Foci of segmentally contracted sarcomeres in trapezius muscle biopsy specimens in myalgic and nonmyalgic human subjects : preliminary results

Objective The myofascial trigger point hypothesis postulates that there are small foci of contracted sarcomeres in resting skeletal muscle. Only one example, in canine muscle, has been published previously. This study evaluated human muscle biopsies for foci of contracted sarcomeres. Setting The Departments of Rehabilitation Sciences and Physiotherapy at Ghent University, Ghent, Belgium. Subjects Biopsies from 28 women with or without trapezius myalgia were evaluated, 14 in each group. Methods Muscle biopsies were obtained from regions of taut bands in the trapezius muscle and processed for light and electron microscopy and for histochemical analysis. Examination of the biopsies was blinded as to group. Results A small number of foci of segmentally contracted sarcomeres were identified. One fusiform segmental locus involved the entire muscle fiber in tissue from a myalgic subject. Several transition zones from normal to contracted sarcomeres were found in both myalgic and nonmyalgic subjects. The distance between Z-lines in contracted sarcomeres was about 25–45% of the same distance in normal sarcomeres. Z-lines were disrupted and smeared in the contracted sarcomeres. Conclusions A small number of foci of segmentally contracted sarcomeres were found in relaxed trapezius muscle in human subjects, a confirmation of the only other example of spontaneous segmental contraction of sarcomeres (in a canine muscle specimen), consistent with the hypothesis of trigger point formation and with the presence of trigger point end plate noise

Building flat space-time from information exchange between quantum fluctuations

We consider a hypothesis in which classical space-time emerges from information exchange (interactions) between quantum fluctuations in the gravity theory. In this picture, a line element would arise as a statistical average of how frequently particles interact, through an individual rate $dt\sim 1/f_t$ and spatially interconnecting rates $dl\sim c/f$. The question is if space-time can be modelled consistently in this way. The ansatz would be opposite to the standard treatment of space-time as insensitive to altered physics at event horizons (disrupted propagation of information) but by extension relate to the connection of space-time to entanglement (interactions) through the gauge/gravity duality. We make a first, rough analysis of the implications this type of quantization would have on the classical structure of flat space-time, and of what would be required of the interactions. Seeing no obvious reason for why the origin would be unrealistic, we comment on expected effects in the presence of curvature.Comment: 22 pages. v3: extended introductio

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

High-resolution mapping of fluoroquinolones in TB rabbit lesions reveals specific distribution in immune cell types.

Understanding the distribution patterns of antibiotics at the site of infection is paramount to selecting adequate drug regimens and developing new antibiotics. Tuberculosis (TB) lung lesions are made of various immune cell types, some of which harbor persistent forms of the pathogen, Mycobacterium tuberculosis. By combining high resolution MALDI MSI with histology staining and quantitative image analysis in rabbits with active TB, we have mapped the distribution of a fluoroquinolone at high resolution, and identified the immune-pathological factors driving its heterogeneous penetration within TB lesions, in relation to where bacteria reside. We find that macrophage content, distance from lesion border and extent of necrosis drive the uneven fluoroquinolone penetration. Preferential uptake in macrophages and foamy macrophages, where persistent bacilli reside, compared to other immune cells present in TB granulomas, was recapitulated in vitro using primary human cells. A nonlinear modeling approach was developed to help predict the observed drug behavior in TB lesions. This work constitutes a methodological advance for the co-localization of drugs and infectious agents at high spatial resolution in diseased tissues, which can be applied to other diseases with complex immunopathology

Large scale evaluation of local image feature detectors on homography datasets

We present a large scale benchmark for the evaluation of local feature detectors. Our key innovation is the introduction of a new evaluation protocol which extends and improves the standard detection repeatability measure. The new protocol is better for assessment on a large number of images and reduces the dependency of the results on unwanted distractors such as the number of detected features and the feature magnification factor. Additionally, our protocol provides a comprehensive assessment of the expected performance of detectors under several practical scenarios. Using images from the recently-introduced HPatches dataset, we evaluate a range of state-of-the-art local feature detectors on two main tasks: viewpoint and illumination invariant detection. Contrary to previous detector evaluations, our study contains an order of magnitude more image sequences, resulting in a quantitative evaluation significantly more robust to over-fitting. We also show that traditional detectors are still very competitive when compared to recent deep-learning alternatives.Comment: Accepted to BMVC 201

Numerical exploration of a hexagonal string billiard

In this paper we are interested in the motion of a ball inside a billiard table bounded by a particular smooth curve. This table belongs to a family of billiards which can all be drawn by a common process: the so-called gardener's string construction. The classical elliptical billiard is, of course, the foremost member of this family. So it should come as no surprise that our hexagonal string billiard shares many basic properties with the latter, but, on the other hand, also exhibits some essential differences with it. We have gathered numerical evidence against the Birkhoff-Poritsky conjecture.Comment: Preprint, 30 pages, 26 figure

LIFT: Learned Invariant Feature Transform

We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description. While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability. We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.Comment: Accepted to ECCV 2016 (spotlight