Total Data Set Research Articles

Autism in patients with eosinophilic gastrointestinal disease: A systematic review with meta-analysis.

Neurodevelopmental disorders, such as autism spectrum disorder (ASD), have been increasingly associated with eosinophilic gastrointestinal disorders (EGID). However, the relationship between these diseases remains unclear. We performed a systematic review with meta-analysis to address this issue. The search was performed according to the PRISMA guidelines using descriptors for ASD and EGIDs from the MEDLINE, Embase, PsycInfo, LILACS, and Web of Science databases. Observational studies with the prevalence of ASD in any EGID were included. The study protocol was registered on the PROSPERO platform under the number CRD42023455177. The total dataset comprised 766,082 participants. The result of the single-arm meta-analysis showed an overall prevalence of ASD in the population with EGID of 21.59% (95% CI: 10.73-38.67). There was an association between EGID and ASD (OR: 3.44; 95% CI: 1.25-2.21), also significant when restricted only to EoE (OR: 3.70; 95% CI: 2.71-5.70). Recent studies have implicated the influence of an inadequate epithelial barrier integrity in the pathogenesis of several diseases. The role of this mechanism can be extended to situations beyond allergic reactions, including other conditions with underlying immunological mechanisms. Several diseases are potentially related to the systemic effect of bacterial translocation in tissues with defective epithelial barriers. Our meta-analysis provides evidence that supports the consideration of EGID in patients with ASD and ASD in patients with EGID. Despite its limitations, the results should also be validated by future studies, preferably using multicenter prospective designs in populations with low referral bias.

Functional roles of biocrusts in enhancing soil quality in a semi-arid environment

Biocrusts play a wide range of ecological roles, especially in modifying surface soil properties and as a natural agent to combat soil degradation in fragile semi-arid and arid ecosystems globally. We examined the importance of biocrust on soil properties and quality in a semi-arid alluvial fan in northeastern Iran. The soil quality index (SQI) was used as the method for the quantitative evaluation of soil with or without biocrusts, using standard linear and nonlinear scoring methods. For this purpose, 17 biological, chemical, and physical soil properties were integrated into two indicator selection methods (total and minimum data set) and SQI has been calculated by the weighted additive method. The spatial patterns of SQI showed that overall the soil quality was (47%) increased in biocrust compared to non-biocrust soil. The increase of microbial biomass nitrogen, basal soil respiration, and microbial biomass carbon, and the decrease of available sodium and percentage sand were the most important indicators controlling the soil quality changes by biocrusts. In addition, biocrusts improved the soil structure by reducing bulk density and increasing the mean weight diameter of aggregates and available water capacity. Cyanobacteria communities of biocrusts were the initiators of soil quality improvement, while moss and cyanolichen communities played a major role from the moderate stages of biocrust occurrence. It is concluded that globally, in dry lands, biocrusts should be considered as signatures of enhanced soil quality.

Coastal transgressive dunefield evolution as a response to multi-decadal shoreline erosion

The Younghusband Peninsula coastal barrier extends ∼190 km and is predominantly comprised of a mix of active, semi-stabilised and stabilised (relict) transgressive and parabolic dunefields. Wave driven shoreline erosion has triggered the rapid development of a transgressive dunefield along a portion the peninsula. This study focuses on a ∼2 km region with the highest regional erosion rates (> −2.0 m/year since 1988), and which has experienced rapid destabilisation and subsequent landward translation of the dunefield. Aerial LIDAR derived datasets of the barrier system from 2008 and 2018 and eight drone photogrammetry surveys between August 2020 and April 2022 are used to compare the rates of change across the beach-dune system. The net and intra-survey changes are used to show the short- to medium-term system response and illustrate the relationship of sediment dynamics to the developing morphology and structure of the dunefield. Shoreline change rates (m/yr) are sourced from the Digital Earth Australia coastlines dataset which provides annual rates for continental Australia from a Landsat time series (1988–2021). When the rate of shoreline change is segmented from the total dataset to the study time-period (2008–2021), the average resulting rates show an increase from −2.2 m/yr to −3.3 m/yr, respectively. This coincides with a landward advance rate of >10 m/yr from 2008 of the dunefield edge, with the fastest eroding dune toes corresponding to regions with the highest rate of dunefield translation and transgression. Net volumetric totals and their ratios of gain to loss of sediments show that significant sediment is lost to offshore during the retrogradation of the dunefield as only a fraction of the stoss slope sediment is transferred landwards. All regions of the dunefield exhibit a net loss of sediments but the high-temporal resolution shows that the magnitude of 3D changes varies through time. An adapted model of erosional stoss slope evolution and dunefield development is advanced and a temporal model of transgressive dunefield evolution is synthesised.

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-remitting multiple sclerosis (RRMS) and explore potential therapeutic applications leveraging machine learning (ML). An ensemble approach was developed using differential gene expression analysis and competitive ML methodologies, interrogating total RNA sequencing datasets from peripheral whole blood of treatment-naïve RRMS and NMO patients and healthy individuals. Pathway analysis of candidate biomarkers informed the biological context of disease, transcription factor activity, and small-molecule therapeutic potential. ML models differentiated between NMO and RRMS patients, with the performance of certain models exceeding 90% accuracy. RNA biomarkers driving model performance were associated with ribosomal dysfunction and viral infection. Regulatory networks of kinases and transcription factors informed biological context and identified potential therapeutic targets. Small-molecule candidates capable of reversing perturbed gene expression were uncovered. Two small molecules identified, mitoxantrone and vorinostat, reinforced discovered expression signatures and highlighted the potential to identify new therapeutic candidates, as their use in NMO patients and experimental autoimmune encephalomyelitis have been previously described. Putative RNA biomarkers were identified that accurately distinguish NMO from RRMS and healthy individuals. The application of multivariate approaches in analysis of RNA sequencing data enhances the discovery of unique RNA biomarkers, accelerating the development of new methods for disease detection, monitoring, and therapeutics. Integrating biological understanding enhances detection of disease-specific signatures and possible therapeutic targets.

Using a soil quality index to evaluate the artificial soil quality response to rehabilitation time

External-soil spray seeding (ESSS) supplies nutrients and water to plants, soils play a pivotal role in the successful rehabilitation of rock-cut slopes. However, few studies have explored how to accurately evaluate the artificial soil quality response to rehabilitation time to determine the effectiveness of the ESSS technique. In this study, taking the rock-cut slopes of Suining Railway Station to study, various soil samples were obtained from 20-year-old, 13-year-old and 9-year-old rehabilitation slopes, where we sampled the surface soil in north- and south-facing slopes, respectively. Fifteen soil indicators were measured as potential indicators of artificial soil quality, and the indicators with significant differences between rehabilitation time considered as a total data set (TDS). Principal component analysis (PCA) and correlation matrix analysis (CMA) were used to determine a minimum data set (MDS), and these indicators within MDS were integrated into a soil quality index (SQI) by standardized scoring functions (SSF) and weighted-additive methods. Results suggested that (1) the SQI was an effective tool for evaluating artificial soil quality, (2) the longer rehabilitation time, the higher artificial soil quality, and (3) the SQI of the north-facing slopes was higher than that of the south-facing slopes in the same rehabilitation years. Nevertheless, all rehabilitation slopes had a low SQI (SQI ≤ 0.560). Overall, ESSS is a technology that can effectively promote the ecological restoration of rock-cut slopes, but there still exist a room for improvement. In the future ecological reconstruction of rock-cut slopes, more attention should be paid to the restoration of soil quality on the south-facing slopes.

Evaluation of the contribution of individual arteries to the cerebral blood supply in patients with Moyamoya angiopathy: comparison of vessel-encoded arterial spin labeling and digital subtraction angiography

PurposeVessel-encoded arterial spin labeling (VE-ASL) is able to provide noninvasive information about the contribution of individual arteries to the cerebral perfusion. The aim of this study was to compare VE-ASL to the diagnostic standard digital subtraction angiography (DSA) with respect to its ability to visualize vascular territories.MethodsIn total, 20 VE-ASL and DSA data sets of 17 patients with Moyamoya angiopathy with and without revascularization surgery were retrospectively analyzed. Two neuroradiologists independently assessed the agreement between VE-ASL and DSA using a 4-point Likert scale (no- very high agreement). Additionally, grading of the vascular supply of subterritories (A1-A2, M1-M6) on the VE-ASL images and angiograms was performed. The intermodal agreement was calculated for all subterritories in total and for the subdivision into without and after revascularization (direct or indirect bypass).ResultsThere was a very high agreement between the VE-ASL and the DSA data sets (median = 1, modus = 1) with a substantial inter-rater agreement (kw = 0.762 (95% CI 0.561–0.963)). The inter-modality agreement between VE-ASL and DSA in vascular subterritories was almost perfect for all subterritories (k = 0.899 (0.865–0.945)), in the subgroup of direct revascularized subterritories (k = 0.827 (0.738–0.915)), in the subgroup of indirect revascularized subterritories (k = 0.843 (0.683–1.003)), and in the subgroup of never revascularized subterritories (k = 0.958 (0.899–1.017)).ConclusionVessel-encoded ASL seems to be a promising non-invasive method to depict the contributions of individual arteries to the cerebral perfusion before and after revascularization surgery.

Performance of Eight Infliximab Population Pharmacokinetic Models in a Cohort of Dutch Children with Inflammatory Bowel Disease.

Efficacy of infliximab in children with inflammatory bowel disease can be enhanced when serum concentrations are measured and further dosing is adjusted to achieve and maintain a target concentration. Use of a population pharmacokinetic model may help to predict an individual's infliximab dose requirement. The aim of this study was to evaluate the predictive performance of available infliximab population pharmacokinetic models in an independent cohort of Dutch children with inflammatory bowel disease. In this retrospective study, we used data of 70 children with inflammatory bowel disease (443 infliximab concentrations) to evaluate eight models that focused on infliximab pharmacokinetic models in individuals with inflammatory bowel disease, preferably aged ≤ 18 years. Predictive performance was evaluated with prior predictions (based solely on patient-specific covariates) and posterior predictions (based on covariates and infliximab trough concentrations). Model accuracy and precision were calculated with relative bias and relative root mean square error and we determined the classification accuracy at the trough concentration target of ≥ 5 mg/L. The population pharmacokinetic model by Fasanmade was identified to be most appropriate for the total dataset (relative bias before/after therapeutic drug monitoring: -20.7%/11.2% and relative root mean square error before/after therapeutic drug monitoring: 84.1%/51.6%), although differences between models were small and several were deemed suitable for clinical use. For the Fasanmade model, sensitivity and specificity for maximum posterior predictions for the next infliximab trough concentration to be ≥ 5 mg/L were respectively 83.5% and 80% with an area under the receiver operating characteristic curve of 0.870. In our paediatric cohort, various models provided acceptable predictive performance, with the Fasanmade model deemed most suitable for clinical use. Model-informed precision dosing can therefore be expected to help to maintain infliximab trough concentrations in the target range.

University students’ metaphors for Canadian multiculturalism: Diverse perceptions

AbstractThis study investigated the beliefs of university students at a university in Canada regarding multiculturalism and multicultural society, through examining the metaphors they use to talk about these concepts. We interviewed 50 students about their perceptions and experiences of multiculturalism in Canada and then asked them to explain their choice of metaphors. Many of the metaphors could be grouped under more general metaphor themes, such as a multicultural society is a varied multicomponent piece of art/craft (comprising, e.g., mosaic and tapestry metaphors) and multicultural society is a container to mix things (comprising, e.g., melting pot and salad bowl metaphors). According to the literature, the former theme is compatible with the view that multiculturalism involves integration while preserving diversity, whereas the latter is associated with the notion of assimilation. Canadian students appeared to be more inclined than international students to use the (positive) varied multicomponent piece of art/craft theme relative to the other metaphors in the total data set. The interview data also revealed marked differences among participants as regards their awareness of metaphor but prompting them to reflect on their choice of metaphors often had an awareness‐raising effect.

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

Optimal design of heat pipes for city gate station heaters by applying genetic and Bayesian optimization algorithms to an artificial neural network model

This study contains a novel idea to improve the performance of city gate station heaters, using pulsating heat pipes with optimized structure. In this research, a hybrid method including Bayesian and genetic algorithms is implemented to optimize the architecture of an artificial neural network (ANN) model and find the optimal operating point for heat pipes. In the previous study, the authors have proposed a neural network-based model to predict the thermal performance of heat pipes, based on a wide range of their design parameters, including dimensions of heat pipe, number of turns, working fluid, inclination angle, filling ratio, and heat input. In the present study, the prediction model architecture is optimized using Bayesian optimization algorithm. The best performance is achieved when iterations of the employed Bayesian optimization is set to 300 iterations or samples, which 150 samples are produced to train the Bayesian model and other 150 iterations are utilized in order to find the optimal ANN structure and hyper-parameter tuning of the model. The optimal ANN model with structure of 9-97-21-68-100-1 has evaluation parameters of 0.980 and 0.014 for R2 and MSE of total dataset, respectively. The performance metrics indicate an improvement in accuracy of the ANN model with a 53.33% reduction in MSE value and a 3.15% increase in R2 score. Then, the genetic algorithm is applied to the optimal model to find the optimum operating parameters of the heat pipe. In this regard, the design parameters of the best heat pipe in order to use in the city gate station heaters, are obtained by setting the genetic algorithm population equal to 300. Finally, as a practical case study, a real heater with capacity of 10,000 SCMH which is located in Iran is selected. Considering constraints and limitations of this heater, the proposed model indicates that a vertical pulsating heat pipe with four turns which is filled 55% with water as the working fluid, has the optimal thermal resistance of 0.104 K/W. Utilizing the optimal heat pipe in the mentioned heater is a promising solution for efficiency improvement and fuel consumption and CO2 emission reduction, which decreases the operating costs of the heater as well.

Assessment of Soil Quality of Smallholder Agroecosystems in the Semiarid Region of Northeastern Brazil

The assessment of soil quality is crucial for the sustainable development of agriculture in semiarid regions. Due to their sensitivity to management practices, soil chemical and physical quality indicators are used for investigating soil quality. This study aimed to assess the soil quality of smallholder agroecosystems from the Brazilian semiarid region. Soil physical and chemical attributes were screened using principal component analysis (PCA) and integrated into a weighted additive soil quality index (SQI). Soil quality was obtained using linear and non-linear scoring methods, a total data set (TDS), and a minimum data set (MDS). The soil quality of the agroecosystems was designated as being of moderate grade. The MDS for soil quality assessment includes cation exchange capacity, C stock, exchangeable sodium percentage, flocculation degree, pH, electrical conductivity, available P, and K+ from twenty-five indicators of the TDS. This MDS mainly reflects the input of manure and crop residues associated with moderate weathering of easily weatherable minerals given the semiarid conditions. The SQI obtained can be used to synthesize the information of the TDS and is a valuable tool to indicate the soil quality of agroecosystems; thereby, it can be used with indicators of sustainable management for application at a regional scale.

Gross regional product in Ukraine: two-dimensional analysis of regularities and territorial features

Purpose. Carrying out an analysis of panel data on individual indicators of the development of the regions of Ukraine in general and their aggregates, distinguished by territorial location and profitability in order to establish the main regularities and specific features of changes in their gross regional product. Methodology. On the basis of a sample of statistical indicators of the socio-economic development of the regions of Ukraine for the period from the beginning of the Russian-Ukrainian war (2014) to the full-scale invasion, the authors created panel series of data with the separation of regional groups according to territorial and income criteria. For each group panel series of data, random effects regression models were built in order to determine the presence of regularities and specific features of regional development in different groups. Findings. An analysis of scientific developments and practical results in the issue of determining the key factors of the formation of the gross regional product was carried out. Given the uncertainty in views on the key factors of regional development, an attempt was made to determine them using a two-dimensional analysis of panel data. For this purpose, a sample of statistical information on the main indicators of socio-economic development of each of the regions of Ukraine was formed and their grouping was carried out according to two criteria: territorial location and profitability according to the indicator of the gross regional product per capita. In general, five groups of oblasts were formed based on territorial characteristics and four groups of oblasts based on income characteristics, and a panel regression model was constructed for each of these groups, including the total set of regional panel data. The received specifications of the models made it possible to form a list of key regularities in the formation of the gross regional product in Ukraine, as well as to determine specific factors of influence on the resulting indicator for each of the groups of oblasts. Originality. On the basis of the given approach to the panel analysis of factors of regional development and, in particular, the formation of the gross regional product, based on a combination of statistical methods for grouping (clustering) regions according to various criteria and panel regression models, the hypothesis regarding the presence of common regularities of regional development in certain groups of oblasts of Ukraine was confirmed. The key factors and specific features of changes in the indicator of the gross regional product in Ukraine in general and in individual regional clusters are determined. Practical value. The possibility of practical use of the obtained results as key risk factors in the process of forming plans for the economic development of individual oblasts in the long term, as well as the application of the proposed approach to the study on key factors of the formation of other indicators of regional development.

Prediction of critical temperature of binary refrigerant mixtures by neural network

Critical temperature (Tc) is an important property of refrigerant mixtures. In this study, a neural network-based model is established to predict the Tc of binary mixtures from the molar fraction and molecular structure of their components. First, graph neural network (GNN) is applied to generate the representation of the molecular structure of single component. The generated representation is added according to the molar fraction as the input of the proposed model. In order to embed the prior knowledge, pre-trained parameters are reused in the proposed model to accelerate convergence and improve accuracy. A dataset that contains 66 binary mixtures and 505 data points is collected from reference to adjust the remaining model parameters. The performance of the obtained model is comprehensively evaluated and it shows good prediction accuracy and generalization ability. The average absolute relative deviation (AARD) in the total dataset is 0.80%. For most of the mixtures, the AARD is less than 1%. Finally, the proposed model is compared with three empirical methods and the equation of state (EOS)-based method in REFPROP, and the model in this study show satisfactory result and significant improved accuracy for some mixture types.

A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents.

Object detection methods based on deep learning have been used in a variety of sectors including banking, healthcare, e-governance, and academia. In recent years, there has been a lot of attention paid to research endeavors made towards text detection and recognition from different scenesor images of unstructured document processing. The article's novelty lies in the detailed discussion and implementation of the various transfer learning-based different backbone architectures for printed text recognition. In this research article, the authors compared the ResNet50, ResNet50V2, ResNet152V2, Inception, Xception, and VGG19 backbone architectures with preprocessing techniques as data resizing, normalization, and noise removal on a standard OCR Kaggle dataset. Further, the top three backbone architectures selected based on the accuracy achieved and then hyper parameter tunning has been performed to achieve more accurate results. Xception performed well compared with the ResNet, Inception, VGG19, MobileNet architectures by achieving high evaluation scores with accuracy (98.90%) and min loss (0.19). As per existing research in this domain, until now, transfer learning-based backbone architectures that have been used on printed or handwritten data recognition are not well represented in literature. We split the total dataset into 80 percent for training and 20 percent for testing purpose and then into different backbone architecture models with the same number of epochs, and found that the Xception architecture achieved higher accuracy than the others. In addition, the ResNet50V2 model gave us higher accuracy (96.92%) than the ResNet152V2 model (96.34%).

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.

Effects of different tillage and fertilizer on soil quality under wheat‐maize rotation in the North China Plain

AbstractThe North China Plain plays an important role in China's crop cultivation regions, with the wheat‐maize rotation serving as the predominant cropping system. Nonetheless, a dearth of research refers to the impact of tillage and fertilizer employment on soil quality (SQ) in this region. In this study, the primary objective centered on utilizing the soil quality index (SQI) derived from both minimum and total datasets (MDS, TDS) by the linear and nonlinear scoring criteria to assess SQ. The results indicated that there was a significant correlation between wheat‐maize yield and SQI, revealing a significant association between yield and key soil parameters such as bulk density, soil porosity, total phosphorus, alkali‐hydrolyzed nitrogen, available phosphorus and cation exchange capacity at 0–60 cm soil layer. The study identifies that substantially elevated SQI mean values were completely under T8 whose values were 0.980 (linear), 0.672 (nonlinear) under MDS and 0.908 (linear), 0.701 (nonlinear) under TDS at 20–40 cm. Importantly, the annual yields were the highest when the SQI values were the maximum. Furthermore, SQ levels within this locale demonstrate a moderate to highly favorable status, ultimately underscoring the area's propitious conditions for crop cultivation. This research provides indispensable theoretical guidance for agricultural practitioners and offers a foundational framework supporting enhanced yields in local grain crops.

ENSO effects on the relationship between aerosols and evapotranspiration in the south of the Amazon biome

The effects of the El Nino-Southern Oscillation (ENSO) events have local, regional, and global consequences for water regimes, causing floods or extreme drought events. Tropical forests are strongly affected by ENSO, and in the case of the Amazon, its territorial extension allows for a wide variation of these effects. The prolongation of drought events in the Amazon basin contributes to an increase in gas and aerosol particle emissions mainly caused by biomass burning, which in turn alter radiative fluxes and evapotranspiration rates, cyclically interfering with the hydrological regime. The ENSO effects on the interactions between aerosol particles and evapotranspiration is a critical aspect to be systematically investigated. Therefore, this study aimed to evaluate the ENSO effect on a site located on the southern portion of the Amazonian region. In addition to quantifying and testing possible differences between aerosols and evapotranspiration under different ENSO classes (El Niño, La Niña and Neutrality), this study also evaluated possible variations in evapotranspiration as a function of the aerosol load. A highly significant difference was found for air temperature, relative humidity and aerosol load between the El Niño and La Niña classes. For evapotranspiration, significant differences were found for the El Niño and La Niña classes and for El Niño and Neutrality classes. Under the Neutrality class, the aerosol load correlated significantly with evapotranspiration, explaining 20% of the phenomenon. Under the El Niño and La Niña classes, no significant linear correlation was found between aerosol load and evapotranspiration. However, the results showed that for the total data set, there is a positive and significant correlation between aerosol and evapotranspiration. It increases with a quadratic fit, i.e., the aerosol favors evapotranspiration rates up to a certain concentration threshold. The results obtained in this study can help to understand the effects of ENSO events on atmospheric conditions in the southern Amazon basin, in addition to elucidating the role of aerosols in feedback to the water cycle in the region.

The kinematics of amblypygid (Arachnida) pedipalps during predation: extreme elongation in raptorial appendages does not result in a proportionate increase in reach and closing speed.

The link between form and function is key to understanding the evolution of unique and/or extreme morphologies. Amblypygids, or whip spiders, are arachnids that often have highly elongated spined pedipalps. These limbs are used to strike at, and secure, prey before processing by the chelicerae. Amblypygi pedipalps are multifunctional, however, being used in courtship and contest, and vary greatly in form between species. Increased pedipalp length may improve performance during prey capture, but length could also be influenced by factors including territorial contest and sexual selection. Here, for the first time, we used high-speed videography and manual tracking to investigate kinematic differences in prey capture between amblypygid species. Across six morphologically diverse species, spanning four genera and two families, we created a total dataset of 86 trials (9-20 per species). Prey capture kinematics varied considerably between species, with differences being expressed in pedipalp joint angle ranges. In particular, maximum reach ratio did not remain constant with total pedipalp length, as geometric scaling would predict, but decreased with longer pedipalps. This suggests that taxa with the most elongated pedipalps do not deploy their potential length advantage to proportionally increase reach. Therefore, a simple mechanical explanation of increased reach does not sufficiently explain pedipalp elongation. We propose other factors to help explain this phenomenon, such as social interactions or sexual selection, which would produce an evolutionary trade-off in pedipalp length between prey capture performance and other behavioural and/or anatomical pressures.

Nonparametric Additive Models for Billion Observations

The nonparametric additive model (NAM) is a widely used nonparametric regression method. Nevertheless, due to the high computational burden, classic statistical techniques for fitting NAMs are not well-equipped to handle massive data with billions of observations. To address this challenge, we develop a scalable element-wise subset selection method, referred to as Core-NAM, for fitting penalized regression spline based NAMs. Specifically, we first propose an approximation of the penalized least squares estimation, based on which we develop an efficient variant of generalized cross-validation (GCV) to select the smoothing parameter and approximate the Bayesian confidence intervals for statistical inference. Theoretically, we show that the proposed estimator approximately minimizes an upper bound of the estimation mean squared error. Moreover, we provide a non-asymptotic approximation guarantee for the proposed estimator and establish the asymptotic optimality of the proposed variant of GCV. Extensive simulations demonstrate the superior accuracy and efficiency of the Core-NAM method. We also apply the proposed method to a total column ozone dataset containing nearly one billion observations, and the results indicate a speed-up by almost a thousand times with comparable performance compared to the full data approach. Supplementary materials for this article are available online.

Arable land in Northwest China: Dynamics analysis of health and productivity in 2000–2020

AbstractArable land health (ALH) and arable land productivity (ALP) are crucial indicators of agricultural ecological functionality and sustainability. The shift of grain‐producing regions in Northwest China has led to arable land degradation, which endangers both food production and sustainable development. Several studies have focused on arable land quantity, distribution, and regional features, but ALH and sustainable ALP have received little attention. For the first time, we investigated the spatio‐temporal distribution and coupling connection between ALH and ALP in Northwest China (NC) using soil health indicators. Our study revealed the following key findings: (1) A minimal dataset of 12 indicators can efficiently replace the total dataset to evaluate the arable land health index in NC. (2) Over the last 20 years, the high and sub‐high grades of ALH increased by 536.8% and 222.6%, respectively, with a shift toward the northwest. During the specified period, ALH with low health and underutilized grade decreased by 46.01% and 69.4%, respectively, primarily in Xinjiang and Inner Mongolia. At the county level, ALP increased by 26%, primarily in northern Xinjiang and southern Gansu. 71% of counties remained stable, while only 3% experienced a decline. (4) The relationship between ALH and ALP were divided into six coupled groups that form an inverted “U” shape. High‐production areas were generally strongly linked, while low‐production areas were weakly coupled. Over the last 20 years, NC has seen significant improvements in ALH, coinciding with an increase in ALP. However, in most cases, the relationship between ALH and ALP were negative or weak.