Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection

A Robust and Sensitive Synthetic Sensor to Monitor the Transcriptional Output of the Cytokinin Signaling Network in Planta

Cytokinins are classic plant hormones that orchestrate plant growth, development, and physiology. They affect gene expression in target cells by activating a multistep phosphorelay network. Type-B response regulators, acting as transcriptional activators, mediate the final step in the signaling cascade. Previously, we have introduced a synthetic reporter, Two Component signaling Sensor (TCS)::green fluorescent protein (GFP), which reflects the transcriptional activity of type-B response regulators. TCS::GFP was instrumental in uncovering roles of cytokinin and deepening our understanding of existing functions. However, TCS-mediated expression of reporters is weak in some developmental contexts where cytokinin signaling has a documented role, such as in the shoot apical meristem or in the vasculature of Arabidopsis (Arabidopsis thaliana). We also observed that GFP expression becomes rapidly silenced in TCS::GFP transgenic plants. Here, we present an improved version of the reporter, TCS new (TCSn), which, compared with TCS, is more sensitive to phosphorelay signaling in Arabidopsis and maize (Zea mays) cellular assays while retaining its specificity. Transgenic Arabidopsis TCSn::GFP plants exhibit strong and dynamic GFP expression patterns consistent with known cytokinin functions. In addition, GFP expression has been stable over generations, allowing for crosses with different genetic backgrounds. Thus, TCSn represents a significant improvement to report the transcriptional output profile of phosphorelay signaling networks in Arabidopsis, maize, and likely other plants that display common response regulator DNA-binding specificities

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Model weights for Lymphoid Aggregates Segmentation (in Pytorch 1.0.1)

Allograft rejection is a major concern in kidney transplantation. Inflammatory processes in patients with kidney allografts involve various patterns of immune cell recruitment and distributions. Lymphoid aggregates (LAs) are commonly observed in patients with kidney allografts and their presence and localization may correlate with severity of acute rejection. Alongside with other markers of inflammation, LAs assessment is currently performed by pathologists manually in a qualitative way, which is both time consuming and far from precise. Here we present the first automated method of identifying LAs and measuring their densities in whole slide images of transplant kidney biopsies. We trained a deep convolutional neural network based on U-Net on 44 core needle kidney biopsy slides, monitoring loss on a validation set (n=7 slides). The model was subsequently tested on a hold-out set (n=10 slides). We found that the coarse pattern of LAs localization agrees between the annotations and predictions, which is reflected by high correlation between the annotated and predicted fraction of LAs area per slide (Pearson R of 0.9756). Furthermore, the network achieves an auROC of 97.78 ± 0.93% and an IoU score of 69.72 ± 6.24 % per LA-containing slide in the test set. Our study demonstrates that a deep convolutional neural network can accurately identify lymphoid aggregates in digitized histological slides of kidney. This study presents a first automatic DL-based approach for quantifying inflammation marks in allograft kidney, which can greatly improve precision and speed of assessment of allograft kidney biopsies when implemented as a part of computer-aided diagnosis system

Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

In healthcare, artificial intelligence (AI) technologies have the potential to create significant value by improving time-sensitive outcomes while lowering error rates for each patient. Diagnostic images, clinical notes, and reports are increasingly generated and stored in electronic medical records. This heterogeneous data presenting us with challenges in data analytics and reusability that is by nature has high complexity, thereby necessitating novel ways to store, manage and process, and reuse big data. This presents an urgent need to develop new, scalable, and expandable AI infrastructure and analytical methods that can enable healthcare providers to access knowledge for individual patients, yielding better decisions and outcomes. In this review article, we briefly discuss the nature of data in breast cancer study and the role of AI for generating "smart data" which offer actionable information that supports the better decision for personalized medicine for individual patients. In our view, the biggest challenge is to create a system that makes data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare outcomes and costs. We highlight some of the challenges in using breast cancer data and propose the need for an AI-driven environment to address them. We illustrate our vision with practical use cases and discuss a path for empowering the study of breast cancer databases with the application of AI and future directions

Receptor-like cytoplasmic kinase MARIS functions downstream of CrRLK1L-dependent signaling during tip growth

Growing plant cells need to rigorously coordinate external signals with internal processes. For instance, the maintenance of cell wall (CW) integrity requires the coordination of CW sensing with CW remodeling and biosynthesis to avoid growth arrest or integrity loss. Despite the involvement of receptor-like kinases (RLKs) of the Catharanthus roseus RLK1-like (CrRLK1L) subfamily and the reactive oxygen species-producing NADPH oxidases, it remains largely unknown how this coordination is achieved. ANXUR1 (ANX1) and ANX2, two redundant members of the CrRLK1L subfamily, are required for tip growth of the pollen tube (PT), and their closest homolog, FERONIA, controls root-hair tip growth. Previously, we showed that ANX1 overexpression mildly inhibits PT growth by oversecretion of CW material, whereas pollen tubes of anx1 anx2 double mutants burst spontaneously after germination. Here, we report the identification of suppressor mutants with improved fertility caused by the rescue of anx1 anx2 pollen tube bursting. Mapping of one these mutants revealed an R240C nonsynonymous substitution in the activation loop of a receptor-like cytoplasmic kinase (RLCK), which we named MARIS (MRI). We show that MRI is a plasma membrane-localized member of the RLCK-VIII subfamily and is preferentially expressed in both PTs and root hairs. Interestingly, mri-knockout mutants display spontaneous PT and root-hair bursting. Moreover, expression of the MRI(R240C) mutant, but not its wild-type form, partially rescues the bursting phenotypes of anx1 anx2 PTs and fer root hairs but strongly inhibits wild-type tip growth. Thus, our findings identify a novel positive component of the CrRLK1L-dependent signaling cascade that coordinates CW integrity and tip growth

A calcium dialog mediated by the FERONIA Signal transduction pathway controls plant sperm delivery

Sperm delivery for double fertilization of flowering plants relies on interactions between the pollen tube (PT) and two synergids, leading to programmed cell death (PCD) of the PT and one synergid. The mechanisms underlying the communication among these cells during PT reception is unknown. We discovered that the synergids control this process by coordinating their distinct calcium signatures in response to the calcium dynamics and growth behavior of the PT. Induced and spontaneous aberrant calcium responses in the synergids abolish the two coordinated PCD events. Components of the FERONIA (FER) signaling pathway are required for initiating and modulating these calcium responses and for coupling the PCD events. Intriguingly, the calcium signatures are interchangeable between the two synergids, implying that their fates of death and survival are determined by reversible interactions with the PT. Thus, complex intercellular interactions involving a receptor kinase pathway and calcium-mediated signaling control sperm delivery in plants