An Assessment of Vegetation Changes in the Three-River Headwaters Region, China: Integrating NDVI and Its Spatial Heterogeneity.

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems.

18F-Fluoro-2-Deoxyglucose Positron Emission Tomography/Computed Tomography Measures of Spatial Heterogeneity for Predicting Platinum Resistance of High-Grade Serous Ovarian Cancer.

BACKGROUND: The purpose of this study is to construct models for predicting platinum resistance in high-grade serous ovarian cancer (HGSOC) derived from quantitative spatial heterogeneity indicators obtained from 18F-FDG PET/CT images. METHODS: A retrospective study was conducted on patients diagnosed with HGSOC. Quantitative indicators of spatial heterogeneity were generated using conventional features and Haralick texture features from both CT and PET images. Three groups of predictive models (conventional, heterogeneity, and integrated) were built. Each group's optimal model was the one with the highest area under curve (AUC). Postoperative immunohistochemical staining for Ki-67 and p53 was conducted. The correlation between the heterogeneity indicators and scores for Ki-67 and p53 was assessed by Spearman's correlation coefficient (ρ). RESULTS: A total of 286 patients (54.6 ± 9.3 years) were enrolled. And 107 spatial heterogeneity indicators were extracted. The optimal models for each group were obtained using the Gradient Boosting Machine (GBM) algorithm. There was an AUC of 0.790 (95% CI: 0.696, 0.885) in the conventional model for the validation set, and an AUC of 0.904 (95% CI: 0.842, 0.966) in the heterogeneity model for the validation set. The integrated model achieved the highest predictive performance, with an AUC value of 0.928 (95% CI: 0.872, 0.984) for the validation set. Spearman's correlation showed that HU_Kurtosis had the strongest correlation with p53 scores with ρ = 0.718, while cluster site entropy had the strongest correlation with Ki-67 scores with ρ = 0.753. CONCLUSIONS: Adding quantitative spatial heterogeneity indicators derived from PET/CT images can improve the prediction of platinum resistance in patients with HGSOC. Spatial heterogeneity indicators were related to Ki-67 and p53 scores.

Landscape Heterogeneity and Environmental Dynamics Improve Predictions of Establishment Success of Colonising Small Founding Populations.

In long-distance dispersal events, colonising species typically begin with a small number of founding individuals. A growing body of research suggests that establishment success of small founding populations can be determined by the context of the colonisation event and the new environment. Here, we illuminate the importance of these sources of context dependence. Using a spatially explicit, temporally dynamic, mechanistic, individual-based simulator of a model amphibian species, the cane toad (Rhinella marina), we simulated colonisation scenarios to investigate how (1) the number of founding individuals, (2) the number of dispersal events, (3) landscape's spatial composition and configuration of habitats ('spatially heterogeneous landscapes') and (4) the timing of arrival with regards to dynamic environmental conditions ('dynamic environmental conditions') influence the establishment success of small founding populations. We analysed the dynamic effects of these predictors on establishment success using running-window logistic regression models. We showed establishment success increases with the number of founding individuals, whereas the number of dispersal events had a weak effect. At ≥ 20 founding individuals, propagule size swamps the effects of other factors, to whereby establishment success is near-certain (≥ 90%). But below this level, confidence in establishment success dramatically decreases as number of founding individuals decreases. At low numbers of founding individuals, the prominent predictors are landscape spatial heterogeneity and dynamic environmental conditions. For instance, compared to the annual mean, founding populations with ≤ 5 individuals have up to 18% higher establishment success when they arrive in 'packed' landscapes with relatively limited and clustered essential habitats and right before the breeding season. Accounting for landscape spatial heterogeneity and dynamic environmental conditions is integral in understanding and predicting population establishment and species colonisation. This additional complexity is necessary for advancing biogeographical theory and its application, such as in guiding species reintroduction efforts and invasive alien species management.

Cooling benefits from urban planting depend on local background canopy cover: A continental cross-city comparison.

Numerous studies have shown that the cooling efficiency (CE) of urban trees varies by cities with different climate backgrounds, and recent research further indicated that there may be large within-city variations in CE. However, how such within-city variations differ across cities, and their relations to the local percent of urban tree canopy (Ptree) remain poorly understood. This study aims to fill this gap based on a comparison study across 118 cities with different biomes and climates in the continental USA. We used the tree canopy layer of the National Land Cover Dataset (NLCD) 2011 to measure urban tree canopy (UTC), and calculated land surface temperature (LST) based on Landsat thermal bands. We found: 1) CE had larger within-city and cross-city spatial variations in cities located in arid and semi-arid biomes. 2) CE was related to Ptree in nonlinear ways in >90 % of the study cities. In most cities (approximately 70 %), CE had an L-shaped relationship with Ptree, showing that CE first declined quickly with the increase of Ptree, but then gradually dropped in a slower way or became relatively stable after Ptree reached a certain threshold. 3) While there was no significant difference in the types of CE-Ptree relationship among biomes and climates, the threshold of Ptree in CE-Ptree nonlinear ways was smaller in arid cities. The results of this threshold linking cooling benefits and current UTC can serve as a useful tool to prioritize locations for urban planting to maximize cooling benefits.

Spatial distribution characteristics and influencing factors of soil organic carbon based on the geographically weighted regression model.

Quantifying the effects of environmental factors on soil organic carbon and spatial distribution is fundamental to soil quality regulation, restoration, and response to climate change. The present study aims to explore the spatial distribution characteristics of the soil organic carbon (SOC) contents in Anhui Province, China, based on national soil data. In addition, we used the geographically weighted regression (GWR) model to quantify the influence degrees of environmental factors on the soil organic carbon density (SOCD). The results showed that the spatial distribution of SOCD in Anhui Province in both 1985 and 2018 was characterized by high in the south and low in the north. The GWR model prediction results of the 0-30 cm SOCD showed local coefficients of determination (local R2) ranging from 0.21 to 0.96 and 0.14 to 0.96 in 1985 and 2018, respectively. Therefore, the predicted results were effective in evaluating the overall spatial distribution of the SOCD in Anhui Province. The regression coefficients of the normalized difference vegetation index (NDVI) and air temperature ranged from - 0.39 to 5.67 and - 0.17 to 3.11, respectively, demonstrating their strong controlling effects on the spatiotemporal variations in the 0-30 cm SOCD in Anhui Province.

It's about (taking up) space: Discreteness of individuals and the strength of spatial coexistence mechanisms.

One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation-dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth-density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation-based approach to quantifying IGRs in this situation, building on our previous approach for spatially non-varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth-density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities.

Redox potential shapes spatial heterogeneity of mixed-cultured electroactive biofilm treating wastewater.

The core of bioelectrochemical systems (BESs) is electrochemically active microorganisms (EAMs), which exert spatial heterogeneity on electrode surface and influences BESs performance. Setting an optimal potential is an effective strategy for improving and optimizing BESs performance, however, how the electrode potential affects spatial structure of microbial community within anode biofilm is not known. Using a complex substrate-fed BES with a wastewater inoculum, this study investigated the community structure and composition of the stratified biofilm developed under the potential of -0.3 V, 0 V, +0.3 V and +0.6 V (vs. saturated calomel electrode) by freezing microtome method and high-throughput sequencing analysis. The spatial heterogeneity of biofilm community was found to be dependent on the electrode potential and a less stratified community structure was observed for +0.6 V than other potentials. Within the biofilms, the inner layers selected more Geobacter and the outer layers enriched more Acinetobacter and Serratia, potentially suggested a stratification of electron transfer pathway and metabolite-based interspecies communications. The results demonstrated the response of spatial heterogeneity of anode biofilm community to the change of electrode potential, which helps to understand the selectivity and enrichment of kinetically efficient anodic microbiome by electron potential.

The MCIB Model: A Novel Theory for Describing the Spatial Heterogeneity of the Tumor Microenvironment.

The tumor microenvironment (TME) can be regarded as a complex and dynamic microecosystem generated by the interactions of tumor cells, interstitial cells, the extracellular matrix, and their products and plays an important role in the occurrence, progression and metastasis of tumors. In a previous study, we constructed an IEO model (prI-, prE-, and pOst-metastatic niche) according to the chronological sequence of TME development. In this paper, to fill the theoretical gap in spatial heterogeneity in the TME, we defined an MCIB model (Metabolic, Circulatory, Immune, and microBial microenvironment). The MCIB model divides the TME into four subtypes that interact with each other in terms of mechanism, corresponding to the four major links of metabolic reprogramming, vascular remodeling, immune response, and microbial action, providing a new way to assess the TME. The combination of the MCIB model and IEO model comprehensively depicts the spatiotemporal evolution of the TME and can provide a theoretical basis for the combination of clinical targeted therapy, immunotherapy, and other comprehensive treatment modalities for tumors according to the combination and crosstalk of different subtypes in the MCIB model and provide a powerful research paradigm for tumor drug-resistance mechanisms and tumor biological behavior.

Analysis of multi-scale effects and spatial heterogeneity of environmental factors influencing purse seine tuna fishing activities in the Western and Central Pacific Ocean.

Understanding the spatial fishing activity distribution characteristics is important for the sustainable development of fisheries. Spatial nonstationarity is always present, especially in marine ecosystems. To explore how marine environmental factors affect the fishing effort of tuna purse seine vessels, data from 2015 to 2020 on the fishing activities of these fleets and environmental variables in the Western and Central Pacific Ocean (WCPO) were analyzed. A Generalized Additive Model (GAM), Geographically Weighted Regression model (GWR), and Multi-Scale Geographically Weighted Regression (MGWR) model were applied to explore the drivers of fishing activity and the impacts of environmental factors on spatial heterogeneity. The results indicate that: (1) The MGWR models has the highest prediction accuracy and effectively reflects the spatial heterogeneity and multi-scale effects of environmental factors in a year. (2) Environmental factors exhibit significant multi-scale effects and spatial heterogeneity on the fishing activities of purse seine tuna vessels. Sea floor depth, salinity at 200 m depth and sea surface temperature show the greatest spatial heterogeneity in their impact on fishing activities. (3) Sea surface temperature, distance to port, and primary productivity and salinity at 200 m depth are key variables influencing the fishing activities of purse seine tuna vessels. These findings are expected to provide scientific and effective guidance for fishery management and sustainable development by assessing the spatial variations in fishing activities at multiple scales.

Integrated analysis to identify biological features and molecular markers of poorly cohesive gastric carcinoma (PCC).

As one of the two main histologic subtypes of gastric cancer (GC), diffuse-type gastric cancer (DGC) containing poorly cohesive gastric carcinoma (PCC) components has a worse prognosis and does not respond well to typical therapies. Despite the large number of studies revealing the complex pathogenic network of DGC, the molecular heterogeneity of DGC is still not fully understood. We obtained single-cell RNA-seq data and bulk data from the tumor immune single cell hub, the public gene expression omnibus, and the cancer genome atlas databases. A series of bioinformatics analyses were performed using R software. Immunofluorescence staining, hematoxylin and eosin staining, western blot, and functional experiments were used for experimental validation. Caudin-3, -4 and -7 were lowly expressed in DGC and their expression levels were further reduced in PCC. The PCC components were mainly located in the deeper layers of the DGC and had a high level of hypoxic Wnt/ß-catenin signaling and stemness. We further identified Insulin Like Growth Factor Binding Protein 7 (IGFBP7) as a marker for PCC components in the deep layer. IGFBP7 is stimulated by hypoxia and promotes cancer cell invasiveness and reduced claudin expression. In addition, programmed death-1 ligand (PD-L1) was specifically expressed in the deep layer, reflecting deep layer-specific immunosuppression. The PCC components are predominantly situated in the deeper layers of DGC. Initial molecular characterization of these PCC components revealed distinct features, including low expression of claudin-3, -4, and -7, high expression of IGFBP7, and the presence of PD-L1. These molecular traits may partially account for the pronounced tumor heterogeneity observed in GC.

Dermal fibroblasts retain site-specific transcriptomic identity in keloids.

BACKGROUND: Human skin displays extensive spatial heterogeneity and maintains distinct positional identity. However, the impact of disease processes on these site-specific differences remains poorly understood, especially in keloid, a skin disorder characterized by pronounced spatial heterogeneity. OBJECTIVE: This study aimed to assess whether the spatial heterogeneity and positional identity observed in different anatomic sites persist in keloids. METHODS: Transcriptome sequencing was conducted on 139 keloid dermal tissues and 19 keloid fibroblast samples spanning seven distinct anatomic sites to identify the spatial transcriptomic heterogeneity. In addition, single-cell RNA sequencing data were utilized to elucidate the contributions of various cell types to the maintenance of positional identity. RESULTS: Keloid dermal tissues from diverse sites were categorized into three anatomic groupings: trunk and extremity, ear, and mandible regions. Enrichment analysis of differentially expressed genes unveiled that keloids across distinct regions retained unique anatomically-related gene expression profiles, reminiscent of those observed in normal skin. Notably, regional disparities consistently prevailed and surpassed inter-donor variations. Single-cell RNA sequencing further revealed that mesenchymal cells, particularly fibroblasts, made major contributions to positional identity in keloids. Moreover, gene expression profiles in primary keloid fibroblasts demonstrated a remarkable persistence of positional identity, enduring even after prolonged in vitro propagation. CONCLUSION: Taken together, these findings imply that keloids remain positional identity and developmental imprinting characteristic of normal skin. Fibroblasts predominantly contribute to the spatial heterogeneity observed in keloids.

Spatial clustering based on geographically weighted multivariate generalized gamma regression.

Geographically Weighted Regression (GWR) is one of the local statistical models that can capture the effects of spatial heterogeneity. This model can be used for both univariate and multivariate responses. However, it should be noted that GWR models require the assumption of error normality. To overcome this problem, we propose a GWR model for generalized gamma distributed responses that can capture the phenomenon of some special continuous distributions. The proposed model is known as Geographically Weighted Multivariate Generalized Gamma Regression (GWMGGR). Parameter estimation is performed using the Maximum Likelihood Estimation (MLE) method optimized with the Bernt-Hall-Hall-Haussman (BHHH) algorithm. To determine the significance of the spatial heterogeneity effect, a hypothesis test was conducted using the Maximum Likelihood Ratio Test (MLRT) approach. We made a spatial cluster based on the estimated model parameters for each response using the k-means clustering method to interpret the obtained results. Some highlights of the proposed method are:•A new model for GWR with multivariate generalized gamma distributed responses to overcome the assumption of normally distributed errors.•Goodness of fit test to test the spatial effects in GWMGGR model.•Spatial clustering of districts/cities in Central Java based on three dimensions of educational indicators.

Spatial heterogeneity: Necessary and feasible for revealing soil trace elements pollution, sources, risks, and their links.

The source diversity and health risk of trace elements (TEs) in soil make it necessary to reveal the relationship between pollution, source, and risk. However, neglect of spatial heterogeneity restricts the reliability of existing identification methods. In this study, spatial heterogeneity is proposed as a necessary and feasible factor for accurately dissecting the pollution-source-risk link of soil TEs. A comprehensive framework is developed by integrating positive matrix factorization, Geodetector, and risk evaluation tools, and successfully applied in a mining-intensive city in northern China. Overall, the TEs are derived from natural background (28.5 %), atmospheric deposition (25.6 %), coal mining (21.8 %), and metal industry (24.1 %). The formation mechanism of heterogeneity for high-variance TEs (Se, Hg, Cd) is first systematically deciphered by revealing the heterogeneous source-sink relationship. Specifically, Se is dominated (76.5 %) by heterogeneous coal mining (q=0.187), Hg is determined (92.6 %) by the heterogeneity of metal mining (q=0.183) and smelting (q=0.363), and Cd is caused (50.9 %) by heterogeneous atmospheric deposition (q>0.254) co-influenced by the terrains and soil properties. Highly heterogeneous sources are also noteworthy for their potential to pose extreme risks (THI=1.122) in local areas. This study highlights the necessity of integrating spatial heterogeneity in pollution and risk assessment of soil TEs.

Spatiotemporal drivers of agricultural non-point source pollution: A case study of the Huang-Huai-Hai Plain, China.

Agricultural non-point source pollution (ANPSP) poses a severe threat to ecological environments, especially in China's major grain-producing regions. Despite the increasing attention, existing studies often overlook the spatial heterogeneity and driving mechanisms of ANPSP within different functional regions. This study addresses this research gap by constructing a bottom-up regional inventory of ANPSP for the Huang-Huai-Hai Plain (HHHP) and applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyse the spatio-temporal patterns of ANPSP from 2000 to 2020. Spatial econometric models were further applied to examine the spatial spillover effects of driving factors from the perspective of Major Function-oriented Zoning (MFZ). The results show that while ANPSP emissions in the HHHP have generally increased over the past two decades, a slight decrease has been observed since 2015. Grain yield capacity and cropping intensity were identified as the primary drivers of ANPSP growth, particularly in urbanised zones (UZs) and main agricultural production zones (MAPZs). The study also highlights significant spatial heterogeneity in the impact of driving factors on ANPSP across different MFZs, with marked differences in both the direct and spatial spillover effects of these factors. This underlines the need for differentiated environmental protection policies tailored to the functions and characteristics of each region. By integrating the LMDI decomposition method with spatial econometric models, this study offers a new framework for understanding the ANPSP dynamics within the context of MFZs, providing policymakers with valuable insights for designing effective, regionally coordinated governance strategies.

New insights into the combined effect of dispersal and local dynamics in a two-patch population model.

Understanding the effect of dispersal on fragmented populations has drawn the attention of ecologists and managers in recent years, and great efforts have been made to understand the impact of dispersal on the total population size. All previous numerical and theoretical findings determined that the possible response scenarios of the overall population size to increasing dispersal are monotonic or hump-shaped, which has become a common assumption in ecology. Against this, we show in this paper that many other response scenarios are possible by using a simple two-patch discrete-time model. This fact evidences the interplay of local dynamics and dispersal and has significant consequences from a management perspective that will be discussed.

Evaluation of an Inverse Method for Quantifying Spatially Variable Mechanics.

Soft biological tissues often function as highly deformable membranes in vivo and exhibit impressive mechanical behavior effectively characterized by planar biaxial testing. The Generalized Anisotropic Inverse Mechanics (GAIM) method links full-field deformations and boundary forces from mechanical testing to quantify material properties of soft, anisotropic, heterogeneous tissues. In this study, we introduced an orthotropic constraint to GAIM to improve the quality and physical significance of its mechanical characterizations. We evaluated the updated GAIM method using simulated and experimental biaxial testing datasets obtained from soft tissue analogs (PDMS and TissueMend) with well-defined mechanical properties. GAIM produced stiffnesses (first Kelvin moduli, K1) that agreed well with previously published Young's moduli of PDMS samples. It also matched the stiffness moduli determined via uniaxial testing for TissueMend, a collagen-rich patch intended for tendon repair. We then conducted the first biaxial testing of TissueMend and confirmed that the sample was mechanically anisotropic via a relative anisotropy metric produced by GAIM. Next, we demonstrated the benefits of full-field laser micrometry in distinguishing between spatial variations in thickness and stiffness. Finally, we conducted an analysis to verify that results were independent of partitioning scheme. The success of the newly implemented constraints on GAIM suggests notable potential for applying this tool to soft tissues, particularly following the onset of pathologies that induce mechanical and structural heterogeneities.

Differential warming at crown scale impacts walnut primary growth onset and secondary growth rate.

Trees are exposed to significant spatio-temporal thermal variations, which can induce intracrown discrepancies in the onset and dynamics of primary and secondary growth. In recent decades, an increase in late winter and early spring temperatures has been observed, potentially accelerating bud break, cambial activation and their potential coordination. Intracrown temperature heterogeneities could lead to asymmetric tree shapes unless there is a compensatory mechanism at the crown level. An original warming experiment was conducted on young Juglans regia trees in a greenhouse. From February to August, the average temperature difference during the day between warmed and control parts was 4°C. The warming treatment advanced the date of budbreak significantly, by up to 14 days. Warming did not alter secondary growth resumption but increased growth rates, leading to higher xylem cell production (twice as many) and to an increase in radial increment (+80% compared to control). Meristems resumptions were asynchronous without coordination in response to temperature. Buds on warmed branches began to swell two weeks prior to cambial division, which was one week earlier than on control branches. A difference in carbon and water remobilisation at the end of bud ecodormancy was noted under warming. Overall, our results argue for a lack of compensatory mechanisms at the crown scale, which may lead to significant changes in tree architecture in response to intra-crown temperature heterogeneities.

Impact of the 2011 Tohoku Earthquake on the species diversity of rocky intertidal sessile assemblages.

The impacts of large-scale disturbance events on the species diversity of rocky intertidal sessile assemblages across multiple spatial scales are not well understood. To evaluate the influence of the 2011 Tohoku Earthquake on alpha and beta diversities of rocky intertidal sessile assemblages, we surveyed sessile assemblages in the mid-shore zone from 2011 to 2019 and compared the data with those collected from 2003 to 2010 before the earthquake at the same region. The census was conducted across 22 study plots on five rocky shores along 30 km of the Sanriku Coast of Japan, which is located 150-160 km north-northwest of the earthquake epicenter. Alpha diversity was measured with three Hill numbers (H 0, H 1, and H 2), which represent the number of equally common species that would exist in a community with the same diversity as the sampled community, with higher values of the subscript indicating more weight placed on abundant species. Beta diversity was measured with two metrics (BD total at two spatial scales). Values were compared between the post-earthquake period (2011-2019) and the pre-earthquake period (2003-2010). The results show that the Tohoku Earthquake significantly altered the species diversity of intertidal sessile assemblages across multiple spatial scales. All diversity metrics obtained at multiple spatial scales (i.e., alpha diversities: H 0, H 1, and H 2; beta diversities: BD total at the shore and regional scales) decreased immediately after the earthquake and then increased in subsequent years. At 2 years after the earthquake, H 0 recovered to within the range of pre-earthquake values and H 1 and H 2 became significantly higher than pre-earthquake values. Most metrics of alpha and beta diversities recovered to pre-earthquake levels after several years, but regional BD total remained low for a longer period.

Fluorescence Spectroscopy With Temperature Functional Tests in the Assessment of Markers of Intracellular Energy Metabolism: Spatial Heterogeneity and Reproducibility of Measurements.

The fluorescence intensities of the cellular respiratory cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD++ (oxidized flavin adenine dinucleotide) reflect energy metabolism in skin and other tissues and can be quantified in vivo by fluorescence spectroscopy (FS). However, the variability of physiological parameters largely determines the reproducibility of measurement results and the reliability of the diagnostic test. In this prospective study, we evaluated the interday reproducibility of NADH and FAD++ fluorescence intensity measurements in the skin of 51 healthy volunteers assessed by the FS at baseline, after local cooling (10°C) and heating of the skin (35°C). Results showed that the fluorescence amplitude of NADH (AFNADH) in forearm skin was the most reproducible of the FS parameters studied. Assessment of AFNADH in the dorsal forearm in combination with a thermal functional test is the most promising method for clinical use for assessing energy metabolism in the skin.

The root microlandscape of arbuscular mycorrhizal fungi.

Understanding the drivers of assemblages of arbuscular mycorrhizal fungi (AMF) is essential to leverage the benefits of AMF for plant growth and health. Arbuscular mycorrhizal fungi are heterogeneously distributed in space even at small scale. We review the role of plant distribution in driving AMF assemblages (the passenger hypothesis), using a transposition of the conceptual framework of landscape ecology. Because rooting systems correspond to habitat patches with limited carrying capacity that differ in quality due to host-preference effects, we suggest considering plant communities as mosaics of AMF microhabitats. We review how predictions from landscape ecology apply to plant community effects on AMF, and the existing evidence that tests these predictions. Although many studies have been conducted on the effect of plant compositional heterogeneity on AMF assemblages, they mostly focused on the effect of plant richness, while only a few investigated the effect of configurational heterogeneity, plant connectivity or plant community temporal dynamics. We propose key predictions and future prospects to fill these gaps. Considering plant communities as landscapes extends the passenger hypothesis by including a spatially explicit dimension and its associated ecological processes and may help understand and manipulate AMF assemblages at small spatial scales.