Accelerated Navigator for Rapid ∆B Field Mapping for Real-Time Shimming and Motion Correction of Human Brain MRI.

∆B shim optimization performed at the beginning of an MR scan is unable to correct for ∆B field inhomogeneities caused by patient motion or hardware instability during scans. Navigator-based methods have been demonstrated previously to be effective for motion and shim correction. The purpose of this work was to accelerate volumetric navigators to allow fast acquisition of the parent navigated sequence with short real-time feedback time and high spatial resolution of the ∆B field mapping.

Projected external doses from an accidental release of ESS spallation-target products: time-dependence and radionuclide contribution.

The Swedish Radiation Safety Authority has presented a report on severe accident scenarios at the European Spallation Source (ESS) for dimensioning the emergency preparedness zones around the facility. The source-term in the scenario consisted of more than 80 tungsten-target spallation products with physical half-life (T½) exceeding 1 hour. The purpose of this study is to establish which of these radionuclides will become of highest importance in terms of the radiological consequences to residents in areas affected by an accident release.

Effect of a novel nutraceutical combination of EstroG-100 and γ-aminobutyric acid (GABA) in attenuating symptoms of menopause in healthy adult women: a randomized double-blinded placebo-controlled study.

Objective: Menopause is associated with a range of symptoms, including hot flashes, mood swings, and others that adversely affect the quality of life of women. This study evaluated the effects of a novel nutraceutical combination containing γ-aminobutyric acid (GABA, 50 mg) and EstroG-100 (514 mg) on these symptom clusters using validated questionnaires.

Methods: Eighty women were randomized into active (age: 53.

Gene set optimization for cancer transcriptomics using sparse principal component analysis.

A common approach for exploring pathway dysregulation in cancer involves the gene set or pathway analysis of tumor transcriptomic data. Unfortunately, the effectiveness of cancer gene set testing is limited by the fact that most gene set collections model gene activity in normal tissue, which can differ significantly from gene activity found within tumors. To address this challenge, we have developed a bioinformatics approach based on sparse principal component analysis (PCA) for optimizing existing gene set collections to reflect the pattern of gene activity in dysplastic tissue and have used this technique to optimize the Molecular Signatures Database (MSigDB) Hallmark collection for 21 solid human cancers profiled via bulk RNA-seq by The Tumor Genome Atlas (TCGA).

Network analysis using Krylov subspace trajectories.

We describe a set of network analysis methods based on the rows of the Krylov subspace matrix computed from a network adjacency matrix via power iteration using a non-random initial vector. We refer to these node-specific row vectors as Krylov subspace trajectories. While power iteration using a random initial starting vector is commonly applied to the network adjacency matrix to compute eigenvector centrality values, this application only uses the final vector generated after numerical convergence.

Cell signaling characterization for spatial transcriptomics (ST) data using network analysis.

We describe a network analysis-based cell-cell communication method for Spatial Transcriptomics (ST) data. For each evaluated ligand-receptor interaction, we define a fully connected, directed and weighted network model where nodes represent the individual ST locations with directed edge weights set to the product of the reduced rank reconstructed expression values for the ligand at the source location and cognate receptor at the target location divided by the squared distance between the locations. Using this network, we compute the weighted in-degree centrality to quantify signaling activity of the target ligand-receptor interaction at each location.

Gene set optimization for single cell transcriptomics.

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis.

An in-vivo approach to quantify in-MRI head motion tracking accuracy: comparison of markerless optical tracking versus fat-navigators.

Purpose: Head-motion tracking and correction remains a key area of research in MRI, but the lack of rigorous evaluation approaches hinders their optimization and comparison. This study introduces an in-vivo framework to assess head-motion tracking methods and compares a markerless optical system (MOS) to a fat-signal navigator (FatNav).

Methods: Six participants underwent 3T brain MRI using a T1-weighted (T1w) pulse-sequence with a fat-navigator module.

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.

Spatial transcriptomics (ST) provides critical insights into the spatial organization of gene expression, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is key to understanding tissue architecture and mechanisms underlying development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially-aware dimensionality reduction method for ST data.

Access to Ear and Hearing Care Globally: A Survey of Stakeholder Perceptions from the Lancet Commission on Global Hearing Loss.

Objective: Characterize global access to ear and hearing care (EHC) to inform future policy recommendations.

Study Design: Survey using convenience sampling.

Setting: Subjects were surveyed via contact lists of the World Health Organization, Global Otolaryngology-Head and Neck Surgery Initiative, and Global HEAR Collaborative.

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.

Spatial transcriptomics (ST) provides critical insights into the complex spatial organization of gene expression in tissues, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is essential for understanding tissue architecture and the mechanisms underlying various biological processes, including development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially aware dimensionality reduction method for spatial transcriptomics (ST) data.

Data Consistent Deep Rigid MRI Motion Correction.

Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies. Current retrospective rigid intra-slice motion correction techniques jointly optimize estimates of the image and the motion parameters. In this paper, we use a deep network to reduce the joint image-motion parameter search to a search over rigid motion parameters alone.

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.

Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis.

Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.

CAraCAl: CAMML with the integration of chromatin accessibility.

Background: A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success.

A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance.

We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer.

Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error.

We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios.

High resolution diffusion imaging in the unfixed post-mortem infant brain at 7T.

Diffusion MRI of the infant brain allows investigation of the organizational structure of maturing fibers during brain development. Post-mortem imaging has the potential to achieve high resolution by using long scan times, enabling precise assessment of small structures. Technical development for post-mortem diffusion MRI has primarily focused on scanning of fixed tissue, which is robust to effects like temperature drift that can cause unfixed tissue to degrade.

Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.

We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported.

Learning Task-Specific Strategies for Accelerated MRI.

Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance. In this work, we propose Tackle as a unified co-design framework for jointly optimizing subsampling, reconstruction, and prediction strategies for the performance on downstream tasks.

Segmentation of supragranular and infragranular layers in ultra-high resolution 7T MRI of the human cerebral cortex.

Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere scans at 120 m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.

A cellular resolution atlas of Broca's area.

Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries.

Cell type-specific interaction analysis using doublets in scRNA-seq.

Summary: Doublets are usually considered an unwanted artifact of single-cell RNA-sequencing (scRNA-seq) and are only identified in datasets for the sake of removal. However, if cells have a juxtacrine interaction with one another and maintain this association through an scRNA-seq processing pipeline that only partially dissociates the tissue, these doublets can provide meaningful biological information regarding the intercellular signals and processes occurring in the analyzed tissue. This is especially true for cases such as the immune compartment of the tumor microenvironment, where the frequency and the type of immune cell juxtacrine interactions can be a prognostic indicator.

Can we predict motion artifacts in clinical MRI before the scan completes?

Subject motion can cause artifacts in clinical MRI, frequently necessitating repeat scans. We propose to alleviate this inefficiency by predicting artifact scores from partial multi-shot multi-slice acquisitions, which may guide the operator in aborting corrupted scans early.

Clinical evaluation of k-space correlation informed motion artifact detection in segmented multi-slice MRI.

Motion artifacts can negatively impact diagnosis, patient experience, and radiology workflow especially when a patient recall is required. Detecting motion artifacts while the patient is still in the scanner could potentially improve workflow and reduce costs by enabling immediate corrective action. We demonstrate in a clinical k-space dataset that using cross-correlation between adjacent phase-encoding lines can detect motion artifacts directly from raw k-space in multi-shot multi-slice scans.