Estimating Diagnostic Test Accuracies for Brachyspira hyodysenteriae Accounting for the Complexities of Population Structure in Food Animals

For swine dysentery, which is caused by Brachyspira hyodysenteriae infection and is an economically important disease in intensive pig production systems worldwide, a perfect or error-free diagnostic test ("gold standard") is not available. In the absence of a gold standard, Bayesian latent class modelling is a well-established methodology for robust diagnostic test evaluation. In contrast to risk factor studies in food animals, where adjustment for within group correlations is both usual and required for good statistical practice, diagnostic test evaluation studies rarely take such clustering aspects into account, which can result in misleading results. The aim of the present study was to estimate test accuracies of a PCR originally designed for use as a confirmatory test, displaying a high diagnostic specificity, and cultural examination for B. hyodysenteriae. This estimation was conducted based on results of 239 samples from 103 herds originating from routine diagnostic sampling. Using Bayesian latent class modelling comprising of a hierarchical beta-binomial approach (which allowed prevalence across individual herds to vary as herd level random effect), robust estimates for the sensitivities of PCR and culture, as well as for the specificity of PCR, were obtained. The estimated diagnostic sensitivity of PCR (95% CI) and culture were 73.2% (62.3; 82.9) and 88.6% (74.9; 99.3), respectively. The estimated specificity of the PCR was 96.2% (90.9; 99.8). For test evaluation studies, a Bayesian latent class approach is well suited for addressing the considerable complexities of population structure in food animals.

Statistical shape modeling of pathological scoliotic vertebrae: A comparative analysis

Statistical shape models (SSMs) have been used widely as a basis for segmenting and interpreting complex anatomical structures. The robustness of these models are sensitive to the registration procedures, i.e., establishment of a dense correspondence across a training data set. In this work, two SSMs based on the same training data set of scoliotic vertebrae, and registration procedures were compared. The first model was constructed based on the original binary masks without applying any image pre- and post-processing, and the second was obtained by means of a feature preserving smoothing method applied to the original training data set, followed by a standard rasterization algorithm. The accuracies of the correspondences were assessed quantitatively by means of the maximum of the mean minimum distance (MMMD) and Hausdorf distance (H(D)). Anatomical validity of the models were quantified by means of three different criteria, i.e., compactness, specificity, and model generalization ability. The objective of this study was to compare quasi-identical models based on standard metrics. Preliminary results suggest that the MMMD distance and eigenvalues are not sensitive metrics for evaluating the performance and robustness of SSMs.

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.

Surgical planning tool for robotically assisted hearing aid implantation

PURPOSE : For the facilitation of minimally invasive robotically performed direct cochlea access (DCA) procedure, a surgical planning tool which enables the surgeon to define landmarks for patient-to-image registration, identify the necessary anatomical structures and define a safe DCA trajectory using patient image data (typically computed tomography (CT) or cone beam CT) is required. To this end, a dedicated end-to-end software planning system for the planning of DCA procedures that addresses current deficiencies has been developed. METHODS :    Efficient and robust anatomical segmentation is achieved through the implementation of semiautomatic algorithms; high-accuracy patient-to-image registration is achieved via an automated model-based fiducial detection algorithm and functionality for the interactive definition of a safe drilling trajectory based on case-specific drill positioning uncertainty calculations was developed. RESULTS :    The accuracy and safety of the presented software tool were validated during the conduction of eight DCA procedures performed on cadaver heads. The plan for each ear was completed in less than 20 min, and no damage to vital structures occurred during the procedures. The integrated fiducial detection functionality enabled final positioning accuracies of [Formula: see text] mm. CONCLUSIONS :    Results of this study demonstrated that the proposed software system could aid in the safe planning of a DCA tunnel within an acceptable time.

Diagnosis of endometriosis with imaging: a review

Endometriosis corresponds to ectopic endometrial glands and stroma outside the uterine cavity. Clinical symptoms include dysmenorrhoea, dyspareunia, infertility, painful defecation or cyclic urinary symptoms. Pelvic ultrasound is the primary imaging modality to identify and differentiate locations to the ovary (endometriomas) and the bladder wall. Characteristic sonographic features of endometriomas are diffuse low-level internal echos, multilocularity and hyperchoic foci in the wall. Differential diagnoses include corpus luteum, teratoma, cystadenoma, fibroma, tubo-ovarian abscess and carcinoma. Repeated ultrasound is highly recommended for unilocular cysts with low-level internal echoes to differentiate functional corpus luteum from endometriomas. Posterior locations of endometriosis include utero-sacral ligaments, torus uterinus, vagina and recto-sigmoid. Sonographic and MRI features are discussed for each location. Although ultrasound is able to diagnose most locations, its limited sensitivity for posterior lesions does not allow management decision in all patients. MRI has shown high accuracies for both anterior and posterior endometriosis and enables complete lesion mapping before surgery. Posterior locations demonstrate abnormal T2-hypointense, nodules with occasional T1-hyperintense spots and are easier to identify when peristaltic inhibitors and intravenous contrast media are used. Anterior locations benefit from the possibility of MRI urography sequences within the same examination. Rare locations and possible transformation into malignancy are discussed.

Combined ultrasmall superparamagnetic particles of iron oxide-enhanced and diffusion-weighted magnetic resonance imaging reliably detect pelvic lymph node metastases in normal-sized nodes of bladder and prostate cancer patients

BACKGROUND: Lymph node staging of bladder or prostate cancer using conventional imaging is limited. Newer approaches such as ultrasmall superparamagnetic particles of iron oxide (USPIO) and diffusion-weighted magnetic resonance imaging (DW-MRI) have inconsistent diagnostic accuracy and are difficult to interpret. OBJECTIVE: To assess whether combined USPIO and DW-MRI (USPIO-DW-MRI) improves staging of normal-sized lymph nodes in bladder and/or prostate cancer patients. DESIGN, SETTING, AND PARTICIPANTS: Twenty-one consecutive patients with bladder and/or prostate cancer were enrolled between May and October 2008. One patient was excluded secondary to bone metastases detected on DW-MRI with subsequent abstention from surgery. INTERVENTION: Patients preoperatively underwent 3-T MRI before and after administration of lymphotropic USPIO using conventional MRI sequences combined with DW-MRI. Surgery consisted of extended pelvic lymphadenectomy and resection of primary tumors. MEASUREMENTS: Diagnostic accuracies of the new combined USPIO-DW-MRI approach compared with the "classic" reading method evaluating USPIO images without and with DW-MRI versus histopathology were evaluated. Duration of the two reading methods was noted for each patient. RESULTS AND LIMITATIONS: Diagnostic accuracy (90% per patient or per pelvic side) was comparable for the classic and the USPIO-DW-MRI reading method, while time of analysis with 80 min (range 45-180 min) for the classic and 13 min (range 5-90 min) for the USPIO-DW-MRI method was significantly shorter (p<0.0001). Interobserver agreement (three blinded readers) was high with a kappa value of 0.75 and 0.84, respectively. Histopathological analysis showed metastases in 26 of 802 analyzed lymph nodes (3.2%). Of these, 24 nodes (92%) were correctly diagnosed as positive on USPIO-DW-MRI. In two patients, one micrometastasis each (1.0x0.2 mm; 0.7x0.4 mm) was missed in all imaging studies. CONCLUSIONS: USPIO-DW-MRI is a fast and accurate method for detecting pelvic lymph node metastases, even in normal-sized nodes of bladder or prostate cancer patients.

Verbal instructions and top-down saccade control.

Few studies have addressed the interaction between instruction content and saccadic eye movement control. To assess the impact of instructions on top-down control, we instructed 20 healthy volunteers to deliberately delay saccade triggering, to make inaccurate saccades or to redirect saccades--i.e. to glimpse towards and then immediately opposite to the target. Regular pro- and antisaccade tasks were used for comparison. Bottom-up visual input remained unchanged and was a gap paradigm for all instructions. In the inaccuracy and delay tasks, both latencies and accuracies were detrimentally impaired by either type of instruction and the variability of latency and accuracy was increased. The intersaccadic interval (ISI) required to correct erroneous antisaccades was shorter than the ISI for instructed direction changes in the redirection task. The word-by-word instruction content interferes with top-down saccade control. Top-down control is a time consuming process, which may override bottom-up processing only during a limited time period. It is questionable whether parallel processing is possible in top-down control, since the long ISI for instructed direction changes suggests sequential planning.

Evaluation of an IgE ELISA with Culicoides spp. extracts and recombinant salivary antigens for diagnosis of insect bite hypersensitivity in Warmblood horses

Insect bite hypersensitivity (IBH) in horses represents an immunoglobulin E (IgE)-mediated hypersensitivity to salivary antigens from biting midges (Culicoides spp.). The aim of this study was to evaluate and compare the performances of IgE ELISAs using recombinant Culicoides spp. Obsoletus group salivary gland antigens or crude whole body extracts ('ObsWBE'), C. nubeculosus recombinant proteins (Culn1, 3, 4, 5, 7, 8 and 10) and Obsoletus group recombinant proteins (Culo1 and 2). IgE levels were measured in plasma of 343 Warmblood horses classified as IBH-affected (n=167) and IBH-unaffected (n=176) according to the owners' descriptions. IBH-affected horses were subdivided based on the severity of their clinical signs at sampling and whether or not their IBH history was considered to be classical. The accuracies of the tests increased when clinical signs at sampling were more pronounced or when the IBH history could be considered as classical. A combination of IgE levels against the three best performing Culicoides spp. recombinant proteins (Culn4, Culo1 and Culo2) and ObsWBE resulted in the best performing test. When IBH-affected horses showing a classical history of the disease and severe clinical signs were compared with IBH-unaffected horses, the Youden's index at the optimal cut-off for the three tests in combination was 0.67. This optimal cut-off had a sensitivity of 70%, a specificity of 97% and a total accuracy of 92%. The performance of the IgE ELISA was affected by the severity of IBH clinical signs at sampling and was improved when IgE levels against several recombinant proteins were combined.

Precise Orbit Determination of Low Earth Satellites at AIUB using GPS and SLR data

An ever increasing number of low Earth orbiting (LEO) satellites is, or will be, equipped with retro-reflectors for Satellite Laser Ranging (SLR) and on-board receivers to collect observations from Global Navigation Satellite Systems (GNSS) such as the Global Positioning Sys- tem (GPS) and the Russian GLONASS and the European Galileo systems in the future. At the Astronomical Insti- tute of the University of Bern (AIUB) LEO precise or- bit determination (POD) using either GPS or SLR data is performed for a wide range of applications for satellites at different altitudes. For this purpose the classical numeri- cal integration techniques, as also used for dynamic orbit determination of satellites at high altitudes, are extended by pseudo-stochastic orbit modeling techniques to effi- ciently cope with potential force model deficiencies for satellites at low altitudes. Accuracies of better than 2 cm may be achieved by pseudo-stochastic orbit modeling for satellites at very low altitudes such as for the GPS-based POD of the Gravity field and steady-state Ocean Circula- tion Explorer (GOCE).

Cone-beam computed tomography-guided stereotactic liver punctures: a phantom study

PURPOSE Images from computed tomography (CT), combined with navigation systems, improve the outcomes of local thermal therapies that are dependent on accurate probe placement. Although the usage of CT is desired, its availability for time-consuming radiological interventions is limited. Alternatively, three-dimensional images from C-arm cone-beam CT (CBCT) can be used. The goal of this study was to evaluate the accuracy of navigated CBCT-guided needle punctures, controlled with CT scans. METHODS Five series of five navigated punctures were performed on a nonrigid phantom using a liver specific navigation system and CBCT volumetric dataset for planning and navigation. To mimic targets, five titanium screws were fixed to the phantom. Target positioning accuracy (TPECBCT) was computed from control CT scans and divided into lateral and longitudinal components. Additionally, CBCT-CT guidance accuracy was deducted by performing CBCT-to-CT image coregistration and measuring TPECBCT-CT from fused datasets. Image coregistration was evaluated using fiducial registration error (FRECBCT-CT) and target registration error (TRECBCT-CT). RESULTS Positioning accuracies in lateral directions pertaining to CBCT (TPECBCT = 2.1 ± 1.0 mm) were found to be better to those achieved from previous study using CT (TPECT = 2.3 ± 1.3 mm). Image coregistration error was 0.3 ± 0.1 mm, resulting in an average TRE of 2.1 ± 0.7 mm (N = 5 targets) and average Euclidean TPECBCT-CT of 3.1 ± 1.3 mm. CONCLUSIONS Stereotactic needle punctures might be planned and performed on volumetric CBCT images and controlled with multidetector CT with positioning accuracy higher or similar to those performed using CT scanners.

Assessment of patient setup errors in IGRT in combination with a six degrees of freedom couch

PURPOSE The range of patient setup errors in six dimensions detected in clinical routine for cranial as well as for extracranial treatments, were analyzed while performing linear accelerator based stereotactic treatments with frameless patient setup systems. Additionally, the need for re-verification of the patient setup for situations where couch rotations are involved was analyzed for patients treated in the cranial region. METHODS AND MATERIALS A total of 2185 initial (i.e. after pre-positioning the patient with the infrared system but before image guidance) patient setup errors (1705 in the cranial and 480 in the extracranial region) obtained by using ExacTrac (BrainLAB AG, Feldkirchen, Germany) were analyzed. Additionally, the patient setup errors as a function of the couch rotation angle were obtained by analyzing 242 setup errors in the cranial region. Before the couch was rotated, the patient setup error was corrected at couch rotation angle 0° with the aid of image guidance and the six degrees of freedom (6DoF) couch. For both situations attainment rates for two different tolerances (tolerance A: ± 0.5mm, ± 0.5°; tolerance B: ± 1.0 mm, ± 1.0°) were calculated. RESULTS The mean (± one standard deviation) initial patient setup errors for the cranial cases were -0.24 ± 1.21°, -0.23 ± 0.91° and -0.03 ± 1.07° for the pitch, roll and couch rotation axes and 0.10 ± 1.17 mm, 0.10 ± 1.62 mm and 0.11 ± 1.29 mm for the lateral, longitudinal and vertical axes, respectively. Attainment rate (all six axes simultaneously) for tolerance A was 0.6% and 13.1% for tolerance B, respectively. For the extracranial cases the corresponding values were -0.21 ± 0.95°, -0.05 ± 1.08° and -0.14 ± 1.02° for the pitch, roll and couch rotation axes and 0.15 ± 1.77 mm, 0.62 ± 1.94 mm and -0.40 ± 2.15 mm for the lateral, longitudinal and vertical axes. Attainment rate (all six axes simultaneously) for tolerance A was 0.0% and 3.1% for tolerance B, respectively. After initial setup correction and rotation of the couch to treatment position a re-correction has to be performed in 77.4% of all cases to fulfill tolerance A and in 15.6% of all cases to fulfill tolerance B. CONCLUSION The analysis of the data shows that all six axes of a 6DoF couch are used extensively for patient setup in clinical routine. In order to fulfill high patient setup accuracies (e.g. for stereotactic treatments), a 6DoF couch is recommended. Moreover, re-verification of the patient setup after rotating the couch is required in clinical routine.

SNPs for parentage testing and traceability in globally diverse breeds of sheep

DNA-based parentage determination accelerates genetic improvement in sheep by increasing pedigree accuracy. Single nucleotide polymorphism (SNP) markers can be used for determining parentage and to provide unique molecular identifiers for tracing sheep products to their source. However, the utility of a particular "parentage SNP" varies by breed depending on its minor allele frequency (MAF) and its sequence context. Our aims were to identify parentage SNPs with exceptional qualities for use in globally diverse breeds and to develop a subset for use in North American sheep. Starting with genotypes from 2,915 sheep and 74 breed groups provided by the International Sheep Genomics Consortium (ISGC), we analyzed 47,693 autosomal SNPs by multiple criteria and selected 163 with desirable properties for parentage testing. On average, each of the 163 SNPs was highly informative (MAF≥0.3) in 48±5 breed groups. Nearby polymorphisms that could otherwise confound genetic testing were identified by whole genome and Sanger sequencing of 166 sheep from 54 breed groups. A genetic test with 109 of the 163 parentage SNPs was developed for matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry. The scoring rates and accuracies for these 109 SNPs were greater than 99% in a panel of North American sheep. In a blinded set of 96 families (sire, dam, and non-identical twin lambs), each parent of every lamb was identified without using the other parent's genotype. In 74 ISGC breed groups, the median estimates for probability of a coincidental match between two animals (PI), and the fraction of potential adults excluded from parentage (PE) were 1.1×10(-39) and 0.999987, respectively, for the 109 SNPs combined. The availability of a well-characterized set of 163 parentage SNPs facilitates the development of high-throughput genetic technologies for implementing accurate and economical parentage testing and traceability in many of the world's sheep breeds.

Improved Space Object Orbit Determination Using CMOS Detectors

CMOS-sensors, or in general Active Pixel Sensors (APS), are rapidly replacing CCDs in the consumer camera market. Due to significant technological advances during the past years these devices start to compete with CCDs also for demanding scientific imaging applications, in particular in the astronomy community. CMOS detectors offer a series of inherent advantages compared to CCDs, due to the structure of their basic pixel cells, which each contains their own amplifier and readout electronics. The most prominent advantages for space object observations are the extremely fast and flexible readout capabilities, feasibility for electronic shuttering and precise epoch registration,and the potential to perform image processing operations on-chip and in real-time. Here, the major challenges and design drivers for ground-based and space-based optical observation strategies for objects in Earth orbit have been analyzed. CMOS detector characteristics were critically evaluated and compared with the established CCD technology, especially with respect to the above mentioned observations. Finally, we simulated several observation scenarios for ground- and space-based sensor by assuming different observation and sensor properties. We will introduce the analyzed end-to-end simulations of the ground- and spacebased strategies in order to investigate the orbit determination accuracy and its sensitivity which may result from different values for the frame-rate, pixel scale, astrometric and epoch registration accuracies. Two cases were simulated, a survey assuming a ground-based sensor to observe objects in LEO for surveillance applications, and a statistical survey with a space-based sensor orbiting in LEO observing small-size debris in LEO. The ground-based LEO survey uses a dynamical fence close to the Earth shadow a few hours after sunset. For the space-based scenario a sensor in a sun-synchronous LEO orbit, always pointing in the anti-sun direction to achieve optimum illumination conditions for small LEO debris was simulated.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.