Characteristics and Influence Factors of Pb(II) Adsorption by Graphene Oxide-Montmorillonite Composite.

This study presents the fabrication of a novel porous composite of graphene oxide-montmorillonite (GO-MMT) through the modification of montmorillonite using the freeze-drying method for the purpose of Pb removal. The characterization of the GO-MMT composite was conducted using scanning electron microscopy, Fourier transform infrared spectrometry, and X-ray diffraction. The results from batch adsorption experiments revealed that the GO-MMT composite exhibited a superior capacity for Pb removal compared to MMT. Furthermore, a single factor experiment confirmed that the dosage of the GO-MMT composite or GO, pH, temperature, and reaction time all significantly influenced the adsorption of Pb by the GO-MMT composite, MMT, or GO. This superiority can be attributed to the presence of oxygen-containing functional groups, the site-blocking effect, and the ion exchange mechanism exhibited by the GO-MMT composite.

Vague-Segment Technique: Automatic Computation of Tumor Stroma Ratio for Breast Cancer on Whole Slides.

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.