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Background Biofilms disperse in response to specific environmental cues, such as reduced oxygen concentration, changes in nutrient concentration and exposure to nitric oxide. Interestingly, biofilms do not completely disperse under these conditions, which is generally attributed to physiological heterogeneity of the biofilm. However, our results suggest that genetic heterogeneity also plays an important role in the non-dispersing population of P. aeruginosa in biofilms after nutrient starvation. Results In this study, 12.2% of the biofilm failed to disperse after 4 d of continuous starvation-induced dispersal. Cells were recovered from the dispersal phase as well as the remaining biofilm. For 96 h starved biofilms, rugose small colony variants (RSCV) were found to be present in the biofilm, but were not observed in the dispersal effluent. In contrast, wild type and small colony variants (SCV) were found in high numbers in the dispersal phase. Genome sequencing of these variants showed that most had single nucleotide mutations in genes associated with biofilm formation, e.g. in wspF, pilT , fha1 and aguR . Complementation of those mutations restored starvation-induced dispersal from the biofilms. Because c-di-GMP is linked to biofilm formation and dispersal, we introduced a c-di-GMP reporter into the wild-type P. aeruginosa and monitored green fluorescent protein (GFP) expression before and after starvation-induced dispersal. Post dispersal, the microcolonies were smaller and significantly brighter in GFP intensity, suggesting the relative concentration of c-di-GMP per cell within the microcolonies was also increased. Furthermore, only the RSCV showed increased c-di-GMP, while wild type and SCV were no different from the parental strain. Conclusions This suggests that while starvation can induce dispersal from the biofilm, it also results in strong selection for mutants that overproduce c-di-GMP and that fail to disperse in response to the dispersal cue, starvation.

Background Manual and semi-automatic analyses of images, acquired in vivo by confocal microscopy, are often used to determine the quality of corneal endothelium in the human eye. These procedures are highly time consuming. Here, we present two fully automatic methods to analyze and quantify corneal endothelium imaged by in vivo white light slit-scanning confocal microscopy. Methods In the first approach, endothelial cell density is estimated with the help of spatial frequency analysis. We evaluate published methods, and propose a new, parameter-free method. In the second approach, based on the stochastic watershed, cells are automatically segmented and the result is used to estimate cell density, polymegathism (cell size variability) and pleomorphism (cell shape variation). We show how to determine optimal values for the three parameters of this algorithm, and compare its results to a semi-automatic delineation by a trained observer. Results The frequency analysis method proposed here is more precise than any published method. The segmentation method outperforms the fully automatic method in the NAVIS software (Nidek Technologies Srl, Padova, Italy), which significantly overestimates the number of cells for cell densities below approximately 1200 mm −2 , as well as previously published methods. Conclusions The methods presented here provide a significant improvement over the state of the art, and make in vivo, automated assessment of corneal endothelium more accessible. The segmentation method proposed paves the way to many possible new morphometric parameters, which can quickly and precisely be determined from the segmented image.

This study compares the ability of μCT image-based registration, 2D structural rigidity analyses and multimodal continuum-level finite element (FE) modeling in evaluating the mechanical stability of healthy, osteolytic, and mixed osteolytic/osteoblastic metastatically involved rat vertebrae. μMR and μCT images (loaded and unloaded) were acquired of lumbar spinal motion segments from 15rnu/rnu rats (five per group). Strains were calculated based on image registration of the loaded and unloaded μCT images and via analysis of FE models created from the μCT and μMR data. Predicted yield load was also calculated through 2D structural rigidity analysis of the axial unloaded μCT slices. Measures from the three techniques were compared to experimental yield loads. The ability of these methods to predict experimental yield loads were evaluated and image registration and FE calculated strains were directly compared. Quantitatively for all samples, only limited weak correlations were found between the image-based measures and experimental yield load. In comparison to the experimental yield load, we observed a trend toward a weak negative correlation with median strain calculated using the image-based strain measurement algorithm (r=−0.405, p=0.067), weak significant correlations (p<0.05) with FE based median and 10th percentile strain values (r=−0.454, −0.637, respectively), and a trend toward a weak significant correlation with FE based mean strain (r=−0.366, p=0.09). Individual group analyses, however, yielded more and stronger correlations with experimental results. Considering the image-based strain measurement algorithm we observed moderate significant correlations with experimental yield load (p<0.05) in the osteolytic group for mean and median strain values (r=−0.840, −0.832, respectively), and in the healthy group for median strain values (r=−0.809). Considering the rigidity-based predicted yield load, we observed a strong significant correlation with the experimental yield load in the mixed osteolytic/osteoblastic group (r=0.946) and trend toward a moderate correlation with the experimental yield load in the osteolytic group (r=0.788). Qualitatively, strain patterns in the vertebral bodies generated using image registration and FEA were well matched, yet quantitatively a significant correlation was found only between mean strains in the healthy group (r=0.934). Large structural differences in metastatic vertebrae and the complexity of motion segment loading may have led to varied modes of failure. Improvements in load characterization, material properties assignments and resolution are necessary to yield a more generalized ability for image-based registration, structural rigidity and FE methods to accurately represent stability in healthy and pathologic scenarios.

Zusammenfassung Klinisch verfügbare KI(künstliche Intelligenz)-basierte Lösungen dienen der Prozessoptimierung und der Objektivierung bildbasierter Quantifizierung für die kardiale Diagnostik. Damit eröffnet sich eine breitere Anwendung der heutigen Hochleistungsmedizin auch außerhalb von universitären Zentren. In dieser Übersichtsarbeit wird über die Praxisrelevanz sowie die Anwendung der KI in der Bildvorverarbeitung und -segmentierung, der Diagnostik, des Phenotypings für die klinische Entscheidungsunterstützung sowie über den heutigen Stand des Datenschutzes und der interdisziplinären Zusammenarbeit berichtet. Die Weiterentwicklung hochqualitativer KI-basierter Lösungen unter Berücksichtigung aktueller Datenschutzvorgaben erfordert eine multidisziplinäre Zusammenarbeit und den Aufbau geeigneter Forschungsinfrastrukturen. KI-Anwendungen in der diagnostischen kardialen Bildanalyse ohne klinische Daten der Patienten und die letztendlich entscheidende kardiologische Expertise erscheinen für eine valide Befundung und damit Therapieentscheidung bei kardiologischen Patienten nicht sinnvoll.

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [ Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.

In attempting to develop non-invasive image based measures for the determination of the biomechanical integrity of healing fractures, traditional μCT based measurements have been limited. This study presents the development and evaluation of a tool for assessment of fracture callus mechanical properties through determination of the geometric characteristics of the fracture callus, specifically along the surface of failure identified during destructive mechanical testing. Fractures were created in tibias of ten male mice and subjected to μCT imaging and biomechanical torsion testing. Failure surface analysis, along with previously described image based measures was calculated using the μCT image data, and correlated with mechanical strength and stiffness. Three-dimensional measures along the surface of failure, specifically the surface area and torsional rigidity of bone, were shown to be significantly correlating with mechanical strength and stiffness. It was also shown that surface area of bone along the failure surface exhibits stronger correlations with both strength and stiffness than measures of average and minimum torsional rigidity of the entire callus. Failure surfaces observed in this study were generally oriented at 45° to the long axis of the bone, and were not contained exclusively within the callus. This work represents a proof of concept study, and shows the potential utility of failure surface analysis in the assessment of fracture callus stability.

Quantification of the actin cytoskeleton is of prime importance to unveil the cellular force sensing and transduction mechanism. Although fluorescence imaging provides a convenient tool for observing the morphology of the actin cytoskeleton, due to the lack of approaches to accurate actin cytoskeleton quantification, the dynamics of mechanotransduction is still poorly understood. Currently, the existing image-based actin cytoskeleton analysis tools are either incapable of quantifying both the orientation and the quantity of the actin cytoskeleton simultaneously or the quantified results are subject to analysis artifacts. In this study, we propose an image recognition-based actin cytoskeleton quantification (IRAQ) approach, which quantifies both the actin cytoskeleton orientation and quantity by using edge, line, and brightness detection algorithms. The actin cytoskeleton is quantified through three parameters: the partial actin-cytoskeletal deviation (PAD), the total actin-cytoskeletal deviation (TAD), and the average actin-cytoskeletal intensity (AAI). First, Canny and Sobel edge detectors are applied to skeletonize the actin cytoskeleton images, then PAD and TAD are quantified using the line directions detected by Hough transform, and AAI is calculated through the summational brightness over the detected cell area. To verify the quantification accuracy, the proposed IRAQ was applied to six artificially-generated actin cytoskeleton mesh work models. The average error for both the quantified PAD and TAD was less than 1.22 ∘ . Then, IRAQ was implemented to quantify the actin cytoskeleton of NIH/3T3 cells treated with an F-actin inhibitor (latrunculin B). The quantification results suggest that the local and total actin-cytoskeletal organization became more disordered with the increase of latrunculin B dosage, and the quantity of the actin cytoskeleton showed a monotonically decreasing relation with latrunculin B dosage.