Principal Component Analysis Research Articles

Epigenetic signatures of asthma: a comprehensive study of DNA methylation and clinical markers

BackgroundAsthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma.MethodsThe Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood from 319 participants from 94 families. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 individuals. Principal component analysis on the clinical asthma markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates.Results221 unique CpGs reached genome-wide significance at timepoint 1 after Bonferroni correction. PC7, which correlated with loadings of eosinophil counts and immunoglobulin levels, accounted for the majority of associations (204). Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points yielded a correlation of 0.81.ConclusionWe identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to a robust DNA methylation profile as a new, stable biomarker for asthma.

Cyanogel-Transformed Porous Palladium and Iron Framework Intermixed with rGO for Wearable Hydrogen Sensing.

Wearable hydrogen (H2) sensing is necessary to monitor the H2 leakage in its transportation and storage, of which ppm-concentration detection limit and fast response at room temperature are highly desired. Here, a wearable H2 sensing working at room temperature is developed with palladium and iron framework intermixed with reduced graphene oxide (rGO//Pd-Fe FW), which is synthesized by combined Pd-Fe cyanogel immobilized with graphene oxide as precursor and in situ reduction. As-prepared rGO//Pd-Fe FW is observed with porous FW structure composed of interconnected Pd-Fe nanoparticles, in which rGO is evenly intermixed. Beneficially, rGO//Pd-Fe FW exhibits 2ppm low detection limit and 2 s fast response (1 v/v% H2) at room temperature. Such excellent H2 sensing performance may be attributed to the synergistic effect of the optimized Pd-Fe FW's catalytic activity, boosted electron transfers between Pd hydride and rGO, and enriched adsorption sites over porous FW's surface. Practically, the perceptron learning algorithm combined with principal component analysis is conducted to identify the H2 leakage, and the wearable H2 sensing devices are built by integrating rGO//Pd-Fe FW over the paper and flexible printed circuit board with reliable sensing responses.

The effect of the menstrual cycle phases on back squat performance, jumping ability and psychological state in women according to their level of performance -a randomized three-arm crossover study

ObjectiveThe influence of the menstrual cycle on practical power performance such as barbell back squats and jumping performance in women has not yet been fully investigated. In addition, the performance level of athletes has not been considered in previous studies. This study aimed to investigate the influence of different cycle phases on acute back squat performance, jumping ability and psychological state concerning the performance level.Methods24 female strength athletes (age: 25.2 ± 3.3 years; height: 169.5 ± 3.4 cm; body weight: 67.7 ± 7.3 kg) were recruited for the study. Level of performance was classified according to Santos et al. (intermittent (n = 13), advanced (n = 6), highly advanced (n = 5)). Participants were tested for 1RM barbell back squat and jumping performance (countermovement and squat jump) as well as two questionnaires assessing their psychological states in the menses (M), late follicular phase (FP) and mid-luteal phase (LP) in three MC. Saliva estradiol and progesterone concentrations with a menstrual cycle diary were used to confirm a normal MC. A principal components analysis for power performance, well-being, relaxation and alertness was carried out and a linear mixed model was used for statistical evaluation.ResultsNo significant differences were found between the MC phases in performance scores (p > 0.05), readiness (p > 0.05) and alertness (p > 0.05). However, a high correlation between MC phases, performance level and back squat performance was detected. Correlation analyses indicate that a higher performance level results in a higher variation depending on the MC of the squat performance. For well-being, a significantly lower score was detected in M than in FP and LP.ConclusionIn general the performance score of the lower body is not influenced by the MC. If strength performance and jumping ability are considered separately, there are indications that strength capability is influenced at a higher performance level. In addition, individual variance was also observed, so this should also be considered. However, further studies are needed to confirm this assumption due to the small sample sizes of the individual performance levels.Trial registrationGerman registry for clinical studies (DRKS00034816, Date: 08/01/2024).

Access to radiotherapy in improving gastric cancer care quality and equality

BackgroundQuality health services could improve patient outcomes and prognosis. Gastric cancer care was of great disparity across genders. Disparities within radiotherapy units could impact gastric cancer care, potentially exacerbating gender-based inequalities.MethodsWe retrieved the disease burden data from Global Burden of Disease 2019. A quality of care index was constructed by applying principal component analysis techniques. The disparity of gastric cancer care across genders was described, and the association of access to radiotherapy with gastric cancer care as well as gender disparity was explored.ResultsMales receive better quality of gastric cancer care than females, and this gender disparity is widening in middle-low socio-development regions. A positive correlation emerges between the density of radiotherapy facilities and an elevated quality of care, and reduced gender-based disparities.ConclusionsThe association between robust radiotherapy access, improved gastric cancer QCI, and reduced gender-based disparities spotlights the imperative of fortifying radiotherapy infrastructure within areas and populations in greatest need.

Multivariate statistical and hydrogeochemical analysis of seasonal groundwater quality variations in coastal villages of Trivandrum district, south India

This study attempts a detailed assessment of the quality of groundwater in the coastal region of Trivandrum District, Kerala where groundwater is the main source of drinking water. Forty groundwater samples were collected during the pre-monsoon and post-monsoon periods. The collected samples were analyzed for physical properties such as electrical conductivity (EC), pH, total dissolved solids (TDS), and total hardness, along with chemical properties, including major cations (Ca2⁺, Mg2⁺, Na⁺, K⁺) and anions (Cl⁻, SO₄2⁻, HCO₃⁻, NO₃⁻). The analysis of groundwater quality reveals significant spatial and seasonal variations caused by both natural and manmade influences. Water Quality Index (WQI), hydrogeochemical plots, and Principal Component Analysis (PCA) were used to analyses the data. The results show that Vakkom, Kazhakottam, Veli-Attipara, and Pozhiyoor show significant deterioration, and areas such as Varkala, Ayroor, and Edava generally maintain good water quality. The Water Quality Index (WQI) assessment indicates that approximately 22.5% of the studied area falls under excellent quality, while 17.5% is classified as poor. The WHO standard and BSI standards were used to derive the WQI. Principal Component Analysis (PCA) identified electrical conductivity, total dissolved solids, and total hardness as the primary factors affecting groundwater quality, explaining 65.17% and 61.03% of the total variance in the pre-monsoon and post-monsoon periods, respectively. Hydrochemical plots collaborate these results, emphasize the influence of rock-water interactions as the main geochemical process, further compounded by pollution from agricultural runoff and urban development. These findings highlight the need for sustainable groundwater management strategies in coastal communities. Effective measures, including pollution mitigation, sustainable agricultural practice, proper waste management, and preservation of freshwater ecosystems, are essential for ensuring the sustainability of groundwater resources.

Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves

The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm

Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-value product in the biorefinery field. As the traditional wet chemical methods for detecting lignin content are labor-intensive, time-consuming and environment-toxic, it is an urgent need to develop high-throughput and environment-friendly techniques for large-scale crop germplasms screening. In this study, we conducted a Fourier transform infrared (FTIR) assay on 150 maize germplasms with a diverse lignin composition to build predictive models for lignin content in maize stalk. Principal component analysis (PCA) was applied to the FTIR spectra for use as model inputs. Classification and advanced gradient boosting machine (GBM) algorithms demonstrated higher predictive accuracy (0.82–0.96) compared to traditional linear and regularization algorithms (0.03–0.04) in the training set. Notably, two optimal models, built using the extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) algorithms, achieved R2 values of over 0.91 in the training set and over 0.82 in the test set. Overall, the combination of FTIR and machine learning (ML) algorithms offers a high-throughput and efficient method for predicting lignin content. This approach holds significant potential for genetic breeding and the effective utilization of maize in industrial production.

Jump for Joy

Principal Component Analysis is used to construct �microstructure modes� that describe the most common flow/return patterns and allow one to separate them into bid-ask symmetric and bid-ask anti-symmetric.

Using metabolomics to explore the effects of epigenetic-modification strategies on the metabolites of Acanthus ilicifolius L. endophytic fungi against ovarian cancer

Ovarian cancer is a serious threat to women's health and safety. So far, people have discovered more than 130 small molecule compounds of natural origin for anti-tumor, of which approximately 50% are of microbial origin. The Acanthus ilicifolius L. species is primarily distributed in the Guangdong, Hainan, and Guangxi regions of China and grows in tidally accessible coastal areas. Recent studies have revealed that Acanthus ilicifolius L. extracts are endowed with a range of pharmacological properties, including anti-inflammatory, hepatoprotective, antioxidant, and antitumor activities. Endophytic fungi are commonly found in the healthy tissue and organs of medicinal plants. These fungi and the plants they inhabit form mutually beneficial symbiotic relationships. Endophytic fungi produce a series of secondary metabolites, with active substances having shown great economic value and applications prospects in drug research and development as well as for the biological control of plant diseases. Secondary metabolites production by endophytic fungi is regulated by specific gene clusters, and several techniques have been used to stimulate the secondary metabolic processes of fungi, including epigenetic-modification and OSMAC (one strain many compounds) strategies, co-culturing, and gene modification. Among these, epigenetic modification has been shown to be effective; this strategy involves the addition of small-molecule epigenetic modifiers to the culture medium, thereby activating silenced biosynthetic gene clusters without altering the DNA sequences of the fungi. This approach facilitates the expression of silenced genes in endophytic fungi, thereby increasing the number and diversity of secondary metabolites. Furthermore, it assists in overcoming the inhibition of microbial secondary-metabolite synthesis under laboratory conditions, and enhances silenced-gene expressions. The advent of novel analytical techniques and bioinformatics has provided a comprehensive, multifaceted, and holistic understanding of fungal metabolism through the development of metabolomics as a research platform. However, few studies have combined anti-ovarian cancer-activity screening with metabolomic approaches in the search for activity-differentiating metabolites from endophytic fungi under the intervention of epigenetic modifiers. Herein, we investigated the impact of epigenetic modifiers on the secondary metabolites of the endophytic Diaporthe goulteri fungus from Acanthus ilicifolius L. to determine their potential anti-ovarian cancer activities. Crude extracts were obtained by controlling three variables: the number of fermentation days, the type of epigenetic modifier, and its concentration, with activities screened using the CCK-8 (cell counting kit-8) method. Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was subsequently employed for non-targeted metabolomic analysis. A multivariate statistical analysis model was constructed using principal component analysis and orthogonal partial least squares-discriminant analysis, which combines model and variable importance projection, with qualitative screening performed and significant changes (variable importance in the projection (VIP)≥1; P<0. 05) determined. Fifteen differential metabolites were identified in the fungal and epigenetic modification group, primarily comprising polyketides, amino acids, derivatives, alkaloids, and organic acids, including prenderol, glycine, valine, 2-ethylcaproic acid, rubratoxin B, finasteride, 6-silaspiro[5.5]undecane, 1-(2-nitrophenoxy)octane, heptadecene, 1-pentadecene, 11-ketoetiocholanolone, 3-(1-ethyl-1,3,3-trimethyl-2,3-dihydro-1H-inden-5-yl)butanal, N2-benzoylarginine, tabutrex, (3aR,6S,6aS)-6-(4-hydroxy-2-methoxy-2-butanyl)-4,4-dimethylhexahydro-1(2H)-pentalenone, and 8-aminoquinoline. The expressions of prenderol, 1-(2-nitrophenoxy)octane, 3-(1-ethyl-1,3,3-trimethyl-2,3-dihydro-1H-inden-5-yl)butanal, N2-benzoylarginine, and 8-aminoquinoline were downregulated, whereas the expressions of the remaining 10 substances were upregulated. Polyketides were the main components that exhibited higher expressions. This study showed that latent active differential metabolites can be searched by combining anti-ovarian cancer-activity screening with metabolomics analysis, thereby providing a reference for the further development of Acanthus ilicifolius L. resources and the subsequent targeted isolation of active compounds.

Assessment of organochlorine pesticide contamination in Astyanax altiparanae from the Alagados Dam, Southern Brazil

Organochlorine pesticides (OCPs) are persistent pollutants previously used in agriculture, known for their ability to bioaccumulate and pose health risks. This study analyzed samples of roe, viscera, and muscle from Astyanax altiparanae fish collected from the Alagados reservoir in Paraná, Brazil. Samples were prepared through extraction and purification, then analyzed using gas chromatography with an electron capture detector (GC/ECD), chosen for its sensitivity in detecting OCPs. The method was validated for precision, accuracy, and detection limits. Detected OCPs included Aldrin (17.1 to 50.6 ng/g in roe), α-endosulfan (3.4 to 23.5 ng/g), p, p’-DDE (4.2 to 134.7 ng/g), Dieldrin (84.7 to 183.1 ng/g), β-endosulfan (6.0 to 51.6 ng/g), and p, p’-DDT (56.6 to 286.8 ng/g). In viscera, concentrations ranged from Aldrin (19.8 to 93.3 ng/g) to p, p’-DDT (52.3 to 89.2 ng/g). Muscle samples showed similar trends. Principal component analysis indicated a link between higher OCP concentrations and increased abdominal width of the fish. While OCP levels were below FAO and WHO limits, risk quotient calculations suggest potential health risks from consuming these fish.

Screening and Identification Quality Markers for Antioxidant Activity of Tetrastigma hemsleyanum from Different Origins using Spectrum-effect Correlation Analysis

Background:: Tetrastigma hemsleyanum (THL) is a traditional medicinal plant with various biological activities, it has been validated that several THL extracts have strong pharmacological activities. Until now, there is no complete and systematic germplasm identification and quality evaluation standard. Objective:: Establishing fingerprint spectra of THL from different sources, evaluating their antioxidant activity and identifying quality markers. Methods:: In this paper, establishing fingerprints and identifying quality markers through UPLC-QTOF- MS/MS. The antioxidant capacity of forty-one batches of THL from multiple regions was determined by 1,1-Diphenyl-2-picrylhydrazyl radical (DPPH), 2,2'-Azinobis-(3-ethylbenzthiazoline-6- sulphonate) (ABTS), and Ferric reducing antioxidant power (FRAP) assays. Results:: The results indicated that THL methanol extracts showed good antioxidative activity and eight THL samples, including THL aerial parts, showed high antioxidative activity. Besides, Ultra Performance Liquid Chromatography (UPLC) fingerprints of THL from different places were established and chromatographic peaks probably correlated with antioxidant capacity were screened by principal component analysis (PCA) and partial least square regression (PLS) analysis. Conclusion:: The chemical constituents of these peaks were identified as possible quality markers for THL quality evaluation. In this study, twelve effective substances were preliminarily determined, such as isovitexin, L-epicatechin, procyanidin B, sitosterol, etc.

Climate change and external debt vulnerability: the case of Sub-Saharan Africa

This study examined the influence of climate change vulnerability on the external debt vulnerability of Sub-Saharan Africa, with a focus on the Vulnerable Twenties (V20s), utilizing panel data spanning from 2015 to 2020. The climate readiness, climate vulnerability, and external debt vulnerability indices were derived via principal component analysis (PCA). The results from the system GMM analysis indicate that climate readiness exhibits a negative and significant impact across short- and long-term periods for the overall sample. However, for the V20s, the negative effect of climate readiness shifts to a positive and significant one over both short and long terms, with the long-term effect being more notable. These findings highlight a vicious cycle between debt and climate crises within the V20s. Thus, prioritizing debt relief over climate finance loans, decarbonizing greenhouse gas emissions, redirecting funds for climate goals, improving debt practices, and investing in public services would break nations’ vicious cycle of debt and climate vulnerability.

Balanço nutricional em mangueiras no semiárido brasileiro utilizando métodos diagnósticos nutricionais

ABSTRACT Nutritional assessment of mango trees based on diagnostic methods considering nutritional balance is recommended. This study aimed to establish optimum nutritional ranges using diagnostic methods, compare them, identify the most efficient diagnostic method, and select the nutrients responding best to the application of the diagnostic method. The study was conducted in commercial mango orchards in the São Francisco Valley. Nutritional content was calculated using the diagnosis and recommendation integrated system (DRIS-Beaufils, DRIS-Jones), modified DRIS (M-DRIS-Beaufils; M-DRIS-Jones), compositional nutrient diagnosis (CND), and mathematical chance (ChM) methods and compared using the chi-square test. Principal component analysis was applied to select the most efficient diagnostic method and the nutrients responsible for the greatest variability. The DRIS-Beaufils, M-DRIS-Beaufils, DRIS-Jones, M-DRIS-Jones, CND, and ChM methods generated nutritional sufficiency ranges for the evaluated cultivars. The nutritional diagnoses of the DRIS-Beaufils and M-DRIS-Beaufils methods were similar and discordant with those of DRIS-Jones, M-DRIS-Jones, and CND. The DRIS-Beaufils method, updated by Maia, proved to be more consistent for the nutritional assessment of mango trees. The nutrients N, P, K, Mg, and S in the Tommy Atkins cultivar; N, P, Mg, S, B, Mn, Zn, Mo, and Cl in the Kent cultivar; and N, P, K, Ca, S, B, Cu, Fe, Zn, Mo, and Cl in the Keitt cultivar showed significant responses to the application of the DRIS-Beaufils method updated by Maia.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

Comparing feature usage in IMU-based gesture control for omnidirectional robot via wearable glove

&lt;span&gt;To improve the intuitiveness of maneuver control on omniwheeled mobile robot, many hand gesture-based robot controls have been developed. The focus of this research is to develop a wearable system for data acquisition from inertial measurement unit (IMU) sensors and compare its features to be used as gesture recognition using the random forest algorithm. With the need of resource constrained device for wearable system based on microcontrollers, we compared the use of Euler and quaternion-based orientation data as input features. As additional comparison, dimension reduction was also carried out using the principal component analysis (PCA) method. Hand gestures are recognized using data obtained by the IMU sensor embedded in the wearable glove. This study compared the accuracy and size of library files embedded in microcontrollers in several feature usage scenarios. The test evaluation results of all scenarios show that the use of all features provides a balance between high accuracy but small file sizes, respectively 99% and 9.2 KB. However, the use of other fewer features, such as by only using 3 Euler data, 4 quaternion data, or by using PCA algorithm (PC=3) can also be used since the accuracy is still above 90%, with a relatively larger file size.&lt;/span&gt;

Understanding of Structure-Property Linkages in Cu-Co-P Alloy Based on Numerical Analysis of Microstructure and Principal Component Analysis

Understanding of Structure-Property Linkages in Cu-Co-P Alloy Based on Numerical Analysis of Microstructure and Principal Component Analysis

Are Oxidative Stress Biomarkers Reliable Part of Multimarker Panel in Female Patients with Type 2 Diabetes Mellitus?

Background: Oxidative stress and inflammation are the key features of metabolic diseases, including type 2 diabetes mellitus (T2D). However, studies that explored redox homeostasis parameters in relation to T2D show discrepant results. Accordingly, we aimed to examine the potential reliability of oxidative stress biomarkers [i.e., determined by malondialdehyde (MDA), advanced oxidation protein products (AOPP) and catalase (CAT)] in addition to traditional cardiometabolic parameters in relation to T2D in female cohort. Methods: A total of 214 women (of them 40.6% T2D) were consecutively recruited in the study. Principal component analysis with varimax rotation was performed to determine the adequate number of factors consisting of anthropometric, traditional cardiometabolic and redox status markers. Results: MDA and AOPP concentrations were lower, but CAT activity was higher in T2D group as compared with controls (P < 0.001, P = 0.002, P < 0.001). Traditional markers related factor (i.e., with positive loading of waist circumference, triglycerides, uric acid, high sensitivity C-reactive protein and negative loadings of high-density lipoprotein cholesterol) was found to be independently related with T2D in multivariate binary regression analysis, whereas oxidative stress related factor (i.e., with positive loading of MDA and AOPP) lost its independent prediction after adjustment for confounding factors (i.e., age, menopausal status, antihypertensive, and hypolipemic therapies). Increased Traditional markers related factor was associated with more than three times higher probability for T2D onset (OR = 3.319, p < 0.001). Conclusion: Oxidative stress biomarkers, i.e., MDA, AOPP, and CAT are not superior over traditional cardiometabolic markers in relation to T2D in female population. Future studies with both gender included are needed to confirm such results.

Neighborhood Ecologies, Cases, and Deaths during the Beginning of the COVID-19 Pandemic: Lessons for Current and Future Epidemics?

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected Black and Latinx communities. Ecologic analyses have shown that counties with a higher percentage of Latinx and Black people have worse COVID-19 outcome rates. Few ecologic analyses have been published at the neighborhood (census tract) level. We sought to determine whether certain sociodemographic neighborhood ecologies were associated with COVID-19 case and death rates in metropolitan Atlanta, Georgia. We used census data and principal-component analysis to identify unique neighborhood ecologies. We then estimated correlation coefficients to determine whether the neighborhood profiles produced by a principal-component analysis were correlated with COVID-19 case and death rates. We conducted geographically weighted regression models to assess how correlation coefficients varied spatially for neighborhood ecologies and COVID-19 outcomes. We identified two unique neighborhood profiles: (1) high percentage of residents, Hispanic ethnicity, without a high school diploma, without health insurance, living in crowded households, and lower percentage older than 65 years; and (2) high percentage of residents, Black race, living in poverty, unemployed, and households receiving Supplemental Nutrition Assistance Program benefits. Profile 1 was associated with COVID-19 case rate (Pearson r = 0.462, P < 0.001) and profile 2 was associated with COVID-19 death rate (Spearman r = 0.279, P < 0.001). Correlations between neighborhood profiles and COVID-19 outcomes varied spatially. Neighborhoods were differentially at risk of COVID-19 cases or deaths depending on their sociodemographic ecology at the beginning of the COVID-19 pandemic. Prevention methods and interventions may need to consider different social determinants of health when addressing potential cases and deaths during future emergent epidemics.

Atributos microbiológicos em Latossolo Vermelho cultivado com cana-de-açúcar na região de cerrado do Brasil Central

ABSTRACT The contribution of plant residues throughout the sugarcane cycles favors the increase of organic matter and the activity of microorganisms in the soil, especially in the surface layers. Soil texture also has an important effect on ecological processes and soil quality. In this context, the objective of this study was to evaluate soil biological attributes in different sugarcane cultivation cycles under mechanized harvesting in an Oxisol in the Savanna region of Central Brazil. The study was conducted in commercial areas under sugarcane cultivation during the 2018/2019 season, which were considered homogeneous in terms of soil and climatic conditions, with the source of variation among the areas being the cultivation cycles (C1: one cultivation cycle; C3: three cultivation cycles; C7: seven cultivation cycles) and a savanna vegetation area selected as a reference. Microbiological variables were determined in two layers, 0-0.1 and 0.1-0.2 m. The variables related to microbial biomass and texture were subjected to principal component analysis. Areas with longer sugarcane cultivation cycles show higher proportion of microbial biomass carbon in the total organic carbon in subsurface layers (microbial quotient). The performance of the soil microbial community, as expressed by total organic carbon and microbial biomass nitrogen indicators, was associated with higher presence of clay and silt, i.e., soil particles smaller than 0.02 mm.