Search Results (23,894)

Vegetation in residential areas plays a crucial role in biodiverse and sustainable cities as it enhances biological diversity, environmental quality, and the human well-being of city residents. However, the distribution of vegetation among these areas is often unequal, leading to disparities in access to its benefits. To promote a more biodiverse and environmentally just city, we investigated how woody plants (trees, shrubs and vines) vary with socioeconomic level in residential streetscapes of Santiago de Chile. Across the city, we sampled woody plants in 120 plots (11 m radius) located in residential streetscapes of three socioeconomic levels: low, medium, and high. A total of 557 woody plants were identified and measured. Of these, only 9.7% corresponded to native species, whereas 90.3% were introduced species. Wealthier residential areas had higher species richness and abundance of woody plants, as well as plants with greater structural size (revealed by height and crown area). In addition, we found that the composition of woody plants differed among socioeconomic levels: Liquidambar styraciflua, Platanus x hispanica, and Pittosporum tobira were more abundant in high socioeconomic areas; Prunus cerasifera, Citrus limon, and Ailanthus altissima were more abundant in medium socioeconomic areas; Robinia pseudoacacia, Acer negundo, and Schinus areira were more abundant in low socioeconomic areas. Our research highlights that woody plant diversity, abundance, structure, and composition vary with socioeconomic level in residential streetscapes. Key insights for reducing these inequalities and achieve a more environmentally just city include: (a) governance and equity-based investment; (b) prioritizing local native species; (c) promoting the use of non-tree woody plants; and (d) empowering communities through capacity building and stewardship. Full article

Recognizable and distinctive streets are essential not only for navigation but also for fostering place identity and therefore socio-environmental sustainability in cities. The recognition depends on both high-level visual features (e.g., buildings, trees, etc.) and low-level ones (e.g., colors, spatial frequencies, etc.). While the former has been examined extensively, the latter remains less understood. This study addresses this gap via a multi-disciplinary perspective, by exploring how low-level visual features influence attention and cognitive processing during street recognition using an eye-tracking device. In the experiment, participants were expected to recognize Huaihai Road in China from other historic, tree-shaded, commercial streets with similar appearance (e.g., the Champs-Élysées in France, Omotesando in Japan, and Shaanxi South Road and Fuxing Middle Road in China). Results showed that removing mid-to-high spatial frequencies significantly improved recognition accuracy, while the absence of color led to a notable decline in accuracy. Markedly, the presence or absence of trees did not significantly affect recognition accuracy, suggesting that trees may be not vital for street recognition. These findings underscore the importance of global visual cues and color in the recognition process and provide practical computational design insights for urban distinctiveness and cultural sustainability. Full article

Background/Objectives: The PB family of “cut-and-paste” DNA transposons shows great promise as genetic manipulation tools while significantly impacting eukaryotic genome evolution. However, their evolutionary profile in beetles (Coleoptera), the most species-rich animal order, remains poorly characterized. Methods: A local tBLASTN search was conducted to mine PiggyBac (PB) transposons across 136 coleopteran insect genomes, using the DDE domain of the PB transposase as the query. Multiple sequence alignment was performed with MAFFT, and a maximum likelihood phylogenetic tree of the transposase DDE domains was constructed using IQ-TREE. Evolutionary dynamics were analyzed by means of K-divergence. Results: Our study reveals PB transposons are widely distributed, highly diverse, and remarkably active across beetles. We detected PB elements in 62 of 136 examined species (45%), classifying them into six distinct clades. A total of 62 PB-containing species harbored intact copies, with most showing recent insertions (K divergence ≈ 0), indicating ongoing transpositional activity. Notably, PB elements from Harmonia axyridis, Apoderus coryli, and Diabrotica balteata exhibit exceptional potential for genetic tool development. Structurally, intact PB elements ranged from 2074 to 3465 bp, each containing a single transposase ORF (500–725 aa). All were flanked by terminal inverted repeats and generated TTAA target site duplications. Conclusions: These findings demonstrate PB transposons have not only shaped historical beetle genome evolution but continue to drive genomic diversification, underscoring their dual significance as natural genome architects and promising biotechnological tools. Full article

Deep learning has become a powerful tool for diagnosing pests and plant diseases, although conventional convolutional neural networks (CNNs) generally suffer from limited interpretability and suboptimal focus on important image features. This study examines the integration of attention mechanisms into two prevalent CNN architectures—ResNet50 and MobileNetV2—to improve the interpretability and classification of diseases impacting date palm trees. Four attention modules—Squeeze-and-Excitation (SE), Efficient Channel Attention (ECA), Soft Attention, and the Convolutional Block Attention Module (CBAM)—were systematically integrated into ResNet50 and MobileNetV2 and assessed on the Palm Leaves dataset. Using transfer learning, the models were trained and evaluated through accuracy, F1-score, Grad-CAM visualizations, and quantitative metrics such as entropy and Attention Focus Scores. Analysis was also performed on the model’s complexity, including parameters and FLOPs. To confirm generalization, we tested the improved models on field data that was not part of the dataset used for learning. The experimental results demonstrated that the integration of attention mechanisms substantially improved both predictive accuracy and interpretability across all evaluated architectures. For MobileNetV2, the best performance and the most compact attention maps were obtained with SE and ECA (reaching 91%), while Soft Attention improved accuracy but produced broader, less concentrated activation patterns. For ResNet50, SE achieved the most focused and symptom-specific heatmaps, whereas CBAM reached the highest classification accuracy (up to 90.4%) but generated more spatially diffuse Grad-CAM activations. Overall, these findings demonstrate that attention-enhanced CNNs can provide accurate, interpretable, and robust detection of palm tree diseases and pests under real-world agricultural conditions. Full article

Background: HER2-positive breast cancer patients receiving chemotherapy and targeted therapy (including anthracyclines and trastuzumab) face an elevated risk of cardiotoxicity, which can lead to long-term cardiovascular complications. Identifying predictive biomarkers is essential for early intervention. Circulating microRNAs (miRNAs), known regulators of gene expression and cardiovascular function, have emerged as potential indicators of cardiotoxicity. This study aims to evaluate the differential expression of circulating miRNAs in HER2-positive breast cancer patients undergoing chemotherapy and to assess their prognostic ability for therapy-induced cardiotoxicity using machine learning models. Methods: Forty-seven patients were assessed for cardiac toxicity at baseline and every 3 months, up to 15 months. Blood samples were collected at baseline. MiRNA expression profiling for 84 microRNAs was performed using the miRCURY LNA miRNA PCR Panel. Differential expression was calculated via the 2−∆∆Ct method. The five most upregulated and five most downregulated miRNAs were further assessed using univariate logistic regression and receiver operating characteristic (ROC) analysis. Five machine learning models (Decision Tree, Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), k-Nearest Neighbors (KNN)) were developed to classify cardiotoxicity based on miRNA expression. Results: Forty-five miRNAs showed significant differential expression between cardiac toxic and non-toxic groups. ROC analysis identified hsa-miR-155-5p (AUC 0.76, p = 0.006) and hsa-miR-124-3p (AUC 0.75, p = 0.007) as the strongest predictors. kNN, SVM, and RF models demonstrated high prognostic accuracy. The decision tree model identified hsa-miR-17-5p and hsa-miR-185-5p as key classifiers. SVM and RF highlighted additional miRNAs associated with cardiotoxicity (SVM: hsa-miR-143-3p, hsa-miR-133b, hsa-miR-145-5p, hsa-miR-185-5p, hsa-miR-199a-5p, RF: hsa-miR-185-5p, hsa-miR-145-5p, hsa-miR-17-5p, hsa-miR-144-3p, and hsa-miR-133a-3p). Performance metrics revealed that SVM, kNN, and RF models outperformed the decision tree in overall prognostic accuracy. Pathway enrichment analysis of top-ranked miRNAs demonstrated significant involvement in apoptosis, p53, MAPK, and focal adhesion pathways, all known to be implicated in chemotherapy-induced cardiac stress and remodeling. Conclusions: Circulating miRNAs show promise as biomarkers for predicting cardiotoxicity in breast cancer patients. Machine learning approaches may enhance miRNA-based risk stratification, enabling personalized monitoring and early cardioprotective interventions. Full article

This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance, drawing upon the multi-methodological framework of combining econometrics with state-of-the-art machine learning approaches. Employing Instrumental Variable (IV) Panel data regressions, viz., 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz., Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz., Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model-Based, and Random Forest—were applied to uncover unknown structures and predict the behavior of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), social (S), and governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labor, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing us to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust. Full article

Detecting active failures is important for the Industrial Internet of Things (IIoT). The IIoT aims to connect devices and machinery across industries. The devices connect via the Internet and provide large amounts of data which, when processed, can generate information and even make automated decisions on the administration of industries. However, traditional active fault management techniques face significant challenges, including highly imbalanced datasets, a limited availability of failure data, and poor generalization to real-world conditions. These issues hinder the effectiveness of prompt and accurate fault detection in real IIoT environments. To overcome these challenges, this work proposes a data augmentation mechanism which integrates generative adversarial networks (GANs) and the synthetic minority oversampling technique (SMOTE). The integrated GAN-SMOTE method increases minority class data by generating failure patterns that closely resemble industrial conditions, increasing model robustness and mitigating data imbalances. Consequently, the dataset is well balanced and suitable for the robust training and validation of learning models. Then, the data are used to train and evaluate a variety of models, including deep learning architectures, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), and conventional machine learning models, such as support vector machines (SVMs), K-nearest neighbors (KNN), and decision trees. The proposed mechanism provides an end-to-end framework that is validated on both generated and real-world industrial datasets. In particular, the evaluation is performed using the AI4I, Secom and APS datasets, which enable comprehensive testing in different fault scenarios. The proposed scheme improves the usability of the model and supports its deployment in a real IIoT environment. The improved detection performance of the integrated GAN-SMOTE framework effectively addresses fault classification challenges. This newly proposed mechanism enhances the classification accuracy up to 0.99. The proposed GAN-SMOTE framework effectively overcomes the major limitations of traditional fault detection approaches and proposes a robust, scalable and practical solution for intelligent maintenance systems in the IIoT environment. Full article

Efforts to improve undergraduate education in mathematics and other STEM fields often work with instructors to implement research-based instructional practices that emphasize active and collaborative learning. To measure the progress and outcomes of such initiatives, researchers need measurement tools that are versatile, meaningful, and inexpensive to use, to know what teaching practices are occurring. Because students spend a great deal of time observing class conditions, they are well positioned to report the teaching that they experienced. We report results from some 2400 student surveys on the use of active and collaborative learning (ACL) approaches in over 200 recitation sections of gateway courses in tertiary mathematics, physics, and computer science. We developed a set of survey items, TAMI-SS, and a compound measure based on the items, called S-ACL for Student-reported Active and Collaborative Learning, that reflects the extent of active and collaborative learning as reported by students. We find that S-ACL scores compare favorably with instructor surveys and observations, and with students’ reports of their classroom experience using established measures. Moreover, S-ACL reflected departments’ progress in implementing ACL in recitations. When focused on specific, observable classroom behaviors, student surveys of instructional practice can be used to measure the progress of instructional change initiatives in mathematics and similar fields. Full article

Tree fruit and nut crops are a critical component of the global economy, producing at least 400 million tonnes of produce in 2022 and nourishing a growing population of approximately 8 billion humans every year. Improved cultivars and growing practices depend upon an understanding of the molecular basis of tree traits and physiology. Over the past 20 years, the proliferation of reference genomes for tree fruit and nut crop species has transformed the study of genetics in these crops, providing a platform for resequencing analyses of large populations, enabling comparative genomic analyses between distant plant species, and allowing the development of molecular markers for use in breeding. However, reference bias and poor transferability of markers limit widespread applicability in many instances. As third-generation sequencing has become more accurate and accessible, a greater number of reference genomes have become available, enabling higher-quality assemblies and wider sampling of genomic diversity. To facilitate the effective use of multiple closely related genomes to create a reference and comparative genomics platform, tools have been developed for the creation of pangenome graphs, a data structure using nodes connected by edges to represent multiple genomes and their sequence variations. Pangenome graphs allow for singular representations of diversity within a species or even a wider genus. Pangenomic analyses at the genus-scale (e.g., Malus, Citrus) have been conducted for Malus and Citrus, and more tree fruit and nut species are likely to follow. As the number of genome sequences and pangenome resources increases, the importance of generating great quantities of transcriptomic and phenomic data will increase as well. This data is essential in the drive to connect genes to traits and overcome traditional breeding bottlenecks, which is needed to develop improved tree fruit and nut crops, which can satisfy global demand. Full article

Background: Kenaf (Hibiscus cannabinus L.) is an important fiber crop belonging to the genus Hibiscus in the Malvaceae family. Research on its chloroplast genome holds significant importance for deciphering the evolutionary relationships of the Hibiscus species, developing genetic markers, and promoting kenaf (H. cannabinus) genetic breeding. Methods: Based on high-throughput sequencing technology, this study completed the sequencing and assembly of the kenaf (H. cannabinus) chloroplast genome. Results: (1) The kenaf (H. cannabinus) chloroplast genome exhibits a typical circular quadripartite structure with a total length of 163,019 bp, including a large single-copy region (LSC) of 90,467 bp, a small single-copy region (SSC) of 19,486 bp, and a pair of inverted repeat regions (IRa/IRb) of 26,533 bp each. The total GC content is 36.62%, among which, the IR region has the highest GC content (42.61%) and the SSC region the lowest (30.87%). (2) A total of 131 genes were annotated, including 85 mRNAs, 37 tRNAs, 8 rRNAs, and 1 pseudogene. Their functions cover photosynthesis (e.g., pet and atp family genes), self-replication (e.g., rpl, rps, and rpo family genes), and genes with unknown functions (e.g., ycf1 and ycf2). A codon usage bias analysis revealed that the relative synonymous codon usage (RSCU) value of the stop codon UAA is the highest (1.6329), and codons ending with A/U are preferentially used (e.g., GCU for alanine with RSCU = 1.778). (3) A repeat sequence analysis identified various interspersed repeat sequences (predominantly 30~31 bp in length, with a relatively high proportion in the 30~40 bp range, including forward and palindromic types) and simple sequence repeats (cpSSRs). Among them, single-base repeat SSRs account for the highest proportion (e.g., (A)8 and (T)9), and specific SSR primers were designed. (4) A comparative evolutionary analysis indicated that the Ka/Ks ratios (nonsynonymous substitution rate/synonymous substitution rate) of core chloroplast genes (e.g., rps2 and rpoC2) in kenaf (H. cannabinus) are all less than 1 (0.145~0.415), suggesting that they are under purifying selection. The collinearity similarity of chloroplast genomes between kenaf (H. cannabinus) and its closely related species reaches over 99.97%, and the IR region boundaries are relatively conserved. The phylogenetic tree shows that kenaf (H. cannabinus) clusters with closely related Hibiscus species with a 100% bootstrap value, indicating a close genetic relationship. Conclusions: This study provides basic data for the functional analysis of the kenaf (H. cannabinus) chloroplast genome, the phylogeny of Hibiscus, and the utilization of genetic resources. Full article

Tropical germplasm has accumulated a large number of genes adapted to a variety of adversities. In this study, we assessed the genetic diversity and population structure of 109 inbred maize lines newly released from the International Maize and Wheat Improvement Center (CIMMYT) in the last few years. The results indicated the following: (1) linkage disequilibrium (LD) analysis showed that tropical maize germplasms had a faster rate of LD decay, suggesting higher recombination rates and genetic diversity; (2) both the phylogenetic tree and structure analysis supported the classification of the material into three subgroups; (3) the results of the principal component analysis were consistent with the population structure analysis, further verifying the reliability of subgroup delineation; (4) the genetic distances between the tropical germplasms from groups 2 and 3 and the elite temperate inbred lines were relatively close, which is suitable for temperate germplasms improvement. The results can help us select suitable tropical germplasms and speed up the process of inbred line development and maize improvement. Full article

Anthracnose is one of the major diseases affecting Dalbergia odorifera T. Chen. However, the soil microbial mechanisms underlying D. odorifera responses to anthracnose remain largely unexplored. This study investigated three planting systems: a Dalbergia odorifera monoculture (J); a mixed plantation of D. odorifera and Pterocarpus macrocarpus (JD); and a composite mixed plantation of D. odorifera, P. macrocarpus, and Clinacanthus nutans (JDY). Using amplicon sequencing technology for soil microbial analysis and combining soil physical and chemical properties with disease severity, we comprehensively analyzed changes in soil microbial community structure and function across different planting modes. The results showed that the diverse mixed mode (JD, JDY) significantly improved soil physicochemical properties and promoted soil nutrient cycling. Redundancy analysis (RDA) indicated that soil organic matter (SOM) and disease severity, quantified by the area under the disease progress curve (AUDPC), were the primary environmental drivers of microbial community variation. Genera positively correlated with SOM and negatively correlated with AUDPC were significantly enriched in JDY and JD, whereas genera showing opposite relationships were predominantly enriched in J. Functional predictions revealed enhanced nutrient-cycling capacities in JD and JDY, with JDY uniquely harboring functional groups such as Arbuscular Mycorrhizal, Epiphyte, and Lichenized taxa. In contrast, microbial functions in the J plantation were mainly limited to environmental amelioration. Co-occurrence network analysis further showed that as planting patterns shifted from J to JDY, microbial communities evolved from competition-dominated networks to cooperative defensive networks, integrating efficient decomposition with strong pathogen suppression potential. The study demonstrates that complex mixed planting systems regulate soil properties, enhance the enrichment of key functional microbial taxa, reshape community structure and function, and ultimately enable ecological control of anthracnose disease. This study provides new perspectives and theoretical foundations for ecological disease management in plantations of rare tree species and for microbiome-based ecological immunization strategies. Full article

Understanding soil respiration (Rs) dynamics in Mediterranean olive groves is crucial for quantifying carbon fluxes under climate change. Soil respiration represents the combined CO₂ efflux from root metabolic activity and microbial decomposition of soil organic matter, processes strongly controlled by soil moisture, temperature, and the quantity and quality of organic matter inputs in semi-arid Mediterranean environments. This study quantified the seasonal and spatial variability of Rs in a traditional rainfed olive orchard planted at a spacing of 11 m between rows and 9 m between trees (≈101 trees ha⁻¹). Continuous measurements were conducted in two contrasting zones, under-canopy (UC) and inter-row (IR), using automated soil CO₂ flux chambers. Annual Rs reached 3.68 Mg CO₂ ha⁻¹ y⁻¹ in UC and 2.21 Mg CO₂ ha⁻¹ y⁻¹ in IR, with substantially higher emissions per unit area beneath the canopy. However, due to its larger surface proportion, the IR zone contributed more to the orchard scale CO₂ budget. Soil water content emerged as the dominant environmental driver of Rs, moderating or suppressing the temperature response during dry periods. These findings highlight the importance of explicitly considering microsite heterogeneity when assessing soil CO₂ efflux and designing sustainable carbon-management strategies in Mediterranean olive agroecosystems. Full article

In urban underground space construction using shield tunnelling, the geological conditions ahead of the tunnel face are often uncertain. Without timely and accurate classification of the ground type, mismatches in operational parameters, uncontrolled costs, and schedule risks are likely to occur. Using observations from an earth pressure balance (EPB) project on an urban railway, a data-driven classification framework is developed that integrates shield tunnelling operating measurements with physically derived quantities to discriminate among soft soil, hard rock, and mixed strata. Principal component analysis (PCA) is performed on the training set, followed by a systematic comparison of tree-based classifiers and hyperparameter optimization strategies to explore the attainable performance. Under unified evaluation criteria, a categorical bosting (CatBoost) model optimized by a Nevergrad combination strategy (NGOpt) attains the highest test accuracy of 0.9625, with macro-averaged precision and macro-averaged recall of 0.9715 and 0.9716, respectively. To mitigate optimism from single-point estimates, stratified bootstrap intervals are reported for the test set. A Monte Carlo experiment applies independent perturbations to the PCA-transformed features, producing low label-flip rates across the three classes, with only minor changes in probability calibration metrics, which suggests consistent decisions under sensor noise and sampling bias. Overall, within the scope of the considered EPB project, the study delivers a compact workflow that demonstrates the feasibility of uncertainty-aware ground-type classification and provides a methodological reference for developing decision-support tools in underground tunnel construction. Full article

Landscape fragmentation as a process of landscape transformation affects the structure and composition of plant communities; however, relationships between fragmentation metrics and vegetation characteristics often remain weakly expressed and difficult to interpret, especially under conditions of multiple natural (wildfires, windstorms, pest outbreaks) and anthropogenic stressors (construction, forest management, agriculture). The aim of this study was to identify the sensitivity of forest community characteristics to landscape fragmentation metrics using methods that are effective at low correlation coefficients. The study analyzed 1694 vegetation relevés of forest communities in the center of the Russian Plain in the territory of the Moscow region. Seven uncorrelated metrics were calculated using the moving window method (2000 m) in Fragstats 4.3. The relationships between selected metrics and 20 community characteristics were evaluated using Spearman’s rank correlation method, assessment of statistically significant differences between classes, and testing for non-linear interactions. The species richness and Shannon index showed no correlation with fragmentation for tree and herb layers; however, the composition of ecological–coenotic groups demonstrated high sensitivity. The proportion of boreal and oligotrophic species, as well as the moss layer abundance, increased with increasing patch size, while nemoral and adventive species dominated in small-contrast patches. Results showed that fragmentation leads to asynchronous responses from ecosystem components, reducing correlations between structure and functioning. The conservation of large connected forest patches is critical for preserving the boreal–oligotrophic complex and moss layer, and is a priority task for climate adaptation. The robustness of the findings is supported by the extensive number of analyzed vegetation relevés. The multi-method approach demonstrated effectiveness in identifying significant ecological patterns under conditions of high multifactorial impact, emphasizing the need for a functionally oriented approach to managing fragmented temperate forests. Full article