Advancing Open Science

Quinoa (Chenopodium quinoa Willd.), an Andean crop with exceptional nutritional value, thrives in ecosystems exposed to intense ultraviolet-B (UV-B) radiation; yet the molecular mechanisms underlying its photoreception remain largely unknown. The UV Resistance locus 8 (UVR8) protein, a member of the Regulator of Chromosome Condensation 1 (RCC1) family, is the primary UV-B photoreceptor in plants. Here, we report the first in silico characterization of the RCC1 gene family in C. quinoa, aimed at identifying and structurally analyzing UVR8 homologs. Genomic analysis uncovered 40 CqRCC1 genes, exhibiting extensive structural diversity. Phylogenetic reconstruction identified two proteins, CqRCC1_20 and CqRCC1_23, as the closest homologs of AtUVR8 from Arabidopsis thaliana. Homology modeling revealed that CqRCC1_20 maintains the canonical seven-bladed β-propeller architecture of UVR8, whereas CqRCC1_23 carries a deletion leading to a six-bladed structure. Both isoforms retain the critical tryptophan residues (W233, W285, W337) and the C-terminal Valine-Proline (VP) motif required for photoperception and Constitutive Photomorphogenic 1 (COP1) interaction. Notably, the CqRCC1_23 model predicts fewer hydrogen bonds at the dimer interface and structural alterations at key regulatory interaction sites. Collectively, these results indicate that quinoa harbors functionally conserved UVR8 isoforms with structural divergence, such as CqRCC1_23, which may influence photoreceptor stability and enable a sustained UV-B response, potentially conferring an adaptive advantage in high-radiation environments.

In intensive vegetable production systems, long-term reliance on chemical fertilizers often leads to soil degradation and microbial imbalance, highlighting the need for sustainable biotillage strategies. In this study, a long-term field experiment examined how vegetable–earthworm co-cultivation (VE) combined with different fertilization regimes affects vegetable yield, soil physicochemical properties, and microbial communities. VE significantly improved vegetable yield, with full chemical fertilization (VE_IF100) and a 30% reduction in chemical fertilizer supplemented with organic fertilizer (VE_IF70) increasing yields by 30.86% and 26.02%, respectively, relative to full fertilization without earthworms (CK_IF100). VE also moderated soil pH toward neutrality. VE_IF100 decreased the soil C/N ratio, whereas VE_IF70 increased it and enhanced available hydrolyzable nitrogen, indicating a more balanced nutrient transformation. Microbial analysis revealed that VE_IF100 reduced bacterial abundance while strongly increasing fungal abundance, decreasing the bacteria-to-fungi ratio from 3.51 to 0.53. In contrast, VE_IF70 restored the bacteria-to-fungi ratio to 1.65 and increased fungal diversity, with the Shannon and Chao1 indices exceeding those in VE_IF100. Bacterial genera associated with nutrient cycling and plant growth promotion (e.g., Brevundimonas, Anaeromyxobacter) were enriched under VE_IF70, while fungal taxa with antagonistic and biocontrol potential (e.g., Chaetomium, Arthrobotrys) also increased. Redundancy analysis identified the soil C/N ratio (ranging from 5.94 to 8.60 across treatments) as a key driver of both bacterial and fungal community structures, whereas pH exerted a stronger influence on fungi. Random forest analysis indicated that the annual total vegetable yield was primarily driven by fertilization and available phosphorus in VE systems, whereas pH and bacterial abundance were the main drivers in CK systems. Overall, earthworm inoculation combined with partial organic fertilizer substitution improved soil conditions, reshaped microbial communities, and maintained high yield, demonstrating a practical strategy for sustainable vegetable production.

Perilla seed oil (PSO), rich in alpha-linolenic acid (ALA), has been traditionally used to relieve exterior syndrome and promote digestion, with modern studies confirming its anti-hyperlipidemic and anti-atherosclerotic properties. This study investigated the lipid-lowering effects of PSO and its underlying mechanisms in high-fat-diet-induced hyperlipidemic rats. Chemical standardization by UPLC-MS and GC-MS identified 591 compounds in PSO, with ALA accounting for 57.5% of its composition. The PSO administration significantly improved the general condition of hyperlipidemic rats, reduced body weight, lowered serum total cholesterol and LDL-C levels, and alleviated liver tissue injury and lipid accumulation. Serum metabolomics analysis revealed that PSO upregulated ALA and eicosapentaenoic acid while downregulating pro-inflammatory metabolites, including arachidonic acid, prostaglandin H2, and prostaglandin E2. Integrated network pharmacology and molecular docking studies identified the PI3K/Akt/NOS3 pathway as the primary signaling mechanism, which was further confirmed by Western blot analysis showing that PSO upregulated expression of p-PI3K, p-Akt, and NOS3 proteins. These results demonstrated that PSO-ameliorated hyperlipidemia, through PI3K/Akt/NOS3 pathway activation, coordinately modulated fatty acid metabolism and endogenous inflammatory responses. Our findings provided scientific evidence supporting PSO as a dietary intervention for managing hyperlipidemia and related metabolic disorders.

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert–Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability.

Concrete is one of the most important and most widely used materials for construction activities around the world. However, it has inherent deficiencies, e.g., brittleness, low impact resistance, low tensile strength, low fire resistance, low durability, and lower resistance to crack formation. Fibers and waste materials of different types are added as partial replacement of cement and aggregates in concrete to improve performance properties and reduce environmental pollution. In the present study, a thorough review of the use of various types of fibers with high and low elastic moduli in concrete to improve mechanical performance and reduce environmental pollution issues has been conducted. This review paper also provides comprehensive information on the different types of waste materials, e.g., biodegradable and non-biodegradable, which are used in concrete. The use of waste materials in concrete reduces the amount of waste sent to landfill and, in addition, improves some mechanical properties of concrete. This review is aimed at evaluating and understanding the strengths and weaknesses of fiber-reinforced concrete by using SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Moreover, this study also concluded that carbon fiber-reinforced concrete proves to be stronger and more durable but more expensive than other fibers. An ideal percentage of natural origin fibers used in concrete can greatly improve the mechanical performance. This study also discussed that waste from polymeric materials can be used in concrete as a partial replacement of cement and other components, e.g., coarse aggregates. It can be inferred that the optimum content of fibers that gives effective results is about 1%, and the reinforcement of concrete with different varieties of wastes as a replacement for fine aggregates should not be more than 2%. Parametric optimization of fiber content will be necessary for the best possible combination of performance properties.

Background: Knee osteoarthritis (KOA) is a progressive joint disorder that leads to pain, functional limitations, and reduced quality of life. While physical therapy (PT) and resistance exercise are effective in managing KOA, creatine supplementation (CS) may provide additional benefits. Aims/Objectives: To determine the additive effects of creatine supplementation alongside physical therapy (PT) and resistance exercise training in individuals with KOA. Methods: A parallel-design, double-blind, randomized controlled trial was conducted on 40 patients with KOA (≤grade III on Kellgren classification), aged 40–70 years. Participants were randomly allocated to either a placebo control group, which received placebo supplementation (maltodextrin) along with PT, including heat therapy, electrotherapy, manual therapy, and resistance exercises, for four weeks, or an experimental group, which received CS instead of maltodextrin in addition to the same treatment. Outcome measures included the visual analog scale (VAS), fall risk, Knee Injury and Osteoarthritis Outcome Score (KOOS), isometric muscle strength (IMS), five-repetition sit-to-stand test (5xSST), knee range of motion (ROM), and body composition analysis. Results: No significant differences were observed between the two groups at baseline. After four weeks of treatment, a significant interaction effect (treatment group x time) was observed for VAS (p = 0.001), fall risk score (p < 0.001), KOOS overall score (p < 0.001), IMS (p < 0.001), and body composition parameters (p < 0.05) in favor of the CS group. However, no significant interaction effect was observed for knee ROM and KOOS QOL subscale. Conclusions: CS, when combined with PT and resistance exercise, may provide additional benefits in terms of pain, function, muscle strength, and body composition parameters in individuals with KOA. However, no supplementary benefits of CS are observed in terms of quality of life and ROM.

Background: Postoperative delirium (POD) is known to involve systemic inflammatory responses, but the characteristics of the immune cell types involved in these responses are unclear. Methods: In this prospective study, we compared relative abundances and transcriptomes of circulating immune cells between patients who experienced POD (n = 11) or not (n = 109) within 7 days after elective cardiac surgery with cardiopulmonary bypass. Blood was sampled before and at 24 h after surgery; features of immune cells were profiled using multi-channel spectral flow cytometry, 10× single-cell RNA sequencing, and measurement of plasma levels of cytokines. Results: Patients with POD were older and with higher incidence of congestive heart failure than patients without POD, and these risk factors in turn positively correlated with preoperative proportion of CD40+/HLA-DR+ monocytes and CD69+CD8+ T cells. In addition, preoperative activation of antigen presentation in monocytes and chemotaxis in CD8+ T cells, as well as elevated plasma levels of chemokines CCL3 and CXCL8, were detected in patients with POD. After cardiac surgery, activation of antigen presentation and chemotaxis were also found in patients with POD. Conclusions: This study described the perioperative landscape of immune cells in POD and found possible links between preoperative immune dysfunction and risk factors, which may guide future research to explore how the immune system contributes to POD and to design preventive strategies.

The growing trend of using artificial intelligence models in many areas increases the need for a proper understanding of their functioning and decision-making. Although these models achieve high predictive accuracy, their lack of transparency poses major obstacles to trust. Explainable artificial intelligence (XAI) has emerged as a key discipline that offers a wide range of methods to explain the decisions of models. Selecting the most appropriate XAI method for a given application is a non-trivial problem that requires careful consideration of the nature of the method and other aspects. This paper proposes a systematic approach to solving this problem using multi-criteria decision-making (MCDM) techniques: ARAS, CODAS, EDAS, MABAC, MARCOS, PROMETHEE II, TOPSIS, VIKOR, WASPAS, and WSM. The resulting score is an aggregation of the results of these methods using Borda Count. We present a framework that integrates objective and subjective criteria for selecting XAI methods. The proposed methodology includes two main phases. In the first phase, methods that meet the specified parameters are filtered, and in the second phase, the most suitable alternative is selected based on the weights using multi-criteria decision-making and sensitivity analysis. Metric weights can be entered directly, using pairwise comparisons, or calculated objectively using the CRITIC method. The framework is demonstrated on concrete use cases where we compare several popular XAI methods on tasks in different domains. The results show that the proposed approach provides a transparent and robust mechanism for objectively selecting the most appropriate XAI method, thereby helping researchers and practitioners make more informed decisions when deploying explainable AI systems. Sensitivity analysis confirmed the robustness of our XAI method selection: LIME dominated 98.5% of tests in the first use case, and Tree SHAP dominated 94.3% in the second.