BreasTDLUSeg: A coarse-to-fine framework for segmentation of breast terminal duct lobular units on histopathological whole-slide images.

Automatic segmentation of breast terminal duct lobular units (TDLUs) on histopathological whole-slide images (WSIs) is crucial for the quantitative evaluation of TDLUs in the diagnostic and prognostic analysis of breast cancer. However, TDLU segmentation remains a great challenge due to its highly heterogeneous sizes, structures, and morphologies as well as the small areas on WSIs. In this study, we propose BreasTDLUSeg, an efficient coarse-to-fine two-stage framework based on multi-scale attention to achieve localization and precise segmentation of TDLUs on hematoxylin and eosin (H&E)-stained WSIs.

Analysis of the acoustoelectric response of SAW gas sensors using a COM model.

Surface acoustic wave (SAW) gas sensors based on the acoustoelectric effect exhibit wide application prospects for in situ gas detection. However, establishing accurate models for calculating the scattering parameters of SAW gas sensors remains a challenge. Here, we present a coupling of modes (COM) model that includes the acoustoelectric effect and specifically explains the nonmonotonic variation in the center frequency with respect to the sensing film's sheet conductivity.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.

Objective: To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA.

Materials And Methods: This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129).

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.

Highly Sensitive Chemiresistive HS Detection at Subzero Temperature over the Sb-Doped SnO@g-CN Heterojunctions under UV Illumination.

NASA has detected HS in the persistently shadowed region of the lunar South Pole through NIR and UV/vis spectroscopy remotely, but in situ detection is generally considered to be more accurate and convincing. However, subzero temperatures in space drastically reduce chemisorbed oxygen ions for gas sensing reactions, making gas sensing at subzero temperature something that has rarely been attempted. Herein, we report an in situ semiconductor HS gas sensor assisted by UV illumination at subzero temperature.

Extracting Lamb wave vibrating modes with convolutional neural network.

In recent years, micro-acoustic devices, such as surface acoustic wave (SAW) devices, and bulk acoustic wave (BAW) devices have been widely used in the areas of Internet of Things and mobile communication. With the increasing demand of information transmission speed, working frequencies of micro-acoustic devices are becoming much higher. To meet the emerging demand, Lamb wave devices with characteristics that are fit for high working frequency come into being.

DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer.

The morphological evaluation of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H& E)-stained histopathological images is the key to breast cancer (BCa) diagnosis, prognosis, and therapeutic response prediction. For now, the qualitative assessment of TILs is carried out by pathologists, and computer-aided automatic lymphocyte measurement is still a great challenge because of the small size and complex distribution of lymphocytes. In this paper, we propose a novel dense dual-task network (DDTNet) to simultaneously achieve automatic TIL detection and segmentation in histopathological images.

Sponge-like loose and porous SnO microspheres with rich oxygen vacancies and their enhanced room-temperature gas-sensing performance.

Structure and surface modification of semiconductor materials are of great importance in gas sensors. In this study, a facile citric acid-assisted solvothermal method a precise calcination process was leveraged to synthesize sponge-like loose and porous SnO microspheres with rich oxygen vacancies (denoted as LP-SnO-O). When this material was used in a gas sensor, it exhibited an extremely high response to 10 ppm hydrogen sulfide gas at room temperature (/ = 9688), which was 54 times higher than that of commercial SnO.

An Algorithm for Solving the Phase Ambiguity Problem of SAW Delay-Line Sensor Systems.

Solving the phase ambiguity problem is crucial to achieving a wide-range and high-precision measurement for the frequency-domain sampling (FDS)-based surface acoustic wave (SAW) delay-line sensor systems. This study proposes an improved phase estimation algorithm called dual-band phase estimation (DBPE) to solve the problem. By using DBPE, the SAW sensor system can obtain an extensive and alterable measuring range without further requirements for sensor design or transmitted signals.

Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Background: To prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA).

Methods: Patients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ).

Prediction of Treatment Response According to ASAS-EULAR Management Recommendations in 1 Year for Hip Involvement in Axial Spondyloarthritis Based on MRI and Clinical Indicators.

Background: To predict the treatment response for axial spondyloarthritis (axSpA) with hip involvement in 1 year based on MRI and clinical indicators.

Methods: A total of 77 axSpA patients with hip involvement (60 males; median age, 25 years; interquartile, 22-31 years old) were treated with a drug recommended by the Assessment of SpondyloArthritis international Society and the European League Against Rheumatism (ASAS-EULAR) management. They were prospectively enrolled according to Assessment in SpondyloArthritis international Society (ASAS) criteria.

High-fidelity acoustic signal enhancement for phase-OTDR using supervised learning.

Phase-measuring phase-sensitive optical time-domain reflectometry (OTDR) has been widely used for the distributed acoustic sensing. However, the demodulated phase signals are generally noisy due to the laser frequency drift, laser phase noise, and interference fading. These issues are usually addressed individually.

Numerical Analysis of Viscous Dissipation in Microchannel Sensor Based on Phononic Crystal.

Phononic crystals with phononic band gaps varying in different parameters represent a promising structure for sensing. Equipping microchannel sensors with phononic crystals has also become a great area of interest in research. For building a microchannels system compatible with conventional micro-electro-mechanical system (MEMS) technology, SU-8 is an optimal choice, because it has been used in both fields for a long time.

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images.

Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer.

Background: Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer.

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function.

Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

Purpose: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs.

Low Computational Cost Distributed Acoustic Sensing Using Analog I/Q Demodulation.

Distributed acoustic sensing based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been widely used in many fields. Phase demodulation of the Φ-OTDR signal is essential for undistorted acoustic measurement. Digital coherent detection is a universal method to implement phase demodulation, but it may cause severe computational burden.

BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches.

Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis.

The integrative analysis of histopathological images and genomic data has received increasing attention for studying the complex mechanisms of driving cancers. However, most image-genomic studies have been restricted to combining histopathological images with the single modality of genomic data (e.g.

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images.

Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease.

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI.

Objectives: To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T-weighted fat suppression (T-FS) and diffusion-weighted MRI (DWI).

Methods: We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T-FS and DWI MRI scan.