Preview of "Data and its (dis)contents: A survey of dataset development and use in machine learning research".

In this preview, we highlight what we believe to be the major contributions of the review and discuss opportunities to build on the work, including by closely examining the incentive structures that contribute to our dataset culture and by further engaging with other disciplines.

The SSU processome interactome in reveals novel protein subcomplexes.

Ribosome assembly is an evolutionarily conserved and energy intensive process required for cellular growth, proliferation, and maintenance. In yeast, assembly of the small ribosomal subunit (SSU) requires approximately 75 assembly factors that act in coordination to form the SSU processome, a 6 MDa ribonucleoprotein complex. The SSU processome is required for processing, modifying, and folding the preribosomal RNA (rRNA) to prepare it for incorporation into the mature SSU.

Mutations in KDSR Cause Recessive Progressive Symmetric Erythrokeratoderma.

The discovery of new genetic determinants of inherited skin disorders has been instrumental to the understanding of epidermal function, differentiation, and renewal. Here, we show that mutations in KDSR (3-ketodihydrosphingosine reductase), encoding an enzyme in the ceramide synthesis pathway, lead to a previously undescribed recessive Mendelian disorder in the progressive symmetric erythrokeratoderma spectrum. This disorder is characterized by severe lesions of thick scaly skin on the face and genitals and thickened, red, and scaly skin on the hands and feet.

Detection of Hyperperfusion on Arterial Spin Labeling using Deep Learning.

Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused).

Deep Learning of Tissue Fate Features in Acute Ischemic Stroke.

In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct a deep learning model of tissue fate based on randomly sampled local patches from the hypoperfusion (Tmax) feature observed in MRI immediately after symptom onset.

A protein interaction map of the LSU processome.

Maturation of the large ribosomal subunit (LSU) in eukaryotes is a complex and highly coordinated process that requires the concerted action of a large, dynamic, ribonucleoprotein complex, the LSU processome. While we know that >80 ribosome biogenesis factors are required throughout the course of LSU assembly, little is known about how these factors interact with each other within the LSU processome. To interrogate its organization and architecture, we took a systems biology approach and performed a semi-high-throughput, array-based, directed yeast two-hybrid assay.

Ribonucleoproteins in archaeal pre-rRNA processing and modification.

Given that ribosomes are one of the most important cellular macromolecular machines, it is not surprising that there is intensive research in ribosome biogenesis. Ribosome biogenesis is a complex process. The maturation of ribosomal RNAs (rRNAs) requires not only the precise cleaving and folding of the pre-rRNA but also extensive nucleotide modifications.