Principal Component Analysis Research Articles

Longitudinal associations between dietary diversity and serum lipid markers in Japanese workers.

The aim of this study was to determine the longitudinal associations between dietary diversity score and serum lipid markers in a five-year follow-up period in Japanese workers. This study included 745 participants aged 20-60 years in 2012-2013 without dyslipidemia at baseline who participated at least once from 2013 to 2017. Dietary intake was assessed using a food frequency questionnaire, and dietary diversity score was determined using the Quantitative Index for Dietary Diversity. Principal component analysis was used to determine three dietary patterns: healthy, western, and sweetener. Lipid markers including total cholesterol, triglycerides, LDL-cholesterol, HDL-cholesterol, and non-HDL-cholesterol were measured. Generalized estimating equations were used for calculating the cumulative mean of lipid profiles in the follow-up period according to the dietary diversity score at baseline with control of confounding factors. Higher dietary diversity score was inversely associated with serum concentrations of LDL cholesterol (p for trend = 0.028), triglycerides (p for trend = 0.029), and non-HDL cholesterol (p for trend = 0.026) in women. The associations except for the association with serum triglycerides were robust after additional adjustment for three dietary patterns (healthy, western, and sweetener). The association with serum triglycerides disappeared after additional adjustment for a healthy pattern. There was no significant association between dietary diversity and dyslipidemia in men in the follow-up period. This study suggests that dietary diversity is beneficial for lipid profiles in Japanese female workers.

Functional Immunoaffinity 3D Magnetic Core-Shell Nanometallic Structure for High-Efficiency Separation and Label-Free SERS Detection of Exosomes.

Tumor exosomes, known as maternal cell messengers, play an important role in cancer occurrence, proliferation, metastasis, immune escape, drug resistance, and other processes and are an entry point for cancer research. However, there is still a lack of an efficient detection technology for exosomes. In this study, the ultrahigh sensitivity SERS nanoprobe with a three dimensional (3D) magnetic core/Au nanocolumn/Au nanoparticles shell strongly coupling multistage structure (Fe3O4@NR-NPs) was constructed by crystal growth of nanocrystals in the confined space of a central radiating single particle mesoporous molecular sieve channel and strong coupling secondary growth of gold particles. The exosomes were confined onto the "hot spot" of plasmonic nanoparticles and rapidly enriched by CD63 antibody functional-Fe3O4@NR-NPs to achieve high sensitivity detection, with the limit of detection of 1 × 103 particles/mL (S/N = 3). The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosomes by principal component analysis (PCA). Finally, this detection method has also been successfully employed for the detection of exosomes in complex samples; this proves that the proposed SERS-based method is a promising tool for clinical cancer screening.

Comparative volatiles profiling of two marjoram products via GC-MS analysis in relation to the antioxidant and antibacterial effects.

Marjoram (Origanum majorana L.), also known as "sweet marjoram" or "sweet oregano" is a Mediterranean herbaceous perennial herb cultivated in Egypt and widely consumed as an herbal supplement for treatment of several ailments. The main goal of this study was to assess volatiles' variation in marjoram samples collected from two different widely consumed commercial products using two different extraction techniques viz. head space solid phase microextraction (HS-SPME) and petroleum ether using gas chromatography mass spectrometry (GC-MS) analysis and multivariate data analysis. A total of 20 major aroma compounds were identified in samples extracted with HS-SPME found enriched in monoterpene hydrocarbons and oxygenated compounds. The major volatiles included β-phellandrene (20.1 and 14.2%), γ-terpinene (13.4 and 11.7%), 2-bornene (12.3 and 11.5%), p-cymene (9.8 and 4.6%) terpenen-4-ol (16.4 and 7.5%), sabinene hydrate (16.02 and 8.8%) and terpineol (4.2 and 3.2%) in MR and MI, respectively. Compared with HS-SPME, 51 aroma compounds were identified in marjoram samples extracted with petroleum ether, found more enriched in aliphatic hydrocarbons (42.8 and 73.8%) in MR and MI, respectively. While a higher identification score was observed in the case of solvent extraction, SPME appeared to be more selective in the recovery of oxygenated terpenes to account more for marjoram aroma. Multivariate data analysis using principal component analysis (PCA) revealed distinct discrimination between volatile composition of both marjoram samples. The total phenolic and flavonoid contents in marjoram samples were at (111.9, 109.1µg GA/mg) and (18.3, 19.5µg rutin eq/mg) in MR and MI, respectively. Stronger antioxidant effects were observed in MR and MI samples with IC50 at 45.5 and 56.8µg/mL respectively compared to IC50 6.57µg/mL for Trolox as assayed using DPPH assay. Moderate anti-bacterial effect was observed in MR and MI samples and expressed as a zone of inhibition mostly against Bacillus subtilis (16.03 and 15.9mm), B. cereus (12.9 and 13.7mm), Enterococcus faecalis (14.03 and 13.97mm), and Enterobacter cloacae (11.6 and 11.6mm) respectively.

Computer-Aided Diagnosis of Early-Stage Retinopathy of Prematurity in Neonatal Fundus Images Using Artificial Intelligence.

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolution Neural Networks (CNN) based pre-trained networks. The characteristic demarcation lines and ridges in early stage ROP are segmented utilising a novel Swin U-Net. 2000 Scale Invariant Feature Transform (SIFT) descriptors were extracted from the segmented ridges and are dimensionally reduced to 50 features using Principal Component Analysis (PCA). Seven ROP-specific features, including six Gray Level Co-occurrence Matrix (GLCM) and ridge length features, are extracted from the segmented image and are fused with the PCA reduced 50 SIFT features. Finally, three ML classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and k- Nearest Neighbor (k-NN), are used to classify the 50 features to predict the early-stage ROP from Normal images. On the other hand, the raw retinal images are classified directly into normal and early-stage ROP using six pre-trained classifiers, namely ResNet50, ShuffleNet V2, EfficientNet, MobileNet, VGG16, and DarkNet19. It is seen that the ResNet50 network outperformed all other networks in predicting early-stage ROP with 89.5% accuracy, 87.5% sensitivity, 91.5% specificity, 91.1% precision, 88% NPV and an Area Under the Curve (AUC) of 0.92. Swin U-Net Convolutional Neural Networks (CNN) segmented the ridges and demarcation lines with an accuracy of 89.7% with 80.5% precision, 92.6% recall, 75.76% IoU, and 0.86 as the Dice coefficient. The SVM classifier using the 57 features from the segmented images achieved a classification accuracy of 88.75%, sensitivity of 90%, specificity of 87.5%, and an AUC of 0.91. The system can be utilised as a point-of-care diagnostic tool for ROP diagnosis of neonates in remote areas.

Distortion-Corrected Posterior Ocular Shape in Myopic Eyes Assessed by Ultrawide OCT Detects Deformations Associated With Vision-Threatening Changes.

To develop a quantitative tool for assessing the posterior ocular shape using widefield, volumetric optical coherence tomography (OCT) in eyes with myopia. This observational, cross-sectional study included 178 eyes from 113 participants. Participants underwent a standardized eye examination, including ocular biometry and a custom ultrawide OCT. True ocular shape was reconstructed by tracing the beam propagation from the system to the posterior eye. Gaussian curvature quantified the localized ocular shape, which was further categorized into five distinct categories. An ocular shape irregularity (OSI) was calculated using principal component analysis. Linear regression with breakpoints analyzed the relationship between ocular shape parameters and axial length (AL). Increased curvature mean and variance were associated with more severe myopia (P < 0.001). Curvature categories (convex, normal, concave, and highly concave) differed significantly between the groups (all P < 0.001). Their correlations with AL revealed significant breakpoints between 27.1 and 27.2 mm. OSI, as a single metric for quantifying ocular shape distortion, was associated with more severe myopia (P < 0.001), and its correlation with AL revealed a breakpoint at 27.2 mm with a fourfold increase in slope steepness beyond this pivot. Determination of OSI was highly reproducible and could also be obtained with fewer scans. Eyes exceeding 27.2 mm in length exhibit pronounced shape changes. Our framework can be seamlessly integrated into commercial OCTs for ocular shape deformation detection, which could aid in identifying eyes with pathologic myopia.

Strategies for feeding tilapia in smallholder aquaculture systems: a study of aquaculture feed access and availability in two districts of Northern Province, Zambia

ABSTRACT Aquaculture contributes to agricultural development, economic growth, and poverty reduction initiatives in Zambia. In northern and rural regions of the country, development requires improvements to the management of local and farm-generated inputs. Fish farmers have limited market access and purchasing power, which restricts access to commercial inputs, such as formulated feeds and chemical fertilizers. Instead, farmers adopt integrated-agriculture-aquaculture (IAA) practices, whereby on-farm resources supply essential inputs. Research is limited regarding local systems of resource management, and this hinders development of innovations for improving efficiency. This mixed-methods study explored local patterns of resource utilization. Smallholders (n = 63) participated in surveys and interviews regarding resource management. A series of quantitative analyses, including descriptive statistics, principal component analyses (PCA), and multinomial logistic regression (logit) models, identified relationships between these resources, household livelihood, and system productivity. Qualitative analyses explored farmer perceptions regarding seasonal resource availability. Results suggested that livestock manure, homemade feeds comprising maize and soybean, garden residues, and kitchen wastes were the most frequently selected inputs. Logit models of feeding intensity found that income, proximity to Luwingu Town, experience in aquaculture, and access to extension services were key household resources. High-intensity feeding strategies incorporated commercial feeds, chemical fertilizers, as well as home-made feeds containing a wide diversity of both high- and low-quality ingredients. Low-intensity feeding strategies incorporated only farm waste and termites. Potential innovations that may improve system performance in resource poor contexts are considered and recommendations for additional research are offered.

Dietary patterns and bone density among school-aged children: a cross-sectional study in China.

Diet is an essential modifiable determinant of bone health, yet the associations between dietary patterns (DPs) and bone mineral density (BMD) in Chinese children remain limited. This study aimed to investigate the relationship between overall diet and low BMD risk among school-aged children in China. A total of 1,099 children aged 9-12 in China were recruited for this cross-sectional study. A semi-quantified food frequency questionnaire (FFQ) was used to assess dietary intake. A priori numerical index, the Chinese Dietary Guidelines Index for Children and Adolescents [CDGI (2021)-C] was utilized to assess dietary quality. Specific DPs were identified by using principal components analysis (PCA). The BMD of the left forearm was assessed using dual-energy X-ray absorptiometry (DXA). Spearman correlation test was conducted to investigate the associations between DPs. Multivariate logistic regression analysis and restricted cubic spline models (RCS) were applied to explore the associations between DPs and BMD. Three distinct DPs were identified: the plant-animal balanced pattern, the grain-tuber-meat pattern, and the bean-dairy pattern. We found a weak but significant positive correlation of the CDGI (2021)-C with the plant-animal balanced pattern (R = 0.318, P < 0.001), and with the bean-dairy pattern (R = 0.266, P < 0.001), respectively. After adjusting for covariates, adherence to the CDGI (2021)-C (Q4 vs. Q1, OR = 0.45, 95% CI: 0.27-0.75), the plant-animal balanced pattern (Q4 vs. Q1, OR = 0.50, 95% CI: 0.31-0.81), and the bean-dairy pattern (Q3 vs. Q1, OR = 0.57, 95% CI: 0.33-0.96) were associated with a lower risk of low BMD. No significant association was observed between the grain-tuber-meat pattern and low BMD (Q4 vs. Q1, OR = 1.09; 95% CI: 0.90-1.31). Adherence to the CDGI (2021)-C and the plant-animal balanced pattern is advantageous for bone health and inversely correlated with the risk of low BMD among school-aged children in China. Additionally, moderate adherence to the bean-dairy pattern may also confer benefits to bone health. A balanced and overall healthy diet should be recommended in our daily life.

Medium and long-term regional water demand prediction using Harris hawks optimisation-backpropagation neural network model.

Precise water demand prediction is essential for the efficient allocation and rational utilisation of regional water resources. This study addressed the challenge associated with medium and long-term water demand prediction by introducing a novel coupled model, HHO-BPNN (Harris Hawks Optimisation-Backpropagation Neural Network). Principal component analysis was employed to reduce the dimensionality of potential water demand factors. The performance of the forecasting models was compared through mean square error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2). The findings indicated that the HHO-BPNN outperformed traditional methods, such as BPNN, support vector machines, and grey prediction model. The study utilised the sliding window method to predict water demand for the next 1, 3, and 5 years for five prefecture-level cities in northern Jiangsu Province, China. High prediction accuracy was achieved across various categories of water demand (agricultural, industrial, domestic, and ecological), with the overall accuracy being impressive at 97%. Additionally, the forecasts aligned well with local developmental plans, suggesting practical applicability for urban planning. This study elucidates the key drivers impacting water demand, providing an effective tool for regional water demand forecasting, facilitating efficient and precise water management and decision-making in the future.

Performance of cash transfer program on farmers’ livelihood: evidence from PM-KISAN scheme of India

IntroductionResearch into various aspects of vulnerability and poverty has played a key role in shaping cash transfer systems. Cash-based direct benefit transfers (DBT) are frequently discussed, as they help reduce corruption and limit intermediary involvement, which often impedes policy implementation in countries like India. Small and marginal farmers (SMFs), constituting 86% of India’s farming population, are essential for sustainable agricultural growth and ensuring food and nutrition security. The Pradhan Mantri Kisan Samman Nidhi (PM-KISAN) scheme provides Rs.6000 annually to the farmer families via DBT mechanism. Despite reports and information from various government sources, significant concerns remain regarding the performance of PM-KISAN scheme in improving living conditions of SMFs.MethodsFor having the highest number of SMFs, a research study was conducted in Bihar and Uttar Pradesh. Two cluster of villages from each of these two states were chosen by simple random sampling (SRS) method. An ex post facto research design was used; data was gathered from 240 SMFs, with 120 beneficiaries and 120 non-beneficiaries, using a stratified disproportionate simple random sampling technique. The study used 18 indicators related to livelihood assets—human capital, social capital, natural capital, physical capital, and financial capital—to generate a Livelihood Index (LI) through Principal Component Analysis (PCA).ResultsThe beneficiaries of the scheme had an average LI score of 0.396, while non-beneficiaries had a score of 0.366. Propensity Score Matching (PSM) was employed to compare the beneficiaries and non-beneficiaries in order to determine the impact of the scheme. In the Livelihood Index of beneficiaries, in comparison to non-beneficiaries, there was a significant improvement of 3.34 to 4.13 percentage points, according to the analysis, which utilized a variety of matching algorithms.DiscussionImplications from the study suggests that PM-KISAN should be integrated into a broader rural development strategy to maximize multiplier effects. By combining cash transfers with complementary initiatives like modern technology adoption and high-yield seeds, farmers can make productive investments that could enhance their livelihood status.

Cyanobacterial Cultures, Cell Extracts, and Individual Toxins Decrease Photosynthesis in the Terrestrial Plants Lactuca sativa and Zea mays

Cyanobacterial harmful algal blooms (cHABs) are increasing due to eutrophication and climate change, as is irrigation of crops with freshwater contaminated with cHAB toxins. A few studies, mostly in aquatic protists and plants, have investigated the effects of cHAB toxins or cell extracts on various aspects of photosynthesis, with variable effects reported (negative to neutral to positive). We examined the effects of cyanobacterial live cultures and cell extracts (Microcystis aeruginosa or Anabaena flos-aquae) and individual cHAB toxins (anatoxin-a, ANA; beta-methyl-amino-L-alanine, BMAA; lipopolysaccharide, LPS; microcystin-LR, MC-LR) on photosynthesis in intact plants and leaf pieces in corn (Zea mays) and lettuce (Lactuca sativa). In intact plants grown in soil or hydroponically, overall net photosynthesis (Pn), but not Photosystem-II (PSII) electron-transport yield (ΦPSII), decreased when roots were exposed to cyanobacterial culture (whether with intact cells, cells removed, or cells lysed and removed) or individual toxins in solution (especially ANA, which also decreased rubisco activity); cyanobacterial culture also decreased leaf chlorophyll concentration. In contrast, ΦPSII decreased in leaf tissue vacuum-infiltrated with cyanobacterial culture or the individual toxins, LPS and MC-LR, though only in illuminated (vs. dark-adapted) leaves, and none of the toxins caused significant decreases in in vitro photosynthesis in thylakoids. Principal component analysis indicated unique overall effects of cyanobacterial culture and each toxin on photosynthesis. Hence, while cHAB toxins consistently impacted plant photosynthesis at ecologically relevant concentrations, the effects varied depending on the toxins and the mode of exposure.

Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration

The accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we proposed an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implemented a multi-frequency microwave sensing system for PCC measurement. This method transcended the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measured insertion loss data across the sensor cross-section at various frequencies and utilized PCA to reduce the dimensionality of high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm was applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing the measurement accuracy through multi-dimensional fusion. The experimental results demonstrated that the multi-frequency microwave sensing system effectively extracted features from the high-dimensional PCC samples and selected the optimal frequency combination. Filed experiments conducted on five coal mills showed that, within a common PCC range of 0–0.5 kg/kg, the system achieved a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system could quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.

Construction and evaluation of a model for efficient identification of photothermal sensitivity of tobacco cultivars based on agronomic traits.

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco.

Three visions about surface water quality

Water quality is a worldwide problem, especially in places where the growth of urban areas alters surface waters that can be analyzed in conjunction with legislation, bioindicators and emerging pollutants. However, the use of these three variables to determine water quality is still scarce. For this reason, the use of these three variables was the objective of this research, which sought answers as to their associative use for the analysis of surface waters such as the Paragominas Igarapé, in the municipality of the same name, Pará, Brazil. Investigative research with qualitative and quantitative scope was used with 72 selected publications, in seven types of publication, whose time frame was between 2005 and 2024.The data obtained and analyzed indicated that Conama Resolution 357 still needs changes and greater flexibility where bioindicators composed of Ephemeroptera, Plecoptera and Trichoptera (n= 8; 80%), and pollutants or contaminants such as microplastics (9.0 ± 6.6). This association was confirmed by principal component analysis (PCA): PC1 = 87%. PC2 = 12.8%. Thus, the use of the association between Conama Resolution 357/2005, with the EPT group and microplastics, may indicate greater veracity in the quality of urban surface waters, such as the Paragominas stream.

Estimation of genetic polymorphism in quince (Cydonia oblonga Mill.) genotypes using morphological traits and molecular (DNA barcoding) characterizations.

Quince (Cydonia oblonga) is a medicinal plant and a member of family Rosaceae. It is native plant of Asia Minor and Europe. It is used in production of jam and jellies and also as a remedy of several ailments. The present study was conducted to evaluate the genetic polymorphism based on morphological and molecular traits. Different varieties of Quince were collected from different ecological zones of Khyber Pakhtunkhwa Pakistan and a total of 26 different morphological traits were recorded among studied genotypes. Based on qualitative morphological trait study, the variety collected from Tindodag was unique one with highest fruit weight (328.82 g). The lowest fruit weight (68.38 g) was recorded for Talash genotype. The Charbagh and Tindodag genotypes showed highest seed length (10.6 mm) while genotypes of Chitral was recorded as lowest (8.4 mm). Statistically, significant level of variation was noted with coefficient of variance ranged from 2.23% to 30.38%. Based on correlation analysis, fruit length had strongly correlation with fruit weight (r = 0.89**), Average Fruit width was found significant with fruit weight (r = 0.90**). Similarly, the Core Width was found strongly significant with Core Length (r = 0.95**). ANOVA analysis indicated 10 quantitative characters to be highly significant, 2 significant and 1 insignificant. Principal component analysis was also computed for the 13 quantitative traits with Eigen value of 0.48 and a total variance of 97.78%. The first principal component shows total variation of 52.52%. In PC2 the total variation was 80.15%, PC3 94.06% while in PC4 it was 97.78%. The NCBI BLAST results shows that all the genotypes have similar origin except Tindodag genotype, which shows differences in its origin. Accession number for all other genotypes is MN216014.1, while accession number of Tindodag genotype is KF861967.1. Based on this study, it can be concluded that Tindodag genotype is unique out of the studied localities. NCBI BLAST have provided further support for the drawn conclusion.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Identification and characterization of novel ferroptosis-related genes in acute myocardial infarction.

Acute myocardial infarction (AMI) is a leading cause of death and morbidity worldwide. Ferroptosis, a form of regulated cell death, plays a critical role in modulating immune functions during AMI. This study aimed to identify ferroptosis-related hub genes that could serve as potential therapeutic targets in the progression of AMI. Bioinformatics was used to identify overlapping genes associated with ferroptosis and the infiltration of 22 immune cells by Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) analysis. The expression of ferroptosis-related genes in AMI was validated across independent datasets, clinical samples, and in vitro cellular experiments. The predictive value for heart failure was evaluated in the first dimension of principal component analysis (PCA) using receiver operating characteristic (ROC) analysis. The study identified 11 key ferroptosis-related genes significantly correlated with immune cell abundance. CIBERSORT analysis highlighted immune dysregulation in AMI. JDP2, DUSP1, TLR4, NFS1, and SLC1A5 were identified as potential biomarkers for AMI progression. Additionally, JDP2, DUSP1, and DDIT4 demonstrated strong predictive value for long-term heart failure. This study highlights the potential association of ferroptosis-related genes with the pathogenesis of AMI, suggesting a role in the molecular mechanisms that may underlie acute coronary events.

Can spatial planning in the Polish Carpathians be enforced? How spatial chaos indicators depict a gap between scientific knowledge and practice

AbstractDespite their strategic hydrological importance for neighbouring areas, the Polish Carpathians are experiencing spatial chaos, which may weaken their adaptability to the progressive climate change. The article attempts to answer the question of whether spatial planning, which is supposed to guarantee spatial order, fulfils its role and whether the knowledge of the natural conditions of spatial development is respected in the spatial planning process. Using GIS techniques, up to 238 communes were analysed in terms of their spatial coverage, the degree of scattered settlement, and the violation of natural barriers by location of buildings in areas that are threatened with mass movements or floods; by settlement on excessively inclined slopes and in areas with adverse climatic conditions. Spearman non-parametric rank correlation analysis and the multidimensional Principal Component Analysis (PCA) technique were performed to investigate relations between spatial chaos indicators and the planning situation. The analysis of the data has revealed that spatial planning does not fulfil its role. Serious errors in location of buildings have been noted even though the communes are covered by local spatial development plans. Scientific knowledge is not sufficiently transferred into planning documents, and bottom-up initiatives cannot replace systemic solutions. There is a need for strengthening the role of environmental studies documents in the spatial planning system. This would facilitate the transfer of scientific knowledge into the planning process and help to protect mountain areas. The development of a special spatial strategy for the Polish Carpathians in compliance with the Carpathian Convention is also recommended.

Identification of Molecular Subtypes and Prognostic Traits Based on Chromosomal Instability Phenotype-Related Genes in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) exhibits significant molecular heterogeneity; however, previous studies have not fully explored its classification into distinct molecular subtypes. Here, we identified LUAD-significant chromosomal instability (CIN) phenotype genes (n = 24) using a TCGA-LUAD cohort (n = 592) and evaluated their ability to predict pathologic grade. Unsupervised clustering and principal component analysis revealed that LUAD patients could be classified into CIN phenotype-related subtypes (GroupLow, GroupModerate, and GroupHigh), each exhibiting distinct transcriptomic patterns. Notably, the GroupHigh showed significantly poor overall survival [OS; hazard ratio (HR) = 1.43, p-value &lt; 10−3] and disease-free survival (DFS; HR = 1.27, p-value &lt; 10−3). Univariate and multivariate analysis confirmed that its expression status was an independent prognostic predictor (p-value &lt; 10−3, HR = 2.18, 95% C.I = 1.26–3.76) of the clinical outcomes, outperforming pathologic grade (p-value &lt; 10−3, HR = 1.2, 95% C.I = 1.08–1.33). Moreover, analysis of surfactant metabolism-related genes revealed higher expression in the GroupLow, which was associated with a favorable prognosis. By integrating multiple independent cohorts (n = 779), we validated these findings and confirmed that CIN phenotype gene status serves as a critical prognostic marker in LUAD. Furthermore, genomic profiling showed that the GroupHigh exhibited frequent mutations in key genes such as KEAP1, LYST, SETD2, and TP53, with oncogenes in this group preferentially showing copy number gains. Our study highlights the significance of CIN phenotype gene status as a predictor of LUAD prognosis and its association with transcriptomic and genomic alterations, paving the way for further clinical validation and potential therapeutic interventions.

Soil test crop response nutrient prescription equations for improving soil health and yield sustainability—a long-term study under Alfisols of southern India

IntroductionEnhancing soil health and nutrient levels through fertilizers boosts agricultural productivity and global food security. However, careful fertilizer use is essential to prevent environmental damage and improve crop yields. The soil test crop response (STCR) is a scientific approach to fertilizer recommendation that ensures efficient use, supporting higher crop production while protecting the environment and preserving resources.MethodologyA long-term field experiment on the STCR approach was initiated in 2017 at the Zonal Agriculture Research Station, University of Agricultural Sciences, Bangalore, India. The experiment aimed to study the impact of STCR-based nutrient prescription along with farmyard manure (FYM) for a targeted yield of soybean (Glycine max), sunflower (Helianthus annuus), dry chili (Capsicum annuum), aerobic rice (Oryza sativa L.), foxtail millet (Setaria italica), okra (Abelmoschus esculentus), and kodo millet (Paspalum scrobiculatum) on yield and changes in soil health in comparison with other approaches of fertilizer recommendation.ResultsThe results showed a significant and positive impact of the integrated use of fertilizer with FYM based on the STCR approach on the productivity of all the crops and soil fertility. Significantly higher yields of soybean (23.91 q ha−1), sunflower (27.13 q ha−1), dry chili (16.67 q ha−1), aerobic rice (65.46 q ha−1), foxtail millet (14.07 q ha−1), okra (26.82 t ha−1), and kodo millet (17.10 q ha−1) were observed in the STCR NPK + FYM approach at yield level 1 compared to the general recommended dose and soil fertility rating approach. This approach outperformed the standard recommendations, enhancing nutrient uptake and efficiency across various crops. Utilizing the principal component analysis, the soil quality index effectively reflected the impact of nutrient management on soil properties, with the STCR NPK + FYM treatment at yield level 1 showing the highest correlation with improved soil physical and chemical parameters.DiscussionThe STCR approach led to improved yield, nutrient uptake, utilization efficiency, and soil health, thanks to a balanced fertilization strategy. This strategy was informed by soil tests and included factors like crop-induced nutrient depletion, baseline soil fertility, the efficiency of inherent and added nutrients through fertilizers and farmyard manure, and the success of yield-targeting techniques in meeting the nutritional needs of crops.

Enhanced soil carbon storage and arbuscular mycorrhizal fungal biomass in a long-term nutrient management under soybean-based cropping system.

To ensure the sustainability of crop production and ecosystem functioning, a thorough understanding of the mechanisms governing soil carbon (C) sequestration and soil health is essential. This study examined the effects of three nutrient management practices (organic, inorganic, and integrated) and two cropping systems (soybean-wheat and soybean-chickpea), on arbuscular mycorrhizal fungi (AMF) and soil C-sequestration in a long-term (12 years) field experiment. We measured the stocks of soil organic carbon, total glomalin-related soil protein, pertinent soil quality parameters such as microbial biomass carbon, and β-glucosidase activity along with AMF biomass [microscopic parameters and 16:1ω5cis phospholipid fatty acid (AM PLFA) and neutral lipid fatty acid (AM NLFA)]. It was observed that the measures of AMF biomass were positively correlated with the soil organic carbon stocks, total glomalin-related soil protein stocks, and soil quality parameters. Organic practice recorded significantly higher AMF spores, mycorrhizal colonization percentage, AM PLFA (2.58 nmol g-1 soil), AM NLFA (7.95 nmol g-1 soil), soil organic carbon stocks (15.78 Mg ha-1), total glomalin-related soil protein stocks (2.10 Mg ha-1), and soil quality parameters such as microbial biomass carbon, and β-glucosidase activity than inorganic and integrated practices. In comparison to soybean-chickpea, C-sequestration was higher in soybean-wheat. Principal component analysis validated the said results and differentiated soybean-wheat under organic practice from the rest of the treatments. In conclusion, our results suggest that organic management in conjunction with soybean-wheat crop rotation enhances AMF and can be recommended for improving soil quality and C sequestration without compromising crop yield.