LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

On variance estimation of target population created by inverse probability weighting.

Inverse probability weighting (IPW) is frequently used to reduce or minimize the observed confounding in observational studies. IPW creates a pseudo-sample by weighting each individual by the inverse of the conditional probability of receiving the treatment level that he/she has actually received. In the pseudo-sample there is no variation among the multiple individuals generated by weighting the same individual in the original sample. This would reduce the variability of the data and therefore bias the variance estimate in the target population. Conventional variance estimation methods for IPW estimators generally ignore this underestimation and tend to produce biased estimates of variance. We here propose a more reasonable method that incorporates this source of variability by using parametric bootstrapping based on intra-stratum variability estimates. This approach firstly uses propensity score stratification and intra-stratum standard deviation to approximate the variability among multiple individuals generated based on a single individual whose propensity score falls within the corresponding stratum. The parametric bootstrapping is then used to incorporate the target variability by re-generating outcomes after adding a random error term to the original data. The performance of the proposed method is compared with three existing methods including the naïve model-based variance estimator, the nonparametric bootstrap variance estimator, and the robust variance estimator in the simulation section. An example of patients with sarcopenia is used to illustrate the implementation of the proposed approach. According to the results, the proposed approach has desirable statistical properties and can be easily implemented using the provided R code.

Above- and belowground biomass allocation and its regulation by plant density in six common grassland species in China.

Above- and belowground biomass allocation is an essential plant functional trait that reflects plant survival strategies and affects belowground carbon pool estimation in grasslands. However, due to the difficulty of distinguishing living and dead roots, estimation of biomass allocation from field-based studies currently show large uncertainties. In addition, the dependence of biomass allocation on plant species, functional type as well as plant density remains poorly addressed. Here, we conducted greenhouse manipulation experiments to study above- and belowground biomass allocation and its density regulation for six common grassland species with different functional types (i.e., C3 vs C4; annuals vs perennials) from temperate China. To explore the density regulation on the biomass allocation, we used five density levels: 25, 100, 225, 400, and 625 plant m-2. We found that mean root to shoot ratio (R/S) values ranged from 0.04 to 0.92 across the six species, much lower than those obtained in previous field studies. We also found much lower R/S values in annuals than in perennials (C. glaucum and S. viridis vs C. squarrosa, L. chinensis, M. sativa and S. grandis) and in C4 plants than in C3 plants (C. squarrosa vs L. chinensis, M. sativa and S. grandis). In addition to S. grandis, plant density had significant effects on the shoot and root biomass fraction and R/S for the other five species. Plant density also affected the allometric relationships between above- and belowground biomass significantly. Our results suggest that R/S values obtained from field investigations may be severely overestimated and that R/S values vary largely across species with different functional types. Our findings provide novel insights into approximating the difficult-to-measure belowground living biomass in grasslands, and highlight that species composition and intraspecific competition will regulate belowground carbon estimation.

Spatial preference behavior of robo-pigeons induced by electrical stimulus targeting fear nuclei.

BACKGROUND: Numerous studies have confirmed that stimulating the mid-brain motor nuclei can regulate movement forcibly for robo-pigeons, but research on behavior modulation using non-motor nuclei is scarce. OBJECTIVE: In this study, we constructed a spatial preference behavior by stimulating the stratum griseum periventriculare (SGP), a nucleus correlated with fear and escape, for robo-pigeons. METHODS: The study was carried out in a square-enclosed experimental field, with a designated box serving as the 'safe' area for the robo-pigeons. If the robo-pigeon exits this area, the SGP will be stimulated. After a brief training period, the robo-pigeons will have a clear spatial preference for the box. RESULTS: The result from five pigeons has shown that, after simple training, the animals develop a spatial preference for the box. They can quickly return to the box in any situation when the SGP is stimulated, with a success rate exceeding 80% (89.0 ± 6.5%). Moreover, this behavior is highly stable and remains consistent, unaffected by changes in the location of the box or the interference box. CONCLUSION: The results prove that using the electrical stimulus could enable animals to accomplish more complex tasks. It may offer a novel approach to regulating pigeon behavior and further advance the study of cyborg animals.

Enhancing ecosystem productivity and stability with increasing canopy structural complexity in global forests.

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.

A national-scale assessment of land subsidence in China's major cities.

China's massive wave of urbanization may be threatened by land subsidence. Using a spaceborne synthetic aperture radar interferometry technique, we provided a systematic assessment of land subsidence in all of China's major cities from 2015 to 2022. Of the examined urban lands, 45% are subsiding faster than 3 millimeters per year, and 16% are subsiding faster than 10 millimeters per year, affecting 29 and 7% of the urban population, respectively. The subsidence appears to be associated with a range of factors such as groundwater withdrawal and the weight of buildings. By 2120, 22 to 26% of China's coastal lands will have a relative elevation lower than sea level, hosting 9 to 11% of the coastal population, because of the combined effect of city subsidence and sea-level rise. Our results underscore the necessity of enhancing protective measures to mitigate potential damages from subsidence.

PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.

Automated recognition of Human Phenotype Ontology (HPO) terms from clinical texts is of significant interest to the field of clinical data mining. In this study, we develop a combined deep learning method named PhenoBERT for this purpose. PhenoBERT uses BERT, currently the state-of-the-art NLP model, as its core model for evaluating whether a clinically relevant text segment (CTS) could be represented by an HPO term. However, to avoid unnecessary comparison of a CTS with each of ∼14,000 HPO terms using BERT, we introduce a two-levels CNN module consisting of a series of CNN models organized at two levels in PhenoBERT. For a given CTS, the CNN module produces only a short list of candidate HPO terms for BERT to evaluate, significantly improving the computational efficiency. In addition, BERT is able to assign an ancestor HPO term to a CTS when recognition of the direct HPO term is not successful, mimicking the process of HPO term assignment by human. In two benchmarks, PhenoBERT outperforms four traditional dictionary-based methods and two recently developed deep learning-based methods in two benchmark tests, and its advantage is more obvious when the recognition task is more challenging. As such, PhenoBERT is of great use for assisting in the mining of clinical text data.

Research progress of novel magnetic two-dimensional carbon composites in photocatalytic degradation of pollutants: a review.

With the improvement of economic level and the development of science and technology, the problem of water pollution needs to be solved now. Various water pollutants have a negative impact on nature and restrict its development. In recent years, photocatalysis is considered to be a promising wastewater treatment method. Two-dimensional carbon materials have become the hotspot of photocatalytic degradation of pollutants because of their excellent conductivity, large specific surface area, and good hydrophilicity. Nevertheless, it is very hard for these photocatalysts based on carbon materials to separate and recover from the system. For solving such a problem, the composition with magnetic components is an effective way which can facilitate separation and keep the catalytic activity of the samples. In this review, the main roles of magnetic carbon-based composites in the field of pollutant degradation are introduced, and their synthesis technology, classification, and application are summarized. In the end, the current challenges and prospects in this field are involved, aiming to provide useful insights and enlightments into the fields of pollutant treatment and photocatalytic degradation.

Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances.

Grasslands are one of the largest coupled human-nature terrestrial ecosystems on Earth, and severe anthropogenic-induced grassland ecosystem function declines have been reported recently. Understanding factors influencing grassland ecosystem functions is critical for making sustainable management policies. Canopy structure is an important factor influencing plant growth through mediating within-canopy microclimate (e.g., light, water, and wind), and it is found coordinating tightly with plant species diversity to influence forest ecosystem functions. However, the role of canopy structure in regulating grassland ecosystem functions along with plant species diversity has been rarely investigated. Here, we investigated this problem by collecting field data from 170 field plots distributed along an over 2000 km transect across the northern agro-pastoral ecotone of China. Aboveground net primary productivity (ANPP) and resilience, two indicators of grassland ecosystem functions, were measured from field data and satellite remote sensing data. Terrestrial laser scanning data were collected to measure canopy structure (represented by mean height and canopy cover). Our results showed that plant species diversity was positively correlated to canopy structural traits, and negatively correlated to human activity intensity. Canopy structure was a significant indicator for ANPP and resilience, but their correlations were inconsistent under different human activity intensity levels. Compared to plant species diversity, canopy structural traits were better indicators for grassland ecosystem functions, especially for ANPP. Through structure equation modeling analyses, we found that plant species diversity did not have a direct influence on ANPP under human disturbances. Instead, it had a strong indirect effect on ANPP by altering canopy structural traits. As to resilience, plant species diversity had both a direct positive contribution and an indirect contribution through mediating canopy cover. This study highlights that canopy structure is an important intermediate factor regulating grassland diversity-function relationships under human disturbances, which should be included in future grassland monitoring and management.

Ecological restoration programs dominate vegetation greening in China.

Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increase rate of 0.0017 yr-1 in the mean growing season NDVI. The ecological restoration programs dominated the vegetation greening in northern China and the southern coastal regions, indicating a good performance of restoration programs in these regions. In contrast, temperature or precipitation dominated the vegetation greening in southwestern China, Inner Mongolia and the implementation regions of several ecological restoration programs in northeastern China. Among the ecological restoration programs except the Three-North Shelterbelt Forest Program, the effect of ecological restoration programs on vegetation greening was stronger than the total effects of temperature and precipitation changes. Our study presents a systematic assessment on the contribution of ecological restoration programs to the vegetation greening in China, accessed the role on vegetation greening of different ecosystem restoration programs. We analyzed the reasons for the differences in the contribution of different ecological restoration programs to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.

Multispecies forest plantations outyield monocultures across a broad range of conditions.

Multispecies tree planting has long been applied in forestry and landscape restoration in the hope of providing better timber production and ecosystem services; however, a systematic assessment of its effectiveness is lacking. We compiled a global dataset of matched single-species and multispecies plantations to evaluate the impact of multispecies planting on stand growth. Average tree height, diameter at breast height, and aboveground biomass were 5.4, 6.8, and 25.5% higher, respectively, in multispecies stands compared with single-species stands. These positive effects were mainly the result of interspecific complementarity and were modulated by differences in leaf morphology and leaf life span, stand age, planting density, and temperature. Our results have implications for designing afforestation and reforestation strategies and bridging experimental studies of biodiversity-ecosystem functioning relationships with real-world practices.

Determinants of trophic cascade strength in freshwater ecosystems: a global analysis.

Top-down cascade effects are among the most important mechanisms underlying community structure and abundance dynamics in aquatic and terrestrial ecosystems worldwide. A current challenge is understanding the factors controlling trophic cascade strength under global environmental changes. Here, we synthesized 161 global sites to analyze how multiple factors influence consumer-resource interactions with fish in freshwater ecosystems. Fish have a profound negative effect on zooplankton and water clarity but positive effects on primary producers and water nutrients. Furthermore, fish trophic levels can modify the strength of trophic cascades, but an even number of food chain length does not have a negative effect on primary producers in real ecosystems. Eutrophication, warming, and predator abundance strengthen the trophic cascade effects on phytoplankton, suggesting that top-down control will be increasingly important under future global environmental changes. We found no influence or even an increasing trophic cascade strength (e.g., phytoplankton) with increasing latitude, which does not support the widespread view that the trophic cascade strength increases closer to the equator. With increasing temporal and spatial scales, the experimental duration has an accumulative effect, whereas the experimental size is not associated with the trophic cascade strength. Taken together, eutrophication, warming, temporal scale, and predator trophic level and abundance are pivotal to understanding the impacts of multiple environmental factors on the trophic cascade strength. Future studies should stress the possible synergistic effect of multiple factors on the food web structure and dynamics.