Alien Registration- Bannon, William J. (Bar Harbor, Hancock County)

https://digitalmaine.com/alien_docs/19926/thumbnail.jp

Economic Empowerment as a Health Care Intervention Among Orphaned Children in Rural Uganda

This study evaluated an economic empowerment intervention to reduce HIV risks among orphaned children in Uganda. Children (n=97) were randomly assigned to receive an economic intervention or to a control arm. Data obtained at baseline and 12-month follow-up revealed differences on HIV prevention attitudes, educational plans, and child-caregiver relationship for intervention arm children relative to control children. Findings lend support to use of economic empowerment interventions for HIV risk reduction among orphaned children

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

Impaired coordination of nutrient intake and substrate oxidation in melanocortin-4 receptor knockout mice

Mutations in the melanocortin-4 receptor (MC4R) are associated with obesity. The obesity syndrome observed in humans with MC4R haploinsufficiency is similar to that observed in MC4R knockout mice: increased longitudinal growth, hyperphagia, and fasting hyperinsulinemia. For comparison with other commonly investigated models of obesity and insulin resistance, we have backcrossed Mc4r-/- mice into the C57BL/6J (B6) background. Female obese Mc4r-/- mice exhibit reduced energy expenditure and an attenuated increase in fatty acid oxidation following exposure to high fat diets compared to obese Lep ob/Lepob mice. The reduced energy expenditure and fatty acid oxidation correlates with changes in hepatic gene expression. The expression of genes involved in fatty acid oxidation increased in obese Lep ob/Lepob mice compared to wild type and obese Mc4r-/- mice. In contrast, a key lipogenic enzyme (fatty acid synthase) is increased in obese Mc4r-/- mice compared to obese Lepob/Lepob mice. Hyperinsulinemia, increased FAS mRNA expression and hepatic steatosis appear to be secondary to obesity in B6 Mc4r-/- mice. However, Mc4r-/- mice in a mixed genetic background develop severe hepatic steatosis at an early age. This might suggest an important role of the MC4R in regulating liver fatty acid metabolism this is masked on the B6 background. Interestingly, the 10- to 20-fold increase in liver triglyceride in this strain of Mc4r-/- mice is not always associated with fasting hyperinsulinemia or increased FAS mRNA expression. This observation suggests changes in liver secondary to triglyceride accumulation lead to hyperinsulinemia and increased hepatic FAS expression in Mc4r-/- mice

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10⁶ 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

DeepCell Kiosk: scaling deep learning–enabled cellular image analysis with Kubernetes

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10⁶ 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console; a persistent deployment is available at https://deepcell.org/

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Comparative analytical performance of multiple plasma Aβ42 and Aβ40 assays and their ability to predict positron emission tomography amyloid positivity

INTRODUCTION: This report details the approach taken to providing a dataset allowing for analyses on the performance of recently developed assays of amyloid beta (Aβ) peptides in plasma and the extent to which they improve the prediction of amyloid positivity. METHODS: Alzheimer's Disease Neuroimaging Initiative plasma samples with corresponding amyloid positron emission tomography (PET) data were run on six plasma Aβ assays. Statistical tests were performed to determine whether the plasma Aβ measures significantly improved the area under the receiver operating characteristic curve for predicting amyloid PET status compared to age and apolipoprotein E (APOE) genotype. RESULTS: The age and APOE genotype model predicted amyloid status with an area under the curve (AUC) of 0.75. Three assays improved AUCs to 0.81, 0.81, and 0.84 (P < .05, uncorrected for multiple comparisons). DISCUSSION: Measurement of Aβ in plasma contributes to addressing the amyloid component of the ATN (amyloid/tau/neurodegeneration) framework and could be a first step before or in place of a PET or cerebrospinal fluid screening study. HIGHLIGHTS: The Foundation of the National Institutes of Health Biomarkers Consortium evaluated six plasma amyloid beta (Aβ) assays using Alzheimer's Disease Neuroimaging Initiative samples. Three assays improved prediction of amyloid status over age and apolipoprotein E (APOE) genotype. Plasma Aβ42/40 predicted amyloid positron emission tomography status better than Aβ42 or Aβ40 alone