Exposure to radon can lead to lung cancer-and it shows in our toenails. Breakthroughs, discoveries, and DIY tips sent every weekday. Donating blood, plasma, organs, and even full bodies saves countless lives every year. But toenail clippings could also become a life-saving body part with a new pilot study from the University of Calgary in Canada. The team is soliciting toenail donations (sorry, only from Canadians) to study a type of cancer that arises far from our feet-lung cancer .

Motivation: Brain aging varies significantly between sexes, yet genetic contributions to these differences remain under - explored. Goal: Identify sex - specific genetic variants linked to accelerated brain aging using structural MRI data. Approach: This study proposes implementing Brain Age Gap Estimates (BrainAGE) with sex - stratified GW AS to uncover genetic associations in T1 - weighted MRI data from the UK Biobank, complemented by Post - GW AS analyses to explore biological pathways and gene expression. Results: Sex - stratified analyses revealed neurotransmitter and mitochondrial response to cellular stress genes linked to brain aging in females and immune - related genes in males. Shared genes suggest common neurostructural roles, advancing understanding of sex - specific genetic determinants in brain aging. Impact: This study highlights the importance of sex - stratified analysis in understanding the genetic associations with brain aging. Findings pave the way for future work on personalized treatments and preventative measures for neurodegeneration based on individual genetic profiles and sex - specific risks.

Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in clinical scenarios. One of the reasons for this problem is that many machine learning models only identify correlations between the input images and the outputs of interest, which can lead to many practical problems, such as encoding of uninformative biases and reduced explainability. Thus, recent research is exploring if integrating a priori causal knowledge into deep learning models is a potential avenue to identify these problems. This work introduces a new causal generative architecture named Masked Causal Flow (MACAW) for neuroimaging applications. Within this context, three main contributions are described. First, a novel approach that integrates complex causal structures into normalizing flows is proposed. Second, counterfactual prediction is performed to identify the changes in effect variables associated with a cause variable. Finally, an explicit Bayesian inference for classification is derived and implemented, providing an inherent uncertainty estimation. The feasibility of the proposed method was first evaluated using synthetic data and then using MRI brain data from more than 23000 participants of the UK biobank study. The evaluation results show that the proposed method can (1) accurately encode causal reasoning and generate counterfactuals highlighting the structural changes in the brain known to be associated with aging, (2) accurately predict a subject's age from a single 2D MRI slice, and (3) generate new samples assuming other values for subject-specific indicators such as age, sex, and body mass index. The code for a toy dataset is available at the following link: https://github.com/vibujithan/macaw-2D.git.

Acquiring accurate external respiratory data during functional Magnetic Resonance Imaging (fMRI) is challenging, prompting the exploration of machine learning methods to estimate respiratory variation (RV) from fMRI data. Respiration induces head motion, including real and pseudo motion, which likely provides useful information about respiratory events. Recommended notch filters mitigate respiratory-induced motion artifacts, suggesting that a bandpass filter at the respiratory frequency band isolates respiratory-induced head motion. This study seeks to enhance the accuracy of RV estimation from resting-state BOLD-fMRI data by integrating estimated head motion parameters. Specifically, we aim to determine the impact of incorporating raw versus bandpass-filtered head motion parameters on RV reconstruction accuracy using one-dimensional convolutional neural networks (1D-CNNs). This approach addresses the limitations of traditional filtering techniques and leverages the potential of head motion data to provide a more robust estimation of respiratory-induced variations.

Artificial Intelligence (AI) represents the frontier of computer science, enabling machines to emulate human intelligence and perform tasks that were once exclusive to human capabilities (Briganti and Le Moine 2020). This rapid progression in AI, driven by Machine Learning (ML) and Deep Learning (DL) innovations, has catalyzed breakthroughs across various industries, including business, communication, healthcare, and education, among others. Utilizing state-of-the-art computational resources, the AI models are trained on extensive datasets and can be used for decision-making on unseen data. Recent advancements in AI algorithms and feature engineering techniques have played a pivotal role in transforming various human-centric fields, notably, healthcare (Esteva et al 2019), image and text generation (Epstein et al 2023), biometrics and cybersecurity (Gavrilova et al 2022), online social media opinion mining (Anzum and Gavrilova 2023), autonomous driving vehicles (Ma et al 2020), and beyond. Despite the impressive capabilities exhibited by recent AI-based systems, a significant challenge lies in their inherent black box nature. Due to the lack of explainability and interpretability of AI models, establishing trust among end users has become critical (von Eschenbach 2021). Therefore, to ensure trustworthiness in AI-empowered systems, it is imperative not only to improve the model's accuracy but also to incorporate explainability and interpretability into the model's architecture and

The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms differ in type and quality between agencies, so their data are usually isolated from one agency to another. Larger agencies may have sufficient data to train and test artificial intelligence (AI) tools but smaller agencies typically do not. To address this gap, we introduce a Federated Learning (FL) approach enabling all agencies to train a predictive model collaboratively without sharing their sensitive data. We demonstrate how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC. This is achieved while preserving the privacy of the people within the data by not sharing identifying information between agencies without their consent. Our experimental results using real-world HHSC data from Calgary, Alberta, demonstrate that our FL approach offers comparable performance with the idealized scenario of training the predictive model with data fully shared and linked between agencies.

This paper utilizes statistical data from various open datasets in Calgary to to uncover patterns and insights for community crimes, disorders, and traffic incidents. Community attributes like demographics, housing, and pet registration were collected and analyzed through geospatial visualization and correlation analysis. Strongly correlated features were identified using the chi-square test, and predictive models were built using association rule mining and machine learning algorithms. The findings suggest that crime rates are closely linked to factors such as population density, while pet registration has a smaller impact. This study offers valuable insights for city managers to enhance community safety strategies.

Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices. Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform. Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals. Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation. Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global age prediction, and rely on techniques, such as saliency maps to interpret their results. These saliency maps highlight regions in the input image that were significant for the model's predictions, but they are hard to be interpreted, and saliency map values are not directly comparable across different samples. In this work, we reframe the age prediction problem from MR images to an image-to-image regression problem where we estimate the brain age for each brain voxel in MR images. We compare voxel-wise age prediction models against global age prediction models and their corresponding saliency maps. The results indicate that voxel-wise age prediction models are more interpretable, since they provide spatial information about the brain aging process, and they benefit from being quantitative.