A data science approach to 138 years of congressional speeches.

The availability of automatic data analysis tools and large databases have enabled new ways of studying language and communication that were not possible in the pre-information era. Here we apply a quantitative analysis to a large dataset of USA congressional speeches made over a period of 138 years. The analysis reveals that the readability index of congressional speeches increased consistently until the 96th congress, and then started to decline.

Quantitative Machine Learning Analysis of Brain MRI Morphology throughout Aging.

Background: While cognition is clearly affected by aging, it is unclear whether the process of brain aging is driven solely by accumulation of environmental damage, or involves biological pathways.

Methods: We applied quantitative image analysis to profile the alteration of brain tissues during aging. A dataset of 463 brain MRI images taken from a cohort of 416 subjects was analyzed using a large set of low-level numerical image content descriptors computed from the entire brain MRI images.

Composite Aging Markers Can Be Used for Quantitative Profiling of Aging.

In the absence of a single marker that reliably reflects biological aging, or even an exact definition of biological age, compound aging scores that combine multiple aging biomarkers into a single composite aging score can quantitatively reflect the age-related changes over time. If aging is viewed as the broad accumulation of alterations over time, a composite score that reflects numerous diverse aspects of these alterations can be used as a quantitative approximation of aging. Profiling the changes of the composite aging score over time shows variations in the pace of aging at different chronological ages, such that the changes over time show distinct stages separated by a short period of rapid aging.

Comprehensive Analysis of Large Sets of Age-Related Physiological Indicators Reveals Rapid Aging around the Age of 55 Years.

Background: While many studies show a correlation between chronological age and physiological indicators, the nature of this correlation is not fully understood.

Objective: To perform a comprehensive analysis of the correlation between chronological age and age-related physiological indicators.

Method: Physiological aging scores were deduced using principal component analysis from a large dataset of 1,227 variables measured in a cohort of 4,796 human subjects, and the correlation between the physiological aging scores and chronological age was assessed.

Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls.

Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project.

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.

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.

A computer analysis method for correlating knee X-rays with continuous indicators.

Purpose: To develop an image analysis method that can automatically find correlations between a set of plain radiographs and continuous clinical or physiological indicators.

Methods: Knee X-rays taken from the Baltimore Longitudinal Study of Aging are used in this study. The computer analysis method is based on the WND-CHARM image feature set filtered by using the Pearson correlation of each feature with the continuous variable, and the estimated value is determined by a weighted nearest neighbor interpolation.

Pattern recognition software and techniques for biological image analysis.

The increasing prevalence of automated image acquisition systems is enabling new types of microscopy experiments that generate large image datasets. However, there is a perceived lack of robust image analysis systems required to process these diverse datasets. Most automated image analysis systems are tailored for specific types of microscopy, contrast methods, probes, and even cell types.

Does cosmic weather affect infant mortality rate?

In this article, the author proposes to consider a link between infant mortality rate (IMR) and galactic cosmic radiation (CR) density. The periodical increase in solar activity increases the effect of the magnetic field of the sun, and therefore weakens galactic cosmic rays hitting the Earth's surface. As a result, embryos in their early stages of development may be less exposed to high-energy ionizing cosmic rays when the solar activity peaks.

Progression analysis and stage discovery in continuous physiological processes using image computing.

We propose an image computing-based method for quantitative analysis of continuous physiological processes that can be sensed by medical imaging and demonstrate its application to the analysis of morphological alterations of the bone structure, which correlate with the progression of osteoarthritis (OA). The purpose of the analysis is to quantitatively estimate OA progression in a fashion that can assist in understanding the pathophysiology of the disease. Ultimately, the texture analysis will be able to provide an alternative OA scoring method, which can potentially reflect the progression of the disease in a more direct fashion compared to the existing clinically utilized classification schemes based on radiology.

Automatic classification of lymphoma images with transform-based global features.

We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content.

An Image Informatics Method for Automated Quantitative Analysis of Phenotype Visual Similarities.

The post genomic era introduced the need to define single gene functions within biological pathways. A systems biology approach can be realized by automating image acquisition and phenotype classification. While machinery for automated data acquisition have been developing rapidly in the past years, the main bottleneck remains the effectiveness of the computer vision algorithms.

Automatic morphological classification of galaxy images.

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using a manually classified images of elliptical, spiral, and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores.

Biometric identification using knee X-rays.

Identification of people often makes use of unique features of the face, fingerprints and retina. Beyond this, a similar identifying process can be applied to internal parts of the body that are not visible to the unaided eye. Here we show that knee X-rays can be used for the identification of individual persons.

Quantitative measurement of aging using image texture entropy.

Motivation: A key element in understanding the aging of Caenorhabditis elegans is objective quantification of the morphological differences between younger and older animals. Here we propose to use the image texture entropy as an objective measurement that reflects the structural deterioration of the C. elegans muscle tissues during aging.

Knee x-ray image analysis method for automated detection of osteoarthritis.

We describe a method for automated detection of radiographic osteoarthritis (OA) in knee X-ray images. The detection is based on the Kellgren-Lawrence (KL) classification grades, which correspond to the different stages of OA severity. The classifier was built using manually classified X-rays, representing the first four KL grades (normal, doubtful, minimal, and moderate).

WND-CHARM: Multi-purpose image classification using compound image transforms.

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image.

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods.

Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images.

IICBU 2008: a proposed benchmark suite for biological image analysis.

New technology for automated biological image acquisition has introduced the need for effective biological image analysis methods. These algorithms are constantly being developed by pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of biological imaging. However, computer scientists do not always have access to biological image datasets that can be used for computer vision research, and biologist collaborators who can assist in defining the biological questions are not always available.

Wndchrm - an open source utility for biological image analysis.

Background: Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.

Methods: Wndchrm is an open source utility for biological image analysis.