are available. Two mechanisms were shown to be important for the outgrowth of tracheal cells: the FGF signaling pathway and the interaction between the tracheal cells and the surrounding mesodermal cells. The Drosophila FGF ligand encoded by branchless (bnl) is localized in groups of cells near tracheal metameres. The tracheal cells expressing the FGF receptor breathless (btl) respond to these sources of FGF ligand and extend towards them. However, this FGF signaling pathway is not sufficient for the formation of continuous dorsal trunk, the only muticellular tube in tracheal system. Recently, it was found out that single mesodermal cells called bridge-cells are essential for the formation of continuous dorsal trunk as they direct the outgrowth of dorsal trunk cells towards the correct targets. The results in this PhD thesis demonstrate that a cell adhesion molecule Capricious (Caps), which is specifically localized on the surface of bridge-cells, plays an essential role in guiding the outgrowing dorsal trunk cells towards their correct targets. When caps is lacking, some bridge-cells cannot stretch properly towards the adjacent posterior tracheal metameres and thus fail to interconnect the juxtaposing dorsal trunk cells. Consequently, discontinuous dorsal trunks containing interruptions at several positions are formed. On the other hand, when caps is ectopically expressed in the mesodermal cells through a twi-GAL4 driver, these mesodermal cells acquire a guidance function through ectopic caps and misguide the outgrowing dorsal trunk cells in abnormal directions. As a result, disconnected dorsal trunks are formed. These loss- and gain-of-function studies suggest that Caps presumably establishes the cell-to-cell contact between the bridge-cells and the tracheal cells and thereby mediates directly the guidance function of bridge-cells. The most similar protein known to Caps is another cell adhesion molecule called Tartan (Trn). Interestingly, trn is expressed in the mesodermal cells but not in the bridge-cells. When trn is lacking, the outgrowth of not only the dorsal trunks but also the lateral trunks are disrupted. However, in contrast to the ectopic expression of caps, the misexpression of trn does not affect tracheal development. Whereas Trn requires only its extracellular domain to mediate the matrix function, Caps requires both its extracellular and intracellular domains to function as a guidance molecule in the bridge-cells. These observations suggest that Trn functions differently from Caps during tracheal morphogenesis. Presumably, Trn mediates a matrix function of mesodermal cells, which support the tracheal cells to extend efficiently through the surrounding mesodermal tissue. In order to determine which domains dictate the functional specificity of Caps, two hybrid proteins CapsEdTrnId, which contains the Caps extracellular domain and the Trn intracellular domain, and TrnEdCapsId, which consists of the Trn extracellular domain and the Caps intracellular domain, were constructed. Gain of function and rescue experiments with these hybrid proteins suggest on one hand that the extracellular domains of Caps and Trn are functionally redundant and on the other hand that the intracellular domain dictates the functional specificity of Caps. In order to identify putative interactors of Caps, yeast two-hybrid screening was performed. An in vivo interaction assay in yeast suggests that Ras64B interacts specifically with the Caps intracellular domain. In addition, an in vitro binding assay reveals a direct interaction between an inactive form of Ras64B and the Caps intracellular domain. ras64B, which encodes a small GTPase, is expressed in the mesodermal cells concurrently as caps. Finally, a gain-of-function study with the constitutively active Ras64B suggests that Ras64B presumably functions downstream of Caps. All these results suggest consistently that the small GTPase Ras64B binds specifically to the Caps intracellular domain and may thereby mediate the guidance function of Caps.

Characterization of the role of Drosophila JAK/STAT signalling in cellular proliferation

Der Janus Kinase / signal transducer and activator of transcription (JAK/STAT) Signal- transduktionsweg wird für viele Entwicklungsvorgänge benötigt und spielt eine zentrale Rolle bei der Hämatopoese und bei der Immunantwort. Obwohl der JAK/STAT-Signalweg in den vergangenen Jahren Gegenstand intensiver Forschung war, erschwert die Redundanz des Signalwegs bei Wirbeltieren genetische Untersuchungen zur Identifizierung derjenigen Mechanismen, die den JAK/STAT-Signalweg regulieren. Der JAK/STAT-Signaltransduktionsweg ist evolutionär konserviert und ebenfalls bei der Taufliege Drosophila melanogaster vorhanden. Im Gegensatz zu Wirbeltieren ist der Signaltransduktionsweg von Drosophila weniger redundant und beinhaltet folgende Hauptkomponenten: den Liganden Unpaired (Upd), den Transmembranrezeptor Domeless (Dome), die einzige JAK-Tyrosinkinase Hopscotch (hop), sowie den Transkriptionsfaktor STAT92E. In der vorliegenden Arbeit wird die Rolle des JAK/STAT-Signalwegs bei der zellulären Proliferation mithilfe der Modellsysteme der Flügel- und der Augen-Imaginalscheiben von Drosophila charakterisiert. "Loss-of-function"- und "Gain-of-function"-Experimente zur Verminderung beziehungs-weise Erhöhung der Signalaktivität zeigten, dass der JAK/STAT-Signalweg eine Rolle bei der zellulären Proliferation der Flügel-Imaginalscheiben spielte, ohne die Zellgröße oder Apoptose zu verändern. Bei der Flügelentwicklung während des zweiten und des frühen dritten Larvalstadiums war die Aktivität des JAK/STAT-Signalwegs sowohl notwendig für die zelluläre Proliferation als auch hinreichend, um Überproliferation anzutreiben. Allerdings änderte sich während der späten dritten Larvalstadien die JAK/STAT-Signalaktivität, sodass endogene STAT92E-Mengen einen anti-proliferativen Effekt im gleichen Gewebe aufwiesen. Weiterhin reichte die ektopische Aktivierung des JAK/STAT-Signalwegs zu diesem späten Entwicklungszeitpunkt aus, um die Mitose zu inhibieren und die Zellen in der Phase G2 des Zellzyklus zu arretieren. Diese Ergebnisse legen den Schluss nahe, dass der JAK/STAT-Signalweg sowohl pro-proliferativ in frühen Flügelscheiben als auch anti-proliferativ zu späten Stadien der Flügelscheiben-Entwicklung wirken kann. Dieser späte anti-proliferative Effekt wurde durch einen nicht-kanonischen Mechanismus der STAT92E-Aktivierung vermittelt, da späte hop defiziente Zellverbände im Vergleich zu Wildtyp-Zellen keine Veränderungen im Ausmaß der zellulären Proliferation aufwiesen. Ferner konnte gezeigt werden, dass eine während der Larvalstadien exprimierte dominant-negative und im N-Terminus deletierte Form von STAT92E (?NSTAT92E) nicht für den anti-proliferativen Effekt verantwortlich ist. Diese Tatsache ist ein weiteres Indiz dafür, dass das vollständige STAT92E den späten anti-proliferativen Effekt verursacht. Um Modulatoren für die von JAK/STAT vermittelte zelluläre Proliferation zu identifieren, wurde ein P-Element-basierter genetischer Interaktions-Screen in einem sensibilisierten genetischen Hintergrund durchgeführt. Insgesamt wurden dazu 2267 unabhängige P-Element-Insertionen auf ihre Wechselwirkung mit der JAK/STAT-Signalaktivität untersucht und 24 interagierende Loci identifiziert. Diese Kandidaten können in folgende Gruppen eingeordnet werden: Zellzyklusproteine, Transkriptionsfaktoren, DNA und RNA bindende Proteine, ein Mikro-RNA-Gen, Komponenten anderer Signaltransduktionswege und Zelladhäsionsproteine. In den meisten Fällen wurden mehrere Allele der interagierenden Kandidatengene getestet. 18 Kandidatengene mit übereinstimmend interagierenden Allelen wurden dann zur weiteren Analyse ausgewählt. Von diesen 18 Kandidaten-Loci wurden 7 mögliche JAK/STAT-Signalwegskomponenten und 6 neue Zielgene des Signalwegs gefunden. Zusammenfassend wurde das Verständnis um STAT92E verbessert. Dieses Protein hat die gleiche Funktion wie das STAT3-Protein der Wirbeltiere und treibt die zelluläre Proliferation voran. Analog zu STAT1 hat STAT92E aber auch einen anti-proliferativen Effekt. Ferner wurden 24 mögliche Modulatoren der JAK/STAT-Signalaktivität identifiziert. Die Charakterisierung dieser Wechselwirkungen eröffnet vielversprechende Wege zu dem Verständnis, wie JAK/STAT die zelluläre Proliferation reguliert und könnte bei der Entwicklung von neuartigen therapeutischen Targets zur Behandlung von Krebskrankheiten und Entwicklungsstörungen beitragen.

Effects of dietary lipid source on growth, digestibility and tissue fatty acid composition of Heterobranchus longifilis fingerlings

One of the major problems facing aquaculture is the inadequate supply of fish oil mostly used for fish feed manufacturing. The continued growth in aquaculture production cannot depend on this finite feed resources, therefore, it is imperative that cheap and readily available substitutes that do not compromise fish growth and fillet quality be found. To achieve this, a 12-week feeding trial with Heterobranchus longifilis fed diets differing in lipid source was conducted. Diets were supplemented with 6% lipid as fish oil, soybean oil, palm oil, coconut oil, groundnut oil and melon seed oil. Triplicate groups of 20 H. longifilis were fed the experimental diets two times a day to apparent satiation, over 84 days. Growth, digestibility, and muscle fatty acid profile were measured to assess diet effects. At the end of the study, survival, feed intake and hepatosomatic index were similar for fish fed experimental diets. However, weight gain, SGR and FCR of fish fed soybean oil-based diet was significantly reduced. Apparent nutrient digestibility coefficients were significantly lower in fish fed soybean, coconut and groundnut oil-based diets. Fillet and hepatic fatty acid compositions differed and reflected the fatty acid compositions of the diets. Docosahexaenoic acid (22:6n-3), 20:5n-3 and 20:4n-6 were conserved in vegetable oils-based diets fed fish possibly due to synthesis of HUFA from 18:3n-3 and 18:4n-6. Palm oil diet was the least expensive, and had the best economic conversion ratio. The use of vegetable oils in the diets had positive effect on growth and fillet composition of H. longifilis.

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment