deconvolution

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

This paper decodes natural scenes from optogenetic responses in mouse visual cortex and shows that patterned stimulation can reproduce visual representations with high fidelity.

Incomplete references leave bulk deconvolution targets non-identifiable, but identification has a computable precision price

With an incomplete deconvolution reference, operator history changes estimates and associations, and the target is not point-identified; fitdrop scores remedies on shrinkage, coverage, certification and false certification, and prices measurements.

A robust approach for preserving and sectioning fragile 3D spheroids for high-quality histological analysis

This paper introduces a method to preserve and section 3D spheroids for high-quality histological analysis, reducing sample loss and maintaining structural integrity.

Instant Prior-Free Resolution Enhancement for Cross-Modality Microscopy

This paper introduces NFR, a prior-free algorithm that enhances microscopy resolution without requiring PSF estimation, achieving results beyond the Sparrow limit and improving super-resolution techniques.

Interleukin-1β Drives Disease Progression in Arrhythmogenic Cardiomyopathy

Human arrhythmogenic cardiomyopathy hearts harbor a lesion niche of POSTN fibroblasts and NLRP3+ inflammatory macrophages; IL-1β neutralization in Dsg2-mutant mice reduced fibrosis, dysfunction, and ventricular ectopy.

Hormonally responsive bovine oviductal organoids recapitulate native oviductal secretions and enhance sperm capacitation

Bovine oviductal organoids treated with estradiol or a progesterone analog reproduce follicular and luteal transcriptomes and secretomes; estradiol-stimulated secretions boost bull sperm capacitation and acrosome reaction.

Enhancing Biomedical Optical Volumetric Imaging via Self-Supervised Orthogonal Learning

This paper introduces VALID, a self-supervised denoising method for volumetric biomedical imaging that improves structural fidelity across multiple modalities.

The RNA editing enzyme ADARB1 is readily detectable in primary auditory neurons and provides a means for automated counting

The paper identifies ADARB1 as a marker for spiral ganglion neurons and validates its use for automated counting across species.

HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology

HEDeST improves spatial transcriptomic deconvolution by integrating histology features, achieving single-cell resolution and outperforming existing methods in simulated and real cancer datasets.

Resolving and Quantifying Viral-Like Particles via Blind Deconvolution

This paper introduces EpiVirQuant, a computer vision framework for automated enumeration and sizing of viral-like particles, showing 18% higher detection than manual counting.

High-fidelity bioimage restoration via adversarial learning

This paper introduces NAFNet GAN, a bioimage restoration framework that achieves high fidelity and low latency, suitable for real-time microscopy applications.

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus

Thermostable RNaseH release of barcoded cDNA keeps sections intact for CODEX imaging, yielding same-section whole-transcriptome and protein maps plus CODEX-guided spot deconvolution in FFPE tissues.

Denoising, Deblurring, and optical Deconvolution for cryo-ET and light microscopy with a physics-informed deep neural network DeBCR

DeBCR improves microscopy image quality through physics-informed deep learning, outperforming existing models in denoising, deblurring, and optical deconvolution across light microscopy and cryo-ET.

HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals

The paper introduces HONeD-in, a method to deconvolve passive diffusion effects from functional MRI signals, revealing active brain activity patterns.

Immunoglobulin G binding as a quantitative marker of hepatocellular death across acute and chronic liver injury

Endogenous immunoglobulin G (IgG) immunostaining quantitatively labels apoptotic and necrotic hepatocytes across acute and chronic murine liver injury models, outperforming serum transaminases and histopathological Ishak scoring.

Cellular deconvolution of the brain with topological magnetic resonance image analysis

The paper introduces STRATA, a machine learning framework that estimates cell type proportions from MRI data, validated across disease models and species.

DeepCor: Denoising fMRI Data with Contrastive Autoencoders

DeepCor improves fMRI denoising by using contrastive autoencoders, outperforming CompCor on simulated data and enhancing connectivity differences in real datasets.

CLEAR-AF: Improved Autofluorescence Subtraction for Multiplexed Tissue Imaging via Polar Transformation and Gaussian Mixture Models

CLEAR-AF improves autofluorescence subtraction in multiplexed tissue imaging by using polar transformation and Gaussian Mixture Models, enhancing specificity, sensitivity, and reproducibility.

RamanMAE: Masked Autoencoders Enable Efficient Molecular Imaging by Learning Biologically Meaningful Spectral Representations

RamanMAE uses masked autoencoders to learn spectral representations from Raman data, enabling efficient processing and noise reduction in biological imaging.

Temporal Deconvolution of Mesoscale Recordings

The paper develops and evaluates four deconvolution methods to recover neuronal spiking rates from wide-field calcium imaging data, showing they outperform existing methods and highlight the importance of accurate temporal deconvolution.

Intravital Multimodal Imaging of Human Cortical Organoids for Chronic Stroke Treatment in Mice

This paper develops a multimodal imaging platform to track human cortical organoids in mice after stroke, showing their survival and maturation in specific brain regions.

Robust Deep Denoising of Soft X-Ray Tomography Data for Biological Research: Targeting Tilt Series Versus Reconstructed Tomograms

This study compares denoising raw tilt series versus reconstructed tomograms in soft X-ray tomography, finding that denoising tilt series provides more reliable results and faster inference.

A deconvolution solution to aliasing confusion in apparently undersampled oblique plane microscopy

The paper introduces a deconvolution method to reconstruct properly sampled volumes from apparently undersampled oblique plane microscopy data, improving upon a previous Fourier-stitching approach.

MuFIX - Enabling Combinations of Concurrent Optogenetics and Lock-in Amplification Fiber Photometry via Removal of Optogenetic Stimulation Crosstalk

MuFIX recovers crosstalk-contaminated photometry responses by interpolating across contaminated segments using multi-frequency lock-in amplification, accurately restoring original signals.

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

This paper introduces a method to distinguish BOLD and non-BOLD signals in fMRI by analyzing cross-cortical depth delay patterns, demonstrating its effectiveness with visual task data.

PENGUIN: A rapid and efficient image preprocessing tool for multiplexed spatial proteomics

PENGUIN is a preprocessing tool for multiplexed spatial proteomics that normalizes signal-to-noise ratios and reduces noise without manual annotation or machine learning training.

Physics-Informed Ellipsoidal Coordinate Encoding Implicit Neural Representation for high-resolution volumetric wide-field microscopy

This paper introduces PIECE-INR, a physics-informed method for high-fidelity 3D fluorescence data reconstruction in wide-field microscopy, achieving state-of-the-art results without requiring ground truth data.

A Biologically Inspired Attention Model for Neural Signal Analysis

This paper introduces SPARKS, a biologically inspired model that achieves high decoding accuracy and interpretability in neural signal analysis across multiple datasets.

QRATER: a collaborative and centralized imaging quality control web-based application.

QRATER is a web-based QC tool for neuroimaging that enables rating of QC images, showing varying time and agreement across tasks and rater experience.

Unsupervised deep learning enables blur-free super-resolution in two-photon microscopy

This paper introduces an unsupervised deep learning method for deblurring, super-resolution, and segmentation of two-photon microscopy images, achieving better performance than conventional methods.