BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis. BI-LAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections.Comment: 15 pages, 6 figure

The Effects on 3D Printed Molds on Metal Castings

Additive manufacture of sand molds via binder jetting enables the casting of complex metal geometries. Various material systems have been created for 3D printing of sand molds; however, a formal study of the materials’ effects on cast products has not yet been conducted. In this paper the authors investigate potential differences in material properties (microstructure, porosity, mechanical strength) of A356 – T6 castings resulting from two different commercially available 3D printing media. In addition, the material properties of cast products from traditional “no-bake” silica sand is used as a basis for comparison of castings produced by the 3D printed molds.Mechanical Engineerin

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research

The X-ray Emissions from the M87 Jet: Diagnostics and Physical Interpretation

We reanalyze the deep Chandra observations of the M87 jet, first examined by Wilson & Yang (2002). By employing an analysis chain that includes image deconvolution, knots HST-1 and I are fully separated from adjacent emission. We find slight but significant variations in the spectral shape, with values of $\alpha_x$ ranging from $\sim 1.2-1.6$. We use VLA radio observations, as well as HST imaging and polarimetry data, to examine the jet's broad-band spectrum and inquire as to the nature of particle acceleration in the jet. As shown in previous papers, a simple continuous injection model for synchrotron-emitting knots, in which both the filling factor, $f_{acc}$, of regions within which particles are accelerated and the energy spectrum of the injected particles are constant, cannot account for the X-ray flux or spectrum. Instead, we propose that $f_{acc}$ is a function of position and energy and find that in the inner jet, $f_{acc} \propto E_\gamma^{-0.4 \pm 0.2} \propto E_e^{-0.2 \pm 0.1}$, and in knots A and B, $f_{acc} \propto E_\gamma^{-0.7 \pm 0.2} \propto E_e^{-0.35 \pm 0.1}$, where $E_\gamma$ is the emitted photon energy and and $E_e$ is the emitting electron energy. In this model, the index $p$ of the injected electron energy spectrum ($n(E_{e}) \propto E_{e}^{-p}$) is $p=2.2$ at all locations in the jet, as predicted by models of cosmic ray acceleration by ultrarelativistic shocks. There is a strong correlation between the peaks of X-ray emission and minima of optical percentage polarization, i.e., regions where the jet magnetic field is not ordered. We suggest that the X-ray peaks coincide with shock waves which accelerate the X-ray emitting electrons and cause changes in the direction of the magnetic field; the polarization is thus small because of beam averaging.Comment: Accepted for publication in ApJ; 21 pages, 9 figures, 2 tables; abstract shortened for astro-ph; Figures 1, 7 and 8 at reduced resolutio

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.Comment: 10 pages, 9 figure

Association of dairy consumption with metabolic syndrome, hypertension and diabetes in 147 812 individuals from 21 countries

Objective: Our aims were to assess the association of dairy intake with prevalence of metabolic syndrome (MetS) (cross-sectionally) and with incident hypertension and incident diabetes (prospectively) in a large multinational cohort study.Methods: The Prospective Urban Rural Epidemiology (PURE) study is a prospective epidemiological study of individuals aged 35 and 70 years from 21 countries on five continents, with a median follow-up of 9.1 years. In the cross-sectional analyses, we assessed the association of dairy intake with prevalent MetS and its components among individuals with information on the five MetS components (n=112 922). For the prospective analyses, we examined the association of dairy with incident hypertension (in 57 547 individuals free of hypertension) and diabetes (in 131 481 individuals free of diabetes).Results: In cross-sectional analysis, higher intake of total dairy (at least two servings/day compared with zero intake; OR 0.76, 95% CI 0.71 to 0.80, p-trend\u3c0.0001) was associated with a lower prevalence of MetS after multivariable adjustment. Higher intakes of whole fat dairy consumed alone (OR 0.72, 95% CI 0.66 to 0.78, p-trend\u3c0.0001), or consumed jointly with low fat dairy (OR 0.89, 95% CI 0.80 to 0.98, p-trend=0.0005), were associated with a lower MetS prevalence. Low fat dairy consumed alone was not associated with MetS (OR 1.03, 95% CI 0.77 to 1.38, p-trend=0.13). In prospective analysis, 13 640 people with incident hypertension and 5351 people with incident diabetes were recorded. Higher intake of total dairy (at least two servings/day vs zero serving/day) was associated with a lower incidence of hypertension (HR 0.89, 95% CI 0.82 to 0.97, p-trend=0.02) and diabetes (HR 0.88, 95% CI 0.76 to 1.02, p-trend=0.01). Directionally similar associations were found for whole fat dairy versus each outcome.Conclusions: Higher intake of whole fat (but not low fat) dairy was associated with a lower prevalence of MetS and most of its component factors, and with a lower incidence of hypertension and diabetes. Our findings should be evaluated in large randomized trials of the effects of whole fat dairy on the risks of MetS, hypertension, and diabetes

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy:development of a pre-treatment decision support tool

PURPOSE: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.MATERIAL AND METHODS: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient's dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation.RESULTS: A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p &lt;.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p &lt;.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p &lt;.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms.CONCLUSION: Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.</p

Contemporary South African Urbanization Dynamics

Abstract The paper provides an overview of urbanization patterns and trends in the current era in South Africa, focusing in particular on the key dynamics and driving forces underlying migration and urbanization. It considers overall demographic trends with regard to migration and urbanization, and points to some of the difficulties with data, and with the analysis of trends and patterns. The paper explores the changing rural context and dynamics, and some of the significant processes in this context: large-scale displacement of black people off farms, the impact of land reform, and conditions in the former homeland areas. Circular migration continues to be an important way in which households in rural areas survive, but some are unable to move, and are falling out of these networks. International migration—the consequence of both conditions in the home country and the draw of the South African economy— is another significant process fuelling mainly urban growth. The paper demonstrates the importance of cities in terms of economic growth and employment, and thus their attractiveness to migrants. Continuing migration to cities is of course a challenge fo

Rabies vaccination of 6‐week‐old puppies born to immunized mothers: a randomized controlled trial in a high‐mortality population of owned, free‐roaming dogs

To achieve global elimination of human rabies from dogs by 2030, evidence-based strategies for effective dog vaccination are needed. Current guidelines recommend inclusion of dogs younger than 3 months in mass rabies vaccination campaigns, although available vaccines are only recommended for use by manufacturers in older dogs, ostensibly due to concerns over interference of maternally-acquired immunity with immune response to the vaccine. Adverse effects of vaccination in this age group of dogs have also not been adequately assessed under field conditions. In a single-site, owner-blinded, randomized, placebo-controlled trial in puppies born to mothers vaccinated within the previous 18 months in a high-mortality population of owned, free-roaming dogs in South Africa, we assessed immunogenicity and effect on survival to all causes of mortality of a single dose of rabies vaccine administered at 6 weeks of age. We found that puppies did not have appreciable levels of maternally-derived antibodies at 6 weeks of age (geometric mean titer 0.065 IU/mL, 95% CI 0.061–0.069; n = 346), and that 88% (95% CI 80.7–93.3) of puppies vaccinated at 6 weeks had titers ≥0.5 IU/mL 21 days later (n = 117). Although the average effect of vaccination on survival was not statistically significant (hazard ratio [HR] 1.35, 95% CI 0.83–2.18), this effect was modified by sex (p = 0.02), with the HR in females 3.09 (95% CI 1.24–7.69) and the HR in males 0.79 (95% CI 0.41–1.53). We speculate that this effect is related to the observed survival advantage that females had over males in the unvaccinated group (HR 0.27; 95% CI 0.11–0.70), with vaccination eroding this advantage through as-yet-unknown mechanisms.Supplementary Materials: Table S1. Results of sensitivity analysis for survival analysis (6 to 13 weeks of age), considering subjects reported as lost or stolen by owners as dead (n = 22); Table S2. Results of sensitivity analysis for survival analysis (6 to 13 weeks of age), censoring subjects that reportedly died from accidents (n = 5).http://www.mdpi.com/journal/tropicalmedhj2021Companion Animal Clinical StudiesProduction Animal StudiesVeterinary Tropical Disease