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Enhancing MRI contrast between sub-chronic myocardial infarct, remote and healthy myocardium using periodic irradiation in the rotating frames
Abstract Relaxations Along a Fictitious Field in the rotating frame of rank 2 (RAFF2) was used to characterize fibrosis-related myocardial diseases with MRI. RAFF2 can be sensitized to molecular dynamics processes by changing the durations of the RAFF2 pulses. Here, we studied the effect of altering the RAFF2 pulse durations for characterization of myocardial infarction (MI). 10 mice underwent myocardial infarct surgery, and the hearts were collected 7 days after the operation. 3 mice hearts were served as healthy control. We tested the influence of stretching factor (TL) on relaxations, where TL was between 0.6 and 2.0, and TL = 1 corresponds to the duration of the RAFF2 pulses TL = 2.25 ms with α 2 = 45°. Here, α 2 = 45° indicates location of the fictitious field in the second rotating frame. The contrast between MI and remote areas were measured as Relative Relaxation Time Difference (RRTD). Significant increase in relaxation time constants between MI and remote areas with relatively high RRTD were found in RAFF2 TL0.6, TL0.8, TL1.8 and TL2.0. With protein analysis, fibronectin was upregulated in MI area and histology showed area of fibrosis. By altering durations RAFF2 pulse, MI areas can be captured from the myocardium, which provides a novel way to evaluate molecular dynamics in MI area. The findings were validated by the results of other MRI methods, protein analyses and histological sections. Enhancement of MRI contrast between MI, remote and healthy myocardium without using any contrast agents can be achieved by changing the duration RAFF2 pulse.
Rank charged system search algorithm for optimization and operations research
Abstract In this paper, we introduce CSSRank, an improved version of the charged system search (CSS) algorithm, designed to address complex optimization problems more efficiently. CSSRank integrates a rank-based reduction selection strategy to enhance exploitation by progressively reducing the number of charged particles used in electric force calculations. To further balance exploration and exploitation, a ranking-based mutation strategy is incorporated, promoting diversity in early iterations and precision in later stages. We evaluated CSSRank on a set of standard benchmark functions and compared its performance with the original CSS algorithm. In addition, CSSRank was tested on two major benchmark suites, CEC 2014 and CEC 2024, and compared against a wide range of state-of-the-art metaheuristic algorithms. The results show that CSSRank outperforms many existing methods on CEC 2014 and performs competitively and close to the best-performing algorithms on CEC 2024, demonstrating both robustness and scalability. For real-world applications, CSSRank was applied to six UCI clustering datasets, where it consistently achieved higher clustering accuracy and more reliable objective values than baseline methods. It was also tested on three complex reservoir operation optimization problems, yielding superior engineering solutions with high reliability, and contributing to improvements in operational cost and resource efficiency. These results confirm the effectiveness, versatility, and reliability of CSSRank across both theoretical and practical optimization tasks, positioning it as a strong candidate for solving complex problems in optimization and operations research.
Genomic and epidemiological insights into the emergence and dominance of MRSA clones in Riyadh’s healthcare facilities
Abstract The emergence and expansion of methicillin-resistant Staphylococcus aureus (MRSA) clones in healthcare facilities pose a significant public health concern due to their adaptability and resistance to commonly used antibiotics. In this study, the genomic and epidemiological characteristics of 81 MRSA isolates collected between February and June 2022 were analyzed. ST5 (25.9%) and ST6 (19.7%) emerged as the dominant sequence types, collectively accounting for 45.6% of infections. Whole-genome sequencing and phenotypic analyses revealed that ST5 clone exhibited a broader multidrug-resistant profile compared to ST6 clone, with higher prevalence of β-lactam, tetracycline, and trimethoprim resistance. ST6 clone showed more variable resistance, including aminoglycoside and macrolide genes, suggesting a possible community origin before adaptation to hospital settings. Phylogenetic analysis demonstrated ongoing microevolution within Clonal Complex 5 (CC5), including the identification of novel single-locus variants such as ST8111. Correlation analyses highlighted significant associations between key resistance genes and their phenotypic profiles, reflecting complex co-resistance mechanisms. Additionally, virulence profiling revealed that ST5 uniquely carried tsst -1, whereas PVL genes were absent in both ST5 and ST6 and appeared only in a small subset of other MRSA clones. The emergence of highly resistant clones like ST672 underscores the potential concern for sustained genomic surveillance. The observed clonal shift from ST239 to ST5 and ST6 signals a dynamic MRSA landscape in Saudi Arabia, emphasizing the need for integrated molecular epidemiology and targeted infection control strategies.
Primacy of feature engineering over architectural complexity for intermittent demand forecasting
Correction: Ptbp1 knockdown induces conversion of rat spinal cord astrocytes into neuron like cells
sc-eQTL unveil immunogenetic architecture of polycystic ovary syndrome
Acclimation to high and low diurnal light is flexible in <i>Chlamydomonas reinhardtii</i>
Chlamydomonas acclimates to repeated low (LL) or high light (HL) days by changing the abundance of photosynthetic complexes and the ultrastructure of its thylakoid membranes. These phenotypes persist through the night phases, suggesting a readiness for the daylight environment that is routinely experienced despite the intervening dark periods [S. Dupuis et al. , Plant Cell 37 , koaf086 (2025), 10.1093/plcell/koaf086]. Here, we investigate how prior acclimation impacts algal fitness upon a change in daylight intensity and how quickly Chlamydomonas can reprogram its photoprotective strategy in a diurnal context. We performed a systems analysis of synchronized populations acclimated to diurnal LL when subjected to HL days and of populations acclimated to diurnal HL when subjected to LL days. In the latter case, diurnal photoacclimation decreased fitness during the first day at a new light intensity: HL-acclimated cells barely increased in size over the first LL period, and they failed to complete a cell cycle. However, although LL-acclimated cells showed severe photodamage after 6 h of HL, they recovered chloroplast form and function later that afternoon and successfully divided at nightfall. These cells rapidly altered their thylakoid membrane ultrastructure, increased their photoprotective quenching capacity, and decreased their inventory of photosystem and antenna proteins by the end of the first HL day. Transcriptomic and proteomic analyses revealed rapid induction of thousands of genes, including those encoding proteases, chaperones, and other proteins involved in the chloroplast unfolded protein response. These results show that the alga is highly flexible and competent to rapidly acclimate to changes in diurnal light intensity.
In vitro anti-inflammatory potential and in vivo anti-arthritis activities of Ximenia caffra extract on antigen-induced arthritis in rats
Abstract The current study estimated the in vitro anti-inflammatory activity and in vivo anti-arthritic activities of the aqueous ethanolic extract of Ximenia caffra ( X. caffra ) seeds extract. It was hypothesized that X. caffra seeds extract, rich in phytochemicals that could modulate inflammatory pathways and protect joint tissues in an antigen-induced arthritis rat model. The chemical composition of X. caffra seeds extract was examined using liquid chromatography high-resolution mass spectrometry (LC-HRMS). Ximenia caffra seeds extract showed promising in vitro anti-inflammatory activity with an IC 50 = 26.01 ± 0.85 µg/ml. To evaluate in vivo efficacy, antigen-induced arthritis was established in rats using Complete Freund’s Adjuvant (CFA), followed by subcutaneous administration of X. caffra extract at doses of 26, 50, and 100 mg/kg body weight, alongside a standard drug control [Methotrexate (MTX), 0.3 mg/kg] in separate groups of animals. Anti-arthritic effects were assessed by measuring joint diameter, arthritic score, body weight, and through histopathological and ultrastructural analyses of joint and muscle tissues as well as osteoclast assessment, cytokine analyses, renal and kidney functions. The optimal dose (26 mg/kg) significantly alleviated arthritis symptoms, restoring joint and muscle morphology toward normal architecture. Ex vivo osteoclast evaluation and flow cytometric apoptosis analysis indicated that X. caffra extract promoted cellular recovery and reduced inflammatory damage. Furthermore, cytokine profiling demonstrated that treatment with X. caffra shifted pro-inflammatory mediators (IL-1β, IL-6, IL-17, IFN-γ) toward an anti-inflammatory balance by elevating IL-4 and regulating IgG1a/IgG2a ratios. Collectively, these results support the hypothesis that X. caffra seeds extract had potent anti-inflammatory and anti-arthritic effects by modulating immune responses and preserving joint integrity, suggesting its potential as a natural therapeutic alternative for rheumatoid arthritis management after further validation.
Host–microbiome mutualism drives urea carbon salvage and acetogenesis during hibernation
Hibernation is a seasonal survival strategy employed by certain mammals that, through torpor use, reduces overall energy expenditure and permits long-term fasting. Although fasting solves the challenge of winter food scarcity, it also removes dietary carbon, a critical biomolecular building block. Here, we demonstrate a process of urea carbon salvage (UCS) in hibernating 13-lined ground squirrels, whereby urea carbon is reclaimed through gut microbial ureolysis and used in reductive acetogenesis to produce acetate, a short-chain fatty acid (SCFA) of major value to the host and its gut microbiota. We find that urea carbon incorporation into acetate is more efficient during hibernation than the summer active season and that while both host and gut microbes oxidize acetate for energy supply throughout the year, the host’s ability to absorb and oxidize acetate is highest during hibernation. Metagenomic analysis of the gut microbiome indicates that genes involved in the degradation of gut mucins, an abundant endogenous nutrient, are retained during hibernation. The hydrogen disposal associated with reductive acetogenesis from urea carbon helps facilitate this mucin degradation by providing a luminal environment that sustains fermentation, thereby generating SCFAs and other metabolites usable by both the host and its gut microbes. Our findings introduce UCS as a mechanism that enables hibernating squirrels and their gut microbes to exploit two key endogenous nutrient sources—urea and mucins—in the resource-limited hibernation season.
Influence of Kerr nonlinearities on THz radiation generation in air by bichromatic femtosecond laser pulses
Social attraction to a predatory black hole: A natural ecological trap for lobsters
Ecological traps occur when animals erroneously choose habitats that lower their fitness, drawn by cues that are adaptive in most instances, but not all. Most ecological traps result from anthropogenic changes that make habitats less beneficial, yet still attractive to unsuspecting animals. Naturally occurring ecological traps are rare, persisting despite their drain on population sustainability. We found an unusual natural ecological trap whereby social juvenile Caribbean spiny lobsters ( Panulirus argus ) are drawn to solution hole dens by the scent of larger conspecifics dwelling there, but where predatory red grouper ( Epinephelus morio ) also lurk. Although lobsters are sensitive to odors from food, healthy and diseased conspecifics, and other predators such as octopus, our experiments revealed that lobsters cannot detect the scent of groupers. As a consequence, mortality of small juvenile lobsters was 30% higher near solution holes occupied by grouper, as compared to larger lobsters that are invulnerable to the gape-limited grouper predator. By preying on small lobsters, grouper negatively skewed lobster size distributions up to 16 m away from the solution hole lair that they patrol. Our results provide one of the clearest examples of a natural ecological trap, in which the normally advantageous social cue of large conspecifics lures young lobsters to what is a predatory death trap. Although anthropogenically driven traps are an evolutionarily new and destabilizing force for animal populations, natural ecological traps like this one are ecological oddities whose effects on populations can be locally intense, yet persist through time.
Knowledge graph-enhanced deep learning for pharmaceutical demand forecasting
Abstract Accurate pharmaceutical demand forecasting is essential to ensure timely drug availability, reduce inventory costs, and improve operational efficiency in healthcare supply chains. However, existing statistical, machine learning, and deep learning approaches often struggle to capture the nonlinear and dynamic demand patterns arising from drug substitutions, comorbidity treatments, and seasonal disease fluctuations. To address this challenge, we propose KG-GCN-LSTM, a novel hybrid model that integrates a pharmaceutical knowledge graph (KG) with deep learning techniques. A clipped Graph Convolutional Network (GCN) is employed to extract feature representations from both the historical demand of the target drug and the related drugs encoded in the knowledge graph. The outputs of the GCN are subsequently processed by a Long Short-Term Memory (LSTM) network to capture temporal dynamics in drug demand. Experiments on real-world pharmacy sales data demonstrate that KG-GCN-LSTM consistently outperforms established benchmarks—including ARIMA, SVR, XGBoost, RNN, CNN-LSTM, TimeMixer and NBEATS, achieving a 3.62% reduction in Symmetric Mean Absolute Percentage Error (SMAPE) relative to NBEATS, while delivering performance comparable to the state-of-the-art TimeMixer. These results highlight the effectiveness of knowledge graph–enhanced deep learning in improving the accuracy and robustness of pharmaceutical demand forecasting, which can support data-driven decision-making in healthcare supply chain management.
Computer-assisted learning in the real world: How Khan Academy influences student math learning
Computer-assisted learning (CAL) offers an affordable way to implement a mastery learning approach in the classroom. However, while experimental research suggests CAL can enhance student outcomes, such findings often rely on experimental conditions not easily replicated in ordinary classroom settings (e.g., opt-in participation, extensive training and support, and high CAL usage targets). To assess the real-world impact of CAL, we draw on a large three-year panel of administrative data covering over 200,000 students in school districts that licensed Khan Academy’s Measures of Academic Progress accelerator, a program designed to support math learning. To identify causal effects, we exploit within-teacher and within-school changes in average classroom CAL practice time—a strategy that yields precise, policy-relevant estimates even at modest usage levels. We find that a classroom with 6.6 h of annual Khan Academy practice (about 11 min per week) experiences a + 0.031 SD gain in math test score performance compared to no practice. For classrooms with higher usage levels, we find approximately linear gains, with projected effects rising to + 0.085 SD at the recommended 30 min per week. Higher-achieving students benefit most, in part because they spend more time on CAL and progress through more skills than lower-performing peers. Teachers might reduce achievement gaps and boost overall gains by encouraging more productive use of the platform (focused on skill mastery)—especially among struggling students.
Numerical investigation on performance of concrete-steel composite beams incorporating multi-transverse holes
Abstract The integration of web openings in reinforced concrete (RC) beams for building services severely compromises shear capacity by disrupting load paths and creating critical stress concentrations. While previous research has focused on external strengthening of traditional RC beams, a significant gap exists regarding the performance of composite beams with embedded steel sections near openings. This study introduces a novel strengthening strategy using internally built-up I-section and T-section steel elements as shear reinforcement. The methodology integrated experimental testing with a validated nonlinear 3D finite element model in ABAQUS to conduct an extensive parametric study. Key investigated parameters included I-section web thickness (0.1–2.5 mm) and flange width (16–64 mm), and T-section compression and tension flange widths (0–64 mm). The key findings were substantial: web openings caused a 14% reduction in strength but a 41% increase in ductility, indicating a brittle failure mode. The incorporation of steel sections effectively reversed this; the I-section (176 × 2 mm web, 40 × 2 mm flanges) in beam BI-W2.0 yielded a remarkable 53.4% increase in ultimate load capacity, outperforming the original solid beam. An optimal I-section web thickness of 2.0 mm was identified, with diminishing returns beyond this point. For T-sections, the tension flange width was far more influential than the compression flange on strength recovery. A fundamental finding was that even a simple steel web alone provided a 43% strength gain, highlighting the critical role of bridging the opening. The reinforcement trade-off was a controlled 16–22% reduction in deflection, enhancing stiffness while maintaining structural safety. The research provides optimized, practical design guidelines for utilizing built-up steel sections to ensure structural integrity in perforated beams, effectively bridging architectural functionality and engineering safety.
Exploring the interplay between protein conformational changes and phosphorylation in a pancreatic cancer cell and stellate cell coculture system
A global exploration of the complex interplay between protein conformational changes and phosphorylation events in cell–cell interactions is crucial for understanding the dynamic nature of protein modifications. This understanding is essential for developing novel targeted therapies for pancreatic cancer, particularly in the context of interactions between pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs). However, protein conformational changes that occur during PCCs–PSCs interactions remain poorly studied, and the relationship between these changes and phosphorylation is not well understood. Here, we present a comprehensive mass spectrometry–based study investigating the interplay between protein conformational alterations and phosphorylation in a coculture system of pancreatic ductal adenocarcinoma cells (PANC-1) and PSCs. Our results demonstrate that 435 proteins exhibiting conformational changes were detected during the coculture of PANC-1 with PSCs, primarily involving proteins associated with the tricarboxylic acid (TCA) cycle, glycolysis/gluconeogenesis, and carbon metabolism. Our findings also highlight a potential association between phosphorylation and protein conformational changes. Moreover, we identified five potential conformational targets, including ACLY, ACO1, ACO2, IDH1, and OGDH, which may provide valuable insights into the molecular pathways underlying gemcitabine resistance in pancreatic cancer. Overall, these results offer insights into protein conformational changes and their potential link to phosphorylation in the context of cancer-stromal cell interactions, paving the way for structure-based, targeted therapeutic strategies for pancreatic cancer treatment.
Default mode network activity is related to efficiency in a combined motion error and gambling task
Antagonistic regulation of nitrogen and drought signaling mediated by NIN-like protein 7 transcription factor in <i>Arabidopsis thaliana</i>
Plants face the constant challenge of reconciling antagonistic environmental signals, such as nutrient-driven growth and water deficit–induced stress responses. However, the molecular mechanisms that integrate these conflicting pathways remain poorly understood. Through a comprehensive transcriptomic meta-analysis in Arabidopsis thaliana , we show that nitrogen (N) supply and water deficit signaling exhibit overlapping and often opposing gene expression responses. Regulatory network modeling identifies the NIN-LIKE PROTEIN 7 (NLP7) transcription factor (TF) as a central integrator of these convergent transcriptional responses. Through combinatorial water deficit and N supply treatments in wild-type and nlp7 mutant plants, we find that NLP7 accounts for 85% of the transcriptional interaction between these pathways. Chromatin immunoprecipitation and sequencing and a cell TF assay to detect TF regulation genome-wide reveal that NLP7 directly downregulate the expression of TFs such as HB6 , NAC6 , NAP , and WRKY18 , which are central regulators of water deficit–mediated stress signaling. Repression of these secondary TFs has distinct downstream effects on gene expression, influencing shared and water deficit–specific responses. Loss of NLP7 enhances water deficit tolerance, characterized by increased water retention, reduced abscisic acid-mediated stomatal aperture, and altered expression of stress-responsive genes. These findings establish NLP7 as a central hub balancing growth and stress responses, providing insight into how plants integrate competing environmental cues.
Albuminuria as a non-invasive biomarker of endothelial dysfunction in patients with COPD
Abstract Endothelial dysfunction (ED) plays a significant role in the pathogenesis of chronic obstructive pulmonary disease (COPD). While albuminuria is currently recognized as a biomarker of generalized ED, data on the evaluation of albuminuria among COPD patients and its association with disease outcome measures are still limited. Thus, the aim of this study was to assess albuminuria among a group of COPD patients and investigate its relationship with clinical and physiological parameters. Sixty adult patients with COPD and forty non-COPD adult smokers were included in this cross-sectional study. All participants were assessed for anthropometric parameters, oxygen saturation (SpO 2 ), spirometry test, 6-minute walk test, flow-mediated dilation (FMD) of the brachial artery, routine laboratory measurements, and urinary albumin-to-creatinine ratio (UACR). Patients with COPD had higher levels of UACR (mg/g) and a greater prevalence of albuminuria than non-COPD smokers. COPD patients with albuminuria had higher body mass index (BMI), more frequent exacerbations, lower SpO 2 , lower FMD, and higher fasting plasma glucose than patients without albuminuria. UACR in COPD patients was associated negatively with FMD and SpO 2 and positively with the number of comorbidities and fasting plasma glucose. The main predictors of albuminuria in COPD patients were high BMI, low SpO 2 , and high fasting plasma glucose. The only independent predictor of albuminuria was the presence of low SpO 2 . Given the significant association with FMD, the gold standard measure of ED, we suggest that the measurement of UACR could be routinely utilized for assessing ED in COPD patients, particularly those with low oxygen saturation.