Search Results (926)

Systems thinking (ST) is a foundational cognitive skillset to advance sustainability education but has not been well examined for learners prior to higher education. This case study research in rural middle schools in the Midwestern U.S. examines systems thinking outcomes of a place-based energy literacy unit focused on energy-efficient building design. The unit employs the science of energy-efficient, green buildings to illuminate the ways in which energy flows between natural and built environments. The unit emphasized electrical, light, and thermal energy systems and the ways these systems interact to create functional and energy-efficient buildings. This study focuses on three case study classrooms where students across schools (n = 89 students) created systems models as part of pre- and post-unit tests (n = 162 models). The unit tests consisted of student drawings, annotations, and writings, culminating into student-developed systems models. Growth from pre- to post-test was observed in both the identification of system elements and the linkages between elements. System elements included in the models were common classroom features, such as windows, lights, and temperature control, suggesting that rooting the unit in place-based teaching may support ST skills. Full article

Macrocyclic organic nanostructures have emerged as crucial components of functional supramolecular materials owing to their unique structural and chemical features, such as their distinctive “infinite” cyclic topology and tunable topology-dependent properties, attracting significant recent attention. However, the controlled synthesis of macrocyclic compounds with well-defined compositions and geometries remains a formidable challenge. On-surface synthesis, capable of constructing nanostructures with atomic precision on various substrates, has become a frontier technique for exploring novel macrocyclic architectures. This review summarizes the recent advances in the on-surface synthesis of macrocycles. It focuses on analyzing the synthetic mechanisms and conformational characterization of macrocycles formed through diverse bonding interactions, including both covalent and non-covalent linkages. This review elucidates the intricate interplay between the thermodynamic and kinetic factors governing macrocyclic structure formation across these bonding types and clarifies the critical influence of the reaction temperature and external conditions on the cyclization efficiency. Ultimately, this study offers design strategies for the precise on-surface synthesis of larger and more flexible macrocyclic compounds. Full article

Background/Objectives: Elevated serum uric acid levels are associated with the occurrence, development, and adverse events of coronary heart disease (CHD) and CHD risk factors. However, the extent of any pathogenic effect of the serum uric acid on CHD and whether CHD risk factors play a confounding or mediating role are still unclear. Methods: The potential causal associations of serum uric acid with CHD were evaluated via cross-trait linkage disequilibrium score regression analysis and Mendelian randomization. The pleiotropy of genetic tools was analyzed via a Bayesian colocalization approach. Moreover, we utilized two-step MR to identify risk factors mediating the relationship between uric acid and CHD. Results: Mendelian randomization results derived from two genetic instrument selection strategies support that serum uric acid levels have a significant causal relationship with coronary artery disease, stable angina pectoris, and myocardial infarction. This causal relationship was partially mediated by diastolic blood pressure, mean arterial pressure, and serum triglycerides. Transcriptomic analysis revealed that serum uric acid may directly contribute to the development of atherosclerosis by inducing transcriptomic changes in macrophages. Conclusions: Our findings highlight that the control of serum urate concentration in the long-term management of CHD patients may be necessary. Well-designed clinical trials and foundational research are presently required to furnish conclusive proof regarding the specific clinical scenarios in which adequate reduction in urate concentrations can confer cardiovascular advantages. Full article

Background. The STING protein is activated by the second messenger cGAMP to promote the innate immune response against infections. Beyond this role, a chronically overactive STING signaling has been described in several disorders. Patients with severe COVID-19 exhibit a hyper-inflammatory response (the cytokine storm) that is in part mediated by the cGAS-STING pathway. Several STING inhibitors may protect from severe COVID-19 by down-regulating several inflammatory cytokines. This pathway has been implicated in the establishment of an optimal antiviral vaccine response. STING agonists as adjuvants improved the IgG titers against the SARS-CoV-2 Spike protein vaccines. Methods. We investigated the association between two common functional STING1/TMEM173 polymorphisms (rs78233829 C>G/p.Gly230Ala and rs1131769C>T/p.His232Arg) and severe COVID-19 requiring hospitalization. A total of 801 non-vaccinated and 105 fully vaccinated (mRNA vaccine) patients, as well as 300 population controls, were genotyped. Frequencies between the groups were statistically compared. Results. There were no differences for the STING1 variant frequencies between non-vaccinated patients and controls. Vaccinated patients showed a significantly higher frequency of rs78233829 C (230Gly) compared to non-vaccinated patients (CC vs. CG + GG; p = 0.003; OR = 2.13; 1.29–3.50). The two STING1 variants were in strong linkage disequilibrium, with the rs78233829 C haplotypes being significantly more common in the vaccinated (p = 0.02; OR = 1.66; 95%CI = 1.01–2.55). We also studied the LTZFL1 rs67959919 G/A polymorphism that was significantly associated with severe COVID-19 (p < 0.001; OR = 1.83; 95%CI = 1.28–2.63). However, there were no differences between the non-vaccinated and vaccinated patients for this polymorphism. Conclusions. We report a significant association between common functional STING1 polymorphisms and the risk of developing severe COVID-19 among fully vaccinated patients. Full article

Chromium slag sites pose severe environmental risks due to hexavalent chromium (Cr(VI)) contamination, characterized by high mobility and toxicity. This study focused on chromium-contaminated soil from a historical chromium slag site in North China, where long-term accumulation of chromate production residues has led to serious Cr(VI) pollution, with Cr(VI) accounting for 13–22% of total chromium and far exceeding national soil risk control standards. To elucidate Cr(VI) transformation mechanisms and elemental linkages, a combined approach of macro-scale condition experiments and micro-scale analysis was employed. Results showed that acidic conditions (pH < 7) significantly enhanced Cr(VI) reduction efficiency by promoting the conversion of CrO₄²⁻ to HCrO₄⁻/Cr₂O₇²⁻. Among reducing agents, FeSO₄ exhibited the strongest effect (reduction efficiency >30%), followed by citric acid and fulvic acid. Temperature variations (−20 °C to 30 °C) had minimal impact on Cr(VI) transformation in the 45-day experiment, while soil moisture (20–25%) indirectly facilitated Cr(VI) reduction by enhancing the reduction of agent diffusion and microbial activity, though its effect was weaker than chemical interventions. Soil grain-size composition influenced Cr(VI) distribution unevenly: larger particles (>0.2 mm) in BC-35 and BC-36-4 acted as main Cr(VI) reservoirs due to accumulated Fe-Mn oxides, whereas BC-36-3 showed increased Cr(VI) in smaller particles (<0.074 mm). μ-XRF and correlation analysis revealed strong positive correlations between Cr and Ca, Fe, Mn, Ni (Pearson coefficient > 0.7, p < 0.01), attributed to adsorption–reduction coupling on iron-manganese oxide surfaces. In contrast, Cr showed weak correlations with Mg, Al, Si, and K. This study clarifies the complex factors governing Cr(VI) behavior in chromium slag soils, providing a scientific basis for remediation strategies such as pH adjustment (4–6) combined with FeSO₄ addition to enhance Cr(VI) reduction efficiency. Full article

Background/Objective: Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique with growing relevance in psychiatry. Its ability to measure cortical hemodynamics positions it as a potential tool for monitoring neurofunctional changes related to treatment. However, the specific features and level of consistency of its use in clinical psychiatric settings remain unclear. A scoping review was conducted under PRISMA-ScR guidelines to systematically map how fNIRS has been used in monitoring treatment response among individuals with psychiatric disorders. Methods: Forty-seven studies published between 2009 and 2025 were included based on predefined eligibility criteria. Data was extracted on publication trends, research design, sample characteristics, fNIRS paradigms, signal acquisition, preprocessing methods, and integration of clinical outcomes. Reported limitations and conflicts of interest were also analyzed. Results: The number of publications increased sharply after 2020, predominantly from Asia. Most studies used experimental designs, with 31.9% employing randomized controlled trials. Adults were the primary focus (93.6%), with verbal fluency tasks and DLPFC-targeted paradigms most common. Over half of the studies used high-density (>32-channel) systems. However, only 44.7% reported motion correction procedures, and 53.2% did not report activation direction. Clinical outcome linkage was explicitly stated in only 12.8% of studies. Conclusions: Despite growing clinical interest, with fNIRS showing promise as a non-invasive neuroimaging tool for monitoring psychiatric treatment response, the current evidence base is limited by methodological variability and inconsistent outcome integration. There is a rising need for the adoption of standardized protocols for both design and reporting. Future research should also include longitudinal studies and multimodal approaches to enhance validity and clinical relevance. Full article

Genomic prediction has emerged as a powerful tool in aquaculture breeding, but its effectiveness depends on the careful selection of informative single nucleotide polymorphisms (SNPs) and the application of appropriate prediction models. This study aimed to enhance genomic prediction accuracy in Russian sturgeon (Acipenser gueldenstaedtii) by optimizing SNP selection strategies and exploring the performance of linear and machine learning models. Three economically important traits—caviar yield, caviar color, and body weight—were selected due to their direct relevance to breeding goals and market value. Whole-genome sequencing (WGS) data were obtained from 971 individuals with an average sequencing depth of 13.52×. To reduce marker density and eliminate redundancy, three SNP selection strategies were applied: (1) genome-wide association study (GWAS)-based prioritization to select trait-associated SNPs; (2) linkage disequilibrium (LD) pruning to retain independent markers; and (3) random sampling as a control. Genomic prediction was conducted using both linear (e.g., GBLUP) and machine learning models (e.g., random forest) across varying SNP densities (1 K to 50 K). Results showed that GWAS-based SNP selection consistently outperformed other strategies, especially at moderate densities (≥10 K), improving prediction accuracy by up to 3.4% compared to the full WGS dataset. LD-based selection at higher densities (30 K and 50 K) achieved comparable performance to full WGS. Notably, machine learning models, particularly random forest, exceeded the performance of linear models, yielding an additional 2.0% increase in accuracy when combined with GWAS-selected SNPs. In conclusion, integrating WGS data with GWAS-informed SNP selection and advanced machine learning models offers a promising framework for improving genomic prediction in sturgeon and holds promise for broader applications in aquaculture breeding programs. Full article

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Background: Foster care alumni face increased health challenges across the domains of mental and physical health, yet there is a paucity of research examining the associations between care experiences, health, and sleep quality in alumni aged 30 and above. Objectives: Our exploratory mixed-method study examined the sleep quality of North American group care leavers aged 30+ to understand whether sleep quality in adulthood is associated with earlier child welfare system experiences during childhood and adolescence. Secondly, we examined the association between sleep quality and overall concurrent health. Methods: Using a convenience sample of 41 alumni of care aged 30–85 and 16 qualitative interviews, we explored the intricate connections between group care leavers’ developmental trauma, sleep quality, and health. Linear regression and qualitative content analysis were utilized to understand how sleep was related to well-being in aging care alumni. Results: Adult sleep was significantly associated with the perceived quality of their youth out-of-home placement experiences (β = 0.421, p < 0.01), controlling for friendship support networks and demographic variables. Adult sleep quality was a significant predictor of overall health (β = −0.328, p < 0.05). Qualitative interviews elucidated insights into the importance and linkages of child welfare system experiences, adult sleep, and well-being. Conclusions: Our research highlights the enduring association between child welfare placement experiences, and sleep functioning well into adulthood, even when accounting for contemporaneous social support and other demographic indicators. Practitioners should be inquiring directly about sleep, and future longitudinal research should delve deeper into the nature of sleep difficulties and their association with health and well-being. Full article

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration. While its etiology remains unclear, both genetic and environmental factors, including oxidative stress, have been implicated in the development of the disease. The base excision repair (BER) pathway plays a critical role in repairing oxidative DNA damage. This study investigated the association between polymorphisms in BER-related genes and MS susceptibility in a Central European population. Ten SNPs across seven BER genes were genotyped in 102 patients with MS and 118 healthy controls. Six SNPs were significantly associated with MS. Increased risk was observed for rs25478 in XRCC1 (OR = 2.37, 95% CI: 1.44–3.91, p < 0.0001), rs3087404 in SMUG1 (OR = 2.80, 95% CI: 1.49–5.26, p = 0.0012), and rs3219493 in MUTYH (OR = 2.23, 95% CI: 1.35–3.67, p = 0.0018). Conversely, reduced risk was associated with rs2307293 in MBD4 (OR = 0.42, 95% CI: 0.23–0.78, p = 0.006), rs3219489 in MUTYH (OR = 0.55, 95% CI: 0.31–0.97, p = 0.038), and rs4135054 in TDG (OR = 0.52, 95% CI: 0.29–0.94, p = 0.031). Haplotype analysis was performed for SNPs in strong linkage disequilibrium. Only rs3219489 and rs3219472 within the MUTYH gene showed strong LD (r² = 0.90), justifying haplotype-based analysis. Among four inferred haplotypes, the rare G–C haplotype was significantly associated with reduced MS risk (Score = −2.10, p = 0.035), suggesting a protective effect of this allele combination. Other SNPs not in LD were analyzed using a multivariable logistic regression model. Significant associations with decreased MS risk were found for rs1052133 in OGG1 (OR = 0.57, p = 0.043), rs2307293 in MBD4 (OR = 0.16, p = 0.010), and rs4135054 in TDG (OR = 0.38, p < 0.001), while rs3087404 in SMUG1 increased MS risk (OR = 1.98, p = 0.013). These results suggest that genetic variation in BER genes, including both single SNP effects and haplotypes, contributes to MS susceptibility. Further studies are warranted to explore the functional consequences of these variants and validate findings in larger, independent cohorts. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Medicinal mushroom production utilising rural cultivation (solid state fermentation) requires approximately six months compared to culinary mushroom production (7 days). Urban cultivation (submerged liquid fermentation) can be used as a sustainable method of producing medicinal mushroom biomass. In this study, chicken patties were fortified with liquid-fermented Ganoderma lucidum flour (GLF) and Pleurotus djamor mushroom biomass flour (PDF) at concentrations of 3%, 6%, and 9%. These were compared to a negative control (0% mushroom flour chicken patty) and a commercial patty. Chicken patties fortified with 3% PDF and 9% GLF recorded the lowest cooking loss, at 5.55% and 10.3%, respectively. Mushroom chicken patties exhibited lower cooking losses and significant changes in colour and texture compared to control samples. Notably, 3% GLF chicken patty achieved the highest overall acceptability score of 6.55 followed by 9% PDF chicken patty (6.08) (p < 0.05). Biomass flour of liquid-fermented Ganoderma lucidum (ENS-GL) and Pleurotus djamor (ENS-PD) were extracted for their endopolysaccharide and analysed for their functional properties. All elemental, FT-IR, and NMR spectroscopy analyses revealed the existence of a comparable beta-glucan polymer structure, linkages, and absorptions when compared to the Laminarin standard. In addition, ENS-GL also proved to possess higher antimicrobial activities and significant antioxidant levels (DPPH-scavenging activity, ferric reduction potential and total phenolic content) compared to ENS-PD. Overall, this study revealed that sustainable liquid-fermented Ganoderma lucidum, a medicinal mushroom, outperformed Pleurotus djamor, a culinary mushroom, as a potential alternative flour for combating hunger in the future. Full article

Record linkage can enhance the utility of data by bringing data together from different sources, increasing the available information about data subjects and providing more holistic views. Doing so, however, can increase privacy risks. To mitigate these risks, a family of methods known as privacy-preserving record linkage (PPRL) was developed, using techniques such as cryptography, de-identification, and the strict separation of roles to ensure data subjects’ privacy remains protected throughout the linkage process, and the resulting linked data poses no additional privacy risks. Building privacy protections into the architecture of the system (for instance, ensuring that data flows between different parties in the system do not allow for transmission of private information) is just as important as the technology used to obfuscate private information. In this paper, we present a technology-agnostic framework for designing PPRL systems that is focused on privacy protection, defining key roles, providing a system architecture with data flows, detailing system controls, and discussing privacy evaluations that ensure the system protects privacy. We hope that the framework presented in this paper can both help elucidate how currently deployed PPRL systems protect privacy and help developers design future PPRL systems. Full article

Bacterial blight (BB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is a major threat to global rice productivity. Although hybrid rice breeding has significantly enhanced yields, persistent genetic vulnerabilities within restorer lines continue to compromise BB resistance. This study addresses this challenge by implementing functional marker-assisted selection (FMAS) to pyramid two broad-spectrum resistance (R) genes, Xa21 and Xa23, into the elite, yet BB-susceptible, restorer line K608R. To enable precise Xa23 genotyping, we developed a novel three-primer functional marker (FM) system (IB23/CB23/IR23). This system complements the established U1/I2 markers used for Xa21. This recombination-independent FMAS platform facilitates simultaneous, high-precision tracking of both homozygous and heterozygous alleles, thereby effectively circumventing the linkage drag limitations typical of conventional markers. Through six generations of marker-assisted backcrossing followed by intercrossing, we generated K608R2123 pyramided lines harboring both R genes in homozygous states, achieving a recurrent parent genome recovery rate of 96.93%, as determined by single nucleotide polymorphism (SNP) chip analysis. The pyramided lines exhibited enhanced resistance against six virulent Xoo pathogenic races while retaining parental yield performance across key agronomic traits. Our FMAS strategy overcomes the historical trade-off between broad-spectrum resistance and the preservation of elite phenotypes, with the developed lines exhibiting resistance coverage complementary to that of both introgressed R genes. This integrated approach provides breeders with a reliable molecular tool to accelerate the development of high-yielding, disease-resistant varieties, demonstrating significant potential for practical deployment in rice improvement programs. The K608R2123 germplasm represents a dual-purpose resource suitable for both commercial hybrid seed production and marker-assisted breeding programs, and it confers synergistic resistance against diverse Xoo races, thereby providing a pivotal breeding resource for sustainable BB control in epidemic regions. Full article