Clinical validation of an automated vessel-segmentation software of the extracranial-carotid arteries based on 3D-MRA: A prospective study

OBJECTIVES: To determine the accuracy of automated vessel-segmentation software for vessel-diameter measurements based on three-dimensional contrast-enhanced magnetic resonance angiography (3D-MRA). METHOD: In 10 patients with high-grade carotid stenosis, automated measurements of both carotid arteries were obtained with 3D-MRA by two independent investigators and compared with manual measurements obtained by digital subtraction angiography (DSA) and 2D maximum-intensity projection (2D-MIP) based on MRA and duplex ultrasonography (US). In 42 patients undergoing carotid endarterectomy (CEA), intraoperative measurements (IOP) were compared with postoperative 3D-MRA and US. RESULTS: Mean interoperator variability was 8% for measurements by DSA and 11% by 2D-MIP, but there was no interoperator variability with the automated 3D-MRA analysis. Good correlations were found between DSA (standard of reference), manual 2D-MIP (rP=0.6) and automated 3D-MRA (rP=0.8). Excellent correlations were found between IOP, 3D-MRA (rP=0.93) and US (rP=0.83). CONCLUSION: Automated 3D-MRA-based vessel segmentation and quantification result in accurate measurements of extracerebral-vessel dimensions.

Semi-automatic segmentation of fetal cardiac cavities: progress towards an automated fetal echocardiogram

OBJECTIVE: To develop a novel application of a tool for semi-automatic volume segmentation and adapt it for analysis of fetal cardiac cavities and vessels from heart volume datasets. METHODS: We studied retrospectively virtual cardiac volume cycles obtained with spatiotemporal image correlation (STIC) from six fetuses with postnatally confirmed diagnoses: four with normal hearts between 19 and 29 completed gestational weeks, one with d-transposition of the great arteries and one with hypoplastic left heart syndrome. The volumes were analyzed offline using a commercially available segmentation algorithm designed for ovarian folliculometry. Using this software, individual 'cavities' in a static volume are selected and assigned individual colors in cross-sections and in 3D-rendered views, and their dimensions (diameters and volumes) can be calculated. RESULTS: Individual segments of fetal cardiac cavities could be separated, adjacent segments merged and the resulting electronic casts studied in their spatial context. Volume measurements could also be performed. Exemplary images and interactive videoclips showing the segmented digital casts were generated. CONCLUSION: The approach presented here is an important step towards an automated fetal volume echocardiogram. It has the potential both to help in obtaining a correct structural diagnosis, and to generate exemplary visual displays of cardiac anatomy in normal and structurally abnormal cases for consultation and teaching.

Comparison of voxel-based 3-D MRI analysis and subtraction ictal SPECT coregistered to MRI in focal epilepsy

While voxel-based 3-D MRI analysis methods as well as assessment of subtracted ictal versus interictal perfusion studies (SISCOM) have proven their potential in the detection of lesions in focal epilepsy, a combined approach has not yet been reported. The present study investigates if individual automated voxel-based 3-D MRI analyses combined with SISCOM studies contribute to an enhanced detection of mesiotemporal epileptogenic foci. Seven consecutive patients with refractory complex partial epilepsy were prospectively evaluated by SISCOM and voxel-based 3-D MRI analysis. The functional perfusion maps and voxel-based statistical maps were coregistered in 3-D space. In five patients with temporal lobe epilepsy (TLE), the area of ictal hyperperfusion and corresponding structural abnormalities detected by 3-D MRI analysis were identified within the same temporal lobe. In two patients, additional structural and functional abnormalities were detected beyond the mesial temporal lobe. Five patients with TLE underwent epileptic surgery with favourable postoperative outcome (Engel class Ia and Ib) after 3-5 years of follow-up, while two patients remained on conservative treatment. In summary, multimodal assessment of structural abnormalities by voxel-based analysis and SISCOM may contribute to advanced observer-independent preoperative assessment of seizure origin.

Reliability of evaluations for the choice of system solutions at the example of automated order-picking systems for bagged goods

There is a steadily increasing pressure on cost-savings and productivity growth in sectors of order-picking such that the wish for rationalization by automation is rising. Special problems are faced trying to automatize handling operations of order-picking articles packed in bags. The mechanical properties of the objects and their hard-to-predict shape and position represent obstacles and are complicating handling operations. A systematic approach in system design is required. This article deals with the properties of such products under aspects of difficulties arising in automated handling and points out a useful system development methodology.

Meta-model for calculating the mean energy demand of automated storage and retrieval systems

Energy efficiency has become an important research topic in intralogistics. Especially in this field the focus is placed on automated storage and retrieval systems (AS/RS) utilizing stacker cranes as these systems are widespread and consume a significant portion of the total energy demand of intralogistical systems. Numerical simulation models were developed to calculate the energy demand rather precisely for discrete single and dual command cycles. Unfortunately these simulation models are not suitable to perform fast calculations to determine a mean energy demand value of a complete storage aisle. For this purpose analytical approaches would be more convenient but until now analytical approaches only deliver results for certain configurations. In particular, for commonly used stacker cranes equipped with an intermediate circuit connection within their drive configuration there is no analytical approach available to calculate the mean energy demand. This article should address this research gap and present a calculation approach which enables planners to quickly calculate the energy demand of these systems.

A systems analysis identifies a feedforward inflammatory circuit leading to lethal influenza infection

For acutely lethal influenza infections, the relative pathogenic contributions of direct viral damage to lung epithelium versus dysregulated immunity remain unresolved. Here, we take a top-down systems approach to this question. Multigene transcriptional signatures from infected lungs suggested that elevated activation of inflammatory signaling networks distinguished lethal from sublethal infections. Flow cytometry and gene expression analysis involving isolated cell subpopulations from infected lungs showed that neutrophil influx largely accounted for the predictive transcriptional signature. Automated imaging analysis, together with these gene expression and flow data, identified a chemokine-driven feedforward circuit involving proinflammatory neutrophils potently driven by poorly contained lethal viruses. Consistent with these data, attenuation, but not ablation, of the neutrophil-driven response increased survival without changing viral spread. These findings establish the primacy of damaging innate inflammation in at least some forms of influenza-induced lethality and provide a roadmap for the systematic dissection of infection-associated pathology.

A versatile gas interface for routine radiocarbon analysis with a gas ion source

In 2010 more than 600 radiocarbon samples were measured with the gas ion source at the MIni CArbon DAting System (MICADAS) at ETH Zurich and the number of measurements is rising quickly. While most samples contain less than 50 mu g C at present, the gas ion source is attractive as well for larger samples because the time-consuming graphitization is omitted. Additionally, modern samples are now measured down to 5 per-mill counting statistics in less than 30 min with the recently improved gas ion source. In the versatile gas handling system, a stepping-motor-driven syringe presses a mixture of helium and sample CO2 into the gas ion source, allowing continuous and stable measurements of different kinds of samples. CO2 can be provided in four different ways to the versatile gas interface. As a primary method. CO2 is delivered in glass or quartz ampoules. In this case, the CO2 is released in an automated ampoule cracker with 8 positions for individual samples. Secondly, OX-1 and blank gas in helium can be provided to the syringe by directly connecting gas bottles to the gas interface at the stage of the cracker. Thirdly, solid samples can be combusted in an elemental analyzer or in a thermo-optical OC/EC aerosol analyzer where the produced CO2 is transferred to the syringe via a zeolite trap for gas concentration. As a fourth method, CO2 is released from carbonates with phosphoric acid in septum-sealed vials and loaded onto the same trap used for the elemental analyzer. All four methods allow complete automation of the measurement, even though minor user input is presently still required. Details on the setup, versatility and applications of the gas handling system are given. (C) 2012 Elsevier B.V. All rights reserved.

Late cardiac sodium current can be assessed using automated patch-clamp

The cardiac late Na (+) current is generated by a small fraction of voltage-dependent Na (+) channels that undergo a conformational change to a burst-gating mode, with repeated openings and closures during the action potential (AP) plateau. Its magnitude can be augmented by inactivation-defective mutations, myocardial ischemia, or prolonged exposure to chemical compounds leading to drug-induced (di)-long QT syndrome, and results in an increased susceptibility to cardiac arrhythmias. Using CytoPatch™ 2 automated patch-clamp equipment, we performed whole-cell recordings in HEK293 cells stably expressing human Nav1.5, and measured the late Na (+) component as average current over the last 100 ms of 300 ms depolarizing pulses to -10 mV from a holding potential of -100 mV, with a repetition frequency of 0.33 Hz. Averaged values in different steady-state experimental conditions were further corrected by the subtraction of current average during the application of tetrodotoxin (TTX) 30 μM. We show that ranolazine at 10 and 30 μM in 3 min applications reduced the late Na (+) current to 75.0 ± 2.7% (mean ± SEM, n = 17) and 58.4 ± 3.5% ( n = 18) of initial levels, respectively, while a 5 min application of veratridine 1 μM resulted in a reversible current increase to 269.1 ± 16.1% ( n = 28) of initial values. Using fluctuation analysis, we observed that ranolazine 30 μM decreased mean open probability p from 0.6 to 0.38 without modifying the number of active channels n, while veratridine 1 μM increased n 2.5-fold without changing p. In human iPSC-derived cardiomyocytes, veratridine 1 μM reversibly increased APD90 2.12 ± 0.41-fold (mean ± SEM, n = 6). This effect is attributable to inactivation removal in Nav1.5 channels, since significant inhibitory effects on hERG current were detected at higher concentrations in hERG-expressing HEK293 cells, with a 28.9 ± 6.0% inhibition (mean ± SD, n = 10) with 50 μM veratridine.

3D morphometry using automated aortic segmentation in native MR angiography: an alternative to contrast enhanced MRA?

INTRODUCTION Native-MR angiography (N-MRA) is considered an imaging alternative to contrast enhanced MR angiography (CE-MRA) for patients with renal insufficiency. Lower intraluminal contrast in N-MRA often leads to failure of the segmentation process in commercial algorithms. This study introduces an in-house 3D model-based segmentation approach used to compare both sequences by automatic 3D lumen segmentation, allowing for evaluation of differences of aortic lumen diameters as well as differences in length comparing both acquisition techniques at every possible location. METHODS AND MATERIALS Sixteen healthy volunteers underwent 1.5-T-MR Angiography (MRA). For each volunteer, two different MR sequences were performed, CE-MRA: gradient echo Turbo FLASH sequence and N-MRA: respiratory-and-cardiac-gated, T2-weighted 3D SSFP. Datasets were segmented using a 3D model-based ellipse-fitting approach with a single seed point placed manually above the celiac trunk. The segmented volumes were manually cropped from left subclavian artery to celiac trunk to avoid error due to side branches. Diameters, volumes and centerline length were computed for intraindividual comparison. For statistical analysis the Wilcoxon-Signed-Ranked-Test was used. RESULTS Average centerline length obtained based on N-MRA was 239.0±23.4 mm compared to 238.6±23.5 mm for CE-MRA without significant difference (P=0.877). Average maximum diameter obtained based on N-MRA was 25.7±3.3 mm compared to 24.1±3.2 mm for CE-MRA (P<0.001). In agreement with the difference in diameters, volumes obtained based on N-MRA (100.1±35.4 cm(3)) were consistently and significantly larger compared to CE-MRA (89.2±30.0 cm(3)) (P<0.001). CONCLUSIONS 3D morphometry shows highly similar centerline lengths for N-MRA and CE-MRA, but systematically higher diameters and volumes for N-MRA.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Analysis of behavioral changes in dairy cows associated with claw horn lesions

Detecting lame cows is important in improving animal welfare. Automated tools are potentially useful to enable identification and monitoring of lame cows. The goals of this study were to evaluate the suitability of various physiological and behavioral parameters to automatically detect lameness in dairy cows housed in a cubicle barn. Lame cows suffering from a claw horn lesion (sole ulcer or white line disease) of one claw of the same hind limb (n=32; group L) and 10 nonlame healthy cows (group C) were included in this study. Lying and standing behavior at night by tridimensional accelerometers, weight distribution between hind limbs by the 4-scale weighing platform, feeding behavior at night by the nose band sensor, and heart activity by the Polar device (Polar Electro Oy, Kempele, Finland) were assessed. Either the entire data set or parts of the data collected over a 48-h period were used for statistical analysis, depending upon the parameter in question. The standing time at night over 12 h and the limb weight ratio (LWR) were significantly higher in group C as compared with group L, whereas the lying time at night over 12 h, the mean limb difference (△weight), and the standard deviation (SD) of the weight applied on the limb taking less weight were significantly lower in group C as compared with group L. No significant difference was noted between the groups for the parameters of heart activity and feeding behavior at night. The locomotion score of cows in group L was positively correlated with the lying time and △weight, whereas it was negatively correlated with LWR and SD. The highest sensitivity (0.97) for lameness detection was found for the parameter SD [specificity of 0.80 and an area under the curve (AUC) of 0.84]. The highest specificity (0.90) for lameness detection was present for Δweight (sensitivity=0.78; AUC=0.88) and LWR (sensitivity=0.81; AUC=0.87). The model considering the data of SD together with lying time at night was the best predictor of cows being lame, accounting for 40% of the variation in the likelihood of a cow being lame (sensitivity=0.94; specificity=0.80; AUC=0.86). In conclusion, the data derived from the 4-scale-weighing platform, either alone or combined with the lying time at night over 12 h, represent the most valuable parameters for automated identification of lame cows suffering from a claw horn lesion of one individual hind limb.

Dielectrophoretic approaches to sample preparation and analysis

Dielectrophoresis (DEP) has been used to manipulate cells in low-conductivity suspending media using AC electrical fields generated on micro-fabricated electrode arrays. This has created the possibility of performing automatically on a micro-scale more sophisticated cell processing than that currently requiring substantial laboratory equipment, reagent volumes, time, and human intervention. In this research the manipulation of aqueous droplets in an immiscible, low-permittivity suspending medium is described to complement previous work on dielectrophoretic cell manipulation. Such droplets can be used as carriers not only for air- and water-borne samples, contaminants, chemical reagents, viral and gene products, and cells, but also the reagents to process and characterize these samples. A long-term goal of this area of research is to perform chemical and biological assays on automated, micro-scaled devices at or near the point-of-care, which will increase the availability of modern medicine to people who do not have ready access to large medical institutions and decrease the cost and delays associated with that lack of access. In this research I present proofs-of-concept for droplet manipulation and droplet-based biochemical analysis using dielectrophoresis as the motive force. Proofs-of-concept developed for the first time in this research include: (1) showing droplet movement on a two-dimensional array of electrodes, (2) achieving controlled dielectric droplet injection, (3) fusing and reacting droplets, and (4) demonstrating a protein fluorescence assay using micro-droplets. ^

Hemodynamic changes associated with manual and automated lateral rotation in mechanically ventilated intensive care patients

Objective: To investigate hemodynamic responses to lateral rotation. ^ Design: Time-series within a randomized controlled trial pilot study. ^ Setting: A medical intensive care unit (ICU) and a medical-surgical ICU in two tertiary care hospitals. ^ Patients: Adult patients receiving mechanical ventilation. ^ Interventions: Two-hourly manual or continuous automated lateral rotation. ^ Measurements and Main Results: Heart rate (HR) and arterial pressure were sampled every 6 seconds for > 24 hours, and pulse pressure (PP) was computed. Turn data were obtained from a turning flow sheet (manual turn) or with an angle sensor (automated turn). Within-subject ensemble averages were computed for HR, mean arterial pressure (MAP), and PP across turns. Sixteen patients were randomized to either the manual (n = 8) or automated (n = 8) turn. Three patients did not complete the study due to hemodynamic instability, bed malfunction or extubation, leaving 13 patients (n = 6 manual turn and n = 7 automated turn) for analysis. Seven patients (54%) had an arterial line. Changes in hemodynamic variables were statistically significant increases ( p < .05), but few changes were clinically important, defined as ≥ 10 bpm (HR) or ≥ 10 mmHg (MAP and PP), and were observed only in the manual-turn group. All manual-turn patients had prolonged recovery to baseline in HR, MAP and PP of up to 45 minutes (p ≤ .05). No significant turning-related periodicities were found for HR, MAP, or PP. Cross-correlations between variables showed variable lead-lag relations in both groups. A statistically, but not clinically, significant increase in HR of 3 bpm was found for the manual-turn group in the back compared with the right lateral position ( F = 14.37, df = 1, 11, p = .003). ^ Conclusions: Mechanically ventilated critically ill patients experience modest hemodynamic changes with manual lateral rotation. A clinically inconsequential increase in HR, MAP, and PP may persist for up to 45 minutes. Automated lateral rotation has negligible hemodynamic effects. ^

Automated soldier medical readiness: Prevalence of " medically not ready" using data from the electronic soldier readiness processing system (ESRP)

Identifying accurate numbers of soldiers determined to be medically not ready after completing soldier readiness processing may help inform Army leadership about ongoing pressures on the military involved in long conflict with regular deployment. In Army soldiers screened using the SRP checklist for deployment, what is the prevalence of soldiers determined to be medically not ready? Study group. 15,289 soldiers screened at all 25 Army deployment platform sites with the eSRP checklist over a 4-month period (June 20, 2009 to October 20, 2009). The data included for analysis included age, rank, component, gender and final deployment medical readiness status from MEDPROS database. Methods.^ This information was compiled and univariate analysis using chi-square was conducted for each of the key variables by medical readiness status. Results. Descriptive epidemiology Of the total sample 1548 (9.7%) were female and 14319 (90.2%) were male. Enlisted soldiers made up 13,543 (88.6%) of the sample and officers 1,746 (11.4%). In the sample, 1533 (10.0%) were soldiers over the age of 40 and 13756 (90.0%) were age 18-40. Reserve, National Guard and Active Duty made up 1,931 (12.6%), 2,942 (19.2%) and 10,416 (68.1%) respectively. Univariate analysis. Overall 1226 (8.0%) of the soldiers screened were determined to be medically not ready for deployment. Biggest predictive factor was female gender OR (2.8; 2.57-3.28) p<0.001. Followed by enlisted rank OR (2.01; 1.60-2.53) p<0.001. Reserve component OR (1.33; 1.16-1.53) p<0.001 and Guard OR (0.37; 0.30-0.46) p<0.001. For age > 40 demonstrated OR (1.2; 1.09-1.50) p<0.003. Overall the results underscore there may be key demographic groups relating to medical readiness that can be targeted with programs and funding to improve overall military medical readiness.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.