Flexible cellulose nanofiber aerogel with enhanced porous structure and its applications in copper(II) removal.

In order to achieve an aerogel with both rigid pore structures and desired flexibility, stiff carboxyl-functionalized cellulose nanofiber (CNFs) were introduced into a flexible polyvinyl alcohol-polyethyleneimine (PVA-PEI) crosslinking network, with 4-formylphenylboronic acid (4FPBA) bridging within the PVA-PEI network to enable dynamic boroxine and imine bond formation. The strong covalent bonds and hydrogen connections between CNF and the crosslinking network enhanced the wet stability of the aerogel while also contributed to its thermal stability. Importantly, the harmonious coordination between the stiff CNF and the flexible polymer chains not only facilitated aerogel flexibility but also enhanced its increased specific surface area by improving pore structure. Moreover, the inclusion of CNF enhanced the adsorption capacity of the aerogel, rendering it effective for removing heavy metal ions. The specific surface area and adsorption capacity for copper ions of the aerogel increased significantly with a 3 wt% addition CNF suspension, reaching 19.74 m2 g-1 and 60.28 mg g-1, respectively. These values represent a remarkable increase of 590.21 % and 213.96 %, respectively, compared to the blank aerogel. The CNF-enhanced aerogel in this study, characterized by its well-defined pore structures, and desired flexibility, demonstrates versatile applicability across multiple domains, including environmental protection, thermal insulation, electrode fabrication, and beyond.

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. Results demonstrated that PS-MPs increased PFOA bioavailability by altering cell membrane permeability, thus aggravating biotoxicity (synergistic effect). Meanwhile, the defense mechanisms (antioxidant system modulation and extracellular polymeric substances secretion) of Chlorella sorokiniana were activated to alleviate toxicity. Additionally, transcriptomic analysis illustrated that co-exposure had more differential expression genes (DEGs; 4379 DEGs) than single-exposure (PFOA: 2533 DEGs; PS-MPs: 492 DEGs), which were mainly distributed in the GO terms associated with the membrane composition and antioxidant system. The molecular regulatory network further revealed that PS-MPs and PFOA primarily regulated the response mechanisms of Chlorella sorokiniana by altering the ribosome biogenesis, photosynthesis, citrate cycle, oxidative stress, and antioxidant system (antioxidant enzyme, glutathione-ascorbate cycle). These findings elucidated that PS-MPs enhanced the effect of PFOA, providing new insights into the influences of MPs and PFOA on algae and the risk assessment of multiple contaminants. ENVIRONMENTAL IMPLICATION: MPs and PFAS, emerging contaminants, are difficult to degrade and pose a non-negligible threat to organisms. Co-pollution of MPs and PFAS is ubiquitous in the aquatic environment, while risks of co-existence to organisms remain unknown. The present study revealed the toxicity and defense mechanisms of microalgae exposure to PS-MPs and PFOA from cellular and molecular levels. According to biochemical and transcriptomic analyses, PS-MPs increased PFOA bioavailability and enhanced the effect of PFOA on Chlorella sorokiniana, showing a synergistic effect. This research provides a basis for assessing the eco-environmental risks of MPs and PFAS.

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. This mixture of unreliable labels and varied data types makes the task of learning robust neural networks notably challenging. We observe that both in-and out-of-distribution samples can almost invariably be ruled out from belonging to certain classes, aside from those corresponding to unreliable ground-truth labels. This opens the possibility of utilizing reliable complementary labels that indicate the classes to which a sample does not belong. Guided by this insight, we introduce a novel approach, termed gray learning (GL), which leverages both ground-truth and complementary labels. Crucially, GL adaptively adjusts the loss weights for these two label types based on prediction confidence levels. By grounding our approach in statistical learning theory, we derive bounds for the generalization error, demonstrating that GL achieves tight constraints even in non-IID settings. Extensive experimental evaluations reveal that our method significantly outperforms alternative approaches grounded in robust statistics.

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. However, this practice can introduce unreliable labeling, negatively affecting ID classification. The distributional vulnerability presents a critical challenge for non-IID deep learning, which aims for OOD-tolerant ID classification by balancing ID generalization and OOD detection. In this paper, we introduce a novel supervision adaptation approach to generate adaptive supervision information for OOD samples, making them more compatible with ID samples. First, we measure the dependency between ID samples and their labels using mutual information, revealing that the supervision information can be represented in terms of negative probabilities across all classes. Second, we investigate data correlations between ID and OOD samples by solving a series of binary regression problems, with the goal of refining the supervision information for more distinctly separable ID classes. Our extensive experiments on four advanced network architectures, two ID datasets, and eleven diversified OOD datasets demonstrate the efficacy of our supervision adaptation approach in improving both ID classification and OOD detection capabilities.

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. To address this issue, we draw out-of-distribution samples from the vicinity distribution of training in-distribution samples for learning to reject the prediction on out-of-distribution inputs. A cross-class vicinity distribution is introduced by assuming that an out-of-distribution sample generated by mixing multiple in-distribution samples does not share the same classes of its constituents. We, thus, improve the discriminability of a pretrained network by finetuning it with out-of-distribution samples drawn from the cross-class vicinity distribution, where each out-of-distribution input corresponds to a complementary label. Experiments on various in-/out-of-distribution datasets show that the proposed method significantly outperforms the existing methods in improving the capacity of discriminating between in-and out-of-distribution samples.

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. We propose a general approach for fine-tuning discriminators by implicit generators (FIG). FIG is grounded on information theory and applicable to standard discriminators without retraining. It improves the ability of a standard discriminator in distinguishing ID and OOD samples by generating and penalizing its specific OOD samples. According to the Shannon entropy, an energy-based implicit generator is inferred from a discriminator without extra training costs. Then, a Langevin dynamic sampler draws specific OOD samples for the implicit generator. Lastly, we design a regularizer fitting the design principle of the implicit generator to induce high entropy on those generated OOD samples. The experiments on different networks and datasets demonstrate that FIG achieves the state-of-the-art OOD detection performance.

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. sorokiniana) under polystyrene microplastics (PS-MPs) and perfluorooctanoic acid (PFOA) stress, including toxicity and defense mechanisms. C. sorokiniana was exposed to PS-MPs (10 mg/L) and PFOA (0.05, 0.5, and 5 mg/L) and their mixtures for 96 h, respectively. We found that the dominant toxicity mechanism of PFOA and PS-MPs to C. sorokiniana was dissimilar. PS-MPs mainly inhibited photosynthesis through shading effect, while PFOA mainly induced oxidative stress by reactive oxygen species. The co-exposure of PFOA and PS-MPs aggravated biotoxicity (maximum inhibition rate: 27.27 ± 2.44%), such as photosynthesis inhibition, physical damage, and oxidative stress, compared with individuals. To alleviate toxicity, C. sorokiniana activated defense mechanisms. Extracellular polymeric substances were the first barrier to protect cells, the effect on its secretion was ordered PS-MPs+5PFOA > PS-MPs > 5PFOA, and IBRv2 values were 2.37, 1.35, 1.11, respectively. Antioxidant system was thought of second defense pathway, the influence order of treatment groups was PS-MPs+5PFOA > 5PFOA > PS-MPs, and its IBRv2 values were 2.89, 1.69, 0.25, respectively. Our findings provide valuable information on the complex impacts of PFOA and PS-MPs, which facilitate the ecological risk assessment of multiple pollutants.

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 (0.05 mg/L PFOA) was slightly negatively affected, while 0.5 and 2.0 mg/L PFOA in R3 and R4 reactors significantly damaged the key enzyme activities of AGS, leading to deterioration of nutrients removal. TN and TP removal efficiencies decreased correspondingly from 79.32% to 78.41% on day 0 to 74.66% and 74.14% in R2 and 68.57% and 67.80% in R3 and 56.94% and 57.47% in R4 on day 7, respectively. In Phase Ⅱ, the key enzyme activities of AGS were obviously renewed dependent on operation strategy adjustment and AGS self-regulation. The performance of AGS in R2 (continuously dosing 0.05 mg/L PFOA) and R4 (stopping dosing PFOA) recovered quite good, while the long-term adverse effects of 0.5 mg/L PFOA on AGS in R3 were still more difficult to be alleviated. In end of Phase Ⅱ (69-97days), the average TN and TP removal efficiencies correspondingly reached 83.31% and 82.09% in R1 (control), 80.67% and 79.62% in R2, 76.38% and 74.27% in R3, and 79.01% and 78.25% in R4, respectively. Further analysis revealed that the effect of PFOA on proteins in extracellular polymeric substances (EPS) was greater than that on polysaccharides. Specifically, short-term dosage of PFOA mainly affected loosely bound EPS, while long-term dosage of PFOA affected tightly bound EPS. Although AGS is severely inhibited by short exposure to 2.0 mg/L PFOA (in R4), after the operation strategy adjustment, EPS content decreased, nutrient and oxygen transport channels of AGS were re-established, which contributed to the recovery of AGS.

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. However, its interpretability is a challenge for building trust and providing insights into the learned relationships. Herein, we systematically investigated how the factors influence nanoparticle uptake during seed priming by ZnO nanoparticles and its effects on seed germination. The properties of the nanoparticles, priming solution, and seeds were considered. Post hoc interpretation and model-based interpretation of machine learning were integrated into two ways to understand the mechanism of nanoparticle uptake during seed priming and its biological effects on seed germination. The results indicated that nanoparticle concentration and ionic strength influenced the shoot fresh weight mainly by controlling the nanoparticle uptake. The nanoparticle uptake had a significant slowdown when the nanoparticle concentration exceeded 50 mg L-1. Although other factors, such as zeta potential and hydrodynamic diameter, had no obvious effects on nanoparticle uptake, their biological effects cannot be ignored. This approach can promote the safer-by-design strategy of nanomaterials for sustainable agriculture.

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. Increased relative contents of reactive oxygen species (ROS) (146.34% and 191.43%) and lactate dehydrogenase (LDH) (185.71% and 316.67%) under PE MPs (20 and 200 n/L) exposure resulted in disruption of cellular structure. The activities of enzymes related to denitrification and phosphorus removal were greatly decreased, while ammonia monooxygenase (AMO) was stable, supporting the high efficiency removal of ammonia nitrogen. High-throughput sequencing demonstrated that the relative abundance of nitrifying and denitrifying bacteria (Nitrospira, Thermomonas, Flavobacterium), and PAOs (Comamonas and Rhodocyclus) were significantly reduced from 4.47%, 3.57%, 2.02%, 9.38%, and 5.45%-2.95%, 2.88%, 1.77%, 8.01%, and 4.86% as the concentration of PE MPs increased from 0 to 200 n/L, respectively. Those findings were consistent with the deterioration in decontamination capability.

Interlayer spacing enlarged 2D 1T-MoS2 and V2CTx 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. Herein, the V2CTx-based hybrids including 1T-MoS2 (1T -MoS2@V2CTx) has been successfully constructed by the magneto-hydrothermal method and proved to be an eminent anode, which can make PIBs have high reversible capacity and eminent rate performance at the same time. Moreover, the combination of 2D 1T-MoS2 and V2CTx not only significantly promotes the transfer of interfacial charges as well as accelerates the transmission and diffusion of electrons and K+, but also helps to alleviate the volume changes caused by the insertion/extraction of large-sized K+ during the cycle, which makes the electrode exhibit good cycle stability. Density functional theory (DFT) indicates that the synergy effect between 1T-MoS2 and V2CTx has significantly strengthened the potassium affinities and ion diffusion kinetics in the 1T-MoS2@V2CTx anode by reducing the ion diffusion energy barrier, thereby showing outstanding K+ storage performance, especially in 1T-MoS2@V2CF2. As a result, the 1T-MoS2@V2CTx anode shows a high reversible capacity of 887.3 mA h g-1 at 0.1 A g-1, eminent rate performance of the capacity maintaining 563.6 mA h g-1 at 2.0 A g-1 and remarkable cycle stability of 601.2/374.7 mA h g-1 with 69.4/56.5% capacity retention after 2000 cycles at 1.0/2.0 A g-1. This work provides a new way for the exquisite design of 2D composite electrodes with excellent performance in PIBs.

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. The results showed that exopolysaccharide and protein in bound-extracellular polymeric substances significantly increased under Ag-NPs stress. These metal-binding groups including C-O-C (exopolysaccharide), CH3/CH2 (proteins), O-H/N-H (hydroxyl group) and C-H (alkyl groups) played a key role in extracellular biosorption. The internalized or strongly bound Ag (1.90%-17.45% of total contents) was higher than the loosely surface biosorption (0.31%-1.79%). The accumulation of glutathione disulfide (GSSG), together with the decline of reduced glutathione/GSSG (GSH/GSSG) ratio in C. reinhardtii cells, indicated a significant oxidative stress caused by exposure of Ag-NPs. The increasing phytochelatin accompanied with the decreasing GSH level indicated a critical role to intracellular detoxification of Ag. Furthermore, upregulation of antioxidant genes (MSOD, QTOX2, CAT1, GPX2, APX and VTE3) can cope with oxidative stress of Ag-NPs or Ag+. The up-regulation of ascorbate peroxidase (APX) and glutathione peroxidase (GPX2) genes and the reduction in GSH contents showed that the toxicity of Ag-NPs could be mediated by an intracellular ascorbate-GSH defense pathway. These findings can provide valuable information on ecotoxicity of Ag-NPs, potential bioremediation and adaptation capabilities of algal cells to Ag-NPs.

Rational design of Ti3C2Cl2 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 Ti3C2Cl2 nanodots-interspersed MXene@NiAl-layered double hydroxides (QD-Ti3C2Cl2@NiAl-LDHs) hybrid electrode with superior pseudocapacitor storage performance has been elaborately designed by electrostatic-assembled. As a result, the QD-Ti3C2Cl2@NiAl-LDHs electrode exhibits a super specific capacitance of 2010.8F g-1 at 1.0 A g-1 and high energy density of 100.5 Wh kg-1 at a power density of 299.8 W kg-1. In addition, 94.1% capacitance retention is achieved after cycling for 10,000 cycles at 1.0 A g-1, outperforming previously reported of two dimensional hybrids electrode for supercapacitor. Furthermore, density functional theory (DFT) calculations show that the superior pseudocapacitor storage performance of the QD-Ti3C2Cl2@NiAl-LDHs may be attributed to the creation of numerous electrochemical active sites and the enhancement of electrical conductivity by the QD-Ti3C2Cl2 MXene. This work provides new strategy for developing excellent pseudocapacitor supercapacitor based on two dimensional hybrid electrode.

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. Here, we present an approach for uncovering causal structure in nanotoxicity datasets by mutual-validated and model-agnostic interpretation methods. Model predictions can be explained from feature importance, feature effects, and feature interactions. The utility of this approach is demonstrated through two case studies, the cytotoxicity of cadmium-containing quantum dots and metal oxide nanoparticles. Further, these case studies indicate the efficacy and impacts at two scales: (i) model interpretation, where the most relevant features for correlating cytotoxicity are identified and their influence on model predictions and interactions with other features are then explained, and (ii) model validation, where the difference among interpretation results of different methods (or the difference between interpretation results and well-known toxicity mechanisms) may reflect some inherent problems in the used dataset (or the developed models). Our approach of integrating machine learning models and interpretation methods provides a roadmap for predicting the toxicity of ENPs in a translucent way.

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). In the present study, we revealed the toxicity mechanism of C. reinhardtii under Ag-NPs stress using flow cytometry (FCM), metabolic methods, and transmission electron microscopy. The results showed that the chloroplasts were damaged and the synthesis of photosynthetic pigments was inhibited under Ag-NPs stress, which inhibited the growth of C. reinhardtii. Meanwhile, Ag-NPs also caused C. reinhardtii to produce excessive reactive oxygen species (ROS), increased malondialdehyde content and changed the permeability of cell membrane, resulting in the acceleration of internalization of Ag-NPs. The decrease of cell size and intracellular chlorophyll autofluorescence was observed with FCM. To deal with the induced excessive ROS that could lead to lethal and irreversible structure damage, C. reinhardtii activated antioxidant enzymes including superoxide dismutase and peroxidase. This study provides new information for better understanding the potential toxicity risks of Ag-NPs in the aquatic environment.

Synthesis and photocatalytic activity study of S-doped WO3 under visible light irradiation.

In this study, a photocatalyst S-doped WO3 was successfully synthesized by the hydrothermal method. The prepared undoped and S-doped WO3 samples were then characterized by XRD, SEM, XPS, and UV-vis DRS. The results showed that the band gap energy of S-doped WO3 was lower than that of the undoped WO3, which led to a better absorption of visible light. Furthermore, the results of XPS analysis suggested that the doping with S element resulted in an increase in lattice oxygen vacancies on the surface of S-WO3, which could effectively improve the photocatalytic activity. The photocatalytic performance of the S-WO3 samples were evaluated by the measurement of methylene blue (MB) degradation under visible light irradiation. The experimental results demonstrated that S-doped WO3 sample exhibited a much better photodegradation performance compared to undoped WO3, with the maximum MB removal efficiency of 78.7% for the 5% S-WO3 sample. Based on the above results, the mechanisms of photodegradation of MB by S-WO3 were discussed.

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 Cu2+ releasing, extracellular adsorption Cu (Cuex) and intracellular assimilation Cu (Cuin). 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 Cu2+ releasing, however, inhibited adsorption and assimilation of metal nanoparticles (MNPs) into microalgal cells, resulting in the reduction of the Cuex and Cuin per microalgal cell. The phenomenon was probably due to the reduced chance of contact between microalgae and MNPs. The internalization of CuO-NPs was also observed in microalgal cells by high resolution transmission electron microscope (HRTEM). Furthermore, the results of fast fourier transform (FFT)/inversed FFT (IFFT) analysis indicated that the CuO-NPs was reduced to Cu2O-NPs in the microalgae cells. The above results suggested that the microalgae can significantly affect the fate of MNPs, and subsequently, influencing the bioavailability and toxicity of MNPs in the aquatic environment.

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 5.45-14.72%) in simulated natural water environment. Cefradine was mainly removed by hydrolysis, adsorption, desorption, photodecarboxylation, and photoisomerization. The effects of C. reinhardtii density, initial solution pH, and different light sources on CFD removal efficiency were investigated. The optimum conditions occurred at a density of algae 10 × 104 cells/mL, a solution pH of 9.0, and the ultraviolet (UV) light. Additionally, the removal kinetics under 16 different conditions was explored. The results showed that the removal of CFD fits well with a pseudo-first-order kinetic, and the half-life times are from 0.8 to 261.6 days. This study summarizes the CFD removal mechanisms in alga-containing water environment, highlights the important role played by light irradiation in eliminating CFD, and obtains the important kinetic data on CFD removal.

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 0.5-10 mg/L. Effects of algae density, initial solution pH, and temperature on the adsorption batch assays were investigated. The optimum conditions for cefradine adsorption occurred at a density of 5 × 106 algae cells/mL, a solution pH of 7.0, and a temperature of 25.0 °C. A Box-Behnken design was employed to evaluate correlations between influential factors and cefradine adsorption. The results showed a significant interaction between algae density and temperature. The maximum removal rate could reach 50.13% under the optimal conditions. Additionally, the adsorption mechanisms were explored through Langmuir and Freundlich isotherm equations, adsorption kinetics, and thermodynamics. The results suggested that the adsorption process was monolayer, spontaneous, and endothermic with an increase in randomness at the algae-solution interface, which followed a pseudo-second-order model. All the data indicated that the alga performed a better removal capacity in the antibiotic-containing wastewater treatment process. This study lays the groundwork for a better understanding of the interaction mechanism between cefradine and Chlamydomonas reinhardtii in water solutions under dark condition.

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. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.