The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

We have described multipotent progenitor-like cells within the major pancreatic ducts (MPDs) of the human pancreas. They express PDX1, its surrogate surface marker P2RY1, and the bone morphogenetic protein (BMP) receptor 1A (BMPR1A)/activin-like kinase 3 (ALK3), but not carbonic anhydrase II (CAII). Here we report the single-cell RNA sequencing (scRNA-seq) of ALK3bright+-sorted ductal cells, a fraction that harbors BMP-responsive progenitor-like cells. Our analysis unveiled the existence of multiple subpopulations along two major axes, one that encompasses a gradient of ductal cell differentiation stages, and another featuring cells with transitional phenotypes toward acinar tissue. A third potential ducto-endocrine axis is revealed upon integration of the ALK3bright+ dataset with a single-cell whole-pancreas transcriptome. When transplanted into immunodeficient mice, P2RY1+/ALK3bright+ populations (enriched in PDX1+/ALK3+/CAII− cells) differentiate into all pancreatic lineages, including functional β-cells. This process is accelerated when hosts are treated systemically with an ALK3 agonist. We found PDX1+/ALK3+/CAII− progenitor-like cells in the MPDs of types 1 and 2 diabetes donors, regardless of the duration of the disease. Our findings open the door to the pharmacological activation of progenitor cells in situ.

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.

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