Type IV Pili Can Mediate Bacterial Motility within Epithelial Cells.

Pseudomonas aeruginosa is among bacterial pathogens capable of twitching motility, a form of surface-associated movement dependent on type IV pili (T4P). Previously, we showed that T4P and twitching were required for P. aeruginosa to cause disease in a murine model of corneal infection, to traverse human corneal epithelial multilayers, and to efficiently exit invaded epithelial cells. Here, we used live wide-field fluorescent imaging combined with quantitative image analysis to explore how twitching contributes to epithelial cell egress. Results using time-lapse imaging of cells infected with wild-type PAO1 showed that cytoplasmic bacteria slowly disseminated throughout the cytosol at a median speed of >0.05 μm s-1 while dividing intracellularly. Similar results were obtained with flagellin (fliC) and flagellum assembly (flhA) mutants, thereby excluding swimming, swarming, and sliding as mechanisms. In contrast, pilA mutants (lacking T4P) and pilT mutants (twitching motility defective) appeared stationary and accumulated in expanding aggregates during intracellular division. Transmission electron microscopy confirmed that these mutants were not trapped within membrane-bound cytosolic compartments. For the wild type, dissemination in the cytosol was not prevented by the depolymerization of actin filaments using latrunculin A and/or the disruption of microtubules using nocodazole. Together, these findings illustrate a novel form of intracellular bacterial motility differing from previously described mechanisms in being directly driven by bacterial motility appendages (T4P) and not depending on polymerized host actin or microtubules.IMPORTANCE Host cell invasion can contribute to disease pathogenesis by the opportunistic pathogen Pseudomonas aeruginosa Previously, we showed that the type III secretion system (T3SS) of invasive P. aeruginosa strains modulates cell entry and subsequent escape from vacuolar trafficking to host lysosomes. However, we also showed that mutants lacking either type IV pili (T4P) or T4P-dependent twitching motility (i) were defective in traversing cell multilayers, (ii) caused less pathology in vivo, and (iii) had a reduced capacity to exit invaded cells. Here, we report that after vacuolar escape, intracellular P. aeruginosa can use T4P-dependent twitching motility to disseminate throughout the host cell cytoplasm. We further show that this strategy for intracellular dissemination does not depend on flagellin and resists both host actin and host microtubule disruption. This differs from mechanisms used by previously studied pathogens that utilize either host actin or microtubules for intracellular dissemination independently of microbe motility appendages

Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications

Tumour T1 changes in vivo are highly predictive of response to chemotherapy and reflect the number of viable tumour cells – a preclinical MR study in mice

BACKGROUND: Effective chemotherapy rapidly reduces the spin–lattice relaxation of water protons (T(1)) in solid tumours and this change (ΔT(1)) often precedes and strongly correlates with the eventual change in tumour volume (TVol). To understand the biological nature of ΔT(1), we have performed studies in vivo and ex vivo with the allosteric mTOR inhibitor, everolimus. METHODS: Mice bearing RIF-1 tumours were studied by magnetic resonance imaging (MRI) to determine TVol and T(1), and MR spectroscopy (MRS) to determine levels of the proliferation marker choline and levels of lipid apoptosis markers, prior to and 5 days (endpoint) after daily treatment with vehicle or everolimus (10 mg/kg). At the endpoint, tumours were ablated and an entire section analysed for cellular and necrotic quantification and staining for the proliferation antigen Ki67 and cleaved-caspase-3 as a measure of apoptosis. The number of blood-vessels (BV) was evaluated by CD31 staining. Mice bearing B16/BL6 melanoma tumours were studied by MRI to determine T(1) under similar everolimus treatment. At the endpoint, cell bioluminescence of the tumours was measured ex vivo. RESULTS: Everolimus blocked RIF-1 tumour growth and significantly reduced tumour T(1) and total choline (Cho) levels, and increased polyunsaturated fatty-acids which are markers of apoptosis. Immunohistochemistry showed that everolimus reduced the %Ki67(+) cells but did not affect caspase-3 apoptosis, necrosis, BV-number or cell density. The change in T(1) (ΔT(1)) correlated strongly with the changes in TVol and Cho and %Ki67(+). In B16/BL6 tumours, everolimus also decreased T(1) and this correlated with cell bioluminescence; another marker of cell viability. Receiver-operating-characteristic curves (ROC) for everolimus on RIF-1 tumours showed that ΔT(1) had very high levels of sensitivity and specificity (ROC(AUC) = 0.84) and this was confirmed for the cytotoxic patupilone in the same tumour model (ROC(AUC) = 0.97). CONCLUSION: These studies suggest that ΔT(1) is not a measure of cell density but reflects the decreased number of remaining viable and proliferating tumour cells due to perhaps cell and tissue destruction releasing proteins and/or metals that cause T(1) relaxation. ΔT(1) is a highly sensitive and specific predictor of response. This MRI method provides the opportunity to stratify a patient population during tumour therapy in the clinic

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible

Recognizing Treelike k-Dissimilarities

A k-dissimilarity D on a finite set X, |X| >= k, is a map from the set of size k subsets of X to the real numbers. Such maps naturally arise from edge-weighted trees T with leaf-set X: Given a subset Y of X of size k, D(Y) is defined to be the total length of the smallest subtree of T with leaf-set Y . In case k = 2, it is well-known that 2-dissimilarities arising in this way can be characterized by the so-called "4-point condition". However, in case k > 2 Pachter and Speyer recently posed the following question: Given an arbitrary k-dissimilarity, how do we test whether this map comes from a tree? In this paper, we provide an answer to this question, showing that for k >= 3 a k-dissimilarity on a set X arises from a tree if and only if its restriction to every 2k-element subset of X arises from some tree, and that 2k is the least possible subset size to ensure that this is the case. As a corollary, we show that there exists a polynomial-time algorithm to determine when a k-dissimilarity arises from a tree. We also give a 6-point condition for determining when a 3-dissimilarity arises from a tree, that is similar to the aforementioned 4-point condition.Comment: 18 pages, 4 figure