A rapid, high-throughput, viral infectivity assay using automated brightfield microscopy with machine learning.

Infectivity assays are essential for the development of viral vaccines, antiviral therapies, and the manufacture of biologicals. Traditionally, these assays take 2-7 days and require several manual processing steps after infection. We describe an automated viral infectivity assay (AVIA), using convolutional neural networks (CNNs) and high-throughput brightfield microscopy on 96-well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

Objective: To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up.

Methods: A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT.

Ribosomal protein L5 facilitates rDNA-bundled condensate and nucleolar assembly.

The nucleolus is the site of ribosome assembly and formed through liquid-liquid phase separation. Multiple ribosomal DNA (rDNA) arrays are bundled in the nucleolus, but the underlying mechanism and significance are unknown. In the present study, we performed high-content screening followed by image profiling with the wndchrm machine learning algorithm.

Predicting physiological aging rates from a range of quantitative traits using machine learning.

It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.

Radiomics in the evaluation of lung nodules: Intrapatient concordance between full-dose and ultra-low-dose chest computed tomography.

Purpose: The purpose of this study was to retrospectively evaluate the quantitative and qualitative intrapatient concordance of pulmonary nodule risk assessment by commercially available radiomics software between full-dose (FD) chest-CT and ultra-low-dose (ULD) chest CT.

Materials And Methods: Between July 2013 and September 2015, 68 patients (52 men and16 women; mean age, 65.5±10.

Computational analysis of morphological and molecular features in gastric cancer tissues.

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of morphological information has been highly recognized in clinical diagnosis and therapeutic strategies. In this study, we used a supervised machine learning algorithm wndchrm to classify hematoxylin and eosin (H&E)-stained images of human gastric cancer tissues.

Age-related changes of the retinal microvasculature.

Purpose: Blood vessels of the retina provide an easily-accessible, representative window into the condition of microvasculature. We investigated how retinal vessel structure captured in fundus photographs changes with age, and how this may reflect features related to patient health, including blood pressure.

Results: We used two approaches.

Transcriptome States Reflect Imaging of Aging States.

In this study, we describe a morphological biomarker that detects multiple discrete subpopulations (or "age-states") at several chronological ages in a population of nematodes (Caenorhabditis elegans). We determined the frequencies of three healthy adult states and the timing of the transitions between them across the lifespan. We used short-lived and long-lived strains to confirm the general applicability of the state classifier and to monitor state progression.

Differential Aging Signals in Abdominal CT Scans.

Rationale And Objectives: Changes in the composition of body tissues are major aging phenotypes, but they have been difficult to study in depth. Here we describe age-related change in abdominal tissues observable in computed tomography (CT) scans. We used pattern recognition and machine learning to detect and quantify these changes in a model-agnostic fashion.

Tomatidine enhances lifespan and healthspan in C. elegans through mitophagy induction via the SKN-1/Nrf2 pathway.

Aging is a major international concern that brings formidable socioeconomic and healthcare challenges. Small molecules capable of improving the health of older individuals are being explored. Small molecules that enhance cellular stress resistance are a promising avenue to alleviate declines seen in human aging.

Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative.

Unlabelled: The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the osteoarthritis initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire 3 years after baseline evaluation.

Cockayne syndrome group A and B proteins converge on transcription-linked resolution of non-B DNA.

Cockayne syndrome is a neurodegenerative accelerated aging disorder caused by mutations in the CSA or CSB genes. Although the pathogenesis of Cockayne syndrome has remained elusive, recent work implicates mitochondrial dysfunction in the disease progression. Here, we present evidence that loss of CSA or CSB in a neuroblastoma cell line converges on mitochondrial dysfunction caused by defects in ribosomal DNA transcription and activation of the DNA damage sensor poly-ADP ribose polymerase 1 (PARP1).

A Longitudinal Support Vector Regression for Prediction of ALS Score.

Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects.

Loss of the integral nuclear envelope protein SUN1 induces alteration of nucleoli.

A supervised machine learning algorithm, which is qualified for image classification and analyzing similarities, is based on multiple discriminative morphological features that are automatically assembled during the learning processes. The algorithm is suitable for population-based analysis of images of biological materials that are generally complex and heterogeneous. Here we used the algorithm wndchrm to quantify the effects on nucleolar morphology of the loss of the components of nuclear envelope in a human mammary epithelial cell line.

Biomedical imaging ontologies: A survey and proposal for future work.

Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as "cell" or "image" or "tissue" or "microscope") that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical definitions thereby also supporting reasoning over the tagged data.

The actin family protein ARP6 contributes to the structure and the function of the nucleolus.

The actin family members, consisting of actin and actin-related proteins (ARPs), are essential components of chromatin remodeling complexes. ARP6, one of the nuclear ARPs, is part of the Snf-2-related CREB-binding protein activator protein (SRCAP) chromatin remodeling complex, which promotes the deposition of the histone variant H2A.Z into the chromatin.

An open data ecosystem for cell migration research.

Cell migration research has recently become both a high content and a high throughput field thanks to technological, computational, and methodological advances. Simultaneously, however, urgent bioinformatics needs regarding data management, standardization, and dissemination have emerged. To address these concerns, we propose to establish an open data ecosystem for cell migration research.

Computational image analysis of colony and nuclear morphology to evaluate human induced pluripotent stem cells.

Non-invasive evaluation of cell reprogramming by advanced image analysis is required to maintain the quality of cells intended for regenerative medicine. Here, we constructed living and unlabelled colony image libraries of various human induced pluripotent stem cell (iPSC) lines for supervised machine learning pattern recognition to accurately distinguish bona fide iPSCs from improperly reprogrammed cells. Furthermore, we found that image features for efficient discrimination reside in cellular components.

Genome-wide modeling of complex phenotypes in Caenorhabditis elegans and Drosophila melanogaster.

Background: The genetic and molecular basis for many intermediate and end stage phenotypes in model systems such as C. elegans and D. melanogaster has long been known to involve pleiotropic effects and complex multigenic interactions.

Systematic repression of transcription factors reveals limited patterns of gene expression changes in ES cells.

Networks of transcription factors (TFs) are thought to determine and maintain the identity of cells. Here we systematically repressed each of 100 TFs with shRNA and carried out global gene expression profiling in mouse embryonic stem (ES) cells. Unexpectedly, only the repression of a handful of TFs significantly affected transcriptomes, which changed in two directions/trajectories: one trajectory by the repression of either Pou5f1 or Sox2; the other trajectory by the repression of either Esrrb, Sall4, Nanog, or Tcfap4.

Improving class separability using extended pixel planes: a comparative study.

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation.

CREB-binding protein levels in the rat hippocampus fail to predict chronological or cognitive aging.

Normal cognitive aging is associated with deficits in memory processes dependent on the hippocampus, along with large-scale changes in the hippocampal expression of many genes. Histone acetylation can broadly influence gene expression and has been recently linked to learning and memory. We hypothesized that CREB-binding protein (CBP), a key histone acetyltransferase, may contribute to memory decline in normal aging.

Biological imaging software tools.

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data.

Molecular characterization of the transition to mid-life in Caenorhabditis elegans.

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions.

Automatic detection of melanoma progression by histological analysis of secondary sites.

We present results from machine classification of melanoma biopsies sectioned and stained with hematoxylin/eosin (H&E) on tissue microarrays (TMA). The four stages of melanoma progression were represented by seven tissue types, including benign nevus, primary tumors with radial and vertical growth patterns (stage I) and four secondary metastatic tumors: subcutaneous (stage II), lymph node (stage III), gastrointestinal and soft tissue (stage IV). Our experiment setup comprised 14,208 image samples based on 164 TMA cores.