Effects of microplastics on soil microorganisms and microbial functions in nutrients and carbon cycling - A review.

The harmful effects of microplastics (MPs) pollution in the soil ecosystem have drawn global attention in recent years. This paper critically reviews the effects of MPs on soil microbial diversity and functions in relation to nutrients and carbon cycling. Reports suggested that both plastisphere (MP-microbe consortium) and MP-contaminated soils had distinct and lower microbial diversity than that of non-contaminated soils. Alteration in soil physicochemical properties and microbial interactions within the plastisphere facilitated the enrichment of plastic-degrading microorganisms, including those involved in carbon (C) and nutrient cycling. MPs conferred a significant increase in the relative abundance of soil nitrogen (N)-fixing and phosphorus (P)-solubilizing bacteria, while decreased the abundance of soil nitrifiers and ammonia oxidisers. Depending on soil types, MPs increased bioavailable N and P contents and nitrous oxide emission in some instances. Furthermore, MPs regulated soil microbial functional activities owing to the combined toxicity of organic and inorganic contaminants derived from MPs and contaminants frequently encountered in the soil environment. However, a thorough understanding of the interactions among soil microorganisms, MPs and other contaminants still needs to develop. Since currently available reports are mostly based on short-term laboratory experiments, field investigations are needed to assess the long-term impact of MPs (at environmentally relevant concentration) on soil microorganisms and their functions under different soil types and agro-climatic conditions.

Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning.

The domain of machine learning is confronted with a crucial research area known as class imbalance (CI) learning, which presents considerable hurdles in the precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph-embedded intuitionistic fuzzy RVFL for CI learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers a plethora of benefits: 1) leveraging graph embedding (GE) to preserve the inherent topological structure of the datasets; 2) employing intuitionistic fuzzy (IF) theory to handle uncertainty and imprecision in the data; and 3) the most important, it tackles CI learning. The amalgamation of a weighting scheme, GE, and IF sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the CI issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.

Plastic Paradox in Blue Carbon Ecosystems.

Plastics are rapidly accumulating in blue carbon ecosystems, i.e., mangrove forests, tidal marshes, and seagrass meadows. Accumulated plastic is diverted from the ocean, but the extent and nature of impacts on blue carbon ecosystem processes, including carbon sequestration, are poorly known. Here, we explore the potential positive and negative consequences of plastic accumulation in blue carbon ecosystems. We highlight the effects of plastic accumulation on organic carbon stocks and sediment biogeochemistry through microbial metabolism. The notion of beneficial plastic accumulation in blue carbon ecosystems is controversial, yet considering the alternative impacts of plastics on oceanic and aboveground environments, this may be the "lesser of evils". Using environmental life cycle impact assessment, we propose a research framework to address the potential positive and negative impacts of plastic accumulation in blue carbon ecosystems. Considering the multifaceted benefits, we prioritize expanding and managing blue carbon ecosystems, which may help with ecosystem conservation, as well as mitigating the negative effects of plastic.

GRA: Graph Representation Alignment for Semi-Supervised Action Recognition.

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness.

Prioritising plastic pollution research in blue carbon ecosystems: A scientometric overview.

The Blue Carbon Ecosystems (BCEs), comprising mangroves, saltmarshes, and seagrasses, located at the land-ocean interface provide crucial ecosystem services. These ecosystems serve as a natural barrier against the transportation of plastic waste from land to the ocean, effectively intercepting and mitigating plastic pollution in the ocean. To gain insights into the current state of research, and uncover key research gaps related to plastic pollution in BCEs, this study conveyed a comprehensive overview using bibliometric, altmetric, and literature synthesis approaches. The bibliometric analysis revealed a significant increase in publications addressing plastic pollution in BCEs, particularly since 2018. Geographically, Chinese institutions have made substantial contributions to this research field compared to countries and regions with extensive BCEs and established blue carbon science programs. Furthermore, many studies have focused on mangrove ecosystems, while limited attention was given to exploring plastic pollution in saltmarsh, seagrass, and multiple ecosystems simultaneously. Through a systematic analysis, this study identified four major research themes in BCE-plastics research: a) plastic trapping by vegetated coastal ecosystems, b) microbial plastic degradation, c) ingestion of plastic by benthic organisms, and d) effects of plastic on blue carbon biogeochemistry. Upon synthesising the current knowledge in each theme, we employed a perspective lens to outline future research frameworks, specifically emphasising habitat characteristics and blue carbon biogeochemistry. Emphasising the importance of synergistic research between plastic pollution and blue carbon science, we underscore the opportunities to progress our understanding of plastic reservoirs across BCEs and their subsequent effects on blue carbon sequestration and mineralisation. Together, the outcomes of this review have overarching implications for managing plastic pollution and optimising climate mitigation outcomes through the blue carbon strategies.

Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm.

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the "STrutred Analysis of the REtina (STARE) Database" accessed on Oct. 2020 (https://cecas.clemson.edu/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm.

A survey on cancer detection via convolutional neural networks: Current challenges and future directions.

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Transformation of Biofilm to Carbon Sinks after Prolonged Droughts Linked with Algal Biodiversity Change.

Global climate change significantly increased the duration of droughts in intermittent rivers, impacting benthic microbial-mediated biogeochemical processes. However, the impact of prolonged droughts on the carbon contribution of intermittent rivers remains poorly understood. In this study, we investigated the potential effects of varying drought gradients (ranging from 20 to 130 days) on benthic biofilms community structure (algae, bacteria, and fungi) and their carbon metabolism functions (ecosystem metabolism and carbon dioxide (CO2) emission fluxes) using mesocosm experiments. Our findings indicate that longer drought durations lead to reduced alpha diversity and community heterogeneity, tighter interdomain networks, and an increased role of stochastic processes in community assembly, with a discernible threshold at around 60 days. Concurrently, the biofilm transforms into a carbon sink following a drought period of 60 days, as evidenced by the transformation of CO2 emission fluxes from 633.25 ± 194.69 to -349.61 ± 277.79 mg m-2 h-1. Additionally, the partial least-squares path model revealed that the resilience of algal communities and network stability may drive biofilm's transformation into a carbon sink, primarily through the heightened resilience of autotrophic metabolism. This study underscores the significance of the carbon contribution from intermittent rivers, as the shift in carbon metabolism functions with increasing droughts could lead to skewed estimations of current riverine carbon fluxes.

Effects of released organic components of solid carbon sources on denitrification performance and the related mechanism.

Here, a hybrid scaffold of polyvinyl alcohol/sodium alginate (PVA/SA) was used to prepare solid carbon sources (SCSs) for treating low carbon/nitrogen wastewater. The four SCSs were divided into two groups, biodegradable polymers group (including polyvinyl alcohol-sodium alginate (PS) and PS-PHBV (PP), and blended SCSs (PS-PHBV-wood chips (PPW) and PS-PHBV-wheat straw (PPS)). After the leaching experiments, no changes occurred in elemental composition and functional groups of the SCSs, and the released dissolved organic matter showed a lower degree of humification and higher content of labile molecules in the blended SCSs groups using EEM and FT-ICR-MS. The denitrification performance of the blended SCSs was higher, with nitrate removal efficiency over 84%. High-throughput sequencing confirmed PPW had the highest alpha-diversity, and the microbial community structure significantly varied among SCSs. Results of functional enzymes and genes show the released carbon components directly affect the NADH level and electron transfer efficiency, ultimately influencing denitrification performance.

Surface modification significantly changed the effects of nano-polystyrene on sediment microbial communities and nitrogen metabolism.

Nanoplastics are ubiquitous in the natural environment, and their ecological risks have received considerable attention. Surface modification is common for nanoplastics and an essential factor affecting their toxicity. However, studies on the potential effects of nanoplastics and their surface-modified forms on functional communities in aquatic systems are still scarce. This study investigated the effects of nano-polystyrene (nPS), amino-modified nPS (nPS-NH2), and carboxylated nPS (nPS-COOH) particles on sediment bacterial and fungal communities and their functions over a 60-day incubation period. The results showed that the fungal community was significantly inhibited by nPS-NH2 exposure, while the bacterial community diversity remained relatively stable in all nPS treatments. Proteobacteria and Ascomycota were the dominant phyla for the bacterial and fungal communities, respectively. Nitrification was inhibited in all nPS treatments, while denitrification was enhanced for nPS-NH2 and nPS-COOH treatments. The activity of four key denitrification enzymes (NAR, NIR, NOR, and NOS) was also significantly improved by nPS, resulting in increased nitrogen and nitrous oxide gas production, and decreased nitrate concentrations in the overlying water. This showed the total increased effect of nPS on the activity of denitrifiers. Our results suggest that surface modification significantly affects the effects of nPS on microbial communities and functions. The potential impacts of nPS on ecological functions should be elucidated with more attention.

Multimodal Neuroimaging based Alzheimer's Disease Diagnosis using Evolutionary RVFL Classifier.

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.

Redefining Lobe-Wise Ground-Glass Opacity in COVID-19 Through Deep Learning and its Correlation With Biochemical Parameters.

During COVID-19 pandemic qRT-PCR, CT scans and biochemical parameters were studied to understand the patients' physiological changes and disease progression. There is a lack of clear understanding of the correlation of lung inflammation with biochemical parameters available. Among the 1136 patients studied, C-reactive-protein (CRP) is the most critical parameter for classifying symptomatic and asymptomatic groups. Elevated CRP is corroborated with increased D-dimer, Gamma-glutamyl-transferase (GGT), and urea levels in COVID-19 patients. To overcome the limitations of manual chest CT scoring system, we segmented the lungs and detected ground-glass-opacity (GGO) in specific lobes from 2D CT images by 2D U-Net-based deep learning (DL) approach. Our method shows accuracy, compared to the manual method (  âˆ¼ 80%), which is subjected to the radiologist's experience. We determined a positive correlation of GGO in the right upper-middle (0.34) and lower (0.26) lobe with D-dimer. However, a modest correlation was observed with CRP, ferritin and other studied parameters. The final Dice Coefficient (or the F1 score) and Intersection-Over-Union for testing accuracy are 95.44% and 91.95%, respectively. This study can help reduce the burden and manual bias besides increasing the accuracy of GGO scoring. Further study on geographically diverse large populations may help to understand the association of the biochemical parameters and pattern of GGO in lung lobes with different SARS-CoV-2 Variants of Concern's disease pathogenesis in these populations.

High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis.

Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.

Eukaryotes contribute more than bacteria to the recovery of freshwater ecosystem functions under different drought durations.

Global climate change mostly impacts river ecosystems by affecting microbial biodiversity and ecological functions. Considering the high functional redundancy of microorganisms, the unknown relationship between biodiversity and ecosystem functions obstructs river ecological research, especially under the influence of increasing weather extremes, such as in intermittent rivers and ephemeral streams (IRES). Herein, dry-wet alternation experiments were conducted in artificial stream channels for 25 and 90 days of drought, both followed by 20 days of rewetting. The dynamic recovery of microbial biodiversity and ecosystem functions (represented by ecosystem metabolism and denitrification rate) were determined to analyse biodiversity-ecosystem-function (BEF) relationships after different drought durations. There was a significant difference between bacterial and eukaryotic biodiversity recovery after drought. Eukaryotic biodiversity was more sensitive to drought duration than bacterial, and the eukaryotic network was more stable under dry-wet alternations. Based on the establishment of partial least squares path models, we found that eukaryotic biodiversity has a stronger effect on ecosystem functions than bacteria after long-term drought. Indeed, this work represents a significant step forward for further research on the ecosystem functions of IRES, especially emphasizing the importance of eukaryotic biodiversity in the BEF relationship.

Dynamic responses of carbon metabolism of sediment microbial communities to Ag nanoparticles: Effects of the single and repeated exposure scenarios.

The widespread use of nanosilver will inevitably lead to their release into aquatic environment, threating the health of freshwater ecosystem. The toxic effects of silver nanoparticles (AgNPs) on sediment microbial diversity, community composition, and functional enzyme activity are well established, while little is known about how sediment microbes dynamically respond to the stress of different AgNPs exposure scenarios. Herein, microcosm experiments were performed to investigate the impacts of repeated (1 mg/L, applied every 6 days for 10 times) and single (10 mg/L) exposure scenarios of AgNPs on the specific functions of sediment microbes (5-60 days). The carbon metabolism of sediment microbial communities was measured using BIOLOG ECO microplates, and carbon metabolic rate and functional diversity indices were calculated. Compared to control group, the maximum carbon source utilization capacity of the microbial community increased by 6.6 and 15.4 % in the single and repeated exposure group, respectively. And the metabolic rates of sediment microorganisms were significant increased by 6.1 % in the repeated exposure group, which suggested that repetitive low-dosing of AgNPs induce a larger alteration of both capacity and rate of microbial carbon metabolism. Notably, different AgNPs exposure scenarios resulted in a shift in the carbon source preference of the microorganisms. After exposure for 60 days, compared with the controls, the ability to utilize polymers was significantly increased by 51.5 and 21.7 % in the single and repeated exposure groups, respectively, and decreased by 33.7 and 10.5 % in the utilization of miscellaneous, both exhibiting significant differences (P < 0.05), implying that AgNPs exposure scenarios affected the microbial-mediated carbon cycling processes in sediment. These results highlight that different exposure scenarios of AgNPs have different effects on the carbon metabolism capacity of microbial communities, thus affecting the carbon cycling processes in which microorganisms are involved.

DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing.

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods.

Aquatic plastisphere: Interactions between plastics and biofilms.

Because of the high production rates, low recycling rates, and poor waste management of plastics, an increasing amount of plastic is entering the aquatic environment, where it can provide new ecological niches for microbial communities and form a so-called plastisphere. Recent studies have focused on the one-way impact of plastic substrata or biofilm communities. However, our understanding of the two-way interactions between plastics and biofilms is still limited. This review first summarizes the formation process and the co-occurrence network analysis of the aquatic plastisphere to comprehensively illustrate the succession pattern of biofilm communities and the potential consistency between keystone taxa and specific environmental behavior of the plastisphere. Furthermore, this review sheds light on mutual interactions between plastics and biofilms. Plastic properties, environmental conditions, and colonization time affect biofilm development. Meanwhile, the biofilm communities, in turn, influence the environmental behaviors of plastics, including transport, contaminant accumulation, and especially the fragmentation and degradation of plastics. Based on a systematic literature review and cross-referencing from these disciplines, the current research focus, and future challenges in exploring aquatic plastisphere development and biofilm-plastic interactions are proposed.

Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based Morphometry.

Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model.

Characterization of dynamic plastisphere and their underlying effects on the aging of biodegradable and traditional plastics in freshwater ecosystems.

Recently, biodegradable plastics (BPs) are emerging as a sustainable alternative to traditional plastics. When released into an aquatic environment, the biodegradable performance of BPs is influenced by biochemical processes, especially the developed plastisphere. However, studies addressing the biodegrading capacity of BPs and traditional plastics within the plastisphere are still limited. Here, we investigated plastisphere community variations and their capacity to biodegrade polyethylene terephthalate (PET) and starch-based plastics (SBP) for four time periods (15, 30, 45, and 80 days) in three freshwaters. Unexpectedly, there is no significant difference in the microbial communities and network structure of the plastisphere between SBP and PET. Moreover, SBP tended to age rapidly at the early stage (0-15 days), while the aging degree of SBP and PET did not display an obvious difference at 80 days. Partial least squares path modeling suggested that plastic aging was mainly dominated by keystone taxa of network and aquatic environmental factors. These results suggest that the aging rate of commercial BPs may not be as fast as we imagine in freshwaters (SBP ≈ PET), and the environmental behaviors of BPs in the aquatic environment should be paid more attention to.

Sediment bacterial and fungal communities exhibit distinct responses to microplastic types and sizes in Taihu lake.

Microplastics (MPs) are emerging contaminants in aquatic environments, yet their impact on sediment microbiota and biogeochemical processes were not well reported. Herein, microcosm experiments were performed to investigate the effects of MPs (Polystyrene, PS and Polyethylene, PE) with three size classes (ranging from 100 nm to 150-200 µm) on sediment bacterial and fungal communities over 60-day incubation from Taihu Lake. High-throughput sequencing revealed the alpha diversities of bacterial and fungal communities were reduced by MPs, dependent on MPs' size and type. Bacterial community structures were significantly altered under all MPs treatments, with clustering for the same size class for PS and PE. Fungal community structures were significantly affected for all MPs, with PS and PE exhibiting different effects. Co-occurrence network analysis suggested MPs changed bacterial and fungal network complexities. Proteobacteria and Ascomycota formed strong associations with other phyla and demonstrated tolerance to MPs exposure. Actinobacteria, Firmicutes, and Chytridiomycota were the main respondents to MPs. The enzyme concentrations were stimulated by MPs, indicating carbon and nitrogen uptakes might be increased. Therefore, PS and PE had similar impacts on the microbial community (particularly bacteria), and sizes of MPs were the main influencing factors. MPs shifted community structure and network with distinct responses from bacteria and fungi, likely leading to the alteration of microbial-involved carbon and nitrogen cycling.