Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device (CF-LVAD) implantation. Abnormalities in mitochondrial oxidative phosphorylation (OxPhos) have been critically implicated in the pathogenesis of neurodegenerative diseases and cerebral ischemia. We hypothesize that prior stroke may be associated with systemic mitochondrial OxPhos abnormalities, and impaired more in post-CF-LVAD patients with risk of developing new stroke.

Hierarchical deep learning for autonomous multi-label arrhythmia detection and classification on real-world wearable electrocardiogram data.

Objective: Arrhythmia detection and classification are challenging because of the imbalanced ratio of normal heartbeats to arrhythmia heartbeats and the complicated combinations of arrhythmia types. Arrhythmia classification on wearable electrocardiogram monitoring devices poses a further unique challenge: unlike clinically used electrocardiogram monitoring devices, the environments in which wearable devices are deployed are drastically different from the carefully controlled clinical environment, leading to significantly more noise, thus making arrhythmia classification more difficult.

Methods: We propose a novel hierarchical model based on CNN+BiLSTM with Attention to arrhythmia detection, consisting of a binary classification module between normal and arrhythmia heartbeats and a multi-label classification module for classifying arrhythmia events across combinations of beat and rhythm arrhythmia types.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues.

Streaming Quantiles Algorithms with Small Space and Update Time.

Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theoretical result by providing a practical variants of their algorithm with improved constants.

A scalable and unbiased discordance metric with H.

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span.

Fast and memory-efficient scRNA-seq -means clustering with various distances.

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient -means++ center finding and -means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction.

Data-Independent Structured Pruning of Neural Networks via Coresets.

Model compression is crucial for the deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst case guarantees on the tradeoff between the compression rate and the approximation error for an arbitrarily new sample.

A phase Ib/IIa, open-label, multiple ascending-dose trial of domagrozumab in fukutin-related protein limb-girdle muscular dystrophy.

Introduction/aims: In this study we report the results of a phase Ib/IIa, open-label, multiple ascending-dose trial of domagrozumab, a myostatin inhibitor, in patients with fukutin-related protein (FKRP)-associated limb-girdle muscular dystrophy.

Methods: Nineteen patients were enrolled and assigned to one of three dosing arms (5, 20, or 40 mg/kg every 4 weeks). After 32 weeks of treatment, participants receiving the lowest dose were switched to the highest dose (40 mg/kg) for an additional 32 weeks.

Longitudinal functional and imaging outcome measures in FKRP limb-girdle muscular dystrophy.

Background: Pathogenic variants in the FKRP gene cause impaired glycosylation of α-dystroglycan in muscle, producing a limb-girdle muscular dystrophy with cardiomyopathy. Despite advances in understanding the pathophysiology of FKRP-associated myopathies, clinical research in the limb-girdle muscular dystrophies has been limited by the lack of normative biomarker data to gauge disease progression.

Methods: Participants in a phase 2 clinical trial were evaluated over a 4-month, untreated lead-in period to evaluate repeatability and to obtain normative data for timed function tests, strength tests, pulmonary function, and body composition using DEXA and whole-body MRI.

Near Optimal Linear Algebra in the Online and Sliding Window Models.

We initiate the study of numerical linear algebra in the sliding window model, where only the most recent updates in a stream form the underlying data set. Although many existing algorithms in the sliding window model use or borrow elements from the smooth histogram framework (Braverman and Ostrovsky, FOCS 2007), we show that many interesting linear-algebraic problems, including spectral and vector induced matrix norms, generalized regression, and lowrank approximation, are not amenable to this approach in the row-arrival model. To overcome this challenge, we first introduce a unified row-sampling based framework that gives algorithms for spectral approximation, low-rank approximation/projection-cost preservation, and -subspace embeddings in the sliding window model, which often use nearly optimal space and achieve nearly input sparsity runtime.