Phase transitions during fruiting body formation in Myxococcus xanthus

The formation of a collectively moving group benefits individuals within a population in a variety of ways such as ultra-sensitivity to perturbation, collective modes of feeding, and protection from environmental stress. While some collective groups use a single organizing principle, others can dynamically shift the behavior of the group by modifying the interaction rules at the individual level. The surface-dwelling bacterium Myxococcus xanthus forms dynamic collective groups both to feed on prey and to aggregate during times of starvation. The latter behavior, termed fruiting-body formation, involves a complex, coordinated series of density changes that ultimately lead to three-dimensional aggregates comprising hundreds of thousands of cells and spores. This multi-step developmental process most likely involves several different single-celled behaviors as the population condenses from a loose, two-dimensional sheet to a three-dimensional mound. Here, we use high-resolution microscopy and computer vision software to spatiotemporally track the motion of thousands of individuals during the initial stages of fruiting body formation. We find that a combination of cell-contact-mediated alignment and internal timing mechanisms drive a phase transition from exploratory flocking, in which cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into streams, which act as slow-spreading, quasi-one-dimensional nematic fluids. These observations lead us to an active liquid crystal description of the myxobacterial development cycle.Comment: 16 pages, 5 figure

Level Set-Based Fast Multi-phase Graph Partitioning Active Contours Using Constant Memory

Abstract. We present multi-phase FastGPAC that extends our dramatic improvement of memory requirements and computational complexity on two-class GPAC, into multi-class image segmentation. Graph partitioning active contours GPAC is a recently introduced approach that elegantly embeds the graph-based image segmentation problem within a continuous level set-based active contour paradigm. However, GPAC similar to many other graph-based approaches has quadratic memory requirements. For example, a 1024x1024 grayscale image requires over one terabyte of working memory. Approximations of GPAC reduce this complexity by trading off accuracy. Our FastGPAC approach implements an exact GPAC segmentation using constant memory requirement of few kilobytes and enables use of GPAC on high throughput and high resolution images. Extension to multi-phase enables segmention of multiple regions of interest with different appearances. We have successfully applied FastGPAC on different types of images, particularly on biomedical images of different modalities. Experiments on the various image types, natural, biomedical etc. show promising segmentation results with substantially reduced computational requirements

Multi-Scale Spatially Weighted Local Histograms in O(1)

Weighting pixel contribution considering its location is a key feature in many fundamental image processing tasks including filtering, object modeling and distance matching. Several techniques have been proposed that incorporate Spatial information to increase the accuracy and boost the performance of detection, tracking and recognition systems at the cost of speed. But, it is still not clear how to efficiently ex- tract weighted local histograms in constant time using integral histogram. This paper presents a novel algorithm to compute accurately multi-scale Spatially weighted local histograms in constant time using Weighted Integral Histogram (SWIH) for fast search. We applied our spatially weighted integral histogram approach for fast tracking and obtained more accurate and robust target localization result in comparison with using plain histogram.Comment: 5 pages, 7 figure

Joint Adaptive Median Binary Patterns for texture classification

a b s t r a c t This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise

Adaptive Median Binary Patterns for Texture Classification

Abstract-This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into a binary pattern texton by fusing MBP and Local Binary Patterns (LBP) operators combined with using self-adaptive analysis window sizes to better capture invariant microstructure information while providing robustness to noise. The AMBP scheme is shown to be an effective mechanism for non-parametric learning of spatially varying image texture statistics. The local distribution of rotation invariant and uniform binary pattern subsets extended with more global joint information are used as the descriptors for robust texture classification. The AMBP is shown to outperform recent binary pattern and filtering-based texture analysis methods on two large texture corpora (CUReT and KTH TIPS2-b) with and without additive noise. The AMBP method is slightly superior to the best techniques in the noiseless case but significantly outperforms other methods in the presence of impulse noise

Overview of contextual tracking approaches in information fusion

Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad

Characteristics of human spermatozoa harvested in culture media with and without serum proteins

This study was aimed to determine the efficiency of synthetic protein-free media in spermatozoa washing, preparation and retention of the activity of washed spermatozoa over short periods in vitro. Normozoospermic semen samples (n = 71) were equally apportioned and washed using synthetic protein-free medium (PFM), minimum essential medium + HSA (MEM) or commercial protein-containing medium (CPC). Washed spermatozoa were cultured in vitro using PFM, MEM or CPC media and held for 24 hrs at 4°C, 15°C, 22°C or 37°C. Spermatozoa activity was evaluated at 0 hr, 4 to 7 hrs and 24 hrs post-wash. The effects of PFM on spermatozoa motility, vitality, membrane integrity and DNA fragmentation level were not significantly different from that of MEM and CPC media at 0 hr, 4 to 7 hrs and 24 hrs post-wash in vitro. Synthetic PFM, MEM and CPC retained spermatozoa activity highest when specimen were held at 22°C and it was significantly higher (p 0.05) were noted in spermatozoa DNA fragmentation (SDF) levels when specimen were held at 22°C or 37°C at 4 to 7 hrs and also after 24 hrs post-wash in vitro in all media. The use of synthetic PFM as an alternative to the commercial protein-containing media in human spermatozoa washing and preparation procedure for an efficient and safer (Assisted Reproduction Technology) ART outcome. Spermatozoa activity can be successfully retained at room temperature post-wash over short periods; spermatozoa may lose viability rapidly if held for long hours at 37°C in all media

The Cell Tracking Challenge: 10 years of objective benchmarking

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101