Impact of residual antibiotics on microbial decomposition of livestock manures in Eutric Regosol: Implications for sustainable nutrient recycling and soil carbon sequestration.

The land application of livestock manure has been widely acknowledged as a beneficial approach for nutrient recycling and environmental protection. However, the impact of residual antibiotics, a common contaminant of manure, on the degradation of organic compounds and nutrient release in Eutric Regosol is not well understood. Here, we studied, how oxytetracycline (OTC) and ciprofloxacin (CIP) affect the decomposition, microbial community structure, extracellular enzyme activities and nutrient release from cattle and pig manure using litterbag incubation experiments. Results showed that OTC and CIP greatly inhibited livestock manure decomposition, causing a decreased rate of carbon (28%-87%), nitrogen (15%-44%) and phosphorus (26%-43%) release. The relative abundance of gram-negative (G-) bacteria was reduced by 4.0%-13% while fungi increased by 7.0%-71% during a 28-day incubation period. Co-occurrence network analysis showed that antibiotic exposure disrupted microbial interactions, particularly among G- bacteria, G+ bacteria, and actinomycetes. These changes in microbial community structure and function resulted in decreased activity of urease, ß-1,4-N-acetyl-glucosaminidase, alkaline protease, chitinase, and catalase, causing reduced decomposition and nutrient release in cattle and pig manures. These findings advance our understanding of decomposition and nutrient recycling from manure-contaminated antibiotics, which will help facilitate sustainable agricultural production and soil carbon sequestration.

Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species.

Spatial and temporal associations between sympatric species underpin biotic interactions, structure ecological assemblages, and sustain ecosystem functioning and stability. However, the resilience of interspecific spatiotemporal associations to human activity remains poorly understood, particularly in mountain forests where anthropogenic impacts are often pervasive. Here, we applied context-dependent Joint Species Distribution Models to a systematic camera-trap survey dataset from a global biodiversity hotspot in eastern Himalayas to understand how prominent human activities in mountain forests influence species associations within terrestrial mammal communities. We obtained 10,388 independent detections of 17 focal species (12 carnivores and five ungulates) from 322 stations over 43,163 camera days of effort. We identified a higher incidence of positive associations in habitats with higher levels of human modification (87%) and human presence (83%) compared to those located in habitats with lower human modification (64%) and human presence (65%) levels. We also detected a significant reduction of pairwise encounter time at increasing levels of human disturbance, corresponding to more frequent encounters between pairs of species. Our findings indicate that human activities can push mammals together into more frequent encounters and associations, which likely influences the coexistence and persistence of wildlife, with potential far-ranging ecological consequences.

Microbial influencers and cotton leaf curl disease (CLCuD) susceptibility: a network perspective.

Biotic stresses, such as plant viruses, e.g., cotton leaf curl virus (CLCuV), can alter root-associated and leaf-associated microbial diversities in plants. There are complex ecological dynamics at play, with each microbe contributing to a multitude of biotic and abiotic interactions, thus deciding the stability of the plant's ecosystem in response to the disease. Deciphering these networks of interactions is a challenging task. The inferential research in microbiome is also at a nascent stage, often constrained by the underlying analytical assumptions and the limitations with respect to the depth of sequencing. There is also no real consensus on network-wide statistics to identify the influential microbial players in a network. Guided by the latest developments in network science, including recently published metrics such as Integrated View of Influence (IVI) and some other centrality measures, this study provides an exposé of the most influential nodes in the rhizospheric and phyllospheric microbial networks of the cotton leaf curl disease (CLCuD) susceptible, partially tolerant, and resistant cotton varieties. It is evident from our results that the CLCuD-resistant Gossypium arboreum possesses an equal share of keystone species, which helps it to withstand ecological pressures. In the resistant variety, the phyllosphere harbors the most influential nodes, whereas in the susceptible variety, they are present in the rhizosphere. Based on hubness score, spreading score, and IVI, the top 10 occurring keystone species in the FDH-228 (resistant) variety include Actinokineospora, Cohnella, Thermobacillus, Clostridium, Desulfofarcimen, and MDD-D21. Elusimicrobia, Clostridium-sensu-stricto_12, Candidatus woesebacteria, and Dyella were identified as the most influential nodes in the PFV-1 (partially tolerant) variety. In the PFV-2 (susceptible) variety, the keystone species were identified as Georginia, Nesterenkonia, Elusimicrobia MVP-88, Acetivibrio, Tepedisphaerales, Chelatococcus, Nitrosospira, and RCP2-54. This concept deciphers the diseased and healthy plant's response to viral disease, which may be microbially mediated.

Tobacco crop rotation enhances the stability and complexity of microbial networks.

Introduction: The effects of continuous cropping and rotation cropping, two important tobacco cultivation practices, on soil microbial communities at different stages remain unclear. Different planting patterns have been shown to influence soil physical and chemical properties, which in turn can affect the composition and diversity of soil microbial communities. Methods: In order to investigate the impact of different planting methods on soil microbial community structure, we selected two representative planting methods: continuous cropping (tobacco) and rotational cropping (tobacco-maize). These methods were chosen as the focal points of our research to explore the potential effects on soil microbial communities. High-throughput sequencing technology was employed to investigate the structure of soil microbial communities, as well as their relationships with soil environmental factors, by utilizing the 16S rRNA, ITS, and 18S genes. Furthermore, the interaction among microorganisms was explored through the application of the Random Matrix Theory (RMT) molecular ecological network approach. Results: There was no significant difference in α diversity, but significant difference in ß diversity based on Jaccard distance test. Compared to continuous cropping, crop rotation significantly increased the abundance of beneficial prokaryotes Verrucomicrobia and Rhodanobacter. These findings indicate that crop rotation promotes the enrichment of Verrucomicrobia and Rhodanobacter in the soil microbial community. AP and NH4-N had a greater effect on the community structure of prokaryotes and fungi in tobacco soil, while only AP had a greater effect on the community structure of protist. Molecular ecological network analysis showed that the network robustness and Cohesion of rotation were significantly higher than that of continuous cropping, indicating that the complexity and stability of molecular ecological networks were higher in the rotational, and the microbial communities cooperated more effectively, and the community structure was more stable. Discussion: From this point of view, rotational cropping is more conducive to changing the composition of soil microbial community, enhancing the stability of microbial network structure, and enhancing the potential ecological functions in soil.

A study on the monitoring of heatwaves and bivariate frequency analysis based on mortality risk assessment in Wuhan, China.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

Polymetallic contamination drives indigenous microbial community assembly dominated by stochastic processes at Pb-Zn smelting sites.

Indigenous microbial communities in smelting areas are crucial for maintaining fragile ecosystem functions. However, the community assembly process and their responses to polymetallic pollution are poorly understood, especially the taxa in each bin from the amplicons that contributed to the assembly process. Herein, microbial diversity, co-occurrence patterns, assembly process and the intrinsic mechanisms across contamination gradients at a typical PbZn smelting site were systematically unravelled by high-throughput sequencing. The results showed a consistent compositional profile among the indigenous communities across sampling sites, wherein genera KD4-96 from Chloroflexi and Sphingomonas from Proteobacteria emerged as the most abundant taxa. Network modularity of the high- and middle-contaminated communities at Pb and Zn smelting sites was >0.44, indicating that community populations were clustered into modules to resist high heavy metal stress. Stochastic processes dominated the community assembly, with the greatest contribution from drift (DR), which was significantly correlated with Pb, Zn, Cr and Cu contents. What's particular was that the DR-controlled bins were dominated by Proteobacteria (typical r-strategists), while the HoS-controlled bins were by Chloroflexi (typical K-strategists). Furthermore, the proportion of DR in the bins dominated by Sphingomonadaceae (phylum Proteobacteria) increased gradually with the increase of heavy metal contents. These discoveries provide essential insights for community control in restoring and mitigating soil degradation at PbZn smelting sites.

Linking active rectal mucosa-attached microbiota to host immunity reveals its role in host-pathogenic STEC O157 interactions.

The rectal anal junction (RAJ) is the major colonization site of Shiga toxin-producing Escherichia coli (STEC) O157 in beef cattle, leading to transmission of this foodborne pathogen from farms to food chains. To date, there is limited understanding on whether mucosa-attached microbiome has a profound impact on host-STEC interactions. In this study, the active RAJ mucosa-attached microbiota and its potential role in host immunity-STEC-commensal interactions were investigated using RAJ mucosal biopsies collected from calves orally challenged with two STEC O157 strains with or without functional stx2a (stx2a + or stx2a-). The results revealed that shifts of microbial diverstives, topological, and assembly patterns were subjected to stx2a production post-challenge and Paeniclostridium and Gallibacterium being the keystone taxa for both microbial interactions and assembly. Additional mucosal transcriptome profiling showed stx2a-dependent host immune responses (i.e. B and T cell signaling, antigen processing and presentation) post-challenge. Further integrated analysis revealed that mucosa-attached beneficial microbes (i.e. Provotella, Faecalibacterium, and Dorea) interacted with host immune genes pre-challenge to maintain host homeostasis, however, opportunistic pathogenic microbes (i.e. Paeniclostridium) could interact with host immune genes after the STEC O157 colonization and interactions were stx2a-dependent. Furthermore, bacterial predicted functions involved in pathogen (O157 and Paeniclostridium) colonization and metabolism were related to host immunities. These findings suggest that host-microbe interactions could shift from beneficial to opportunistic pathogenic bacteria-driven during the pathogen colonization and be dependent on the production of particular virulence factors, highlighting the potential regulatory role of mucosa-attached microbiota in affecting pathogen-commensal-host interactions under STEC O157 infection in calves.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication.

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Global current research status and future hotspots in osteoporotic fracture based on bibliometric assessment and visualization techniques.

BACKGROUND: In the past decade, the evolution of themes in the field of osteoporotic fractures has changed from epidemiology and prediction of long-term morbidity, risk assessment of osteoporotic fractures, and zoledronic acid and denosumab in the treatment of osteoporosis to treatment guidelines for osteoporosis and the side effects caused by anti-osteoporotic drugs. AIM: To understand the trends and hotspots in osteoporotic fracture research. METHODS: Original articles were retrieved between January 1, 2010, and December 31, 2019, from the Web of Science Core Collection database. CiteSpace software facilitated the analysis and visualization of scientific productivity and emerging trends. RESULTS: Nine studies were identified using bibliometric indices, including citation, centrality, and sigma value, which might indicate a growing trend. Through clustering, we identified six major hot subtopics. Using burst analysis, top-5 references with the strongest bursting strength after 2017 were identified, indicating a future hotspot in this field. CONCLUSION: Current hot subtopics in osteoporotic fracture research include atypical femoral fractures, androgen deprivation therapy, denosumab discontinuation, hip fractures, trabecular bone score (TBS), and bone phenotype. Management and prevention of secondary fractures in patients with osteoporotic fractures, TBSs, and long-term administration strategy for zoledronic acid are expected to become research hotspots.

Seasonal variations in soil microbial community co-occurrence network complexity respond differently to field-simulated warming experiments in a northern subtropical forest.

Global warming may reshape seasonal changes in microbial community diversity and co-occurrence network patterns, with significant implications for terrestrial ecosystem function. We conducted a 2-year in situ field simulation of the effects of warming on the seasonal dynamics of soil microbial communities in a northern subtropical Quercus acutissima forest. Our study revealed that warming had no significant effect on the richness or diversity of soil bacteria or fungi in the growing season, whereas different warming gradients had different effects on their diversity in the nongrowing season. Warming also changed the microbial community structure, increasing the abundance of some thermophilic microbial species and decreasing the abundance of some symbiotrophic microorganisms. The co-occurrence network analysis of the microbial community showed that warming decreased the complexity of the intradomain network in the soil bacterial community in the growing and nongrowing seasons but increased it in the fungal community. Moreover, increasing warming temperatures increased the complexity of the interdomain network between bacteria and fungi in the growing season but decreased it in the nongrowing season, and the keystone species in the interdomain network changed with warming. Warming also reduced the proportion of positive microbial community interactions, indicating that warming reduced the mutualism, commensalism, and neutralism of microorganisms as they adapted to soil environmental stress. The factors affecting the fungal community varied considerably across warming gradients, with the bacterial community being significantly affected by soil temperature, MBC, NO3--N and NH4+-N, moreover, SOC and TN significantly affected fungal communities in the 4 °C warming treatment.. These results suggest that warming increases seasonal differences in the diversity and complexity of soil microbial communities in the northern subtropical region, significantly influencing soil dynamic processes regulating forest ecosystems under global warming.

A rare case of coexistence of meningioma and glioma in a single patient: A case report from Nepal.

INTRODUCTION AND IMPORTANCE: Primary brain tumors are less frequent as compared to metastatic brain tumors. Meningioma and glioma are common primary brain tumors. The patho-physiologies of meningioma and glioma are disparate. The concurrence of these two lesions in same patient is extremely rare, only few such cases are documented till now. CASE PRESENTATION: We present a case of an elderly male presenting with sudden weakness of left limbs. He had weakness in his left limbs (muscle power was 4/5 and 4/5 respectively). There was no weakness in his right limbs. There was decreased sensation in his left hand and below left knee, while sensations of his right limbs were intact. Bilateral plantar reflex was down going. Bulk and tone of his limbs were normal. His higher mental function and cranial nerves were normal. There was no facial deviation. Cerebellar sign, meningeal irritation, clonus was absent. The rest of systemic examination findings were regular. CT scan and MRI of brain revealed right frontal meningioma and right parietal high-grade glioma. He underwent right temporoparietal craniotomy and excision of both tumors. Histopathological examination confirmed the diagnosis of fibrous meningioma and high-grade glial tumor. He was discharged in stable condition. CLINICAL DISCUSSION: This case demonstrates rare phenomenon of simultaneous occurrence of meningioma and glioma in the same patient without any known predisposing factors. The exact mechanism behind this phenomenon is unclear. CONCLUSION: Clinicians should be aware of the possibility of co-existence of multiple primary brain tumors with different histologies in same patient.

Capabilities of Microbial Consortia from Disparate Environment Matrices in the Decomposition of Nature Organic Matter by Biofiltration.

Dissolved organic matter (DOM) plays a pivotal role in drinking water treatment, influencing the performance of unit processes and final water quality (e.g. disinfection byproduct risk). Biofiltration is an effective method of reducing DOM, but currently lacks a comprehensive appreciation of the association between microbial profiles and biofiltration performance. In this study, bench-scale biofiltration units inoculated with microbial consortia from river and soil matrices were operated successively for comparing their efficacy in terms of DOM removal. The results showed that biofiltration units receiving soil microbes were significantly superior (p < 0.05) to those receiving river inoculated microbes in terms of decomposing DOM recalcitrant fractions and reducing DBP formation potential, resulting in DOC and DBP precursor removals of up to 58.4 % and 87.9 %, respectively. Characterization of the taxonomic composition revealed that differences in the microbial assembly of the two biofilter groups were subject to deterministic rather than stochastic factors. Furthermore, more complicated interspecific relationships and niche structures in soil inoculated biofilters were deciphered by co-occurrence network, providing a plausible profile on a taxonomic division of labor in DOM stepwise degradation. Accordingly, the contribution of microbial compositions was found to be of greater importance than the GAC mass and biomass attached to the media. Thus, this study has advanced the understanding of microbial-mediated DOM decomposition in biofiltration, and also provided a promising strategy for enhancing the process for water use via developing appropriate engineered consortia of bacteria.

Stable microbial community diversity across large-scale Antarctic water masses.

The distinctive environmental attributes of the Southern Ocean underscore the indispensability of microorganisms in this region. We analyzed 208 samples obtained from four separate layers (Surface, Deep Chlorophyll Maximum, Middle, and Bottom) in the neighboring seas of the Antarctic Peninsula and the Cosmonaut Sea to explore variations in microbial composition, interactions and community assembly processes. The results demonstrated noteworthy distinctions in alpha and beta diversity across diverse communities, with the increase in water depth, a gradual rise in community diversity was observed. In particular, the co-occurrence network analysis exposed pronounced microbial interactions within the same water mass, which are notably stronger than those observed between different water masses. Co-occurrence network complexity was higher in the surface water mass than in the bottom water mass. Yet, the surface water mass exhibited greater network stability. Moreover, in the phylogenetic-based ß-nearest taxon distance analyses, deterministic processes were identified as the primary factors influencing community assembly in Antarctic microorganisms. This study contributes to exploring diversity and assembly processes under the complex hydrological conditions of Antarctica.

Fertilization regime changes rhizosphere microbial community assembly and interaction in Phoebe bournei plantations.

Fertilizer input is one of the effective forest management practices, which improves soil nutrients and microbial community compositions and promotes forest productivity. However, few studies have explored the response of rhizosphere soil microbial communities to various fertilization regimes across seasonal dynamics. Here, we collected the rhizosphere soil samples from Phoebe bournei plantations to investigate the response of community assemblages and microbial interactions of the soil microbiome to the short-term application of four typical fertilizer practices (including chemical fertilizer (CF), organic fertilizer (OF), compound microbial fertilizer (CMF), and no fertilizer control (CK)). The amendments of organic fertilizer and compound microbial fertilizer altered the composition of rhizosphere soil bacterial and fungal communities, respectively. The fertilization regime significantly affected bacterial diversity rather than fungal diversity, and rhizosphere fungi responded more sensitively than bacteria to season. Fertilization-induced fungal networks were more complex than bacterial networks. Stochastic processes governed both rhizosphere soil bacterial and fungal communities, and drift and dispersal limitation dominated soil fungal and bacterial communities, respectively. Collectively, these findings demonstrate contrasting responses to community assemblages and interactions of rhizosphere bacteria and fungi to fertilizer practices. The application of organic fertilization strengthens microbial interactions and changes the succession of key taxa in the rhizosphere habitat. KEY POINTS: • Fertilization altered the key taxa and microbial interaction • Organic fertilizer facilitated the turnover of rhizosphere microbial communities • Stochasticity governed soil fungal and bacterial community assembly.

The Potential of Co-Fermentation of Whole-Plant Cassava with Piper sarmentosum: A Comprehensive Study of Fermentation Quality, Antioxidant Activity, Bacterial Community Structure, and Microbial Ecological Networks in Novel Foods.

The objective of this study was to explore the preservation of food products through the co-fermentation of whole-plant cassava and Piper sarmentosum (PS) without additives. We assessed fermentation quality, antioxidant activity, bacterial community structure, function profile, and microbial ecological network features. Our results demonstrate that co-fermentation of whole-plant cassava with 10% PS significantly improves food quality. The co-fermented samples exhibited enhanced lactic acid concentrations and increased antioxidant activity, with reduced pH values and concentrations of acetic acid, butyric acid, and ammonia-N(NH3-N) compared to whole-plant cassava fermented alone. In addition, PS addition also optimized microbial community structure by elevating the total abundance of lactic acid bacteria and influenced bacterial predicted functions. Furthermore, our analysis of co-occurrence networks reveals that co-fermentation impacts microbial network features, including module numbers and bacterial relative abundances, leading to altered complexity and stability of the networks. Moreover, out study also highlights the impact of ferment undesirable bacteria like Pseudomonas aeruginosa and unclassified_Muribaculaceae playing crucial roles in microbial network complexity and stability. These findings provide valuable insights into the anaerobic fermentation process and offers strategies for regulating food fermentation quality.

The application of varying amount of green manure combined with nitrogen fertilizer altered the soil bacterial community and rice yield in karst paddy areas.

Long-term application of green manure (GM) and nitrogen (N) fertilizers markedly improved soil fertility and boosted rice yield in ecologically fragile karst paddy fields. However, the precise response mechanisms of the soil bacterial community to varying amounts of green manure alone and in combination with N fertilizer in such environments remain poorly elucidated. In this study, we investigated the soil bacterial communities, keystone taxa, and their relationship with soil environmental variables across eight fertilization treatments. These treatments included group without N addition (N0M0, no N fertilizer and no GM; N0M22.5, 22.5 t/ha GM; N0M45, 45 t/ha GM, N0M67.5, 67.5 t/ha GM) and group with N addition (NM0, N fertilizer and no GM; NM22.5, N fertilizer and 22.5 t/ha GM; NM45, N fertilizer and 45 t/ha GM; NM67.5, N fertilizer and 67.5 t/ha GM). The results revealed that increasing green manure input significantly boosted rice yield by 15.51-22.08% and 21.84-35% in both the group without and with N addition, respectively, compared to N0M0 treatment. Moreover, with escalating green manure input, soil TN, AN, AK, and AP showed an increasing trend in the group without N addition. However, following the addition of N fertilizer, TN and AN content initially rose, followed by a decline due to the enhanced nutrient availability for rice. Furthermore, the application of a large amount of N fertilizer decreased the C: N ratio in the soil, resulting in significant changes in both the soil microbial community and its function. Particularly noteworthy was the transition of keystone taxa from their original roles as N-fixing and carbon-degrading groups (oligotrophs) to roles in carbon degradation (copiotrophs), nitrification, and denitrification. This shift in soil community and function might serve as a primary factor contributing to enhanced nutrient utilization efficiency in rice, thus significantly promoting rice yield.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer.

BACKGROUND: Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC. AIM: To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI). METHODS: Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model. RESULTS: Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively. CONCLUSION: A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

Exploring seasonal variations, assembly dynamics, and relationships of bacterial communities in different habitats of marine ranching.

Offshore coastal marine ranching ecosystems provide habitat for diverse and active bacterial communities. In this study, 16S rRNA gene sequencing and multiple bioinformatics methods were applied to investigate assembly dynamics and relationships in different habitats. The higher number of edges in the water network, more balanced ratio of positive and negative links, and more keystone species included in the co-occurrence network of water. Stochastic processes dominated in shaping gut and sediment community assembly (R2 < 0.5), while water bacterial community assembly were dominated by deterministic processes (R2 > 0.5). Dissimilarity-overlap curve model indicated that the communities in different habitats have general dynamics and interspecific interaction (P < 0.001). Bacterial source-tracking analysis revealed that the gut was more similar to the sediment than the water bacterial communities. In summary, this study provides basic data for the ecological study of marine ranching through the study of bacterial community dynamics.

[Interaction Effects of Vegetation and Soil Factors on Microbial Communities in Alpine Steppe Under Degradation].

To clarify the regulating effect of vegetation and soil factors on microbial communities in the alpine steppe under degradation on the Qinghai-Xizang Plateau, the alpine steppe in the Sanjiangyuan area of the Qinghai-Tibet Plateau was chosen. We analyzed the differences in vegetation and soil factors in different stages of degradation （non-degradation, moderate degradation, and severe degradation） and detected the variations in microbial community characteristics in the alpine steppe under different degradation stages using high-throughput sequencing technology. Eventually, redundancy analysis （RDA） and multiple regression matrixes （MRM） based on the similarity or dissimilarity matrix were used to identify key environmental factors regulating microbial （bacterial and fungal） community changes under degradation. The results showed that the degradation of the alpine steppe significantly changed the community coverage, height, biomass, and important value of graminae； significantly reduced the contents of soil organic matter, total nitrogen, total phosphorus, and silt； and increased the soil bulk density and sand content. Degradation did not change the composition of bacteria and fungi, but their composition proportions changed and also resulted in the loss of microbial richness （Chao1 index and Richness index） but did not significantly change the microbial diversity （Shannon index）. With the occurrence of degradation, the vegetation characteristics, soil physicochemical properties, and microbial diversity showed a consistent change trend. Combined with the characteristics of the network topology changes （the number of nodes and clustering coefficient significantly decreased）, it was found that degradation of the alpine steppe led to the decline of interspecies interactions, decentralization of network, and homogenization of microorganisms, but the cooperation relations among the species were maintained （positive correlation connections accounted for more than 90% in all degradation stages）. Under the alpine steppe degradation, the vegetation-soil interaction had the greatest effect on soil bacterial community, whereas soil physicochemical properties had the greatest influence on soil fungal community. Specifically, vegetation community height, biomass, and soil bulk density were the mutual factors regulating soil microorganisms, whereas the vegetation Simpson index, important value of graminae, soil total phosphorus, total potassium, and silt content were the unique factors affecting the soil bacterial community, and soil pH and total nitrogen content were the particular factors affecting the soil fungal community.

Disentangling the assembly patterns and drivers of microbial communities during thermal stratification and mixed periods in a deep-water reservoir.

Effect of periodic thermal stratification in deep-water reservoirs on aquatic ecosystems has been a research hotspot. Nevertheless, there is limited information on the response patterns of microbial communities to environmental changes under such specialized conditions. To fill this gap, samples were collected from a typical deep-water reservoir during the thermal stratification period (SP) and mixed period (MP). Three crucial questions were answered: 1) How microbial communities develop with stratified to mixed succession, 2) how the relative importance of stochastic and deterministic processes to microbial community assembly, shifted in two periods, and 3) how environmental variables drive microbial co-occurrence networks and functional group alteration. We used Illumina Miseq high-throughput sequencing to investigate the dynamics of the microbial community over two periods, constructed molecular ecological networks (MENs), and unraveled assembly processes based on null and neutral models. The results indicated that a total of 33.9 % and 27.7 % of bacterial taxa, and 23.1 % and 19.4 % of fungal taxa were enriched in the stratified and mixed periods, respectively. Nitrate, water temperature, and total phosphorus drove the variation of microbial community structure. During the thermal stratification period, stochastic processes (dispersal limitation) and deterministic processes (variable selection) dominated the assembly of bacterial and fungal communities, followed by a shift to stochastic processes dominated by dispersal limitation in two communities. The MENs results revealed that thermal stratification-induced environmental stresses increased the complexity of microbial networks but decreased its robustness, resulting in more vulnerable ecological networks. Therefore, this work provides critical ecological insights for the longevity and sustainability of water quality management in an artificially regulated engineered system.