Effects of silver nanoparticles with different dosing regimens and exposure media on artificial ecosystem.

Due to the wide use of silver nanoparticles (AgNPs) in various fields, it is crucial to explore the potential negative impacts on the aquatic environment of AgNPs entering into the environment in different ways. In this study, comparative experiments were conducted to investigate the toxicological impacts of polyvinylpyrrolidone-coated silver nanoparticles (PVP-AgNPs) with two kinds of dosing regimens, continuous and one-time pulsed dosing, in different exposure media (deionized water and XiangJiang River water). There were a number of quite different experimental results (including 100% mortality of zebrafish, decline in the activity of enzymes, and lowest number and length of adventitious roots) in the one-time pulsed dosing regimen at high PVP-AgNP concentration exposure (HOE) compared to the three other treatments. Meanwhile, we determined that the concentration of leached silver ions from PVP-AgNPs was too low to play a role in zebrafish death. Those results showed that HOE led to a range of dramatic ecosystem impacts which were more destructive than those of other treatments. Moreover, compared with the continuous dosing regimen, despite the fact that higher toxicity was observed for HOE, there was little difference in the removal of total silver from the aquatic environment for the different dosing regimens. No obvious differences in ecological impacts were observed between different water columns under low concentration exposure. Overall, this work highlighted the fact that the toxicity of AgNPs was impacted by different dosing regimens in different exposure media, which may be helpful for assessments of ecological impacts on aquatic environments.

Advancement of Ag-Graphene Based Nanocomposites: An Overview of Synthesis and Its Applications.

Graphene has been employed as an excellent support for metal nanomaterials because of its unique structural and physicochemical properties. Silver nanoparticles (AgNPs) with exceptional properties have received considerable attention in various fields; however, particle aggregation limits its application. Therefore, the combination of AgNPs and graphene based nanocomposites (Ag-graphene based nanocomposites) has been widely explored to improve their properties and applications. Excitingly, enhanced antimicrobial, catalytic, and surface enhanced Raman scattering properties are obtained after their combination. In order to have a comprehensive knowledge of these nanocomposites, this Review highlights the chemical and biological synthesis of Ag-graphene nanocomposites. In particular, their applications as antimicrobial agents, catalysts, and sensors in biomedicine, agricultural protection, and environmental remediation and detection are covered. Meanwhile, the factors that influence the synthesis and applications are also briefly discussed. Furthermore, several important issues on the challenges and new directions are also provided for further development of these nanocomposites.

Applications of white rot fungi in bioremediation with nanoparticles and biosynthesis of metallic nanoparticles.

White rot fungi (WRF) are important environmental microorganisms that have been widely applied in many fields. To our knowledge, the application performance of WRF in bioremediation can be greatly improved by the combination with nanotechnology. And the preparation of metallic nanoparticles using WRF is an emerging biosynthesis approach. Understanding the interrelation of WRF and nanoparticles is important to further expand their applications. Thus, this mini-review summarizes the currently related reports mainly from the two different point of views. We highlight that nanoparticles as supports or synergistic agents can enhance the stability and bioremediation performance of WRF in wastewater treatment and the biosynthesis process and conditions of several important metallic nanoparticles by WRF. Furthermore, the potential toxicity of nanoparticles on WRF and challenges encountered are also discussed. Herein, we deem that this mini-review will strengthen the basic knowledge and provide valuable insight for the applications of WRF and nanoparticles.

Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image.

Accurate tissue segmentation in histopathological images is essential for promoting the development of precision pathology. However, the size of the digital pathological image is great, which needs to be tiled into small patches containing limited semantic information. To imitate the pathologist's diagnosis process and model the semantic relation of the whole slide image, We propose a semi-supervised pixel contrastive learning framework (SSPCL) which mainly includes an uncertainty-guided mutual dual consistency learning module (UMDC) and a cross image pixel-contrastive learning module (CIPC). The UMDC module enables efficient learning from unlabeled data through mutual dual-consistency and consensus-based uncertainty. The CIPC module aims at capturing the cross-patch semantic relationship by optimizing a contrastive loss between pixel embeddings. We also propose several novel domain-related sampling methods by utilizing the continuous spatial structure of adjacent image patches, which can avoid the problem of false sampling and improve the training efficiency. In this way, SSPCL significantly reduces the labeling cost on histopathological images and realizes the accurate quantitation of tissues. Extensive experiments on three tissue segmentation datasets demonstrate the effectiveness of SSPCL, which outperforms state-of-the-art up to 5.0% in mDice.

MG-Trans: Multi-Scale Graph Transformer With Information Bottleneck for Whole Slide Image Classification.

Multiple instance learning (MIL)-based methods have become the mainstream for processing the megapixel-sized whole slide image (WSI) with pyramid structure in the field of digital pathology. The current MIL-based methods usually crop a large number of patches from WSI at the highest magnification, resulting in a lot of redundancy in the input and feature space. Moreover, the spatial relations between patches can not be sufficiently modeled, which may weaken the model's discriminative ability on fine-grained features. To solve the above limitations, we propose a Multi-scale Graph Transformer (MG-Trans) with information bottleneck for whole slide image classification. MG-Trans is composed of three modules: patch anchoring module (PAM), dynamic structure information learning module (SILM), and multi-scale information bottleneck module (MIBM). Specifically, PAM utilizes the class attention map generated from the multi-head self-attention of vision Transformer to identify and sample the informative patches. SILM explicitly introduces the local tissue structure information into the Transformer block to sufficiently model the spatial relations between patches. MIBM effectively fuses the multi-scale patch features by utilizing the principle of information bottleneck to generate a robust and compact bag-level representation. Besides, we also propose a semantic consistency loss to stabilize the training of the whole model. Extensive studies on three subtyping datasets and seven gene mutation detection datasets demonstrate the superiority of MG-Trans.

A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image.

Pathological primary tumor (pT) stage focuses on the infiltration degree of the primary tumor to surrounding tissues, which relates to the prognosis and treatment choices. The pT staging relies on the field-of-views from multiple magnifications in the gigapixel images, which makes pixel-level annotation difficult. Therefore, this task is usually formulated as a weakly supervised whole slide image (WSI) classification task with the slide-level label. Existing weakly-supervised classification methods mainly follow the multiple instance learning paradigm, which takes the patches from single magnification as the instances and extracts their morphological features independently. However, they cannot progressively represent the contextual information from multiple magnifications, which is critical for pT staging. Therefore, we propose a structure-aware hierarchical graph-based multi-instance learning framework (SGMF) inspired by the diagnostic process of pathologists. Specifically, a novel graph-based instance organization method is proposed, namely structure-aware hierarchical graph (SAHG), to represent the WSI. Based on that, we design a novel hierarchical attention-based graph representation (HAGR) network to capture the critical patterns for pT staging by learning cross-scale spatial features. Finally, the top nodes of SAHG are aggregated by a global attention layer for bag-level representation. Extensive studies on three large-scale multi-center pT staging datasets with two different cancer types demonstrate the effectiveness of SGMF, which outperforms state-of-the-art up to 5.6% in the F1 score.

Stability, transport and ecosystem effects of graphene in water and soil environments.

Graphene nanomaterials (GMs), such as graphene oxide (GO) and reduced graphene oxide (rGO), have been widely applied in various fields. Due to the rapid increase in production and application, the inevitable release of GMs into water and soil environments poses potential health and ecosystem risks. Upon exposure, the behavior, transport, and fate of GMs may be altered after interacting with the relevant environmental conditions. GMs can affect the microbial communities as well. Thus, it is imperative to understand the interaction between the GMs and the environmental systems for predicting their risks. For this purpose, this review highlights the influence of the most relevant environmental factors on the stability, aggregation, and transformation of GMs in aquatic environments. Moreover, the transport of GMs and microbial communities changes have also been presented based on the recent findings. To the best of our knowledge, this review covered most of the recent related studies and will allow for accurate predictions of the fate and risks associated with GMs. In consideration of the diversity of GMs and the complexity of environmental factors, further studies should be focused on their inherent properties and amicable development.