Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity

KiDS+VIKING-450 and DES-Y1 combined: Mitigating baryon feedback uncertainty with COSEBIs

We present cosmological constraints from a joint cosmic shear analysis of the Kilo-Degree Survey (KV450) and the Dark Energy Survey (DES-Y1), which were conducted using Complete Orthogonal Sets of E/B-Integrals (COSEBIs). With COSEBIs, we isolated any B-modes that have a non-cosmic shear origin and demonstrate the robustness of our cosmological E-mode analysis as no significant B-modes were detected. We highlight how COSEBIs are fairly insensitive to the amplitude of the non-linear matter power spectrum at high k-scales, mitigating the uncertain impact of baryon feedback in our analysis. COSEBIs, therefore, allowed us to utilise additional small-scale information, improving the DES-Y1 joint constraints on S8 = σ8(Ωm/0.3)0.5 and Ωm by 20%. By adopting a flat ΛCDM model we find S8 = 0.755−0.021+0.019, which is in 3.2σ tension with the Planck Legacy analysis of the cosmic microwave background

Gene Expression Analysis of In Vitro Cocultures to Study Interactions between Breast Epithelium and Stroma

The interactions between breast epithelium and stroma are fundamental to normal tissue homeostasis and for tumor initiation and progression. Gene expression studies of in vitro coculture models demonstrate that in vitro models have relevance for tumor progression in vivo. For example, stromal gene expression has been shown to vary in association with tumor subtype in vivo, and analogous in vitro cocultures recapitulate subtype-specific biological interactions. Cocultures can be used to study cancer cell interactions with specific stromal components (e.g., immune cells, fibroblasts, endothelium) and different representative cell lines (e.g., cancer-associated versus normal-associated fibroblasts versus established, immortalized fibroblasts) can help elucidate the role of stromal variation in tumor phenotypes. Gene expression data can also be combined with cell-based assays to identify cellular phenotypes associated with gene expression changes. Coculture systems are manipulable systems that can yield important insights about cell-cell interactions and the cellular phenotypes that occur as tumor and stroma co-evolve

High reproducibility using sodium hydroxide-stripped long oligonucleotide DNA microarrays

Recently, long oligonucleotide (60- to 70-mer) microarrays for two-color experiments have been developed and are gaining widespread use. In addition, when there is limited availability of mRNA from tissue sources, RNA amplification can and is being used to produce sufficient quantities of cRNA for microarray hybridization. Taking advantage of the selective degradation of RNA under alkaline conditions, we have developed a method to "strip" glass-based oligonucleotide microarrays that use fluorescent RNA in the hybridization, while leaving the DNA oligonucleotide probes intact and usable for a second experiment. Replicate microarray experiments conducted using stripped arrays showed high reproducibility, however, we found that arrays could only be stripped and reused once without compromising data quality. The intraclass correlation (ICC) between a virgin array and a stripped array hybridized with the same sample showed a range of 0.90-0.98, which is comparable to the ICC of two virgin arrays hybridized with the same sample. Using this method, once-stripped oligonucleotide microarrays are usable, reliable, and help to reduce costs

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics

Down-regulation of sfrp1 in a mammary epithelial cell line promotes the development of a cd44high/cd24low population which is invasive and resistant to anoikis

<p>Abstract</p> <p>Background</p> <p>The Wnt family of secreted proteins is implicated in the regulation of cell fate during development, as well as in cell proliferation, morphology, and migration. Aberrant activation of the Wnt/β-catenin signaling pathway leads to the development of several human cancers, including breast cancer. Secreted frizzled-related protein 1 (SFRP1) antagonizes this pathway by competing with the Frizzled receptor for Wnt ligands resulting in an attenuation of the signal transduction cascade. Loss of SFRP1 expression is observed in breast cancer, along with several other cancers, and is associated with poor patient prognosis. However, it is not clear whether the loss of SFRP1 expression predisposes the mammary gland to tumorigenesis.</p> <p>Results</p> <p>When SFRP1 is knocked down in a non-malignant immortalized mammary epithelial cell line (76 N TERT), nuclear levels of β-catenin rise and the Wnt pathway is stimulated. The SFRP1 knockdown cells exhibit increased expression of the pro-proliferative Cyclin D1 gene and increased cellular proliferation, undergo a partial epithelial-mesenchymal transition (EMT), are resistant to anchorage-independent cell death, exhibit increased migration, are significantly more invasive, and exhibit a CD24^low/CD44^highcell surface marker expression pattern.</p> <p>Conclusion</p> <p>Our study suggests that loss of SFRP1 allows non-malignant cells to acquire characteristics associated with breast cancer cells.</p

Fostering eABCD: Asset-Based Community Development in Digital Service-Learning

The continuing expansion of digital service-learning is bringing emergent dynamics to the field of community engagement, including the challenge of fostering asset-based views of community partners in online spaces. “Online disinhibition” (Suler, 2004) can prompt harsh critique or insensitive language that would not have occurred during face-to-face relationships. Traditionally, the field of community engagement has drawn on asset-based community development (Kretzmann & McKnight, 1993), which calls for relationship-driven, asset-based, and internally focused partnerships, to encourage ethical and positive interactions with community members. However, this theory was not originally intended for digital, text-based interactions. This article explores how aspects of asset-based community development might be enacted in online partnerships, in electronic asset-based community development (eABCD). A case study of a digital writing partnership between college students and rural youth is used to illustrate how students can be supported in asset-based, relationship-driven, and internally focused interactions in online service-learning collaborations