Principal Component Analysis Research Articles

In silico study of some plant compounds as potential anticancer agents targeting MALT1 allosteric domain

Mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) is the only human paracaspase, that serves as an adaptor protein and controls substantial genes expressed in the activation, proliferation of lymphocyte, and immune reactions by triggering the IKK/NF-kB signaling pathway. However, unusual MALT1-mediated NF-kB signaling pathway has been identified in multiple diseases like cancer, therefore making MALT1 a promising therapeutic target. There are scanty numbers of MALT1 inhibitors, thus the need to discover more compounds with less or no toxicity issue, that are cheap and pharmacologically efficient is of pertinence. Hence, our present study was to identify phyto-small molecules that could bind the allosteric interface of MALT1 using in silico methods. Total of 34 plant molecules were selected and screened for druglikeness, after which they were docked via Maestro 11.1 against the allosteric site of MALT1. The molecule with a binding score (kcal/mol) better than the control drug was subjected to molecular dynamics (MD) simulations of 100 ns via Desmond, free energy perturbations, principal component and Pearson correlation analyses. Our findings from this computational study presents cyanidin (–8.822 kcal/mol) as better binder to the allosteric site of MALT1 based on the molecular docking and pharmacokinetic profiling than thioridazine. Similarly, cyanidin-MALT1 complex showed significant stability and exhibiting contacts with critical amino acid residues in the site of interest than thioridazine-MALT1 complex. Hence, cyanidin is a potential allosteric inhibitor of MALT1. However, an urgent need for in vitro and in vivo validations is required to ascertain the efficacy of cyanidin in the fight against cancer and other MALT1-related diseases.

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Abstract In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square (RMS) value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through Kernel Principal Component Analysis (KPCA), thereby enabling a more nuanced representation of the gearbox's degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process's parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox's RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic predictive maintenance for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of predictive maintenance in the context of gearboxes.

Psychometric evaluation of the extended inclusion of nature in self scale in Chinese adults through the Rasch analysis

Abstract How humans connect with nature impacts the well‐being of both people and ecosystems. Being disconnected from nature can lead to attitudes and behaviours that ultimately damage our physical and mental health and cause irreparable harm to the planet. To measure the human‐nature connection, measurements with good psychometric properties are warranted. By synthesizing the existing instruments, we suggested the distinctive advantages of the Extended Inclusion of Nature in Self scale (EINS), such as using multiple graphical spatial metaphors. Based on the back‐translation method, we translated the EINS into a Chinese version (EINS‐C). Then, using the Rasch model and the classical test theory, we elaborately examined the psychometric properties of the scale with a sample of 786 Chinese adults. Exploratory factor analysis, principal components analysis of Rasch residuals, and confirmative factor analysis together provided robust evidence for the one‐factor structure of the EINS‐C. Category probability curves showed the satisfying functioning of the rating scale. The scale further exhibited high criterion validity, convergent validity, internal consistency and test–retest reliability. The Rasch model fit statistics were also substantial for items. Each item presented a moderate difficulty, suggesting high discrimination power for levels of self‐nature connection. Scores on the scale could be meaningfully compared across genders, as no substantial differential item functioning was found. The EINS‐C consequently demonstrated generally robust psychometric properties, which could be applied to the Chinese‐speaking community, approximately 16 per cent of the world's population. This high‐quality and convenient tool for evaluating and monitoring the relationship between humans and nature lays a foundation for and might facilitate conservation‐related research and policy formulation. Read the free Plain Language Summary for this article on the Journal blog.

Unravelling wheat genotypic responses: insights into salinity stress tolerance in relation to oxidative stress, antioxidant mechanisms, osmolyte accumulation and grain quality parameters

BackgroundSalt stress is a prominent abiotic stressor that imposes constraints on grain yield and quality across various crops, including wheat (Triticum aestivum). This study focused on assessing the genetic diversity of 20 wheat genotypes categorized as tolerant, moderately tolerant, and sensitive with three genotypes of unknown tolerance. To address salinity stress-related problems, different morpho-physiological, osmoprotectant, biochemical, yield, and grain quality-related parameters were analyzed under control (pH 8.0, EC 3.9) and saline-sodic (pH 9.4, EC 4.02) conditions in field.ResultsFindings revealed noteworthy variations among the genotypes in response to salinity stress. Greater accumulation of Na+ and lower K+ content were observed in response to salt stress in the sensitive varieties HD1941 and K9162. Proline, a stress indicator, exhibited significantly (p ≤ 0.05) greater accumulation in response to salinity stress, particularly in the tolerant cultivars KRL210 and KH65. Salt stress induced the most significant decrease (p ≤ 0.05) in spike length, thousand-grain weight, and hectolitre weight coupled with increased protein content in sensitive varieties, resulting in diminished yield.ConclusionCorrelation analysis of parameters under salinity stress showed that SOD, proline, and K+ contents can be used as the most efficient screening criteria for salinity stress during early developmental stages. Principal component analysis revealed that DBW187, DBW303, and DBW222 varieties were tolerant to salinity stress and exhibited an effective antioxidant system against salinity. This study will facilitate salt-tolerant wheat breeding in terms of the identification of tolerant lines by screening for limited traits in a wide range of germplasms.

Automated Disease Detection in Silkworms Using Machine Learning Techniques

Silkworm diseases pose a major threat to the sericulture industry, with early detection remaining a challenge due to limited infrastructure. This study focuses on detecting Grasserie disease, which can rapidly spread in silkworm rearing units, leading to significant economic losses. A novel dataset of 668 healthy and 574 Grasserie-affected silkworm images forms the basis of this research. The study applies machine learning techniques, using the Histogram Oriented Gradient (HOG) feature descriptor combined with Kernel Principal Component Analysis (KPCA) and supervised classifiers. The integration of Support Vector Machines (SVM) with HOG and KPCA achieved high accuracy (93.16%), recall (93.38%), and precision (91.94%), offering a faster, more accurate alternative to manual detection methods. This approach holds great potential for developing real-time, IoT-based diagnostic tools that enable farmers to quickly identify infected silkworms, reducing disease spread and economic losses, and can be extended to other agricultural applications requiring early disease detection.

Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.

Lipidomic profiling of amniotic fluid reveals aberrant fetal lung development and fetal growth disrupted by lipid disorders during gestational asthma

This study aimed to investigate how maternal asthma during pregnancy disrupts fetal lung development by altering lipid metabolism in the amniotic fluid, which is crucial for fetal development. A pregnancy-induced asthma model was established in female rats using house dust mite (HDM) as a common allergen. The fetuses were divided into four groups based on whether the mother and fetus were exposed to the allergen: PBS+PBS, PBS+HDM, HDM+PBS, and HDM+HDM. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to analyze changes in the lipid profile of the amniotic fluid and bronchoalveolar lavage fluid (BALF). Principal component analysis (PCA) and ChemRICH methods were used to explore the potential relationship between lipid metabolism abnormalities and impaired fetal lung development. The results indicate that maternal asthma exacerbates asthma-related inflammatory markers in fetuses, leading to pathological changes in the lungs and elevated levels of cytokines IL-5, IL-13, and IgE. Additionally, 18 differential lipids, primarily oxygenated lipids, were identified in the amniotic fluid after modeling, suggesting an enhanced oxidative stress environment for the fetus. This environment causes metabolic disturbances in various lipid groups in fetal lungs, with the HDM+HDM group showing significant abnormalities in lipids critical for lung development, including phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and fatty acids (FA). In conclusion, gestational asthma can reshape the lipid profile in the amniotic fluid and BALF, significantly disrupting fetal growth and lung development. Restoring normal lipid metabolism in the amniotic fluid and fetal lungs may offer a potential therapeutic approach to managing aberrant fetal lung development in asthmatic mothers.

Women’s autonomy, neonatal, infant and under-five mortality in the Upper East Region of Ghana

Child mortality remains a major health challenge in Sub-Saharan Africa. Child survival is greatly influenced by household circumstances and mother’s healthcare choices. Notwithstanding tremendous investment in women empowerment in Ghana, there is limited empirical evidence on whether women’s autonomy translates into better child mortality outcomes. To examine the association between women’s autonomy and neonatal, infant and under-five mortality in the Upper East Region of Ghana. Data were obtained from a randomized cluster household survey among 15–49 years old women in seven districts in the Upper East Region. Data analysis was restricted to 3,243 women who reported ever having given birth. Based on Principal Component Analysis (PCA), we constructed an autonomy index categorized into least, moderate, and high autonomy based on responses to six questions regarding household decision-making. Bivariate and multivariate logistic regressions were used to assess the association of women’s autonomy status and mortality outcomes. Attaining secondary education or higher was significantly associated with infant mortality (adjusted odds ratio (aOR) = 0.39, CI = 0.16, 0.94) and under-five mortality (aOR = 0.39, CI = 0.18–0.87). Also, maternal age was significantly associated with neonatal, infant, and under-five mortality, while living in rural setting was significantly associated with lower risk of neonatal (aOR = 0.38, CI = 0.19–0.75) and under-five (aOR = 0.63, CI = 0.48–0.83) mortality. However, we found that compared to women with least autonomy, infants of those with moderate autonomy (aOR = 1.76, CI 1.07–2.89) and high autonomy (aOR = 1.75; CI = 1.04–2.93) were significantly more likely to die. In this study setting, women’s autonomy was not predictive of child mortality. Interventions that aim to improve child mortality should pay attention to community and family level factors that promote increase utilization of essential early childhood interventions.

Nutritional Qualities, Metabolite Contents, and Antioxidant Capacities of Yardlong Beans (Vigna unguiculata subsp. sesquipedalis) of Different Pod and Seed Colors.

Studying the effects of genetic and environmental factors on plant biochemical components helps in selecting the best varieties for the food industry and breeding programs. This study analyzed the nutritional qualities, secondary metabolites, and antioxidant activities of 14 field-grown yardlong beans accessions and how they are affected by differences in pod and seed colors. The analyzed parameters varied significantly among the yardlong bean accessions, with variances ranging from 1.36% in total unsaturated fatty acid content to 51.01% in DPPH• scavenging activity. Accessions YLB4, YLB7, and YLB14 performed the best, showing antioxidant indices of 100.00, 70.10, and 67.88%, respectively. Among these, YLB14 showed a characteristic property, having the highest levels of vitamin C (2.62 mg/g) and omega-6 to omega-3 ratio (2.67). It also had the second highest dietary fiber (21.45%), stearic acid (4.44%), and linoleic acid (40.39%) contents, as well as the lowest thrombogenicity index (0.38). Although cluster and principal component analyses did not clearly separate the yardlong beans based on pod or seed color, analysis of variance revealed that these factors and their interaction had significant effects on total phenol, DPPH• scavenging activity, ABTS•+ scavenging activity, and reducing power. In contrast, the nutritional parameters, except for dietary fiber, were not significantly affected by pod and seed color variations. Therefore, consuming yardlong beans of different pod and seed colors may not affect the overall nutrient intake. In general, this study identified yardlong beans with green pods and black seeds as good sources of antioxidants. Accordingly, further metabolomics and genomics studies are suggested to thoroughly explore their characteristics.

Eco-Morphological Responses of Camponotus japonicus (Hymenoptera: Formicidae) to Varied Climates and Habitats.

Ants are a highly adaptable group of insects that have globally established themselves in diverse climates and habitats. This study investigates the influence of climate and habitat on the morphological traits of Camponotus japonicus across 22 sites in mainland China. These sites span three climate zones (mid-temperate, warm temperate, and subtropical) and three habitat types (urban parks, farmlands, and sparse woodlands). Principal component analysis (PCA) was used to determine the principal axis of morphological variation, while hypervolume analysis and centroid distance calculation were used to verify the environmental filtering hypothesis and the optimal transfer hypothesis. The results support both hypotheses showing that climate and habitat significantly affect the morphological space of C. japonicus workers. In particular, the morphological space is more constrained in mid-temperate farmlands, while workers in sparse woodlands exhibit greater morphological variation. In contrast, urban parks are characterized by higher stability and reduced morphological differences. Additionally, robust regression analysis reveals that environmental factors such as temperature, precipitation, humidity, and altitude are closely linked to the morphological traits of the workers. Understanding how ant morphology responds to external environmental changes enhances our understanding of their adaptability and their essential ecological roles across various ecosystems.

Generating pooled quality control samples of volatile organic compounds

Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace are influenced by several confounding factors not represented in reference standards. In this study, we propose a method of generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow with thermal desorption tubes. Sample tubes were desorbed and split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of multiple QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects.

Evaluation of golfers’ performances in the ladies professional golf association tour based on bootstrapped data envelopment analysis and latent growth curve model

A latent growth curve model and bootstrapped data envelopment analysis were used to examine the performance and changes in professional golfers on the Ladies Professional Golf Association tour. The panel data for both analyses were obtained for the last three seasons (from 2020 to 2022). The dependent variable was the official money list of players, whereas the independent variables consisted of efficiency factors, technical factors (number of birdies, eagles, and hole-in-ones), mental factors (driving and putting accuracies), and career length. Each factor, other than the efficiency factor, was determined by the weighted combination of parenthesized variables using principal component analysis and was measured as two latent variables of the intercept and the slope of the growth pattern for the three seasons. The efficiency factor was measured using data envelopment analysis and its z with the output factors of scoring average and the percentage of rounds in the 60s, and the input factors of driving distance, greens in regulation, number of putts per green hit in regulation, and sand savings. The results confirm the homogeneity in players’ efficiency and prove Penick’s claim that golf performance is dependent on various factors and that golf is psychological.

Numerical Study of Low Engine Order Excitations due to Manufacturing Variability Part 1: Characterization of the Geometric Scatter and its Effect on Forced Response

Abstract The geometric scatter in the surfaces of blades and vanes due to manufacturing variability has an impact on the dynamics but also on the aerodynamics. The loss of the spatial periodicity of the flow due to the geometric variations generates an aerodynamic forcing with a wave number different from the airfoil count on the adjacent rows. In this paper, deviations from the nominal geometry are obtained from a set of scanned parts. The average manufactured geometry is computed through the point-by-point deviations between the scanned parts and the nominal geometry. Then, Principal Component Analysis is applied to the deviations from the average geometry to find a way to describe them in a simple manner. The cold geometries are reconstructed using the mean geometry and the PCA geometric modes; afterwards, a cold-to-hot process is applied to obtain the hot geometry, and finally the resulting aerodynamics are obtained using an in-house CFD solver. The described method is applied to a representative row constituted by packets of vanes. Firstly, the average packet geometry is used to analyze the aerodynamic forcing associated to the geometric variation of the airfoils inside the vane packet. After that, a conceptual study is carried out under some simplified assumptions in order to relate the geometric variability to random low engine order disturbances, using the geometric modes obtained from the PCA analysis. In both cases, the induced vibrations on the adjacent rotor row are comparable to the excitation due to the nominal blade-passing.

Feature Engineering for Agile Requirement Management Using Semantic Analysis

Efficient management and prioritization of software requirements are critical challenges in agile projects, where requirements constantly evolve due to changing user needs, business goals, and regulatory updates. This paper explores the role of semantic feature extraction in enabling adaptive management strategies. Using the PROMISE Expanded Dataset and the Coquina Dataset, we employed TF-IDF weighted Word2Vec for advanced tokenization and feature extraction. Latent Dirichlet Allocation (LDA) was used to analyze how preprocessing steps like stop word removal impact topic representation, revealing that removing stop words improved topic specificity and coherence. To address class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) was applied, enhancing the model's ability to handle underrepresented classes effectively. Principal Component Analysis (PCA) reduced the dimensionality of TF-IDF weighted Word2Vec embeddings from 100 features to 30, while Analysis of Variance (ANOVA) identified the most significant features for classification. The results obtained identified three features to have p-values below 0.05 as statistically significant, p-value = 0.0000605, p-value = 0.00000000469, and p-value = 0.0024. These extracted features could be used as input to training machine learning models for predicting and managing software requirements adaptively during agile development. With the reduction of ambiguities and sentiments of the user at the requirement phase, the development phase could be undertaken seamlessly with ease.

Assessment of pesticide residues: a comprehensive analysis of seasonal trends and health implications

This study assessed the presence of eight pesticide residues in the Indus River, Mianwali, Pakistan, focusing on three sampling sites (S1, S2, and S3) in water, sediment, and the fish species Cyprinus carpio during both dry and wet seasons. Analysis was conducted using gas chromatography with an electron capture detector. Results indicated elevated pesticide concentrations in both seasons, with levels of 0.84 and 0.62 μg/L in water, 12.47 and 9.21 μg/g/dw in sediment, and 17.33 and 12.17 μg/g/ww in fish, with higher concentrations observed during the dry season. Cypermethrin and carbofuran were the primary pesticides detected in water, while endosulfan and cypermethrin were dominant in sediment and fish tissue, often exceeding standard safety thresholds. Principal Component Analysis (PCA) and cluster analysis revealed stronger correlations between sediment and fish muscle, with varying associations among pesticides across seasons. The Hazard Index (HI) surpassed 1 in both seasons, signaling potential health risks to humans. These findings underscore the substantial risk agricultural pesticides pose to the aquatic ecosystem and food chain, highlighting the urgent need for sustainable agricultural practices and stricter regulations to minimize pesticide use and encourage eco-friendly pest management strategies.

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.

Multi-way modelling of oral microbial dynamics and host-microbiome interactions during induced gingivitis

Gingivitis—the inflammation of the gums—is a reversible stage of periodontal disease. It is caused by dental plaque formation due to poor oral hygiene. However, gingivitis susceptibility involves a complex set of interactions between the oral microbiome, oral metabolome and the host. In this study, we investigated the dynamics of the oral microbiome and its interactions with the salivary metabolome during experimental gingivitis in a cohort of 41 systemically healthy participants. We use Parallel Factor Analysis (PARAFAC), which is a multi-way generalization of Principal Component Analysis (PCA) that can model the variability in the response due to subjects, variables and time. Using the modelled responses, we identified microbial subcommunities with similar dynamics that connect to the magnitude of the gingivitis response. By performing high level integration of the predicted metabolic functions of the microbiome and salivary metabolome, we identified pathways of interest that describe the changing proportions of Gram-positive and Gram-negative microbiota, variation in anaerobic bacteria, biofilm formation and virulence.

Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record

Abstract. For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach ∼ 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ∼ 10 %–15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients &gt;0.98 and overall differences &lt;10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Sulfated Bile Acids in Serum as Potential Biomarkers of Disease Severity and Mortality in COVID-19.

The fight against coronavirus disease 2019 (COVID-19) continues. Since the pandemic's onset, several biomarkers have been proposed to assess the diagnosis and prognosis of this disease. This research aimed to identify potential disease severity biomarkers in serum samples of patients with COVID-19 during the disease course. Data were collected using untargeted and targeted mass spectrometry methods. The results were interpreted by performing univariate and multivariate analyses. Important metabolite classes were identified by qualitative untargeted metabolomics in 15 serum samples from survivors of COVID-19. Quantitative targeted metabolomics on a larger patient cohort including 15 non-survivors confirmed serum 3-sulfate bile acids (i.e. GLCA-3S) were significantly increased in non-survivors compared to survivors during the early disease stage (p-value < 0.0001). Notably, it was associated with a higher risk of mortality (odds ratio of 26). A principal component analysis showed the ability to discriminate between survivors and non-survivors using the BA concentrations. Furthermore, increased BA-S is highly correlated with known parameters altered in severe clinical conditions.