Variance Component Analysis Research Articles

Exploring online public survey lifestyle datasets with statistical analysis, machine learning and semantic ontology

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.

Genome-wide association study reveals genetic basis and candidate genes for chlorophyll content of leaves in maize (Zea mays L.).

The chlorophyll content (CC) directly affects photosynthesis, growth, and yield. However, the genetic basis of CC is still unclear in maize (Zea mays L.). Here, we conducted a genome-wide association study using mixed linear model for CC of the fifth leaves at seedling stage (CCFSS) and the ear leaves at filling stage (CCEFS) for 334 maize inbred lines. The heritability estimates for CCFSS and CCEFS, obtained via variance components analysis using the lme4 package in R, were 70.84% and 78.99%, respectively, indicating that the CC of leaves is primarily controlled by genetic factors. A total of 15 CC-related SNPs and 177 candidate genes were identified with a p-value < 4.49 × 10-5, which explained 4.98-7.59% of the phenotypic variation. Lines with more favorable gene variants showed higher CC. Meanwhile, Gene Ontology (GO) analysis implied that these candidate genes were probably related to chlorophyll biosynthesis. In addition, gene-based association analyses revealed that six variants in GRMZM2G037152, GRMZM5G816561, GRMZM2G324462, and GRMZM2G064657 genes were significantly (p-value < 0.01) correlated with CC, of which GRMZM2G064657 (encodes a phosphate transporter protein) and GRMZM5G816561 (encodes a cytochrome P450 protein) were specifically highly expressed in leaves tissues. Interestingly, these candidate genes were previously reported to involve in the regulation of the contents of chlorophyll in plants or Chlamydomonas. These results may contribute to the understanding of genetic basis and molecular mechanisms of maize CC and the selection of maize varieties with improved CC.

Analytic racecraft: Race-based averages create illusory group differences in perceptions of racism.

Research practices used by social scientists to understand and dismantle the psychological foundations that uphold racist hierarchies can backfire when they rely on racecraft. Racecraft ideology assumes the reality of race(s), an assumption that shapes study designs and inferences to the detriment of theoretical and practical goals. I showcase how racecraft manifests in studies seeking to quantify how perceptions of sociopolitical stimuli differ across racialized perceivers (e.g., black, white, latinx). The typical analysis for quantifying perceptions focuses on comparing group averages, which assumes the existence of discrete "races" whose perceptions can be sufficiently summarized by averages. Across three studies, I used variance component analyses on racism ratings of anti-immigrant tweets from differently racialized perceivers (N = 1,211) to show there was much larger disagreement than agreement within race categories, even when there were average differences in perceptions across race categories. This analysis shows how analytic practices can bolster different assumptions about the nature of race, some of which reify the illusion that race categories are stable cohesive groups. Researchers can improve their analytic inferences and avoid producing race-reifying caricatures of peoples' perceptions by adding variance mapping to their toolkits and attending to racialization as a dynamic process-needed improvements within the psychological study of race and racism, group-based beliefs, and antiracist research endeavors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PRODUCCIÓN Y RENTABILIDAD DE MAÍCES NATIVOS AZULES BAJO DIFERENTES DENSIDADES DE POBLACIÓN Y RÉGIMENES HÍDRICOS

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; It is necessary to increase maize production due to the increase in global food demand. Producers in Mexico prefer to grow native varieties of maize, which have a wide genetic diversity that has not been fully studied. These native maize are being lost due in part to their low yields, as well as their low or no profitability. Despite this, in this germplasm it is possible the presence of genotypes that show high grain production and economic gains. Increasing plant density can be a viable strategy that contributes to improving the production systems of these native maize. &lt;strong&gt;Objective&lt;/strong&gt;: To analyze the agronomic behavior and profitability of blue native maize genotypes in two population densities and two water regimes. &lt;strong&gt;Methodology&lt;/strong&gt;: The study was carried out in the spring summer 2022 cycle in Nuevo León, Mexico (24°19'11.4"N, 99°56'34.8"W, 1980 m.a.s.l.). The treatments were the combination of four genotypes (Mimbres, Ascensión, Siberia and a Hybrid as a control), two water regimes (irrigated and rainfed) and two population densities (62,500 and 83,333 plants ha&lt;sup&gt;-1&lt;/sup&gt;), which were distributed in a random complete block design with split-split plot design. Analysis of variance, Pearson correlation and principal components were carried out to understand the behavior and relationship between variables. &lt;strong&gt;Results&lt;/strong&gt;: Grain yield was 38% higher in irrigation compared to rainfed. Increasing the density to 83,333 plants ha&lt;sup&gt;-1&lt;/sup&gt; also contributed to increasing grain yield and economic profitability, only in native maize. The number of grains per m&lt;sup&gt;2&lt;/sup&gt; and grain weight per ear were the components that were mostly associated with the benefit-cost ratio and grain yield. &lt;strong&gt;Implications&lt;/strong&gt;: The native maize studied are important genetic resources with a favorable response to high plant density, therefore, increasing the density to 83,333 plants ha&lt;sup&gt;-1&lt;/sup&gt; is a strategy that could be explored to increase grain production and profitability of native maize, in addition, this proposal could be more easily adopted by producers, since it would not significantly affect their production cost. &lt;strong&gt;Conclusions&lt;/strong&gt;: It was feasible to cultivate native maize at a density of 83,333 plants ha&lt;sup&gt;-1&lt;/sup&gt; both under irrigation and rainfed conditions, since only with this density and such maize genotypes was it possible to obtain the highest grain yield and economic gains.&lt;/p&gt;

Do leaf traits shape herbivory in tropical montane rainforests? A multispecies approach

AbstractThe co‐evolutionary arms race between herbivores and plants forces plants to evolve protection strategies that reduce the palatability of the plant modules attacked by the herbivores. These characteristics of traits have consequences for both the survival of plant individuals and the composition of plant communities. Thus, correlating traits of for instance leaves with herbivory is an important step toward understanding the dynamics of plant populations and communities. Traits can either be measured using conventional lab methods or recently developed spectral sensing techniques. We examined whether leaf traits of trees are related to herbivory in a multispecies approach. Furthermore, we explored whether leaf traits characterized by spectral sensing provide similar relations to herbivory as lab‐based leaf traits. We established nine 1‐ha square plots evenly distributed over three different forest types in Ecuadorian tropical montane rainforests where we estimated herbivory as the leaf area loss (in square centimeters) of 20 (±5) leaves sampled from the canopies of 380 tree individuals belonging to 51 tree species (7 ± 1 individuals/species) using lab‐ and spectral‐sensing‐based methods. For each methodological approach, we ran 100 linear mixed‐effects models with all respective leaf traits as predictor and herbivory as response variables for data subsets containing one randomly selected tree individual of each species to estimate the range of the regression coefficients for each trait. Automated stepwise backward selections determined the frequency of each trait having an important influence on herbivory. We found no clear relations between leaf traits and herbivory for neither lab‐ nor spectral‐sensing‐based traits. A nested variance component analysis demonstrated that the observed variability was mainly due to the variation in trait concentrations between tree individuals of a species. Our results suggest that snapshot data lead to a mismatch between herbivory and the concentrations of traits during the peak of herbivory. Another explanation could be that environmental conditions or processes along the food web are more important in structuring herbivory than leaf traits.

Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Rice is an important food crop with a crucial role in global food security. Study on correlation between morphoagronomic traits and yield is an important step in the development of superior rice varieties. Research data were collected from 20 rice varieties grown in the experimental field. A total of 10 morphoagronomic trait variables were measured, including plant height, number of productive tillers, flowering age, dry weight of biomass, hollow grain, 1,000-grain weight, grain weight, panicle number, panicle length, and harvest index. Data were analyzed using variance, correlation, and principal component analyses. The results of PCA showed that 3 principal components (PC) explained 80.88% of the data variation. PC1 was associated with plant height, flowering age, biomass dry weight, empty grain, panicle length, grain weight per clump. PC2 was associated with 1,000-grain weight and harvest index, while PC3 was associated with number of productive tillers and number of panicles. Correlation analysis showed that 1,000-grain weight was positively correlated with harvest index, while biomass dry weight was negatively correlated. These results suggest that all observed morphoagronomic traits except biomass dry weight have an important role in increasing rice yield. Furthermore, the results of this study provide basic informations for the development of high-yielding rice varieties.

121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.

Use of Phenomics in the Selection of UAV-Based Vegetation Indices and Prediction of Agronomic Traits in Soybean Subjected to Flooding

Flooding is a frequent environmental stress that reduces soybean growth and grain yield in many producing areas in the world, such as the United States, Southeast Asia, and Southern Brazil. In these regions, soybean is frequently cultivated in lowland areas in crop rotation with rice, which provides numerous technical, economic, and environmental benefits. In this context, the identification of the most important spectral variables for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics, with faster and more reliable results enabled using multispectral sensors mounted on unmanned aerial vehicles (UAVs). Accordingly, this research aimed to identify the optimal UAV-based multispectral vegetation indices for characterizing the response of soybean genotypes subjected to flooding and to test the best linear model fit in predicting tolerance scores, relative maturity group, biomass, and grain yield based on phenomics analysis. Forty-eight soybean cultivars were sown in two environments (flooded and non-flooded). Ground evaluations and UAV-image acquisition were conducted at 13, 38, and 69 days after flooding and at grain harvest, corresponding to the phenological stages V8, R1, R3, and R8, respectively. Data were subjected to variance component analysis and genetic parameters were estimated, with stepwise regression applied for each agronomic variable of interest. Our results showed that vegetation indices behave differently in their suitability for more tolerant genotype selection. Using this approach, phenomics analysis efficiently identified indices with high heritability, accuracy, and genetic variation (&gt;80%), as observed for MSAVI, NDVI, OSAVI, SAVI, VEG, MGRVI, EVI2, NDRE, GRVI, BNDVI, and RGB index. Additionally, variables predicted based on estimated genetic data via phenomics had determination coefficients above 0.90, enabling the reduction in the number of important variables within the linear model.

Extent of Equivalence of Results for Urine Albumin among 3 Candidate Mass Spectrometry Reference Measurement Procedures.

Urine albumin (UA) is an important biomarker of chronic kidney disease. Current in vitro diagnostic medical devices (IVD-MDs) for measuring UA are not standardized, and median results among IVD-MDs differ by approximately 45%. Since fixed decision values are used to interpret UA, higher-order reference measurement procedures (RMPs) are needed for metrological traceability. Three candidate liquid chromatography-tandem mass spectrometry RMPs have been developed for UA. Eight single-donation human urine samples were measured by 3 candidate RMPs. Results were compared using t-test and variance component analysis. The mean results for each urine sample from each RMP laboratory were not statistically different from the overall mean value by t-test. The median total CV including contributions from bias and imprecision among the 3 RMP laboratories was 6.23% using variance component analysis for each sample. The allowable bias to the RMP for an end-user IVD-MD was ≦9.0% or ≦3.0% based on the desirable or optimal total allowable error of 30% or 24%, respectively. A maximum allowable standard uncertainty for an RMP result was determined to be 4.3% or 3.3% for desirable or optimal performance, respectively. The standard uncertainties for all of the RMP laboratories meet the desirable and optimal standard uncertainty specifications. The candidate RMPs for UA in these 3 laboratories have suitable agreement of results and uncertainties for use as higher-order RMPs in the metrological traceability of end-user IVD-MDs for measuring UA.

Diversity analysis of panicle traits in Chinese prickly ash germplasm resources and their influence on its systematic classification

This study aimed to reveal the diversity and variation in panicle traits of the Chinese prickly ash and clarify their influence on the its systematic classification to provide a theoretical basis and technical support for the efficient utilization of Chinese prickly ash germplasm resources and breeding. Sixteen panicle traits were identified from 35 Chinese prickly ash germplasm resources from 2021 to 2022. The diversity of these panicle traits and their role in the plant’s systematic classification were studied using variance, correlation, cluster, and principal component analyses. Cluster analysis showed that the 35 Chinese prickly ash germplasm resources could be divided into two groups with Euclidean distances of 25. Further analysis showed that yield traits such as panicle length, panicle width, primary branching, grain number per panicle, and grain weight per panicle were significantly positively correlated with grain chlorophyll content, while grain anthocyanin content was negatively correlated with both panicle (panicle length, panicle width, panicle length to width ratio, primary branching, grain number per panicle, and grain weight per panicle) and grain characteristics (single grain weight, thousand-grain weight, grain length, grain width and fruit shape index). In conclusion, Chinese prickly ash germplasms have diverse panicle traits. Z. armatum has dark green grains, long and wide panicles, a long conical shape, many primary branches, high grain weight, and high grain number per panicle. In contrast, Z. bungeanum has bright red seeds, a panicle width larger than its length, short and conical panicles, a small number of primary branches, and low grain weight per panicle and number of grains per panicle. Overall, Z. armatum had a significant yield advantage over Z. bungeanum.

Calculation of reference intervals for the concentrations of α-tocopherol and retinol in serum using indirect data-mining procedures

BackgroundEstablishing adequate reference intervals (RIs) for vitamins A and E is essential for diagnosing and preventing deficiencies. Due to the current boom in data mining and its easy applicability, more laboratories are establishing RIs using indirect methods. Our study aims to obtain RIs using four indirect data-mining procedures (Bhattacharya, Hoffmann, Kosmic, and RefineR) for vitamins A and E. Material and methods8943 individuals were collected to establish the RIs. After using different data cleaning steps and checking whether these data should be divided according to age and gender based on multiple linear regression and variance component analyses, indirect RIs were calculated using specific Excel spreadsheets or R-packages software. ResultsA total of 2004 records were eligible. For vitamin A, the RIs obtained were (1.11 − 2.68) µmol/L, (1.13 − 2.70) µmol/L, (1.13 − 2.71) µmol/L, and (1.17 − 2.66) µmol/L using the Bhattacharya, Hoffmann, Kosmic and RefineR approaches, respectively. For vitamin E, these intervals were (17.3 − 49.9) µmol/L (Bhattacharya), (17.3 − 48.9) µmol/L (Hoffmann), (19.6 − 50.3) µmol/L (Kosmic), and (19.4 − 50.9) µmol/L (RefineR). In all cases, the RIs were comparable. ConclusionsSuitable RIs for vitamins A and E were calculated using four indirect methods that are suitable and adapted to our population’s demographic characteristics.

Absence of association between maternal adverse events and long-term gut microbiome outcomes in the Australian autism biobank

IntroductionMaternal immune activation (MIA) and prenatal maternal stress (MatS) are well-studied risk factors for psychiatric conditions such as autism and schizophrenia. Animal studies have proposed the gut microbiome as a mechanism underlying this association and have found that risk factor-related gut microbiome alterations persist in the adult offspring. In this cross-sectional study, we assessed whether maternal immune activation and prenatal maternal stress were associated with long-term gut microbiome alterations in children using shotgun metagenomics. MethodsThis cross-sectional study included children diagnosed with autism (N = 92), siblings without a diagnosis (N = 42), and unrelated children (N = 40) without a diagnosis who were recruited into the Australian Autism Biobank and provided a faecal sample. MIA exposure was inferred from self-reported data and included asthma/allergies, complications during pregnancy triggering an immune response, auto-immune conditions, and acute inflammation. Maternal stress included any of up to 9 stressful life events during pregnancy, such as divorce, job loss, and money problems. Data were analysed for a total of 174 children, of whom 63 (36%) were born to mothers with MIA and 84 (48%) were born to mothers who experienced maternal stress during pregnancy (where 33 [19%] experienced both). Gut microbiome data was assessed using shotgun metagenomic sequencing of the children's faecal samples. ResultsIn our cohort, MIA, but not MatS, was associated with ASD. Variance component analysis revealed no associations between any of the gut microbiome datasets and neither MIA nor MatS. After adjusting for age, sex, diet and autism diagnosis, there was no significant difference between groups for bacterial richness, α-diversity or β-diversity. We found no significant differences in species abundance in the main analyses. However, when stratifying the cohort by age, we found that Faecalibacterium prausnitzii E was significantly decreased in MIA children aged 11–17. DiscussionConsistent with previous findings, we found that children who were born to mothers with MIA were more likely to be diagnosed with autism. Unlike within animal studies, we found negligible microbiome differences associated with MIA and maternal stress. Given the current interest in the microbiome-gut-brain axis, researchers should exercise caution in translating microbiome findings from animal models to human contexts and the clinical setting.

Psychometric evaluation of the Chinese version of the febrile convulsion knowledge scale for parents/caregivers: translation and validation study

BackgroundFever is one of the most common clinical symptoms of respiratory diseases in children. Once the child has a fever, parents and caregivers are mainly concerned that the child may have a febrile convulsion. A lack of cognitive ability not only leads to anxiety but also aggravates or delays the time of children’s medical treatment and even seriously affects the prognosis because of improper management of fever patients.Therefore, it is necessary to clarify the degree of mastery of knowledge related to febrile convulsions, implement targeted guidance and health education, and ensure that parents and caregivers receive correct and reasonable first aid treatment. The purpose of this study was to translate the Febrile Convulsion Knowledge Scale for Parents/Caregivers into Chinese and to verify its reliability and validity for Chinese parents and caregivers of children.MethodsThe Brislin traditional translation model was used to translate the Febrile Convulsion Knowledge Scale for Parents/Caregivers from English to Chinese, following authorization from the original author of the scale. This involved literal translation, back translation, and cultural adaptation. A convenience sampling method was used to select 402 parents and caregivers of children in the pediatric ward and pediatric infusion clinic of a Grade III hospital in Liaoning Province. The item analysis method was employed to assess item differentiation, while the Delphi method was used to analyze content validity. Scale reliability was evaluated through the calculation of internal consistency and test-retest reliability. Exploratory and confirmatory factor analyses were conducted to explore and verify the underlying factor structure and scale validity.ResultsThe Chinese version of the Febrile Convulsion Knowledge Scale for Parents/Caregivers consists of 3 dimensions and 8 items. The Cronbach’s alpha coefficient was 0.828, with each dimension having coefficients of 0.806, 0.720, and 0.702. The split-half reliability and test-retest reliability were 0.716 and 0.790, respectively. The Chinese version has good reliability. Exploratory factor analysis revealed that the Bartlett sphericity test was 394.52 (p < 0.001) and that the KMO value was 0.802 > 0.600, indicating suitability for factor analysis. Principal component analysis and orthogonal rotation of maximum variance were performed on the data, and items with a load greater than 0.40 within a single factor were selected for inclusion. The resulting three-factor structure explained 70.78% of the total variance. All model fitting indices were within the acceptable range, indicating the good structural validity of the Chinese version. The results of both exploratory and confirmatory factor analyses support this conclusion.ConclusionsThe Chinese version of the Febrile Convulsion Knowledge Scale for Parents/Caregivers has good reliability and validity. It can be used as a tool for clinical pediatric nurses to evaluate the knowledge of parents and caregivers of children with febrile convulsion and provide the basis for the design and implementation of targeted training plans according to the results obtained from the Chinese scale.

Mycorrhizal‐herbivore interactions and the competitive release of subdominant tallgrass prairie species

Abstract Plant‐microbial‐herbivore interactions play a crucial role in the structuring and maintenance of plant communities and biodiversity, yet these relationships are complex. In grassland ecosystems, herbivores have the potential to greatly influence the survival, growth and reproduction of plants. However, few studies examine interactions of above‐ and below‐ground grazing and arbuscular mycorrhizal (AM) mycorrhizal symbiosis on plant community structure. We established experimental mesocosms containing an assemblage of eight tallgrass prairie grass and forb species in native prairie soil, maintained under mycorrhizal and nonmycorrhizal conditions, with and without native herbivorous soil nematodes, and with and without grasshopper herbivory. Using factorial analysis of variance and principal component analysis, we examined: (a) the independent and interacting effects of above‐ and below‐ground herbivores on AM symbiosis in tallgrass prairie mesocosms, (b) independent and interacting effects of above‐ and below‐ground herbivores and mycorrhizal fungi on plant community structure and (c) potential influences of mycorrhizal responsiveness of host plants on herbivory tolerance and concomitant shifts in plant community composition. Treatment effects were characterized by interactions between AM fungi and both above‐ground and below‐ground herbivores, while herbivore effects were additive. The dominance of mycorrhizal‐dependent C4 grasses in the presence of AM symbiosis was increased (p &lt; 0.0001) by grasshopper herbivory but reduced (p &lt; 0.0001) by nematode herbivory. Cool‐season C3 grasses exhibited a competitive release in the absence of AM symbiosis but this effect was largely reversed in the presence of grasshopper herbivory. Forbs showed species‐specific responses to both AM fungal inoculation and the addition of herbivores. Biomass of the grazing‐avoidant, facultatively mycotrophic forb Brickellia eupatorioides increased (p &lt; 0.0001) in the absence of AM symbiosis and with grasshopper herbivory, while AM‐related increases in the above‐ground biomass of mycorrhizal‐dependent forbs Rudbeckia hirta and Salvia azurea were eradicated (p &lt; 0.0001) by grasshopper herbivory. In contrast, nematode herbivory enhanced (p = 0.001) the contribution of Salvia azurea to total biomass. Synthesis. Our research indicates that arbuscular mycorrhizal symbiosis is the key driver of dominance of C4 grasses in the tallgrass prairie, with foliar and root herbivory being two mechanisms for maintenance of plant diversity.

The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers

BackgroundThis study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models.Materials and methods135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification.ResultsThe principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92.ConclusionsThe Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model’s classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat’s impact on radiomic features in medical imaging.

Untargeted metabolomics of blood plasma samples of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths in the world. HCC is often diagnosed late because patients with early-stage cancer have no apparent symptoms. Therefore, it is desirable to find a reliable method for an early diagnosis based on the detection of metabolites – biomarkers, that can be detected in the early stages of the disease. Untargeted metabolomics is often used as a tool to find a suitable biomarker for several diseases. In this work, untargeted metabolomics was performed on blood plasma samples of HCC patients and compared with healthy individuals and patients with liver cirrhosis. A combination of liquid chromatography and high-resolution mass spectrometry was used as an analytical method. More than a thousand peaks were detected in the blood plasma samples, from which mainly amino acids, carboxylic acids, lipids, and their derivatives were evaluated as potential biomarkers. The data obtained were statistically processed using the analysis of variance, correlation analysis, and principal component analysis.

Nutritional quality of kabuli and desi chickpea in Argentina: Effects of environment

AbstractThe environment to which a crop is exposed during the growing season has a significant impact on seed composition. The objectives of this study were to (1) estimate the contribution of genotype (G), environment (E), and their interaction (G × E) to the agronomic features and seed quality of chickpea and (2) identify climatic variables that significantly affect chickpea nutritional composition. A total of 10 pre‐commercial and commercial cultivars of Cicer arietinum L. were evaluated in 15 environments in Argentina. Average plant height ranged between 44.7 and 55.2 cm, 100‐seed weight was 38 and 27 g, and grain yield was 750 and 1500 kg ha−1 for kabuli and desi chickpea, respectively. The results obtained from the variance component analysis showed statistically significant (p &lt; 0.05) contributions of the effects of G, E, and G × E interaction on the nutritional quality of chickpea seeds. Protein, carbohydrates, and mineral content were mostly affected by E, whereas oil content was G‐dependent. Only tocopherols were affected by G × E interaction. The total phenotypic variance is mostly composed of environmental effects captured during the seed filling period. High mean daily air temperature had a negative effect on carbohydrates and increased protein and mineral content. The fatty acid profile and gamma tocopherol contents were affected by accumulated precipitation and evapotranspiration. Air humidity was negatively correlated with protein content and iodine value. Results from this research are useful for breeders to broaden the genetic background of chickpea genotypes and for farmers to identify climatic conditions that impact grain quality.

Influence of regional and yearly weather patterns on multi-mycotoxin occurrence in Austrian wheat: a liquid chromatographic-tandem mass spectrometric and multivariate statistics approach.

Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi-toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute-and-shoot LC-MS/MS-based multi-analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi-mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. The results presented in this study underline the added value of combining targeted LC-MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Connecting Water Quality and Ecosystem Services for Valuation and Assessment of a Groundwater Reserve Area in South-East Mexico

Even though the role of ecosystem services is known, the identification and assessment of water-related services is usually absent or often less represented as an ecosystem service. Progress in water quality indicator definition and compliance with regulations has been made; however, the relationship between water quality degradation and benefits to individuals and ecosystems remains little recognized. Here, we present an assessment of water quality and identification of ecosystem services in south-east Mexico. This study was performed within the geohydrological reserve zone of the Ring of Sinkholes, Yucatán Peninsula. Thirteen ecosystem services provided by the aquifer were identified. Water quality was evaluated in sinkholes based on national and international norms, considering different sinkhole uses. Results show a dynamic system, without saltwater intrusion and good to excellent water quality. The research demonstrates the relationship between ecosystem services and water quality, showing pressure in services related to uses for aquatic life protection and to a lesser extent those related to consumption. Current productive activities showed no pressure at this time. Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) exhibited a significant difference in parameters and campaigns, but not between sinkholes. A long-lasting monitoring program for water quality is necessary to accurately evaluate the status of ecosystem services provided by the aquifer. Moreover, it is necessary to assess aquifers as ecosystems with economic, ecologic and socio-cultural importance. Effective water governance requires a balance of interests between all parties, within a legal and institutional framework.

Improving the Yield and Quality of Tomato by Using Organic Fertilizer and Silicon Compared to Reducing Chemical Nitrogen Fertilization

Essential macronutrient nitrogen (N) is crucial for plant growth and yield, but excessive chemical N fertilizer not only increases unnecessary production costs but also causes environmental pollution. Therefore, reducing N fertilizer use by increasing organic fertilizer use is crucial for sustainable agriculture. In this study, we investigated the effects of three nitrogen levels—the recommended rate (N), a 20.0% reduced rate (0.8N), and a 40.0% reduced rate (0.6N)—and two levels of organic fertilizer—a normal dose (M) and a four-times the normal dose (4M)—combined with root application of the beneficial element silicon (Si) on the photosynthetic characteristics, yield, and fruit quality of the tomato cultivar ‘Tianxi No. 5’. Compared with M + N treatment, the longitudinal diameter, transverse diameter, fruit weight, and fruit yield of tomato fruit in 4M + 0.6N treatment significantly increased by 12.4%, 14.6%, 14.5%, and 12.8%, respectively, while the yield was further improved with Si application. In addition, a reduction in N fertilizer and an increase in organic fertilizer, combined with Si application, improved fruit quality parameters such as concentrations of vitamin C, lycopene, phenols, flavonoids, sucrose, fructose, etc., and promoted sugar metabolism-related enzyme activity (sucrose synthase, invertase, and sucrose phosphate synthase) and the accumulation of N in the fruit. The principal component analysis and three-factor analysis of variance (ANOVA) of the fruit quality and yield indices showed that nitrogen fertilizer, organic fertilizer, silicon fertilizer, and the interaction of the three had significant effects on the quality and yield of tomato fruits, and that the 4M + 0.6N + Si treatment had the best combined effect on the yield and quality of the tomatoes. Thus, a moderate reduction in chemical N fertilizer, combined with increased organic fertilizer and Si, could be an effective agronomic practice for improving the yield and quality of tomatoes.