AI-Enabled Contextual Representations for Image-based Integration in Health and Safety

Recent advancements in the area of Artificial Intelligence (AI) have made it the field of choice for automatically processing and summarizing information in big-data domains such as high-resolution images. This approach, however, is not a one-size-fits-all solution, and must be tailored to each application. Furthermore, each application comes with its own unique set of challenges including technical variations, validation of AI solutions, and contextual information. These challenges are addressed in three human-health and safety related applications: (i) an early warning system of slope failures in open-pit mining operations; (ii) the modeling and characterization of 3D cell culture models imaged with confocal microscopy; and (iii) precision medicine of biomarker discovery from patients with glioblastoma multiforme through digital pathology. The methodologies and results in each of these domains show how tailor-made AI solutions can be used for automatically extracting and summarizing pertinent information from big-data applications for enhanced decision making

Differentiating Benign and Malignant Phenotypes in Breast Histology Sections with Quantum Cascade Laser Microscopy

The proposed research has two complementary goals: increasing productivity of pathologists and explore the utility of chemical imaging for diagnostic applications. The first goal is driven by an emerging shortage of pathologists is predicted for the near future; hence, increased productivity will be a necessity. The second goal is motivated by simplifying sample preparation, avoid fixation and staining, and increase throughput in a clinical setting. The efficacy of the stain-free protocol is evaluated through a pilot project of clinical samples that are scanned by a Quantum Cascade Laser InfRared (QCL-IR) microscope.QCL-IR microscopy has the potential to emerge as a unique modality for the diagnostics of histology sections. The pilot experiment is designed to evaluate whether benign and malignant breast histology sections can be differentiated using chemical profiling and without the use of spatial information. The experiment consists of fifteen independent samples that were randomly selected from paraffin-embedded blocks with 8 benign and 7 malignant labels. Spectral data are normalized and then used for both visualization and classification. Visualization is based on consensus clustering of the spectral signature within each group of the benign or malignant phenotype. Classification has been evaluated with four methods of tree-based, convolutional neural networks, Bayesian model, and an encoder module for compression followed by softmax classification. The latter has the best performance with using only 20\% of the data for training to arrive at the classification accuracy of 100\%. Direct analysis of the spectral signatures indicates that both malignant and benign samples express the similar spectral peaks; however, peaks corresponding to several nucleic acid sequences and protein groups are over expressed in malignant tissues

Gps-Free Maintenance Of A Free-Space-Optical Link Between Two Autonomous Mobiles

Free-Space-Optical (FSO) communication has the potential to provide optical-level wireless communication speeds. It can also help solve the wireless capacity problem experienced by the traditional RF-based technologies. Despite its capacity advantages, FSO communication is prone to mobility. Since the FSO transceivers are highly directional, they require establishment and maintenance of line-of-sight (LOS) between each other. We consider two autonomous mobile nodes, each with one FSO transceiver mounted on a movable head capable of rotating 360 degree. We propose a novel scheme that deals with the problem of automatic maintenance of LOS alignment between the two nodes with mechanical steering of the FSO transceivers. We design protocols to maintain an FSO link between the mobiles satisfying a minimum received power or signal-to-noise ratio (SNR). We also present a prototype implementation of such mobile node with FSO transceivers. The effectiveness of the alignment protocol is evaluated by analyzing the results obtained from both simulations and also experiments conducted using the prototype. The results show that, by using such mechanically steerable transceivers and a simple auto-alignment mechanism, it is possible to maintain optical wireless links in a mobile setting with nominal disruption

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes

Abstract Background Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., regions, boundaries) contributes to improved nuclear segmentation. Results We have demonstrated that training of deep encoder-decoder convolutional networks overcomes complexities associated with multiple nuclear phenotypes, where we evaluate alternative architecture of deep learning for an improved performance against the simplicity of the design. In addition, improved nuclear segmentation is achieved by color decomposition and combining region- and boundary-based features through a fusion network. The trained models have been evaluated against approximately 19,000 manually annotated nuclei, and object-level Precision, Recall, F1-score and Standard Error are reported with the best F1-score being 0.91. Raw training images, annotated images, processed images, and source codes are released as a part of the Additional file 1. Conclusions There are two intrinsic barriers in nuclear segmentation to H&E stained images, which correspond to the diversity of nuclear phenotypes and perceptual boundaries between adjacent cells. We demonstrate that (i) the encoder-decoder architecture can learn complex phenotypes that include the vesicular type; (ii) delineation of overlapping nuclei is enhanced by fusion of region- and edge-based networks; (iii) fusion of ENets produces an improved result over the fusion of UNets; and (iv) fusion of networks is better than multitask learning. We suggest that our protocol enables processing a large cohort of whole slide images for applications in precision medicine

Additional file 1 of Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes

Supplementary materials. (ZIP 120,330 kb

CD36⁺ Fibroblasts Secrete Protein Ligands That Growth-Suppress Triple-Negative Breast Cancer Cells While Elevating Adipogenic Markers for a Model of Cancer-Associated Fibroblast

Tumor and stroma coevolve to facilitate tumor growth. Hence, effective tumor therapeutics would not only induce growth suppression of tumor cells but also revert pro-tumor stroma into anti-tumoral type. Previously, we showed that coculturing triple-negative or luminal A breast cancer cells with CD36+ fibroblasts (FBs) in a three-dimensional extracellular matrix induced their growth suppression or phenotypic reversion, respectively. Then, we identified SLIT3, FBLN-1, and PENK as active protein ligands secreted from CD36+ FBs that induced growth suppression of MDA-MB-231 breast cancer cells and determined their minimum effective concentrations. Here, we have expanded our analyses to include additional triple-negative cancer cell lines, BT549 and Hs578T, as well as HCC1937 carrying a BRCA1 mutation. We show that the ectopic addition of each of the three ligands to cancer-associated fibroblasts (CAFs) elevates the expression of CD36, as well as the adipogenic marker FABP4. Lastly, we show that an agonist antibody for one of the PENK receptors induces growth suppression of all cancer cell lines tested but not for non-transformed MCF10A cells. These results clearly suggest that proteins secreted from CD36+ FBs induce not only growth suppression of tumor cells through binding the cognate receptors but also increasing adipogenic markers of CAFs to reprogram tumor stroma

Influence of Simulated Microgravity on Mammary Epithelial Cells Grown as 2D and 3D Cultures

During space travel, astronauts will experience a unique environment that includes continuous exposure to microgravity and stressful living conditions. Physiological adaptation to this is a challenge and the effect of microgravity on organ development, architecture, and function is not well understood. How microgravity may impact the growth and development of an organ is an important issue, especially as space flight becomes more commonplace. In this work, we sought to address fundamental questions regarding microgravity using mouse mammary epithelial cells in 2D and 3D tissue cultures exposed to simulated microgravity. Mouse mammary HC11 cells contain a higher proportion of stem cells and were also used to investigate how simulated microgravity may impact mammary stem cell populations. In these studies, we exposed mouse mammary epithelial cells to simulated microgravity in 2D and then assayed for changes in cellular characteristics and damage levels. The microgravity treated cells were also cultured in 3D to form acini structures to define if simulated microgravity affects the cells’ ability to organize correctly, a quality that is of key importance for mammary organ development. These studies identify changes occurring during exposure to microgravity that impact cellular characteristics such as cell size, cell cycle profiles, and levels of DNA damage. In addition, changes in the percentage of cells revealing various stem cell profiles were observed following simulated microgravity exposure. In summary, this work suggests microgravity may cause aberrant changes in mammary epithelial cells that lead to an increase in cancer risk

Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines

Human breast tumors are not fully autonomous. They are dependent on nutrients and growth-promoting signals provided by the supporting stromal cells. Within the tumor microenvironment, one of the secreted macromolecules by tumor cells is activin A, where we show to downregulate CD36 in fibroblasts. Downregulation of CD36 in fibroblasts also increases the secretion of activin A by fibroblasts. We hypothesize that overexpression of CD36 in fibroblasts inhibits the formation of solid tumors in subtypes of breast cancer models. For the first time, we show that co-culturing organoid models of breast cancer cell lines of MDA-MB-231 (e.g., a triple-negative line) or MCF7 (e.g., a luminal-A line) with CD36+ fibroblasts inhibit the growth and normalizes basal and lateral polarities, respectively. In the long-term anchorage-independent growth assay, the rate of colony formation is also reduced for MDA-MB-231. These observations are consistent with the mechanism of tumor suppression involving the downregulation of pSMAD2/3 and YY1 expression levels. Our integrated analytical methods leverage and extend quantitative assays at cell- and colony-scales in both short- and long-term cultures using brightfield or immunofluorescent microscopy and robust image analysis. Conditioned media are profiled with the ELISA assay