Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Assessing causal associations between neurodegenerative diseases and neurological tumors with biological aging: a bidirectional Mendelian randomization study

BackgroundAging is a significant risk factor for many neurodegenerative diseases and neurological tumors. Previous studies indicate that the frailty index, facial aging, telomere length (TL), and epigenetic aging clock acceleration are commonly used biological aging proxy indicators. This study aims to comprehensively explore potential relationships between biological aging and neurodegenerative diseases and neurological tumors by integrating various biological aging proxy indicators, employing Mendelian randomization (MR) analysis.MethodsTwo-sample bidirectional MR analyses were conducted using genome-wide association study (GWAS) data. Summary statistics for various neurodegenerative diseases and neurological tumors, along with biological aging proxy indicators, were obtained from extensive meta-analyses of GWAS. Genetic single-nucleotide polymorphisms (SNPs) associated with the exposures were used as instrumental variables, assessing causal relationships between three neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis), two benign neurological tumors (vestibular schwannoma and meningioma), one malignant neurological tumor (glioma), and four biological aging indicators (frailty index, facial aging, TL, and epigenetic aging clock acceleration). Sensitivity analyses were also performed.ResultsOur analysis revealed that genetically predicted longer TL reduces the risk of Alzheimer’s disease but increases the risk of vestibular schwannoma and glioma (All Glioma, GBM, non-GBM). In addition, there is a suggestive causal relationship between some diseases (PD and GBM) and DNA methylation GrimAge acceleration. Causal relationships between biological aging proxy indicators and other neurodegenerative diseases and neurological tumors were not observed.ConclusionBuilding upon prior investigations into the causal relationships between telomeres and neurodegenerative diseases and neurological tumors, our study validates these findings using larger GWAS data and demonstrates, for the first time, that Parkinson’s disease and GBM may promote epigenetic age acceleration. Our research provides new insights and evidence into the causal relationships between biological aging and the risk of neurodegenerative diseases and neurological tumors

Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study

BackgroundMeningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear.MethodMendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data. It’s a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome.ResultsThis research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-β, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood.ConclusionThese findings suggest that TNF-β, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas

Porous cobalt oxide nanowires: Notable improved gas sensing performances

The porous Co3O4 nanowires have been successfully synthesized via modified template method. A possible growth mechanism governing the formation of such 1D nanowires is proposed. The as-prepared products have been characterized by X-ray Powder Diffraction (XRD), Extended X-ray Absorption Fine-structure (EXAFS), High-resolution Transmission Electron Microscopy (HRTEM) and N-2 adsorption/desorption analysis. Our systematic studies have revealed that the porous Co3O4 nanowires show excellent gas sensing performances, which demonstrate the potential application of the 1D nanostructured Co3O4 in the detection of the ethanol gas as a sensor material. The improved performances are owing to its large specific surface area and porous morphology

Species–size networks elucidate the effects of biodiversity on aboveground biomass in tropical forests

Although biodiversity has been shown to profoundly affect ecosystem function in forests, the processes which it impacts are limited understood. Various plant species with diverse sizes interact to form complex networks to complete resource use processes, but little is known about the role of species-size networks in influencing ecosystem function and biodiversity-ecosystem function relationships. Using a dataset encompassing 423 species and 32,067 individuals, we constructed species–tree diameter and species–tree height networks for two sampling areas (0.04 and 0.09 ha), and then calculated the network modularity for species and size interlinked specialization and nestedness, species or sizes with relatively few links are a subset of those with more network links. We analyzed the relationships between modularity, nestedness, and the aboveground biomass. The direct and indirect effects of species abundance and richness on the aboveground biomass through network structures were explored using structural equation modelling. Regardless of the species–tree diameter or height network, modularity was positively associated with the aboveground biomass, independent of the sampling area, while nestedness was negatively associated. Species abundance negatively affected the modularity, but positively affected nestedness, whereas species richness had the opposite effect. Species abundance and richness affected the aboveground biomass indirectly through modularity and nestedness, but the effect of modularity was greater than that of nestedness. Our study confirms that the importance of species–size networks in the context of the aboveground biomass has similar effects on species–diameter and height networks across sampling areas. It clarifies that modularity from interactions between species and individuals is a useful indicator to reveal the mechanism by which plant diversity acquires resources for biomass production. These results provide new insights into biodiversity–ecosystem function relationships from a network perspective