31 research outputs found
Bannekar Junior High Basketball Team
No full textFront Row L to R: Leanord Helms, Alvin Washington, Ronald Warner, Kenneth Diggs, Burton Bellfield; Second row: Thomas Gray, Caleb Carter, Walter Booker, James Evans, Mr. Harr S. Davis, Coachhttps://dh.howard.edu/ebhend_bsp/1115/thumbnail.jp
Development of Machine Learning Models with Applications in Cardiovascular Research
No full textCardiovascular disease (CVD) is one of the leading burdens on modern healthcare globally in terms of mortality, loss of health and healthcare costs. CVD covers all conditions that affect the heart and circulatory systems. Artificial intelligence (AI) and machine learning (ML) are being increasingly leveraged to help improve diagnosis, prognosis, treatment, and management of CVDs. This thesis aims to develop ML approaches that can generate novel, meaningful insights into several aspects of cardiovascular research. First, in Chapter 3 we use convolutional neural networks (CNN) to quantify the effect ECG data format has on ML predictive performance, through the clinical task of detecting myocardial infarction, providing the first results in determining the optimal ECG data format for ML modelling. The remaining analysis leverages the unsupervised, probabilistic ML technique generative topographic mapping (GTM). The analysis aims to generate 2-dimensional representations of data and propose different approaches that can identify large macro-clusters within the reduced dimension. Doing this gives an understanding of which patients/participants within a data set are clinically similar, along with interpretable visualisations that explain the rationale behind each cluster. Chapter 4 contains the first outline of this methodology, developed on a noncardiovascular dataset, to demonstrate the generalisability of such a methodology. Through this approach, we propose a novel freedom of expression index that provides an understanding of the level of restrictions placed on the population of a country. This index is defined by macro clusters generated through aggregating the normalised information contained in the GTM reference vector outputs. Chapter 5 applies this methodology to generate clinically relevant AF phenotypes for specific patient cohorts, from the general and the critical care populations. We propose a new methodological approach to achieve this that implements hierarchical clustering, again on the GTM reference vector outputs, to generate the phenotypes. Finally, Chapters 6 and 7 investigate the athlete’s heart, defined as the physiological changes that the heart undergoes due to exercising for prolonged periods. Chapter 6 contains an in-depth scoping review, evaluating the current ML applications in athlete’s heart and identifying the gaps for future research. Chapter 7 investigates features automatically extracted from ECG recordings from elite footballers, cyclists, rugby league players, and ultra runners to further the understanding of healthy athlete’s hearts. The methodology in Chapter 5 was further developed here to define a novel approach that uses magnification factors to define neighbourhoods in the 2-dimensional data representation, to carry out constrained hierarchical clustering on the reference vectors
Ihna Thayer Frary Home photograph
No full textThis is a photograph of the I.T. Frary Home on Bellfield Avenue in Cleveland. It measures 5.5" x 3.5" (12.7 x 8.89 cm). This photograph was taken by Ihna Thayer Frary. The Ihna Thayer Frary Audiovisual Collection was given to the Ohio Historical Society by Mr. Frary in two sections. One was in March of 1963 and the remainder in May of 1965 by his sons, Dr. Spencer G. and Allen T. Frary following their father's death. I.T. Frary (1873-1965) was the publicity and membership secretary for the Museum of Art in Cleveland, Ohio. He taught for many years at the Cleveland Institute of Art and Western Reserve University's School of Architecture. He did much research of Ohio and American architecture and was the author of seven major works and numerous scholarly articles on architectural and art history. One of his major works was Early Homes of Ohio published in 1936
From Paper to Plastic: Epigraphic Squeezes, Photogrammetry, and 3D Printing
No full textEarly Classicists and Archeologists of the 19th and 20th centuries utilized paper in a very interesting way. Bringing entire stone structures back to an epigraphist\u27s home institution would indeed have been a problematic undertaking so they used paper to make impressions of the inscriptions they wanted to study after leaving a cultural heritage site. This process is called making squeezes.
Join Charlie Harper, Ph.D. and Andrew R. Mancuso at the Kelvin Smith Library for a discussion and demonstration on this process, view historic squeezes from prominent Classics faculty of CWRU held in our Special Collections, and learn about new technologies that are being employed for preservation and research today. Attendees are also invited to take their newfound knowledge into the field for an optional workshop at the nearby Doan Brook walls to make their own squeezes. The discussion and demonstration will take place in the Hatch Reading Room on the 2nd floor of Kelvin Smith Library from 11 AM - 12 PM. Afterward, participants wishing to go into the field can meet at the corner of North Park Blvd. and Bellfield Ave. in Cleveland Heights (free street parking) by 1 PM and stay as long as they like
Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks
No full textBackground: Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. Methods: This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. Results: The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. Conclusions: The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages
Tracking Therapy Response in Glioblastoma Using 1D Convolutional Neural Networks
No full textGlioblastoma (GB) is a malignant brain tumour with no cure, even after the best treatment. The evaluation of a therapy response is usually based on magnetic resonance imaging (MRI), but it lacks precision in early stages, and doctors must wait several weeks until they are confident information is produced, facing an uncertain time window. Magnetic resonance spectroscopy (MRS/MRSI) can provide additional information about tumours and their environment but is not widely used in clinical settings since the spectroscopy format is not standardised as MRI is, and doctors are not familiarised with outputs/interpretation. This study aims to improve the assessment of the treatment response in GB using MRSI data and machine learning, including state-of-the-art one-dimensional convolutional neural networks. Preclinical (murine) GB data were used for developing models that successfully identified tumour regions regarding their response to treatment (or the lack thereof). These models were accurate and outperformed previous methods, potentially providing new opportunities for GB patient management. Background: Glioblastoma (GB) is a malignant brain tumour that is challenging to treat, often relapsing even after aggressive therapy. Evaluating therapy response relies on magnetic resonance imaging (MRI) following the Response Assessment in Neuro-Oncology (RANO) criteria. However, early assessment is hindered by phenomena such as pseudoprogression and pseudoresponse. Magnetic resonance spectroscopy (MRS/MRSI) provides metabolomics information but is underutilised due to a lack of familiarity and standardisation. Methods: This study explores the potential of spectroscopic imaging (MRSI) in combination with several machine learning approaches, including one-dimensional convolutional neural networks (1D-CNNs), to improve therapy response assessment. Preclinical GB (GL261-bearing mice) were studied for method optimisation and validation. Results: The proposed 1D-CNN models successfully identify different regions of tumours sampled by MRSI, i.e., normal brain (N), control/unresponsive tumour (T), and tumour responding to treatment (R). Class activation maps using Grad-CAM enabled the study of the key areas relevant to the models, providing model explainability. The generated colour-coded maps showing the N, T and R regions were highly accurate (according to Dice scores) when compared against ground truth and outperformed our previous method. Conclusions: The proposed methodology may provide new and better opportunities for therapy response assessment, potentially providing earlier hints of tumour relapsing stages
AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations
No full textBackground Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications, clinical risk scores are commonly employed, but their predictive accuracy is generally limited, given the inherent complexity and heterogeneity of patients with AF. By classifying different presentations of AF into coherent and manageable clinical phenotypes, the development of tailored prevention and treatment strategies can be facilitated. In this study, we propose an artificial intelligence (AI)-based methodology to derive meaningful clinical phenotypes of AF in the general and critical care populations. Methods Our approach employs generative topographic mapping, a probabilistic machine learning method, to identify micro-clusters of patients with similar characteristics. It then identifies macro-cluster regions (clinical phenotypes) in the latent space using Ward’s minimum variance method. We applied it to two large cohort databases (UK-Biobank and MIMIC-IV) representing general and critical care populations. Findings The proposed methodology showed its ability to derive meaningful clinical phenotypes of AF. Because of its probabilistic foundations, it can enhance the robustness of patient stratification. It also produced interpretable visualisation of complex high-dimensional data, enhancing understanding of the derived phenotypes and their key characteristics. Using our methodology, we identified and characterised clinical phenotypes of AF across diverse patient populations. Interpretation Our methodology is robust to noise, can uncover hidden patterns and subgroups, and can elucidate more specific patient profiles, contributing to more robust patient stratification, which could facilitate the tailoring of prevention and treatment programs specific to each phenotype. It can also be applied to other datasets to derive clinically meaningful phenotypes of other conditions
Comparing levels of job satisfaction in the countries of Western and Eastern Europe
No full textAgainst the plethora of studies of the factors influencing job satisfaction, this paper makes three contributions. First, in contrast to most studies of job satisfaction which are country-specific, the scope of this paper extends to 33 different countries. Comparing different countries on the basis of their mean job satisfaction scores ignores inequality in the distribution of scores between the countries’ individual respondents: the paper’s second contribution is to construct “equity-sensitive” job satisfaction scores for each country and, using these indicators, to compare their achievements with respect to job satisfaction. The third purpose of the paper is to answer the question posed in the title. The reason that West European countries have higher levels of job satisfaction compared to East European countries could, in part, be because they are better endowed with the “attributes” that promote job satisfaction; it could also, in part, be due to the “responses” of workers in West European countries, to a given set of attributes, being more conducive to job satisfaction than the corresponding responses of workers in East European countries. In this paper we estimate the relative importance of attributes and coefficients in determining differences in levels of job satisfaction between the two sets of countries. We do this by using the estimates from an ordered logit model to decompose the probability of being at a particular level of satisfaction into its “attributes” and “coefficients” parts. The empirical foundation for the study is provided by data for over 20,000 employed respondents, pertaining to the year 2000, obtained from the 1999-2002 Values Survey Integrated Data File.Job satisfaction; inequality; ordered logit; decomposition analysis
Simvastatin in aneurysmal subarachnoid haemorrhage (STASH): A multicentre randomised phase 3 trial
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