The Johns Hopkins University

Microsoft Technology Licensing, LLC

Provincial Health Services Authority

Peng Huang

Aaron William Ogus

Cheng Huang

Jacob Rubin Lorch

Saraswati Vaidyanathan Sukumar

Christopher Umbricht

Antonio Wolff

Leslie M Cope

Mary Jo Steele Fackler

Zhe Zhang

Kala Visvanathan

Yuliang Li

Stephen Lam

A device may obtain first information relating to one or more first lung nodules identified in first imaging of a chest of a patient and second information relating to one or more second lung nodules identified in second imaging of the chest of the patient. The device may provide the first information and the second information to a machine learning model. The device may determine, using the machine learning model, a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information. The risk of lung cancer may have an inverse correlation to the elapsed time and a direct correlation to the differences. The device may perform one or more actions based on the risk of lung cancer that is determined

Lung cancer prediction

A system includes determination of a plurality of meta-copysets of a plurality of storage devices, each of the meta-copysets including a first number of storage devices, determination of a first copyset of a second number of storage devices from a first one of the meta-copysets, where the second number is less than the first number, storage of first data in a fault-tolerant format using the first copyset, determination to migrate the first data from a first storage device of the first copyset, and, in response to the determination to migrate the first data, determine a second copyset of the second number of storage devices including a storage device from the first meta-copyset which is not in the first copyset, and the storage devices of the first copyset except for the first storage device of the first copyset and storage of the first data in a fault-tolerant format using the second copyset.

Meta-copysets for fault-tolerant data storage

To better understand the biology of hormone receptor-positive and negative breast cancer and to identify methylated gene markers of disease progression, a genome-wide methylation array analysis was performed on 103 primary invasive breast cancers and 21 normal breast samples using the Illumina Infinium HumanMethylation27 array that queried 27,578 CpG loci. Forty CpG loci showed differential methylation specific to either ER-positive or ER-negative tumors. Each of the 40 ER-subtype-specific loci was validated in silico using an independent, publicly available methylome dataset from The Cancer Genome Atlas (TCGA). In addition, 100 methylated CpG loci were identified that were significantly associated with disease progression. Arrays containing the ER-subtype-specific loci and their use in methods of diagnosis and treatment of breast cancer are provided.

Genome-wide methylation analysis and use to identify genes specific to breast cancer hormone receptor status and risk of recurrence

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Baltimore, MD, United States of America

Peng Huang