Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.

Background: Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (FL).

Purpose: To assess the performance and reliability of FL for renal tumor segmentation and classification in multi-institutional MRI datasets.

Study Type: Retrospective multi-center study.

The Bjøntegaard Bible Why Your Way of Comparing Video Codecs May Be Wrong.

In this paper, we provide an in-depth assessment on the Bjøntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and processing energies. These metrics are evaluated for visual data types such as classic perspective video, 360° video, point clouds, and screen content.

An integrative pipeline for circular RNA quantitative trait locus discovery with application in human T cells.

Motivation: Molecular quantitative trait locus (QTL) mapping has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by genome-wide association studies. Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of various human diseases. However, a well-developed computational pipeline for circRNA QTL (circQTL) discovery is still lacking.

Perception of rigidity in three- and four-dimensional spaces.

Our brain employs mechanisms to adapt to changing visual conditions. In addition to natural changes in our physiology and those in the environment, our brain is also capable of adapting to "unnatural" changes, such as inverted visual-inputs generated by inverting prisms. In this study, we examined the brain's capability to adapt to hyperspaces.

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations.

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number and content making a comprehensive evaluation of their performances became more important.

End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

Objectives: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients.

Methods: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal.

LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays.

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits.

Circall: fast and accurate methodology for discovery of circular RNAs from paired-end RNA-sequencing data.

Background: Circular RNA (circRNA) is an emerging class of RNA molecules attracting researchers due to its potential for serving as markers for diagnosis, prognosis, or therapeutic targets of cancer, cardiovascular, and autoimmune diseases. Current methods for detection of circRNA from RNA sequencing (RNA-seq) focus mostly on improving mapping quality of reads supporting the back-splicing junction (BSJ) of a circRNA to eliminate false positives (FPs). We show that mapping information alone often cannot predict if a BSJ-supporting read is derived from a true circRNA or not, thus increasing the rate of FP circRNAs.