Global Thresholds Research Articles

Global thresholds for the climate-driven effects of vegetation restoration on runoff and soil erosion

Global thresholds for the climate-driven effects of vegetation restoration on runoff and soil erosion

Exploring soil pollution patterns in Ghana's northeastern mining zone using machine learning models

This study assessed the pollution status and effectiveness of machine learning models in predicting pollution indices in soils from a mining area in Northeastern Ghana. 552 soil samples were analysed with an Energy Dispersive X-ray Fluorescence (ED-XRF) spectrometer for their elemental concentrations. Four pollution indices; Nemerow Integrated Pollution Index (NIPI), degree of contamination (Cdeg), modified degree of contamination (mCdeg) and Pollution Load Index (PLI). Additionally, the Multivariate Adaptive Regression Splines (MARS) machine learning approach were used. The high CV%, skewness, and kurtosis values show a high degree of variability and uneven distribution patterns which denotes dispersed hotspots that can be interpreted as an influence of gold anomalies and illegal mining activities in the area. V (120.86 mg/L), Cr (242.42 mg/L), Co (30.92 mg/L) Ba (337.62 mg/L), and Zn (35.42 mg/L) recorded values higher than the global and regional contaminant thresholds. The NIPI shows that 46.74% and 26.81% of samples are slightly and moderately polluted respectively. The Cdeg analysis supports these findings, with 36.96% and 41.49% of samples classified as having “moderate” to “considerable” contamination, respectively. The PLI indicates progressive soil quality deterioration (43.84%) of samples reflecting substantial environmental disturbance. The pollution indices show the effect of illegal mining on Shaega, Buin and other areas in the eastern boundary of the study. The MARS models developed for the study demonstrated high predictive capabilities with an R2 value of 0.9665 for model 1 (NIPI), and RMSE and MAE values of 0.8227 and 0.4287 respectively. For model 2 (Cdeg), R2 value of 0.9863, RMSE and MAE of 1.0416 and 0.6181, respectively. Model 3 (mCdeg) produced an R2 value of 0.9844, RMSE and MAE of 0.1225 and 0.0670. These findings suggest MARS models can be an integral tool for soil quality analysis in cooperation with pollution indices. The study suggests that remedial and legislative measures be implemented to address the issue of illegal mining in the area.

Dynamics of urban latent heat in response to climate change and urbanization: What would be a global threshold?

Urban latent heat is influenced by climate change-induced greening, CO2 fertilization, and the urban heat island effect (Effect 1), which offsets reductions in latent heat caused by urbanization’s expansion of impermeable surfaces (Effect 2). The interaction between these effects remains unclear. The Hong Kong-Shenzhen region, characterized by a stable Leaf Area Index, intense anthropogenic activities, and abundant water resources, amplifies Effect 1. This region serves as an experimental site to explore global thresholds where these impacts reach equilibrium. Using Landsat and ERA5-Land data, monthly latent heat fluxes were computed from 1990 to 2019 using a stomatal process-based urban energy balance model. Shenzhen experienced a decline in latent heat (slope: −0.02), whereas Hong Kong showed an increase (slope: 0.08), confirming the presence of such thresholds. The region was segmented into 129 units, establishing a global benchmark indicating that Effect 1 significantly influences regional latent heat when the vegetation-to-impermeable surface ratio exceeds 2.71. Climate change impacts on Shenzhen’s latent heat increased by 94 %, compared to Hong Kong’s 55 %. Hong Kong demonstrates greater resilience to climate change than Shenzhen. This study underscores the importance of assessing urban dynamics for developing climate mitigation and sustainability strategies.

Fertilization regulates global thresholds in soil bacteria.

Global patterns in soil microbiomes are driven by non-linear environmental thresholds. Fertilization is known to shape the soil microbiome of terrestrial ecosystems worldwide. Yet, whether fertilization influences global thresholds in soil microbiomes remains virtually unknown. Here, utilizing optimized machine learning models with Shapley additive explanations on a dataset of 10,907 soil samples from 24 countries, we discovered that the microbial community response to fertilization is highly dependent on environmental contexts. Furthermore, the interactions among nitrogen (N) addition, pH, and mean annual temperature contribute to non-linear patterns in soil bacterial diversity. Specifically, we observed positive responses within a soil pH range of 5.2-6.6, with the influence of higher temperature (>15°C) on bacterial diversity being positive within this pH range but reversed in more acidic or alkaline soils. Additionally, we revealed the threshold effect of soil organic carbon and total nitrogen, demonstrating how temperature and N addition amount interacted with microbial communities within specific edaphic concentration ranges. Our findings underscore how complex environmental interactions control soil bacterial diversity under fertilization.

Investigating the carcinogenic and non-carcinogenic health hazards of heavy metal ions in Spinacia oleracea grown in agricultural soil treated with biochar and humic acid.

This study addressed the bioaccumulation and human health risk among the consumption of Spinacia oleracea grown in agricultural soil treated with humic acid (189-2310ppm) and biochars (0.00-5.10%.wt). The biochars came from two local feedstocks of rice-husk (RH) and sugar-beet-pulp (SBP) pyrolyzed at temperatures 300 and 600°C. Total concentrations of Cu, Cd, and Ni found in both the soil and biomass/biochar exceeded global safety thresholds. The bioaccumulation levels of HMs in spinach leaves varied, with Fe reaching the highest concentration at 765.27mgkg-1 and Cd having the lowest concentration at 3.31mgkg-1. Overall, the concentrations of Zn, Cd, Pb, and Ni in spinach leaves exceeded the safety threshold limits, so that its consumption is not recommended. The assessment of hazard quotient (HI) for the HMs indicated potential health hazards for humans (HI > 1) from consuming the edible parts of spinach. The biochar application rates of 4.35%wt and 0.00%.wt resulted in the highest (3.69) and lowest (3.15) HI values, respectively. The cumulative carcinogenic risk (TCR) ranged from 0.0085 to 0.0119, exceeding the cancer risk threshold. Introducing 5.10%wt biomass/biochar resulted in a 36% rise in TCR compared to the control. The utilization of humic acid alongside HMs-polluted biochars results in elevated levels of HMs bioaccumulation exceeding the allowable thresholds in crops (with a maximum increase of 49% at 2000ppm humic acid in comparison to 189ppm). Consequently, this raised the HI by 46% and the TCR by 22%. This study demonstrated that the utilization of HMs-polluted biochars could potentially pose supplementary health hazards. Moreover, it is evident that the utilization of HMs-polluted biochars in treating metal-contaminated soil does not effectively stabilize or reduce pollution.

Climate data for building simulations with urban heat island effects and nature-based solutions

As cities face a changing climate, buildings will be subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under climate change is essential for maintaining sustainable and resilient communities. To better prepare building simulation climate data with urban effects, a computationally efficient approach is used to generate “urbanized” data, where the city’s unique signature is obtained through the dynamic Weather Research and Forecasting model for the Ottawa, Canada region. We demonstrate this process using existing climate data and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. The data consists of several 31-year time series of climate variables such as temperature, humidity, wind speed and direction, pressure, cloud cover, and precipitation over different global warming thresholds. Such a dataset allows building practitioners to evaluate building performance under both historical and future climate conditions, as well as to evaluate the impacts of nature-based solutions to mitigate future climate change risks.

Global critical soil moisture thresholds of plant water stress

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

1.Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data.2.To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these soundscapes using both BirdNET and manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min).3.Since BirdNET provides a confidence score for each identification, minimum confidence thresholds can be used to filter out identifications with low scores, thus retaining only the most reliable ones. The volume of ecoacoustic data used in this study did not allow us to estimate species-specific minimum confidence thresholds for most taxa, so we instead used and evaluated global confidence thresholds selected for optimized results when consistently applied across all species.4.Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the inevitable trade-off between precision and recall does not allow to obtain satisfactory results for both metrics at the same time. We found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied, and that acoustic datasets of extended duration seem to be currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. We estimate, however, that the usage of species- and context-specific minimum confidence thresholds would allow to substantially improve the global performance benchmarks obtained in this study.5.We conclude that a judicious use of AI-based identifications provided by BirdNET can represent a powerful method to assist in the assessment of bird community composition through the automated analysis of ecoacoustic data, especially when applied to acoustic datasets of extended duration.

An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

M3W: Multistep Three-Way Clustering.

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

A binarization method for logos

In the field of digital image processing, the binarization of logo images presents a significant challenge due to the diversity in their color distribution and graphical structures. Logos, as distinctive visual symbols, are integral to brand recognition and commercial advertising. Traditional binarization methods often struggle with this diversity, leading to difficulties in preserving the original color and details of the images. To address this issue, this paper introduces a novel binarization approach specifically designed for logo images. The method comprises three stages: preprocessing, binarization, and noise removal. In the binarization stage, the Otsu method is adapted from a global to a local thresholding technique, combining both global and local thresholds for each pixel. The efficacy of this method is demonstrated through comparative experiments with logos of varying colors and structures, showing superior performance in maintaining image color and detail restoration over traditional methods. Additionally, the preprocessing and noise removal steps further enhance the method's effectiveness in handling logos with rich background colors and complex details. Overall, this approach proves to be highly adaptable across a wide range of logo types.

Gender-specific prolactin thresholds to determine prolactinoma size: a novel Bayesian approach and its clinical utility.

In clinical practice, the size of adenomas is crucial for guiding prolactinoma patients towards the most suitable initial treatment. Consequently, establishing guidelines for serum prolactin level thresholds to assess prolactinoma size is essential. However, the potential impact of gender differences in prolactin levels on estimating adenoma size (micro- vs. macroadenoma) is not yet fully comprehended. To introduce a novel statistical method for deriving gender-specific prolactin thresholds to discriminate between micro- and macroadenomas and to assess their clinical utility. We present a novel, multilevel Bayesian logistic regression approach to compute observationally constrained gender-specific prolactin thresholds in a large cohort of prolactinoma patients (N = 133) with respect to dichotomized adenoma size. The robustness of the approach is examined with an ensemble machine learning approach (a so-called super learner), where the observed differences in prolactin and adenoma size between female and male patients are preserved and the initial sample size is artificially increased tenfold. The framework results in a global prolactin threshold of 239.4 μg/L (95% credible interval: 44.0-451.2 μg/L) to discriminate between micro- and macroadenomas. We find evidence of gender-specific prolactin thresholds of 211.6 μg/L (95% credible interval: 29.0-426.2 μg/L) for women and 1,046.1 μg/L (95% credible interval: 582.2-2,325.9 μg/L) for men. Global (that is, gender-independent) thresholds result in a high sensitivity (0.97) and low specificity (0.57) when evaluated among men as most prolactin values are above the global threshold. Applying male-specific thresholds results in a slightly different scenario, with a high specificity (0.99) and moderate sensitivity (0.74). The male-dependent prolactin threshold shows large uncertainty and features some dependency on the choice of priors, in particular for small sample sizes. The augmented datasets demonstrate that future, larger cohorts are likely able to reduce the uncertainty range of the prolactin thresholds. The proposed framework represents a significant advancement in patient-centered care for treating prolactinoma patients by introducing gender-specific thresholds. These thresholds enable tailored treatment strategies by distinguishing between micro- and macroadenomas based on gender. Specifically, in men, a negative diagnosis using a universal prolactin threshold can effectively rule out a macroadenoma, while a positive diagnosis using a male-specific prolactin threshold can indicate its presence. However, the clinical utility of a female-specific prolactin threshold in our cohort is limited. This framework can be easily adapted to various biomedical settings with two subgroups having imbalanced average biomarkers and outcomes of interest. Using machine learning techniques to expand the dataset while preserving significant observed imbalances presents a valuable method for assessing the reliability of gender-specific threshold estimates. However, external cohorts are necessary to thoroughly validate our thresholds.

Establishing the Patient Acceptable Symptom State Thresholds for Patient-Reported Outcomes after Operatively Treated Tibial Plateau Fractures.

Define patient-acceptable symptom state (PASS) thresholds and factors affecting PASS thresholds for Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) and Knee Injury and Osteoarthritis Outcome Score (KOOS) following operatively treated tibial plateau fractures. Retrospective cohort. Single Level I academic trauma center. All patients (n = 159) who underwent fixation of a tibial plateau fracture from 2016 to 2021 and completed patient-reported outcome measures (PROMs) at minimum 1-year follow-up were enrolled for the study. PASS thresholds for global outcome (PASS-Global), pain (PASS-Pain), and function (PASS-Function) were determined using anchor-based questions such as "How satisfied are you today with your injured lower extremity?" with answer choices of very satisfied, satisfied, neutral, unsatisfied, and very unsatisfied. PASS thresholds for each PROM were calculated using 3 methods: (1) 80% specificity, (2) 75th percentile, and (3) Youden Index. Sixty percent of patients were satisfied with their global outcome and 53% with function. Using 80% specificity, 75th percentile, and Youden Index, PASS-Global thresholds were 48.5, 44.5, and 47.9 for PROMIS-PF and 56.3, 56.2, and 56.3 for KOOS-QOL, respectively. PASS-Pain threshold for KOOS-Pain was 84.4, 80.6, and 80.6, respectively. PASS-Function thresholds were 48.9, 46.8, and 48 for PROMIS-PF and 94.1, 90.2, and 86.8 for KOOS-ADL, respectively. Younger patients and those with bicondylar fractures or infections were associated with significantly lower PASS-Pain thresholds. Schatzker II fractures, lateral column involvement, or isolated lateral approach resulted in significantly higher PASS-Global and PASS-Function thresholds. This study defines global, functional, and pain PASS thresholds for tibial plateau fractures. Patients with bicondylar fractures, infections, and medial column involvement were more often unsatisfied. These thresholds are valuable references to identify patients who have attained satisfactory outcomes and to counsel patients with risk factors for unsatisfactory outcomes following tibial plateau fractures. Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Automated Built-Up Infrastructure Land Cover Extraction Using Index Ensembles with Machine Learning, Automated Training Data, and Red Band Texture Layers

Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.

Automated Mapping of Land Cover Type within International Heterogenous Landscapes Using Sentinel-2 Imagery with Ancillary Geospatial Data.

A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates (winter and non-winter) of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers (such as global impervious surface and global tree cover) to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global (all seven scenes) and regional (arid, tropics, and temperate) adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class (68.4% vs. 73.1%), six-class (79.8% vs. 82.8%), and five-class (80.1% vs. 85.1%) schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies (five classes: Esri 80.0 ± 3.4%, region corrected 85.1 ± 2.9%). The results highlight not only performance in line with an intensive deep learning approach, but also that reasonably accurate models can be created without a full annual time series of imagery.

Defining global thresholds for anaemia: a challenging mission

Defining global thresholds for anaemia: a challenging mission

Double diffusive convection in bidispersive porous media with couple stresses effect and relatively large macropores

In this study, we have investigated the problem of double-diffusive convection in a porous medium that exhibits bidispersive characteristics and is influenced by couple stresses. The model employed considers the Darcy theory in the micropores, while utilizing the Brinkman theory in the macropores. The system encompasses two competing effects: a temperature gradient that induces instability and a salt gradient that enhances system stability. The study involved conducting analyses of linear instability and nonlinear stability. The eigenvalue systems obtained from both the linear and nonlinear theories were solved using the Chebyshev collocation method. To ensure the accuracy of this numerical method, the results were cross-validated by comparing them with the analytical solution of the eigenvalue system derived from the linear instability theory. Notably, the Chebyshev collocation method exhibited high accuracy even in the presence of high-order derivatives arising from the couple stresses term. Finally, numerical computations were performed to determine and extensively discuss the linear instability thresholds and global nonlinear thresholds. The findings contribute valuable insights into the behavior of the system under investigation. The results show that the stability thresholds of bidisperse flow in porous media are lower than those of single-phase flow. Moreover, the results confirm the stabilizing effect of the Brinkman parameter, couple stresses parameter, and permeability ratio parameter. It has also noted the stabilizing effect of the concentration Rayleigh number if the layer is salted below and the destabilizing effect if the layer is salted above.

Influence of dark adaptation on the perceptual threshold of electrically evoked phosphenes

Aims/Purpose: Phosphenes are light phenomena in the eye that have been studied since the 18th century. They can be evoked mechanically or by electrostimulation, for example. The perception of phosphenes and their threshold, the limit of phosphene perception, has been the subject of numerous studies. In this work, the dynamics of the threshold shift of electrically evoked phosphenes during dark adaptation is recorded. Statistically it was tested whether the phosphene thresholds differ before and after dark adaptation.Methods: A study was conducted on 16 healthy subjects (7 w, 9 m, 28.5 ± 6.8 yr). Each subject sat approximately 30 cm in front of a black cloth‐covered semi‐circular screen placed on a table. On the table was a signal generator (33250A, Agilent Technologies Inc, Santa Clara, CA, USA) with a rotary control for the subject's own setting of the current intensity for the current stimulation (sinusoidal, 20 Hz) using a transcranial electrical stimulator (DC‐Stimulator PLUS, neuroConn GmbH, Ilmenau, Germany). The electrodes were a ring electrode at one eye and a rectangular electrode at the back of the head. After the subject had been instructed, and the electrodes had been attached, the subject had to set the phosphene threshold in room light conditions (125 cd/sqm) 10 times in succession (baseline B1, bright adapted). Lightproof glasses were then worn. For 40 min, each subject adjusted the threshold at intervals of 2 min. After 40 min, 10 more threshold values were recorded (baseline B2, dark adapted). Means were calculated for each subject for both baselines B1 and B2 and tested for discrimination using the Wilcoxon signed‐rank test.Results: All subjects were able to successfully complete the test. The global thresholds before and after dark adaptation were 132.3 ± 13.8 μA and 329.5 ± 38.0 μA respectively. The difference between the conditions B1 and B2 is significant (p = 0.0002). All measurement curves of the threshold progression show an increasing behaviour with a pronounced plateau after approximately 15 min.Conclusions: We successfully performed a study on the generation of phosphenes during current stimulation. The dynamics of the threshold progression from light to dark adaptation could be recorded. A significant threshold shift is shown for all subjects.

Aridity creates global thresholds in soil nitrogen retention and availability.

Identifying tipping points in the relationship between aridity and gross nitrogen (N) cycling rates could show critical vulnerabilities of terrestrial ecosystems to climate change. Yet, the global pattern of gross N cycling response to aridity across terrestrial ecosystems remains unknown. Here, we collected 14,144 observations from 451 15 N-labeled studies and used segmented regression to identify the global threshold responses of soil gross N cycling rates and soil process-related variables to aridity index (AI), which decreases as aridity increases. We found on a global scale that increasing aridity reduced soil gross nitrate consumption but increased soil nitrification capacity, mainly due to reduced soil microbial biomass carbon (MBC) and N (MBN) and increased soil pH. Threshold response of gross N production and retention to aridity was observed across terrestrial ecosystems. In croplands, gross nitrification and extractable nitrate were inhibited with increasing aridity below the threshold AI ~0.8-0.9 due to inhibited ammonia-oxidizing archaea and bacteria, while the opposite was favored above this threshold. In grasslands, gross N mineralization and immobilization decreased with increasing aridity below the threshold AI ~0.5 due to decreased MBN, but the opposite was true above this threshold. In forests, increased aridity stimulated nitrate immobilization below the threshold AI ~1.0 due to increased soil C/N ratio, but inhibited ammonium immobilization above the threshold AI ~1.3 due to decreased soil total N and increased MBC/MBN ratio. Soil dissimilatory nitrate reduction to ammonium decreased with increasing aridity globally and in forests when the threshold AI ~1.4 was passed. Overall, we suggest that any projected increase in aridity in response to climate change is likely to reduce plant N availability in arid regions while enhancing it in humid regions, affecting the provision of ecosystem services and functions.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q 2 = 0.7634, R 2 train = 0.8666) and ANN[4-3-1] (Q 2 = 0.8699, R 2 train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q 2 = 0.504, R 2 train = 0.805) and CoMSIA/SEDHA (Q 2 = 0.619, R 2 train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a–e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies. Communicated by Ramaswamy H. Sarma