Spatial Variance Research Articles

Catchment-scale stream network spatio-temporal models, applied to the freshwater stages of a diadromous fish species, longfin eel (Anguilla dieffenbachii)

Spatio-temporal modelling frameworks are important tools for evaluating changes in the population structure of freshwater species, to inform population assessments. However, in the stream networks occupied by freshwater species, two data points in space are more related by physical connectivity than by straight-line, Euclidian distance. Therefore, there is a need for spatio-temporal modelling frameworks that represent the relatedness of points based on physical connectivity. We developed such a modelling framework, known as the “VAST stream network modelling framework", using the widely-employed vector autoregressive spatio-temporal modelling platform VAST and the Ornstein-Uhlenbeck process for spatial variance. Here, we demonstrate the VAST stream network spatio-temporal modelling platform with two applications to the freshwater stages of a diadromous fish species, the endemic New Zealand longfin eel (Anguilla dieffenbachii), and a simulation experiment. The first real-world application considers a single category (longfin eel) and models its probability of encounter in the Waitaki catchment, while the second distinguishes between two categories (smaller and larger longfin eels) and models their numerical densities in the greater Waikato region. These two applications demonstrate how stream network spatio-temporal models can usefully inform freshwater resource managers, providing insights into changes in fish encounter probability and density for different fish length classes and estimates of effective river length occupied. The simulation experiment uses the Waikato model as an operating model to evaluate alternative sampling scenarios for accuracy, precision and coverage, to advise on data requirements. A yearly sample size of 30 sites over a 20-year time series had the greatest precision and accuracy in trend estimates among all scenarios investigated. We also found that, for the scenario with declining female spawning biomass, increasing the yearly sample size from 15 sites to 30 sites in a 9-year time series increased the precision and resulted in unbiased estimates. There is a need to optimise sampling schemes for the freshwater stages of diadromous species, including longfin eel, and we, therefore, recommend further simulation experiments to explore scenarios of population trends, habitat features and sampling approaches.

Associations between Maternal Risk Factors and Intrinsic Placental and Fetal Brain Functional Properties in Congenital Heart Disease.

The relationship between maternal risk factors (MRFs) (particularly pre-gravid obesity, diabetes, and hypertension) and congenital heart disease (CHD) to placental and fetal brain outcomes is poorly understood. Here, we tested the hypothesis that MRF and CHD would be associated with reduced intrinsic placental and fetal brain function using a novel non-invasive technique. Pregnant participants with and without MRF and fetal CHD were prospectively recruited and underwent feto-placental MRI. Using intrinsic properties of blood oxygen level dependent imaging (BOLD) we quantified spatiotemporal variance of placenta and fetal brain. MRFs and CHD were correlated with functional characteristics of the placenta and fetal brain. Co-morbid MRF (hypertension, diabetes, and obesity) reduced spatiotemporal functional variance of placenta and fetal brain (p < 0.05). CHD predicted reduced fetal brain temporal variance compared to non-CHD (p < 0.05). The presence of both MRF and CHD was associated with reduced intrinsic pBOLD temporal variance (p = 0.047). There were no significant interactions of MRFs and CHD status on either temporal or spatial variance of intrinsic brain BOLD. MRF and CHD reduced functional characteristic of placenta and brain in fetuses. MRF modification and management during pregnancy may have the potential to not only provide additional risk stratification but may also improve neurodevelopmental outcomes.

Beyond classification: directly training spiking neural networks for semantic segmentation

Spiking neural networks (SNNs) have recently emerged as the low-power alternative to artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event-driven processing. Due to their energy efficiency, SNNs have a high possibility of being deployed for real-world, resource-constrained systems such as autonomous vehicles and drones. However, owing to their non-differentiable and complex neuronal dynamics, most previous SNN optimization methods have been limited to image recognition. In this paper, we explore the SNN applications beyond classification and present semantic segmentation networks configured with spiking neurons. Specifically, we first investigate two representative SNN optimization techniques for recognition tasks (i.e., ANN-SNN conversion and surrogate gradient learning) on semantic segmentation datasets. We observe that, when converted from ANNs, SNNs suffer from high latency and low performance due to the spatial variance of features. Therefore, we directly train networks with surrogate gradient learning, resulting in lower latency and higher performance than ANN-SNN conversion. Moreover, we redesign two fundamental ANN segmentation architectures (i.e., Fully Convolutional Networks and DeepLab) for the SNN domain. We conduct experiments on three semantic segmentation benchmarks including PASCAL VOC2012 dataset, DDD17 event-based dataset, and synthetic segmentation dataset combined CIFAR10 and MNIST datasets. In addition to showing the feasibility of SNNs for semantic segmentation, we show that SNNs can be more robust and energy-efficient compared to their ANN counterparts in this domain.

Island carrying capacity for three development types: ecological resource, agricultural production, and urban construction

The evaluation of island carrying capacity is the premise for conducting the island spatial planning and can contributes to guiding island ecological conservation and socioeconomic developments. In this study, the island carrying capacities were evaluated for the three major development types, namely, ecological resource, agricultural production, and urban construction, based on the quantity and quality. The occupancy and vacancy rates of island carrying capacity were measured in different scenarios. Miaodao Archipelago and Dongtou Archipelago in China were selected as the study area to demonstrate the evaluation. The former is constituted all by spatially isolated rocky islands, while the latter is featured by the coexistence of the sandy and rocky islands and the connection with the mainland by bridges. The results indicated the high variances of occupancy and vacancy rates of island carrying capacity at multiple spatial scales. Across the two archipelagos, climate conditions, island composition, and spatial connections with the mainland controlled the spatial variance at this scale. For different types of islands, the sandy island presented distinctly higher occupancy and vacancy rates for agricultural production than the rocky islands. At the single island scale within the same archipelago, the developments of agricultural production and urban construction distinctly increased with the increase in the island area. The dependency of island constructions on the external world in Dongtou Archipelago was higher than that in Miaodao Archipelago. Meanwhile, the difference in traffic conditions between the two archipelagos did not distinctly influence the food dependency on the external world. Then, practical suggestions in terms of quantity control and quality promotion were proposed to improve the island carrying capacity.

The spatial effect of alcohol availability on violence: A geographically weighted regression analysis

Studies have identified an association between proximity to outlets selling alcohol and violent crime. The strength of this association has been known to vary spatially as not all areas have the same accessibility to alcohol outlets, suggesting local context matters. Within this evidence base, however, studies have not routinely incorporated spatial variance into their models. Moreover, whilst it is acknowledged that the characteristics of the alcohol outlets (for example distinguishing between on- and off-trade premises) is important in studying the varying pathways to violent crime, studies have not often explored the role/contribution of outlet sales volume in this relationship.This study used open-source police and alcohol outlet data to explore the effect of alcohol availability and sales volume on violent crime, across small areas in England. Spatial associations were calculated using geographically weighted regression controlling for contextual variables, such as deprivation and population characteristics.Associations between alcohol outlets and violence varied spatially, for both on- and off-trade outlets and for different premise types, e.g. pubs and bars. Increased proximity to high-sales outlets was associated with an increased risk of violence in inner city contexts, where there is a concentration of night-life spaces. Through incorporating space, this study highlighted the spatially varying associations between different types of alcohol outlets and violence across small areas in England; so that licensing boards and local authorities can determine which appropriate measures are needed for targeting areas with high alcohol availability, to reduce risk of violence at the small area level.

Tipping points in spatial ecosystems driven by short-range correlated noise.

Complex spatial systems can experience critical transitions or tippings on crossing a threshold value in their response to stochastic perturbations. While previous studies have well characterized the impact of white noise on tipping, the effect of correlated noise in spatial ecosystems remains largely unexplored. Here, we investigate the effect of both multiplicative and additive Ornstein-Uhlenbeck (OU) correlated noise on the occurrence of critical transitions in spatial ecosystems. We find that decreasing the noise correlation time of OU (exponentially correlated) noise aggravates the chance of critical transitions in spatiotemporal ecological systems. Our results hold good and are supported by the analysis of three well-studied spatial ecological models of varying nonlinearity. We also compute spatial early warning indicators (e.g., spatial variance, spatial skewness, and spatial correlation) to determine their reliability in anticipating tipping points with variations in noise correlation. The indicators of critical transitions exhibit mixed success in forewarning the occurrence of a tipping point, as indicated by the distribution of Kendall's rank correlation.

Habitat-specific responses of soil organic matter decomposition to Spartina alterniflora invasion along China's coast.

Plant invasions cause a fundamental change in soil organic matter (SOM) turnover. Disentangling the biogeographic patterns and key drivers of SOM decomposition and its temperature sensitivity (Q10 ) under plant invasion is a prerequisite for making projections of global carbon feedback. We collected soil samples along China's coast across saltmarshes to mangrove ecosystems invaded by the smooth cordgrass (Spartina alterniflora Loisel.). Microcosm experiments were carried out to determine the patterns of SOM decomposition and its thermal response. Soil microbial biomass and communities were also characterized accordingly. SOM decomposition constant dramatically decreased along the mean annual temperature gradient, whereas the cordgrass invasion retarded this change (significantly reduced slope, p < 0.05). The response of Q10 to invasion and the soil microbial quotient peaked at midlatitude saltmarshes, which can be explained by microbial metabolism strategies. Climatic variables showed strong negative controls on the Q10 , whereas dissolved carbon fraction exerted a positive influence on its spatial variance. Higher microbial diversity appeared to weaken the temperature-related response of SOM decomposition, with apparent benefits for carbon sequestration. Inconsistent responses to invasion were exhibited among habitat types, with SOM accumulation in saltmarshes but carbon loss in mangroves, which were explained, at least in part, by the SOM decomposition patterns under invasion. This study elucidates the geographic pattern of SOM decomposition and its temperature sensitivity in coastal ecosystems and underlines the importance of interactions between climate, soil, and microbiota for stabilizing SOM under plant invasion.

Integrated human behavior and tick risk maps to prioritize Lyme disease interventions using a 'One Health' approach

Lyme disease (LD) risk is emerging rapidly in Canada due to range expansion of its tick vectors, accelerated by climate change. The risk of contracting LD varies geographically due to variability in ecological characteristics that determine the hazard (the densities of infected host-seeking ticks) and vulnerability of the human population determined by their knowledge and adoption of preventive behaviors. Risk maps are commonly used to support public health decision-making on Lyme disease, but the ability of the human public to adopt preventive behaviors is rarely taken into account in their development, which represents a critical gap. The objective of this work was to improve LD risk mapping using an integrated social-behavioral and ecological approach to: (i) compute enhanced integrated risk maps for prioritization of interventions and (ii) develop a spatially-explicit assessment tool to examine the relative contribution of different risk factors.The study was carried out in the Estrie region located in southern Québec. The blacklegged tick, Ixodes scapularis, infected with the agent of LD is widespread in Estrie and as a result, regional LD incidence is the highest in the province. LD knowledge and behaviors in the population were measured in a cross-sectional health survey conducted in 2018 reaching 10,790 respondents in Estrie. These data were used to create an index for the social-behavioral component of risk in 2018. Local Empirical Bayes estimator technique were used to better quantify the spatial variance in the levels of adoption of LD preventive activities. For the ecological risk analysis, a tick abundance model was developed by integrating data from ongoing long-term tick surveillance programs from 2007 up to 2018. Social-behavioral and ecological components of the risk measures were combined to create vulnerability index maps and, with the addition of human population densities, prioritization index maps. Map predictions were validated by testing the association of high-risk areas with the current spatial distribution of human cases of LD and reported tick exposure. Our results demonstrated that social-behavioral and ecological components of LD risk have markedly different distributions within Estrie. The occurrence of human LD cases or reported tick exposure in a municipality was positively associated with tick density and the prioritization risk index (p < 0.001).This research is a second step towards a more comprehensive integrated LD risk assessment approach, examining social-behavioral risk factors that interact with ecological risk factors to influence the management of emerging tick-borne diseases, an approach that could be applied more widely to vector-borne and zoonotic diseases.

Consumption-based emissions at city level in China and the spatial heterogeneity analysis of the influential factors.

It is of great significance to identify the critical influential factors of pollutant emissions for emission mitigation. However, city disparity implies different priorities for regional mitigation. This study aims to estimate the consumption-based emissions of 309 prefecture-level cities in China based on the multi-region input-output table and the sectoral NOx emission inventory and investigate the emission transfer phenomenon among cities and sectors. In addition, a geographically weighted regression method is used to analyze the spatial heterogeneity in the driving factors of regional consumption-based emissions. The results reveal that the top 10 cities in consumption-based emissions account for 25.2% of emissions and contribute 22.6% to GDP. The consumption-based emissions are mainly driven by local demand (72.79%) at the regional level and by construction activities (94.43%) at the sectoral level. Besides, the results also show the spatial variances in contributions of driving forces to consumption-based emissions. Economic growth has been identified as the most important factor which promotes consumption-based emissions. However, disposable personal income, per capita road area, urbanization, and percentage of tertiary industry GDP are conducive to reduce consumption-based emissions in some cities of China. It could be concluded that policies without consideration of the emissions from a consumption perspective are difficult to achieve effective emission reduction.

3-D H-scan ultrasound imaging of relative scatterer size using a matrix array transducer and sparse random aperture compounding

H-scan ultrasound (US) is a high-resolution imaging technique for soft tissue characterization. By acquiring data in volume space, H-scan US can provide insight into subtle tissue changes or heterogenous patterns that might be missed using traditional cross-sectional US imaging approaches. In this study, we introduce a 3-dimensional (3-D) H-scan US imaging technology for voxel-level tissue characterization in simulation and experimentation. Using a matrix array transducer, H-scan US imaging was developed to evaluate the relative size of US scattering aggregates in volume space. Experimental data was acquired using a programmable US system (Vantage 256, Verasonics Inc, Kirkland, WA) equipped with a 1024-element (32 × 32) matrix array transducer (Vermon Inc, Tours, France). Imaging was performed using the full array in transmission. Radiofrequency (RF) data sequences were collected using a sparse random aperture compounding technique with 6 different data compounding approaches. Plane wave imaging at five angles was performed at a center frequency of 8 MHz. Scan conversion and attenuation correction were applied. To generate the 3-D H-scan US images, a convolution filter bank (N = 256) was then used to process the RF data sequences and measure the spectral content of the backscattered US signals before volume reconstruction. Preliminary experimental studies were conducted using homogeneous phantom materials embedded with spherical US scatterers of varying diameter, i.e., 27 to 45, 63 to 75, or 106–126 μm. Both simulated and experimental results revealed that 3-D H-scan US images have a low spatial variance when tested with homogeneous phantom materials. Furthermore, H-scan US is considerably more sensitive than traditional B-mode US imaging for differentiating US scatterers of varying size (p = 0.001 and p = 0.93, respectively). Overall, this study demonstrates the feasibility of 3-D H-scan US imaging using a matrix array transducer for tissue characterization in volume space.

Novel database and cut-off value for bacterial amoA gene revealed a spatial variability pattern of the ammonia-oxidizing bacteria community from river to sea

Ammonia-oxidizing bacteria (AOB) catalyze the first step of nitrification, oxidizing ammonia to nitrite, and are characterized by amoA gene encoding ammonia monooxygenase. To analyze the AOB community effectively, an integral taxonomy database containing 14,058 amoA sequences and the optimal cut-off value at 95 % for OTU clustering were determined. This method was evaluated to be efficient by the analysis of environmental samples from the river, estuary, and sea. Using this method, a significant spatial variance of the AOB community was found. The diversity of AOB was highest in the estuary and lowest in the ocean. Nitrosomonas were the predominant AOB in the sediments of the freshwater river and estuary. Nearly all the AOB-amoA sequences belonged to uncultured bacterium in the sediments of deep sea. In general, an integral AOB taxonomic database and a suitable cut-off value were constructed for the comprehensive exploration of the diversity of AOB from river to sea.

A manager's guide to using eDNA metabarcoding in marine ecosystems.

Environmental DNA (eDNA) metabarcoding is a powerful tool that can enhance marine ecosystem/biodiversity monitoring programs. Here we outline five important steps managers and researchers should consider when developing eDNA monitoring program: (1) select genes and primers to target taxa; (2) assemble or develop comprehensive barcode reference databases; (3) apply rigorous site occupancy based decontamination pipelines; (4) conduct pilot studies to define spatial and temporal variance of eDNA; and (5) archive samples, extracts, and raw sequence data. We demonstrate the importance of each of these considerations using a case study of eDNA metabarcoding in the Ports of Los Angeles and Long Beach. eDNA metabarcoding approaches detected 94.1% (16/17) of species observed in paired trawl surveys while identifying an additional 55 native fishes, providing more comprehensive biodiversity inventories. Rigorous benchmarking of eDNA metabarcoding results improved ecological interpretation and confidence in species detections while providing archived genetic resources for future analyses. Well designed and validated eDNA metabarcoding approaches are ideally suited for biomonitoring applications that rely on the detection of species, including mapping invasive species fronts and endangered species habitats as well as tracking range shifts in response to climate change. Incorporating these considerations will enhance the utility and efficacy of eDNA metabarcoding for routine biomonitoring applications.

Forest stand-by-environment interaction invalidates the use of space-for-time substitution for site index modeling under climate change

Quantification and prediction of forest site productivity potential under climate changes are core issues of growth and yield modelling and crucial for the effective management of forest stands. Climatic factors typically enter these models as long-term baseline data of mean air temperature and sum of precipitation. Such models frequently are parameterized using spatially distributed data, an approach that can be considered as indirect space-for-time substitution (SFTS), which is based on the assumption that the growth response across spatial environmental gradients is equivalent to the dynamic growth response in time when site factors shift in situ due to environmental changes over time. In site index modelling this assumption has never been thoroughly tested. We applied procedures to test the validity of the SFTS approach using altitudinal transect analysis of data from long-term experimental plots with Norway spruce (Picea abies) in southwest Germany. The following procedures were applied 1. Partitioning of spatial and temporal variance components of site index and air temperature; 2. Test of the equivalence of spatial and temporal variation in models predicting site index from air temperature; 3. Bias analysis of the SFTS- and the altitudinal range-specific individual models. The results show that the response of site index to changes in growing season air temperature as estimated by the SFTS-based approach is significantly different from the observed temporal dynamics within the plots. The estimated sensitivity of site index to changes in growing season air temperature is significantly larger in the SFTS-model. Bias trends increase with increasing air temperature in all altitudinal ranges. We found significant effects of stand-by-environment interaction on site index responses to changing growing season air temperature. Possible reasons for the found differences are discussed. Furthermore, based on an independent data set from the German National Forestry Inventory (NFI), a SFTS-based climate-sensitive site index model was compared to a state-space modelling approach (SSM) which considers the dynamics of environmental changes over time. The results show that the SSM is statistically superior to the SFTS approach, corroborating the findings of the altitudinal transect analysis. Finally, future site index dynamics of Norway spruce were simulated on the basis of climate change scenarios using the SSM approach. We recommend for all climate sensitive forest growth and yield models based on spatially and temporally distributed data to properly account for within- and between-group effects to avoid erroneous model predictions under climate change.

Global and regional changes in carbon dioxide emissions: 1970–2019

We introduce new frameworks to study spatio-temporal patterns in carbon dioxide emissions, demographic trends and economic patterns across 50 countries over the past 50 years. Our analysis is broken up into four sections. First, we introduce a new method to classify countries into one of three characteristic emissions classes based on a one, two or three-segment piecewise linear model. We reveal that most countries are best represented by a piecewise linear model with one change point. Next, we perform a decade-by-decade study of carbon dioxide trajectories. There, we demonstrate notable changes in cluster structures in each decade. We then study the spatial propagation of emissions over time, highlighting a peak in spatial dispersion in 2000, beyond which there has been a gradual decline in spatial emissions variance over space. Finally, we use carbon dioxide, GDP, and population data and apply dimensionality reduction and clustering to group countries based on similarity in their real and carbon economies.

What explains spatial variations of COVID-19 vaccine hesitancy?: a social-ecological-technological systems approach

While COVID-19 vaccines have been available since December 2020 and efforts have been made to vaccinate the maximum population, a large number of people are continuing to be hesitant, prolonging the pandemic in the US. While most previous studies investigated social, economic, and demographic variables that are associated with COVID-19 vaccine hesitancy, we added ecological and technological variables to better understand the spatial variations of vaccine rates in the contiguous United States using spatial regression and geographically weighted regression (GWR) models. We aim to identify spatially varying social, ecological, and technological factors that are associated with COVID-19 vaccination rates, which can aid in identifying and strengthening the public health system and vaccination programs that can eventually facilitate and overcome vaccination hesitancy. We found six statistically significant predictors; two predictors, % Republican voters (r = 0.507, p < .001) and % Black population (r = −0.360, p < .001) were negatively correlated with the vaccination rates, whereas four remaining predictors, % Population with college degree (r = 0.229, p < 001), NRI Score (r = 0.131, p < .001), % Population with broadband access (r = 0.020, p < 001), and Health facilities per 10 000 population (r = 0.424, p < 001) were positively correlated with the vaccination rates at the county level. GWR results show spatially varying relationships between vaccination rate and explanatory variables, indicating the need for regional-specific public health policy. To achieve widespread vaccination, addressing social, ecological, and technological factors will be essential. We draw particular attention to the spatial variances even among positively and negatively associated factors. This research also calls for a reexamination of existing practices, including vaccination communication and other public health policies, local and national public health organizations, telecommunications agents, and mobilization of resources by the public and private sectors.

Drivers of population differentiation in phenotypic plasticity in a temperate conifer: A 27-year study.

Phenotypic plasticity is a main mechanism for organisms to cope with changing environments and broaden their ecological range. Plasticity is genetically based and can evolve under natural selection, such that populations within a species show distinct phenotypic responses to the environment if evolved under different conditions. Understanding how intraspecific variation in phenotypic plasticity arises is critical to assess potential adaptation to ongoing climate change. Theory predicts that plasticity is favored in more favorable but variable environments. Yet, many theoretical predictions about benefits, costs, and selection on plasticity remain untested. To test these predictions, we took advantage of three genetic trials in the northern Rocky Mountains, USA, which assessed 23 closely located Pinus ponderosa populations over 27 years. Mean environmental conditions and their spatial patterns of variation at the seed source populations were characterized based on six basic climate parameters. Despite the small area of origin, there was significant genetic variation in phenotypic plasticity for tree growth among populations. We found a significant negative correlation between phenotypic plasticity and the patch size of environmental heterogeneity at the seed source populations, but not with total environmental spatial variance. These results show that populations exposed to high microhabitat heterogeneity have evolved higher phenotypic plasticity and that the trigger was the grain rather than the total magnitude of spatial heterogeneity. Contrary to theoretical predictions, we also found a positive relationship between population plasticity and summer drought at the seed source, indicating that drought can act as a trigger of plasticity. Finally, we found a negative correlation between the quantitative genetic variance within populations and their phenotypic plasticity, suggesting compensatory adaptive mechanisms for the lack of genetic diversity. These results improve our understanding of the microevolutionary drivers of phenotypic plasticity, a critical process for resilience of long-lived species under climate change, and support decision-making in tree genetic improvement programs and seed transfer strategies.

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms’ biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

A Novel Method to Achieve Precision and Reproducibility in Exposure Parameters for Low-Frequency Pulsed Magnetic Fields in Human Cell Cultures.

The effects of extremely low-frequency electromagnetic field (ELF-MF) exposure on living systems have been widely studied at the fundamental level and also claimed as beneficial for the treatment of diseases for over 50 years. However, the underlying mechanisms and cellular targets of ELF-MF exposure remain poorly understood and the field has been plagued with controversy stemming from an endemic lack of reproducibility of published findings. To address this problem, we here demonstrate a technically simple and reproducible EMF exposure protocol to achieve a standardized experimental approach which can be readily adopted in any lab. As an assay system, we chose a commercially available inflammatory model human cell line; its response to magnetic fields involves changes in gene expression which can be monitored by a simple colorimetric reporter gene assay. The cells were seeded and cultured in microplates and inserted into a custom-built, semi-automated incubation and exposure system which accurately controls the incubation (temperature, humidity, ) and magnetic-field exposure conditions. A specific alternating magnetic field (<1.0% spatial variance) including far-field reduction provided defined exposure conditions at the position of each well of the microplate. To avoid artifacts, all environmental and magnetic-field exposure parameters were logged in real time throughout the duration of the experiment. Under these extensively controlled conditions, the effect of the magnetic field on the cell cultures as assayed by the standardized operating procedure was highly reproducible between experiments. As we could fully define the characteristics (frequency, intensity, duration) of the pulsed magnetic field signals at the position of the sample well, we were, for the first time, able to accurately determine the effect of changing single ELF-MF parameters such as signal shape, frequency, intensity and duty cycle on the biological response. One signal in particular (10 Hz, duty cycle, rectangular, bipolar, T) provided a significant reduction in cytokine reporter gene expression by in our model cell culture line. In sum, the accuracy, environmental control and data-logging capacity of the semi-automated exposure system should greatly facilitate research into fundamental cellular response mechanisms and achieve the consistency necessary to bring ELF-MF/PEMF research results into the scientific mainstream.

Escaping speed of bacteria from confinement

Microswimmers such as bacteria exhibit large speed fluctuation when exploring their living environment. Here, we show that the bacterium Escherichia coli with a wide range of length speeds up beyond its free-swimming speed when passing through narrow and short confinement. The speedup is observed in two modes: for short bacteria with L <20 μm, the maximum speed occurs when the cell body leaves the confinement, but a flagellar bundle is still confined. For longer bacteria (L ≥ 20 μm), the maximum speed occurs when the middle of the cell, where the maximum number of flagellar bundles locate, is confined. The two speed-up modes are explained by a vanishing body drag and an increased flagella drag—a universal property of an “ideal swimmer.” The spatial variance of speed can be quantitatively explained by a simple model based on the resistance matrix of a partially confined bacterium. The speed change depends on the distribution of motors, and the latter is confirmed by fluorescent imaging of flagellar hooks. By measuring the duration of slowdown and speedup, we find that the effective chemotaxis is biased in filamentous bacteria, which might benefit their survival. The experimental setup can be useful to study the motion of microswimmers near surfaces with different surface chemistry.

Analysis of the heterogeneity of landscape risk evolution and driving factors based on a combined GeoDa and Geodetector model

Liaoning Province, one of China’s top 13 grain-producing areas, has recently implemented various ecological construction and protection plans. To ensure regional environmental protection, national food security, and the development of scientific ecological protection strategies, it is crucial to understand the spatial and temporal evolutionary patterns and driving forces of the landscape’s ecological risk. This study used spatial statistics and spatial autocorrelation to examine the scale response features of landscape risk in Liaoning Province. The best study scale was then selected and the GeoDa software platform was used to examine the pattern of landscape risk evolution in Liaoning Province from 2000 to 2020. Finally, the main spatial drivers of ecological risk in Liaoning Province were analyzed hierarchically according to the magnitude of the explanatory power of the q-statistic by combining the driving force-pressure-state-impact-response model with Geodetector. Several experiments were conducted to determine the optimal taxonomy and classification number for the discretization of each driver. The results showed that (1) the Liaoning River had a high-risk value and thus required careful consideration to maintain sustainable and healthy growth of the area and conserve ecological environment of the southeastern coastline of Liaoning Province. (2) The township scale was regarded as the ideal scale because all nine scales of landscape risk indices in Liaoning Province showed positive spatial connections and the greatest significant difference with it. (3) Clustering and significance analysis determined that townships in Liaoning Province with higher landscape risk indices were more likely to cluster spatially and had less spatial variance. (4) The two factors with the greatest impact on landscape risk in Liaoning Province were temperature and gross domestic product per capita. Landscape risk was the result of the combined influence of these multiple factors. Hence, the carrying capacity of the environment itself cannot be ignored while pursuing economic growth. Our findings offer theoretical support for government to formulate ecological protection policies.