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Emotion dysregulation as a marker in adolescent mental health with EEG-based prediction model
Single phase extraction method for determination of dithianon residues in fruits and vegetables using LC ESI (-) MS/MS
Abstract Dithianon is a non-systemic fungicide, applied in some agricultural products. Dithianon residues in food cause health problems for humans so it is recommended to be analyzed in fruits and vegetables. Four different extracting solvents were compared to get the optimum one. Quantitative analysis was done using a liquid chromatography triple quadruple mass spectrometer in different agricultural products. The in-house validation process was carried out based on SANTE guideline. The results demonstrated an average recovery rate between 85 and 113%, with relative standard deviations (RSDs) ≤ 8% for all tested food matrices in repeatability and RSD wR % = 16% in within-Laboratory reproducibility. Good linearity at r2 > 0.99 was obtained for 0.001–0.5 µg/ml dithianon calibration curves. Limit of quantifications (LOQs) for the method ranged between 0.01 and 0.05 µg/g with expanded measured uncertainty Uexp = ± 42%. Our method is simple, fast and reliable for the determination of dithianon residues in food so, it is recommended to be applied in the routine analysis. The method’s practicality was confirmed by analyzing fifty market samples from Egypt. No dithianon residues were detected, a finding consistent with its limited national registration and underscoring the method’s utility for ensuring compliance and food safety.
A simulation of thermal coupling characteristics on deep-sea residual oil recovery via pipeline using thermophysical properties
Univariate and multivariate signal processing spectrophotometric determination of an antihypertensive combination in line with the United Nations sustainable development goals
Abstract Effective management of hypertension typically involves multiple medications. This underscores the pharmaceutical industry’s demand for simple, cost-effective, and environmentally sustainable analytical methods capable of handling complex, multicomponent formulations. This study’s primary goal was to compare and validate univariate and multivariate spectrophotometric techniques for analyzing fixed-dose antihypertensive formulations of Telmisartan (TEL), Chlorthalidone (CHT), and Amlodipine (AML). Successive Ratio Subtraction paired with Constant Multiplication (SRS-CM) and Successive Derivative Subtraction paired with Constant Multiplication (SDS-CM) were the developed univariate methods. The cited drugs were successfully quantified at their respective maxima: 295.7 nm for TEL, 275.0 nm for CHT, and 359.5 nm for AML. On the other hand, the SDS-CM method enabled their determination using first-derivative spectra, with TEL identified at P 282.5–313 nm, CHT at 287.0 nm, and AML at P 231-246 nm. Also, Interval-Partial Least Squares (iPLS) and Genetic Algorithm-Partial Least Squares (GA-PLS) were applied as multivariate techniques. In contrast to full-spectrum modeling alone, the results showed that adding variable selection techniques greatly improved the model’s performance. Following ICH guidelines, the proposed techniques were used to quantify the cited medications in tablets. The validity of the results was confirmed by statistical comparison with the reported method. The study was further expanded to assess the content uniformity of the dosage units in compliance with USP. Three environmental complementary assessment tools were employed: the Analytical Greenness Metric (AGREE), the Blue Applicability Grade Index (BAGI) and White Analytical Chemistry (RGB12). This study also aligns with several UN Sustainable Development Goals (UN-SDGs), emphasizing commitment to green pharmaceutical research. Sustainability was verified using the NQS index, confirming the method’s compliance with responsible analytical practices.
Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression
Abstract Despite increasing awareness of microplastics as contaminants, their sources and abundance factors remain poorly understood. This study found widespread microplastic pollution in the Lupit River. Microplastic concentrations were determined from surface water samples once per season. Samples were analyzed across rural, residential, informal settlement, and commercial zones during both seasons. Microplastic concentration (particles/m 2 ) was modeled using multiple linear regression with four predictors: population, seasonality, macroplastic frequency (items/m-hr), and volumetric flow rate (m 3 /s). To enhance model stability, multicollinear variables (velocity, width, and depth) were removed. Concentrations were lower in the wet season due to dilution and flushing and higher in the dry season likely from accumulation and weathering. The final model showed strong explanatory power (R 2 = 0.690, adjusted R 2 = 0.643), with population and seasonality as significant predictors ( p < .001). Seasonality had a negative effect (β = – 621.90) due to dilution. Surprisingly, population correlated negatively (β = -0.0217), suggesting better waste infrastructure in denser areas. Macroplastic abundance and flow rate were not statistically significant due to slow weathering and local microplastic accumulation. The model’s standard error (301.6 particles/m 2 ) accounted for 16.9% of the mean concentration (1,780 particles/m 2 ), signifying good prediction accuracy. The developed predictive model provides a low-cost tool for estimating microplastic levels in data-scarce areas which can assist agencies in tracking pollution trends over time and to assess the effectiveness of waste management policies. Future research should validate the model across different catchments and temporal scales to enhance its utility in long-term monitoring.
Nutrient enhanced yogurt dressing using microencapsulated anthocyanins from red onion by-products for functional food applications
A moderated mediation approach to enhancing autonomous learning in university physical education
Combined multi-omics and brain pathology reveal novel biomarkers for alzheimer’s disease
Manipulating transient SOT-MRAM switching dynamics for efficiency improvement and probabilistic switching
Abstract In this paper, we investigate the effect of transient dynamics in the switching process for spin-orbit torque magnetic random-access memory (SOT-MRAM) devices stabilized by in-plane uniaxial magnetocrystalline anisotropy. We develop theory for the interaction between spin torques and effective fields during a magnetization write trajectory and apply this framework to find regions of failed and successful switching. We focus particularly on a “quasi-stochastic” regime located between regions of deterministic failed and successful switching and caused by the interplay between torque-driven and precession-driven magnetization evolution during the switching process. We demonstrate a series of minor alterations to device geometry, material characteristics, and electrical inputs that use transient phenomena to lower the switching barrier—thereby allowing for SOT-MRAM switching with significantly lower currents and faster write speeds than the traditional architecture. Furthermore, we demonstrate that at elevated temperatures, the unpredictable stochastic regime evolves into a probabilistic “transition band” with clearly defined, montotonic, and tunable regions of probabilistic operation. Through this addition of control mechanisms through electrical inputs, our framework paves the way for the creation of a fast, efficient probabilistic bit (p-bit) for the field of probabilistic computing.
OCT analysis of white clover leaves affected by regional ozone stress
Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment
Baicalein inhibits amyloid beta42 aggregation through disruption of the Asp23-Lys28 salt bridge
Research waste in randomized clinical trials of degenerative spinal diseases: a cross-sectional study
Improving disinfection and packaging strategies for the effective preservation of welsh onion (Allium fistulosum L.)
Extracellular RNA as a molecular driver and therapeutic target in abdominal aortic aneurysms
Abstract Abdominal aortic aneurysms (AAAs) are characterized by chronic inflammation, matrix degradation and smooth muscle cell (SMC) loss, leading to vessel dilation and rupture, with no current pharmaceutical management options. Since recent studies have highlighted the role of extracellular (ex) nucleic acids in promoting inflammation and tissue damage in cardiovascular conditions, we aimed to characterize the contribution of exDNA and exRNA to AAA pathogenesis and evaluate their potential as therapeutic targets in established disease. Circulating exDNA was elevated in patients and mouse models, while plasma levels of exRNA were not associated with AAA development. When RNase A or DNase I was administered to mice with established disease, the targeted degradation of exRNA, but not exDNA, significantly attenuated aneurysm growth. The RNase A treatment produced systemic anti-inflammatory effects (reduced monocyte/granulocyte count) and showed the potential to improve local vascular conditions by preserving SMC integrity, reducing macrophage infiltration and protease expression. These effects were not observed in DNase I-treated animals. In conclusion, while circulating exDNA showed AAA biomarker potential, targeting exRNA by systemic RNase A treatment effectively mitigated aneurysm progression in established disease through pleiotropic modulation of inflammation and tissue remodeling, presenting a novel and promising therapeutic strategy.
Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures
IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases
A comprehensive framework for solution space exploration in community detection
Abstract Community detection algorithms are essential tools for understanding complex networks, yet their results often vary between runs and are affected by node input order and the presence of outliers, undermining reproducibility and interpretation. This paper addresses these issues by introducing a framework for systematic exploration of the solution space, obtained through repeated runs of a given algorithm with permuted node orders. A Bayesian model assesses convergence, estimates solution probabilities, and provides a defensible stopping rule that balances accuracy and computational cost. Building on this process, we propose a taxonomy of solution spaces that offers clear diagnostics of partition reliability across algorithms and a shared vocabulary for interpretation. Applied to a real-world network, the approach shows that different algorithms produce various types of solution space, highlighting the importance of systematic exploration of the solutions before drawing scientific conclusions.
Long-read methylome analysis of Oleidesulfovibrio alaskensis G20 biofilm under copper stress
Abstract This study represents the first investigation of 5-methyl cytosine (5mC) DNA methylation patterns in sulfate-reducing bacterial (SRB) biofilms under copper (Cu) stress, utilizing Oxford Nanopore Technologies (ONT) sequencing. DNA methylation is a crucial epigenetic modification that is dynamic and regulates the signals to modulate molecular mechanisms across biological systems. The regulatory roles of DNA methylation in prokaryotic systems remain comparatively understudied than in eukaryotes. Bacteria are highly sensitive to environmental changes and therefore may utilize additional mechanisms like DNA methylation to combat the stresses. Our previous studies, utilizing microscopy and growth analyses, revealed that Oleidesulfovibrio alaskensis G20 (OA G20) biofilms responded to Cu stress. However, the DNA methylation patterns associated with this response remain unexplored, leaving a critical gap in our understanding of the epigenetic mechanisms regulating OA G20 biofilms under Cu stress. This study aims to address this knowledge gap by identifying 5mC DNA methylation in biofilms of OA G20 under Cu stress. To achieve our goal OA G20 biofilms cultivated under 30 µM-Cu ion stress along with control and sequenced through ONT sequencing. DNA methylation analysis was performed using the MicrobeMod pipeline identifying three methylated motifs: TCCG, CCCGCCCG, and CGGGAT in control (0 µM-Cu). TCCG was identified as the predominant methylated motif, with analysis revealing 78,022 genomic positions in the control condition. Of these, 61.7% exhibited 5mC modifications, 33.9% remained unmodified, and 4.4% showed uncharacterized modifications. In contrast, the 30 µM-Cu biofilm showed methylation in only two motifs, TCCG and GCANCTGCGS. Analysis of TCCG revealed 63,315 genomic positions, with 62.7% (39,706 sites) showing methylation and 33.2% (20,990 sites) remaining unmethylated. A total of 1418 common methylated positions were identified for both conditions and there were 341 and 424 genomic positions identified for motif TCCG above 75% methylation in the 0 µM and 30 µM-Cu biofilm samples, respectively. Differential methylation analysis revealed significant variations in methylation patterns across several key genes of crucial molecular pathways, important for biofilm formation, including ATP-Binding Cassettes (ABC) transporters, phosphohydrolase, flagellar biosynthesis, chemotaxis, cobalamin synthase, histidine kinase, and uncharacterized proteins.