Automated plantar contact area estimation in a dynamic state using K-Means clustering.

BACKGROUND: Estimation of plantar contact area (PCA) can be used for a variety of purposes such as classification of foot types and diagnosis of foot abnormalities. While some techniques have been developed for assessing static PCA, understanding dynamic PCA may improve understanding of gait biomechanics. This study aims (1) to develop an approach to estimate PCA from video images of footprints during walking and (2) to assess the accuracy and generalizability of this method. METHODS: A sample of 41 ambulatory, young adults (age = 24.3 ± 3.2 years, mass = 67.2 ± 16.9 kg, height = 1.63 ± 0.08 m) completed 10 trials walking on a raised transparent plexiglass platform. Foot contact during walking was recorded using a video camera placed under the platform. An image processing algorithm, Clustering Segmentation, was developed based on identifying color intensity between the PCA and the rest of the foot and plantar contact morphology. RESULTS: The proposed approach was compared to manual hand tracing, which is widely accepted as the Gold Standard, as well as with an earlier automated approach (Lidstone et al., 2019). Results showed that Clustering Segmentation followed the Gold Standard closely in all phases of gait. The maximum PCA and the maximum PCA length and width generally increased with foot size, indicating that the algorithm could successfully estimate the PCA across a wide range of foot sizes. Results also showed that the proposed approach for obtaining the PCA may be used to characterize various foot types in a dynamic state. CONCLUSION: Clustering Segmentation algorithm eliminates the need for subjective interpretation of the PCA. The results showed that the algorithm was considerably faster and more accurate than the earlier automated method. The proposed algorithm will be appropriate for assessment of foot abnormalities and provides complementary information to gait analysis.

Operational resilience: concepts, design and analysis.

Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Multi-scale imaging and informatics pipeline for in situ pluripotent stem cell analysis.

Human pluripotent stem (hPS) cells are a potential source of cells for medical therapy and an ideal system to study fate decisions in early development. However, hPS cells cultured in vitro exhibit a high degree of heterogeneity, presenting an obstacle to clinical translation. hPS cells grow in spatially patterned colony structures, necessitating quantitative single-cell image analysis. We offer a tool for analyzing the spatial population context of hPS cells that integrates automated fluorescent microscopy with an analysis pipeline. It enables high-throughput detection of colonies at low resolution, with single-cellular and sub-cellular analysis at high resolutions, generating seamless in situ maps of single-cellular data organized by colony. We demonstrate the tool's utility by analyzing inter- and intra-colony heterogeneity of hPS cell cycle regulation and pluripotency marker expression. We measured the heterogeneity within individual colonies by analyzing cell cycle as a function of distance. Cells loosely associated with the outside of the colony are more likely to be in G1, reflecting a less pluripotent state, while cells within the first pluripotent layer are more likely to be in G2, possibly reflecting a G2/M block. Our multi-scale analysis tool groups colony regions into density classes, and cells belonging to those classes have distinct distributions of pluripotency markers and respond differently to DNA damage induction. Lastly, we demonstrate that our pipeline can robustly handle high-content, high-resolution single molecular mRNA FISH data by using novel image processing techniques. Overall, the imaging informatics pipeline presented offers a novel approach to the analysis of hPS cells that includes not only single cell features but also colony wide, and more generally, multi-scale spatial configuration.

Inferring species richness and turnover by statistical multiresolution texture analysis of satellite imagery.

BACKGROUND: The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover. METHODOLOGY/PRINCIPAL FINDINGS: We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or [Formula: see text] diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or [Formula: see text] diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season. CONCLUSIONS/SIGNIFICANCE: Texture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.

Paracrine and epigenetic control of trophectoderm differentiation from human embryonic stem cells: the role of bone morphogenic protein 4 and histone deacetylases.

Our understanding of paracrine and epigenetic control of trophectoderm (TE) differentiation is limited by available models of preimplantation human development. Simple, defined media for selective TE differentiation of human embryonic stem cells (hESCs) were developed, enabling mechanistic studies of early placental development. Paracrine requirements of preimplantation human development were evaluated with hESCs by measuring lineage-specific transcription factor expression levels in single cells and morphological transformation in response to selected paracrine and epigenetic modulators. Bone morphogenic protein 4 (BMP4) addition to feeder-free pluripotent stem cells on matrigel frequently formed CDX2-positive TE. However, BMP4 or activin A inhibition alone also produced a mix of mesoderm and extraembryonic endoderm under these conditions. Further, BMP4 failed to form TE from adherent hESC maintained in standard feeder-dependent monolayers. Given that the efficiency and selectivity of BMP4-induced TE depended on medium components, we developed a basal medium containing insulin and heparin. In this medium, BMP4 induction of TE was dose dependent and with activin A inhibition by SB431542 (SB), approached 100% of cells. This paracrine stimulation of pluripotent cells transformed colony morphology from a cuboidal to squamous epithelium quantitatively on day 3, and produced significant multinucleated syncytiotrophoblasts by day 8. Addition of trichostatin A, a histone deacetylase (HDAC) inhibitor, reduced HDAC3, histone H3K9 methylation, and slowed differentiation in a dose-dependent manner. Modulators of BMP4- or HDAC-dependent signaling might adversely influence the timing and viability of early blastocyst developed in vitro. Since blastocyst development is synchronized to uterine receptivity, epigenetic regulators of TE differentiation might adversely affect implantation in vivo.

Spatio-temporal data fusion for 3D+T image reconstruction in cerebral angiography.

This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss-Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain.

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.

Functional MRI activity characterization using response time shift estimates from curve evolution.

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brain's response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.