Automatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.

Purpose: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.

Methods And Materials: We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training.

18S rRNA Metagenomic Analysis of Nodular Gill Disease in Swiss Rainbow Trout (Oncorhynchus mykiss).

Nodular gill disease (NGD) is a serious proliferative gill condition that affects farmed salmonids, particularly in Europe. While the cause of NGD remains unknown (and maybe multifactorial), various amoebae are often isolated from the gills of affected fish and can in some cases be seen associated with lesions by histopathology. The present study aimed to quantify the abundance of different amoeba species directly from the gills of rainbow trout affected by NGD and healthy controls.

Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI.

Background: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learning, but they need abundant annotated data for training. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations without annotation and its associated costs.

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas.

Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

Dead reckoning of protist viability with propidium monoazide (PMA)-quantitative PCR; a case study using Neoparamoeba perurans.

The ability to distinguish between viable and non-viable protozoan parasites is central to improved human and animal health management. While conceptually simple, methods to differentiate cell viability in situ remain challenging. Amoebic gill disease, caused by Neoparamoeba perurans is a parasitic disease impacting Atlantic salmon aquaculture globally.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant.

Reconfiguration of the visual code and retinal cell type complement in closely related diurnal and nocturnal mice.

How does evolution act on neuronal populations to match computational characteristics to functional demands? We address this problem by comparing visual code and retinal cell composition in closely related murid species with different behaviours. are diurnal and have substantially thicker inner retina and larger visual thalamus than nocturnal High-density electrophysiological recordings of visual response features in the dorsal lateral geniculate nucleus (dLGN) reveals that attains higher spatiotemporal acuity both by denser coverage of the visual scene and a selective expansion of elements of the code characterised by non-linear spatiotemporal summation. Comparative analysis of single cell transcriptomic cell atlases reveals that realignment of the visual code is associated with increased relative abundance of bipolar and ganglion cell types supporting OFF and ON-OFF responses.

High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

Background: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts.

Purpose: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI.

MRI-based prostate cancer classification using 3D efficient capsule network.

Background: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information.

Improved 18S rDNA profiling of parasite communities in salmonid tissues using a host blocking primer.

Sensitive screening of eukaryotic communities in aquaculture for research and management is limited by the availability of technologies that can detect invading pathogens in an unbiased manner. Amplicon sequencing of 18S ribosomal DNA (rDNA) provides a potential pan-diagnostic test to overcome these biases; however, this technique is limited by a swamping effect of host DNA on low abundance parasite DNA. In this study, we have adapted a host 18S rDNA blocking assay to amplify eukaryotic DNA from salmonid tissue for amplicon sequencing.

Modulating signalling lifetime to optimise a prototypical animal opsin for optogenetic applications.

Animal opsins are light activated G-protein-coupled receptors, capable of optogenetic control of G-protein signalling for research or therapeutic applications. Animal opsins offer excellent photosensitivity, but their temporal resolution can be limited by long photoresponse duration when expressed outside their native cellular environment. Here, we explore methods for addressing this limitation for a prototypical animal opsin (human rod opsin) in HEK293T cells.

Synthetic CT generation from MRI using 3D transformer-based denoising diffusion model.

Background And Purpose: Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI-to-CT transformer-based improved denoising diffusion probabilistic model (MC-IDDPM) to translate MRI into high-quality sCT to facilitate radiation treatment planning.

Methods: MC-IDDPM implements diffusion processes with a shifted-window transformer network to generate sCT from MRI.

Early in vivo Radiation Damage Quantification for Pediatric Craniospinal Irradiation Using Longitudinal MRI for Intensity Modulated Proton Therapy.

Purpose: Proton vertebral body sparing craniospinal irradiation (CSI) treats the thecal sac while avoiding the anterior vertebral bodies in an effort to reduce myelosuppression and growth inhibition. However, robust treatment planning needs to compensate for proton range uncertainty, which contributes unwanted doses within the vertebral bodies. This work aimed to develop an early in vivo radiation damage quantification method using longitudinal magnetic resonance (MR) scans to quantify the dose effect during fractionated CSI.

Systematic review of how racialized health inequities are addressed in Epidemiologic Reviews articles (1979-2021): a critical conceptual and empirical content analysis and recommendations for best practices.

Critical analysis of the determinants of current and changing racialized health inequities, including the central role of racism, is an urgent priority for epidemiology, for both original research studies and epidemiologic review articles. Motivating our systematic overview review of Epidemiologic Reviews articles is the critical role of epidemiologic reviews in shaping discourse, research priorities, and policy relevant to the social patterning of population health. Our approach was first to document the number of articles published in Epidemiologic Reviews (1979-2021; n = 685) that either: (1) focused the review on racism and health, racial discrimination and health, or racialized health inequities (n = 27; 4%); (2) mentioned racialized groups but did not focus on racism or racialized health inequities (n = 399; 59%); or (3) included no mention of racialized groups or racialized health inequities (n = 250; 37%).

Amoeba species colonizing the gills of rainbow trout (Oncorhynchus mykiss) in Swiss aquaculture.

Nodular gill disease (NGD) is an infectious condition characterized by proliferative gill lesions leading to respiratory problems, oxygen deficiency and mortality in fish. Globally, NGD primarily impacts freshwater salmonids in intensive aquaculture systems. In recent years, numerous outbreaks of severe gill disease have affected more than half of the larger rainbow trout (Oncorhynchus mykiss) farms in Switzerland, mainly during spring and early summer.

Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models.

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools.

Impact of Age on Overall Survival Among Children With Wilms Tumor: A Population-based Registry Analysis.

Objectives: International trials have reported conflicting findings on whether the association between age and worse overall survival (OS) among children with Wilms tumor (WT) is due to age as an independent prognostic factor or the observation of more advanced disease at older ages. We sought to further elucidate this relationship using a population-based registry analysis.

Methods: The Surveillance, Epidemiology, and End Results database was queried for all patients diagnosed with WT under the age of 20.

Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.

Accurate tracking of anatomic landmarks is critical for motion management in liver radiation therapy. Ultrasound (US) is a safe, low-cost technology that is broadly available and offer real-time imaging capability. This study proposed a deep learning-based tracking method for the US image-guided radiation therapy.