Hide-and-seek in vegetation: time-to-detection is an efficient design for estimating detectability and occurrence

Ecology and conservation require reliable data on the occurrence of animals and plants. A major source of bias is imperfect detection, which, however, can be corrected for by estimation of detectability. In traditional occupancy models, this requires repeat or multi-observer surveys. Recently, time-to-detection models have been developed as a cost-effective alternative, which requires no repeat surveys and hence costs could be halved. We compared the efficiency and reliability of time-to-detection and traditional occupancy models under varying survey effort. Two observers independently searched for 17 plant species in 44100m(2) Swiss grassland quadrats and recorded the time-to-detection for each species, enabling detectability to be estimated with both time-to-detection and traditional occupancy models. In addition, we gauged the relative influence on detectability of species, observer, plant height and two measures of abundance (cover and frequency). Estimates of detectability and occupancy under both models were very similar. Rare species were more likely to be overlooked; detectability was strongly affected by abundance. As a measure of abundance, frequency outperformed cover in its predictive power. The two observers differed significantly in their detection ability. Time-to-detection models were as accurate as traditional occupancy models, but their data easier to obtain; thus they provide a cost-effective alternative to traditional occupancy models for detection-corrected estimation of occurrence.

Automated detection of the osseous acetabular rim using three-dimensional models of the pelvis

An automated algorithm for detection of the acetabular rim was developed. Accuracy of the algorithm was validated in a sawbone study and compared against manually conducted digitization attempts, which were established as the ground truth. The latter proved to be reliable and reproducible, demonstrated by almost perfect intra- and interobserver reliability. Validation of the automated algorithm showed no significant difference compared to the manually acquired data in terms of detected version and inclination. Automated detection of the acetabular rim contour and the spatial orientation of the acetabular opening plane can be accurately achieved with this algorithm.

Evaluation of a new commercial enzyme-linked immunosorbent assay for the detection of porcine antibodies against Trichinella spp

Trichinellosis is a zoonotic disease that is caused by the nematode Trichinella spp. Both European Union regulations and guidelines from the World Organization for Animal Health foresee the possibility of conducting serological surveillance for Trichinella spp. A newly developed commercial enzyme-linked immunosorbent assay (ELISA) was evaluated against 2 existing diagnostic techniques: an in-house ELISA and an in-house Western blot. A total of 875 Trichinella larva-negative samples of pigs and 93 Trichinella larva-positive samples of both naturally and experimentally infected pigs were included in the study. Bayesian modeling techniques were used to correct for the absence of a perfect reference test. The sensitivity and specificity of the commercial ELISA was 97.1-97.8% and 99.5-99.8%, respectively. Sensitivity analysis demonstrated high stability in the models. In a serological surveillance system, ELISA-positive samples should be tested by a confirmatory test. The Western blot is a suitable test for this purpose. With the use of the results of the models, the sensitivity and specificity of a test protocol in both ELISA and Western blot were 95.9% and 99.9%, respectively. The high sensitivity and specificity were achieved with a lower limit of detection than that of the routine artificial digestion test, suggesting that serological surveillance is a valuable alternative in surveillance for Trichinella spp. in pig production.

Validation of a Western Blot for the detection of anti-Trichinella spp. antibodies in domestic pigs

Trichinellosis is a zoonotic disease in humans caused by Trichinella spp. According to international regulations and guidelines, serological surveillance can be used to demonstrate the absence of Trichinella spp. in a defined domestic pig population. Most enzyme-linked immunosorbent assay (ELISA) tests presently available do not yield 100% specificity, and therefore, a complementary test is needed to confirm the diagnosis of any initial ELISA seropositivity. The goal of the present study was to evaluate the sensitivity and specificity of a Western Blot assay based on somatic Trichinella spiralis muscle stage (L1) antigen using Bayesian modeling techniques. A total of 295 meat juice and serum samples from pigs negative for Trichinella larvae by artificial digestion, including 74 potentially cross-reactive sera of pigs with other nematode infections, and 93 meat juice samples from pigs infected with Trichinella larvae were included in the study. The diagnostic sensitivity and specificity of the Western Blot were ranged from 95.8% to 96.0% and from 99.5% to 99.6%, respectively. A sensitivity analysis showed that the model outcomes were hardly influenced by changes in the prior distributions, providing a high confidence in the outcomes of the models. This validation study demonstrated that the Western Blot is a suitable method to confirm samples that reacted positively in an initial ELISA.

Theoretical models of planetary system formation: mass vs. semi-major axis

Context. Planet formation models have been developed during the past years to try to reproduce what has been observed of both the solar system and the extrasolar planets. Some of these models have partially succeeded, but they focus on massive planets and, for the sake of simplicity, exclude planets belonging to planetary systems. However, more and more planets are now found in planetary systems. This tendency, which is a result of radial velocity, transit, and direct imaging surveys, seems to be even more pronounced for low-mass planets. These new observations require improving planet formation models, including new physics, and considering the formation of systems. Aims: In a recent series of papers, we have presented some improvements in the physics of our models, focussing in particular on the internal structure of forming planets, and on the computation of the excitation state of planetesimals and their resulting accretion rate. In this paper, we focus on the concurrent effect of the formation of more than one planet in the same protoplanetary disc and show the effect, in terms of architecture and composition of this multiplicity. Methods: We used an N-body calculation including collision detection to compute the orbital evolution of a planetary system. Moreover, we describe the effect of competition for accretion of gas and solids, as well as the effect of gravitational interactions between planets. Results: We show that the masses and semi-major axes of planets are modified by both the effect of competition and gravitational interactions. We also present the effect of the assumed number of forming planets in the same system (a free parameter of the model), as well as the effect of the inclination and eccentricity damping. We find that the fraction of ejected planets increases from nearly 0 to 8% as we change the number of embryos we seed the system with from 2 to 20 planetary embryos. Moreover, our calculations show that, when considering planets more massive than ~5 M⊕, simulations with 10 or 20 planetary embryos statistically give the same results in terms of mass function and period distribution.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Debris discs around M stars: non-existence versus non-detection

Motivated by the reported dearth of debris discs around M stars, we use survival models to study the occurrence of planetesimal discs around them. These survival models describe a planetesimal disc with a small number of parameters, determine if it may survive a series of dynamical processes and compute the associated infrared excess. For the Wide-field Infrared Survey Explorer (WISE) satellite, we demonstrate that the dearth of debris discs around M stars may be attributed to the small semimajor axes generally probed if either: (1) the dust grains behave like blackbodies emitting at a peak wavelength coincident with the observed one; (2) or the grains are hotter than predicted by their blackbody temperatures and emit at peak wavelengths that are shorter than the observed one. At these small distances from the M star, planetesimals are unlikely to survive or persist for time-scales of 300 Myr or longer if the disc is too massive. Conversely, our survival models allow for the existence of a large population of low-mass debris discs that are too faint to be detected with current instruments. We gain further confidence in our interpretation by demonstrating the ability to compute infrared excesses for Sun-like stars that are broadly consistent with reported values in the literature. However, our interpretation becomes less clear and large infrared excesses are allowed if only one of these scenarios holds: (3) the dust grains are hotter than blackbody and predominantly emit at the observed wavelength; (4) or are blackbody in nature and emit at peak wavelengths longer than the observed one. Both scenarios imply that the parent planetesimals reside at larger distances from the star than inferred if the dust grains behaved like blackbodies. In all scenarios, we show that the infrared excesses detected at 22 μm (via WISE) and 70 μm (via Spitzer) from AU Mic are easily reconciled with its young age (12 Myr). Conversely, the existence of the old debris disc (2–8 Gyr) from GJ 581 is due to the large semimajor axes probed by the Herschel PACS instrument. We elucidate the conditions under which stellar wind drag may be neglected when considering dust populations around M stars. The WISE satellite should be capable of detecting debris discs around young M stars with ages ∼10 Myr.

Image-based vs. mesh-based statistical appearance models of the human femur: Implications for finite element simulations.

Statistical appearance models have recently been introduced in bone mechanics to investigate bone geometry and mechanical properties in population studies. The establishment of accurate anatomical correspondences is a critical aspect for the construction of reliable models. Depending on the representation of a bone as an image or a mesh, correspondences are detected using image registration or mesh morphing. The objective of this study was to compare image-based and mesh-based statistical appearance models of the femur for finite element (FE) simulations. To this aim, (i) we compared correspondence detection methods on bone surface and in bone volume; (ii) we created an image-based and a mesh-based statistical appearance models from 130 images, which we validated using compactness, representation and generalization, and we analyzed the FE results on 50 recreated bones vs. original bones; (iii) we created 1000 new instances, and we compared the quality of the FE meshes. Results showed that the image-based approach was more accurate in volume correspondence detection and quality of FE meshes, whereas the mesh-based approach was more accurate for surface correspondence detection and model compactness. Based on our results, we recommend the use of image-based statistical appearance models for FE simulations of the femur.

Identification of gunshots to the head by detection of RNA in backspatter primarily expressed in brain tissue

Traces of backspatter recovered from the inside of the barrel of a gun that was used to deliver suicidal or homicidal contact shots may be a source of valuable forensic evidence and first systematic investigations of the persistence of victim DNA from inside firearms have been presented. The aim of the present study was to include victim RNA in such analyses to determine the origin of tissues in addition and parallel to standard DNA profiling for forensic identification purposes. In a first step, suitable mRNA (C1orf61) and micro-RNAs (miR-124a and miR-124*) that are primarily expressed in brain tissue were selected from potential candidates and confirmed using quantitative PCR (qPCR). Secondly, a co-extraction procedure for RNA and DNA was established and brain differentiability of the selected RNAs was demonstrated via qPCR using samples from experimental shots at ballistic models. In a third step, this procedure was successfully applied to analyse samples from real casework comprising eight cases of suicidal contact shots. In this pilot study, we are first to report the possibility of co-extracting mRNA, miRNA and DNA from ballistic trace samples collected from the inside of firearms and we demonstrate that RNA and DNA based analyses can be performed in parallel to produce informative and highly complementary evidence.

Improved bioassay for the detection of porcine tumor necrosis factor using a homologous cell line: PK(15)

Close similarities of various physiological parameters makes the pig one of the preferred animal models for the study of human diseases, especially those involving the cardiovascular system. Unfortunately, the use of pig models to study diseases such as viral hemorrhagic fevers and endotoxic shock syndrome have been hampered by the lack of the necessary immunological tools to measure important immunoregulatory cytokines such as tumor necrosis factor (TNF). Here we describe a TNF-bioassay which is based on the porcine kidney cell line PK(15). Compared to the widely used murine fibroblastoid cell line L929, the PK(15) cell line displays a 100-1000-fold higher sensitivity for porcine TNF-alpha, a higher sensitivity for human TNF-alpha, and a slightly lower sensitivity for murine TNF-alpha. Using a PK(15) bioassay we can detect recombinant TNF-alpha as well as cytotoxic activity in the supernatants of lipopolysaccharide (LPS)-activated porcine monocytes at high dilutions. This suggests that the sensitivity of the test should permit the detection of TNF in biological specimens such as pig serum.

Detection and functional portrayal of a novel class of dihydrotestosterone derived selective progesterone receptor modulators (SPRM).

In early pregnancy, abortion can be induced by blocking the actions of progesterone receptors (PR). However, the PR antagonist, mifepristone (RU38486), is rather unselective in clinical use because it also cross-reacts with other nuclear receptors. Since the ligand-binding domain of human progesterone receptor (hPR) and androgen receptor (hAR) share 54% identity, we hypothesized that derivatives of dihydrotestosterone (DHT), the cognate ligand for hAR, might also regulate the hPR. Compounds designed and synthesized in our laboratory were investigated for their affinities for hPRB, hAR, glucocorticoid receptor (hGRα) and mineralocorticoid receptor (hMR), using whole cell receptor competitive binding assays. Agonistic and antagonistic activities were characterized by reporter assays. Nuclear translocation was monitored using cherry-hPRB and GFP-hAR chimeric receptors. Cytostatic properties and apoptosis were tested on breast cancer cells (MCF7, T-47D). One compound presented a favorable profile with an apparent neutral hPRB antagonistic function, a selective cherry-hPRB nuclear translocation and a cytostatic effect. 3D models of human PR and AR with this ligand were constructed to investigate the molecular basis of selectivity. Our data suggest that these novel DHT-derivatives provide suitable templates for the development of new selective steroidal hPR antagonists.

Detecting outlying studies in meta-regression models using a forward search algorithm.

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.