Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Synthetic plastics are ubiquitously used in a broad range of applications, including food and drink packaging. Plastics often contain chemical additives, including bisphenols, phthalates, and terephthalic acid, which can degrade under thermal stress. The environmental presence of these chemicals is cause for public concern, especially in consumer products that utilize plastic packaging, as many have been identified as endocrine disruptors. This study sought to determine exposure to phthalates, bisphenols, and terephthalic acid by quantifying a broad spectrum of these analytes within three bottled water brands at varying temperature exposure levels using the combination of solid phase extraction followed by isotope dilution liquid chromatography-tandem mass spectrometry. Monobenzyl phthalate was detected in two of the three brands after bottles were heated to ~100 °C, ranging from 98 – 107 ng/L, and bisphenol A was detected in one brand at ~100 °C at an average concentration of 748 ± 36 ng/L. Subsequent mass loading calculations demonstrated that bioaccumulation of BPA from Brand C after high levels of temperature exposure well exceeded the tolerable daily intake (TDI). Findings in this study indicate that consumers should not be expected to incur harmful exposures to the target compounds under normal conditions as analytes were not measured in water bottle samples at 25 °C or 60 °C. Further studies should explore a more nuisance approach to heating over long durations, including that of ultraviolet exposure.
Diabetes affects millions of people globally and can lead to other severe health complications when undiagnosed or not properly managed. The incidence of diabetes has rapidly increased over the past several years, however, not all individuals have access to affordable or convenient healthcare. We hypothesize that wastewater-based epidemiology (WBE) has the potential to assess community health status by analyzing biomarkers indicative of human health and disease, including diabetes. Used in tandem with current methods, monitoring indicators of diabetes in community wastewater could provide a comprehensive assessment tool for disease prevalence in large and small populations. Specifically, the proposed targeted biomarker evaluated in this study to indicate population-wide diabetes prevalence was 8-hydroxy-2’- deoxyguanosine (8-OHdG). This work combines a rigorous literature review and initial laboratory studies to explore the possibility of diabetes monitoring at the community level using WBE. Here, 24-hour composite wastewater samples were collected from within two wastewater sub-catchments of Greater Tempe, AZ. Overall goals of this study were to: i) Determine the feasibility to detect endogenous markers of diabetes in community wastewater; ii) Assess the potential impact of confounding factors, such as smoking, cancer, and atherosclerosis, through a literature analysis; and iii) Evaluate the socioeconomic status and demographics of the study population. Preliminary results of the experiments suggest this methodology to be feasible, as indicated by the observation of detectable signals of 8-OHdG in community wastewater collected from the sewer infrastructure; however, future work and continued experimentation will be required to address low signal intensity and assay precision and accuracy. Thus, the work presented here provides valuable proof-of-concept data, with detailed information on the method employed and identified opportunities to further determine the relationship between 8-OHdG concentrations in municipal wastewater and diabetes prevalence at the community level.