Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Exploring the influence of nasal vestibule structure on nasal obstruction using CFD and Machine Learning method.

Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to determine anatomical features which have a large influence on airflow through a combination of Computational Fluid Dynamics (CFD) and machine learning method. Firstly, the aerodynamic characteristics of the nasal vestibule are detailedly analyzed using the CFD method. Based on CFD simulation results, we divide the nasal vestibule into two types with distinctly different airflow patterns, which is consistent with clinical findings. Secondly, we explore the relationship between anatomical features and aerodynamic characteristics by developing a novel machine learning model which could predict airflow patterns based on several anatomical features. Feature mining is performed to determine the anatomical feature which has the greatest impact on respiratory function. The method is developed and validated on 41 unilateral nasal vestibules from 26 patients with nasal obstruction. The correctness of the CFD analysis and the developed model is verified by comparing them with clinical findings.

Revelation of hidden 2D atmospheric turbulence strength fields from turbulence effects in infrared imaging.

Turbulence exists widely in the natural atmosphere and in industrial fluids. Strong randomness, anisotropy and mixing of multiple-scale eddies complicate the analysis and measurement of atmospheric turbulence. Although the spatially integrated strength of atmospheric turbulence can be roughly measured indirectly by Doppler radar or laser, direct measurement of two-dimensional (2D) strength fields of atmospheric turbulence is challenging. Here we attempt to solve this problem through infrared imaging. Specifically, we propose a physically boosted cooperative learning framework, termed the PBCL, to quantify 2D turbulence strength from infrared images. To demonstrate the capability of the PBCL, we constructed a dataset with 137,336 infrared images and corresponding 2D turbulence strength fields. The experimental results show that cooperative learning brings performance improvements, enabling the PBCL to simultaneously learn turbulence strength fields and inhibit adverse turbulence effects in images. Our work demonstrates the potential of imaging in measuring physical quantity fields.

Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes.

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.

Effects of UV/Ag-TiO2/O3 advanced oxidation on unicellular green alga Dunaliella salina: implications for removal of invasive species from ballast water.

The UV/Ag-TiO2/O3 process was investigated for ballast water treatment using Dunaliella salina as an indicator. Inactivation curves were obtained, and the toxicity of effluent was determined. Compared with individual unit processes using ozone or UV/Ag-TiO2, the inactivation efficiency of D. salina by the combined UV/Ag-TiO2/O3 process was enhanced. The presence of ozone caused an immediate decrease in chlorophyll a (chl-a) concentration. Inactivation efficiency and ch1-a removal efficiency were positively correlated with ozone dose and ultraviolet intensity. The initial total residual oxidant (TRO) concentration of effluent increased with increasing ozone dose, and persistence of TRO resulted in an extended period of toxicity. The results suggest that UV/Ag-TiO2/O3 has potential for ballast water treatment.