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3D Printed Ti-based implants and scaffolds have gained substantial attention in the medical field. These medical multi-metals have shown significant achievements in terms of their mechanical, antibacterial, angiogenic properties, and immune regulatory functions. This has led to breakthrough improvements in osteogenesis. Both the physicochemical properties (roughness, hydrophilicity, and corrosion resistance) and mechanical properties (pore shape, pore size, and porosity) of 3D Printed porous Ti-alloys directly affect osteogenesis. The structure of the pores and the presence of different types of metal in these alloys may promote osteogenesis by stimulating the polarisation of macrophages from M1 to M2. It may also induce angiogenesis by regulating the expression of angiogenesis-related genes that encode proteins such as VEGF. Neovascularization around these implants may rapidly induce contact osteogenesis and bone integration. This review elucidates the mechanisms by which 3D Printed porous Ti alloys affect osteogenesis and provides important insights and directions for the development of 3D Printed Ti-based multimaterials.

BackgroundGiven the increasing interest in the role of gut microbiota in glioblastoma multiforme (GBM), our objective was to examine the potential causal relationship between gut microbiota and GBM, as well as the mediating effects of specific metabolites.MethodsA bidirectional two-sample Mendelian randomization (MR) analysis was conducted to investigate the associations between 196 microbial taxa and GBM. A two-step MR technique was used to identify significant mediators in this relationship. Subsequently, a mediation analysis was performed to explore and quantify the mediating effects of specific metabolites on the causal relationship between gut microbiota and GBM.ResultsFive taxa showed significant associations with GBM. Among them, family Victivallaceae [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.21, 3.13; p = 0.005] and genus Lactococcus (OR: 1.81; 95% CI: 1.04, 3.15; p = 0.036) were positively correlated with the risk of GBM, while phylum Cyanobacteria had a protective effect against GBM (OR: 0.45; 95% CI: 0.22, 0.89; p = 0.021). The mediation analysis revealed that the connections among family Victivallaceae, genus Lactococcus, phylum Cyanobacteria and GBM were mediated by Methyl-4-hydroxybenzoate sulfate, phosphoethanolamine and dehydroepiandrosterone sulfate. Each of these accounted for 7.27, 7.98, and 8.65%, respectively.ConclusionOur study provides evidence supporting a potential causal association between certain gut microbiota taxa and GBM. The study highlights the central role of gut microbiota in GBM pathogenesis and their interactions with vital serum metabolites. This paves the way for potential novel therapeutic interventions in GBM management.

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

Northern Xinjiang is the center of gravity for Xinjiang's economic development, and "production-living-ecological space" (PLES) is of great significance to the optimization of regional territorial spatial structure. Taking northern Xinjiang as our study area, we analyze the spatial and temporal variations of the ecological and environmental effects of PLES evolution from 2000 to 2020. To do so, we apply the grid cell method, the ecological and environmental quality index, the global autocorrelation model, and the kernel density estimation. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in ecological and environmental quality. Results show that from 2000 to 2020, PLES in the study area is mainly ecological accommodation land. However, there is an obvious trend showing a decrease in ecological accommodation land area and an increase in other land types. Production land and living land increased the most, among which agricultural land and industrial land were mainly converted from grassland ecological land and water ecological land, respectively. The quality of the ecological environment is basically stable during the study period, with an overall slight declining trend, which indicates the quality of ecological environment has decreased slightly. The spatial distribution of the eco-environmental effect shows a significant spatial autocorrelation pattern of "dense in the west and sparse in the east", along with a spatial clustering phenomenon. The main factor of spatial variation in ecological quality is elevation, and the interactions between the factors are mainly non-linearly and double-factor enhanced. The spatial differentiation of ecological environmental quality in the study area is the result of multiple factors. Exploring the ecological and environmental effects of PLES evolution and its driving factors can provide a scientific basis for the implementation of territorial spatial planning and ecological environmental protection in northern Xinjiang.

Xinjiang is located in arid northwest China, which is a key area for promoting the high-quality development of the regional ecological environment. In recent years, against a background of increasing human activities and rapid natural changes, Xinjiang has faced enormous ecological challenges. This paper utilizes land-use data from 2000 to 2020 to verify the region’s current state of the ecosystem. Additionally, it uses the value equivalent factor per unit area, ecosystem service value (ESV) loss and gain matrix, and double-factor spatial autocorrelation analysis to study the spatial and temporal variabilities of ESV in Xinjiang and its attribution to spatial correlation. The results show that (1) the ESV in Xinjiang exhibits an overall increasing trend during 2000–2020, with a total increase of about CNY 18.202 billion. Regulation-service ESV takes the main position in the single-service function, accounting for about 67.18% of the total ESV. In northern Xinjiang, the ESV demonstrates a decreasing trend, dropping by about CNY 16.885 billion, while in southern Xinjiang, the ESV shows an increasing trend, rising by CNY 35.086 billion. (2) For the study period, the main loss of ESV in Xinjiang is the conversion of ecological land with a high ESV into cropland or barren land with a low ESV. The conversion of bare land to grassland led to the largest increase in ESV (about CNY 209.308 billion), whereas the conversion of grassland to barren land led to the largest loss (about CNY 183.046 billion). (3) There are positive correlations among ESV, net primary productivity (NPP), and human activity intensity (HAI). However, all of the relationships weaken year by year. The spatial agglomeration of ESV ∩ NPP is significantly greater than that of ESV ∩ HAI, so NPP is the dominant factor in the spatial correlation of ESV in Xinjiang. The findings of this study provide a scientific basis for promoting high-quality regional ecological development in China’s arid northwest.

The Bay of Bengal (BoB) is conventionally believed to be a low productive, oligotrophic marine ecosystem, where the diazotroph communities presumed to play a vital role in adding “new” nitrogen through the nitrogen fixation process. However, the diazotroph communities in the oceanic region of the BoB are still poorly understood though it represents most of the seawater volume. The present study investigated a detailed account of the bacterioplankton community structure and distribution in the oceanic BoB during the winter monsoon using high throughput sequencing targeting the 16S rRNA and nifH genes. Our study observed diverse groups of bacterioplankton communities in the BoB including both cyanobacterial and non-cyanobacterial phylotypes. Cyanobacteria (Prochlorococcus spp. and Synechococcus spp.) and Proteobacteria (mainly α-, γ-, and δ-Proteobacteria) were the most abundant groups within the bacterial communities, possessing differential vertical distribution patterns. Cyanobacteria were more abundant in the surface waters, whereas Proteobacteria dominated the deeper layers (75 m). However, within the diazotroph communities, Proteobacteria (mainly γ-Proteobacteria) were the most dominant groups than Cyanobacteria. Function prediction based on PICRUSt revealed that nitrogen fixation might more active to add fixed nitrogen in the surface waters, while nitrogen removal pathways (denitrification and anammox) might stronger in deeper layers. Canonical correspondence analysis (CCA) indicated that temperature, salinity, and silicate were major environmental factors driving the distribution of bacterial communities. Additionally, phosphate was also an important factor in regulating the diazotroph communities in the surface water. Overall, this study provided detailed information on bacterial communities and their vital role in the nitrogen cycles in oligotrophic ecosystems.

Bottom trawling has widespread impact on the structure and composition of benthic communities. In this study, soft-bottom macrofauna in a heavily trawled area and an adjacent marine park that has been closed to bottom trawling for more than ten years were sampled, allowing to investigate the impact of and recovery from trawling. Both areas were equally affected by seasonal summer hypoxia. Polychaetes were the most abundant at trawled (78-86%) and non-trawled (85-94%) sites in both summer and winter. The non-trawled sites inside the marine park area had a lower level of species composition and abundance as compared to the trawled sites in summer, whereas in winter the benthic communities at these trawled and non-trawled sites appeared much similar with the majority of the identified taxa being dominated by new recruiting juveniles in both sampling areas. Results of permutational multivariate analysis of variance (PERMANOVA) on transformed infaunal abundance and biomass data revealed significant differences (p

Graph contrastive learning (GCL) has gained considerable attention as a self-supervised learning technique that has been successfully employed in various applications, such as node classification, node clustering, and link prediction. Despite its achievements, GCL has limited exploration of the community structure of graphs. This paper presents a novel online framework called Community Contrastive Learning (Community-CL) for simultaneously learning node representations and detecting communities in a network. The proposed method employs contrastive learning to minimize the difference in the latent representations of nodes and communities in different graph views. To achieve this, learnable graph augmentation views using a graph auto-encoder (GAE) are proposed, followed by a shared encoder that learns the feature matrix of the original graph and augmentation views. This joint contrastive framework enables more accurate representation learning of the network and results in more expressive embeddings than traditional community detection algorithms that solely optimize for community structure. Experimental results demonstrate that Community-CL achieves superior performance compared to state-of-the-art baselines in community detection. Specifically, the NMI of Community-CL is reported to be 0.714 (0.551) on the Amazon-Photo (Amazon-Computers) dataset, which represents a performance improvement of up to 16% compared with the best baseline.

Anthropogenic nutrient input to coastal waters is one of the most common disturbances within inshore marine benthic communities. Organic enrichment in sediments leads to the reduction or disappearance of sensitive organisms, and influences the quality and quantity of primary producers which serve as food sources for the benthic fauna. Such changes, in turn, affect the energy flow and food-web interactions in benthic communities. To examine how organic enrichment may alter marine benthic trophic relationship, a stable isotope (δ13C and δ15N) analysis of the potential food sources and a range of meiofauna and polychaetes from an organically polluted and a relatively unpolluted site was compared in subtropical waters of Hong Kong. Results indicated that some omnivorous infauna shifted from a mainly carnivorous diet at the unpolluted site to a largely herbivorous diet at the organically polluted site. This dietary shift is likely to be related to the oxygen stress, prey limitation and increased abundance and nutritional quality of primary producers in the eutrophic area, resulting in an increase in utilization of plant materials as the major food source. The present findings suggest that such changes in trophic position induced by organic enrichment can provide further insights into the structure and function of coastal benthic communities under pollution stress.

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions; on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

Stable isotope tracer addition can enhance the isotopic differences of primary producers for a wider food-web resolution than the use of natural abundance stable isotopes (NASIs) alone, particularly in ecosystems where primary producers have similar NASI values. To investigate the food sources and the trophic status of benthic infauna in an intertidal area near a Halophila minor seagrass bed within inner Tai Tam Bay, Hong Kong, China, a 15N addition experiment was conducted, and the results were compared with those from NASI data. Only benthic microalgae (BMA) were labeled by applying 15N-enriched NH4Cl to the sediment daily for the first 7 days during a 21-day study. In contrast to the NASI results, Bayesian mixing models based on the isotope tracer experiment suggested a larger dietary contribution of BMA for nematode Daptonema sp. and copepods, whereas a higher reliance on phytoplankton and seagrass detritus was noted for polychaete Neanthes sp. However, both NASI and isotope tracer addition demonstrated that seagrass detritus was a major food source for nematode Spilophorella sp. The present isotope tracer experiment also revealed a contrasting result of the relatively lower contribution of meiofauna in the diets of carnivores/omnivores as compared to the results of NASIs. This finding suggested that the isotope values in these consumers may have not reached an equilibrium with the added isotope in the study period. Thus, there is a need for applying NASI coupled with isotope tracer addition in the investigation of ecosystems in which primary producers have similar isotope values, especially in ecosystems with lower tissue turnover rates, in order for a more accurate determination of dietary contribution and trophic status of consumers in the food-web study.

Sediment is thought to be a vital reservoir for antibiotic resistance genes (ARGs). Often, studies describing and comparing ARGs and their potential hosts in sediment are based on single DNA extractions. To date, however, no study has been conducted to assess the influence of DNA extraction efficiency on ARGs in sediment. To determine whether the abundance of ARGs is underestimated, we performed five successive extraction cycles with a widely used commercial kit in 10 sediment samples collected from the Haihe River and Bohai Bay. Our results showed that accumulated DNA yields after five extractions were 1.8–3.1 times higher than that by single DNA extractions. High-throughput sequencing showed that insufficient DNA extraction could generate PCR bias and skew community structure characterization in sediment. The relative abundances of some pathogenic bacteria, such as Enterobacteriales, Lactobacillales, and Streptomycetales, were significantly different between single and successive DNA extraction samples. In addition, real-time fluorescent quantitative PCR (qPCR) showed that ARGs, intI1, and 16S rRNA gene abundance strongly increased with increasing extraction cycles. Among the measured ARGs, sulfonamide resistance genes and multidrug resistance genes were dominant subtypes in the study region. Nevertheless, different subtypes of ARGs did not respond equally to the additional extraction cycles; some continued to have linear growth trends, and some tended to level off. Additionally, more correlations between ARGs and bacterial communities were observed in the successive DNA extraction samples than in the single DNA extraction samples. It is suggested that 3–4 additional extraction cycles are required in future studies when extracting DNA from sediment samples. Taken together, our results highlight that performing successive DNA extractions on sediment samples optimizes the extractable DNA yield and can lead to a better picture of the abundance of ARGs and their potential hosts in sediments.

Microbial communities are composed of many rare species and a few abundant species. Considering the disproportionate importance of rare species for ecosystem functioning, it is important to understand the mechanisms structuring the rare and abundant components of a diverse community in response to environmental changes. Here, we used a 16S ribosomal RNA gene sequencing approach to investigate the bacterial community diversity in the Eastern Indian Ocean (EIO) during the monsoon and intermonsoon. We employed a phylogenetic null model and network analysis to evaluate the assembly processes and co-occurrence pattern of the microbial community. We found that higher bacterial diversity was detected in the intermonsoon with high temperature and low Chlorophyll a concentrations and N/P ratios. The balance between ecological deterministic processes and stochastic processes varied with seasons in the EIO. Meanwhile, conditionally rare taxa (CRT) were more likely modulated by variable selection processes than always rare taxa (ART) and abundant taxa (AT) (CRT > ART > AT). By linking assembly process and species co-occurrence, we demonstrated that the microbial co-occurrence associations tended to be higher when deterministic processes (mainly variable selection) were weaker. This negative trend was observed in rare species rather than abundant species. The linkage could enhance our understanding of the underlying mechanisms underpinning the generation and maintenance of microbial community diversity.

Due to the leaps of progress in the 5G telecommunication industry, commodity pricing and consumer choice are frequently subject to change and competition in the search for optimal supply and demand. We here utilize a two-stage extensive game with complete information to mathematically describe user-supplier interactions on a social network. Firstly, an example of how to apply our model in a practical 5G wireless system is shown. Then we build a prototype that offers multiple services to users and provides different outputs for suppliers, where in addition, the user and supplier quantities are independently distributed. Secondly, we then consider a scenario in which we wish to maximize social welfare and determine if there is a perfect answer. We seek the subgame perfect Nash equilibrium and show that it exists, and also show that when both sides reach it, social welfare likewise reaches its maximum. Finally, we provide numerical results that corroborate the efficacy of our approach on a practical example in the 5G background.

Abstract The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning.

Zooplankton play a pivotal role in connecting primary producers and high trophic levels, and changes in their temporal and spatial distribution may affect the entire marine ecosystem. The spatial and seasonal taxonomic composition patterns of mesozooplankton in Bohai Bay were investigated in relation to a number of water parameters. Bohai Bay is a eutrophic semi-enclosed bay with dynamic physico-chemical conditions influenced by terrestrial inputs and seawater intrusion. The results showed that under the condition of terrigenous input, the diversity of mesozooplankton species near the eutrophic Haihe River Estuary and Jiyun River Estuary was lower than that in the central Bohai Bay, with gelatinous Oikopleura dioica as the dominant species. The mesozooplankton diversity was highest in the bay mouth affected by seawater intrusion, and the dominant oceanic species, mainly copepods Corycaeus affinis, Calanus sinicus, and Oithona similis, entered the inner bay from the bay mouth. Meanwhile, the abundance of mesozooplankton in summer was significantly higher than that in autumn. Compared with historical data, the dominant species in Bohai Bay has evolved from arrow worm Sagitta crassa to copepod Paracalanus parvus, probably due to global warming, indicating the effects of human activities on the succession of mesozooplankton community.

Shifts in phytoplankton phenology were observed in the Strait of Malacca (SM) and Sunda Shelf (SS), which were speculated to be potentially related to global warming and climate anomaly events. Such interactions between phytoplankton structure and physico-chemical factors were less known in narrow straits. Therefore, the spatial distribution pattern and diversity of surface phytoplankton assemblage, local hydrology, and nutrient regimes were investigated over the SM and SS (South China Sea, SCS) during 2017 and 2018 pre-monsoon season (spring). Diatoms, dinoflagellates, and cyanobacteria were representatives of microphytoplankton in the survey area. Total phytoplankton abundance peaked near Singapore Strait (SGS) and diminished toward SS. From the lower ratio of diatoms to dinoflagellates (

Network segregation—a critical problem in real-life networks—can reveal the emergence of conflicts or signal an impending collapse of the whole system. However, the strong heterogeneity of such networks and the various definitions for key nodes continue to pose challenges that limit our ability to foresee segregation and to determine the main drivers behind it. In this paper, we show that a multi-agent leader–follower consensus system can be utilized to define a new index, named leadership, to identify key leaders in real-life networks. And then, this paper explores the emergence of network segregation that is driven by these leaders based on the removal or the rewiring of the relations between different nodes in agreement with their contribution distance. We finally show that the observed leaders-driven segregation dynamics reveals the dynamics of heterogeneous attributes that critically influence network structure and its segregation. Thus, this paper provides a theoretical method to study complex social interactions and their roles in network segregation, which ultimately leads to a closed-form explanation for the emergence of imbalanced network structure from an evolutionary perspective.