Perfluorobutanoic acid weakens the heterogeneous aggregation of microplastics and microalgae: Perspective from physicochemical properties, extracellular polymeric substances secretion and DLVO theory.

Microplastics (MPs) and per- and poly-fluoroalkyl substances extensively coexist in aquatic environments and potentially endanger organisms. Microalgae may decrease the effective concentration of pollutants via hetero-aggregation with MPs and adsorption of emerging contaminants. However, the potential influence of coexistent pollutants on hetero-aggregation of MPs and microalgae remains unknown.

Influence of perfluorooctanoic acid on alkaline anaerobic fermentation of waste activated sludge: Perspective from volatile fatty acids production and sludge reduction.

Alkaline anaerobic fermentation is an effective approach for resource utilization and reduction of waste activated sludge (WAS). Perfluorooctanoic acid (PFOA) is widespread in WAS, however, its potential impact on alkaline anaerobic fermentation of WAS remains largely unknown. Hence, this study focused on investigating the influence of PFOA on volatile fatty acids (VFAs) production and sludge reduction during alkaline anaerobic fermentation (pH = 10 ± 0.

Feasibility of simultaneous optimization of Anammox start-up and nitrogen removal performance by intermittent dosing of nanoscale zero-valent iron.

The long acclimation period and sensitivity to environmental conditions of Anammox are the bottlenecks for its promotion and application. An innovative strategy was adopted to accelerate functional microbial enhancement and improve nitrogen removal performance by inoculating cryopreserved Anammox sludge and activated sludge with intermittent dosing of nanoscale zero-valent iron (nZVI). The acclimation time was shortened by 76 days with nitrogen removal efficiency (NRE) reaching up to 91.

Polystyrene microplastics enhanced the effect of PFOA on Chlorella sorokiniana: Perspective from the cellular and molecular levels.

Microplastics (MPs) commonly coexist with other contaminants and alter their toxicity. Perfluorooctanoic acid (PFOA), an emerging pollutant, may interact with MPs but remain largely unknown about the joint toxicity of PFOA and MPs. Hence, this research explored the single and joint effects of PFOA and polystyrene microplastics (PS-MPs) on microalgae (Chlorella sorokiniana) at the cellular and molecular levels.

Gray Learning From Non-IID Data With Out-of-Distribution Samples.

The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-independent and identically distributed (non-IID) datasets comprising both in-and out-of-distribution samples. In an ideal scenario, the majority of samples would be in-distribution, while samples that deviate semantically would be identified as out-of-distribution and excluded during the annotation process. However, experts may erroneously classify these out-of-distribution samples as in-distribution, assigning them labels that are inherently unreliable.

Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection.

The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to distributional vulnerability in deep neural networks, which can subsequently lead to high-confidence predictions for OOD samples. This is mainly due to the absence of OOD samples during training, which fails to constrain the network properly. To tackle this issue, several state-of-the-art methods include adding extra OOD samples to training and assign them with manually-defined labels.

Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data.

Deep neural networks for image classification only learn to map in-distribution inputs to their corresponding ground-truth labels in training without differentiating out-of-distribution samples from in-distribution ones. This results from the assumption that all samples are independent and identically distributed (IID) without distributional distinction. Therefore, a pretrained network learned from in-distribution samples treats out-of-distribution samples as in-distribution and makes high-confidence predictions on them in the test phase.

Revealing the Distributional Vulnerability of Discriminators by Implicit Generators.

In deep neural learning, a discriminator trained on in-distribution (ID) samples may make high-confidence predictions on out-of-distribution (OOD) samples. This triggers a significant matter for robust, trustworthy and safe deep learning. The issue is primarily caused by the limited ID samples observable in training the discriminator when OOD samples are unavailable.

Response mechanisms of Chlorella sorokiniana to microplastics and PFOA stress: Photosynthesis, oxidative stress, extracellular polymeric substances and antioxidant system.

Co-pollution of microplastics and per- and polyfluoroalkyl substances (PFAS) is prevailing in the aquatic environment. However, the risks of coexisting microplastics and PFAS on organisms remain unknown. This study investigated the response mechanisms of Chlorella sorokiniana (C.

Tolerance and recovery of aerobic granular sludge: Impact of perfluorooctanoic acid.

The widespread use of perfluorooctanoic acid (PFOA) has rendered its frequent detection in wastewater. The tolerance and recovery of aerobic granular sludge (AGS) to PFOA were investigated in short-term (Phase Ⅰ) and long-term (Phase Ⅱ, operation strategy adjustment: shortening aeration time and prolonging anaerobic and anoxic time). Results showed that in Phase Ⅰ, the performance of R2 reactor (0.

Response of aerobic granular sludge under polyethylene microplastics stress: Physicochemical properties, decontamination performance, and microbial community.

Microplastics are widely detected in sewage and sludge in wastewater treatment plants and can thereby influence biological processes. In this study, the overall impacts of polyethylene microplastics (PE MPs) and their toxicity mechanisms on aerobic granular sludge (AGS) were investigated. Particle structure, settling properties, particle size distribution, and extracellular polymeric substance characteristics of AGS were significantly affected by PE MPs with concentrations of 20 and 200 n/L.

Integrating machine learning interpretation methods for investigating nanoparticle uptake during seed priming and its biological effects.

Seed priming by nanoparticles is an environmentally-friendly solution for alleviating malnutrition, promoting crop growth, and mitigating environmental stress. However, there is a knowledge gap regarding the nanoparticle uptake and the underlying physiological mechanism. Machine learning has great potential for understanding the biological effects of nanoparticles.

Interlayer spacing enlarged 2D 1T-MoS and VCT MXene as superior anodes for boosting potassium-ion diffusion coefficient.

Two-dimensional (2D) materials used in potassium ion batteries (PIBs) have high theoretical capacitance and excellent rate characteristics. However, the origin of low diffusion of potassium ions and poor storage kinetics still remain challenge mainly due to the large size of potassium ions (0.138 nm) and narrow 2D interlayer spacing.

Rational design of TiCCl MXenes nanodots-interspersed MXene@NiAl-layered double hydroxides for enhanced pseudocapacitor storage.

Although electrodes based on two dimensional hybrids with interstratification-assemble have been widely studied for supercapacitors, the performance enhancement still remains challenge mainly due to the random dispersion of surface passivated two dimensional nanosheets. Herein, a new covalent surface functionalization of MXene-based TiCCl nanodots-interspersed MXene@NiAl-layered double hydroxides (QD-TiCCl@NiAl-LDHs) hybrid electrode with superior pseudocapacitor storage performance has been elaborately designed by electrostatic-assembled. As a result, the QD-TiCCl@NiAl-LDHs electrode exhibits a super specific capacitance of 2010.

Defense pathways of Chlamydomonas reinhardtii under silver nanoparticle stress: Extracellular biosorption, internalization and antioxidant genes.

Numerous studies have been investigated the toxic effects of silver nanoparticle (Ag-NPs) on algae; however, little attention has been paid to the defense pathways of algae cells to Ag-NPs. In the study, Chlamydomonas reinhardtii (C. reinhardtii) was selected as a model organism to investigate the defense mechanisms to Ag-NPs exposure.

Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning.

Safety concerns of engineered nanoparticles (ENPs) hamper their applications and commercialization in many potential fields. Machine learning has been proved as a great tool to understand the complex ENP-organism-environment relationship. However, good-performance machine learning models usually exist as black boxes, which may be difficult to build trust and whose ways of expressing knowledge rarely directly map to forms familiar to scientists.

Toxicity mechanism of silver nanoparticles to Chlamydomonas reinhardtii: photosynthesis, oxidative stress, membrane permeability, and ultrastructure analysis.

Silver nanoparticles (Ag-NPs) are widely used in daily life and inevitably discharged into the aquatic environment, causing increasingly serious pollution. Research on the toxicity of Ag-NPs is still in infancy, little information is available on the relationships between oxidative stress and antioxidant, as well as damaging degrees of Ag-NPs to cellular structural components of Chlamydomonas reinhardtii (C. reinhardtiii).

Synthesis and photocatalytic activity study of S-doped WO under visible light irradiation.

In this study, a photocatalyst S-doped WO was successfully synthesized by the hydrothermal method. The prepared undoped and S-doped WO samples were then characterized by XRD, SEM, XPS, and UV-vis DRS. The results showed that the band gap energy of S-doped WO was lower than that of the undoped WO which led to a better absorption of visible light.

Effect of Chlamydomonas reinhardtii on the fate of CuO nanoparticles in aquatic environment.

In this study, the effect of Chlamydomonas reinhardtii on the fate of CuO nanoparticles (CuO-NPs) in aquatic environment were investigated in terms of the colloidal stability, the free Cu releasing, extracellular adsorption Cu (Cu) and intracellular assimilation Cu (Cu). The results showed that, with the increasing microalgal density, the absolute value of zeta potential of CuO-NPs decreased and the mean hydrodynamic diameter (MHD) became larger, leading to a better aggregation and settling behavior of CuO-NPs. The microalgae also promoted the free Cu releasing, however, inhibited adsorption and assimilation of metal nanoparticles (MNPs) into microalgal cells, resulting in the reduction of the Cu and Cu per microalgal cell.

Degradation of cefradine in alga-containing water environment: a mechanism and kinetic study.

Large quantities of antibiotics are manufactured, used, and eventually discharged into alga-containing water environment as prototypes, by-products, or transformation products. Different activities of Chlamydomonas reinhardtii toward cefradine (CFD) were studied, and the results indicated that CFD is resistant (removal rate of 5.45-14.

Study on the interaction mechanism between cefradine and Chlamydomonas reinhardtii in water solutions under dark condition.

Our research investigated the hormesis effect of cefradine on the specific growth rates (μ) of single-celled algae (Chlamydomonas reinhardtii) from aqueous solutions. We found the specific growth rate of C. reinhardtii slightly increased with cefradine concentrations within the range 0.

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value.

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true.

[Clinical application of reverse radial hypothenar flap for finger soft tissue defect].

Objective: To investigate the therapeutic effect of reverse radial hypothenar flap for finger soft tissue defect.

Methods: From Mar. 2006 to Mar.