Transcriptome analysis of mammary epithelial cell gene expression reveals novel roles of the extracellular matrix

Background The unique lactation strategy of the tammar wallaby (Macropus eugeni) has been invaluable in evaluating the role of lactogenic hormones and the extracellular matrix (ECM) in the local control of mammary gland function. However molecular pathways through which hormones and ECM exert their effect on wallaby mammary gland function remain unclear. This study undertakes transcriptome analysis of wallaby mammary epithelial cells (WallMEC) following treatment with mammary ECM from two distinct stages of lactation. Methods WallMEC from MID lactation mammary glands were cultured on ECM from MID or LATE lactation and treated for 5 days with 1 μg/ml cortisol, 1 μg/ml insulin, 0.2 µg/ml prolactin, 650 pg/ml triodothyronine and 1 pg/ml estradiol to induce lactation. WallMEC RNA from triplicate ECM treatments was used to perform RNAseq. Results ECM from MID and LATE lactation differentially regulated key genes in sugar and lipid metabolism. Seven pathways including galactose metabolism, lysosome, cell adhesion molecules (CAM), p53 signaling, the complement and coagulation and Nod-like receptor signaling pathways were only significantly responsive to ECM in the presence of hormones. The raw RNA-seq data for this project are available on the NCBI Gene Expression Omnibus (GEO) browser (accession number GSE81210). Conclusions A potential role of ECM in regulation of the caloric content of milk, among other functions including apoptosis, cell proliferation and differentiation has been identified. General significance This study has used a non-eutherian lactation model to demonstrate the synergy between ECM and hormones in the local regulation of mammary function. © 2017 The Author

An automated framework for large scale retrospective analysis of ultrasound images

Objective: Large scale retrospective analysis of fetal ultrasound (US) data is important in the understanding of the cumulative impact of antenatal factors on offspring's health outcomes. Although the benefits are evident, there is a paucity of research into such large scale studies as it requires tedious and expensive effort in manual processing of large scale data repositories. This study presents an automated framework to facilitate retrospective analysis of large scale US data repositories. Method: Our framework consists of four modules: (1) an image classifier to distinguish the Brightness (B)-mode images; (2) a fetal image structure identifier to select US images containing user-defined fetal structures of interest (fSOI); (3) a biometry measurement algorithm to measure the fSOIs in the images and, (4) a visual evaluation module to allow clinicians to validate the outcomes. Results: We demonstrated our framework using thalamus as the fSOI from a hospital repository of more than 80,000 patients, consisting of 3,816,967 antenatal US files (DICOM objects). Our framework classified 1,869,105 B-mode images and from which 38,786 thalamus images were identified. We selected a random subset of 1290 US files with 558 B-mode (containing 19 thalamus images and the rest being other US data) and evaluated our framework performance. With the evaluation set, B-mode image classification resulted in accuracy, precision, and recall (APR) of 98.67%, 99.75% and 98.57% respectively. For fSOI identification, APR was 93.12%, 97.76% and 80.78% respectively. Conclusion: We introduced a completely automated approach designed to analyze a large scale data repository to enable retrospective clinical research. © 2013 IEEE

Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen. Also, the whole image does not encode sufficient local information about structures to identify different structures in different locations. Here we propose a method to automatically classify 14 different fetal structures in 2-D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image. Our method trains two feature extractors by fine-tuning pre-trained convolutional neural networks with the whole ultrasound fetal images and the discriminant regions of the fetal structures found in the whole image. The novelty of our method is in integrating the classification decisions made from the global and local features without relying on priors. In addition, our method can use the classification outcome to localize the fetal structures in the image. Our experiments on a data set of 4074 2-D ultrasound images (training: 3109, test: 965) achieved a mean accuracy of 97.05%, mean precision of 76.47% and mean recall of 75.41%. The Cohen κ of 0.72 revealed the highest agreement between the ground truth and the proposed method. The superiority of the proposed method over the other non-fusion-based methods is statistically significant (p < 0.05). We found that our method is capable of predicting images without ultrasound scanner overlays with a mean accuracy of 92%. The proposed method can be leveraged to retrospectively classify any ultrasound images in clinical research

Plane identification in fetal ultrasound images using saliency maps and convolutional neural networks

Fetal development is noninvasively assessed by measuring the size of different structures in ultrasound (US) images. The reliability of these measurements is dependent upon the identification of the correct anatomical viewing plane, each of which contains different fetal structures. However, the automatic classification of the anatomical planes in fetal US images is challenging due to a number of factors, such as low signal-to-noise-ratios and the small size of the fetus. Current approaches for plane classification are limited to simpler subsets of the problem: only classifying planes within specific body regions or using temporal information from videos. In this paper, we propose a new general method for the classification of anatomical planes in fetal US images. Our method trains two convolutional neural networks to learn the best US and saliency features. The fusion of these features overcomes the challenges associated with US fetal imaging by emphasising the salient features within US images that best discriminate different planes. Our method achieved higher classification accuracy than a state-of-the-art baseline for 12 of the 13 different planes found in a clinical dataset of fetal US images. © 2016 IEEE

Potentials of data mining and cloud computing in the fish poisoning and relevant economic contexts: A conceptual review

Fish poisoning can be life threatening and it is important to detect the causes of fish poisoning, and the type of fish poisoning, reliably and accurately. However, the existing research on fish poisoning has generally focused on statistical methods and specific areas within the fish poisoning field. This review based research also highlights the possibilities of data mining in this area, especially by reflecting how the different data mining methods may address different issues of uncertainty linked to fish poisoning. The article also discusses the possibility of cloud computing in this context and the economic benefit that may be brought forth by using a data mining based system within cloud computing architecture. Additionally it demonstrates an example of utilizing data mining in the fish poisoning research area. The work is expected to guide empirical research, especially for the Fijian context. © 2016 IEEE

Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets

© 2013 IEEE. We derived an automated algorithm for accurately measuring the thalamic diameter from 2-D fetal ultrasound (US) brain images. The algorithm overcomes the inherent limitations of the US image modality: Nonuniform density; missing boundaries; and strong speckle noise. We introduced a 'guitar' structure that represents the negative space surrounding the thalamic regions. The guitar acts as a landmark for deriving the widest points of the thalamus even when its boundaries are not identifiable. We augmented a generalized level-set framework with a shape prior and constraints derived from statistical shape models of the guitars; this framework was used to segment US images and measure the thalamic diameter. Our segmentation method achieved a higher mean Dice similarity coefficient, Hausdorff distance, specificity, and reduced contour leakage when compared to other well-established methods. The automatic thalamic diameter measurement had an interobserver variability of-0.56 2.29 mm compared to manual measurement by an expert sonographer. Our method was capable of automatically estimating the thalamic diameter, with the measurement accuracy on par with clinical assessment. Our method can be used as part of computer-assisted screening tools that automatically measure the biometrics of the fetal thalamus; these biometrics are linked to neurodevelopmental outcomes

Intra-Breath Assessment Of Respiratory Impedance Reveals Unique Pattern Of Bronchodilator Response In Wheezy Children

Intra-Breath Assessment Of Respiratory Impedance Reveals Unique Pattern Of Bronchodilator Response In Wheezy Childre

Ecotoxicology

This chapter of Expert Evidence is designed to provide background information for lawyers engaged in investigations or hearings related to chemicals and their potential impacts in the environment. The chapter details procedures for measurement, interpretation and prediction of effects with laboratory- and field-based data. A series of case studies relevant to the Australian environment are presented

Partial cross sections of fragments of uracil in collisions with (a) C^{5 +}, (b) O^{7 +} ions

<p><strong>Figure 7.</strong> Partial cross sections of fragments of uracil in collisions with (a) C^{5 +}, (b) O^{7 +} ions. Parts (c) and (d) show the ratio of fragments with regards to the total ionization for C^{5 +} and O^{7 +} respectively. The symbols , Δ, , , represent mass 112 (Ur⁺), 69, 42, 28 and proton respectively.</p> <p><strong>Abstract</strong></p> <p>Ionization and fragmentation of uracil molecules (C₄H₄N₂O₂, <em>m</em> = 112 amu) in collisions with fast highly charged C, O and F ions have been investigated using a time-of-flight mass spectrometer. The measurement of total ionization cross sections (TCS) is reported for different charge states (q), such as F^{<em>q</em> +} with <em>q</em> = 5–8; O^{<em>q</em> +} with <em>q</em> = 5,7; C^{<em>q</em> +} with <em>q</em> = 5 and 6. These studies reveal a (q/v)^~1.5 dependence of TCS, in contrast, to the well-known q²-dependence in ion–atom collisions. Scaling properties of the TCS with projectile energy and charge states are obtained. The experimental results for TCS measurements are compared with the theoretical calculations performed within classical and quantum mechanical frameworks. The trends in energy dependence of the TCSs is qualitatively well reproduced by the different models and more specifically by the classical description, which provides the best agreement with measurements.</p

Plot of uracil deposition rate versus time

<p><strong>Figure 2.</strong> Plot of uracil deposition rate versus time.</p> <p><strong>Abstract</strong></p> <p>Ionization and fragmentation of uracil molecules (C₄H₄N₂O₂, <em>m</em> = 112 amu) in collisions with fast highly charged C, O and F ions have been investigated using a time-of-flight mass spectrometer. The measurement of total ionization cross sections (TCS) is reported for different charge states (q), such as F^{<em>q</em> +} with <em>q</em> = 5–8; O^{<em>q</em> +} with <em>q</em> = 5,7; C^{<em>q</em> +} with <em>q</em> = 5 and 6. These studies reveal a (q/v)^~1.5 dependence of TCS, in contrast, to the well-known q²-dependence in ion–atom collisions. Scaling properties of the TCS with projectile energy and charge states are obtained. The experimental results for TCS measurements are compared with the theoretical calculations performed within classical and quantum mechanical frameworks. The trends in energy dependence of the TCSs is qualitatively well reproduced by the different models and more specifically by the classical description, which provides the best agreement with measurements.</p