A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection.

Camouflaged object detection (COD) and salient object detection (SOD) are two distinct yet closely-related computer vision tasks widely studied during the past decades. Though sharing the same purpose of segmenting an image into binary foreground and background regions, their distinction lies in the fact that COD focuses on concealed objects hidden in the image, while SOD concentrates on the most prominent objects in the image. Building universal segmentation models is currently a hot topic in the community.

CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT), typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis in case of misalignment between SPECT and CT images. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans.

Non-additive dosage-dependent effects of TaGS3 gene editing on grain size and weight in wheat.

Loss-of-function mutations induced by CRISPR-Cas9 in the TaGS3 gene homoeologs show non-additive dosage-dependent effects on grain size and weight and have potential utility for increasing grain yield in wheat. The grain size in cereals is one of the component traits contributing to yield. Previous studies showed that loss-of-function (LOF) mutations in GS3, encoding Gγ subunit of the multimeric G protein complex, increase grain size and weight in rice.

scMMAE: masked cross-attention network for single-cell multimodal omics fusion to enhance unimodal omics.

Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.

Exposing image splicing traces in scientific publications via uncertainty-guided refinement.

Recently, a surge in image manipulations in scientific publications has led to numerous retractions, highlighting the importance of image integrity. Although forensic detectors for image duplication and synthesis have been researched, the detection of image splicing in scientific publications remains largely unexplored. Splicing detection is more challenging than duplication detection due to the lack of reference images and more difficult than synthesis detection because of the presence of smaller tampered-with areas.

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy.

Super-Resolution Microscopic Imaging of Lipid Droplets in Living Cells via Carbonized Polymer Dot-Based Polarity-Responsive Nanoprobe.

Lipid droplets (LDs) are dynamic subcellular organelles that participate in various physiological processes, and their abnormality can also lead to various diseases. Tracing the dynamics of LDs in living cells will be valuable for understanding cell physiological states. Here, we employed a structured light illumination super-resolution imaging assisted with a carbonized polymer dot (CPD)-based fluorescence nanoprobe to track the travel paths of LDs and other organelles.

CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans.

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

3D printed VEGF-CPO biomaterial scaffold to promote subcutaneous vascularization and survival of transplanted islets for the treatment of diabetes.

Diabetes is a complex metabolic disease and islet transplantation is a promising approach for the treatment of diabetes. Unfortunately, the transplanted islets at the subcutaneous site are also affected by various adverse factors such as poor vascularization and hypoxia. In this study, we utilize biocompatible copolymers l-lactide and D,l-lactide to manufacture a biomaterial scaffold with a mesh-like structure via 3D printing technology, providing a material foundation for encapsulating pancreatic islet cells.

Face anti-spoofing with cross-stage relation enhancement and spoof material perception.

Face Anti-Spoofing (FAS) seeks to protect face recognition systems from spoofing attacks, which is applied extensively in scenarios such as access control, electronic payment, and security surveillance systems. Face anti-spoofing requires the integration of local details and global semantic information. Existing CNN-based methods rely on small stride or image patch-based feature extraction structures, which struggle to capture spatial and cross-layer feature correlations effectively.

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.

Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image.

CRISPR-based editing of the ω- and γ-gliadin gene clusters reduces wheat immunoreactivity without affecting grain protein quality.

Wheat immunotoxicity is associated with abnormal reaction to gluten-derived peptides. Attempts to reduce immunotoxicity using breeding and biotechnology often affect dough quality. Here, the multiplexed CRISPR-Cas9 editing of cultivar Fielder was used to modify gluten-encoding genes, specifically focusing on ω- and γ-gliadin gene copies, which were identified to be abundant in immunoreactive peptides based on the analysis of wheat genomes assembled using the long-read sequencing technologies.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.

Background: Single-photon emission computed tomography (SPECT) provides a mechanism to perform absorbed-dose quantification tasks for [Formula: see text]-particle radiopharmaceutical therapies ([Formula: see text]-RPTs). However, quantitative SPECT for [Formula: see text]-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

A list-mode multi-energy window low-count SPECT reconstruction method for isotopes with multiple emission peaks.

SPECT provides a mechanism to perform absorbed-dose quantification tasks for $\alpha$-particle radiopharmaceutical therapies ($\alpha$-RPTs). However, quantitative SPECT for $\alpha$-RPT is challenging due to the low number of detected counts, the complex emission spectrum, and other image-degrading artifacts. Towards addressing these challenges, we propose a low-count quantitative SPECT reconstruction method for isotopes with multiple emission peaks.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images.

Effects of exogenous retinoic acid on ocular parameters in Guinea pigs with form deprivation myopia.

Myopia is a common chronic eye disease, this study is to investigate the effects of exogenous retinoic acid (RA) on intraocular parameters, especially choroidal thickness (CT) and retinal thickness (RT), in guinea pigs with form deprivation myopia (FDM). A total of 80 male guinea pigs were divided randomly into 4 groups: Control, FDM, FDM + RA, and FDM + Citral groups. The FDM + RA group was given 24 mg/kg RA dissolved in 0.

Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.

Background: Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application.

Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT.

Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been using deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantages of increased radiation dose, increased scanner cost, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images.

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.

Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image.

Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images.

Synthetic images generated by simulation studies have a well-recognized role in developing and evaluating imaging systems and methods. However, for clinically relevant development and evaluation, the synthetic images must be clinically realistic and, ideally, have the same distribution as that of clinical images. Thus, mechanisms that can quantitatively evaluate this clinical realism and, ideally, the similarity in distributions of the real and synthetic images, are much needed.

Protocol to develop a microfluidic human corneal barrier-on-a-chip to evaluate the corneal epithelial wound repair process.

Organs-on-chips are microfluidic devices for cell culturing to simulate tissue- or organ-level physiology, providing new solutions other than traditional animal tests. Here, we describe a microfluidic platform consisting of human corneal cells and compartmentalizing channels to achieve fully integrated human cornea's barrier effects on the chip. We detail steps to verify the barrier effects and physiological phenotypes of microengineered human cornea.