213 research outputs found

    A New and Fast Technique to Generate Offspring after Germ Cells Transplantation in Adult Fish: The Nile Tilapia (Oreochromis niloticus) Model

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    Background: Germ cell transplantation results in fertile recipients and is the only available approach to functionally investigate the spermatogonial stem cell biology in mammals and probably in other vertebrates. In the current study, we describe a novel non-surgical methodology for efficient spermatogonial transplantation into the testes of adult tilapia (O. niloticus), in which endogenous spermatogenesis had been depleted with the cytostatic drug busulfan. Methodology/Principal Findings: Using two different tilapia strains, the production of fertile spermatozoa with donor characteristics was demonstrated in adult recipient, which also sired progeny with the donor genotype. Also, after cryopreservation tilapia spermatogonial cells were able to differentiate to spermatozoa in the testes of recipient fishes. These findings indicate that injecting germ cells directly into adult testis facilitates and enable fast generation of donor spermatogenesis and offspring compared to previously described methods. Conclusion: Therefore, a new suitable methodology for biotechnological investigations in aquaculture was established, with a high potential to improve the production of commercially valuable fish, generate transgenic animals and preserv

    A diffuse reflectance comparative study of benzil inclusion within microcrystalline cellulose and beta-cyclodextrin

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    Diffuse reflectance and laser-induced techniques were used to study photochemical and photophysical processes of benzil adsorbed on two solid powdered supports, microcrystalline cellulose and beta-cyclodextrin. In both substrates, a distribution of ground-state benzil conformers exists, largely dominated by skew conformations where the carbonyl groups are twisted one to the other. Room temperature phosphorescence was observed in air-equilibrated samples in both cases. The decay times vary greatly and the largest lifetime was obtained for benzil/beta-cyclodextrin, showing that this host's cavity accommodates benzil well, enhancing its room temperature phosphorescence. Triplet - triplet absorption of benzil entrapped in cellulose was detected and benzil ketyl radical formation also occurred. With benzil included into beta-cyclodextrin, and following laser excitation, benzoyl radicals were detected on the millisecond timescale. Product analysis and identification of laser-irradiated benzil samples in the two hosts clearly showed that the main degradation photoproducts were benzoic acid and benzaldehyde. The main differences were a larger benzoic acid/benzaldehyde ratio in the case of cellulose and the formation of benzyl alcohol in this support

    Classification of heterogeneous microarray data by maximum entropy kernel

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    <p>Abstract</p> <p>Background</p> <p>There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are commonly used in microarray analyses with support vector machines (SVMs) to approach a wide range of classification problems. However, the standard vectorial data kernel family (linear, RBF, etc.) that takes vectorial data as input, often fails in prediction if the data come from different platforms or laboratories, due to the low gene overlaps or consistencies between the different datasets.</p> <p>Results</p> <p>We introduce a new type of kernel called maximum entropy (ME) kernel, which has no pre-defined function but is generated by kernel entropy maximization with sample distance matrices as constraints, into the field of SVM classification of microarray data. We assessed the performance of the ME kernel with three different data: heterogeneous kidney carcinoma, noise-introduced leukemia, and heterogeneous oral cavity carcinoma metastasis data. The results clearly show that the ME kernel is very robust for heterogeneous data containing missing values and high-noise, and gives higher prediction accuracies than the standard kernels, namely, linear, polynomial and RBF.</p> <p>Conclusion</p> <p>The results demonstrate its utility in effectively analyzing promiscuous microarray data of rare specimens, e.g., minor diseases or species, that present difficulty in compiling homogeneous data in a single laboratory.</p
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