R2 Values Research Articles

Streamflow prediction in ungauged basins: How dissimilar are drainage basins?

Hydrological processes leading to flow in river channels are extremely complex, which is why we cannot use any fundamental law to fully explain river flow dynamics at basin scale. The general approach for hydrological modelling considers that every catchment’s response to precipitation input is unique, which means different catchments respond differently to the same precipitation input. In other words, river basins are sufficiently dissimilar to warrant having hydrological models with multiple free-parameters for predicting discharge. It is assumed that free-parameters are related to static catchment characteristics. However, past efforts to relate model parameters with observable catchment characteristics have not been very successful. We thus need a framework to quantify catchment dissimilarities exhibited by catchment characteristics because there is no need of free-parameters if we decide to ignore dissimilarities. Thus, relative role of climate and catchment characteristics in streamflow response of the basin needs to be quantified. In this study, we have considered daily data from 3534 gauging stations spread across the globe to compare HBV, a widely used hydrological model, with Dynamic Budyko (DB) model, a recently proposed calibration-free model. The HBV model assumes that both climate and catchment characteristics are important in modeling streamflow response, whereas a calibration-free DB model essentially assumes that streamflow response is largely governed by climate. The performance of HBV run with regionalized parameters and DB in terms of median coefficient of determination (R2) and weighted R2 (WR2) are (0.43, 0.45) and (0.24, 0.27), respectively. The corresponding values of R2 and WR2 from quantile-quantile comparison are (0.94, 0.94) and (0.60, 0.59). Results here seem to support of idea of developing calibration-free models by appreciating similarities between basins, particularly if the objectives are prediction in ungauged basins and assessment of climate change impact on streamflow.

Precision forecasting of fertilizer components’ concentrations in mixed variable-rate fertigation through machine learning

Accurate monitoring of fertilizer concentration and variable irrigation components is crucial for achieving precision irrigation through variable-rate fertigation. However, technological and cost constraints pose challenges in effectively managing mixed variable-rate fertigation. This study addresses these challenges by integrating machine learning (ML) with easily monitored physical parameters, such as electrical conductivity (EC), pH, and temperature, to predict fertilizer solution components and concentrations in mixed variable-rate fertigation. Cubic spline interpolation (CSI) was used to enhance the training dataset. Six ML algorithms—Multivariate Linear Regression (MLR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETs), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGB)—were used to develop prediction models, with their performance evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Akaike information criterion (AIC). The Extended Fourier Amplitude Sensitivity Test (EFAST) assessed the sensitivity of the ML models to physical parameters. All of the ML models, except for MLR, particularly SVM, demonstrated superior performance in predicting fertilizer solution components’ concentrations, with R2 values between 0.989 and 0.997 and RMSE values between 0.089 and 0.210. The CSI significantly enhanced model performance, resulting in larger R2 values and smaller RMSE values. The AIC results and sensitivity analysis confirmed the exceptional performance of SVM, emphasizing its suitability for predicting fertilizer components using easily measured physical parameters. The developed ML models offer valuable insights for decision-makers managing irrigation and fertilization in mixed variable-rate fertigation.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Dengue hemorrhagic fever prediction in coastal area using geographically weighted regression

Dengue hemorrhagic fever (DHF) is still a health problem globally, including in Indonesia. Geographical and climatic conditions in coastal areas are different from other areas, which may impact differences in environmental risk factors for dengue. This study aims to create a prediction model for the incidence of DHF in coastal areas. The research was conducted in Bantul Regency, Indonesia, involving data from 2015-2019. Dengue incidence data were collected from the health office. Climatic data were from climatology station. Data on altitude and shoreline distance were obtained by geographic information system (GIS) processing. Population density and wide settlement area are obtained from the Bureau of Statistics. The geographically weighted regression (GWR) analysis was carried out using GWR4. The results showed that GWR with a weighting of Fixed Bi-Square Kernel obtained an R2 value of 0.7768, better than the global model (R2 0.5254). It indicates that DHF (Y) in Bantul Regency is 77.68% determined by population density (X1), altitude (X2), settlement area (X3), shoreline distance (X4) and rainfall (X5) and the remaining 22.32% are influenced by other variables which are not investigated. Geographically, the predictor variables explain the DHF incidence with a strong category in the central region, and weak in coastal area.

Machine learning assists the sensor array constructed by the tri-emission carbon dots to detect multiple metal ions

Metal ions are related to the health of daily life, which urges to be detected and identified by efficient sensors. We first synthesize tri-emission carbon dots for the functional determination of six metal ions and obtain the multi-dimension data to construct the fluorescent sensor array in the work. Inspired by the data-driven method, machine learning is employed in a sensor array to detect and analyze the six metal ions. The ion classification and concentration models are constructed to identify metal ion types and corresponding concentrations. The classification model could correctly discriminate six metal ions, reaching 99.5 % accuracy and the concentration model exhibits an excellent performance range from 30 to 330 μM, with a mean concentration difference value of 0.12 μM and R2 value of 0.9845 for the unknown sample. Furthermore, six metal ions in tap water also can be distinguished with 96 % accuracy and the recovery rate is between 101.89 % and 106.37 %. This work shows that the machine learning combined sensor array has significant advantages in detecting multiple metal ions and provides a novel thought for analyzing sensing data, accelerating the development of data-driven strategies in the sensing field. © 2001 Elsevier Science. All rights reserved.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

The performance of interacting metal surfaces having sliding motion is enhanced by fabricating microfeatures on-top of the surfaces. Laser powder bed fusion (LPBF) is used to fabricate the microfeatures of IN625 on-top of a flat surface. In LPBF, geometrical characteristics of microfeatures are based on the energy supplied and the volume of material melted (layer thickness) at that instant of time. There is very few research reported in open literature on the effect of layer thickness on microfeatures geometry during LPBF. In this work, a series of single tracks is fabricated with different combinations of laser power and scanning speed for 75 μm, 100 μm, and 125 μm layer thickness and its effect on single track geometrical characteristics and microstructure was evaluated. It has been found that laser power and scanning speed have a significant influence on the track geometry irrespective of layer thickness. The minimum linear energy density of 2.5 J/mm is considered to fabricate the continuous track formation for investigated layer thickness. From statistical analysis, it is observed that the laser power and scanning speed shows higher contribution on track width and remelting depth, respectively. Whereas, layer thickness shows a higher influence on track height and contact angle of single track as compared to laser power and scanning speed. In 125 μm layer thickness, the volume of material melting is increased which encourages the gravity effect to deform the melt pool and produces larger track width compare to 100 μm layer thickness. Further, the cell spacing is gradually increased with layer thickness resulting increase in the solidification period. The shape controllability of the single track is significantly increased with layer thickness, but as keeps on increasing layer thickness due to the larger volume of molten metal, the melt pool losses ability to maintain convex shapes. An Artificial Neural Network (ANN) model is developed to understand the relationship between the LPBF process parameters and single track geometrical characteristics to predict the responses for a wide range of process parameters. The multiple response neural network model shows excellent agreement with experimental results with R2 value greater than 0.95.

Machine learning assisted improved desalination pilot system design and experimentation for the circular economy

Desalination is among the most feasible solutions to supply sustainable and clean drinking water in water scarcity areas. In this regard, Multi-Effect Desalination (MED) systems are particularly preferred for harsh feeds (high temperature and salinity) because of their robust mode of operation for water production. However, maintaining the efficient operation of the MED systems is challenging because of the large system design and variables' interdependencies that are sensitive to the distillate production. Therefore, this research leverages the power of machine learning and optimization to estimate the optimal operating conditions for the maximum distillate production from the MED system. In the first step, detailed experimentation is conducted for distillate production against hot water temperature (HWT) varying from 38 to 70 °C, and feed water temperature (FWT) is changed from 34 to 42 °C. Whereas, the feed flow rate (FFR) is investigated to be varied nearly from 3.6 to 8.7 LPM in the three stages, i.e., FFR-S1, FFR-S2 and FFR-S3. The compiled dataset is used to make the process models of the MED system by three ML-based algorithms, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) under rigorous hyperparameters optimization. GPR exhibited superior predictive performance than those of ANN and SVM on R2 value of 0.99 and RMSE of 0.026 LPM. Monte Carlo technique-based variable significance analysis revealed that the HWT has the highest effect on distillate production with a percentage significance of 95.6 %. Then Genetic Algorithm is used to maximize the distillate production with the GPR model embedded in the optimization problem. The GPR-GA driven maximum distillate production is estimated on HWT = 70 ± 0.5 °C, FWT = 40 ± 2.5 °C, FFR-S1 = 6 ± 2.6 LPM, FFR-S2 = 7 ± 1 LPM and FFR-S3 = 7 ± 1. The ML-GA-based system analysis and optimization of the MED system can boost the distillate production that promotes operation excellence and circular economy from the desalination sector.

Optimizing Methylene Blue Dye Adsorption onto Liquid Natural Rubber-Based Hydrogel: Kinetics, Isotherms and Reusability

In recent years, notable advancements have taken place in the textile industry, particularly with the widespread use of synthetic dyes such as methylene blue (MB). However, the environmental impact of these dyes has raised significant concerns. Their potential to influence both chemical and biochemical demand poses risks, leading to potential disruptions in aquatic plant photosynthesis. Additionally, concerns exist regarding the toxicity and potential carcinogenicity of these dyes to humans. This study achieved successful hydrogel synthesis through the efficient utilization of ultrasonic methods, combining liquid natural rubber (LNR), acrylic acid (AAc), and pectin (Pc) to adsorb MB from aqueous solutions. Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM) were employed to analyze the structure. Utilizing Response Surface Methodology (RSM), the study investigated the effects of the AAc:LNR weight ratio and Pc weight on hydrogel preparation for MB removal. This led to the development of a quadratic polynomial model with an ANOVA-derived R2 value of 0.9970. Optimal conditions for hydrogel production were identified as a 3.00 g/g AAc:LNR weight ratio and 0.0325g of Pc, resulting in an impressive 99.07% MB removal effectiveness. The limit of detection (LOD) for methylene blue adsorption was calculated at 0.64 ppm. The kinetics and isotherms of MB removal were described by the pseudo-second-order and Freundlich models, respectively. Furthermore, the investigation into hydrogel reusability demonstrated its capability for up to five utilization cycles. The LNR/AAc/Pc hydrogel exhibits promising potential as an effective, cost-efficient, and environmentally conscious adsorbent for MB removal. This makes it applicable to water treatment scenarios involving cationic dyes.

Predictive modelling of strength characteristics of stabilized medium expansive soils

This research explores the use of Artificial Neural Network (ANN) modeling to predict the Unconfined Compression Strength (UCS) of stabilized medium expansive soil, aiming to optimize the parameters influencing soil stabilization. A database comprising 240 data points was compiled from laboratory tests involving seven input parameters: Stabilizers content (SCBA & WMD), Curing Period (CP), Liquid Limit (L.L), Plasticity Index (PI), Specific Gravity (Gs), and Free Swell (FS). ANN modeling, employing Levenberg-Marquardt algorithms, demonstrated successful UCS prediction. Increasing the number of neurons improved model accuracy, with optimum results achieved with 14 neurons. With 14 neurons for LMA, the R and R² value reaches 0.99, 0.98 Moreover, plots between experimental and predicted values shows strong correlation as majority of predicted UCS is closed to line of fit. Also error shows large count of data points closed to line of zero error. Sensitivity analysis highlighted SCBA, WMD, CP, L.L, and P.I as significant contributors to UCS prediction, emphasizing their importance in soil stabilization. The study underscores the effectiveness of ANN modeling in predicting soil strength and recommends 4% SCBA and 20% WMD for optimal stabilization. Overall, this research presents a comprehensive approach to predicting UCS in stabilized expansive soil, offering insights into parameter optimization and model enhancement for future geotechnical applications.

Predictive models for photosynthetic active radiation irradiance in temperate climates

This research evaluated 10 different empirical models designed for predicting Photosynthetically Active Radiation (PAR) at higher latitudes, addressing atmospheric conditions specific to these regions. The research introduces the Musleh-Rahman (MR) model, which substitutes Diffuse Horziontal Irradiance (DHI) with Clear Direct Normal Irradiance (DNI), Ozone and Aerosol Optical Depth at 550 nm (AOD550) sourced for satellite reanalysis data, achieving a Mean Bias Deviation (MBD) of 0.19 % and Root Mean Square Error (RMSE) of 12.42 W/m2. Furthermore, when applied to six untested locations, results demonstrate that the MR model outperformed the best performing empirical model with an MBD improvement of 3.68 % and an RMSE of 4.28 W/m2, whereas, when compared to machine learning models, the Light Gradient Boost Model (LGBM), had an MBD of −3.85 %. The MR model also maintained consistency across seasonal and density evaluations, attaining an R2 value as high as 0.9709, thereby highlighting the significant benefits of integrating satellite-sourced atmospheric data into PAR prediction models. Moreover, the research illustrated that substituting DHI with Clear DNI, Ozone, and AOD550 not only reduces MBD and boosts R2 values but also amplifies the model's applicability and accuracy in capturing early PAR peaks and reducing overestimations through precise adjustments in Ozone and AOD550 levels. This highlights the benefits of incorporating satellite-derived atmospheric data into PAR predictions models.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Estimating human pharmacokinetic parameters forelectronic nicotine delivery system products from chemical analyses of their aerosols.

The ability of Electronic Nicotine Delivery Systems (ENDS) to deliver nicotine is central to their function to substitute for cigarettes, allowing people who smoke to switch away from smoking, thus reducing their exposure to harmful chemicals in cigarette smoke. The nicotine concentration in ENDS e-liquid has proved to be a poor predictor of nicotine uptake in users. Using meta-analytic methods to analyze 12 pharmacokinetic studies of nicotine-salt closed-system ENDS, this paper examines whether the mass of nicotine/puff of aerosol can predict Cmax in pharmacokinetic studies. Cmax values were available for 38 products, in 58 use conditions (including both controlled [3s] and ad libitum puffing), comprising 1769 participant observations. Nicotine/puff data reflected chemical analyses of aerosol obtained under nonintense (3s) or intense (6s) machine puffing. Meta-regression analyses (weighted by reliability of Cmax estimate) assessed the relationship of nicotine/puff to Cmax. In some models, empirical data were used to impute the variation in Cmax or the nicotine/puff value under intense puffing. In simple linear models, Cmax was significantly associated with nicotine/puff under all combinations of intense/nonintense and controlled/ad-libitum conditions, with R2 values of 0.71-0.77. More complex models based on quadratic effects or log[nicotine/puff] did not generally improve upon more parsimonious linear models. Application of the model illustrates the divergence between nicotine concentration in e-liquids and expected Cmax when other ENDS parameters vary. The meta-analytic model may have utility in settings where clinical pharmacokinetic data are not available, including product development.

A New Perspective on Estimation of Gas Flaring Volume From Space: OLI/TIRS, VIIRS, and TROPOMI

AbstractGas flaring (GF) has the negative impact on the environment, climate, and human health. So, regular monitoring of flares and quantification of their volume is necessary. Iran has many natural oil/gas processing plants and petrochemical companies which are concentrated in the southern region. Pars Special Economic Energy Zone (PSEEZ) is an industry part with different kinds of active flares, thus a significant potential source of environmental impacts due to gas flaring. Remotely sensed data are used in gas‐flaring detection, volume estimation, and pollution emission. In this study, we applied day/nighttime radiation and air pollutant data to estimate gas flaring volumes. We developed artificial neural network models (ANN) for finding the relationship between the field measurement of GF volume as the dependent variable and shortwave infrared and thermal infrared bands of Landsat 8, M10 band of Visible Infrared Imaging Radiometer Suite, and air pollutant (NO2, CO, O3, and SO2) of TROPOMI as independent variables. Results showed that R2 values were 0.73 for the ANN model from 2018 to 2019. The sensitivity analysis demonstrated that the thermal infrared bands of B10 and B11 of Landsat 8 had the most important role in the estimation of gas flaring volume. In contrast, the SWIR bands of Landsat 8 and all TROPOMI products were insignificant. The findings of this research help to shed light on the use of remotely sensed data in estimating the volume of gas flaring at the regional/global scale by integration of the ANN model.

Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

Nanoparticulated Bimodal Contrast Agent for Ultra-High-Field Magnetic Resonance Imaging and Spectral X-ray Computed Tomography.

Bimodal medical imaging based on magnetic resonance imaging (MRI) and computed tomography (CT) is a well-known strategy to increase the diagnostic accuracy. The most recent advances in MRI and CT instrumentation are related to the use of ultra-high magnetic fields (UHF-MRI) and different working voltages (spectral CT), respectively. Such advances require the parallel development of bimodal contrast agents (CAs) that are efficient under new instrumental conditions. In this work, we have synthesized, through a precipitation reaction from a glycerol solution of the precursors, uniform barium dysprosium fluoride nanospheres with a cubic fluorite structure, whose size was found to depend on the Ba/(Ba + Dy) ratio of the starting solution. Moreover, irrespective of the starting Ba/(Ba + Dy) ratio, the experimental Ba/(Ba + Dy) values were always lower than those used in the starting solutions. This result was assigned to lower precipitation kinetics of barium fluoride compared to dysprosium fluoride, as inferred from the detailed analysis of the effect of reaction time on the chemical composition of the precipitates. A sample composed of 34 nm nanospheres with a Ba0.51Dy0.49F2.49 stoichiometry showed a transversal relaxivity (r2) value of 147.11 mM-1·s-1 at 9.4 T and gave a high negative contrast in the phantom image. Likewise, it produced high X-ray attenuation in a large range of working voltages (from 80 to 140 kVp), which can be attributed to the presence of different K-edge values and high Z elements (Ba and Dy) in the nanospheres. Finally, these nanospheres showed negligible cytotoxicity for different biocompatibility tests. Taken together, these results show that the reported nanoparticles are excellent candidates for UHF-MRI/spectral CT bimodal imaging CAs.

Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.

Dynamics of acyl carrier protein in de novo fatty acid synthesis by Enterococcus faecalis based on NMR spectroscopy and molecular dynamics simulation

Fatty acid synthesis (FAS) is essential for the production of biological components such as cell membrane building blocks and metabolism-related compounds. There are two types of bacterial FAS: de novo FAS and FAS through the incorporation of external fatty acids. Enterococcus faecalis possesses two distinct acyl carrier proteins (ACPs), AcpA (EfAcpA) and AcpB (EfAcpB), which serve as cofactors in the two types of FAS. We previously showed through NMR spectroscopy that EfAcpA comprises only three long helices, while EfAcpB consists of four helices, including a short α3 helix, similar to other bacterial ACPs. An increase in melting temperature (Tm) from 64.0 to 76.1 °C confirmed that protein structural stability increased in the presence of calcium ions. Using NMR spectroscopy, two metal binding sites were identified in EfAcpA: site A was located at the start of the α2 helix while site B was situated near the α2 helix and α2α3 loop. To understand the importance of structural flexibility of EfAcpA in de novo FAS, we investigated its motional properties using backbone spin relaxation and molecular dynamics simulations. The α2α3 loop in EfAcpA displayed high flexibility, as indicated by low heteronuclear NOE values. The residues Val51, Glu54, and Gly58 exhibited significant R2 values, likely due to the movement of this loop. EfAcpA created a novel cavity towards the α1α2 loop, in contrast to conventional cavity formation in most bacterial ACPs. This unique behavior was attributed to the flexibility exhibited by the α2α3 loop. The structural and motional characteristics of EfAcpA confirmed that its conformational plasticity is a crucial factor influencing acyl chain transfers in de novo FAS. Given the increasing antibiotic resistance observed for E. faecalis in clinical settings, the findings of this study may contribute to the development of more effective pathogen management strategies targeting FAS.

Consensus holistic virtual screening for drug discovery: a novel machine learning model approach

In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology.Scientific contributionWe presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.Graphical

Rapid quality assessment and traceability of ginger powder from Northeast India and Indian market based on near infrared spectroscopic fingerprinting.

Ginger (Zingiber officinale Rosc.) varies widely due to varying concentrations of phytochemicals and geographical origin. Rapid non-invasive quality and traceability assessment techniques ensure a sustainable value chain. The objective of this study is the development of suitable machine learning models to estimate the concentration of 6-gingerol and check traceability based on the spectral fingerprints of dried ginger samples collected from Northeast India and the Indian market using near-infrared spectrometry. Samples from the market and Northeast India underwent High Performance Liquid Chromatographic analysis for 6-gingerol content estimation. Near infrared (NIR) Spectrometer acquired spectral data. Quality prediction utilized partial least square regression (PLSR), while fingerprint-based traceability identification employed principal component analysis and t-distributed stochastic neighbor embedding (t-SNE). Model performance was assessed using RMSE and R2 values across selective wavelengths and spectral fingerprints. The standard normal variate pretreated spectral data over the wavelength region of 1,100-1,250 nm and 1,325-1,550 nm showed the optimal calibration model with root mean square error of calibration and R2 C (coefficient of determination for calibration) values of 0.87 and 0.897 respectively. A lower value (0.24) of root mean square error of prediction and a higher value (0.973) of R2 P (coefficient of determination for prediction) indicated the effectiveness of the developed model. t-SNE performed better clustering of samples based on geographical location, which was independent of gingerol content. The developed NIR spectroscopic model for Indian ginger samples predicts the 6-gingerol content and provides geographical traceability-based identification to ensure a sustainable value chain, which can promote efficiency, cost-effectiveness, consumer confidence, sustainable sourcing, traceability, and data-driven decision-making.