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Abstract Evaluation of water quality is crucial for managing surface water effectively, ensuring its suitability for human use, and sustaining the environment. In the lower Danube River basin, various methods were employed to assess surface water quality for irrigation, drinking, human health risk purposes and the main mechanism control the surface water chemistry. These methods included water quality indicators (WQIs), complex statistical analyses, geographic information systems (GIS), Monte Carlo simulation, and geochemical modeling. Physicochemical analyses of surface water samples revealed primarily Ca–Mg–HCO3 − is the dominant water types. Principal component analysis (PCA), ionic ratios and piper, chloro alkaline index, Chadha, and Gibbs diagrams identified three distinct water characteristics influenced by water-rocks interaction, evaporation, ions exchange, and human activities. The geochemical modeling showed Danube River water’s strong ability to dissolve gypsum, halite, and anhydrite (SI 1) compared to adults, indicating a 2.1 times higher overall non-carcinogenic risk hazard index. However, Monte Carlo simulation demonstrated negligible iron, manganese, and nitrate health hazards for both age groups. These findings are valuable for water quality management decisions, contributing to long-term resource sustainability.

Marketable yield is the main objective in tomato production, but fair values for quality parameters are also becoming very important. A research project was undertaken for two years to assess the impact of water-saving techniques on yield, fruit quality and water use efficiency (WUE) of processing tomato (cv. Pullrex) in the Mediterranean environment. Additionally, to better understand how irrigation may affect tomato traits, different statistical techniques were applied to the results. Total yield was reduced by 37% on average in both years when half of the crop evapotranspiration (ET) was restored, and maximum marketable tomato yield was obtained under irrigation when 100% of the crop ET was restored. Irrigation cut at tomato veraison (irrigation cutback treatment) did not affect the yield, enhanced fruit quality and maximised WUE, thus contributing to water saving. Through the application of irrigation cutback toward the end of the tomato cycle, there is a possibility to improve tomato quality and, at the same time, save irrigation water. Principal component analysis confirmed that the cutback of irrigation was well correlated with tomato quality.

Abstract Objectives The aim of this study is to examine correlations between different oral rinse matrix metalloproteinase (MMP)‐8 protein species in western blot (WB) analysis, quantitative MMP‐8 measurements, and patient‐related factors. Elevated activated MMP‐8 (aMMP‐8) associate with periodontitis and a diagnostic point‐of‐care technology has been developed based on aMMP‐8. In WB, different MMP‐8 protein species can be analyzed. Relative abundancy of fragmented 20–25 kDa forms in WB has been associated with and reflects MMP‐8 activation and related fragmentation and elevated quantitative aMMP‐8 measurements. Material and Methods A random sample of 192 participants from a periodontal disease screening study was used for this study. Oral rinse samples for biomarker analyses were collected before clinical periodontal examinations. aMMP‐8 immunofluorometric (IFMA) and WB analysis (utilizing the same monoclonal antibody, 8708), polymorphonuclear leukocyte (PMN) elastase activity test and tissue inhibitor of metalloproteinases (TIMP)‐1 ELISA levels were performed from the oral rinse samples. Distinct MMP‐8 protein species were differentiated in the WB analysis. Principal component (PC) analysis was conducted to explore correlation patterns between the different species. Adjusted correlation analysis between the extracted PCs of WB and aMMP‐8 IFMA levels and multilevel regression analysis were conducted to explore if the other periodontal disease‐related biomarkers and clinical surrogate measures and patient‐related factors are co‐variating with the extracted components. Results Distinct correlation patterns between the MMP‐8 protein species were observed. The first four PCs explained 89% of the whole variance in PC analysis. Statistically significant correlation (p

Background: Dementia is the leading cause of disability and imposes a significant burden on society. Previous studies have suggested an association between metabolites and cognitive decline. Although the metabolite composition differs between Western and Asian populations, studies targeting Asian populations remain scarce. Methods: This cross-sectional study used data from a cohort survey of community-dwelling older adults aged ≥60 years living in Miyagi, Japan, conducted by Tohoku Medical Megabank Organization between 2013 and 2016. Forty-three metabolite variables quantified using nuclear magnetic resonance spectroscopy were used as explanatory variables. Dependent variable was the presence of cognitive decline (≤23 points), assessed by the Mini-Mental State Examination. Principal component (PC) analysis was performed to reduce the dimensionality of metabolite variables, followed by logistic regression analysis to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline. Results: A total of 2,940 participants were included (men: 49.0%, mean age: 67.6 years). Among them, 1.9% showed cognitive decline. The first 12 PC components (PC1–PC12) accounted for 71.7% of the total variance. Multivariate analysis showed that PC1, which mainly represented essential amino acids, was associated with lower odds of cognitive decline (OR 0.89; 95% CI, 0.80–0.98). PC2, which mainly included ketone bodies, was associated with cognitive decline (OR 1.29; 95% CI, 1.11–1.51). PC3, which included amino acids, was associated with lower odds of cognitive decline (OR 0.81; 95% CI, 0.66–0.99). Conclusion: Amino acids are protectively associated with cognitive decline, whereas ketone metabolites are associated with higher odds of cognitive decline.

Abstract Heart shape captures variation in cardiac structure beyond traditional phenotypes of mass and volume. Although observational studies have demonstrated associations with cardiometabolic risk factors and diseases, its genetic basis is less understood. We utilised cardiovascular magnetic resonance images from 45,683 UK Biobank participants to construct a heart shape atlas from bi-ventricular end-diastolic surface mesh models through principal component (PC) analysis. Genome-wide association studies were performed on the first 11 PCs that captured 83.6% of shape variance. We identified 43 significant loci, 14 were previously unreported for cardiac traits. Genetically predicted PCs were associated with cardiometabolic diseases. In particular two PCs (2 and 3) linked with more spherical ventricles being associated with increased risk of atrial fibrillation. Our study explores the genetic basis of multidimensional bi-ventricular heart shape using PCA, reporting new loci and biology, as well as polygenic risk scores for exploring genetic relationships of heart shape with cardiometabolic diseases.

Abstract Opioid use disorders cause major morbidity and mortality, and there is a pressing need for novel mechanistic targets and biomarkers for diagnosis and prognosis. Exposure to mu-opioid receptor (MOR) agonists causes changes in cytokine and inflammatory protein networks in peripheral blood, and also in brain glia and neurons. Individuals with heroin use disorder (iHUD) show dysregulated levels of several cytokines in the blood. However, there is limited data on a comprehensive panel of such markers in iHUD versus healthy controls (HC), especially considered as a multi-target biomarker. We used a validated proximity extension assay for the relative quantification of 92 cytokines and inflammatory proteins in the serum of iHUD on medication-assisted therapy (MAT; n = 21), compared to HC (n = 24). Twenty-nine targets showed significant group differences (primarily iHUD>HC), surviving multiple comparison corrections (p = 0.05). These targets included 19 members of canonical cytokine families, including specific chemokines, interleukins, growth factors, and tumor necrosis factor (TNF)-related proteins. For dimensionality reduction, data from these 19 cytokines were entered into a principal component (PC) analysis, with PC1 scores showing significant group differences (iHUD > HC; p

Mitigation of the environment from hazardous pesticides is clamant for all living things. The behavior of the fungicide Fuberidazole was investigated toward biodegradation. Biotransformation experiments were conducted by bacterial strains isolated from soils including, Xanthomonas citri (XC), and Pseudomonas syringae (PS), and fungal strains including, Aspergillus flavus (AF), Aspergillus niger (AN) and Penicillium chrysogenum (PC). Analysis and quantification of Fuberidazole degradation and its metabolites were performed by gas chromatography mass spectrometry and UV-visible spectrophotometry. Pseudomonas syringae and Penicillium chrysogenum displayed great bioremedial potential and degraded 91 and 86% of Fuberidazole after 35 days. Significant metabolites such as Benzimidazole 2-carboxylic acid, Benzimidazole, and 1-(1H-benzimidazole-2-yl)ethanone were produced in the current experiment. The increasing order of half-life for all strains was obtained as, PS (10.3) < PC (10.6) < AN (11) < AF (11.9) = XC (11.9) days. Experimental results demonstrated the capability of bacterial cells to efficiently utilize Fuberidazole as a nutrition source and the significance of favorable environmental conditions for its dissipation.

Abstract Background The allometric body shape index (ABSI) and hip index (HI), as well as multi-trait body shape phenotypes, have not yet been compared in their associations with inflammatory markers. The aim of this study was to examine the relationship between novel and traditional anthropometric indexes with inflammation using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts. Methods Participants from EPIC (n = 17,943, 69.1% women) and UK Biobank (n = 426,223, 53.2% women) with data on anthropometric indexes and C-reactive protein (CRP) were included in this cross-sectional analysis. A subset of women in EPIC also had at least one measurement for interleukins, tumour necrosis factor alpha, interferon gamma, leptin, and adiponectin. Four distinct body shape phenotypes were derived by a principal component (PC) analysis on height, weight, body mass index (BMI), waist (WC) and hip circumferences (HC), and waist-to-hip ratio (WHR). PC1 described overall adiposity, PC2 tall with low WHR, PC3 tall and centrally obese, and PC4 high BMI and weight with low WC and HC, suggesting an athletic phenotype. ABSI, HI, waist-to-height ratio and waist-to-hip index (WHI) were also calculated. Linear regression models were carried out separately in EPIC and UK Biobank stratified by sex and adjusted for age, smoking status, education, and physical activity. Results were additionally combined in a random-effects meta-analysis. Results Traditional anthropometric indexes, particularly BMI, WC, and weight were positively associated with CRP levels, in men and women. Body shape phenotypes also showed distinct associations with CRP. Specifically, PC2 showed inverse associations with CRP in EPIC and UK Biobank in both sexes, similarly to height. PC3 was inversely associated with CRP among women, whereas positive associations were observed among men. Conclusions Specific indexes of body size and body fat distribution showed differential associations with inflammation in adults. Notably, our results suggest that in women, height may mitigate the impact of a higher WC and HC on inflammation. This suggests that subtypes of adiposity exhibit substantial variation in their inflammatory potential, which may have implications for inflammation-related chronic diseases.

Objectives: The relationship between weight-related load and bone mineral density (BMD)/bone microstructure under normal load conditions using high-resolution peripheral quantitative computed tomography (HR-pQCT) remains unconfirmed. The study aims to investigate the differences in effect of body mass index (BMI) on BMD/bone microstructure of loaded and unloaded bones, respectively, in Japanese postmenopausal women. Methods: Fifty-seven postmenopausal women underwent HR-pQCT on the tibia and radius. Correlation analysis, principal component (PC) analysis, and hierarchical multiple regression were performed to examine the relationship between BMI and HR-pQCT parameters. Results: Several microstructural parameters of the tibia and radius correlated with BMI through a simple correlation analysis, and these relationships remained unchanged even with an age-adjusted partial correlation analysis. PC analysis was conducted using seven bone microstructure parameters. The first PC (PC1) reflected all parameters of trabecular and cortical bone microstructures, except for cortical porosity, whereas the second PC (PC2) reflected only cortical bone microstructure. Hierarchical multiple regression analysis indicated that BMI was more strongly related to BMD/bone microstructure in the tibia than in the radius. Furthermore, BMI was associated with trabecular/cortical BMD, and PC1 (not PC2) of the tibia and radius. Thus, BMI was strongly related to the trabecular bone microstructure rather than the cortical bone microstructure. Conclusions: Our data confirmed that BMI is associated with volumetric BMD and trabecular bone microstructure parameters in the tibia and radius. However, although BMI may be more related to HR-pQCT parameters in the tibia than in the radius, the magnitude of association is modest.

IntroductionAnemia is a risk factor for dementia development. However, few studies have examined the relationship between brain volume and anemia. This study aimed to analyze the association between anemia and brain volume using magnetic resonance imaging data from brain health checkups.MethodParticipants underwent brain health checkups between January 2015 and March 2022. Blood samples were collected to measure hemoglobin concentrations and mean corpuscular volumes. The modified Mini-Mental State Examination (MMSE) was used to evaluate cognitive function. Magnetic resonance images were analyzed using voxel-based Morphometry to evaluate the overall patterns of brain volume. After extracting the principal components (PCs) from PC analysis, we investigated their association with MMSE scores and anemia.ResultsThis study included 1,029 participants and identified principal components, representing smaller volume in the frontal lobe (PC1), and smaller volume in the limbic system to the temporal lobe (PC2). A higher PC2 score was significantly associated with a lower MMSE score. Male participants with anemia had smaller bilateral PC1 volumes and left hippocampal volumes, and female participants with anemia had smaller bilateral PC2 volumes and hippocampus volumes.DiscussionPC2 may represent the extent of disease affecting limbic system volume, such as Alzheimer’s disease. Our results suggest that anemia may be associated with smaller volumes in the limbic system, especially in women. Further studies are required to determine which type of anemia is more strongly correlated with smaller brain volumes.

Aims To investigate a comprehensive panel of biomarkers and risk of aortic stenosis (AS) in a prospective population-based study.Methods Anthropometric, metabolic, and inflammatory biomarkers were measured in the Metabolic Syndrome in the Men Study of 10,144 Finnish men without AS at baseline. Cases of AS were identified from the medical records. Cox regression analysis was used to identify variables predicting AS over a follow-up time of 10.8 years. Principal component (PC) analysis was applied to the biomarkers that predicted AS. Cox regression analysis was used to investigate the resulting PCs as AS predictors.Results AS was diagnosed in 116 men (1.1%), with a median age of 62 years. In Cox regression analyses, fasting, 30 min, and 120 min plasma insulin, and proinsulin, with hazard ratios (HR) ranging from 1.38 (1.12-1.69, p = 2.1E-3) to 1.44 (1.23-1.68, p = 4.0E-6), Matsuda index [HR 0.68 (0.56-0.82, p = 6.9E-5)], and serum C-peptide [HR 1.47 (1.22-1.77, p = 5.0E-5)] were associated with incident AS, in addition to age, systolic blood pressure, BMI, waist circumference, waist/hip ratio, height, body fat mass, fat-free mass, and hs-CRP, and remained significant after adjustments, or if diabetic subjects were excluded. PC 1, consisting of fasting plasma insulin, C-peptide, Matsuda index, waist/hip ratio, and urine albumin excretion, and PC 2, consisting of age, body fat mass, and systolic blood pressure, were significantly associated with AS [HRs 1.37(1.09-1.73) and 1.77 (1.45-2.17), respectively].Conclusion Biomarkers reflecting insulin resistance are risk factors for AS, a novel finding indicating that insulin resistance is important in the pathogenesis of AS.

Carbon steel butt welds are widely used in aircraft carrier, shipbuilding, railway, aerospace and other industries. As a new technology of ultrasonic non-destructive testing, Total Focusing Method (TFM) technology is a method of image reconstruction in which the value of each constituent datum of the image results from focused ultrasound. TFM may also be understood as a broad term encompassing a family of processing techniques for image reconstruction from full matrix capture (FMC). This paper compares resolution and focus between TFM and conventional phased array technology, by using phased array test block type B. The paper also introduces the testing system, including equipment, probe, wedge, calibration block as per ASME, PC analysis software, for detection of 21mm thickness carbon steel welds. In order to improve the defect detection ability, a customized software on board was designed based on TFM technology, and an appropriate propagation mode was adopted for different types of defects, such as longitudinal cracks on surface, lack of fusion, porosities, slag inclusions. The test results showing that compared with conventional phased array technology, the defect information such as length and depth detected by TFM are nearer to the actual situation, and the shape is closer to the physical one. TFM technology has more advantages in quantitative and qualitative capabilities in testing carbon steel butt welds, and can meet the requirements of on-site testing.

BackgroundRelapsed and refractory Diffuse large B cell lymphoma (DLBCL) can be successfully treated with axicabtagene ciloleucel (axi-cel), a CD19-directed autologous chimeric antigen receptor T cell (CAR-T) therapy. Diagnostic image-based features could help identify the patients who would clinically respond to this advanced immunotherapy.PurposeThe aim of this study was to establish a radiomic image feature-based signature derived from positron emission tomography/computed tomography (PET/CT), including metabolic tumor burden, which can predict a durable response to CAR-T therapy in refractory/relapsed DLBCL.MethodsWe conducted a retrospective review of 155 patients with relapsed/refractory DLBCL treated with axi-cel CAR-T therapy. The patients’ disease involvement was evaluated based on nodal or extranodal sites. A sub-cohort of these patients with at least one nodal lesion (n=124) was assessed, while an overlapping sub-cohort (n=94) had at least one extranodal lesion. The lesion regions were characterized using 306 quantitative imaging metrics for PET images and CT images independently. Principal component (PC) analysis was performed to reduce the dimensionality in feature-based functional categories: size (n=38), shape (n=9), and texture (n=259). The selected features were used to build prediction models for survival at 1 year and tested for prognosis to overall/progression-free survival (OS/PFS) using a Kaplan-Meier (KM) plot.ResultsThe Shape-based PC features of the largest extranodal lesion on PET were predictive of 1-year survival (AUC 0.68 [0.43,0.94]) and prognostic of OS/PFS (p

Abstract The APOBEC/AID family is known for its mutator activity, and recent evidence also supports the potential impact of ADARs. Furthermore, the mutator impacts of APOBEC/ADAR mutations have not yet been investigated. Assessment of pancancer TCGA exomes identified enriched somatic variants among exomes with nonsynonymous APOBEC1, APOBEC3B, APOBEC3C, ADAR, and ADARB1 mutations, compared to exomes with synonymous ones. Principal component (PC) analysis reduced the number of potential players to eight in cancer exomes/genomes, and to five in cancer types. Multivariate regression analysis was used to assess the impact of the PCs on each COSMIC mutational signature among pancancer exomes/genomes and particular cancers, identifying several novel links, including SBS17b, SBS18, and ID7 mainly determined by APOBEC1 mRNA levels; SBS40, ID1, and ID2 by age; SBS3 and SBS16 by APOBEC3A/APOBEC3B mRNA levels; ID5 and DBS9 by DNA repair/replication (DRR) defects; and SBS7a-d, SBS38, ID4, ID8, ID13, and DBS1 by ultraviolet (UV) radiation/ADARB1 mRNA levels. APOBEC/ADAR mutations appeared to potentiate the impact of DRR defects on several mutational signatures, and some factors seemed to inversely affect certain signatures. These findings potentially implicate certain APOBEC/ADAR mutations/mRNA levels in distinct mutational signatures, particularly APOBEC1 mRNA levels in aging-related signatures and ADARB1 mRNA levels in UV radiation-related signatures.

Abstract This study presents a comprehensive analysis of mineralization exploration in the Egyptian Eastern Desert (ED), one of the most sought-after areas for those interested in mining industry, by integrating Landsat-9 images and geophysical magnetic data. Employing advanced techniques like Principal Component (PC) analysis, Minimum Noise Fraction (MNf) transform, and Band-Ratio (B-Ratio), the research focuses on mapping lithological units, hydrothermal alteration regions, and structural elements. Composite images derived from specific PC, and MNf bands, and B-Ratio exhibit superior lithological unit identification. The findings emphasize that there are significant variations in the types of rocks extend from the southern to the northern parts of the ED. Hydrothermal alteration mapping, guided by B-Ratio results, aids qualitative lithological discrimination. A novel false color composite image optimizes Landsat-9 B-Ratios, enhancing rock unit discrimination. Correlation analyses reveal associations between mineralization types and major lithological units, while exploration of the magnetic anomaly map highlights its role in correlating mineralization sites. Structural features, analyzed through Center for Exploration-Targeting Grid-Analysis (CET-GA) and Center for Exploration-Targeting Porphyry-Analysis (CET-GA) with Tilt Derivative of RTP (TDR) techniques, contribute to a robust association between regions with medium to high structural density and porphyry intrusions and mineralization. The study significantly supports the advanced exploration geoscience, providing insights into the geological structures and dynamics governing mineralization in the Egyptian ED.

Abstract Aims The association between microRNAs (miRNAs) and established cardiac biomarkers is largely unknown. We aimed to measure the association between plasma miRNAs and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP), cardiac troponin I, soluble urokinase‐type plasminogen activator receptor (suPAR), and galectin‐3 with cardiac structure and function and clinical outcomes. Methods and results We quantified 32 plasma miRNAs using the FirePlex miRNA assay and measured biomarkers in 139 individuals with symptomatic heart failure (HF). We used principal component (PC) analysis and linear regression to evaluate the association between miRNAs and biomarkers with ventricular size and function by echocardiography and Cox modelling for the incidence of a first composite event of HF hospitalization, heart transplant, left ventricular assist device implant, or death. The mean (standard deviation) age at baseline was 64.3 (12.4) years, 33 (24%) were female, and 122 (88%) were White. A total of 45 events occurred over a median follow‐up of 368 (interquartile range 234, 494) days. Baseline NT‐proBNP (β = −2.0; P = 0.001) and miRNA PC2 (β = 2.6; P = 0.002) were associated with baseline left ventricular ejection fraction. NT‐proBNP (β = 20.6; P = 0.0004), suPAR (β = −39.6; P = 0.005), and PC4 (β = 21.1; P = 0.02) were associated with baseline left ventricular end‐diastolic volumes. NT‐proBNP [hazard ratio (HR) 1.67, 95% confidence interval (CI) 1.28–2.18, P = 0.0002], galectin‐3 (HR 2.02, 95% CI 1.05–3.91, P = 0.036), PC3 (HR 1.75, 95% CI 1.23–2.49, P = 0.002), and PC4 (HR 1.67, 95% CI 1.1–2.52, P = 0.016) were independently associated with incident events. Conclusions Biomarkers and miRNA PCs are associated with cardiac structure and function and incident cardiovascular outcomes. Combining information from miRNAs provides prognostic information beyond biomarkers in HF.

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.

In situ visible–near infrared spectroscopy holds great potential in providing information supporting field applications, decision making and management in soil science, especially combined with the information present in the archived soil spectra. However, soil moisture can drastically affect the reflectance curve and reduce prediction accuracy. The external parameter orthogonalization (EPO) can remove the moisture effect but the effect of elimination is urgently needed to be improved. Herein, firstly, we implemented EPO on 50 bootstrapped calibration datasets, which generated from the local soil spectra library, with 255 combinations of PC1 to PC8, resulting in a total of 50*255 models in PLSR and cubist, respectively. Secondly, we calculated the mean prediction results from these 255 combinations and selected the top 10 validation performance results. Thirdly, we performed correlation analysis on SOM with each segment induced by singular value decomposition on the difference of laboratory and in situ spectra to determine which PC should be removed in the modified EPO. Results revealed that top 10 prediction results with principles without PC2 and the moisture effect was mainly in PC1, and PC2 with significant correlation with SOM were removed from the EPO procedure. EPO with removing PC2 (namely Modified EPO) improved the correlation of SOM with some optional bands that directly and indirectly were associated with SOM to improve the SOM prediction accuracy. Modified EPO improved the accuracy in predicting SOM with increased R2 (9 %–44 % and 7 %–17 %) and root mean square error (1 %–9 % and 63 %–68 %) in the PLSR and Cubist model, respectively. Our study highlights the advantage of modified EPO in improving the elimination efficiency of water in spectra, and of PC analysis biplots in approximating the removed PCs.

Abstract Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting for ~ 32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.

Abstract Body shape phenotypes combining multiple anthropometric traits have been linked to postmenopausal breast cancer (BC). However, underlying biological pathways remain poorly understood. This study investigated to what extent the associations of body shapes with postmenopausal BC risk is mediated by biochemical markers. The study included 176,686 postmenopausal women from UK Biobank. Four body shape phenotypes were derived from principal component (PC) analysis of height, weight, body mass index, waist and hip circumferences, and waist-to-hip ratio (WHR). The four-way decomposition of the total effect was used to estimate mediation and interaction effects simultaneously as well as the mediated proportions. After 10.9 years median follow-up, 6,396 incident postmenopausal BC were diagnosed. There was strong evidence of positive associations between PC1 (general obesity) and PC2 (tall, low WHR), and BC risk. The association of PC1 with BC risk was positively mediated by testosterone and negatively by insulin-like growth factor-1 (IGF-1), with the overall proportion mediated (sum of the mediated interaction and pure indirect effect (PIE)) accounting for 11.4% (95% confidence intervals: 5.1 to 17.8%) and -12.2% (-20.5% to -4.0%) of the total effect, respectively. Small proportions of the association between PC2 and BC were mediated by IGF-1 (PIE: 2.8% (0.6 to 4.9%)), and sex hormone-binding globulin (SHBG) (PIE: -6.1% (-10.9% to -1.3%)). Our findings are consistent with differential pathways linking different body shapes with BC risk, with a suggestive mediation through testosterone and IGF-1 in the relationship of a generally obese body shape and BC risk, while IGF-1 and SHBG may mediate a tall/lean body shape-BC risk association.

This study aims to map the non-sulphide Zinc (Zn)-bearing gossans at the Gujer Zn deposit area, Central Iran, using Landsat 8 Operational Land Imager (OLI) and GeoEye-1 satellites. The colour composites, Principal Component Analysis (PCA), and Support Vector Machine (SVM) were adopted for image analysis. Zn-bearing gossans contain Fe-oxyhydroxide minerals displaying spectral characteristics in visible and infrared (IR) wavelengths. The application of colour composites using GeoEye-1 images resulted in the delineation of gossans (real target) and ferruginous sandstones (false targets) having the same colour tone in the study area. IR spectroscopy of ore samples showed that hemimorphite exhibits low absorption in shortwave infrared (SWIR) wavelengths. Consequently, the Crosta-PC analysis was conducted using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI to enhance only ore gossans. Five target zones were specified using the Crosta technique. The SVM method was performed to increase the accuracy of image analysis using the Radial Basis Function (RBF) kernel. The SVM-RBF method accomplished enhancing ore gossans by defining a new target zone. According to the results, the application of the Crosta technique using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI can specify ore gossans and eliminate the interfering effect of ferruginous sandstones in similar geological settings. The SVM-RBF can improve the results of image processing using PC entry of Landsat OLI bands. GeoEye-1 images are useful for the initial assessment of geological units in the region and for delineating the accurate boundary of ore gossans derived from Landsat 8 OLI data.

Mato Grosso state is the biggest maize producer in Brazil, with the predominance of cultivation concentrated in the second harvest. Due to the need to obtain more accurate and efficient data, agricultural intelligence is adapting and embracing new technologies such as the use of satellites for remote sensing and geographic information systems. In this respect, this study aimed to map the second harvest maize cultivation areas at Canarana-MT in the crop year 2019/2020 by using geographic object-based image analysis (GEOBIA) with different spatial, spectral, and temporal resolutions. MSI/Sentinel-2, OLI/Landsat-8, MODIS-Terra and MODIS-Aqua, and PlanetScope imagery were used in this assessment. The maize crops mapping was based on cartographic basis from IBGE (Brazilian Institute of Geography and Statistics) and the Google Earth Engine (GEE), and the following steps of image filtering (gray-level co-occurrence matrix—GLCM), vegetation indices calculation, segmentation by simple non-iterative clustering (SNIC), principal component (PC) analysis, and classification by random forest (RF) algorithm, followed finally by confusion matrix analysis, kappa, overall accuracy (OA), and validation statistics. From these methods, satisfactory results were found; with OA from 86.41% to 88.65% and kappa from 81.26% and 84.61% among the imagery systems considered, the GEOBIA technique combined with the SNIC and GLCM spectral and texture feature discriminations and the RF classifier presented a mapping of the corn crop of the study area that demonstrates an improved and aided the performance of automated multispectral image classification processes.

Due to the inhomogeneity of electron number density, radio waves emitted by pulsars undergo scattering as they pass through the interstellar medium (ISM). However, a connection between large-scale pulsar scattering data and the structure of the Galactic ISM has yet to be established. In this paper, we explore the capability of pulsar scattering time data in discovering structures in the ISM. Using a large data set of scattering time measurements for 473 pulsars, we fit the pulsar reduced scattering intensity as a function of Galactic latitude and distance, constructing a smooth model of the Galactic pulsar scattering distribution. By comparing this smooth distribution with observational data, we identify two ISM structures responsible for pulsar scattering, one is associated with the Vela supernova remnant region within the Gum Nebula, while the other is a newly discovered structure—a distant superbubble, G38, located at a distance of 2.3 kpc with a size of ~50 pc. Analysis of the correlation coefficient of the pulsar scattering distribution shows that the correlation is dominated by structures smaller than 0.15 kpc—the closest separation approachable by the current data set. As measurements of the pulsar scattering time continue to increase in the future, they can potentially become an independent tool for exploring structures in the ISM.

Modern treat-to-target management of rheumatoid arthritis (RA) involves titration of drug therapy to achieve remission, requiring close monitoring of disease activity through frequent clinical assessments. Accelerometry offers a novel method for continuous remote monitoring of RA activity by capturing fluctuations in mobility, sedentary behaviours, physical activity and sleep patterns over prolonged periods without the expense, inconvenience and environmental impact of extra hospital visits. We aimed to (a) assess the feasibility, usability and acceptability of wearable devices in patients with active RA; (b) investigate the multivariate relationships within the dataset; and (c) explore the robustness of accelerometry outcomes to downsampling to facilitate future prolonged monitoring. Eleven people with active RA newly starting an arthritis drug completed clinical assessments at 4-week intervals for 12 weeks. Participants wore an Axivity AX6 wrist device (sampling frequency 100 Hz) for 7 days after each clinical assessment. Measures of macro gait (volume, pattern and variability), micro gait (pace, rhythm, variability, asymmetry and postural control of walking), sedentary behaviour (standing, sitting and lying) and physical activity (moderate to vigorous physical activity [MVPA], sustained inactive bouts [SIBs]) and sleep outcomes (sleep duration, wake up after sleep onset, number of awakenings) were recorded. Feasibility, usability and acceptability of wearable devices were assessed using Rabinovich’s questionnaire, principal component (PC) analysis was used to investigate the multivariate relationships within the dataset, and Bland–Altman plots (bias and Limits of Agreement) and Intraclass Correlation Coefficient (ICC) were used to test the robustness of outcomes sampled at 100 Hz versus downsampled at 50 Hz and 25 Hz. Wearable devices obtained high feasibility, usability and acceptability scores among participants. Macro gait outcomes and MVPA (first PC) and micro gait outcomes and number of SIBs (second PC) exhibited the strongest loadings, with these first two PCs accounting for 40% of the variance of the dataset. Furthermore, these device metrics were robust to downsampling, showing good to excellent agreements (ICC ≥ 0.75). We identified two main domains of mobility, physical activity and sleep outcomes of people with RA: micro gait outcomes plus MVPA and micro gait outcomes plus number of SIBs. Combined with the high usability and acceptability of wearable devices and the robustness of outcomes to downsampling, our real-world data supports the feasibility of accelerometry for prolonged remote monitoring of RA disease activity.

Feedlot cattle were assessed for the consistency of enrichment brush use, higher incidence of natural behaviour, and less agonistic or abnormal behaviour compared to cattle without the brush. Cattle were assigned to one of two treatment pens, (1) access to a vertical grooming brush (EB; n = 89) or (2) no access control (CON; n = 80), for a period of 107 days. A Principal Component (PC) analysis was used on pen-side demeanour scores. Statistical analysis on pen-side behaviour and demeanour PCs tested for impact of treatment, day, and time. Frequency of brush use and the duration of each cow–brush interaction did not decrease over the assessment period. Self-grooming and allogrooming did not differ between treatments and were similar in frequency to brush grooming, resulting in total grooming being higher in the EB treatment group (p < 0.05). Cattle in the EB pen were scored as more content (PC 2; p < 0.05) and sociable (PC 3; p < 0.01), while CON cattle were scored as more anxious (PC 3; p < 0.05). Overall, this study suggests that the provision of a brush for enrichment enhanced lot-fed cattle wellbeing and permitted prolonged engagement, making it an effective enrichment device.

The physicochemical and microbiological properties of traditional Portuguese ready-to-eat dry fermented sausage chouriça de carne samples from 14 regional producers were analysed and subjected to multivariate analysis to determine the relationships between them and to evaluate how the quality and safety of these sausages is affected by these properties. Producer-mean values for physicochemical analyses were quite variable, with intervals of 4.87–6.11 for pH, 0.803–0.965 for aw, moisture 19.5–48.5%, protein 32.0–60.1% (db), fat 22.0–53.3% (db), ash 3.52–9.69% (db), and carbohydrates 1.66–13.5% (db). Mesophilic counts varied (5.61–8.68 log CFU/g), while lactic acid bacteria were generally high (MRS: 8.21–10.2; M17: 7.66–10.0 log CFU/g). S. aureus was enumerated in levels up to 2.55 log CFU/g, while presumptive C. perfringens never surpassed 2 log CFU/g. Salmonella spp. and Listeria spp. were also detected in the samples tested. Principal component (PC) analysis yielded a three-dimension solution that explained 60% of the data variation; PC1 (26%) characterized chorizo formulations with more meat, while PC2 (19.3%) described sausages with longer/rapid fermentation, and PC3 (14.5%) highlighted chouriços with poorer hygiene. Cluster analysis identified three quality groups: (i) chorizos with high moisture, high protein content, and lowest pH; (ii) sausages with low moisture, high fat content, and elevated pH; and (iii) chouriças with high moisture and high protein but lower fat contents, low pH, and improved hygiene. Lastly, factor analysis yielded a varimax-rotated three-factor solution that explained 65% of the data, with similar results to PCA; factor 1 (23.5%) depicted chorizos with low pH but high moisture, factor 2 (20.8%) described sausages with more meat in the formulation, and factor 3 (20.6%) longer or rapid fermentation. Overall, the results evidenced the great variability in the quality attributes of Portuguese chorizo sausages, very likely to arise from multiple recipes, ingredients, and manufacturing practices. The definition of quality clusters is expected to play a crucial role for the self-denominated “artisanal” food companies to benchmark their chouriço sausages against the proper artisanal quality group.

Visual cues strongly influence an individual's self-esteem and have fundamental sociopsychological functions. The color and shape of the face are important information for visual cues and are hypothesized to be correlated with each other. However, few studies have examined these relationships. Thus, this study determined the association between color and shape of the face. For this purpose, we evaluated Chinese women in their 30s and 40s (n = 166). Three-dimensional (3D) image-capture devices that provide shape morphology along with standardized photographs (color information) were used to obtain 3D images of women. The coordinates and red‒green–blue color data on the 3D images were utilized to perform principal component (PC) analysis, and shape and color PCs were generated. A canonical variate analysis was then conducted to check for significant correlations between the shape and color PCs. As a result, 6 significant correlations were found (p

ABSTRACT The objective of this study was to estimate the genetic parameters and predict selection of genetic gains and genetic diversity of 12 Andropogon lateralis ecotypes collected in the State of Rio Grande do Sul, Brazil. To estimate genetic parameters and predict selection gains, the REML/BLUP technique was applied. Genetic diversity among the ecotypes was evaluated by two clustering methods (optimization and hierarchical) and principal component (PC) analysis, the latter method also used to discard variables. The genetic parameters studied showed high potential for selection of important agronomic forage traits for livestock production. Results showed that the 12 A. lateralis Nees ecotypes exhibited high genetic variability for the studied forage characters and indicated parental prosperity for crosses within the genetic breeding program. Principal component analysis showed that number of total vegetative tillers, leaf:stem ratio, number of reproductive tillers, and leaf dry matter yield accounted for 80.6% of the observed variation in PC1. These variables are important characteristics for quantifying the dry matter production and nutritional value of forage plants, and they can help to discriminate amongst ecotypes. Ecotypes sourced from the Pelotas, Piratini, Passo Fundo, Bagé, and Montenegro regions showed superior forage production when evaluated by the BLUP methodology. Therefore, this group was identified as the most suitable for selection and crossing purposes. Tocher’s cluster analysis grouped the ecotypes into five divergent groups. Principal component and UPGMA hierarchical methods were also efficient at separating the ecotypes.

Abstract Indoleamine 2,3‐dioxygenase 1 (IDO1), the key enzyme in the catabolism of the essential amino acid tryptophan (Trp) through kynurenine pathway, induces immune tolerance and is considered as a critical immune checkpoint, but its impacts as a metabolism enzyme on glucose and lipid metabolism are overlooked. We aim to clarify the potential role of IDO1 in aerobic glycolysis in pancreatic cancer (PC). Analysis of database revealed the positive correlation in PC between the expressions of IDO1 and genes encoding important glycolytic enzyme hexokinase 2 (HK2), pyruvate kinase (PK), lactate dehydrogenase A (LDHA) and glucose transporter 1 (GLUT1). It was found that IDO1 could modulate glycolysis and glucose uptake in PC cells, Trp deficiency caused by IDO1 overexpression enhanced glucose uptake by stimulating GLUT1 translocation to the plasma membrane of PC cells. Besides, Trp deficiency caused by IDO1 overexpression suppressed the apoptosis of PC cells via promoting glycolysis, which reveals the presence of IDO1–glycolysis–apoptosis axis in PC. IDO1 inhibitors could inhibit glycolysis, promote apoptosis, and exhibit robust therapeutic efficacy when combined with GLUT1 inhibitor in PC mice. Our study reveals the function of IDO1 in the glucose metabolism of PC and provides new insights into the therapeutic strategy for PC.

As the application of hyperspectral images is increasing, many researchers attempt to extend existing pansharpening techniques to hyperspectral images. This paper focuses on the application of Ehlers fusion to hyperspectral image sharpening. Ehlers fusion involves two crucial algorithms: filter technique in the frequency domain and intensity transform. In this study, different filter types and intensity transform methods were analysed separately. With a combination of filter types and intensity transforms, the fusion procedure was implemented to test data sets. The spectral profiles of the pixels of the images were then used as a tool to control the quality of the fused image. Finally, the performance of Ehlers fusion is compared with Principle Component (PC) analysis, Gram-Schmidt transform (Gram-Schmidt), High-Pass Filtering in the spatial domain (HPF), and Wavelet Principal Component (Wavelet-PC) analysis using the same input data. The comparison shows that Ehlers high-pass filter fusion shows outstanding performance both on spatial enhancement and colour preservation.

Soil microbial functional diversity under maize grown in different agricultural management practices was determined using the Biolog EcoPlates and other microbial and biochemical methods. Comparisons of substrate utilization and the diversity indices showed differences in community composition of microorganisms related to different cultivation techniques and seasons. The soil samples collected in spring were characterized by statistically significant lower indices of biological activity in comparison to the soil collected from the flowering stage of maize. The soils collected in spring from the plots with full tillage had a similarly high biological activity as the soils obtained from maize flowering season. The principal component of PC analysis, showed the strong correlation between the parameters of soil quality and biodiversity indicators. Selected indicators of soil microbial diversity explained 71.51% of biological variability in soils. Based on the PC analysis, two major groups of soils have been indicated. Management practices and seasons were two important factors affecting soil microbial communities.

Abstract Background Schools may be high-leverage points for the promotion of physical activity (PA), yet little is known about school built and social environments among youth at high risk of obesity. Purpose To characterise school built and social environments that may be salient for PA and to examine associations between school PA environments and PA in youth at risk of obesity. Methods Data from youth attending 206 schools (314 youth in 2005–2008, and 129 youth in 2008–2010) within the QUALITY cohort study, a longitudinal investigation of youth at high risk of obesity were used. Features of schools, based on built, policy/programming and social environments were identified using principal components (PC) analysis. Gender-stratified generalized estimating equation (GEE) models were used to explore associations between school features and accelerometer measured mean counts per minute (MCPM), mean daily moderate-to-vigorous physical activity (MVPA) and the odds of meeting MVPA guidelines cross-sectionally and prospectively using 90% confidence intervals. Results Nine PCs were identified. Associations were observed between PA and 7 of the 9 PCs. The social environment seemed to be particularly important. Social Norms to Promote PA was associated with an increase in girls’ baseline MCPM and MVPA. High Willingness to Promote PA was associated with boys’ MCPM, MVPA, and odds of meeting MVPA guidelines, at both baseline and follow-up. Conclusion School built and social contexts may be associated with PA cross-sectionally and over time. Further studies are necessary to confirm the direction and magnitude of effects and to establish their relevance to school-based health promotion efforts.

Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.

Abstract Graphene-based nanomaterials have attracted significant attention in the field of nanomedicine due to their unique atomic arrangement which allows for manifold applications. However, their inherent high hydrophobicity poses challenges in biological systems, thereby limiting their usage in biomedical areas. To address this limitation, one approach involves introducing oxygen functional groups on graphene surfaces, resulting in the formation of graphene oxide (GO). This modification enables improved dispersion, enhanced stability, reduced toxicity, and tunable surface properties. In this review, we aim to explore the interactions between GO and the biological fluids in the context of theranostics, shedding light on the formation of the “protein corona” (PC) i.e., the protein-enriched layer that formed around nanosystems when exposed to blood. The presence of the PC alters the surface properties and biological identity of GO, thus influencing its behavior and performance in various applications. By investigating this phenomenon, we gain insights into the bio-nano interactions that occur and their biological implications for different intents such as nucleic acid and drug delivery, active cell targeting, and modulation of cell signalling pathways. Additionally, we discuss diagnostic applications utilizing biocoronated GO and personalized PC analysis, with a particular focus on the detection of cancer biomarkers. By exploring these cutting-edge advancements, this comprehensive review provides valuable insights into the rapidly evolving field of GO-based nanomedicine for theranostic applications. Graphical Abstract

Among 272 patients undergoing a screening colonoscopy, DNA methylation of DNMT3B and MTHFR, genes encoding enzymes critical to one-carbon metabolism, was quantified in blood leukocytes using Sequenom EpiTYPER®. DNA methylation was quantified in 66 and 28 CpG sites of DNMT3B and MTHFR respectively, and conceptualized using two approaches. First, measures representing average methylation across all CpG sites were created. Second, unsupervised principal component (PC) analysis was used as a pattern derivation and data-reduction approach, to develop two summary variables (PC1 and PC2). These two summary variables represented methylation around the transcription start site (PC1) and in the gene-coding area (PC2) for both DNMT3B and MTHFR. The data contained in this article presents the variation of methylation levels for individual CpG sites within the DNMT3B and MTHFR genes and possible correlations uncovered using PC analysis. The data are related to the research article “Gene-specific DNA methylation of DNMT3B and MTHFR and colorectal adenoma risk” in Mutation Research – Fundamental and Molecular Mechanisms of Mutagenesis.

Iron (Fe) toxicity, generated from excess reduced ferrous Fe (Fe2+) ion formation within the soil under submerged condition, is a potent environmental stress that limits lowland rice production. Total 11 diverse Thai rice genotypes, including a recognized tolerant genotype Azucena and a susceptible genotype IR64, were evaluated against 5 Fe2+ levels [0 (control), 150, 300, 600 and 900 mg/L] to screen the tested genotypes for their Fe-toxicity tolerance and to classify them as a sensitive/tolerant category. The evaluation was conducted by a germination study, followed by a polyhouse study on growth, yield and physiochemical performances. Results showed significant variations in Fe2+-tolerance across genotypes. Increasing Fe2+ level beyond 300 mg/L was detrimental for germination and growth of all the tested genotypes, although germination responses were negatively affected at Fe2+ ≥ 300 mg/L. Physiochemical responses in the form of leaf greenness, net photosynthetic rate, membrane stability index and Fe contents in leaf and root were the most representative of Fe2+-toxicity-mediated impairments on overall growth and yield. Difference in physiochemical responses was effectively correlated with the contrasting ability of the genotypes on lowering excess Fe2+ in tissues. Analysis of average tolerance and stress tolerance index unveiled that the genotypes RD85 and RD31 were the closest to the tolerant check Azucena and the sensitive check IR64, respectively. The unweighted pair group method with arithmetic means clustering revealed three major clusters, with cluster II (four genotypes) being Fe2+ tolerant and cluster I (four genotypes) being Fe2+ sensitive. Principal component (PC) analysis and genotype by trait-biplot analysis showed that the first two components explained 90.5% of the total variation, with PC1 accounting for 56.6% and PC2 for 33.9% of the total variation. The identified tolerant rice genotypes show potentials for cultivation in Fe2+-toxic lowlands for increased productivity. The findings contribute to the present understanding on Fe2+-toxicity response and provide a basis for future genotype selection or rice crop improvement programs against Fe2+-toxicity.

Winged bean [Psophocarpus tetragonolobus (L.) DC.] is an underutilized legume with the potential to contribute to nutrition and food security globally, particularly in sub-Saharan Africa (SSA). However, more attention needs to be paid to exploiting its full potential due to a lack of adequate knowledge of the existing genetic diversity in the available winged bean germplasm. To bridge this gap, thirty accessions of winged beans sourced from the Gene Bank of the International Institute of Tropical Agriculture (IITA), Ibadan, were evaluated for seed morphometric and selected agro-morphological traits at three agro-ecological zones in Nigeria. The data obtained were subjected to analysis of variance (ANOVA), principal component (PC) analysis, cluster analysis, and correlation analysis. Significant differences (p < 0.05) were observed among the accessions for all measured traits. The first two PCs accounted for 88.2% of the variation observed among the accessions for all measured traits. Accessions were grouped into three clusters based on the agro-morphological traits and three clusters under the multi-spectral imaging (MSI) system. TPt-31 had the highest seed yield per plant, pod weight per plant, and early maturity, while TPt-7 had an extended flowering and maturity period, the highest number of pods per peduncle and pods per plant, as well as the lowest seed yield. These accessions could be a good resource for future winged bean improvement programs. The result also confirmed that the MSI system is an invaluable tool for discriminating among accessions of the same crop species. The findings of this study provide insight into the genetic diversity of winged bean germplasm, which could contribute to improving its yield and quality in SSA and globally.

Phaseolus species are cultivated worldwide as a primary food source for human consumption. Common bean (Phaseolus vulgaris L.) and runner bean (Phaseolus coccineus L.) landraces are often cultivated together. The purpose was to document the traditional knowledge held by farmers regarding bean landraces, to describe the diversity through seed morphological descriptors (five quantitative and five qualitative traits), to understand the geographic distribution using Moran’s I statistic, and to analyze the diversity through the Shannon–Wiener Diversity–Equity (H′) index and the Shannon J’ homogeneity index at the community level to better understand the richness of this geographic area. A total of 361 common and runner bean accessions were collected in 10 Andean rural communities of Cotacachi, North Ecuador. We identified 47 landraces, predominantly kept by indigenous female farmers, with limited access to formal education, highlighting the role of this disadvantaged population in agrobiodiversity conservation. The cluster analysis revealed three groups (cophenetic correlation coefficient = 0.6). In the principal component (PC) analysis, 94% of the variation was explained by PC1 and PC2. A positive spatial autocorrelation (Moran’s I: 0.24; z-score: 2.20; p-value: 0.03) was identified, confirming a spatial structure. The Morochos community showed the highest diversity (H′ = 1.55). The information of the diversity and distribution of common and runner bean variability provided in this study is a contribution to further research focused on conservation.

Background: The influence of prenatal exposure to per- and poly- fluoroalkyl substances (PFAS) on birth size and offspring adiposity is unclear, especially for the newer, shorter-chained replacement PFAS. Methods: In the GUSTO multi-ethnic Singaporean mother-offspring cohort, 12 PFAS were measured in 783 cord plasma samples using ultra-performance-liquid chromatography-tandem-mass-spectrometer (UPLC-MS/MS). Outcomes included offspring anthropometry, other indicators of body composition/metabolic health, and MRI-derived abdominal adiposity (subset) at birth and 6 years of age. PFAS were modeled individually, in categories of long-chain and short-chain PFAS, and as scores of three principal components (PC) derived using PC analysis (PC1, PC2, and PC3 reflect predominant exposure patterns to “very-long-PFAS”, “long-PFAS”, and “short-PFAS”, respectively). Associations with outcomes were assessed using multivariable linear regressions, adjusted for important covariates such as maternal sociodemographic and lifestyle factors. Results: Overall, cord PFAS levels showed either no or positive associations (mostly for long-chain PFAS) with birth weight, length and head circumference. In general, PFAS were associated with higher neonatal abdominal adiposity, driven by shorter-chain PFAS. Perfluoroheptanoic acid (PFHpA) was associated with higher volumes of superficial subcutaneous adipose tissue (sSAT) (3.75 [1.13, 6.37] mL per SD increase in PFAS) and internal adipose tissue (IAT) (1.39 [0.41, 2.38] mL). Higher levels of perfluorobutanesulfonic acid (PFBS), short-chain PFAS, and PC3 were associated with higher IAT volume (β range 1.22–1.41 mL/SD, all P

Black cumin (Nigella sativa L.) seed oil has been used for its medicinal and aromatic values. Some studies revealed the presence of variability among N. sativa genotypes in seed oil content and yield. In Ethiopia, very few studies were conducted to investigate the variability of N. sativa genotypes by using biochemical traits. Thus, this study was conducted at Debre Zeit and Kulumsa Agricultural Research Centers’ experimental sites under field conditions during the 2021 cropping season to investigate the variability of Ethiopian N. sativa genotypes based on biochemical traits. Sixty-four genotypes were used and arranged in an 8×8 simple lattice design with two replications. Essential oils (EOs) and fixed oils were extracted by the respective methods of hydro distillation and solvent extraction. The univariate, bivariate, and multivariate analyses of the collected data were performed. Combined analysis of variance (ANOVA) revealed significant differences among genotypes in fixed oil yield per hectare (FOY), EO content (EOC), and EO yield per hectare (EOY). EOY had a significant positive correlation with FOY and EOC. It is expected to improve all biochemical traits by 17.39%–94.62% over the improved varieties by selection of the top 5% landraces. Therefore, genotypes 90504, 219970, and 013_ATH were the top 5% best performed landraces by FOY and EOY over the improved varieties. So, through selection, it would also be possible to improve the studied biochemical traits of the genotypes. The principal component (PC) analysis (PCA) of four biochemical traits showed 85.86% of the total variance captured by the first two PCs. EOY and FOY were the main contributor traits to the variation in the first PC, whereas FOC and EOC were the main contributor traits to the variation in the second PC. The genotypes were grouped into three different clusters based on four biochemical traits with significant intercluster distance. This showed that there was sufficient diversity among the genotypes which can be exploited for the future N. sativa improvement program in Ethiopia.

Turmeric is one of the potential spice crops having importance in culinary, colouring in textiles and therapeutic in pharmaceutical industries. The present investigation was carried out to estimate the genetic diversity of 21 turmeric genotypes representing different geographical locations of India. The principal component (PC) analysis indicated that the most of the variation among the genotypes was contributed by the first two principal components (61.38%), which were largely governed by plant height, number of leaves per plant, leaf lamina length, leaf area, total leaf area, collar girth and weight of the mother rhizomes per clump. These traits showed high positive correlation with first two PCs and influenced significantly for grouping. Based on PC correlation analysis, it is evident that morphological and yield attributing traits of PC1 and PC2 are influenced and contributed for most of the variation among the genotypes. The cluster analysis revealed that the 21 genotypes fall into five clusters, and among them most divergent with distinct genotypes were cluster I, III and cluster IV. However, IISR Pragati, Rajendra Sonali and NDH 8 were found superior for fresh rhizome yield and Acc. 849 was found unique with robust mother rhizome. The present study contributes to the knowledge of genetic diversity and defining strategies for yield improvement in turmeric.

ABSTRACTThe endophytic fungal community associated with leaves of Ficus carica L. (Moraceae) from Argentina was investigated. Fifteen fungal isolates were isolated and identified by molecular methods into the genera Alternaria, Cladosporium, Curvularia, Diaporthe, Epicoccum, Myrothecium, Neofusicoccum, Nigrospora, Preussia and Ustilago. Cladosporium cladosporioides and Curvularia lunata were the most frequently isolated species. The fungal metabolic profiles were obtained by automated TLC and NMR and analysed by PC Analysis. Antifungal and antibacterial activity was assessed by bioautographic assays. In addition, the biotransforming ability of the fungal isolates was tested on F. carica extracts. Five isolates (33.3%) exhibited inhibitory activity against at least one of the microorganisms tested. Most of the fungal endophytes were able to metabolise the flavonoid rutin 1, and the coumarin psoralen 3 present in F. carica extracts. Further investigations of the psoralen biotransforming ability performed by the selected endophyte Alternaria alternata F8 showed the accumulation of the 6,7-furan-hydrocoumaric acid derivative 4 as the main biotransformation product. Our results corroborate that F. carica can live symbiotically with rich and diverse endophytic communities adding insights about their ecological interactions.

Abstract Background Blueberries (Vaccinium section Cyanococcus) are an economically important fruit crop in the United States. Understanding genetic structure and relationships in blueberries is essential to advance the genetic improvement of horticulturally important traits. In the present study, we investigated the genomic and evolutionary relationships in 195 blueberry accessions from five species (comprising 33 V. corymbosum, 14 V. boreale, 81 V. darrowii, 29 V. myrsinites, and 38 V. tenellum) using single nucleotide polymorphisms (SNPs) mined from genotyping-by-sequencing (GBS) data. Results GBS generated ~ 751 million raw reads, of which 79.7% were mapped to the reference genome V. corymbosum cv. Draper v1.0. After filtering (read depth > 3, minor allele frequency > 0.05, and call rate > 0.9), 60,518 SNPs were identified and used in further analyses. The 195 blueberry accessions formed three major clusters on the principal component (PC) analysis plot, in which the first two PCs accounted for 29.2% of the total genetic variance. Nucleotide diversity (π) was highest for V. tenellum and V. boreale (0.023 each), and lowest for V. darrowii (0.012). Using TreeMix analysis, we identified four migration events and deciphered gene flow among the selected species. In addition, we detected a strong V. boreale lineage in cultivated blueberry species. Pairwise SweeD analysis identified a wide sweep (encompassing 32 genes) as a strong signature of domestication on the scaffold VaccDscaff 12. From this region, five genes encoded topoisomerases, six genes encoded CAP-gly domain linker (which regulates the dynamics of the microtubule cytoskeleton), and three genes coded for GSL8 (involved in the synthesis of the cell wall component callose). One of the genes, augustus_masked-VaccDscaff12-processed-gene-172.10, is a homolog of Arabidopsis AT2G25010 and encodes the protein MAINTENANCE OF MERISTEMS-like involved in root and shoot growth. Additional genomic stratification by admixture analysis identified genetic lineages and species boundaries in blueberry accessions. The results from this study indicate that V. boreale is a genetically distant outgroup, while V. darrowii, V. myrsinites, and V. tenellum are closely related. Conclusion Our study provides new insights into the evolution and genetic architecture of cultivated blueberries.

Knowledge about the extent of variability and the association among traits are of a high value for any breeding efforts. The objective of this investigation is to evaluate the agro-morphological traits in a set of durum wheat genotypes under supplemental irrigation and dry land conditions. Results showed that principal component (PC) analysis had grouped the measured traits into four main components that altogether accounted for 77% of the total variation under non-stressed condition and 87% under water-stressed condition. With regard to the first four PCs, peduncle length, agronomic score, grain yield, vigority, test weight, days to physiological maturity and thousand kernel weight have shown to be the most important variables affecting the performance of durum wheat under non-stressed condition. In the first four PCs at the water- stressed condition, agronomic score, grain yield, vigority, days to physiological maturity, test weight and peduncle length have been shown to be the important variables under water-stressed condition. The results of factor analysis relatively confirmed the results of PC analysis. Our findings indicated that a selection strategy should take into consideration of agronomic score and days to physiological maturity under non-stressed condition while plant height and spike length under water-stressed condition. Therefore, the above-mentioned traits could be used as indirect selection criteria for genetic improvement of grain yield in durum wheat, especially in early generations of breeding programmes

Background: The increasing level of waste generated and disposed of especially in urban centers arising from population explosion and rapid industrialization is a global concern, because it may lead to the pollution of potentially toxic element (PTE) in surrounding soils and groundwater. This research is aimed at the investigation of PTE contamination in soil around a municipal solid waste open dump facility and their human health risk in Ibadan, Southwestern Nigeria. Methods: Forty (40) soil samples were collected at 20 cm depth within the facility (WFC), close to the facility (CFC), and far from the facility (FFC). The soil samples were analysed for Cd, Co, Cu, Mn, Ni, Pb, and Zn using atomic absorption spectrometry technique. Results: On the basis of the mean concentration, PTEs decrease in the following order Zn > Mn > Pb > Cu > Co > Ni > Cd. The contamination statuses of Cd, Cu, Pb and Zn were high-very high at WFC, moderate-high at CFC, and low-moderate at FFC. The principal component (PC) analysis showed three loadings; PC1 composed of Cd, Cu, Pb, and Zn that were attributed to anthropogenic input from the materials dumped within facility while PC2 (Co and Ni) and PC3 (Mn) were sourced from the geogenic origin from weathering of rocks and soils. Only the ecological risk potential (Er) of Cd and Pb was high at WFC, considerable at CFC and low at FFC and affect the overall ecological risk (RI) of WFC and CFC. The hazard quotient and hazard index of non-carcinogenic risk were only > 1 for Cd especially for children at WFC and therefore, may pose health risk in the vicinity of the facility. The carcinogenic risk (CR) is within permissible limit. Conclusion: The anthropogenic activities in the municipal solid waste facility have impacted the soil of the area by some PTEs and posed potential human health risk to the people living close to the site. Therefore, constant monitoring of the area by regulatory bodies is recommended to reduce the impact of the PTEs on the soil.

Loss of biodiversity, which has an impact on both plant development and genetic advancement, disrupts the fundamental services that ecosystems provide to humanity. Variability assessment is a challenging topic. Multivariate statistics can be useful for comparing and evaluating genetic variability. The data of 16 different morpho-physiological variables were subjected to several multivariate approaches, including principal component (PC) and correlation coefficient analysis, in order to assess the diversity of the twenty Turkey berry genotypes. The correlation coefficients discovered through this analysis were used to gauge the strength of the association between the traits. In this study, Pearson correlation analysis revealed a significant correlation between the observed phenotypic traits. Among these traits, number of branches per plant , number of leaves, leaf area, number of flowers per cluster, number of flower cluster per plant, number of fruits per cluster, number of fruit cluster per plant, and fruit diameter exhibited a positive and significant correlation with fresh fruit yield per plant, whereas days of first flowering exhibited a negative and significant correlation with fresh fruit yield per plant. Principal component (PC) analysis revealed that the first three PCs had Eigen values greater than 1, accounting for 81% of the overall variation. For morphological features, PC 1 accounted for the largest variability of 57% of the overall variation, and the lowest contribution (10%) was recorded by PC3. The genotypes St007, St006, St008, St010, St011, St001, St009, St018, St020, St003, St004, and St005 were shown to be more varied and better performers in terms of fresh fruit yield and yield contributing features based on the primary factor scores. According to research findings, the genotypes of Turkey berries identified in this work would serve as useful genetic tools for boosting the productivity of the fruits for upcoming breeding endeavors, especially in light of the unpredictability of climate change.

Anthropogenic radiofrequency electromagnetic radiation (RF-EMR) from wireless technologies has increased dramatically. The boar semen used for artificial insemination is essential in sustaining the pig industry, and additionally it is also exposed to the effects of the RF-EMR of wireless technologies. Furthermore, there are no data on the effects of RF-EMR on semen quality, and this is the first analysis of sperm’s morphometric parameters for assessing the effect of RF-EMR on the spermatozoa subpopulations of boars. This study investigated the effect of RF-EMR on in vitro exposed breeding boar semen spermatozoa motility and the proportions of spermatozoa subpopulations according to their morphometric head and tail parameters. The semen samples of 12 boars were divided into control and experimental groups. The samples in the experimental group were exposed in a gigahertz transverse electromagnetic chamber at a frequency of 2500 MHz (the frequency band used in 5G technology) and an electric field strength of 10 Vm−1 for two hours. After exposure, the spermatozoa motility was evaluated for both groups. A morphometric analysis of the semen smears was performed using SFORM software (Version 1.0; VAMS, Zagreb, Croatia). The progressive spermatozoa motility was significantly reduced in the experimental group (74.7% vs. 85.7%). PC analysis and cluster analysis revealed two spermatozoa subpopulations: S1, spermatozoa with a more regular head shape and a smaller midpiece outline, and S2, spermatozoa with a more elongated head shape and a larger midpiece outline. The experimental semen samples had a greater proportion of the S1 spermatozoa subpopulation (68.2% vs. 64.4%). The effect of RF-EMR at 2500 MHz on the in vitro exposed boar semen resulted in decreased progressive spermatozoa motility and a lower proportion of the spermatozoa subpopulation with a higher fertilizing potential.

This study aimed to evaluate whether supplemental feeding at milking (SFAM) positively influences the quantitative−qualitative milk parameters due to improving some welfare assessment traits of multiparous Anatolian buffalo cows confined in semi-open free-stall barns. A total of 76 Anatolian buffalo cows at approximately 90 days in milk were selected to encompass four groups (OSF-2nd, NSF-2nd, OSF-≥3rd and NSF-≥3rd), considering offering (OSF) or not (NSF) supplemental feed at milking and the parity (2nd) and (≥3rd). Data of evaluated variables such as the following ones—(i) subjectively scored welfare assessment traits (temperament, udder hygiene and body condition), (ii) milk yield per milking (MYM), (iii) milk components, and (iv) milk physical traits—were analysed using a linear mixed model and principal component (PC) analysis. The OSF improved the temperament, udder hygiene and body condition scores compared to the NSF. The MYM, the fat content and the fat-to-protein ratio of the OSF were higher than those of the NSF, but milk mineral and electrical conductivity of the OSF were lower than those of the NSF. The parity of cows did not affect the evaluated variables. Four parameters (milk density value and lactose, solids-not-fat and protein contents) could be identified in the PC2 versus PC1 plot. In conclusion, the SFAM enhanced the milk yield and qualitative milk parameters due to improving the welfare status of indoor buffalo cows, regardless of parity.

ABSTRACT: Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Principal component (PC) analysis has received considerable attention as a technique for the extraction of meteorological signals from hyperspectral infra-red sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS). In addition to achieving substantial bit-volume reductions for dissemination purposes, the technique can also be used to generate reconstructed radiances in which random instrument noise has been reduced. Studies on PC analysis of hyperspectral infrared sounder data have been undertaken in the context of numerical weather prediction, instrument monitoring and geophysical variable retrieval, as well as data compression. This study examines the potential of PC analysis for chemistry applications. A major concern in the use of PC analysis for chemistry is that the spectral features associated with trace gases may not be well represented in the reconstructed spectra, either due to deficiencies in the training set or due to the limited number of PC scores used in the radiance reconstruction. In this paper we show examples of reconstructed IASI radiances for several trace gases: ammonia, sulphur dioxide, methane and carbon monoxide. It is shown that care must be taken in the selection of spectra for the initial training set: an iterative technique, in which outlier spectra are added to a base training set, gives the best results. For the four trace gases examined, key features of the chemical signatures are retained in the reconstructed radiances, whilst achieving a substantial reduction in instrument noise. A new regional re-transmission service for IASI is scheduled to start in 2010, as part of the EUMETSAT Advanced Retransmission Service (EARS). For this EARS-IASI service it is intended to include PC scores as part of the data stream. The paper describes the generation of the reference eigenvectors for this new service.