Cluster Heat Map Research Articles

Evaluation of volatile safety in children's play mats based on non-targeted screening and risk prioritization.

Given the widespread use of play mats and their close contact with children, it is crucial to identify potential chemical hazards associated with these products. In this study, 34 play mats, comprising four materials (3 EPE, 15 XPE, 5 PVC, 11 EVA), were subjected to non-targeted screening based on headspace gas chromatography-Orbitrap high-resolution mass spectrometry (HS-GC-Orbitrap HRMS), which screened out 71 volatile substances. The 20 significantly different substances were recognized by partial least squares discriminant analysis (PLS-DA), while the characteristic substances in each type of play mat were determined through clustering heat map and Venn diagram. To facilitate the rapid identification of high-risk substances, a risk scoring scheme was developed, taking into account detection rates, average peak areas, and substance hazardousness. The substances were then ranked based on their total risk scores. The distribution of peak areas for the top 14 high-risk substances, including α-methylstyrene, formamide, and toluene, in products was detailed, highlighting the need for further attention to these chemicals. Overall, the study found the volatile chemical safety of the four play mat types to be in the order of EPE > XPE > PVC > EVA. The differentiation analysis and risk scoring scheme proposed in this paper provide significant insights for the efficient regulation and quality improvement of play mats and other products.

Silicon Nanoparticles (SiO2 NPs) Boost Drought Tolerance in Grapevines by Enhancing Some Morphological, Physiological, and Biochemical Traits

AbstractDrought is a significant abiotic stress that adversely affects plant growth, development, and metabolic processes, posing a global threat to food security. In recent years, nanotechnology has emerged as a promising strategy to mitigate environmental challenges and has been tested on various plant species; however, its application to grapevines remains largely unexplored. This study aims to investigate the potential of silicon nanoparticles (SiO2 NPs) to modulate morphological, physiological, and biochemical parameters in grapevine saplings (5 BB/Crimson Seedless, 41 B/Crimson Seedless, and 1103 P/Crimson Seedless) under drought stress conditions. Saplings were treated with foliar applications of different concentrations of SiO2 NP solutions (0 (control), 1, 10, and 100 ppm) under well-irrigated (90–100% field capacity) and drought stress (40–50% field capacity) conditions. The results indicated that the application of SiO2 NPs at 10 ppm concentration enhanced growth parameters (such as leaf area, leaf number, shoot fresh and dry weight, root fresh and dry weight) and chlorophyll content under both well-irrigated and drought conditions. Additionally, there was an improvement in leaf relative water content (RWC) and stomatal conductance, correlating with increased protein content. Under drought conditions, oxidative stress indicators, including drought index, leaf temperature, membrane injury index, hydrogen peroxide (H2O2) content, and malondialdehyde (MDA) levels, showed a significant decrease. Concurrently, proline content, total phenolic compounds, and activities of antioxidant enzymes (SOD, CAT, and APX), which increased under drought stress, were reduced in the presence of 10 ppm SiO2 NPs, suggesting a mitigation of oxidative stress. To comprehensively evaluate the effects of SiO2 NP concentrations, irrigation regimes, and rootstock/variety combinations, correlation analysis, principal component analysis (PCA), and hierarchical clustering heatmap analysis were performed. Collectively, the findings suggest that the observed drought-induced oxidative stress tolerance in grapevine saplings in the presence of 10 ppm SiO2 NPs may result from the influence of antioxidant systems leading to a balanced redox potential. These results support the argument that SiO2 NPs play a crucial role in enhancing drought tolerance, with implications for managing drought stress in other agricultural crops as well.

Compare Analysis of Codon Usage Bias of Nuclear Genome in Eight Sapindaceae Species.

Codon usage bias (CUB) refers to the different frequencies with which various codons are utilized within a genome. Examining CUB is essential for understanding genome structure, function, and evolution. However, little was known about codon usage patterns and the factors influencing the nuclear genomes of eight ecologically significant Sapindaceae species widely utilized for food and medicine. In this study, an analysis of nucleotide composition revealed a higher A/T content and showed a preference for A/T at the third codon position in the eight species of Sapindaceae. A correspondence analysis of relative synonymous codon usage explained only part of the variation, suggesting that not only natural selection but also various other factors contribute to selective constraints on codon bias in the nuclear genomes of the eight Sapindaceae species. Additionally, ENC-GC3 plot, PR2-Bias, and neutrality plot analyses indicated that natural selection exerted a greater influence than mutation pressure across these eight species. Among the eight Sapindaceae species, 16 to 26 optimal codons were identified, with two common high-frequency codons: AGA (encoding Arg) and GCU (encoding Ala). The clustering heat map, which included the 8 Sapindaceae species and 13 other species, revealed two distinct clusters corresponding to monocots and dicots. This finding suggested that CUB analysis was particularly effective in elucidating evolutionary relationships at the family level. Collectively, our results emphasized the distinct codon usage characteristics and unique evolutionary traits of the eight Sapindaceae species.

Predicting Rate Constants of Hydrogen Abstraction Reactions between OH/HO2 and Alkanes by Machine Learning Models.

The hydrogen abstraction reactions by small radicals from fuel molecules play an important role in the oxidation of fuels. However, experimental measurements and/or theoretical calculations of their rate constants under combustion conditions are very challenging due to their high reactivity. Machine learning offers a promising approach to predicting thermal rate constants. In this work, three machine learning methods, XGB, FNN, and XGB-FNN hybrid algorithms, were employed to train and predict the rate constants of the hydrogen abstraction reactions between alkanes and OH/HO2. Six descriptors were selected according to the Pearson correlation coefficients, the importance of descriptors, and the clustering heat map. It was proven that the XGB-FNN model is the most robust. The constructed XGB-FNN model achieved an average deviation of 89.13% for the alkanes + OH reactions and 190.93% for the alkanes + HO2 reactions on their respective prediction sets. The model was also used to predict the rate constants of the reactions involving larger alkanes, demonstrating its extrapolation capability. Furthermore, the model has the ability to distinguish the reactivity of the reactions with the hydrogen atom abstracted at different sites of alkane.

Plasmodium knowlesi Infection Is Associated With Elevated Circulating Biomarkers of Brain Injury and Endothelial Activation.

Malaria remains a major public health concern with substantial morbidity and mortality worldwide. In Malaysia, the emergence of Plasmodium knowlesi has led to a surge in zoonotic malaria cases and deaths in recent years. Signs of cerebral involvement have been observed in a noncomatose, fatal case of knowlesi infection, but the potential impact of this malaria species on the brain remains unexplored. To address this gap, we investigated circulating levels of brain injury, inflammation, and vascular biomarkers in a cohort of knowlesi-infected patients and controls. Archived plasma samples from 19 Malaysian patients with symptomatic knowlesi infection and 19 healthy, age-matched controls were analyzed. Fifty-two biomarkers of brain injury, inflammation, and vascular activation were measured. Wilcoxon tests were used to examine group differences, and biomarker profiles were explored through hierarchical clustering heatmap analysis. Bonferroni-corrected analyses revealed significantly elevated brain injury biomarker levels in knowlesi-infected patients, including S100B (P < .0001), Tau (P = .0007), UCH-L1 (P < .0001), αSyn (P < .0001), Park7 (P = .0006), NRGN (P = .0022), and TDP-43 (P = .005). Compared to controls, levels were lower in the infected group for BDNF (P < .0001), CaBD (P < .0001), CNTN1 (P < .0001), NCAM-1 (P < .0001), GFAP (P = .0013), and KLK6 (P = .0126). Hierarchical clustering revealed distinct group profiles for brain injury and vascular activation biomarkers. Our findings highlight for the first time a potential impact of P knowlesi infection on the brain, with specific changes in cerebral injury and endothelial activation biomarker profiles. Further studies are warranted to investigate the pathophysiology and clinical significance of these altered markers, through neuroimaging and long-term neurocognitive assessments.

Quality Characteristics and Volatile Components of Chili Oil Prepared from the Combination of Shuanla and Erjingtiao Peppers.

This study aimed to investigate the influence of varying weight ratios of Shuanla and Erjingtiao peppers (10:0, 8:2, 6:4, 5:5, 4:6, 2:8, and 0:10, corresponding to samples PA, PB, PC, PD, PE, PF, and PG, respectively) on the sensory attributes, chromatism, acid values (AVs), peroxide values (POVs), capsaicinoids, and volatile organic compounds (VOCs) of seven chili oil samples. GC-IMS was employed to detect the VOCs of the chili oil samples, which were subsequently analyzed using multivariate statistical methods. The results revealed significant differences in pungency among the samples, with the PA sample exhibiting the strongest pungency. The PG sample demonstrated the highest values for a*, b*, and C*, while the PA sample displayed the highest L* and h*. The AVs of seven samples ranged from 0.490 ± 0.005 to 1.727 ± 0.015 mg/g. The POVs of the chili oil samples, ranging from 0.094 ± 0.000 to 0.127 ± 0.002 g/100 g, were significantly lower than those of extra virgin olive oil, 0.183 ± 0.001 g/100 g. The contents of capsaicinoids ranged from 15.26 ± 0.07 g/kg in the PA sample to 0.38 ± 0.00 g/kg in the PG sample (p < 0.05). Additionally, 56 volatile flavor substances were identified, and 10 key flavor compounds (ROAV ≥ 1) were screened among them. Multivariate data analysis via OPLS-DA indicated that 20 VOCs (VIP > 1) could serve as flavor markers in a clustering heat map to differentiate among the seven chili oil varieties. The findings of this study provide a valuable reference for the promotion of Shuanla and Erjingtiao peppers in chili oil production and the development of specific flavor profiles in chili oil to cater to diverse consumer preferences.

Transcriptomic analysis of regulatory mechanisms in the telogen-anagen transition of ovine hair follicles

BackgroundDorper sheep are celebrated for their fast maturation and superior meat quality, with some shedding their wool each spring. Wool shedding occurs naturally due to the hair follicle (HF) cycle, but its regulatory mechanisms remain unclear and need further investigation.ResultsIn this study, shedding and non-shedding sheep were selected from the same Dorper flock. Skin samples were collected in September of the first year and January and March of the following years. RNA sequencing was performed on these samples. Principal component analysis (PCA) was used to assess the results. A total of 2536 differentially expressed genes (DEGs) were identified. Using a clustering heatmap and fuzzy clustering analysis three distinct gene expression patterns were identified: A pattern (high expression in anagen), T1 pattern, and T2 pattern (high expression in telogen). For each pattern, differentially expressed genes (DEGs) were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Combining this with pathway expression analysis, six A-pattern and fourteen T-pattern pathways linked to telogen-anagen transition in the HF cycle were identified. Networks of key pathways were then constructed. Additionally, key genes were identified in the telogen-anagen transition, including one A-pattern gene and seven T-pattern (T1, 1; T2, 6) genes, using the Maximal Clique Centrality (MCC) tool in Cytoscape. Predicted transcription factors (TFs) involved in key pathways, such as LEF and STAT5B, were identified. Finally, RNA-seq results were confirmed by RT-qPCR.ConclusionThis study highlights critical genes and pathways in the telogen-anagen transition, and transcriptome sequencing along with bioinformatics analysis provides new insights into the regulatory mechanisms of the HF cycle and development.

Individual and Combined Effects of Nanoplastics and Cadmium on the Rhizosphere Bacterial Community of Sedum alfredii Hance.

Nanoplastics (NPs) and cadmium (Cd) coexist in soil, but the combined effects of NPs and Cd on the rhizosphere bacterial community remain unknown. In this study, high-throughput sequencing and PICRUSt2 functional analysis were employed to explore the individual and combined effects of polystyrene (PS) NPs (low concentration [N1, 100 mg·kg-1] and high concentration [N2, 1000 mg·kg-1]) and Cd (low concentration [C1, 0.6 mg·kg-1] and high concentration [C2, 4 mg·kg-1]) on the diversity, structural composition, and function of the rhizosphere bacterial community associated with Sedum alfredii Hance. Individually, PS NPs and Cd significantly reduced the soil pH, while the combined treatments induced a more significant decrease in pH. In contrast, combined PS NPs and Cd significantly increased the diethylenetriaminepentaacetic acid-Cd (DTPA-Cd) and total Cd concentrations. Compared with individual treatments, C2N2 significantly increased DPTA-Cd by 4.08%. N1 had no significant effect on the Chao1, observed species, or Shannon indices, while N2 significantly reduced the richness and diversity of the rhizosphere bacteria and altered their community structure. Furthermore, adding PS NPs exacerbated the effect of Cd on rhizosphere bacterial communities. Compared with individual Cd treatments, C2N2 significantly reduced the relative abundances of Actinobacteriota, Bacteroidota, Crenarchaeota, and Myxococcota by 19.76%, 2.01%, 1.49%, and 2.00%, respectively, and significantly increased the relative abundance of Acidobacteriota by 16.05%. A cluster heat map showed that the combined treatments attenuated glycan biosynthesis and metabolic function and enhanced the metabolism of cofactors and vitamins. These findings illuminate rhizosphere processes under co-contamination with heavy metals and PS NPs, supporting the practical application of phytoremediation to alleviate combined Cd and PS NP pollution.

Enhancing the Photosynthetic and Yield Performance of Rice in Saline Soil by Foliar-Applying Cost-Effective Compounds as Sources of Carbon Dioxide and Potassium

Although rice is highly sensitive to salinity, it is considered one of the best crops to grow in salt-affected mudflat soils to alleviate the salinity problem. Applying chemical compounds for an increase in leaf CO2 and nutrient levels can help mitigate the negative impact of salinity on plants in a cost-effective manner. To identify the benefits of using lithovit (Liv), ethanol (Eth), and potassium carbonate (KC) as a source of CO2 and K to enhance rice production in salt-affected soils, a field study was conducted to assess the effects of these compounds on the agro-physiological parameters of two rice genotypes (Giza178 and Giza179) in saline soils. The compounds were applied as a foliar spray at a concentration of 30 mM each before and after the heading growth stage. The results indicated that the genotype, application time, compounds, and their potential two-way interactions significantly influenced all agro-physiological parameters, with only a few exceptions. The genotype Giza 179 exhibited higher pigment contents, photosynthetic capacity, relative water content (RWC), grain yield, and most yield components compared to Giza 178, with increases ranging from 2.1% to 37.9%. Foliar application of different compounds resulted in a 9.7–37.9% increase in various parameters and a 34.6–43.2% decrease in the number of unfilled grains (NUFG) per panicle compared to untreated treatment. Foliar application of different compounds before heading resulted in an increase in various parameters by 4.8–16.1% and a decrease in the NUFG per panicle by 22.9% compared to those applied after heading. Heatmap clustering analysis revealed that foliar application of Liv before heading was the most effective treatment in enhancing various parameters for both genotypes and mitigating the negative effects of salinity stress on the NUFG. This was followed by Eth and KC before heading for Giza 179. Applying Eth and KC to the leaves after heading had a moderate positive impact on most parameters for Giza 179, outperforming the application after heading for Giza 178. Overall, our findings indicate that spraying readily available compounds that elevate CO2 and K levels in rice leaves can help alleviate the negative impacts of salt stress and improve rice production in salt-affected soils in a cost-effective manner.

Qualitative chemical characterization of salva-de-marajó (Lippia origanoides, Verbenaceae) preparations

Lippia origanoides Kunth (LO, Verbenaceae), commonly known in Brazil as salva-de-marajó, is an aromatic plant native to the Americas. Quilombola women from Oriximiná (Pará State, Brazil) use decoctions and decoction vapors of LO aerial parts orally or in sitz baths to alleviate symptoms of dysmenorrhea. This study aimed to evaluate LO decoctions and essential oils through a chemo-qualitative approach. Aerial parts of LO were collected from two individuals, and other two commercial samples were acquired from a public market. Aqueous extract of each sample was obtained by decoction and spray drying. The spray-dried decoctions were chemically characterized using UPLC–APCI(−)–IT–MS2 and cluster heatmap analysis to discern compositional patterns. Essential oils were obtained through hydrodistillation and analyzed using GC–MS and GC–FID. In total, 12 compounds were tentatively identified from LO decoctions. Their product ion spectra were characteristic of flavonoid aglycones (5) and glycosides (4), and phenylpropanoids, including two stilbene glycosides and one hydroxycinnamic acid glycoside. Variations were noted in the chemical fingerprints of the decoctions, but the flavonoid glycosides orientin, isoorientin, and vitexin emerged as potential markers for the species. The essential oils of the samples contained high amounts of oxygenated monoterpenoids, with a predominance of the p-menthanes carvacrol (up to 61.9 %) and/or thymol (up to 17.0 %), and p-cymene (up to 24.9 %). This study was the first to explore the chemical composition of decoctions derived from LO, highlighting the species as an untapped source of phenolic glycosides, an aspect that has received limited discussion thus far.

Effects of steroid hormones and their mixtures on western mosquitofish (Gambusia affinis)

Steroid hormones, including estrone (E1), androstadienedione (ADD), and androstenedione (AED), are prevalent in aquatic ecosystems and pose ecological risks due to their disruptive influence on fish populations. However, little consideration has been given to the endocrine disrupting effects of fish exposed to complex mixtures of hormones in the real world. In this study, adult female western mosquitofish (Gambusia affinis) were exposed to two concentrations of E1 (100 ng/L for E1L and 5,000 ng/L for E1H), ADD (100 ng/L for ADDL and 10,000 ng/L for ADDH), and AED (100 ng/L for AEDL and 10,000 ng/L for AEDH) as well as four binary mixture treatments (ADDL+E1L, ADDH+E1H, AEDL+E1L, and AEDH+E1H). After 42 d, their basic physiological parameters, secondary sex characteristics, gonadal health, embryo numbers, and HPG axis-related gene expression were evaluated. Results showed that the P/D ratio of hemal spines in AEDH+E1H exhibited a pronounced reduction, approximately half that of E1H. Moreover, the number of embryos in ADDH+E1H and AEDH+E1H was reduced by approximately 3-fold compared to E1H. Correspondingly, G. affinis exposure to ADDH+E1H and AEDH+E1H increased the proportion of degenerated oocytes. Exposure to combined treatments led to significant changes in the transcription of HPG axis-related genes in fish and displayed a certain degree of interaction. Furthermore, cluster heatmap analysis of target genes demonstrated that ADD+E1 and AED+E1 (both high and low concentrations) were far apart from ADD, AED and E1. Collectively, these observations imply the presence of antagonistic interactions in combined treatments, and the negative impact on the growth, maturation, and endocrine system of G. affinis varies accordingly.

Comparative Performance of Ionic and Agro-Physiological Traits for Detecting Salt Tolerance in Wheat Genotypes Grown in Real Field Conditions.

Studying the physiological mechanisms underlying the traits associated with salt tolerance in genotypes could lead to the discovery of new genetic resources for salt tolerance. In this study, the mechanisms of salt tolerance were evaluated, based on ionic, physiological, and agronomic traits in four varieties that differ in their salt tolerance and in 18 F8 recombinant inbred lines (RILs) grown in real field conditions. The salt tolerance of plant materials was assessed under both normal (3.5 mM NaCl) and high salinity stress (150 mM NaCl) conditions for two consecutive years. Different growth and physiological traits were assessed 75 days after sowing, while ion contents in the shoots, grain yield, and its components were determined at the maturity stage. Multivariate analysis was used to conduct a comprehensive evaluation of salt tolerance across various genotypes and traits. The ANOVA results showed significant differences (p ≤ 0.05 and 0.001) among salinity, genotypes, and their interactions for all ionic and agro-physiological traits, with a few exceptions. Salinity stress resulted in a considerable increase in Na+ content and canopy temperature (CT), with a simultaneous decrease of 11.3% to 94.5% in other ionic and agro-physiological traits compared to the control treatment. However, the salt-tolerant genotypes showed minimal increases in Na+ content and CT, as well as decreases in other ionic and agro-physiological traits when compared to salt-sensitive genotypes under salinity stress. All ionic and agro-physiological traits exhibited strong correlations with each other under salinity stress, but these correlations were weak or insignificant under control conditions. The principal component analysis identified Na+ and CT as negative indicators and other ionic and agro-physiological traits as positive indicators for salt tolerance under salinity stress. The negative indicators were strongly linked to salt-sensitive genotypes, while the positive indicators were closely associated with salt-tolerant genotypes. Heatmap clustering, using multiple traits, successfully differentiated the salt-tolerant genotypes from the salt-sensitive ones. The salt-tolerant group showed a significant reduction in Na+ content by 36.9%, in CT by 10.0%, and in HI by 16.7%, along with an increase of 6.3-51.4% in other ionic and agro-physiological traits compared to the salt-sensitive group. In conclusion, the mechanisms associated with Na+ exclusion and high K+/Na+ and Ca2+/Na+ ratios, as well as chlorophyll and relative water content, along with low CT, resulted in significant improvements in growth and yield under salinity stress conditions. Given that the effectiveness of various ionic and agro-physiological traits in evaluating salt tolerance in wheat has been proven in real field conditions, these traits will play a key role in the development of salt-tolerant wheat genotypes.

HS-SPME-GC-MS combined with multivariate statistical analysis reveals off-flavor composition and biomarker generation mechanism of whole milk powder

Milk powder is prone to producing off-flavor when it is not handled properly during production or storage. In order to identify the off-flavor compounds of whole milk powder (WMP), headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) combined with cluster heatmap analysis was used, orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component analysis (PCA) were used to investigate the off-flavor substances of whole milk powder. Cluster heatmap analysis showed that there were certain differences in the relative contents of volatiles among the eight milk powders. Further, OPLS-DA was applied to the relative contents of volatile substances in milk powders with and without unpleasant odor, a to obtain the main difference components of the 28 with variable importance in projection (VIP) greater than 1, with off-flavor substances being the main ones. Finally, the volatile compounds positively correlated with off-flavors in milk powder were screened by principal component analysis and combined with relative aroma activity values greater than 1 to identify (E)-2-hexenal and (E,E)-2,4-decadienal as the major off-flavor compounds of milk powder, and also described the mechanism of generation of (E)-2-hexenal (a marker of soya flavor) and (E,E)-2,4-decadienal (a marker of grass flavor) in milk powder. The results provide a reference for the analysis of flavor profile and possible off-flavor components of milk powder, which is beneficial for the optimization and improvement of whole milk powder.

Transcriptome analysis to identify genes related to programmed cell death resulted from manipulating of BnaFAH ortholog by CRISPR/Cas9 in Brassica napus

Fumarylacetoacetate hydrolase (FAH) catalyzes the final step of the tyrosine degradation pathway. In this study, we isolated and characterized two homologous BnaFAH genes in Brassica napus L. variant Westar, and then used CRISPR/Cas9-mediated targeted mutagenesis to generate a series of transgene-free mutant lines either with single or double-null bnafah alleles. Among these mutant lines, the aacc (bnafah) double-null mutant line, rather than the aaCC (bnaa06fah) mutant line, exhibited programmed cell death (PCD) under short days (SD). Histochemical staining and content measurement confirmed that the accumulation of reactive oxygen species (ROS) in bnafah was significantly higher than that in bnaa06fah. To further elucidate the mechanism of PCD, we performed transcriptomic analyses of bnaa06fah and bnafah at different SD stages. A heatmap cluster of differentially expressed genes (DEGs) revealed that PCD may be related to various redox regulatory genes involved in antioxidant activity, ROS-responsive regulation and calcium signaling. Combined with the results of previous studies, our work revealed that the expression levels of BnaC04CAT2, BnaA09/C09SAL1, BnaA08/C08ACO2, BnaA07/C06ERO1, BnaA08ACA1, BnaC04BIK1, BnaA09CRK36 and BnaA03CPK4 were significantly different and that these genes might be candidate hub genes for PCD. Together, our results underscore the ability of different PCD phenotypes to alter BnaFAH orthologs through gene editing and further elucidated the molecular mechanisms of oxidative stress-induced PCD in plants.

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer.

Dysregulated lipid metabolism promotes the progression of various cancer types, including breast cancer. The present study aimed to explore the lipidomic profiles of patients with breast cancer, providing insights into the correlation between lipid compositions and tumor subtypes characterized by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Briefly, 30 patients with breast cancer were categorized into four groups based on their HR and HER2 status: HR+ no HER2 expression (HER2-0), HR+ HER2-low; HR+ HER2-positive (pos) and HR- HER2-pos. The lipidomic profiles of these patients were analyzed using high-throughput liquid chromatography-mass spectrometry. The data were processed through principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and random forest (RF) classification to assess the lipidomic variations and significant lipid features among these groups. The profiles of the lipids, particularly triglycerides (TG) such as TG(16:0-18:1-18:1)+NH4, were significantly different across the groups. PCA and PLS-DA identified unique lipid profiles in the HR+ HER2-pos and HR+ HER2-0 groups, while RF highlighted phosphatidylinositol-3,4,5-trisphosphate(21:2)+NH4 as a crucial lipid feature for accurate patient grouping. Advanced statistical analysis showed significant correlations between lipid carbon chain length and the number of double bonds within the classifications, providing insights into the role of structural lipid properties in tumor biology. Additionally, a clustering heatmap and network analysis indicated significant lipid-lipid interactions. Pathway enrichment analysis showed critical biological pathways, such as the 'Assembly of active LPL and LIPC lipase complexes', which has high enrichment ratio and statistical significance. In conclusion, the present study underscored that lipidomic profiling is crucial in understanding the metabolic alterations associated with different breast cancer subtypes. These findings highlighted specific lipid features and interactions that may serve as potential biomarkers for breast cancer classification and target pathways for therapeutic intervention. Furthermore, advanced lipidomic analyses can be integrated to decipher complex biological data, offering a foundation for further research into the role of lipid metabolism in cancer progression.

Identification and comparison of protein composition of biofilms in response to EGCG from Enterococcus faecalis and Staphylococcus lugdunensis, which showed opposite patterns in biofilm-forming abilities

Bacterial biofilm is resistant to conventional antibiotic treatments, leading to complications associated with many infection-related human diseases. Epigallocatechin Gallate (EGCG), a phenolic catechin enriched in green tea, is recognized for its anti-bacterial and anti-biofilm activities. In this study, we examined the protein components of the biofilms formed in the absence or presence of EGCG using Enterococcus faecalis and Staphylococcus lugdunensis, which had shown opposing patterns in biofilm formation. A clustering heatmap revealed that the two microorganisms expressed the different protein sets in response to EGCG. Proteins that were noticeably upregulated included those associated with stress responsiveness and gluconeogenesis in E. faecalis, and gene modification in S. lugdunensis. Conversely, downregulated proteins were related to tRNA-modifying enzyme activity in E. faecalis, and anabolic metabolism in S. lugdunensis. Among the proteins identified only in EGCG-responsive biofilms, enzymes involved in de novo purine biosynthesis were enriched in E. faecalis, while proteins likely to cause DNA instability and pathogenicity changes were abundantly present in S. lugdunensis. The classification based on gene ontology (GO) terms by microorganism exhibited that metabolic process or catabolic activity was at the top rank in E. faecalis with more than 33 proteins, and in S. lugdunensis, localization or transport was highly ranked with 4 proteins. These results support the hypothesis that EGCG might cause different cellular programs in each microorganism. Finally, comparison of the proteomes between two groups that form biofilms to similar extents discovered that 2 proteins were commonly found in the weak biofilm-forming groups (E. faecalis and EGCG-responding S. lugudunensis), whereas 9 proteins were common among the strong biofilm-forming groups (S. lugdunensis and EGCG-responding E. faecalis). It was suggested that these proteins could serve as potential indicators to detect the presence and predict the extent of biofilm formation by multiple microorganisms. Taken all together, proteomics data and analyses performed in this study provided useful and new information on the proteins embedded in the biofilms formed at the specific conditions, which can aid in diagnosis and the development of tailored treatment strategies.

Data-driven designing of organic electrode materials for batteries and property prediction

In recent years, data driven methods particularly machine learning have brought a significant revolution in materials discovery specially designing new organic compounds for the batteries. This work presents data-driven designing of organic electrode materials for batteries. In order to train machine learning models, data is collected from previous studies. Importantly, four trained machine learning models are used to predict the redox potential values of the electrode materials. Among others, random forest and bagging regression emerged as the best trained models with an impressive R2 value of 0.72. Using Dragon Software, 4000 descriptors are calculated. Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) tool of RDkit is used to design 20,000 new organic compounds. 50 compounds are selected on the basis of their redox potential. Similarity analysis is applied by using cluster plot and heatmap. Interestingly, the synthetic accessibility score of the newly designed compounds is below 4, which indicates that they can be easily synthesized. This work paves the way for accelerated screening and rational designing of high-performance electrode material for batteries.

Characterization of Changes in Ripening Process of Volatile Apple Compounds Based on HS-SPME-GC-MS Analysis

The aim of this study was to identify the aromatic compounds present in different apple varieties and to gain insights into the changes in the aromatic compounds during ripening. Three apple varieties (“Red Astrachan”, “Ning Qiu”, and “Golden Delicious”) at different stages of ripening were selected for this study; their peel and pulp were analyzed via headspace solid-phase microextraction (HS-SPME)–gas chromatography–mass spectrometry (HS-SPME-GC-MS), and 30 volatile compounds were identified. The samples’ differences were analyzed using heat map cluster analysis, principal component analysis (PCA), and an independent samples t-test. The results showed that the content of aromatic compounds in the peel was higher than that in the pulp. The relative content of esters in the aromatic compounds of the three apple varieties followed the order of pulp &gt; peel and “Ning Qiu” &gt; “Golden Delicious” &gt; “Red Astrachan”. This suggests that “Ning Qiu” combines the advantages of its parents in terms of its aroma content. The highest concentrations of aroma compounds in “Red Astrachan” and “Ning Qiu” accumulate before the ripening stage, and care should be taken to choose an appropriate harvesting time according to the different needs during production. The main compounds of “Red Astrachan” are aldehydes and C8 esters, while those of “Ning Qiu” and “Golden Delicious” are alkenes and esters. After analyzing the relative odor activity values (rOAVs) of key volatile compounds and their aroma descriptors during the harvest period, acetic acid pentyl ester, butanoic acid hexyl ester, hexanoic acid hexyl ester, and 2-ethyl-1-Hexanol were found to contribute the most to the overall flavor of the peel and pulp. “Ning Qiu” combines the parental advantages of the concentrated peel of “Red Astrachan” and the astringent pulp of “Golden Delicious”, with compounds in its composition that give a pleasing aroma. Mature “Ning Qiu” fruits have a more intense aroma and fruity flavor. The development of flavor-specific varieties has provided the theoretical basis for future research in molecular hybridization, molecular-assisted breeding, and the molecular biology of apple flavor synthesis and metabolism.

Algal EPS modifies the toxicity potential of the mixture of polystyrene nanoplastics (PSNPs) and triphenyl phosphate in freshwater microalgae Chlorella sp.

Triphenyl phosphate (TPP) and polystyrene nanoplastics (PSNPs) are prevalent freshwater contaminants obtained mainly from food packaging, textiles and electronics. Algal extracellular polymeric substances (EPS), a part of natural organic matter, may influence these pollutants' behaviour and toxicity. The presence of EPS can enhance the aggregation of TPP-PSNP mixtures, and reduce the bioavailability, and thus the toxicity potential. Understanding the mutual interactions between TPP, PSNPs, and EPS in the aquatic environment is a prerequisite for the environmental risk assessment of these chemicals. The study examines the toxicity effects of various surface-modified PSNPs (1 mg/L of plain, animated, and carboxylated) and TPP (0.05, 0.5, and 5 mg/L) in pristine and combined forms on freshwater microalgae, Chlorella sp., as a model organism. The physical-chemical interactions of algal EPS (10 mg/L) with PSNPs and TPP and their mixtures were studied. The toxicity potential of the PSNPs was estimated by quantifying growth inhibition, oxidative stress, antioxidant activity, and photosynthesis in the cells. TPP toxicity increased in the presence of the PSNPs, however the addition of algal EPS reduced the combined toxic effects. EPS plays a protective role by reducing oxidative stress and enhancing photosynthetic efficiency in the algal cells. The Pearson modeling analysis indicated a positive correlation between growth inhibition, and reactive oxygen species, malondialdehyde production. The cluster heatmap and correlation mapping revealed a strong correlation among the oxidative stress, growth inhibition, and photosynthetic parameters. The study clearly highlights the potential of EPS in mitigating the risk of mixed emerging pollutants in the aquatic environment.

Assessment of genetic diversity among Ixora genotypes through morphological and RAPD molecular marker analysis

Ixora a genus in the Rubiaceae family commonly known as jungle geranium, was the focus of the study. However, classifying Ixora species has been challenging due to environmental influences on phenotypic traits. This study explores the genetic diversity and morphological traits of 11 Ixora genotypes from various regions in India using 15 RAPD markers and morphological assessments. A total of 210 bands were generated, Unweighted Pair Group Method for Arithmetic Average (UPGMA) Cluster analysis revealed significant genetic relationships among the genotypes. Notably, Genotypes 9, 10 and 11 shared similar banding patterns with marker OPF 02, while Genotypes 4 and 7, despite differing growth habits, showed close genetic ties and shared RAPD markers OPA03 and OPE04. A unique marker (OPA03 - 340 bp) was identified exclusively in Genotype 4. Principal Component Analysis (PCA) indicated that 2 principal components explained 77.074 % of the total variance, highlighting plant height and leaf number as significant traits. Heat map clustering further illustrated phenotypic variability, grouping genotypes based on morphological similarities. This study demonstrates the utility of RAPD markers in distinguishing Ixora genotypes and provides valuable insights into morphological traits, guiding future breeding programs and genetic research.