TreeRipper web application: towards a fully automated optical tree recognition software

Background: Relationships between species, genes and genomes have been printed as trees for over a century. Whilst this may have been the best format for exchanging and sharing phylogenetic hypotheses during the 20(th) century, the worldwide web now provides faster and automated ways of transferring and sharing phylogenetic knowledge. However, novel software is needed to defrost these published phylogenies for the 21(st) century. Results: TreeRipper is a simple website for the fully-automated recognition of multifurcating phylogenetic trees (http://linnaeus.zoology.gla.ac.uk/similar to jhughes/treeripper/). The program accepts a range of input image formats (PNG, JPG/JPEG or GIF). The underlying command line c++ program follows a number of cleaning steps to detect lines, remove node labels, patch-up broken lines and corners and detect line edges. The edge contour is then determined to detect the branch length, tip label positions and the topology of the tree. Optical Character Recognition (OCR) is used to convert the tip labels into text with the freely available tesseract-ocr software. 32% of images meeting the prerequisites for TreeRipper were successfully recognised, the largest tree had 115 leaves. Conclusions: Despite the diversity of ways phylogenies have been illustrated making the design of a fully automated tree recognition software difficult, TreeRipper is a step towards automating the digitization of past phylogenies. We also provide a dataset of 100 tree images and associated tree files for training and/or benchmarking future software. TreeRipper is an open source project licensed under the GNU General Public Licence v

Cancer cell sensitivity to bortezomib is associated with survivin expression and p53 status but not cancer cell types

<p>Abstract</p> <p>Background</p> <p>Survivin is known playing a role in drug resistance. However, its role in bortezomib-mediated inhibition of growth and induction of apoptosis is unclear. There are conflicting reports for the effect of bortezomib on survivin expression, which lacks of a plausible explanation. Methods: In this study, we tested cancer cells with both p53 wild type and mutant/null background for the relationship of bortezomib resistance with survivin expression and p53 status using MTT assay, flow cytometry, DNA fragmentation, caspase activation, western blots and RNAi technology.</p> <p>Results</p> <p>We found that cancer cells with wild type p53 show a low level expression of survivin and are sensitive to treatment with bortezomib, while cancer cells with a mutant or null p53 show a high level expression of survivin and are resistant to bortezomib-mediated apoptosis induction. However, silencing of survivin expression utilizing survivin mRNA-specific siRNA/shRNA in p53 mutant or null cells sensitized cancer cells to bortezomib mediated apoptosis induction, suggesting a role for survivin in bortezomib resistance. We further noted that modulation of survivin expression by bortezomib is dependent on p53 status but independent of cancer cell types. In cancer cells with mutated p53 or p53 null, bortezomib appears to induce survivin expression, while in cancer cells with wild type p53, bortezomib downregulates or shows no significant effect on survivin expression, which is dependent on the drug concentration, cell line and exposure time.</p> <p>Conclusions</p> <p>Our findings, for the first time, unify the current inconsistent findings for bortezomib treatment and survivin expression, and linked the effect of bortezomib on survivin expression, apoptosis induction and bortezomib resistance in the relationship with p53 status, which is independent of cancer cell types. Further mechanistic studies along with this line may impact the optimal clinical application of bortezomib in solid cancer therapeutics.</p

Effects of FVB/NJ and C57Bl/6J strain backgrounds on mammary tumor phenotype in inducible nitric oxide synthase deficient mice

The ability to genetically manipulate mice has led to rapid progress in our understanding of the roles of different gene products in human disease. Transgenic mice have often been created in the FVB/NJ (FVB) strain due to its high fecundity, while gene-targeted mice have been developed in the 129/SvJ-C57Bl/6J strains due to the capacity of 129/SvJ embryonic stem cells to facilitate germline transmission. Gene-targeted mice are commonly backcrossed into the C57Bl/6J (B6) background for comparison with existing data. Genetic modifiers have been shown to modulate mammary tumor latency in mouse models of breast cancer and it is commonly known that the FVB strain is susceptible to mammary tumors while the B6 strain is more resistant. Since gene-targeted mice in the B6 background are frequently bred into the polyomavirus middle T (PyMT) mouse model of breast cancer in the FVB strain, we have sought to understand the impact of the different genetic backgrounds on the resulting phenotype. We bred mice deficient in the inducible nitric oxide synthase (iNOS) until they were congenic in the PyMT model in the FVB and B6 strains. Our results reveal that the large difference in mean tumor latencies in the two backgrounds of 53 and 92 days respectively affect the ability to discern smaller differences in latency due to the Nos2 genetic mutation. Furthermore, the longer latency in the B6 strain enables a more detailed analysis of tumor formation indicating that individual tumor development is not stoichastic, but is initiated in the #1 glands and proceeds in early and late phases. NO production affects tumors that develop early suggesting an association of iNOS-induced NO with a more aggressive tumor phenotype, consistent with human clinical data positively correlating iNOS expression with breast cancer progression. An examination of lung metastases, which are significantly reduced in PyMT/iNOS(−/−) mice compared with PyMT/iNOS(+/+) mice only in the B6 background, is concordant with these findings. Our data suggest that PyMT in the B6 background provides a useful model for the study of inflammation-induced breast cancer

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

iNOS Ablation Does Not Improve Specific Force of the Extensor Digitorum Longus Muscle in Dystrophin-Deficient mdx4cv Mice

Nitrosative stress compromises force generation in Duchenne muscular dystrophy (DMD). Both inducible nitric oxide synthase (iNOS) and delocalized neuronal NOS (nNOS) have been implicated. We recently demonstrated that genetic elimination of nNOS significantly enhanced specific muscle forces of the extensor digitorum longus (EDL) muscle of dystrophin-null mdx4cv mice (Li D et al J. Path. 223:88–98, 2011). To determine the contribution of iNOS, we generated iNOS deficient mdx4cv mice. Genetic elimination of iNOS did not alter muscle histopathology. Further, the EDL muscle of iNOS/dystrophin DKO mice yielded specific twitch and tetanic forces similar to those of mdx4cv mice. Additional studies suggest iNOS ablation did not augment nNOS expression neither did it result in appreciable change of nitrosative stress markers in muscle. Our results suggest that iNOS may play a minor role in mediating nitrosative stress-associated force reduction in DMD