Search Results (8,624)

Intelligent transportation systems (ITS) play a crucial role in building sustainable and resilient urban mobility by improving traffic efficiency, reducing energy consumption, and lowering emissions. The integration of IoT technologies, particularly long-range low-power networks such as LoRaWAN, enables energy-efficient communication between vehicles and road infrastructure, supporting the sustainability goals of smart cities. However, the widespread deployment of IoT devices also introduces significant cybersecurity risks that may compromise the safety, reliability, and long-term sustainability of transportation systems. To address this challenge, we propose a method for generating synthetic network data that simulates normal traffic and DDoS attacks by randomly selecting distribution parameters for features like packets per second and unique device addresses, enabling evaluation of machine learning algorithms (e.g., Gradient Boosting, Random Forest, SVM, XGBoost) using F1-score and AUC metrics in a controlled environment. By enhancing cybersecurity and resilience in ITS, our research contributes to the development of safer, more energy-efficient, and sustainable transportation infrastructures. Full article

Classifying low-concentration Gas Sensor Array (GSA) data is hard due to low SNR, sensor heterogeneity, drift, and small samples. We benchmark time-series-to-image (TS2I) CNNs against time-series classifiers, after reproducing a strong FE-ELM baseline under a shared fold manifest. Using the GSA-LC and GSA-FM datasets, we compare FE-ELM, vector baselines, time-series methods, and TS2I-CNNs with 20 × 5 repeated stratified cross-validation (n = 100). ROCKET delivers the best accuracy on both datasets and is significantly better than TCN and MiniROCKET (paired tests with Holm–Bonferroni, p < 0.05): on GSA-FM, accuracy 0.9721 ± 0.0480 (95% CI [0.9627, 0.9815]) with Macro-F1 0.9757; on GSA-LC, 0.9578 ± 0.0433 (95% CI [0.9493, 0.9663]) with Macro-F1 0.9555. Among image-based models, CNN-RP is the most robust, whereas CNN-GASF lags, especially on GSA-LC. RGB fusion strategies (e.g., with MTF) are dataset-dependent and often inconsistent, and transfer learning with ResNet-18 offers no consistent advantage. Overall, ROCKET ranks first across folds, while CNN-RP is the most reliable TS2I alternative under low-concentration conditions. These results provide a reproducible, fair benchmark for e-nose applications and practical guidance for model selection, while clarifying both the potential and limitations of TS2I. Full article

Background/Objectives: High-frequency ultrasonography (HFUS) has gained increasing attention in dermatology as a non-invasive imaging technique capable of visualizing cutaneous structures with high resolution. In cutaneous T-cell lymphomas (CTCL), including mycosis fungoides (MF)/Sézary syndrome (SS), HFUS may provide an objective method for assessing disease activity and monitoring treatment response. This study aimed to evaluate the clinical utility of HFUS in detecting therapy-induced changes in subepidermal low-echogenic band (SLEB) thickness. Methods: We conducted a prospective, single-center study between May 2021 and May 2025. Thirty-three patients with histologically confirmed MF (n = 31) or SS (n = 2) underwent HFUS at baseline and after 4–8 weeks of treatment. SLEB thickness was measured before (E1) and after early treatment (E2). Patients received systemic agents, phototherapy, or topical regimens. Statistical analysis included mixed-model ANOVA with repeated measures to assess SLEB changes, and post hoc tests were applied to explore the influence of therapy type, age, and gender. Results: Among 31 evaluable patients with MF, HFUS revealed a significant reduction in SLEB thickness after treatment (0.90 ± 1.10 mm vs. 0.69 ± 0.89 mm; F(1,29) = 8.88, p = 0.006, η² = 0.23). The type of early therapy (systemic vs. topical) did not significantly affect outcomes (p = 0.452). Age emerged as a relevant factor: patients ≥ 66 years exhibited higher baseline SLEB values and a significant reduction post-treatment (p < 0.001), whereas no comparable effect was observed in younger patients. Gender did not significantly influence SLEB changes. Conclusions: HFUS is a sensitive and clinically applicable imaging tool for monitoring treatment response in MF/SS. Reductions in SLEB thickness were observed across therapeutic modalities and aligned with early clinical improvement. HFUS may serve as a valuable adjunct to standard clinical and histopathological evaluation in the routine management of MF/SS. Full article

The SETD6 protein lysine methyltransferase monomethylates specific lysine residues in a diverse set of substrates which contain the target lysine residue in a highly variable amino acid sequence context. To investigate the mechanism underlying this multispecificity, we analyzed SETD6 substrate recognition using AlphaFold 3 docking and peptide SPOT array methylation experiments. Structural modeling of the SETD6–E2F1 complex suggested that substrate binding alone is insufficient to restrict SETD6 activity to only one lysine residue, pointing to additional sequence readout at the target site. Methylation of mutational scanning peptide SPOT arrays derived from four different SETD6 substrates (E2F1 K117, H2A.Z K7, RELA K310, and H4 K12) revealed sequence preferences of SETD6 at positions −1, +2, and +3 relative to the target lysine. Notably, glycine or large aliphatic residues were favored at −1, isoleucine/valine at +2, and lysine at +3. These preferences, however, were sequence context dependent and variably exploited among different substrates, indicating conformational variability of the enzyme–substrate interface. Mutation of SETD6 residue L260, which forms a contact with the +2 site in the available SETD6-RELA structure, further demonstrated substrate-specific differences in recognition at the +2/+3 sites. Together, these findings reveal a versatile mode of peptide recognition in which the readout of each substrate position depends on the overall substrate peptide sequence. These findings can explain the multispecificity of SETD6 and similar mechanisms may underlie substrate selection in other protein methyltransferases. Full article

Background: Most immunologically “cold” tumors do not respond durably to checkpoint blockade because tumor antigen (TA) release and presentation are insufficient to prime effective T-cell immunity. While prior work demonstrated synergy between cisplatin and a TLR7/8/9 agonist (CR108) in 4T1 tumors, the underlying mechanism—particularly whether chemotherapy functions as a broad antigen-releasing agent enabling TLR-driven immune amplification—remained undefined. Methods: Using murine models of breast (4T1), melanoma (B16-F10), and colorectal cancer (CT26), we tested multiple chemotherapeutic classes combined with CR108. We quantified intratumoral and systemic soluble TAs, antigen presentation and cross-priming by antigen-presenting cells, tumor-infiltrating lymphocytes, and cytokine production by flow cytometry/ICS. T-cell receptor β (TCRβ) repertoire dynamics in tumor-draining lymph nodes were profiled to assess amplitude and breadth. Tumor microenvironment remodeling was analyzed, and public datasets (e.g., TCGA basal-like breast cancer) were interrogated for expression of genes linked to TA generation/processing and peptide loading. Results: Using cisplatin + CR108 in 4T1 as a benchmark, we demonstrate that diverse chemotherapies—especially platinum agents—broadly increase the repertoire of soluble tumor antigens available for immune recognition. Across regimens, chemotherapy combined with CR108 increased T-cell recognition of candidate TAs and enhanced IFN-γ⁺ CD8⁺ responses, with platinum agents producing the largest expansions in soluble TAs. TCRβ sequencing revealed increased clonal amplitude without loss of repertoire breadth, indicating focused yet diverse antitumor T-cell expansion. Notably, therapeutic efficacy was not predicted by canonical damage-associated molecular pattern (DAMP) signatures but instead correlated with antigen availability and processing capacity. In human basal-like breast cancer, higher expression of genes involved in TA generation and antigen processing/presentation correlated with improved survival. Conclusions: Our findings establish an antigen-centric mechanism underlying chemo–TLR agonist synergy: chemotherapy liberates a broadened repertoire of tumor antigens, which CR108 then leverages via innate immune activation to drive potent, T-cell-mediated antitumor immunity. This framework for rational selection of chemotherapy partners for TLR7/8/9 agonism and support clinical evaluation to convert “cold” tumors into immunologically responsive disease. Full article

As the core refrigeration equipment in cooling systems, the operational state of chiller units is crucial for ship support, equipment cooling, and mission stability. However, because of their sensitivity and the complexity of operating environments, obtaining large volumes of complete, fault-labeled data is difficult in practical engineering appli-cations. This limitation makes it challenging for traditional data-driven approaches to deliver accurate fault diagnoses. Furthermore, data collected from different devices or under varying operating conditions often differ significantly in both feature dimensions and distributions, i.e., data heterogeneity, which further complicates model transfer. To address these challenges, this study proposes a deep transfer learning–based fault di-agnosis method designed to leverage abundant knowledge from the source domain while adaptively learning features of the target domain. Given the persistent difficulties in collecting sufficient high-quality labeled fault data, traditional data-driven models continue to face restricted diagnostic performance on target equipment. At the same time, data heterogeneity across devices or operating conditions intensifies the challenge of cross-domain knowledge transfer. To overcome these issues, this study develops a heterogeneous transfer learning method that integrates a dual-channel autoencoder, domain adversarial training, and pseudo-label self-training. This combination enables precise small-sample knowledge transfer from the source to the target domain. Specifi-cally, the dual-channel autoencoder is first applied to align heterogeneous feature di-mensions. Then, a Gradient Reversal Layer (GRL) and a domain discriminator are in-troduced to extract domain-invariant features. In parallel, high-confidence pseu-do-labeled samples from the target domain are incorporated into joint training to im-prove generalization and robustness. Experimental results confirm that the method achieves high fault diagnosis accuracy in typical industrial application scenarios, ena-bling effective identification of common faults in various types of chiller units under conventional operating conditions, the proposed method achieves higher accuracy and F1-scores in multi-class fault diagnosis tasks compared with both traditional approaches and existing transfer learning methods. These findings provide a novel perspective for advancing the intelligent operation and maintenance of chiller units. Full article

Schizophrenia is a highly polygenic and clinically heterogeneous disorder. We first review layer-specific evidence across genetics, epigenetics, transcriptomics, proteomics, and patient-derived induced pluripotent stem cell (iPSC) models, then integrate cross-layer findings. Genetics research identifies widespread risk architecture. Hundreds of loci from common, rare, and CNV analyses. Epigenetics reveals disease-associated DNA methylation and histone-mark changes. These occur at neuronally active enhancers and promoters, together with chromatin contacts that link non-coding risk to target genes. Transcriptomics show broad differential expression, isoform-level dysregulation, and disrupted co-expression modules. These alterations span synaptic signaling, mitochondrial bioenergetics, and immune programs. Proteomics demonstrates coordinated decreases in postsynaptic scaffold and mitochondrial respiratory-chain proteins in cortex, with complementary inflammatory signatures in serum/plasma. iPSC models recapitulate disease-relevant phenotypes: including fewer synaptic puncta and excitatory postsynaptic currents, electrophysiological immaturity, oxidative stress, and progenitor vulnerability. These same models show partial rescue under targeted perturbations. Integration across layers highlights convergent pathways repeatedly supported by ≥3 independent data types: synaptic signaling, immune/complement regulation, mitochondrial/energetic function, neurodevelopmental programs and cell-adhesion complexes. Within these axes, several cross-layer convergence genes/proteins (e.g., DLG4/PSD-95, C4A, RELN, NRXN1/NLGN1, OXPHOS subunits, POU3F2/BRN2, PTN) recur across cohorts and modalities. Framing results through cross-layer and shared-pathway convergence organizes heterogeneous evidence and prioritizes targets for mechanistic dissection, biomarker development, and translational follow-up. Full article

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines—including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception. Full article

The complex jet-ground interactions of Short Take-off and Vertical Landing (STOVL) aircraft are critical to flight safety and performance, yet studying them with traditional full-scale wind tunnel tests is prohibitively expensive and time-consuming, hindering design optimization. This study addresses this challenge by developing and numerically verifying a “pressure ratio–momentum–geometry” multi-dimensional similarity framework, enabling accurate and efficient scaled-model analysis. Systematic simulations of an F-35B-like configuration demonstrate the framework’s high fidelity. For a representative curved nozzle configuration (e.g., the F-35B three-bearing swivel duct nozzle, 3BSD), across scale factors ranging from 1:1 to 1:15, the plume deflection angle remains stable at 12° ± 1°. Concurrently, axial force (F) and mass flow rate (Q) strictly follow the square scaling relationship ($F\propto 1/n^2, Q\propto 1/n^2)$, with deviations from theory remaining below 0.15% and 0.58%, respectively, even at the 1:15 scale, confirming high-fidelity momentum similarity, particularly in the near-field flow direction. Second, a 1:13.25 scale aircraft model, constructed using Froude similarity principles, exhibits critical parameter agreement (intake total pressure and total temperature) of the prototype-including vertical axial force, lift fan mass flow, and intake total temperature—all less than 1.5%, while the critical intake total pressure error is only 2.2%. Fountain flow structures and ground temperature distributions show high consistency with the full-scale aircraft, validating the reliability of the proposed “pressure ratio–momentum–geometry” multi-dimensional similarity criterion. The framework developed herein has the potential to reduce wind tunnel testing costs and shorten development cycles, offering an efficient experimental strategy for STOVL aircraft research and development. Full article

Fluoride is a widespread environmental toxin that disrupts metabolic and endocrine functions, but its impact on pancreatic inflammation and hormone secretion remains unclear. This study examined how chronic fluoride exposure affects pancreatic inflammation and secretory function in rats. Pregnant Wistar rats received sodium fluoride (NaF) at 50 mg/L in drinking water during gestation and lactation. Male offspring continued exposure until 3 months old. Controls received fluoride-free water. Pancreatic tissue and serum were collected. Fluoride levels were measured potentiometrically. Eicosanoids were quantified by SPE and HPLC. Serum insulin, glucagon, and somatostatin were measured by ELISA. Histological and biochemical markers of inflammation and oxidative stress were assessed. Fluoride exposure did not lead to significant accumulation in the pancreas or serum. However, fluoride-exposed rats exhibited a significant decrease in serum insulin and somatostatin concentrations, while glucagon levels remained unchanged. Additionally, the pancreas of fluoride-treated animals showed markedly elevated levels of pro-inflammatory eicosanoids, including prostaglandin E2, leukotrienes A4 and B4, and HETE/HODE derivatives, indicating activation of cyclooxygenase and lipoxygenase pathways. Sustained low-dose fluoride exposure induced pancreatic inflammation and disrupted endocrine homeostasis in rats. These findings suggest that chronic fluoride intake may impair insulin secretion and promote pre-diabetic alterations, warranting further research. Full article

Raptors, being at top of the food chain, serve as important models to study the impact of changing climate, as they are more vulnerable due to their unique ecology. They are vulnerable to extinction, with 52% species declining population and 18% are threatened globally. The effect of climate change on raptors is poorly studied in the Eastern Himalayan region. The present study offers a complete investigation of climate change effects on the raptors in the northeast region of the Eastern Himalayas, employing ensemble species distribution modeling. The future predictions were employed to model the climate change across two socioeconomic pathways (SSP) i.e. SSP245 and SSP585 for the periods 2041–2060 and 2061–2080. Specifically, five algorithms were employed for the ensemble model, viz. boosted regression tree (BRT), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and random forest (RF). The study highlights worrying results, as only 10.5% area of the NE region is presently suitable for Falco severus, 11.4% for the critically endangered Gyps tenuirostris, and a mere 6.9% area is presently suitable for the endangered Haliaeetus leucoryphus. The most influential covariates were precipitation of the driest quarter, precipitation of the wettest month, and temperature seasonality. Future projection revealed reduction of 33–41% in suitable habitats for F. severus, G. tenuirostris is expected to lose 53–96% of its suitable habitats, and H. leucoryphus has lost nearly 94–99% of its suitable habitats. Such decline indicates apparent habitat fragmentation, with shrinking habitat patches. Full article

The deposition of amyloid beta (Aβ) in the human brain is a hallmark of Alzheimer’s disease (AD). Aβ has been shown to exert a wide range of effects on neurons in cell and animal models. Here, we take advantage of differentiated neurons from iPSC-derived neural stem cells of human donors to examine its effects on human neurons. Specifically, we employed two types of neurons from genetically distinct donors: one male carrying APO E2/E2 (M E2/E2) and one female carrying APO E3/E3 (F E3/E3). Genome-wide RNA-sequencing analysis identified 64 and 44 genes that were induced by Aβ in M E2/E2 and F E3/E3 neurons, respectively. GO and pathway analyses showed that Aβ-induced genes in F E3/E3 neurons do not constitute any statistically significant pathways whereas Aβ-induced genes in M E2/E2 neurons constitute a complex network of activated pathways. These pathways include those promoting inflammatory responses, such as IL1β, IL4, and TNF, and those promoting cell migration and movement, such as chemotaxis, migration of cells, and cell movement. These results strongly suggest that the effects of Aβ on neurons are highly dependent on their genetic background and that Aβ can promote strong responses in inflammation and cell migration in some, but not all, neurons. Full article

(1) Background: Methylobacterium radiotolerans (M. radiotolerans) is a fastidious, aerobic, Gram-negative bacillus primarily found in environmental sources such as soil and sewage, with rare clinical isolation. Its identification remains challenging due to poor growth with conventional culture methods. (2) Case presentation: A 42-year-old male patient with early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) presented with M. radiotolerans bacteremia during hospitalization. The organism was successfully isolated from peripheral blood using the Myco/F Lytic culture vial (Becton, Dickinson and Company, Lincoln, MT, USA). Comparative analysis demonstrated markedly superior growth of M. radiotolerans in Myco/F Lytic culture vials compared with Plus Aerobic/F Lytic and Lytic/10 Anaerobic/F culture vials (Becton, Dickinson and Company, Lincoln, MT, USA). Antimicrobial susceptibility testing, performed with the epsilometer test (E-test) and Bauer–Kirby disk diffusion (BK) method, guided the selection of an appropriate therapeutic regimen. The patient’s infection was ultimately controlled following targeted antimicrobial therapy. (3) Conclusions: M. radiotolerans demonstrates a distinct growth preference for the Myco/F Lytic culture medium. This observation highlights the importance of considering alternative culture media in cases of rare or fastidious bacterial infections that cannot be reliably detected using conventional Plus Aerobic/F Lytic or Lytic/10 Anaerobic/F culture vials, which are typically employed for clinical isolation of aerobic and anaerobic bacteria. Full article

(1) Background: Marine eutrophication represents a formidable challenge to sustainable global aquaculture, posing a severe threat to marine ecosystems and impeding the achievement of UN Sustainable Development Goal 14. Current methodologies for identifying eutrophication events and tracing their drivers from vast, heterogeneous text data rely on manual analysis and thus have significant limitations. (2) Methods: To address this issue, we developed a novel automated attribution analysis framework. We first pre-trained a domain-specific model (Aquaculture-BERT) on a 210-million-word corpus, which is the foundation for constructing a comprehensive Aquaculture Eutrophication Knowledge Graph (AEKG) with 3.2 million entities and 8.5 million relations. (3) Results: Aquaculture-BERT achieved an F1-score of 92.1% in key information extraction, significantly outperforming generic models. The framework successfully analyzed complex cases, such as Xiamen harmful algal bloom, generating association reports congruent with established scientific conclusions and elucidating latent pollution pathways (e.g., pond aquaculture–nitrogen input–Phaeocystis bloom). (4) Conclusions: This study delivers an AI-driven framework that enables the intelligent and efficient analysis of aquaculture-induced eutrophication, propelling a paradigm shift toward the deep integration of data-driven discovery with hypothesis-driven inquiry. The framework provides a robust tool for quantifying the environmental impacts of aquaculture and identifying pollution sources, contributing to sustainable management and achieving SDG 14 targets. Full article

Accurate tree health monitoring in orchards is essential for optimal orchard production. This study investigates the efficacy of a deep learning-based object detection single-step method for detecting tree health on multispectral UAV imagery. A modified Mask R-CNN framework is employed with four different backbones—ResNet-50, ResNet-101, ResNeXt-101, and Swin Transformer—on three image combinations: (1) RGB images, (2) 5-band multispectral images comprising RGB, Red-Edge, and Near-Infrared (NIR) bands, and (3) three principal components (3PCs) computed from the reflectance of the five spectral bands and twelve associated vegetation index images. The Mask R-CNN, having a ResNeXt-101 backbone, and applied to the 5-band multispectral images, consistently outperforms other configurations, with an F1-score of 85.68% and a mean Intersection over Union (mIoU) of 92.85%. To address the class imbalance, class weighting and focal loss were integrated into the model, yielding improvements in the detection of the minority class, i.e., the unhealthy trees. The tested method has the advantage of allowing the detection of unhealthy trees over UAV images using a single-step approach. Full article