Global Dataset Research Articles

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Global musical diversity is largely independent of linguistic and genetic histories

Music is a universal yet diverse cultural trait transmitted between generations. The extent to which global musical diversity traces cultural and demographic history, however, is unresolved. Using a global musical dataset of 5242 songs from 719 societies, we identify five axes of musical diversity and show that music contains geographical and historical structures analogous to linguistic and genetic diversity. After creating a matched dataset of musical, genetic, and linguistic data spanning 121 societies containing 981 songs, 1296 individual genetic profiles, and 121 languages, we show that global musical similarities are only weakly and inconsistently related to linguistic or genetic histories, with some regional exceptions such as within Southeast Asia and sub-Saharan Africa. Our results suggest that global musical traditions are largely distinct from some non-musical aspects of human history.

Transcriptional Landscape and Dynamics Involved in Sugar and Acid Accumulation during Apple Fruit Development.

In fleshy fruit, sugars and acids are central components of fruit flavor and quality. To date, the mechanisms underlying transcriptional regulation of sugar and acid during fruit development remain largely unknown. Here, we combined ATAC-seq with RNA-seq to investigate the genome-wide chromatin accessibility and to identify putative transcription factors related to sugar and acid accumulation during apple (Malus domestica) fruit development. By integrating the differentially accessible regions (DARs) and differentially expressed genes (DEGs), we generated a global dataset of promoter-accessibility- and expression-increased genes (PEIGs). Using this strategy, we constructed a transcriptional regulatory network enabling screening for key transcription factors and target genes involved in sugar and acid accumulation. Among these transcription factors, five fruit-specific Dof (DNA binding with one finger) genes were selected to confirm their regulatory effects, and our results showed that they could affect sugar or acid concentration by regulating the expression of sugar or acid metabolism-related genes in apple fruits. Our transcriptional regulatory network provides a suitable platform to identify candidate genes that control sugar and acid accumulation. Meanwhile, our dataset will aid in analyzing other characteristics of apple fruit that have not been illuminated previously. Overall, these findings support a better understanding of the regulatory dynamics during apple fruit development and lay a foundation for quality improvement of apple.

A global time series of traffic volumes on extra-urban roads

Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R2 = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.

Will voters polarize over pandemic restrictions? Theory and evidence from COVID-19

In a pandemic, voters will polarize over the choice of restrictions their society needs to impose. This is because in the eyes of voters, optimal restrictions vary with individual and aggregate productivity, vaccination status, and government fiscal transfer capacity. Specifically, less productive voters and those with a limited vaccination status prefer milder restrictions. In addition, voters in countries with superior vaccination programs prefer harsher restrictions, but the effect is weaker when their government has stronger fiscal transfer capacity. These propositions have been tested using the largest global dataset of pandemic restrictions and vaccination rates and are broadly confirmed. Consequently, the paper explains both cross-country variations in restrictions during the COVID-19 pandemic and their development as vaccination programs gained traction. It also offers policy insights into the level of restrictions for future pandemics that different voters will deem tolerable.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Reconstructing long-term natural flows by ensemble machine learning

Large-scale human activities have caused significant disruption to the natural flow regime of rivers worldwide, leading to substantial deviations from river flows’ natural state. The natural hydrological regime of rivers holds great significance for the conservation and management of riverine ecosystems, which relies on the accurate reconstruction of natural river flows. However, the application of existing high spatiotemporal resolution large-scale natural flow datasets at the regional scale has not been adequately assessed, thereby limiting their regional-scale utility. In this study, utilizing global reanalysis datasets and observation data, in combination with ensemble machine learning models, we constructed long-term natural flow time series for inflows into the Pearl River Delta and evaluated the performance of these reconstructed inflows based on observed flow records during undisturbed periods. The reconstruction shows that the ensemble model, the AutoGluon model, indicates the best performance when compared with other machine learning models and outperforms the existing publicly available large-scale datasets in terms of evaluation metrics for the corresponding natural flows. The results of Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) indicate that climate variability, reservoir regulation and human activities have significantly altered the natural flow regime of the Pearl River Delta. Specifically, human activities generally tend to smooth natural flow variability in the Pearl River Delta. This research is expected to provide an efficient and reliable method for reconstructing natural flows and offers guidance for water resource management and ecosystem conservation.

Global drivers of the conservation-invasion paradox.

The conservation-invasion paradox (CIP) refers to a long-term phenomenon wherein species threatened in their native range can sustain viable populations when introduced to other regions. Understanding the drivers of CIP is helpful for conserving threatened species and managing invasive species, which is unfortunately still lacking. We compiled a global data set of 1071 introduction events, including 960 CIP events (successful establishment of threatened species outside its native range) and 111 non-CIP events (unsuccessful establishment of threatened species outside its native range after introduction), involving 174 terrestrial vertebrates. We then tested the relative importance of various predictors at the location, event, and species levels with generalized linear mixed models and model averaging. Successful CIP events occurred across taxonomic groups and biogeographic realms, especially for the mammal group in the Palearctic and Australia. Locations of successful CIP events had fewer native threat factors, especially less climate warming in invaded regions. The probability of a successful CIP event was highest when species introduction efforts were great and there were more local congeners and fewer natural enemies. These results can inform threatened species ex situ conservation and non-native invasive species mitigation.

Does Financial Inclusion Matter to Population Health? Insight From a Global Dataset

AbstractCurrently, extensive empirical studies exist on the impact of financial inclusion on socio-economic outcomes such as economic growth, inequality, and poverty. However, research on financial inclusion and population health is limited and still at the infant stage. Therefore, this article adds to knowledge by inquiring into the health implications of financial inclusion using a global panel dataset for 121 countries between 2004 and 2020. After accounting for endogeneity with heteroskedasticity-based instrumental variable regression and cross-sectional dependency with the Driscol–Kraay estimator, we documented that financial inclusion improves population health outcomes. We found that the effect of financial inclusion on population health outcomes also depends on the level of information and communication technology penetration and existing socio-economic conditions in a country. Following this, we showed that financial inclusion improves population health outcomes in countries with lower income inequality and higher GDP per capita, mobile and internet penetration. We further found heterogeneity in the results across geographical regions and income groups. Following these findings, we suggest that financial inclusion policies could enhance population health.

A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance

ABSTRACT During the last two decades, a great number of global products about evapotranspiration ( E ) and terrestrial water storage ( S ) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of E and S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of E product while various S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing E , offset by decreasing S . Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins.

A global phosphosite-correlated network map of Thousand And One Kinase 1 (TAOK1)

Thousand and one amino acid kinase 1 (TAOK1) is a sterile 20 family Serine/Threonine kinase linked to microtubule dynamics, checkpoint signaling, DNA damage response, and neurological functions. Molecular-level alterations of TAOK1 have been associated with neurodevelopment disorders and cancers. Despite their known involvement in physiological and pathophysiological processes, and as a core member of the hippo signaling pathway, the phosphoregulatory network of TAOK1 has not been visualized. Aimed to explore this network, we first analyzed the predominantly detected and differentially regulated TAOK1 phosphosites in global phosphoproteome datasets across diverse experimental conditions. Based on 709 qualitative and 210 quantitative differential cellular phosphoproteome datasets that were systematically assembled, we identified that phosphorylation at Ser421, Ser9, Ser965, and Ser445 predominantly represented TAOK1 in almost 75% of these datasets. Surprisingly, the functional role of all these phosphosites in TAOK1 remains unexplored. Hence, we employed a robust strategy to extract the phosphosites in proteins that significantly correlated in expression with predominant TAOK1 phosphosites. This led to the first categorization of the phosphosites including those in the currently known and predicted interactors, kinases, and substrates, that positively/negatively correlated with the expression status of each predominant TAOK1 phosphosites. Subsequently, we also analyzed the phosphosites in core proteins of the hippo signaling pathway. Based on the TAOK1 phosphoregulatory network analysis, we inferred the potential role of the predominant TAOK1 phosphosites. Especially, we propose pSer9 as an autophosphorylation and TAOK1 kinase activity-associated phosphosite and pS421, the most frequently detected phosphosite in TAOK1, as a significant regulatory phosphosite involved in the maintenance of genome integrity. Considering that the impact of all phosphosites that predominantly represent each kinase is essential for the efficient interpretation of global phosphoproteome datasets, we believe that the approach undertaken in this study is suitable to be extended to other kinases for accelerated research.

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Increasing impervious surface area (ISA) is a major contributor to the rising land surface temperature (LST). Understanding the warming effect of ISA quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced LST change globally remain to be determined over past decades. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using Terra MODIS LST product (1km) and the global artificial impervious area dataset (30m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1km), albedo (500m), population (100m), air temperature (0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the δLST in urban areas shows a negative trend during daytime (–0.2971°C/% and –0.0870°C/%) and a negative trend at night (0.0938°C/% and 0.0048°C/%) in arid and temperate regions, respectively. While in tropical regions, the δLST remains positive throughout the day, but it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909°C/%) and rural (0.3262°C/%) areas. Besides, the impervious surface has a distinct impact on LST in summer and winter, particularly at night. (3) In temperate regions, δALB (negative) and δAT (positive) are dominant factors in regulating δLST during daytime and nighttime. However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Corporate Bond Issuance Over Financial Stress Episodes: A Global Perspective

We use a merged global data set of security-level corporate bond issuance and firm-level financial statement data to show that, in contrast to earlier periods of financial stress, during the COVID pandemic nonfinancial firms around the world were more likely to issue bonds, driven by a boom in local-currency-denominated issuance. We observe a distinct cross-regional difference in the characteristics of issuing firms, finding that in advanced economies issuance during COVID was driven by less risky firms, as predicted by existing theories; in emerging markets, only issuance of U.S. dollar denominated bonds came from larger or less risky firms.

Wasted efforts of elite Marathon runners under a warming climate primarily due to atmospheric oxygen reduction

Meteorological conditions exert influences on sport performances via complex processes. Previous studies conventionally investigate the effects of weather conditions on marathon performance by following events held at the same places, which brings large uncertainties due to the changed participants. Via following each athlete to eliminate these uncertainties, we show that elite athletes’ marathon-running performance tends to monotonically worsen as ambient temperature rises except when it is extremely cold based on the best 16 athletes from each of the sex continents. It worsens by 0.56 (0.39 for men and 0.71 for women) min/°C when it rises beyond 15 °C. Theoretical analysis based on global marathon performance and weather observation datasets shows that more than half of this effect is mediated by reduced oxygen partial density as warmer temperature expands air and increases vapor pressure for both the men and women athletes, which is confirmed by the methods of Baron–Kenny. This atmospheric thermodynamic mechanism has not been emphasized previously. We also show that world-top athletes’ marathon performance approximately linearly worsens as ambient pressure decreases and slightly worsens as relative humidity rises if not extremely wet. These results substantially differ from the previous ones following the events instead of each athlete. Multi-variable changes in climate system are projected to slow Olympic marathon athletes by 2.51 and 1.06 min by the end of the 21st century in relative to 2020 under the high and intermediate emission scenario, respectively. Therefore, future progression of marathon performance is likely to be substantially slowed or even halted by climate changes without efficient greenhouse gas mitigation.

Liver cancer in young adults: Validity of global datasets.

Liver cancer in young adults: Validity of global datasets.

Global census of the significance of giant mesopelagic protists to the marine carbon and silicon cycles

Thriving in both epipelagic and mesopelagic layers, Rhizaria are biomineralizing protists, mixotrophs or flux-feeders, often reaching gigantic sizes. In situ imaging showed their contribution to oceanic carbon stock, but left their contribution to element cycling unquantified. Here, we compile a global dataset of 167,551 Underwater Vision Profiler 5 Rhizaria images, and apply machine learning models to predict their organic carbon and biogenic silica biomasses in the uppermost 1000 m. We estimate that Rhizaria represent up to 1.7% of mesozooplankton carbon biomass in the top 500 m. Rhizaria biomass, dominated by Phaeodaria, is more than twice as high in the mesopelagic than in the epipelagic layer. Globally, the carbon demand of mesopelagic, flux-feeding Phaeodaria reaches 0.46 Pg C y−1, representing 3.8 to 9.2% of gravitational carbon export. Furthermore, we show that Rhizaria are a unique source of biogenic silica production in the mesopelagic layer, where no other silicifiers are present. Our global census further highlights the importance of Rhizaria for ocean biogeochemistry.

New Empirical Source-Scaling Laws for Crustal Earthquakes Incorporating Fault Dip and Seismogenic-Thickness Effects

Abstract New global source-scaling relations for the aspect ratio and rupture area for crustal earthquakes that include the width-limited effect and a possible free-surface effect are derived using a global dataset of finite-fault rupture models. In contrast to the commonly used scaling relations between moment magnitude (M), fault length (L), width (W), and area, we built self-consistent scaling relations by relating M to the aspect ratio (L/W) and to the fault area to model the change in the aspect ratio once the rupture width reaches the down-dip width limit of the fault. The width-limited effect of large-magnitude earthquakes depends on the fault dip and a regional term for the seismogenic thickness. The magnitude scaling of the aspect ratio includes a break in the magnitude scaling that is dip angle dependent. This dip angle-dependent magnitude scaling in the magnitude–area relation is modeled by a trilinear relation incorporating a dip-related transition range. The effect of the free surface was observed using a normalized depth term and parameterizing the source by the depth of the top of the fault rupture; it is more apparent in the area scaling relation. The scaling differences are related to the fault geometry, not to the rake angle, as commonly assumed. Finally, the corresponding L and W scaling relations obtained by converting the area and aspect ratio models to L and W models not only show good agreement with the previous regional scaling laws on average but also provide better fault-specific application due to the inclusion of a fault-specific dip angle and seismogenic thickness.

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

Modeling and prediction of high-precision global evapotranspiration: based on a different model of physical relationships

Abstract The interchange of water vapor between the land and the atmosphere is influenced by actual evapotranspiration (AET). A nonlinear model (AET-SWC-PET-GPP, ASPG) was developed in this study to combine potential evapotranspiration (PET), soil water content (SWC), and gross primary productivity (GPP) in order to quantitatively estimate AET. The Fluxnet Network 2015 global flux station dataset was used to compared to the AET models (AET-SWC, AS; AET-SWC-PET, ASP and AET-SWC-PET2, ASP2) with various combinations of influencing factors. The results show that the simulation accuracy of the ASPG model is higher than that of AS, ASP, and ASP2, with improvements in a coefficient of determination (R2) of 45.3, 8.1, and 5.7%, respectively.The ASPG performed well for various vegetation types, geographical regions, and time scales. It was also discovered that the fitting coefficients vary depending on the type of vegetation, each with its own range of values.The ASPG model put forth in this study can be used to more effectively estimate AET quantitatively on a global scale and can serve as a theoretical foundation for the accurate calculation of global evapotranspiration and the wise use of water resources.