Distinct Spatial Characteristics Research Articles

Occurrence and environmental risks of antibiotics in surface water of China

Antibiotics as emerging pollutants are frequently detected in surface water, raising concerns about the associated risk of antibiotic resistance genes (ARGs). Despite the widespread apprehension, there are still research gaps in the occurrence of antibiotic pollution in surface water and the associated ecological risks to aquatic organisms in China. Here, we established a dataset of antibiotic pollution in surface water in China during 2018-2022, which encompassed 3 368 concentration values of 128 antibiotics reported in 124 articles. Our analysis showed that antibiotic concentrations were predominantly in the ng/L-μg/L range, reaching up to 26 μg/L. Notably, sulfonamides (e.g., sulfamethoxazole) and quinolones (e.g., ciprofloxacin) were frequently reported at high concentrations. The pollution degree of antibiotics represented by sulfamethoxazole, ciprofloxacin, roxithromycin, and tetracycline exhibited no significant variation across different years but was lower in summer than that in spring and autumn. Additionally, distinct spatial distribution characteristics of the pollution were observed. According to calculation results of the aquatic ecological risk assessment model and the weighted frequency, we proposed a list of priority antibiotics including clarithromycin, erythromycin, sulfamethoxazole, ofloxacin, and oxytetracycline in surface water. Last but not least, this study points out the deficiencies in current research on the occurrence and ecological risks of antibiotics in surface water of China and provides viable screening strategies and monitoring recommendations in this context.

The neonatal liver hosts a spontaneously occurring neutrophil population, exhibiting distinct spatial and functional characteristics from adults.

The elusive nature of the liver immune system in newborns remains an important challenge, casting a shadow over our understanding of how to effectively treat and prevent diseases in children. Therefore, deeper exploration into the intricacies of neonatal immunology might be crucial for improved pediatric healthcare. Using liver intravital microscopy, we unveiled a significant population of granulocytes in the hepatic parenchyma of fetuses and newborns. Utilizing high-dimensional immunophenotyping, we showed dynamic alterations predominantly in granulocytes during neonatal development. Liver intravital microscopy from birth through adulthood captures real-time dynamics, showing a substantial presence of Ly6G + cells that persisted significantly up to 2 weeks of age. Using CyTOF, we characterized neonatal Ly6G + cells as neutrophils, confirmed by morphology and immunohistochemistry. Surprisingly, the embryonic liver hosts a distinct population of neutrophils established as early as the second gestational week, challenging conventional notions about their origin. Additionally, we observed that embryonic neutrophils occupy preferentially the extravascular space, indicating their early establishment within the liver. Hepatic neutrophils in embryos and neonates form unique cell clusters, persisting during the initial days of life, while reduced migratory capabilities in neonates are observed, potentially compensating with increased reactive oxygen species (ROS) release in response to stimuli. Finally, in vivo imaging of acute neutrophil behavior in a newborn mouse, subjected to focal liver necrosis, unveils that neonatal neutrophils exhibit a reduced migratory response. The study provides unprecedented insights into the intricate interplay of neutrophils within the liver, shedding light on their functional and dynamic characteristics during development.

Research on the Control and Performance of Integrated Self-Assembled Micro-Scale Structure of NC-Coated CL-20

A novel self-assembly approach was employed to produce micro-spherical composite energetic material (EM) comprising 2,4,6,8,10,12-Hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane/nitrocellulose (CL-20/NC) via the spray-drying method, with precise control over parameters such as droplet diameter, ambient temperature, and nozzle injection rate. In this method, NC was utilized as a coating for CL-20 to imbue it with distinct spatial characteristics, thereby mitigating its high sensitivity. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses were conducted to investigate the morphology of the CL-20/NC micro-spheres. Additionally, differential scanning calorimetry (DSC) was employed to study the thermal decomposition kinetics of both CL-20 and CL-20/NC. XRD findings revealed that the crystal structure of CL-20/NC micro-spheres prepared using acetone as the solvent remained unchanged, albeit with noticeable attenuation in diffraction peaks. DSC analysis indicated an increase of 4.87 K and 7.64 K in the thermal explosion critical temperature (Tb) and peak temperature (Te) of CL-20, respectively. Furthermore, the apparent activation energy was enhanced by 18.65 kJ·mol−1, signifying improved thermal stability. SEM analysis confirmed that the micro-spheres’ size ranged from 0.5 μm to 5 μm, displaying a regular spherical shape. Notably, the impact sensitivity (H50) of CL-20/NC tripled compared to raw CL-20.

The urban hierarchy and agglomeration effects influence the response of NPP to climate change and human activities

In recent years, global climate changes and human activities have significantly reshaped ecosystems, posing a challenging aspect for research—accurately understanding how climate and human activities affect vegetation. This study employs the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatiotemporal distribution of Net Primary Productivity (NPP) in the Central Yunnan Urban Agglomeration (CYUA) from 1982 to 2019. It measures human activity intensity using the Comprehensive Human Activity Intensity Index (CHAII), which is derived from human activity intensity of land surface, population density, economic conditions, and remote sensing data. Spatial autocorrelation analysis is used to assess human activity clustering. The study employs Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to quantify the impacts of climate change and human activities on NPP in CYUA across different spatiotemporal scales. The findings are as follows: (1) Human activities in CYUA exhibit a spatial distribution radiating from Kunming City and display distinct spatial autocorrelation characteristics. (2) Increasing human activity reduces NPP. In areas with higher and more concentrated human activity, it suppresses NPP, while positively affecting neighboring areas. The threshold of human activity intensity affecting NPP varies across space and time, with higher thresholds in cities with greater human activity intensity compared to those with lower intensity. (3) NPP and precipitation exhibit a closely positive correlation. Reduced precipitation weakens this correlation. Rising temperatures since 2000 have negatively impacted NPP, especially in regions with higher human activity intensity. (4) This study provides a new institutional perspective for the analysis and formulation of adaptive strategies in regions where ecosystems are jointly impacted by climate change and human activities. Future research can further refine the study of NPP responses to human activities in cities with varying development levels, diverse urban organizational structures, and different urban expansion patterns.

Resilience, efficiency fluctuations, and regional heterogeneity in disaster: An empirical study on logistics

While resilience analysis can provide important insights into risk management strategies, research gaps exist in terms of logistics efficiency, regional economic environment, and spatial clustering patterns. Therefore, our study capitalizes on the dramatic variations in logistics activities among regions during COVID-19 to examine regional logistics resilience in the disaster and explore potential spatial clustering patterns. We measure regional logistics resilience through changes in performance outcomes, specifically efficiency fluctuation. To analyze spatial clustering patterns, (regional heterogeneity) presented by logistics efficiency, we propose a new multi-stage approach that integrates the data envelopment analysis with the K-medoids algorithm and analyzes data from the Chinese logistic industry. The results reveal that efficiency fluctuations exhibit distinct spatial distribution characteristics, with more pronounced negative fluctuations in areas of important logistics activities nodes and islands, and more widespread negative trends in coastal areas. Moreover, regions with high freight volumes and logistics specialization demonstrate a sustained high level and quality of logistics efficiency. Furthermore, consumption capacity and economic development appear to positively influence fluctuations in logistics efficiency. The findings hold implications for enhancing regional logistics resilience in the disaster and contribute valuable insights to regional logistics risk management.

Unravelling spatiotemporal patterns of solar photovoltaic plants development in China in the 21st century

Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km2, which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China’s PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China’s PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China’s optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation

AbstractGlobal positioning system data play a crucial role in comprehending an individual’s life due to its ability to provide geographic positions and timestamps. However, it is a challenge to identify the transportation mode used during a trajectory due to the large amount of spatiotemporal data generated, and the distinct spatial characteristics exhibited. This paper introduces a novel approach for transportation mode identification by transforming trajectory data features into image representations and employing these images to train a neural network based on vision transformers architectures. Existing approaches require predefined temporal intervals or trajectory sizes, limiting their adaptability to real-world scenarios characterized by several trajectory lengths and inconsistent data intervals. The proposed approach avoids segmenting or changing trajectories and directly extracts features from the data. By mapping the trajectory features into pixel location generated using a dimensionality reduction technique, images are created to train a deep learning model to predict five transport modes. Experimental results demonstrate a state-of-the-art accuracy of 92.96% on the Microsoft GeoLife dataset. Additionally, a comparative analysis was performed using a traditional machine learning approach and neural network architectures. The proposed method offers accurate and reliable transport mode identification applicable in real-world scenarios, facilitating the understanding of individual’s mobility.

Study on the historical landscape evolution of the core reserve of Guangfu Ancient City based on computer simulation

Abstract In the conservation of the historical landscape within the Guangfu Ancient City Core Protection Area, traditional descriptions via drawings and textual narratives often exhibit subjective biases. To address this, a theoretical model for the historical architectural landscape has been developed to objectively measure and characterize the spatial patterns and morphologies of the landscape. This study introduces Exploratory Spatial Data Analysis (ESDA) techniques, utilizing spatial weights and spatial correlation measures as primary analytical tools. Additionally, meta-cellular automata are employed to simulate the spatial and temporal evolution of the historical landscape. Geographical Information Systems (GIS) and Remote Sensing (RS) technologies have been leveraged to establish a spatial database and facilitate the visualization of the historical landscape’s evolutionary processes. This integrated model and technological approach enable a detailed analysis of the architectural landscape characteristics and the spatial structure’s evolution within the study area. The findings reveal distinct spatial distribution characteristics, with an undulation index ranging from 5 to 20 and a predominant index over 7, accounting for 15.67% of the land. Based on these insights, the study proposes strategic interventions to safeguard the historical integrity of Guangfu Ancient City’s core conservation area.

A study of spatial distribution and dynamic change in monthly FVC of urban parks.

The study on the spatial distribution and dynamic change in monthly Fractional Vegetation Cover (FVC) of parks provides a scientific basis for vegetation management and optimization in urban parks. This research focuses on two comprehensive parks located in Xinxiang, China-People's Park and Harmony Park, using multi-spectral Unmanned Aerial Vehicle (UAV) images as the data source and considering monthly periods. Monthly FVC data was obtained using the method of Dimidiate Pixel Model based on the Normalized Difference Vegetation Index (NDVI). The dynamic changes of monthly FVC at regional scale were described through the dynamic changes in the monthly FVC mean and in the FVC areas at various scales, and the dynamic changes in the monthly FVC were analyzed using the coefficient of variation and curve change trends. Furthermore, the dynamic changes in FVC areas at various scales in the parks were analyzed using standard deviation and curve change trends. Subsequently, the differential method was used to analyze the monthly FVC dynamic changes at pixel scale. The results indicate: (1) In terms of the spatial distribution characteristics in monthly FVC of urban parks, both parks exhibit the highest ratio of bare area in January and February. The proportions of FVC for People's Park are 59.17% and 64.46%, while for Harmony Park they are 69.10% and 51.92%, showing the most distinct spatial distribution characteristics. The high and very high coverage areas in each month are mainly distributed on the outskirts of the park, while the medium, medium-low, and low coverage areas are mainly located in the central and middle parts of the park. The overall FVC of the park shows a trend of high coverage on the periphery and low coverage in the center. (2) In the spatial-temporal dynamic change in FVC at regional scale, the average monthly FVC changes exhibit an overall "∩" -shaped pattern. The peak and minimum FVC values for different parks occur at different times. The peak FVC for People's Park appears in August, while for Harmony Park it appears in June, with corresponding FVC values of 0.46 and 0.50, respectively. The minimum FVC for People's Park occurs in February, and for Harmony Park it occurs in January, with FVC values of 0.17 and 0.15, respectively. Among the dynamic change in FVC areas at various scales, the areas of bare and highest-coverage exhibit the greatest fluctuations, with the ascending and descending changes and rates of bare and highest-coverage areas generally showing opposite trends. (3) In terms of the spatial-temporal dynamic changes in FVC at pixel scale in urban parks, overall, FVC shows moderate improvement from February-August, and moderate degradation from January-February and from August-December. The degradation and improvement are primarily slight. The most significant improvement in monthly FVC occurs in March-April, with a predominant type of significant improvement in FVC changes. People's Park and Harmony Park show the most significant degradation in FVC during September-October and October-November, respectively, with a predominant type of significant degradation in FVC changes. During the periods of most significant improvement and degradation in monthly FVC, the spatial distribution of significant improvement and degradation areas primarily occurs in the periphery and middle parts of the parks. FVC in urban parks decreases from January to February and from August to December, while it increases from February to August, with relatively good conditions from June to August. Vegetation optimization should consider: balancing recreational and ecological functions overall, controlling the proportion of bare land, and enhancing the canopy structure of vegetation in low coverage areas or the coverage of hard surfaces; locally increasing the proportion of evergreen plants and moderately increasing planting density. In addition, parks should strengthen management to reduce the impact of flooding and maintain the health of vegetation.

Numerical and theoretical analysis on soil arching effect of prefabricated piles as deep foundation pit supports

This study presents a detailed investigation into the soil arching effects within deep foundation pits (DFPs), focusing on their mechanical behavior and implications for structural design. Through rigorous 3D finite element modeling and parameter sensitivity analyses, the research explores the formation, geometric characteristics, and spatial distribution of soil arching phenomena. The investigation encompasses the influence of key parameters such as elastic modulus, cohesion, and internal friction angle on the soil arching effect. The findings reveal that soil arching within DFPs exhibits distinct spatial characteristics, with the prominent arch axis shifting as excavation depth progresses. Optimal soil arching is observed when the pile spacing approximates three times the pile diameter, enhancing soil retention and minimizing deformation risks. Sensitivity analyses highlight the significant impact of soil parameters on soil arching behavior, underscoring the critical role of cohesive forces and internal friction angles in shaping arching characteristics. By elucidating the interplay between soil parameters and soil arching effects, the research provides insights for optimizing pile spacing and structural stability.

Environmental efficiency evaluation of China's power system considering factor's spatial dependence: A dynamic spatial network DEA approach

Evaluating and improving the environmental efficiency of the power system is the key to achieving sustainable development. However, the classic DEA approach is susceptible to producing biased results due to its inability to address spatial dependence across factors. Considering the spatial dependence of inputs and outputs, this paper proposes a dynamic spatial network DEA model with slacks-based measure (SNSBM) to examine the environmental efficiency of China's power systems, with a focus on the undesirable outputs of CO2, NOx, and SO2. The investigation incorporates the Moran's I test to account for the spatial interactions of patent counts, coal consumption, CO2 emissions, and renewable power generation. Compared to the classic network DEA, the SNSBM results reveal distinct spatial characteristics and a heightened level of differentiation, which highlights the importance of eliminating spatial dependence. From a dynamic perspective, environmental efficiency shows a visible surge in transmission and distribution divisions after 2015, confirming the success of China's market-oriented reform. In addition, CO2 is the largest contributor to the environmental inefficiency of the power systems, followed by NOx and SO2. This study highlights the importance of spatial dependence and provides many insightful policy recommendations for improving the environmental efficiency of China's power systems.

Study on Excavation Response of Deep Foundation Pit Supported by SMW Piles Combined with Internal Support in Soft Soil Area

Based on a foundation pit project in Fuzhou, China, the influence of foundation pit excavation on the supporting structure and surrounding environment in a soft-soil area is studied. This study was based on actual monitoring data and investigated the variations in the supporting structure, surrounding constructions, and groundwater levels during excavation. The analysis of the monitoring data demonstrates the presence of pronounced ‘spatial effects’ and ‘temporal effects’ on the deformation of the support structure and surrounding structures. The deformation of the support structure and surrounding structures exhibits distinct spatial distribution characteristics at different locations along the excavation pit wall. Typically, more significant deformations are observed in the middle section of the pit wall, while deformations decrease as the distance from the pit corner decreases. The support structure’s and surrounding structures’ deformation characteristics vary during different construction stages. During the excavation phase, the rate of deformation increase in the support structure and surrounding structures is notably higher. In contrast, during the construction of the underground basement floor and the backfilling phase of the excavation pit, the rate of deformation increase in the support structure and surrounding structures is relatively lower. Throughout the entire construction period of the excavation pit, the groundwater level in the vicinity of the pit exhibits a fluctuating trend. Apart from the influence of rainfall, the overall variation in groundwater level is minimal, indicating the effective water-sealing performance of the combined Soil-Mixing Wall (SMW) support structure within the circular enclosure.

Integrative risk assessment method via combining geostatistical analysis, random forest, and receptor models for potentially toxic elements in selenium-rich soil

Revealing the spatial features and source of associated potentially toxic elements (PTEs) is crucial for the safe use of selenium (Se)-rich soils. An integrative risk assessment (GRRRA) approach based on geostatistical analysis (GA), random forest (RF), and receptor models (RMs) was first established to investigate the spatial distribution, sources, and potential ecological risks (PER) of PTEs in 982 soils from Ziyang City, a typical natural Se-rich area in China. RF combined with multiple RMs supported the source apportionment derived from the RMs and provided accurate results for source identification. Then, quantified source contributions were introduced into the risk assessment. Eighty-three percent of the samples contain Cd at a high PER level in local Se-rich soils. GA based on spatial interpolation and spatial autocorrelation showed that soil PTEs have distinct spatial characteristics, and high values are primarily distributed in this research areas. Absolute principal component score/multiple line regression (APCS/MLR) is more suitable than positive matrix factorization (PMF) for source apportionment in this study. RF combined with RMs more accurately and scientifically extracted four sources of soil PTEs: parent material (48.91%), mining (17.93%), agriculture (8.54%), and atmospheric deposition (24.63%). Monte Carlo simulation (MCS) demonstrates a 47.73% probability of a non-negligible risk (RI > 150) caused by parent material and 3.6% from industrial sources, respectively. Parent material (64.20%, RI = 229.56) and mining (16.49%, RI = 58.96) sources contribute to the highest PER of PTEs. In conclusion, the GRRRA method can comprehensively analyze the distribution and sources of soil PTEs and effectively quantify the source contribution to PER, thus providing the theoretical foundation for the secure utilization of Se-rich soils and environmental management and decision making.

Immune landscape and regulatory mechanisms in human atherosclerotic coronary plaques: Evidence from single-cell and bulk transcriptomics

Atherosclerosis is a chronic immuno-inflammatory disease, however, the immune landscape and regulatory mechanisms have not been clear. We detected seven principal immune cell clusters with distinct phenotypic and spatial characteristics using single-cell RNA-sequencing of aortic immune cells from patients with acute coronary syndrome and stable angina pectoris. Then we acquired 265 differentially expressed immune-related genes and the high scores were mainly found in T cells and monocytes, which were differentially regulated in atherosclerotic coronary plaques. The CCL signaling pathway was the most relevant pattern in the T cells and CCL5-CCR1 and CCL5-CCR5 ligand-receptor pairs played a vital role in the CCL signaling pathway. Further comparative analysis indicated MCH-I signaling was the most relevant pattern in the T cells and HLA ligand-related ligand-receptor pairs played a vital role. Functional analysis of the single-cell and bulk transcriptomics pointed to multiple pathways, such as antigen presentation and immune response. Nineteen common differentially expressed immune-related genes were found in both immune cells and the human peripheral blood mononuclear cells. Nine common differentially expressed transcription factors were differentially expressed in both T cell and monocyte clusters from the coronary plaques and human peripheral blood mononuclear cells and the network demonstrated that CEBPB might play an essential role in the transcriptional regulation of atherosclerosis as a hub transcription factor. The definition of immune cell diversity and heterogeneity by single-cell level analysis of aortic immune cell subsets not only unveils cell-type-specific pathways and new immune mechanisms but also discovers the functional correlation of immune cells in human atherosclerosis. Our findings provide great promise for the discovery of novel molecular mechanisms and precise therapeutic targets for atherosclerosis.

Analysis of spatial pattern and influencing factors of private clinics in the main urban area of Guiyang in China from 2021 to 2022 based on multi-source data

BackgroundPrivate clinics are important places for residents to obtain daily medical care. However, previous researches mainly focused on public medical institutions but ignored the issue of systematic allocation of social medical resources such as clinics. It is critical to understand the private clinics distribution to analyze the rational allocation of medical resources and the spatial difference.MethodsBased on the field survey, land census, population density, and economic data from Guiyang, this study analyzes the spatial pattern of private clinics in the main urban area of Guiyang and the influencing factors by using spatial analysis methods such as kernel density, standard deviation ellipses, and geo-detector.ResultsThe private clinics in the main urban area of Guiyang are characterized by "inner dense, outer sparse dense," showing an overall spatial clustering feature of "four cores and two belts with many points" and "dense inside and sparse outside." Different types of private clinics have distinct spatial distribution characteristics and agglomeration forms. The growth of private clinics is closely linked to the population growth of mountainous cities. The most important factors influencing the spatial pattern of private clinics are residential land factors, followed by traffic factors and population density. The impact of economic, natural, and spatial factors is minimal. When using a geo-detector, the results of multi-factor interaction differ from those of single factors, and factor interactions have greater explanatory power than single factors in clinic distribution.ConclusionThis study investigates the geographic distribution and influencing variables of private clinics in typical mountain cities and identifies the causes of the current disparity in the distribution of healthcare resources. It is necessary to gradually develop the primary healthcare system in mountainous cities with legislation, counterpart support, and social resources. While ensuring equal access to health care for low-income people and mobile populations, various medical needs of community members should be fully considered and implemented as soon as possible.

Mechanical Behavior of Steel–Concrete Joint in a Bidirectional Curved Pylon of a Long-Span Cable-Stayed Bridge

The complex mechanical behavior of a steel–concrete joint (SCJ) in a bidirectional curved pylon of a cable-stayed bridge is investigated using finite-element analysis (FEA) and a 1:4 scaled full-section model test. Formulas for the stress distribution and stress nonuniformity coefficients of the concrete in the SCJ are proposed to evaluate the force transmission characteristic of the SCJ. The FEA results indicate that variations in the section stiffness at both the bearing plate and the end of the steel structure result in abrupt stress changes and distinct spatial force characteristics of the SCJ. The experimental results reveal that the SCJ exhibits better force resistance than the concrete and steel transition segments, and the force transfer is dominant through the bearing plate. The proposed formulas are validated by using the measured results, which reveal significant nonuniformity in the stress distribution. The stiffness variation at the bearing plate, nonuniform concrete stress distribution, and spatial force characteristics should be considered during the design of similar SCJs in spatial pylons.

Bacterial taxonomic and functional profiles from Bohai Sea to northern Yellow Sea.

Microbial distribution patterns are the result of a combination of biotic and abiotic factors, which are the core issues in microbial ecology research. To better understand the biogeographic pattern of bacteria in water environments from the Bohai Sea to the northern Yellow Sea, the effects of environmental factors, and spatial distance on the structure of bacterial communities in marine water were investigated using high-throughput sequencing technology based on 16S rRNA genes. The results showed that Proteobacteria, Bacteroidetes, Actinobacteri, Desulfobacterota, and Bdellovibrionota were the dominant phyla in the study area. A clear spatial pattern in the bacterial community was observed, and environmental factors, including salinity, nutrient concentration, carbon content, total phosphorus, dissolved oxygen, and seawater turbidity emerged as the central environmental factors regulating the variation in bacterial communities. In addition, the study provides direct evidence of the existence of dispersal limitation in this strongly connected marine ecological system. Therefore, these results revealed that the variation in bacterial community characteristics was attributed to environmental selection, accompanied by the regulation of stochastic diffusion. The network analysis demonstrated a nonrandom co-occurrence pattern in the microbial communities with distinct spatial distribution characteristics. It is implied that the biogeography patterns of bacterial community may also be associated with the characteristics of co-occurrence characterize among bacterial species. Furthermore, the PICRUSt analysis indicated a clear spatial distribution of functional characteristics in bacterial communities. This functional variation was significantly modulated by the environmental characteristics of seawater but uncoupled from the taxonomic characteristics of bacterial communities (e.g., diversity characteristics, community structure, and co-occurrence relationships). Together, this findings represent a significant advance in linking seawater to the mechanisms underlying bacterial biogeographic patterns and community assembly, co-occurrence patterns, and ecological functions, providing new insights for identifying the microbial ecology as well as the biogeochemical cycle in the marine environment.

Ecosystem Variability along the Estuarine Salinity Gradient: A Case Study of Hooghly River Estuary, West Bengal, India

Hooghly River, a ~460 km long distributary of the Ganga River, passes through a highly industrialized Metropolis-Kolkata in West Bengal, India, and eventually empties into the Bay of Bengal at Gangasagar. To determine the patterns and drivers of planktonic community, spatiotemporal variations in water quality and micronutrient content and planktic prokaryotic and microeukaryotic abundance and diversity across the salinity gradient (0.1 to 24.6 PSU) in the Hooghly River estuary (HRE) were studied. Plankton and water samples were collected at six sites during October 2017, February 2018, and June 2018. The biotic parameters—phytoplankton (Chlorophyll a), total bacterial abundance (cfu), and copepods—were significantly higher in the downstream estuarine sites than in the upstream riparian sites; conversely, rotifer and cladoceran abundances were significantly higher at upstream stations. The most culturable bacterial strains were isolated from the two freshwater sites and one at the confluence (estuarine) and are characterized as Bacillus subtilis, Pseudomonas songnenesis, and Exiguobacterium aurantiacum. Among zooplankton, rotifers (0.09 ± 0.14 ind L−1) and cladocerans (5.4 ± 8.87 ind L−1) were recorded in higher abundance and negatively correlated with bacterial concentrations at upstream stations. On the temporal scale, February samples recorded lower proportions of bacterivorous zooplankton at the three upstream stations. Cluster analysis separated samples on the basis of seasons and water mass movement. The February samples showed distinct spatial characteristics, as three freshwater (FW) stations grouped together and segregated at second 2nd hierarchical level, whereas the three estuarine stations formed a separate cluster at the 50% similarity level. Samples collected in October 2017 and June 2018 exhibited mixed attributes. June samples recorded higher influence of freshwater discharge. The zooplankton abundance showed significant negative correlation with Chl a. Our results demonstrate the relative role of river continuum, land-driven lateral discharge, and seawater intrusion in shaping community structure, which needs to be considered in management and conservation planning of aquatic ecosystems, especially in highly productive and overexploited HRE.

Switching nanoscale temperature fields with high-order plasmonic modes in transition metal nanorods.

Depending on the photoirradiation conditions, metal nanostructures exhibit various plasmonic modes, including dipolar, quadrupolar, and hexapolar modes. This work demonstrates numerically that these high-order plasmonic modes can be used to switch nanoscale temperature distributions during the plasmonic heating of a manganese (Mn) nanorod. The key feature of Mn is its low thermal conductivity. Generally, when noble metal nanostructures are used for plasmonic heating, the nanostructure surface will be almost isothermal regardless of the order of the excited plasmonic modes because of the high thermal conductivity of noble metals, e.g., the thermal conductivity of gold is 314 W m-1 K-1. However, unlike noble metals, Mn has a significantly lower thermal conductivity of 7.8 W m-1 K-1. Due to this lower thermal conductivity, the distinct spatial characteristics of the high-order plasmonic modes can be transcribed clearly into nanoscale temperature fields, which are achieved by generating polarization currents by high-order plasmons within the nanorod. These findings strongly suggest that high-order plasmonic modes hold significant potential for the advanced and precise manipulation of heat generation at the nanometer scale in thermoplasmonics.

Remote sensing-based multi-scale characterization of ecohydrological indicators (EHIs) in India

The carbon, water, and energy cycles play an important role in the terrestrial ecosystem's functioning. Climate change and hydroclimatic disturbances are primary factors that influence these cycles, yet little is known about the major ecohydrological indicators (EHIs) that characterize these cycles. In addition, it is essential to analyze spatiotemporal variations and driving factors of EHIs to comprehend how well terrestrial ecosystems can preserve their structure and function in the face of hydroclimatic perturbations. We assessed the three important EHIs, namely water use efficiency (WUEe), rain use efficiency (RUEe), and light use efficiency (LUEe), as well as their controlling factors, in India from 2002 to 2017 at various spatial scales (major river basins, climatic zones, and land cover types). In general, high EHI values were found in high productivity ecosystems (e.g., forest ecosystems) compared to low productivity ecosystems (e.g., cropland and grassland ecosystems). WUEe and LUEe have similar characteristics and were higher in mountain, tropical wet, and tropical wet-dry zones, whereas lower in arid zones. RUEe shows distinct spatial characteristics with higher values in semi-arid zones and lower values in arid zones. The drivers investigated in this study include CO2 concentrations, evapotranspiration (ET), humidity, leaf area index (LAI), normalized difference vegetation index (NDVI), precipitation (PRECIP), soil moisture (SM), solar radiation (SR), temperature (TEMP), vapor pressure deficit (VPD), and wind speed (WS). All three EHIs were found sensitive to TEMP and SR at a national scale, whereas CO2 was a significant driver in arid ecosystems. Other controlling factors (e.g., VPD, SM, and humidity) also played a significant role at smaller spatial scales. Further, this study finds that undisturbed ecosystems (only climate influenced) have slightly higher values of EHIs compared to disturbed (climate-human influenced) ecosystems. Based on the results, in future, it can be expected that an increase in the incident SR and TEMP will decrease the EHIs in India. This study helps understand the coupling of water, carbon, and energy cycles, and the findings from this research can be a reference for ecosystem conservation and restoration.