Improving the Reliability of Scale-Free Image Morphometrics in Applications with Minimally Restrained Livestock Using Projective Geometry and Unsupervised Machine Learning.

Advances in neural networks have garnered growing interest in applications of machine vision in livestock management, but simpler landmark-based approaches suitable for small, early stage exploratory studies still represent a critical stepping stone towards these more sophisticated analyses. While such approaches are well-validated for calibrated images, the practical limitations of such imaging systems restrict their applicability in working farm environments. The aim of this study was to validate novel algorithmic approaches to improving the reliability of scale-free image biometrics acquired from uncalibrated images of minimally restrained livestock. Using a database of 551 facial images acquired from 108 dairy cows, we demonstrate that, using a simple geometric projection-based approach to metric extraction, a priori knowledge may be leveraged to produce more intuitive and reliable morphometric measurements than conventional informationally complete Euclidean distance matrix analysis. Where uncontrolled variations in image annotation, camera position, and animal pose could not be fully controlled through the design of morphometrics, we further demonstrate how modern unsupervised machine learning tools may be used to leverage the systematic error structures created by such lurking variables in order to generate bias correction terms that may subsequently be used to improve the reliability of downstream statistical analyses and dimension reduction.

Disseminated Intravascular Coagulation Score Predicts Mortality in Patients with Liver Disease and Low Fibrinogen Level.

BACKGROUND: Alongside its original diagnostic intention, the International Society on Thrombosis and Haemostasis' (ISTH) disseminated intravascular coagulation (DIC) score predicts mortality in various patient groups. OBJECTIVES: We investigated whether coagulopathy quantified by the DIC score can predict 30-day mortality in patients with liver disease and low fibrinogen levels. METHODS: We retrospectively analyzed all patients admitted to the Vienna General Hospital between 2003 and 2014 with a fibrinogen level of <150 mg/dL, a history of liver disease, and ≥2 pathological DIC parameters. We used a Cox regression and receiver operating characteristic analysis to assess the predictive value of the ISTH DIC score in its original (DIC-2001) and revised form (DIC-2018). RESULTS: A total of 1,333 patients were screened, and 388 of these patients (38% female, median age: 58 years, interquartile range: 48-66 years) were analyzed. The DIC-2001 (hazard ratio [HR]: 2.08, 95% confidence interval [CI]: 1.78-2.59, p < 0.001) and DIC-2018 (HR: 1.73, 95% CI: 1.51-2.05, p < 0.001) predicted 30-day mortality. The results remained robust in several sensitivity analyses. CONCLUSION: The ISTH DIC-2001 and DIC-2018 scores predicted 30-day mortality in patients with liver disease and low fibrinogen levels. The DIC score deserves further investigation in this population as it likely reflects different dimensions of the underlying disease.

Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities from Different Countries.

The present study investigates U.S. Department of Agriculture inspection records in the Agricultural Quarantine Activity System database to estimate the probability of quarantine pests on propagative plant materials imported from various countries of origin and to develop a methodology ranking the risk of country-commodity combinations based on quarantine pest interceptions. Data collected from October 2014 to January 2016 were used for developing predictive models and validation study. A generalized linear model with Bayesian inference and a generalized linear mixed effects model were used to compare the interception rates of quarantine pests on different country-commodity combinations. Prediction ability of generalized linear mixed effects models was greater than that of generalized linear models. The estimated pest interception probability and confidence interval for each country-commodity combination was categorized into one of four compliance levels: "High," "Medium," "Low," and "Poor/Unacceptable," Using K-means clustering analysis. This study presents risk-based categorization for each country-commodity combination based on the probability of quarantine pest interceptions and the uncertainty in that assessment.

Tailor-made PAT platform for safe syngas fermentations in batch, fed-batch and chemostat mode with Rhodospirillum rubrum.

Recently, syngas has gained significant interest as renewable and sustainable feedstock, in particular for the biotechnological production of poly([R]-3-hydroxybutyrate) (PHB). PHB is a biodegradable, biocompatible polyester produced by some bacteria growing on the principal component of syngas, CO. However, working with syngas is challenging because of the CO toxicity and the explosion danger of H2 , another main component of syngas. In addition, the bioprocess control needs specific monitoring tools and analytical methods that differ from standard fermentations. Here, we present a syngas fermentation platform with a focus on safety installations and process analytical technology (PAT) that serves as a basis to assess the physiology of the PHB-producing bacterium Rhodospirillum rubrum. The platform includes (i) off-gas analysis with an online quadrupole mass spectrometer to measure CO consumption and production rates of H2 and CO2 , (ii) an at-line flow cytometer to determine the total cell count and the intracellular PHB content and (iii) different online sensors, notably a redox sensor that is important to confirm that the culture conditions are suitable for the CO metabolization of R. rubrum. Furthermore, we present as first applications of the platform a fed-batch and a chemostat process with R. rubrum for PHB production from syngas.

Application of Bayesian modeling of autologous antibody responses against ovarian tumor-associated antigens to cancer detection.

Biomarkers for early detection of epithelial ovarian cancer (EOC) are urgently needed. Patients can generate antibodies to tumor-associated antigens (TAAs). We tested multiplex detection of antibodies to candidate ovarian TAAs and statistical modeling for discrimination of sera of EOC patients and controls. Binding of serum antibody of women with EOC or healthy controls to candidate TAA-coated microspheres was assayed in parallel. A Bayesian model/variable selection approach using Markov Chain Monte Carlo computations was applied to these data, and serum CA125 values, to determine the best predictive model. The selected model was subjected to area under the receiver-operator curve (AUC) analysis. The best model generated an AUC of 0.86 [95% confidence interval (95% CI), 0.78-0.90] for discrimination between sera of EOC patients and healthy patients using antibody specific to p53, NY-CO-8, and HOXB7. Inclusion of CA125 in the model provided an AUC of 0.89 (95% CI, 0.84-0.92) compared with an AUC of 0.83 (95% CI, 0.81-0.85) using CA125 alone. However, using TAA responses alone, the model discriminated between independent sera of women with nonmalignant gynecologic conditions and those with advanced-stage or early-stage EOC with AUCs of 0.71 (95% CI, 0.67-0.76) and 0.70 (95% CI, 0.48-0.75), respectively. Serum antibody to p53 and HOXB7 is positively associated with EOC, whereas NY-CO-8-specific antibody shows negative association. Bayesian modeling of these TAA-specific serum antibody responses exhibits similar discrimination of patients with early-stage and advanced-stage EOC from women with nonmalignant gynecologic conditions and may be complementary to CA125.