Search Results (4,696)

Diaphorina citri Kuwayama is a key pest of citrus and insect vector of Huanglongbing (HLB), also known as citrus greening disease, causing significant losses in Florida and other regions. The naturally occurring effective ladybeetle predators and their impact on D. citri reduced from years of insecticide use against this pest and are not available commercially. Additionally, most species are large-sized, while most eggs and neonates of D. citri are in hard-to-reach locations such as unopened leaves, which makes access difficult for them. We evaluated a commercially available small-sized predatory ladybeetle Rhyzobius lophanthae Blaisdell against D. citri immatures. A single adult consumed an average of 24.9 eggs and 8.7 first and second instar nymphs of D. citri within 24 h. Beetles exhibited Type II functional response against nymphs with an attack rate of 0.92 h⁻¹ and a handling time of 0.08 h. Their consumption rate increased with nymphal density up to twenty per shoot. In the field test, beetles lived 10 days longer when confined with new shoots infested with D. citri immatures in a voile fabric sleeve cage in citrus trees every two days, versus seven days. In an open field release of R. lophanthae in a citrus orchard, these ladybeetles were found foraging in sentinel and neighboring trees infested with D. citri. The consumption rate of R. lophanthae on D. citri immatures and its survival in Florida orchards suggest its potential for biological control and Integrated Pest Management. Full article

In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically automate such processes, an adaptive online fault diagnosis framework is required, which consecutively selects variables to measure and updates its inference with additional information at each measurement step. In this paper, adaptive online fault detection models—grafted composite decision trees—are proposed for such a framework. While conventional decision trees themselves can serve two required objectives of the framework, information from monitored variables can be less utilized because decision trees do not consider if required input variables are always monitored when the models are trained. On the other hand, the proposed grafted composite decision tree models are designed to fully utilize both monitored and robot-measured variables at any stage in a given measurement sequence by grafting two types of trees together: a prior-tree trained only with observed variables and sub-trees trained with robot-measurable variables. The proposed method was validated on a cooling water system in a nuclear power plant with multiple leak scenarios, in which improved measurement selection and increase in inference confidence in each measurement step are demonstrated. The performance comparison between the proposed models and the conventional decision tree model clearly illustrates how the acquired information is fully utilized for the best inference while providing the best choice of the next variable to measure, maximizing information gain at the same time. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

Background: While prostate-specific membrane antigen (PSMA)-targeted imaging has revolutionized metastatic detection, unspecific bone uptake (UBU)—particularly in the ribs—is a common but diagnostically challenging finding in prostate cancer (PCa) patients. This review aims to synthesize current evidence on PSMA-avid rib lesions in PCa and to propose a structured approach for differentiating true metastases from benign mimics. Methods: A comprehensive literature search across PubMed, EMBASE, Scopus, and Web of Science identified relevant studies on PSMA imaging interpretation, tracer-specific patterns, rib lesion morphology, and clinical correlates. Data on uptake intensity, CT features, lesion number, location, tracer type, patient-specific risk factors, and follow-up behavior were extracted and analyzed. Results: Most solitary rib lesions are benign, particularly in low-risk patients or when located in the anterior/lateral arcs. Metastatic lesions are more likely to present as multiple foci, show cortical destruction on CT, exhibit high uptake intensity, and occur in patients with elevated PSA, high Gleason score, or ongoing androgen deprivation. ¹⁸F-PSMA-1007 is especially prone to UBU in the ribs compared to ⁶⁸Ga-PSMA-11. Based on these variables, we propose a clinical decision tree to guide interpretation of PSMA-avid rib lesions. Conclusions: Accurate interpretation of rib lesions on PSMA PET/CT requires a multimodal, context-sensitive approach. Our diagnostic decision tree guides precise differentiation of benign versus metastatic rib lesions, enhancing staging accuracy and clinical decision-making. Biomarker-guided therapies offer potential for personalized treatment, though rib-specific validation remains a critical need. Full article

Unmanned aerial vehicle (UAV)-captured RGB imagery, with high spatial resolution and ease of acquisition, is increasingly applied to individual tree crown detection (ITCD). However, ITCD in dense subtropical forests remains challenging due to overlapping crowns, variable crown size, and similar spectral responses between neighbouring crowns. This paper investigates to what extent the ITCD accuracy can be improved by using dual-seasonal UAV-captured RGB imagery in different subtropical forest types: urban broadleaved, planted coniferous, and mixed coniferous–broadleaved forests. A modified YOLOv8 model was employed to fuse the features extracted from dual-seasonal images and perform the ITCD task. Results show that dual-seasonal imagery consistently outperformed single-seasonal datasets, with the greatest improvement in mixed forests, where the F1 score range increased from 56.3%–60.7% (single-seasonal datasets) to 69.1%–74.5% (dual-seasonal datasets) and the AP value range increased from 57.2%–61.5% to 70.1%–72.8%. Furthermore, performance fluctuations were smaller for dual-seasonal datasets than for single-seasonal datasets. Finally, our experiments demonstrate that the modified YOLOv8 model, which fuses features extracted from dual-seasonal images within a dual-branch module, outperformed both the original YOLOv8 model with channel-wise stacked dual-seasonal inputs and the Faster R-CNN model with a dual-branch module. The experimental results confirm the advantages of using dual-seasonal imagery for ITCD, as well as the critical role of model feature extraction and fusion strategies in enhancing ITCD accuracy. Full article

Understanding how vegetation structure influences invertebrate diversity is critical for tropical forest conservation because invertebrates play key roles in ecosystem functioning. This study investigates the role of vegetation and selected microhabitats in shaping harvestmen assemblages across primary and planted forests in the Amazon rainforest. Our findings challenge the traditional view that vegetation quantity alone drives invertebrate distribution, revealing that specific plant species play a key role in shaping harvestmen assemblages. Notably, Geaya sp. (Sclerosomatidae) was strongly associated with specific arboreal species, especially Tetragastris altissima and Attalea maripa, and was identified as a bioindicator of trees. Tree diversity provides critical habitats in primary forests, illustrating how changes in tree composition can disproportionately impact specialist species. Two species of harvestmen were also identified as bioindicators of forest quality. For instance, Geaya sp. was exclusively linked to primary forests, while the cosmetid Gryne sp. emerged as moderately associated with this type of forest with high structural complexity. By identifying the specific relationships between harvestmen and vegetation, this study demonstrates their potential for monitoring ecosystem health and emphasizes the importance of preserving keystone plant species to maintain ecological integrity in tropical forests. Full article

Early software defect prediction research primarily relied on software metric features. However, such features struggle to fully capture semantic information in source code. Consequently, methods based on semantic features have gradually become mainstream. Nevertheless, the empirical value of traditional metric features remains significant. To address this, this paper proposes a multi-modal feature fusion method based on a cross-attention gating mechanism for software defect prediction (GMCA-SDP). This method aims to effectively fuse multiple modal features of source code to improve defect prediction performance. Unlike previous approaches, GMCA-SDP can simultaneously integrate traditional metric features and various code semantic features. During the feature fusion stage, it considers both the contribution differences among different types of features and the information interaction between modalities. This paper selects three types of modal features as input for GMCA-SDP: traditional metric features, semantic features extracted from the abstract syntax tree, and structural features extracted from the control flow graph. Our experiments on nine open-source projects demonstrate that the GMCA-SDP method outperforms six mainstream defect prediction models, with average improvements of 18.7% in F1, 10.9% in AUC, and 14.1% in G-mean. Full article

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

The tire-road friction coefficient (TRFC) is a critical parameter that significantly influences vehicle safety, handling stability, and driving comfort. Existing estimation methods based on vehicle dynamics suffer from a substantial decline in accuracy under conditions with insufficient excitation, while vision-based approaches are often limited by the generalization ability of their datasets, making them less effective in complex and variable real-driving environments. To address these challenges, this paper proposes a novel, low-cost fusion method for TRFC estimation that integrates weather conditions and road image data. The proposed approach begins by employing semantic segmentation to partition the input images into distinct regions—sky and road. The segmented images will be fed into the road recognition network and the weather recognition network for road type and weather classification. Furthermore, a fusion decision tree incorporating an uncertainty modeling mechanism is introduced to dynamically integrate these multi-source features, thereby enhancing the robustness of the estimation. Experimental results demonstrate that the proposed method maintains stable and reliable estimation performance even on unseen road surfaces, outperforming single-modality methods significantly. This indicates its high practical value and promising potential for broad application. Full article

(1) Background: Nitrogen is a key element that is essential for plant growth, and it is absorbed by roots from the soil. Nitrogen stress severely limits forest tree productivity; therefore, elucidating the molecular mechanisms underlying nitrogen stress tolerance in forest trees is critical for sustainable forestry. (2) Methods: Phenotypic analyses of wild-type (WT) Populus ussuriensis (P. ussuriensis) plantlets grown in vitro were carried out at different time points under both normal and low-nitrogen conditions. Transcriptome analyses of roots were performed at 0, 12, 24, 48, 96, and 336 h under low-nitrogen stress via RNA-seq. A gene regulatory network (GRN) for nitrogen-metabolism-associated DEGs was constructed using a three-gene module framework and a bottom-up Gaussian Graphical Model algorithm. (3) Results: WT P. ussuriensis plantlets grown in vitro exhibited a synergistic response characterized by increased root biomass and suppressed shoot growth. Transcriptome analyses identified 8289 DEGs enriched in nitrogen metabolism, ROS scavenging, root development, and phytohormone signaling. A total of 443 differentially expressed transcription factors (TFs) (mainly MYB, AP2/ERF, and bHLH) were detected. A nitrogen-metabolism-associated GRN comprising 60 nodes was established. (4) Conclusions: Transcriptomic data and nitrogen metabolism pathway predictions from this study establish a systematic foundation for investigating molecular adaptation mechanisms in P. ussuriensis roots under nitrogen stress. Full article

Concerns about potential negative impacts of human activity on macrofungal diversity are spreading globally, yet research on this topic remains scarce. This study focuses on forest grazing (silvopasture), a popular economic practice whose impacts on macrofungal diversity are underexplored. Through investigation and comparison of macrofungal diversity and selected environmental factors in three types of subtropical forests (secondary mixed forests, dense-tree plantations and sparse-tree plantations) before and after two years of grazing at an intensity of 10 goats per hectare in South China, three key findings emerged: (1) Macrofungal alpha-diversity increased significantly after grazing, associated with an increase in large plant remains and a decrease in litterfall thickness; (2) dominance was monopolized by few taxa before grazing but became more balanced among a number of taxa after grazing; and (3) dominance of endemic taxa decreased in two of the three types of forests after grazing. Such findings suggest that grazing may create additional niches through foraging, trampling and excretion by livestock and thus recruit diverse macrofungi but may also lead to homogenization of fungal florae across regions and thus result in recessive beta-diversity loss. As this study heavily relies on taxonomy, allied updates for ambiguous taxa recognized in analyses are additionally proposed. Full article

The clinical heterogeneity of type 1 Gaucher disease (GD1) underscores the limited correlation between the GBA1 genotype and phenotype. This study examined GD1 patients from the Spanish Gaucher Disease Registry carrying heterozygous GBA1 genotypes distinct from NM_000157: c.[1226A>G](N370S); [1448T>C](L444P). Among 374 patients with GD1, 195 (52.1%) had alternative heterozygous combinations, including variants corresponding to severe (37.9%) or moderate (42.1%) mutation, whereas only 20% patients harbored mild variants—all of them in combination with N370S. Descriptive statistics and predictive models based on logistic regression and decision trees were applied. Patients carrying N370S with a different L444P variant showed significantly higher rates of advanced bone disease (59.9%) compared to those with homozygous N370S (38.3%) or N370S; L444P (41.0%) (p = 0.002). Decision tree analysis identified the bone marrow burden score (S-MRI) as the strongest predictor of osteopenia/osteoporosis at diagnosis. Genotype also emerged as a key discriminator for Parkinson’s disease: patients with non-N370S; L444P genotypes showed a markedly higher likelihood of developing Parkinsonism. Overall, GD1 patients with genotypes other than N370S; L444P present more severe phenotypes, particularly with greater skeletal involvement and neurological complications. These findings highlight the importance of genotype stratification and predictive modeling in improving risk assessment and clinical management in GD1. Full article

During a survey of the west coast of South Korea, two new Pseudochromadora species were recorded from Yeongjongdo Island. Descriptions of two new species, an updated list of valid species within the genus, a tabular key, partial sequences of mtCOI, near full-length SSU, and the D2–D3 region of LSU rDNA, together with phylogenetic analyses are provided. The two new species are classified as Pseudochromadora based on having a two-portioned cephalic capsule, unispiral amphidial fovea, lateral alae extending from the posterior end of the pharynx as far as the tail, and presence of copulatory thorns, as well as a short conical tail. The two species are distinguished from each other by their different types of labial regions of the cephalic capsule (round-shaped vs. hat-shaped). The two species, despite being found in the same locality, are morphologically and molecularly distinct from one another. Pairwise Kimura 2-parameter (K2P) distances between the two new species were 10.6% (18S) and 27.2% (28S), values consistent with interspecific divergence observed among congeners. Phylogenetic analyses showed both species as distinct lineages within Pseudochromadora. In the 28S rDNA tree, each was retrieved as a well-supported monophyletic clade with congeners, whereas in the 18S tree, all congeners including the two new species formed a single clade, except for P. plurichela, which branched outside the main group. These results highlight potential paraphyly within Pseudochromadora and suggest that overlooked morphological traits may hold phylogenetic significance, warranting further investigation. Full article

Sulfur metabolism plays an important role in plant growth and environmental adaptation. Sulfate transporters (SULTRs) are essential players that mediate sulfur acquisition and distribution in many plants, thereby influencing the cellular redox homeostasis under abiotic stress. In this study, we identified 16 putative HvSULTRs genes in barley at the genome-wide level. The conservation and divergence of the SULTR gene family were assessed through a phylogenetic tree and gene structure analysis, revealing that these genes are closely distributed along the chromosomes. Furthermore, the expression pattern of SULTRs in multiple tissues, including flower, root, leaf, stem, seeds, female, male, root meristem, and apical meristem, were analyzed among ten land plants using a public database. Interestingly, the expression of HvSULTR2, HvSULTR4, and HvSULTR5 was upregulated after four days of heat treatment, suggesting their importance in barley’s adaptive response to heat stress. In addition, HvSULTR11 was confirmed to be localized at the plasma membrane and display functional interactions with Hv14-3-3A/Hv14-3-3D. In addition, haplotypes of the HvSULTR11 based on SNP (Single Nucleotide Polymorphism) were divided into ten types across 123 barley varieties. Together, these results provide a new clue to clarify the molecular mechanism of SULTRs in stress response and a new candidate gene resource to enhance the stress (e.g., heat and drought) tolerance in barley. Full article

Hygrophila polysperma is a type of amphibious plant that originates from Acanthaceae. Here, we report its first complete chloroplast (cp) genome. The complete cp genome is 146,675 bp in length with 38.3% of GC content. There are 130 genes including 86 protein coding genes, 36 tRNA genes, and 8 rRNA genes in this genome. Simple short sequence (SSR) analysis found 30 SSRs, 24 of which are located in a large single-copy region. Nucleotide diversity identified six most divergent sequences (trns-GCU, psaA-pafI, psaI-pafII, ycf2, rpl32, and ycf1) among 3 close-related species, H. polysperma, H. ringens, and Asteracantha longifolia. A phylogenetic tree among H. polysperma and another 30 related species was constructed based on the common coding sequence of the cp genome and showed that H. polysperma is most closely related to H. ringens (both belong to subtribe Hygrophilinae) and, together, they form a clade that is sister to A. longifolia. This study provides a basis for systemic and evolution studies as well as the development of molecular markers for species identification and genetic breeding. Full article