Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and the scarcity of pathologists specializing in cerebral tumors hinder comprehensive clinical tests for precise diagnosis. To address these challenges, we first established a brain tumor dataset of 3,520 cases collected from multiple centers.

Integrative spatial analysis reveals tumor heterogeneity and immune colony niche related to clinical outcomes in small cell lung cancer.

Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells.

Entity-level multiple instance learning for mesoscopic histopathology images classification with Bayesian collaborative learning and pathological prior transfer.

Background: Entity-level pathologic structures with independent structures and functions are at a mesoscopic scale between the cell-level and slide-level, containing limited structures thus providing fewer instances for multiple instance learning. This restricts the perception of local pathologic features and their relationships, causing semantic ambiguity and inefficiency of entity embedding.

Method: This study proposes a novel entity-level multiple instance learning.

OCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.

Background: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various ovarian cancer-related cells, based on morphological and immunohistochemical pathology images, is deemed an important step.

Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.

In the context of chronic liver diseases, where variability in progression necessitates early and precise diagnosis, this study addresses the limitations of traditional histological analysis and the shortcomings of existing deep learning approaches. A novel patch-level classification model employing multi-scale feature extraction and fusion was developed to enhance the grading accuracy and interpretability of liver biopsies, analyzing 1322 cases across various staining methods. The study also introduces a slide-level aggregation framework, comparing different diagnostic models, to efficiently integrate local histological information.

Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.

Objective: Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in model predictions. Therefore, it is crucial to analyze pathology-related predictive uncertainty in glomerular lesion recognition and unveil its relationship with pathological properties and its impact on model performance.

Prognostic factors affecting ALS progression through disease tollgates.

Background And Objectives: Understanding factors affecting the timing of critical clinical events in ALS progression.

Methods: We captured ALS progression based on the timing of critical events (tollgates), by augmenting 6366 patients' data from the PRO-ACT database with tollgate-passed information using classification. Time trajectories of passing ALS tollgates after the first visit were derived using Kaplan-Meier analyses.

Invasive carcinoma segmentation in whole slide images using MS-ResMTUNet.

Identifying the invasive cancer area is a crucial step in the automated diagnosis of digital pathology slices of the breast. When examining the pathological sections of patients with invasive ductal carcinoma, several evaluations are required specifically for the invasive cancer area. However, currently there is little work that can effectively distinguish the invasive cancer area from the ductal carcinoma in situ in whole slide images.

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

Purpose: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficult to obtain a training set with multiple stains because the labeling of pathology images is very time-consuming and tedious. Therefore, in this paper, we proposed an unsupervised stain augmentation-based method for segmentation of glomerular instances.

Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition.

Background: Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function.

Methods: Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists.

Stretching of immersed polyelectrolyte brushes in shear flow.

The way that polymer brushes respond to shear flow has important implications in various applications, including antifouling, corrosion protection, and stimuli-responsive materials. However, there is still much to learn about the behaviours and mechanisms that govern these responses. To address this gap in knowledge, our study uses X-ray reflectivity to investigate how poly(styrene sulfonate) (PSS) brushes stretch and change in different environments, such as isopropanol (a poor solvent), water (a good solvent), and aqueous solutions containing various cations (Cs, Ba, La, and Y).

Kinetics of Shear-Induced Structural Ordering in Dense Colloids.

The macroscopic rheological response of a colloidal solution is highly correlated with the local microscopic structure, as revealed by an Rheo-SAXS experiment with a high temporal resolution. Oscillatory shear can induce a strain-controlled ordering-to-disorder transition, resulting in a shear-thickening process that is different from the normal shear-thickening behavior that is driven by hydrodynamics and particle friction. We reveal that there is a complex time-dependent kinetics toward structural ordering under different applied strains.

Molecular subtypes and tumor microenvironment infiltration signatures based on cuproptosis-related genes in colon cancer.

Background: Colon cancer is one of the common cancers, and its prognosis remains to be improved. The role of cuproptosis as a newly discovered form of cell death in the development of colon cancer has not been determined.

Methods: Based on 983 colon cancer samples in the TCGA database and the GEO database, we performed a comprehensive genomic analysis to explore the molecular subtypes mediated by cuproptosis-related genes.

Unpaired virtual histological staining using prior-guided generative adversarial networks.

Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring.

Expression site agnostic histopathology image segmentation framework by self supervised domain adaption.

Motivation: With the sites of antigen expression different, the segmentation of immunohistochemical (IHC) histopathology images is challenging, due to the visual variances. With H&E images highlighting the tissue structure and cell distribution more broadly, transferring more salient features from H&E images can achieve considerable performance on expression site agnostic IHC images segmentation.

Methods: To the best of our knowledge, this is the first work that focuses on domain adaptive segmentation for different expression sites.

Unsupervised domain adaptive tumor region recognition for Ki67 automated assisted quantification.

Purpose: Ki67 is a protein associated with tumor proliferation and metastasis in breast cancer and acts as an essential prognostic factor. Clinical work requires recognizing tumor regions on Ki67-stained whole-slide images (WSIs) before quantitation. Deep learning has the potential to provide assistance but largely relies on massive annotations and consumes a huge amount of time and energy.

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.

Polymers in Lithium-Sulfur Batteries.

Lithium-sulfur batteries (LSBs) hold great promise as one of the next-generation power supplies for portable electronics and electric vehicles due to their ultrahigh energy density, cost effectiveness, and environmental benignity. However, their practical application has been impeded owing to the electronic insulation of sulfur and its intermediates, serious shuttle effect, large volume variation, and uncontrollable formation of lithium dendrites. Over the past decades, many pioneering strategies have been developed to address these issues via improving electrodes, electrolytes, separators and binders.

Advanced Materials for Energy-Water Systems: The Central Role of Water/Solid Interfaces in Adsorption, Reactivity, and Transport.

The structure, chemistry, and charge of interfaces between materials and aqueous fluids play a central role in determining properties and performance of numerous water systems. Sensors, membranes, sorbents, and heterogeneous catalysts almost uniformly rely on specific interactions between their surfaces and components dissolved or suspended in the water-and often the water molecules themselves-to detect and mitigate contaminants. Deleterious processes in these systems such as fouling, scaling (inorganic deposits), and corrosion are also governed by interfacial phenomena.

Sharpening Nanofiltration: Strategies for Enhanced Membrane Selectivity.

Nanofiltration plays an increasingly large role in many industrial applications, such as water treatment (e.g., desalination, water softening, and fluoride removal) and resource recovery (e.

An in situ shearing x-ray measurement system for exploring structures and dynamics at the solid-liquid interface.

Revealing interfacial structure and dynamics has been one of the essential thematic topics in material science and condensed matter physics. Synchrotron-based x-ray scattering techniques can deliver unique and insightful probing of interfacial structures and dynamics, in particular, in reflection geometries with higher surface and interfacial sensitivity than transmission geometries. We demonstrate the design and implementation of an in situ shearing x-ray measurement system, equipped with both inline parallel-plate and cone-and-plate shearing setups and operated at the advanced photon source at Argonne National Laboratory, to investigate the structures and dynamics of end-tethered polymers at the solid-liquid interface.

Enrichment and Distribution of Pb Ions in Zwitterionic Poly(cysteine methacrylate) Brushes at the Solid-Liquid Interface.

Cysteine-based polyzwitterionic brushes have been prepared via a two-step route. First, poly(allyl methacrylate) (PAMA) brushes have been grown from the surface of silicon substrates using surface-initiated atom transfer radical polymerization. The obtained PAMA brushes with free pendant vinyl groups were further modified via radical thiol-ene addition reaction to attach l-cysteine moieties.

Direct Observation of Structural Deformation Immunity for Understanding Oxygen Plasma Treatment-Enhanced Resistive Switching in HfO-Based Memristive Devices.

Oxygen ions' migration is the fundamental resistive switching (RS) mechanism of the binary metal oxides-based memristive devices, and recent studies have found that the RS performance can be enhanced through appropriate oxygen plasma treatment (OPT). However, the lack of experimental evidence observed directly from the microscopic level of materials and applicable understanding of how OPT improves the RS properties will cause significant difficulties in its further application. In this work, we apply scanning probe microscope (SPM)-based techniques to study the OPT-enhanced RS performance in prototypical HfO based memristive devices through in situ morphology and electrical measurements.

Improving the Electronic Transporting Property for Flexible Field-Effect Transistors with Naphthalene Diimide-Based Conjugated Polymer through Branching/Linear Side-Chain Engineering Strategy.

n-Type organic/polymeric semiconductors with high electron mobilities are highly demanded for future flexible organic circuits. Except for developing a new conjugated backbone, recent studies show that side-chain engineering also plays an indispensable role in boosting the charge-transporting property. In this paper, we report a new polymer PNDI2T-DTD with a representative n-type naphthalene diimide (NDI)-bithiophene backbone for high-performance n-type flexible thin-film transistors through branching/linear side-chain engineering strategy.