Search Results (23,700)

Enzymatic browning, driven by polyphenol oxidase (PPO), remains a major postharvest challenge for potato (Solanum tuberosum L.), reducing product quality, shelf life, and consumer acceptance. To mitigate this trait in the elite tetraploid cultivar ‘Yagana-INIA’, we applied a geminivirus-derived CRISPR–Cas9 system to edit the StPPO genes most highly expressed in tubers, StPPO1 and particularly StPPO2. A paired-gRNA strategy generated a double-cut deletion in StPPO1, while StPPO2 editing required a complementary single-gRNA screening workflow. High-resolution fragment analysis and sequencing identified three StPPO2-edited lines, including one that lacked GFP, Cas9, and Rep/RepA sequences, confirming a transgene-free editing outcome. Edited tubers exhibited visibly reduced browning relative to wild type, and biochemical assays showed decreased PPO activity consistent with targeted disruption of StPPO2. Amplicon sequencing verified monoallelic editing at the gRNA2 site in the non-transgenic line. These results demonstrate the utility of a replicon-based CRISPR system for achieving targeted, transgene-free edits in tetraploid potato and identify a non-GM StPPO2-edited line with improved postharvest quality under Chile’s regulatory framework. Full article

Climate change and land-use intensification are speeding up the loss of ecosystem services that support human health, food security, and environmental stability. Vegetative interventions—such as urban forests in cities and cover crops in farming systems—are increasingly seen as nature-based solutions for climate adaptation. However, their benefits are often viewed separately. This review combines 20 years of research to explore how these strategies, together, improve provisioning, regulating, supporting, and cultural ecosystem services across various landscapes. Urban forests help reduce urban heat islands, improve air quality, manage stormwater, and offer cultural and health benefits. Cover crops increase soil fertility, regulate water, support nutrient cycling, and enhance crop yields, with potential for carbon sequestration and biofuel production. We identify opportunities and challenges, highlight barriers to adopting these strategies, and suggest integrated frameworks—including spatial decision-support tools, incentive programs, and education—to encourage broader use. By connecting urban and rural systems, this review underscores vegetation as a versatile tool for resilience, essential for reaching global sustainability goals. Full article

The COVID-19 pandemic led to an unprecedented slowdown in global economic and transportation activities, offering a unique opportunity to assess the relationship between human activity and atmospheric pollution. This study analyzes global variations in major air pollutants and meteorological conditions during the pandemic period using multi-satellite and reanalysis datasets. Nitrogen dioxide (NO₂) data were obtained from the OMI sensor aboard NASA’s Aura satellite, while carbon monoxide (CO) observations were taken from the MOPITT instrument on Terra. Reanalysis products from MERRA-2 were used to assess CO, sulfur dioxide (SO₂), black carbon (BC), organic carbon (OC), and key meteorological variables, including temperature, precipitation, evaporation, wind speed, and direction. Average concentrations of pollutants for April, May, and June 2020, representing the lockdown phase, were compared with the average values of the same months during 2017–2019, representing pre-pandemic conditions. The difference between these multi-year means was used to quantify spatial changes in pollutant levels. Results reveal widespread reductions in NO₂, CO, SO₂, and BC concentrations across major industrial and urban regions worldwide, consistent with decreased anthropogenic activity during lockdowns. Meteorological analysis indicates that the observed reductions were not primarily driven by short-term weather variability, confirming that the declines are largely attributable to reduced emissions. Unlike most previous studies, which examined local or regional air-quality changes, this work provides a consistent global-scale assessment using harmonized multi-sensor datasets and uniform temporal baselines. These findings highlight the strong influence of human activities on atmospheric composition and demonstrate how large-scale behavioral and economic shifts can rapidly alter air quality on a global scale. The results also provide valuable baseline information for understanding emission–climate interactions and for guiding post-pandemic strategies aimed at sustainable air-quality management. Full article

Instance selection is a critical preprocessing technique for enhancing data quality and improving machine learning model efficiency. However, existing algorithms for regression tasks face a fundamental trade-off: non-heuristic methods offer high precision but suffer from sequential dependencies that hinder parallelization, while heuristic methods support parallelization but often yield coarse-grained results susceptible to local optima. To address these challenges, we propose CRDISA, a novel distributed instance selection algorithm driven by a formalized cognitive reasoning logic. Unlike traditional approaches that evaluate subsets, CRDISA transforms each instance into an independent “Instance Expert” capable of reasoning about the global data distribution through a unique difference knowledge base. For regression tasks with continuous outputs, we introduce a soft partitioning strategy to define adaptive error boundaries and a bidirectional voting mechanism to robustly identify high-quality instances. Although the fine-grained reasoning implies high computational complexity, we implement CRDISA on Apache Spark using an optimized broadcast mechanism. This architecture provides linear scalability in wall-clock time, enabling scalable processing without sacrificing theoretical rigor. Experiments on 22 datasets demonstrate that CRDISA achieves an average compression rate of 31.7% while maintaining predictive accuracy ($R^2=0.681$) comparable to or better than state-of-the-art methods, proving its superiority in balancing selection granularity and distributed efficiency. Full article

Background/Objective: Systematic reviews (SRs) with network meta-analysis (NMA) support evidence-based decision-making by enabling both direct and indirect comparisons across multiple interventions. Given the expanding use of Janus kinase (JAK) inhibitors in rheumatoid arthritis (RA), the methodological rigor of SRs with NMA is essential for trustworthy conclusions. This study is aimed at evaluating the methodological quality of SRs with NMA assessing the efficacy and/or safety of JAK inhibitors in RA. Methods: PubMed and Embase were searched for full-text SRs with NMAs evaluating JAK inhibitors as a therapeutic class in RA. Eligible publications were English-language articles reporting efficacy and/or safety outcomes. Narrative reviews, letters, duplicates, reviews focused on a single JAK inhibitor, and reviews without quantitative synthesis were excluded. Three independent reviewers assessed methodological quality using AMSTAR 2. Descriptive statistics were used to summarize findings. Results: Of the 222 records identified, 18 SRs with NMA met the inclusion criteria: 5 focused on efficacy, 5 on safety, and 8 assessed both. The most consistently fulfilled AMSTAR 2 items were a clearly defined PICO question (100%), duplicate study selection (100%), and reporting of conflicts of interest (100%). Common shortcomings included lack of protocol registration (44%), incomplete reporting of the search strategy (39%), and absence of publication bias assessment (50%). Risk-of-bias assessment varied by review focus: all safety reviews complied (100%), compared with 20% of efficacy reviews and 37% of mixed reviews. Conclusions: Most SRs with NMA of JAK inhibitors in RA present relevant methodological limitations, particularly in protocol registration, search reporting, and risk-of-bias assessment. Methodological standards were generally higher in safety-focused reviews, underscoring the need for more consistent and rigorous conduct and reporting, especially in efficacy and mixed reviews, to strengthen confidence in NMA-derived conclusions. Full article

Chronic obstructive pulmonary disease (COPD) is a major health burden worldwide and in Taiwan, ranking as the third leading cause of death globally, and its prevalence in Taiwan continues to rise. Readmission within 14 days is a key indicator of disease instability and care efficiency, driven jointly by patient-level physiological vulnerability (such as reduced lung function and multiple comorbidities) and healthcare system-level deficiencies in transitional care. To mitigate the growing burden and improve quality of care, it is urgently necessary to develop an AI-based prediction model for 14-day readmission. Such a model could enable early identification of high-risk patients and trigger multidisciplinary interventions, such as pulmonary rehabilitation and remote monitoring, to effectively reduce avoidable early readmissions. However, medical data are commonly characterized by severe class imbalance, which limits the ability of conventional machine learning methods to identify minority-class cases. In this study, we used real-world clinical data from multiple hospitals in Kaohsiung City to construct a prediction framework that integrates data generation and ensemble learning to forecast readmission risk among patients with chronic obstructive pulmonary disease (COPD). CTGAN and kernel density estimation (KDE) were employed to augment the minority class, and the impact of these two generation approaches on model performance was compared across different augmentation ratios. We adopted a stacking architecture composed of six base models as the core framework and conducted systematic comparisons against the baseline models XGBoost, AdaBoost, Random Forest, and LightGBM across multiple recall thresholds, different feature configurations, and alternative data generation strategies. Overall, the results show that, under high-recall targets, KDE combined with stacking achieves the most stable and superior overall performance relative to the baseline models. We further performed ablation experiments by sequentially removing each base model to evaluate and analyze its contribution. The results indicate that removing KNN yields the greatest negative impact on the stacking classifier, particularly under high-recall settings where the declines in precision and F1-score are most pronounced, suggesting that KNN is most sensitive to the distributional changes introduced by KDE-generated data. This configuration simultaneously improves precision, F1-score, and specificity, and is therefore adopted as the final recommended model setting in this study. Full article

Background: Workplace violence (WPV) is a pervasive occupational hazard in healthcare that undermines staff safety and quality of care. In Bulgaria, WPV remains widespread and underreported, despite recent legislative initiatives. This study assessed healthcare professionals’ perceptions of the effectiveness of WPV prevention strategies and examined how prior exposure shapes these perceptions. Methods: A nationwide cross-sectional online survey was conducted in December 2024 with 944 healthcare professionals from multiple sectors. Participants rated the perceived effectiveness of 11 prevention strategies, including environmental/security measures, organizational, and national-level interventions, on a three-point scale. Friedman ANOVA with Kendall’s W assessed overall strategy rankings, while Mann–Whitney U tests with rank-biserial correlations compared specific effectiveness ratings between subgroups defined by WPV exposure (experienced or witnessed vs. not exposed in the previous 12 months). Results: In the previous 12 months, 34.7% of respondents reported direct WPV, and 43.4% had either experienced or witnessed incidents. Friedman ANOVA indicated significant differences in perceived effectiveness across strategies (Kendall’s W = 0.13), with stronger differentiation among violence-exposed respondents (W = 0.37) than among non-exposed respondents (W = 0.09). National-level interventions and security/response measures were consistently ranked the highest. Mann–Whitney tests showed significantly higher endorsement of most strategies among violence-exposed professionals, with large effect sizes for security measures and enforcement of sanctions. Conclusions: Bulgarian healthcare professionals view WPV prevention as requiring a multicomponent approach that integrates robust national policy with organizational and environmental measures. Direct exposure to violence is associated with stronger support for security-focused and national interventions. These findings inform context-specific, evidence-based WPV prevention programs for Bulgarian healthcare facilities. Full article

Adaptive life histories emerge through their environmentally dependent effects on fitness. Those effects are consequences of habitat quality and the density-dependent decisions that organisms make on habitat choice. Density dependence for ideal organisms maximizing fitness through habitat selection is uniquely revealed by their habitat isodars, lines in the state space of species’ densities that confer equal fitness between habitats coupled by dispersal. We use isodars to structure simple simulations of habitat selection in stable and stochastic environments. The simulations demonstrate an indirect effect of ideal habitat selection that can dampen otherwise wide fluctuations in abundance and their impact on pace-of-life strategies. The ability of habitat selection to equalize fitness between habitats also has a direct effect on life history evolution. Habitat selection can promote phenotypically plastic life histories between habitats that might otherwise convey divergent genetically fixed strategies. The direct and indirect effects on life history demonstrate that it is not just habitat that requires our concern in managing and conserving nature, but how those activities are likely to impinge on habitat selection. Full article

Abdominal pain is the cardinal symptom of irritable bowel syndrome (IBS) and the primary determinant of disease burden and healthcare utilization. Despite its diagnostic centrality and high prevalence across all IBS subtypes, effective management remains a clinical challenge. This narrative review explores the pathophysiological mechanisms underlying IBS-related pain, emphasizing the role of visceral hypersensitivity, altered brain–gut communication, and luminal factors such as gas and distension. We examine current guideline recommendations, real-world treatment patterns, and evidence supporting both pharmacological and non-pharmacological interventions. Particular focus is placed on the fixed-dose combination of alverine citrate/simeticone, which targets both motor and sensory pathways. Mechanistic studies demonstrate its smooth muscle relaxant, antinociceptive, and anti-inflammatory actions. Clinical trials support its efficacy in reducing pain, improving quality of life, and lowering healthcare resource use. Despite these advances, several unmet needs remain, including subtype-specific treatment strategies, mechanistic biomarkers, and broader access to integrated care. The review concludes with a call for more personalized, mechanism-based approaches to pain management in IBS, with alverine citrate/simeticone offering a pragmatic option within this evolving therapeutic framework. Full article

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies. Full article

To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy. Full article

This study investigates whether national innovation systems contribute to sustainable well-being in emerging EU Member States by examining the long-run relationship between innovation performance and a multidimensional Quality of Life Index (QoLI). Using a balanced panel covering 2013–2024 for ten countries, the analysis integrates the Global Innovation Index, economic development dynamics, and demographic pressure to assess whether innovation-led progress translates into broad societal benefits. Panel cointegration tests confirm a stable long-run equilibrium among variables, while FMOLS estimation reveals three key results: (i) While the bivariate Pearson correlation indicates a positive association between innovation capacity and quality of life, the multivariate FMOLS estimation reveals a statistically significant negative long-run effect of innovation performance on QoLI, once economic development and demographic pressures are jointly controlled for. (ii) Economic development contributes positively to sustainable well-being, reinforcing the role of income-driven improvements in living conditions, and (iii) population size exerts a strong negative effect, reflecting demographic stress and unequal access to essential services. The findings indicate an innovation–well-being gap in which technological progress advances faster than the institutional and social mechanisms needed to ensure equitable diffusion. These results underscore the need to reorient innovation strategies toward inclusive growth, social accessibility, and environmental resilience so that innovation systems can effectively support sustainable well-being in emerging European economies. Full article

Climate change poses a threat to global rice production by increasing the frequency and intensity of extreme weather events. The widespread cultivation of genetically uniform modern varieties has narrowed the genetic base of rice, increasing its vulnerability to these increased pressures. Rice landraces are traditional rice varieties that have been cultivated by farming communities for centuries and are considered crucial resources of genetic diversity. These landraces are adapted to a wide range of agro-ecological environments and exhibit valuable traits that provide tolerance to various biotic stresses, including drought, salinity, nutrient-deficient soils, and the increasing severity of climate-related temperature extremes. In addition, many landraces possess diverse alleles associated with resistance to biotic stresses, including pests and diseases. In addition, rice landraces exhibit great grain quality characters including high levels of essential amino acids, antioxidants, flavonoids, vitamins, and micronutrients. Hence, their preservation is vital for maintaining agricultural biodiversity and enhancing nutritional security, especially in vulnerable and resource-limited regions. However, rice landraces are increasingly threatened by genetic erosion due to widespread adoption of modern high-yielding varieties, habitat loss, and changing farming practices. This review discusses the roles of rice landraces in developing resilient and climate-smart rice cultivars. Moreover, the Pantiange Heigu landrace, cultivated at one of the highest altitudes globally in Yunnan Province, China, has been used as a case study for integrated conservation by demonstrating the successful combination of in situ and ex situ strategies, community engagement, policy support, and value-added development to sustainably preserve genetic diversity under challenging environmental and socio-economic challenges. Finally, this study explores the importance of employing advanced genomic technologies with supportive policies and economic encouragements to enhance conservation and sustainable development of rice landraces as a strategic imperative for global food security. By preserving and enhancing the utilization of rice landraces, the agricultural community can strengthen the genetic base of rice, improve crop resilience, and contribute substantially to global food security and sustainable agricultural development in the face of environmental and socio-economic challenges. Full article

Inflammatory Bowel Diseases (IBD), which include Crohn’s disease (CD) and ulcerative colitis (UC), are chronic, immune-mediated disorders marked by persistent and recurrent inflammation of the gastrointestinal tract. Over the past two decades, major advances in understanding the immunologic and molecular pathways that drive intestinal injury have transformed the therapeutic landscape. This progress has enabled the development of novel biologics and small-molecule agents that more precisely target dysregulated immune responses, thereby improving clinical outcomes and quality of life for many patients. Despite these therapeutic advances, IBD remains a highly heterogeneous condition. Patients differ widely in disease phenotype, progression, and response to specific treatments. Consequently, selecting the most effective therapy for an individual patient requires careful consideration of clinical features, molecular markers, and prior treatment history. The shift toward personalized, prediction-based treatment strategies aims to optimize the timing and choice of therapy, minimize unnecessary exposure to ineffective drugs, and ultimately alter the natural course of disease. In this review, we provide a comprehensive overview of current evidence guiding drug positioning in IBD, with particular emphasis on biologic therapies and small-molecule inhibitors. We also examine emerging biomarkers, clinical predictors of response, and real-world factors that influence therapeutic decision-making. Finally, we discuss the challenges and limitations that continue to hinder widespread implementation of personalized strategies, underscoring the need for further research to integrate precision medicine into routine IBD care. Full article

With the ongoing advancement of urbanization, the renewal of old urban areas has emerged as a central front in enhancing urban quality, with street space improvement playing a pivotal role in advancing sustainable urban development. This study focuses on Chengdu, a highly urbanized megacity, employing a combination of multi-point continuous street view photography, spatial mapping, and landscape design interventions to systematically examine human activity patterns, commercial dynamics, and pathways for spatial optimization along the street-facing interfaces of old residential neighborhoods and their adjacent urban streets. The findings reveal that: (1) commercializing the street-facing façades enhances local employment opportunities; (2) window-type fences demonstrate superior adaptability by effectively balancing commercial accessibility with resident safety; and (3) a diverse mix of commercial types sustains the vitality of street-level economies in these areas. These results not only offer actionable spatial strategies for the renovation of old residential zones in Chengdu but also contribute transferable insights for urban regeneration efforts globally. Full article