Set Of Predictors Research Articles

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

ObjectiveThis study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB. MethodsInitially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared. ResultsAn OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved. ConclusionsThis study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

Machine learning models identify predictive features of patient mortality across dementia types

BackgroundDementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to flag patients at risk of near-term mortality and identify factors contributing to mortality risk across different dementia types.MethodsHere, we developed machine-learning models predicting dementia patient mortality at four different survival thresholds using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer’s Coordinating Center (NACC). We built multi-factorial XGBoost models using a small set of mortality predictors and conducted stratified analyses with dementiatype-specific models.ResultsOur models achieved an area under the receiver operating characteristic curve (AUC-ROC) of over 0.82 utilizing nine parsimonious features for all 1-, 3-, 5-, and 10-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia.ConclusionsThis study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management. Parsimonious machine-learning models can be used to predict dementia patient mortality with a limited set of clinical features, and dementiatype-specific models can be applied to heterogeneous dementia patient populations.

Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches

Abstract QSPR is a powerful tool for elucidating the correlation between chemical structure and property for both natural and synthesized compounds. In the present work, the half-wave reduction potential for a set of aziridinylquinones (Anticancer Agents [AA]) is modelled using a quantitative structure-electrochemistry relationship (QSER) based on multilinear regression (MLR) and artificial neural network (ANN). Molecular descriptors introduced in this work were computed using the Dragon software (V5). Before the model’s generation, using the Kennard and Stone algorithm, the data set of 84 aziridinylquinones was divided into training and prediction sets consisting of 70 % and 30 % of data points. Quantitative Structure Electrochemistry Relationship (QSER) models were developed using the Genetic Algorithm Multiple Linear Regressions (GA-MLR) and an Artificial Neural Network (ANN). The coefficient of determination (R 2) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated R 2 and RMSEval values for the MLR model were 0.858 and 0.054, respectively. The R 2 and RMSEval values for the ANN training set were calculated to be 0.914 and 0.050, respectively. Findings show that GA is a powerful tool for selecting variables in QSER analysis. Comparing the two employed regression methods showed that ANN is superior to MLR in predictive ability.

From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping

Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i–iv) in the computation process. Pedotransfer functions (PTFs) were built in the form of i) advanced machine learning methods and ensemble models, and trained on the ii) national soil hydrophysical dataset. The set of predictors used in PTFs was supplemented by iii) additional environmental auxiliary variables. Spatial layers of the soil hydraulic parameters were generated using iv) 100 m resolution information on primary soil properties, namely DOSoReMI.hu. HU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties (water content at saturation, field capacity and wilting point, saturated hydraulic conductivity and van Genuchten parameters for the description of the moisture retention curve) with national coverage at 100 m spatial resolution down to 2 m depth for six GSM standard depth layers. The HU-SoilHydroGrids has significantly lower squared error in the case of describing the moisture retention curve and hydraulic conductivity than the EU-SoilHydroGrids. The derived 3D soil hydraulic database (ver1.0) is presently available in National Laboratory for Water Science and Water Safety for project partners in order to test its functional performance in describing hydrological and ecological processes.

Disentangling slope‐scale spatial variability of saturated hydraulic conductivity in the black soil region of northeast China using noise‐assisted multivariate empirical mode decomposition

AbstractReliable estimates of saturated hydraulic conductivity (Ks) are usually difficult to obtain, as Ks is regulated by a variety of soil processes acting at different spatial scales that may obscure each other's impacts. We hypothesized that these scale‐specific relationships could be well characterized with the aid of the noise‐assisted multivariate empirical mode decomposition (NA‐MEMD), thereby serving as a solid foundation for an accurate Ks prediction. The objective was to evaluate whether the incorporation of NA‐MEMD could improve the estimation of Ks based on the multiscale associations it unraveled. On a typical slope transect of 860 m in the black soil region of northeast China, Ks, mollic epipedon thickness, bulk density, soil organic carbon content, total (ϕ) and effective porosities (ϕe), and particle size distribution were investigated at every 20 m. Prior to NA‐MEMD, Ks was most strongly correlated with ϕe, and the linear regression models based on ϕe solely were satisfactory for Ks estimation at the scale of investigation. Adding other predictors significantly improved Ks prediction in calibration, but impaired it in validation. Upon decomposition by NA‐MEMD, Ks was found to be significantly associated with each attribute at two scales of oscillation at least. Summing up the estimates of each Ks component derived from the properties at the equivalent oscillation scales, the results outperformed the traditional multiple linear regressions made at the investigation scale, when the same sets of predictors were used. The application of NA‐MEMD, moreover, could save the tedious measurements of ϕe and ϕ. Excluding these two porosity‐related properties, Ks estimates obtained by incorporating NA‐MEMD were statistically similar or even better than those involving them before NA‐MEMD. These findings demonstrate the great potential of NA‐MEMD in untangling scale‐dependent relationships of Ks with various processes and hold important implications for future estimations of Ks and other hydraulic properties in the black soil region of northeast China and similar regions.

Joint prototype and coefficient prediction for 3D instance segmentation

Abstract3D instance segmentation is crucial for applications demanding comprehensive 3D scene understanding. Here, a novel method is introduced that simultaneously learns coefficients and prototypes. Employing an overcomplete sampling strategy, the method produces an overcomplete set of instance predictions, from which the optimal ones are selected through a Non‐Maximum Suppression (NMS) algorithm during inference. The obtained prototypes are visualizable and interpretable. The method demonstrates superior performance on S3DIS‐blocks, consistently outperforming existing methods in mRec and mPrec. Moreover, it operates 32.9% faster than the state‐of‐the‐art. Notably, with only 0.8% of the total inference time, the method exhibits an over 20‐fold reduction in the variance of inference time compared to existing methods. These attributes render the method well‐suited for practical applications requiring both rapid inference and high reliability.

Pion and kaon electromagnetic and gravitational form factors

A unified set of predictions for pion and kaon elastic electromagnetic and gravitational form factors is obtained using a symmetry-preserving truncation of each relevant quantum field equation. A key part of the study is a description of salient aspects of the dressed graviton + quark vertices. The calculations reveal that each meson’s mass radius is smaller than its charge radius, matching available empirical inferences; and meson core pressures are commensurate with those in neutron stars. The analysis described herein paves the way for a direct calculation of nucleon gravitational form factors.

Geographical Origin Traceability of Atractylodes macrocephala Koidz. Using Mass Spectrometry Data Fusion and Ensemble Learning

The use of data fusion based with multiple analytical techniques was investigated to provide the accurate geographical origin identification of Atractylodes macrocephala Koidz. (AMK). Inductively coupled plasma - mass spectrometry (ICP-MS), gas chromatography - mass spectrometry (GC-MS) and liquid chromatography - mass spectrometry (LC-MS) were used to characterize Hubei, Zhejiang, and Hunan production regions. After the implementation of data fusion, the ensemble learning method multi-forest joint network (MFJN) and classic machine learning methods were used to identify the AMK production regions. The MFJN based upon high-level data fusion distinguished AMK samples from different regions with the highest accuracy. The classification accurate rate of AMK in the prediction set was 95%, which was significantly better than the results obtained using twenty-five mineral element or nine bioactive component data sets. The results showed that mass spectrometry data fusion in combination with MFJN is suitable for the geographic origin determination of AMK and has potential to ensure this product’s fair trade.

Convolutional neural networks for hydrothermal vents substratum classification: An introspective study

The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning semantically rich representations directly from images, this work investigates the application of state-of-the-art network architectures, originally employed in the classification of non-seabed images, for the task of hydrothermal vent substrata image classification. In assessing their potential, we conduct a study on the generalization, complementarity and human interpretability aspects of those architectures. Specifically, we independently trained deep learning models with the selected architectures using images obtained from three distinct sites within the Lucky-Strike vent field and assessed the models' performances on-site as well as off-site. To investigate complementarity, we evaluated a classification decision committee (CDC) built as an ensemble of networks in which individual predictions were fused through a majority voting scheme. The experimental results demonstrated the suitability of the deep learning models for deep-sea substratum classification, attaining accuracies reaching up to 80% in terms of F1-score. Finally, by further investigating the classification uncertainty computed from the set of individual predictions of the CDC, we describe a semiautomatic framework for human annotation, which prescribes visual inspection of only the images with high uncertainty. Overall, the results demonstrated that high accuracy values of over 90% F1-score can be obtained with the framework, with a small amount of human intervention.

Utilization of texture features of volumetric ADC maps in differentiating between serous cystadenoma and intraductal papillary neoplasms.

The rising incidence of incidental detection of pancreatic cystic neoplasms has compelled radiologists to determine new diagnostic methods for the differentiation of various kinds of lesions. We aim to demonstrate the utility of texture features extracted from ADC maps in differentiating intraductal papillary mucinous neoplasms (IPMN) from serous cystadenomas (SCA). This retrospective study was performed on 136 patients (IPMN = 87, SCA = 49) split into testing and training datasets. A total of 851 radiomics features were extracted from volumetric contours drawn by an expert radiologist on ADC maps of the lesions. LASSO regression analysis was used to determine the most predictive set of features and a radiomics score was developed based on their respective coefficients. A hyper-optimized support vector machine was then utilized to classify the lesions based on their radiomics score. A total of four Wavelet features (LHL/GLCM/LCM2, HLL/GLCM/LCM2, /LLL/First Order/90percent, /LLL/GLCM/MCC) were selected from all of the features to be included in our classifier. The classifier was optimized by altering hyperparameters and the trained model was applied to the validation dataset. The model achieved a sensitivity of 92.8, specificity of 90%, and an AUC of 0.97 in the training data set, and a sensitivity of 83.3%, specificity of 66.7%, and AUC of 0.90 in the testing dataset. A support vector machine model trained and validated on volumetric texture features extracted from ADC maps showed the possible beneficence of these features in differentiating IPMNs from SCAs. These results are in line with previous regarding the role of ADC maps in classifying cystic lesions and offers new evidence regarding the role of texture features in differentiation of potentially neoplastic and benign lesions.

Causally‐Informed Deep Learning to Improve Climate Models and Projections

AbstractClimate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Rapid discrimination of xiaokeng green tea geographical origin using surface-enhanced Raman spectroscopy coupled with chemometrics

Xiaokeng green tea (XKGT), named for its origin, brings high economic benefits to the region due to its superior quality, but it is susceptible to fraud. In this study, 131 roasted green teas from different origins in Anhui Province were used as samples to investigate the ability of surface-enhanced Raman spectroscopy (SERS) combined with chemometrics to accurately discriminate green tea origins in narrow regions. First, spherical Ag nanoparticles (AgNPs) were prepared as SERS substrates. A stratified 5-fold cross-validation method was used to divide the modelling samples. The radial basis function neural network (RBFNN), convolutional neural network (CNN), and random forest (RF) models were built, and four different preprocessing methods were compared. The results showed that the optimised RBFNN model with normalisation, as the preprocessing method, had an average prediction set accuracy of 97.69% in distinguishing samples from the Xiaokeng tea area from other tea areas. The RBFNN model was further used to differentiate tea samples from four different origins within Anhui Province, namely Xiaokeng Village (XK), Chizhou City except Xiaokeng Village (CZ), Lu'an City (L'A) and Huangshan City (HS), with an average accuracy of 91.85% for the prediction set. These findings point to the potential of combining SERS with chemometrics as an effective method for discriminating the geographic origins of XKGT in narrow regions.

Психологические предикторы несвоевременного обращения больных раком предстательной железы за онкологическим лечением

<p>Among oncological diseases, prostate cancer ranks third after lung and stomach cancer. A timely (early) application of patients for medical help, early diagnosis and early treatment are the factors of a favourable course and survival rate. The purpose of the study is to identify a set of psychological predictors associated with untimely primary care-seeking in men with prostate cancer. 81 men diagnosed with prostate cancer took part in the study, aged from 53 to 85 years old (mean age — 64.4, standard deviation — 8.8), 42 men at early stages of the disease (I, II), 39 men at late stages (III, IV), who first sought professional medical help in the Chelyabinsk Regional Clinical Centre of Oncology and Nuclear Medicine, Chelyabinsk, Russia. Seeking medical help at the early stages of the disease (I, II stages) is timely, or an early application, while treatment at later stages (III, IV) is late treatment. The study used survey methods, in particular, psychodiagnostic techniques: the World Assumptions Scale (R. Janoff-Bulman; adapted by M.A. Padun, A.V. Kotelnikova), the Level of Subjective Control method (E.F. Bazhin, E.A. Golynkina, L.M. Etkind), the Life Orientation Test (M.F. Scheier, C.S. Carpenter, adapted by D.A. Tsiring, K.Y. Evnina), the S. Maddi test of resilience, adapted by D.A. Leontiev, E.I. Rasskazova, the SF-36 “Quality of Life Assessment” questionnaire (the translation into Russian and the approbation of the methodology was carried out by the Institute of Clinical and Pharmacological Research, St. Petersburg). The study found that men who do not seek medical help in a timely manner experience intense pain that interferes with their daily activity, they are also internalized in the field of industrial relations, showing more personal responsibility for successes and failures in the professional sphere. Also, men with prostate cancer who seek help in the late stages of the disease are close to a pessimistic expectation of the future.</p>

Development and Validation of a Risk Score Predicting Death Without Transplant in Adult Heart Transplant Candidates

The US heart allocation system prioritizes medically urgent candidates with a high risk of dying without transplant. The current therapy-based 6-status system is susceptible to manipulation and has limited rank ordering ability. To develop and validate a candidate risk score that incorporates current clinical, laboratory, and hemodynamic data. A registry-based observational study of adult heart transplant candidates (aged ≥18 years) from the US heart allocation system listed between January 1, 2019, and December 31, 2022, split by center into training (70%) and test (30%) datasets. Adult candidates were listed between January 1, 2019, and December 31, 2022. A US candidate risk score (US-CRS) model was developed by adding a predefined set of predictors to the current French Candidate Risk Score (French-CRS) model. Sensitivity analyses were performed, which included intra-aortic balloon pumps (IABP) and percutaneous ventricular assist devices (VAD) in the definition of short-term mechanical circulatory support (MCS) for the US-CRS. Performance of the US-CRS model, French-CRS model, and 6-status model in the test dataset was evaluated by time-dependent area under the receiver operating characteristic curve (AUC) for death without transplant within 6 weeks and overall survival concordance (c-index) with integrated AUC. A total of 16 905 adult heart transplant candidates were listed (mean [SD] age, 53 [13] years; 73% male; 58% White); 796 patients (4.7%) died without a transplant. The final US-CRS contained time-varying short-term MCS (ventricular assist-extracorporeal membrane oxygenation or temporary surgical VAD), the log of bilirubin, estimated glomerular filtration rate, the log of B-type natriuretic peptide, albumin, sodium, and durable left ventricular assist device. In the test dataset, the AUC for death within 6 weeks of listing for the US-CRS model was 0.79 (95% CI, 0.75-0.83), for the French-CRS model was 0.72 (95% CI, 0.67-0.76), and 6-status model was 0.68 (95% CI, 0.62-0.73). Overall c-index for the US-CRS model was 0.76 (95% CI, 0.73-0.80), for the French-CRS model was 0.69 (95% CI, 0.65-0.73), and 6-status model was 0.67 (95% CI, 0.63-0.71). Classifying IABP and percutaneous VAD as short-term MCS reduced the effect size by 54%. In this registry-based study of US heart transplant candidates, a continuous multivariable allocation score outperformed the 6-status system in rank ordering heart transplant candidates by medical urgency and may be useful for the medical urgency component of heart allocation.

Estimation of the Relative Chlorophyll Content of Carya illinoensis Leaves Using Fractional Order Derivative of Leaf and Canopy Scale Hyperspectral Data

Chlorophyll is a crucial physiological and biochemical indicator that impacts plant photosynthesis, accumulation of photosynthetic products, and final yield. The measurement and analysis of chlorophyll content in plants can provide valuable insights into their nutritional status and overall health. The non-destructive and efficient estimation of relevant plant physiological and biochemical indicators using hyperspectral technology can provide a reliable method for collecting data on nutrient levels and health status during plant growth and development. Fifty-three Carya illinoensis plants of Jiande and Changlin series known for their exceptional qualities and significant economic benefits were used as the research object for collecting their leaf and canopy hyperspectral data. Firstly, fractional order derivative (FOD) was used for spectral preprocessing. Secondly, the spectral response relationship between spectrum and relative chlorophyll content (soil and plant analyzer development, SPAD) was explored by combining single-band and two-band spectral index (normalized difference spectral index, NDSI). The correlation coefficient of Pearson correlation analysis was used to estimate the linear correlation between variables. Finally, the correlation between the spectral feature variables and SPAD was analyzed and calculated. Top 10 absolute values of the correlation coefficients were screened out as modeling variables. eXtreme gradient boosting (XGBoost) machine learning algorithm was used to construct the optimal estimation model of SPAD of Carya illinoensis leaves. Results showed that the correlation between leaf and canopy spectrum after FOD pretreatment and SPAD was substantially improved, compared with raw spectrum. FOD combined with leaf and canopy NDSI was more effective than single band in improving the correlation between spectral characteristics and target components, which was increased by 0.166 and 0.338, respectively. The leaf spectrum could estimate SPAD more accurately than that of canopy spectrum. The optimal SPAD model was the 0.5th-order derivative transformation combined with two-band leaf spectral index (NDSI) model. The R2 P was 0.788, and the RMSEP was 0.842 in prediction set. On one hand, this study confirms the feasibility of rapid and non-destructive estimation of SPAD of Carya illinoensis leaves using hyperspectral technology. On the other hand, FOD combined with two-band spectral indices can significantly improve the response relationship between spectral characteristics and target variables, enrich hyperspectral data processing methods, and propose a novel approach for the detection of plant nutrient level and health.

Classification of wheat grain varieties using terahertz spectroscopy and convolutional neural network

Wheat quality and quantity differ in diverse climates. Therefore, it is essential to identify the variety before purchasing and warehousing. In this study, a study on variety discrimination for 12 wheat varieties (strong-gluten wheat, medium-gluten wheat, weak-gluten wheat) using the Terahertz time-domain spectroscopy (THz-TDS) technology in combination with a Convolutional neural network (CNN). Firstly, the original Time-domain spectra (TDS) of wheat in the range of 0.1–2.0 THz were acquired, and the Frequency domain spectra (FDS), the absorption coefficient spectra in the range of 0.2–1.0 THz were obtained through Fourier Transform. Then, Competitive adaptive reweighted sampling (CARS) algorithms were applied to screen the feature spectrum. Finally, the Support vector machine (SVM), the Least square support vector machine (LS-SVM), the Back-propagation neural networks (BPNN) and CNN models were constructed using feature spectral data. By comparing the four models, it was found that the calibration set accuracy and prediction set accuracy of the CNN model reached 98.7% and 97.8% respectively, with an error recognition rate of only 2.2%. The research results show that combining THz-TDS technology with CNN has the advantages of accurate recognition and high efficiency. It can identify different wheat varieties and can be used for seed classification and quality detection.

Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.

Dry bean is a nutrient-dense food targeted in biofortification programs to increase seed iron and zinc levels. The underlying assumption of breeding for higher mineral content is that enhanced iron and zinc levels will deliver health benefits to the consumers of these biofortified foods. This study characterized a diversity panel of 275 genotypes comprising the Yellow Bean Collection (YBC) for seed Fe and Zn concentration, Fe bioavailability (FeBio), and seed yield across 2years in two field locations. The genetic architecture of each trait was elucidated via genome-wide association studies (GWAS) and the efficacy of genomic prediction (GP) was assessed. Moreover, 82 yellow breeding lines were evaluated for seed Fe and Zn concentrations as well as seed yield, serving as a prediction set for GP models. Large phenotypic variability was identified in all traits evaluated, and variations of up to 2.8 and 13.7-fold were observed for Fe concentration and FeBio, respectively. Prediction accuracies in the YBC ranged from a low of 0.12 for Fe concentration, to a high of 0.72 for FeBio, and an accuracy improvement of 0.03 was observed when a QTN, identified through GWAS, was used as a fixed effect for FeBio. This study provides evidence of the lack of correlation between FeBio estimated in vitro and Fe concentration and highlights the potential of GP in accurately predicting FeBio in yellow beans, offering a cost-effective alternative to the traditional assessment of using Caco2 cell methodologies.

Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest

ABSTRACT Monitoring changes in carbon stocks through forest biomass assessment is crucial for carbon cycle studies. However, challenges in obtaining timely and reliable ground measurements hinder creation of the spatially continuous maps of forest aboveground biomass density (AGBD). This study proposes an approach for generating spatially continuous maps of forest aboveground biomass density (AGBD) by combining Global Ecosystem Dynamics Investigation (GEDI) LiDAR-based data with open-access earth observation (EO) data. The key contribution of the study lies in the systematic evaluation of various model configurations to select the optimal model for AGBD generation. The evaluation considered various model configurations, including predictor sets, spatial resolution, beam selection, and sensitivity thresholds. We used a Random Forest model, trained through five-fold cross-validation on 80% of the total data, to estimate AGBD in the Indian forest region. Model performance was assessed using the 20% independent test dataset. Results, using Sentinel-1 and 2 predictors, yielded R2 values of 0.55 to 0.60 and RMSE of 48.5 to 56.3 Mg/ha. Incorporating forest and agroclimatic zone attributes improved performance (R2: 0.59 to 0.69, RMSE: 42.2 to 53.3 Mg/ha). The selection of the top 15 predictors, which favoured features from Sentinel-2, DEM, forest attributes, and agroclimatic zones, and GEDI data with sensitivity >0.98, yielded the optimal model with an R2 of 0.64 and RMSE of 46.59 Mg/ha. The results underscore the significance of incorporating attributes like forest and agro-climatic zones and the need for an optimal model selection considering predictor types and GEDI shot characteristics. The top-performing model is validated in Simdega, Jharkhand (R2: 0.74, RMSE: 39.3 Mg/ha), demonstrating the methodological potential of this approach. Overall, this study emphasizes the methodological prospects of integrating multi-source open-access EO data to produce spatially continuous aboveground biomass (AGB) maps through data fusion.

Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network.

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Investigating Factors that Affect Reading Literacy Skills in PISA Turkey Sample

Abstract Introduction: In this study, it was aimed to examine the relationships between the variables that related the reading skills of the students and the variables related to the opportunities that the student, family, and the school have. Variables related to students’ reading skills (reading skills scores/PV1READ, perception of competence in reading/SCREADCOMP and perception of difficulty in reading/SCREADDIFF) comprised the criterion variable set, and variables related to students’ socio-economic and cultural characteristics (cultural possessions/CULTPOS, home educational resources/HEDRES, index of economic, social, and cultural status/ESCS, joy/like reading/JOYREAD, teacher’s stimulation of reading engagement perceived by student/STIMREAD, subjective well-being: Sense of belonging to school/BELONG, student-teacher ratio/STRATIO, shortage of educational material/EDUSHORT, student behavior hindering learning/STUBEHA, teacher behavior hindering learning/TEACHBEHA) comprised the predictive variable set. Methods: Between two sets of variables, used canonical correlation analysis to examine, simultaneously, the relationship between these two sets and the contribution of the variables to each set. Turkey data of the PISA 2018 organized by OECD was used as the sample. PISA 2018 Turkey data consisted of 6890 Turkish students from the 15-year-old age group. The variables PV1READ, SCREADCOMP, SCREADCOMP, SCREADDIFF, CULTPOS, HEDRES, ESCS, BELONG, STIMREAD and JOYREAD in the student survey and the variables STRATIO, STUBEHA, TEACHBEHA, EDUSHORT in the school survey in the PISA 2018 were used as data collection tools. Results: A summary of the results of the canonical correlation analysis revealed that the most important factor in the predictive variable set was liking/enjoying reading, followed by the student behaviours that hinder learning, economic and socio-cultural status, cultural position, sense of belonging to the school, and teacher behaviours that hinder learning, respectively. In the criterion variable set consisting of students’ reading skills, the most important factor was the perception of reading competence, followed by reading scores and perception of reading difficulty, respectively. In this context, it can be said that the variable that had the most relationship with the reading literacy skills of the students was the variable of like/enjoy reading. Discussion: It is important for teachers to include additional materials that students can enjoy in the teaching process. Positive school climate is one of the factors that help increase student achievement. Limitations: This study focused on variables related to students’ socio-economic and cultural characteristics and school-related variables as predictors of reading literacy. In addition to the variables, studies can examine the effect of the categorical variables such as gender and school type. Conclusions: The results of the study showed that the variable that had the most relationship with the reading literacy skills of the students was the variable of like/enjoy reading. This variable was followed by student behaviours that hinder learning and the socio-economic cultural status of the students respectively. In line with the results of this study, positive school climate is one of the factors that help increase student achievement. In order for the school climate to be positive, student or teacher behaviors that prevent learning should be minimized. Thus, students can learn more easily in a school climate where there are no obstacles to learning. Finally, as the socio-economic and cultural status of the students increased, it was observed that the reading scores increased.