Search Results (18,475)

Aberrant glycosylation is a recognized hallmark of cancer, establishing Golgi α-mannosidase II (GMII) as strategic therapeutic target. While the natural alkaloid swainsonine demonstrated potent anticancer activity, its clinical use is hampered by toxicity from off-target inhibition of the lysosomal α-mannosidase (LMan). This review surveys computational methodologies advancing inhibitor development from empirical observations to precision structural optimization. We examine the evolution from Molecular Docking to advanced Quantum Mechanics (QM) and Molecular Dynamics (MD), highlighting their combined role in modeling metalloenzyme flexibility and energetics. Analysis reveals that selectivity relies on exploiting peripheral structural divergences, organelle-specific pH gradients, and distinct substrate conformational itineraries. In this context, electronic structure calculations and pKa predictions prove critical for designing “electrostatic switches”, inhibitors binding neutrally at Golgi pH while incurring lysosomal repulsion. Structurally, targeting the non-conserved “anchor site”, mimicking specific transition-state ring distortions and utilizing conformationally restricted scaffolds represent the most effective strategies. Integrating dynamic sampling with rigorous energetic profiling is therefore crucial for developing the next generation of safe, selective GMII inhibitors. Full article

Although canine parvovirus (CPV) vaccination has been widely implemented, CPV continues to circulate in dog populations and poses a potential cross-species transmission risk to wildlife, including giant pandas. Recent increases in CPV-2c detection in China highlight the need for molecular surveillance and standardized immunoreagents for diagnosis and epitope mapping. This study aimed to isolate a representative CPV-2c strain from China and develop VP2-targeted nanobody-based recognition molecules to support antigen monitoring and detection optimization. Canine and giant panda samples were collected in Sichuan Province, and CPV was isolated in F81 cells, followed by VP2 gene sequencing and phylogenetic analysis. A secretion expression system in Bacillus subtilis was established to produce VP2-targeting nanobodies, and a canine Fc-fused format of Nb10 (Nb10-Fc) was constructed. Immunoreactivity was evaluated via immunoassays, and structural modeling and molecular docking were performed to predict binding interfaces. The results showed that CPV-2c was the dominant genotype in Sichuan, with CPV L4 being a representative strain that exhibited 100% identity in VP2 with a giant panda-derived CPV-2c strain. Nb10 and Nb10-Fc demonstrated strong reactivity in Western blotting and immunofluorescence assays. The Fc-fusion improved detection sensitivity, offering potential in vivo application benefits. This study provides a standardized VP2-specific nanobody and molecular system for CPV-2c surveillance, antigenic studies, and diagnostic optimization. Full article

Background/Objectives: Osteosarcoma is the most common primary malignant bone tumour, affecting children and young adults. Recent evidence suggests that extracellular vesicles (EVs), small membrane-bound nanoparticles released by all cell types, play a key role in intercellular communication within the tumour microenvironment. Therefore, we aimed to investigate the effects of osteoblast-derived EVs (OB-EVs) on osteosarcoma cell behaviour and to characterise the transcriptional and miRNA-mediated mechanisms underlying these effects. Methods: Phenotypic assays were performed to assess metabolic activity, proliferation, apoptosis, and invasion ability of human osteosarcoma cell lines after treatment with OB-EVs. Illumina-based RNAseq was conducted on RNA isolated from OB-EVs-treated cells, and qRT-PCR was assessed using commercially available TaqMan miRNA cards on RNA isolated from OB-EVs. Results: In U2OS cells, OB-EVs reduced metabolic activity (1.30-fold decrease, p = 0.0137) and proliferation (1.70-fold decrease, p = 0.017) while increasing apoptosis (1.15-fold increase, p = 0.014). In MG63, OB-EVs increased proliferation (4.9-fold increase, p = 0.020) without affecting tumour cell aggressiveness, while normal osteoblast behaviour was not affected by OB-EVs. MNNG/HOS cells treated with OB-EVs for 48 h showed substantial transcriptomic changes, with 296 differentially expressed genes (97 up- and 199 down-regulated in OB-EVs treated cells versus untreated cells), indicating a direct impact of OB-EVs on gene expression. Intriguingly, Gene Set Enrichment Analysis (GSEA) showed trends consistent with modulation of signalling pathways, including Wnt/β-catenin and NOTCH. Conversely, miRNA profiling of OB-EVs identified 13 highly expressed miRNA. Integration of transcriptomic and miRNA target prediction data highlighted convergent pathway-level signals, suggesting that OB-EVs may modulate tumour-associated regulatory networks. Conclusions: Taken together, these findings indicate that OB-EVs modulate osteosarcoma cell phenotype, with miRNA shuttling representing a potentially relevant contributing mechanism. The integrative analysis suggests that pathways associated with proliferation and cellular homeostasis, including Wnt/β-catenin signalling, may be involved, although further functional validation is required to confirm these mechanisms. Full article

Chicken (Gallus gallus domesticus) domestication, likely associated with dry-rice farming in central Thailand, has led to substantial loss of ancestral immune-related genetic diversity in commercial chicken lineages. This study addresses allelic loss by providing the first comprehensive analysis of the highly polymorphic BG1 gene, an MHC-linked marker across the wild–domestic interface in Thailand and Vietnam, using high-depth Illumina amplicon sequencing. Genomic DNA from 47 Thai and Vietnamese chicken populations was extracted using a salting-out protocol following ethical sampling. Allelic variation was examined by targeting the BG1 intron 15–exon 16 region using triplicate PCR and Salus Pro NGS sequencing. Evolutionary dynamics and selection pressures were analyzed using AmpliSAS, MrBayes, and Datamonkey, while AlphaFold 3 was used to predict and validate 3D protein structures. We identified 98 novel alleles and 172 polymorphic sites within the BG1 intron 15–exon 16 region encoding an Ig-like domain. Extensive allele sharing between indigenous chickens and red junglefowl indicated strong balancing selection and trans-species polymorphism. Selection analyses showed that purifying selection conserved structural integrity at codons 9, 13, and 18, while variation at other sites enhanced immune recognition. AlphaFold 3 modeling confirmed conservation of the β-sandwich fold across variants, maintaining stability of the Immunoreceptor Tyrosine-based Inhibition Motif (ITIM). Thus, despite the regional gene flow, geographic isolation has shaped distinct signatures, as evidenced by the presence of 38 unique Thai and 9 unique Vietnamese alleles in addition to breed-specific private markers in the Betong (BG1*TH88), Decoy (BG1*TH91), and Tre (BG1*VN54) populations. A notable adaptive outlier under positive selection (ω = 1.357) was detected in the Dong Tao population, suggesting a recent selective sweep. These findings support the mission of the Siam Chicken Bioresource Project (SCBP) to utilize indigenous breeds as genetic reservoirs and provide a molecular basis for restoring resilience traits in domestic poultry to enhance global food security. Full article

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

Background: Ursolic acid (UA) is a natural pentacyclic triterpenoid with notable antitumor activity, yet its poor water solubility and insufficient targeting restrict clinical translation. Methods: Forty novel ursolic acid-phosphine derivatives bearing seven distinct lipophilic cationic moieties were synthesized via C28 modification and structurally characterized by ¹H NMR and ¹³C NMR. Their antitumor activities in PC3-M cells were evaluated via in vitro assays. Mechanistic investigations were performed using transcriptomic analysis and Western blot. Molecular docking was performed to predict the binding profile of Compound 25 with FGFR1. In vivo antitumor efficacy and biosafety were assessed in RM-1 xenograft models in C57BL/6 mice. Results: Compound 25 (bearing a tris(3,5-dimethylphenyl)phosphine group at the C28 position with an alkyl chain length of five methylene units) exhibited the most potent activity against PC3-M cells, dose-dependently inhibiting proliferation, migration, and invasion and inducing apoptosis. It triggered mitochondrial apoptosis via ROS accumulation and disrupted cytoskeletal homeostasis by suppressing the FGFR1/KRAS/RAC1/PIP4K2 axis. Molecular docking results suggested its strong binding affinity and specificity. In vivo studies confirmed its significant antitumor effect and favorable safety. Conclusions: These results highlight the potential of Compound 25 as a promising lead compound and provide valuable insights for further medicinal chemistry optimization and the development of novel anticancer drugs derived from ursolic acid. Full article

Pedestrian trajectory prediction is vital for crowd analysis and human–-robot interaction. Recent deep models enhance accuracy by modeling social interactions and scene context, but they often remain opaque and rarely address privacy risks associated with learning individualized motion patterns. We propose CAMP, a Context-Aware, Multimodal, and Privacy-preserving pedestrian trajectory prediction framework designed around a role-aligned multimodal architecture, in which trajectory representations, dynamic scene cues, and explicit spatial interaction constraints are modeled through complementary branches. In CAMP, the trajectory encoder separates shared motion regularities from individualized motion tendencies, the optical-flow encoder captures motion-centric transient scene dynamics, and the potential-field encoder provides an interpretable spatial cost prior for obstacle avoidance and social interaction modeling. A Transformer-based decoder fuses these modalities to predict future trajectory distributions. To reduce the exposure of personalized motion patterns, we apply targeted DP-SGD only to the individual branch during the private fine-tuning stage, while treating the remaining frozen components as post-processing under the stated threat model. Experiments on the ETH/UCY benchmark show that CAMP achieves competitive ADE/FDE performance under the reported setting, while its private variant DP-CAMP maintains a reasonable utility–privacy trade-off across several reported privacy budgets. Full article

The Papanicolaou (Pap) smear, a cornerstone of cervical cancer prevention, has emerged as a compelling, though unconventional, biospecimen for the detection of ovarian cancer (OC). This structured narrative review synthesizes the evolving evidence on the utility of cervicovaginal cytology and molecular analysis of Pap test material for OC detection. While conventional cytology provides a proof of concept, its sensitivity is low, ranging from incidental detection of OC in 0.004% of routine screens to 19.3% in patients with known OC. Specific cytologic findings, however, carry significant predictive value: atypical glandular cells (AGC) confer a two-fold increased OC risk, and psammoma bodies (PB) are strongly associated with serous malignancies. Driven by the sensitivity limitations of morphology, the field has undergone a paradigm shift towards molecular detection. Foundational studies confirmed tumor-derived DNA, including hallmark TP53 mutations, is detectable in Pap samples years before diagnosis, though sensitivity is constrained by low DNA abundance and confounded by background clonal mutations. To overcome this, strategies have expanded to target broader genomic signatures, such as somatic copy number alterations (EVA test: 75% sensitivity, 96% specificity), and multi-gene mutation panels (PapSEEK: 33–45% sensitivity). The most promising advances lie in multi-omic approaches, particularly DNA methylation biomarkers, which have demonstrated sensitivities up to 81% with high specificity. Collectively, this evidence argues against repurposing the Pap test as a standalone OC screen but supports its strategic integration into a risk-stratified clinical algorithm. We propose a “reflex-to-molecular” model where high-risk cytology (e.g., AGC, PB) automatically triggers advanced molecular testing on the same sample. This model efficiently leverages existing infrastructure to triage high-risk women for definitive diagnostics. Prospective validation of this integrated approach is the essential next step toward transforming this test into a sentinel for malignancies of the upper female reproductive tract. Full article

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using acoustic emission (AE) signals. Specifically, we adapt the Audio Spectrogram Transformer (AST), a convolution-free, purely attention-based model that operates directly on audio spectrograms. The Transformer architecture enables the modeling of long-range dependencies, while the model learns discriminative representations directly from AE data without relying on manually engineered features. A selective fine-tuning strategy was implemented by adding layer-freezing functionality to the AST training pipeline, enabling different freezing configurations during fine-tuning. This allowed earlier pretrained representations to be preserved while adapting the later layers to the target AE signals, thereby reducing the risk of overfitting in the small-data setting. In addition, validation-driven early stopping was implemented to further improve generalization during fine-tuning. The model was initialized with ImageNet and AudioSet pretrained weights to exploit general-purpose representations learned from large-scale datasets. The AE data were acquired under varying pressure conditions on a hydraulic test rig designed to simulate hydraulic cylinder leakage. The datasets were partitioned into fine-tuning, validation, and evaluation subsets and labeled into three wear states: unworn, semi-worn, and worn. In addition, data augmentation techniques were applied to the fine-tuning data to increase diversity and mitigate class imbalance. The adapted model achieved 97.92% classification accuracy across all wear conditions and pressure settings, demonstrating its ability to learn discriminative wear-related patterns directly from AE data. Furthermore, the framework’s versatility was further assessed on a bearing strip dataset acquired from the same hydraulic test rig. Using the same fine-tuning configuration, the model achieved 95.65% accuracy and 100% recall for the worn state. These findings highlight the potential of transformer-based architectures for data-efficient, end-to-end AE-based diagnostics across hydraulic system components. Full article

In Saccharomyces cerevisiae (S. cerevisiae), sister kinetochores are mono-oriented during meiosis I to ensure accurate homolog segregation, a process dependent on Hrr25 kinase activity. However, its direct interactors remain poorly defined. To address this, we performed a yeast two-hybrid (Y2H) screen using Hrr25 as bait. HRR25 was cloned into a Y2H vector and functionally validated by complementation of a temperature-sensitive hrr25-ts mutant. Screening across three reading frames identified three putative interactors: Hed1, Cyr1, and Rep1. Additional open reading frames (ORFs), including DAD1, SYS1, and YDR015C were identified but were oppositely oriented to the GAL4 activation domain. Structural modeling and phosphorylation prediction identified high-confidence Hrr25 target residues, including S70/T73 on Hed1, S323 on Rep1, and S198/S527 on Cyr1, whereas Sys1 and YDR015C lacked favorable sites. Although Dad1 was not validated as a direct interactor from Y2H, S63 was identified as a favorable phosphorylation site, and its full-length ORF in the interacting clone and known biological role supported its inclusion. Among the meiotic candidates, Hed1 may link Hrr25 activity to homologous recombination, while Dad1 represents a plausible target for regulating kinetochore–microtubule interactions. Collectively, these findings identify new candidate interactors and substrates of Hrr25 and suggest a broader role in coordinating recombination and kinetochore function during meiosis, warranting further experimental validation. Full article

Accurate short-horizon downlink throughput prediction is essential for automation in high-density 5G deployments (e.g., stadiums and events), where user load, scheduling decisions, and interference conditions change rapidly and produce highly variable user-perceived rates. This paper benchmarks lightweight regression models for per-user throughput prediction from readily available radio access network (RAN) key performance indicators (KPIs) and studies a risk-aware extension that augments point forecasts with calibrated uncertainty and an abstention (deferral) rule. Experiments use a strictly time-ordered train/calibration/test protocol on the Liverpool 5G High-Density Demand (L5GHDD) dataset. The target is strongly zero-inflated (about 62% of samples at 0 Mbps) and heavy-tailed, creating regimes where average-error optimization can mask rare but operationally important bursts. In the point-prediction benchmark, the best model is a tuned two-stage support vector regressor with a mean absolute error (MAE) of $0.452$ Mbps, while the strongest single-stage model attains a weighted mean absolute percentage error (WMAPE) of 56.200%. For uncertainty quantification, we compare standard split conformal prediction against two input-adaptive alternatives. Constant-width split conformal attains 88.900% marginal coverage for a nominal 90% target with an average interval width of $2.288$ Mbps, but width-based deferral is degenerate because all intervals have the same size. Variable-length conformal intervals preserve near-nominal coverage (91.100%) while producing informative width variation: normalized conformal reduces the average width to $1.344$ Mbps, and conformalized quantile regression reduces it to $0.641$ Mbps. At a deferral threshold of $1.5$ Mbps, constant-width conformal defers all samples, whereas normalized conformal still acts on 61.200% of samples with selective MAE $0.219$ Mbps. These results show that input-adaptive uncertainty is necessary for meaningful selective prediction in heteroscedastic 5G throughput dynamics. Full article

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

In accordance with the national strategy of “carbon peaking by 2030 and carbon neutrality by 2060” and Hunan Province’s target of achieving carbon peaking in the construction sector by 2030, this study uses carbon emission data from Hunan’s construction sector for the period 2005–2022 as a research sample to conduct research on carbon emission accounting, analysis of influencing factors, and peak prediction. The carbon emission coefficient method was employed to calculate industry-wide carbon emissions. Using the STIRPAT model combined with ridge regression, we identified and quantified the driving factors of carbon emissions. A CNN-LSTM-Attention hybrid deep learning model was constructed, and three development scenarios—high-carbon, baseline, and low-carbon—were established to simulate the evolution of carbon emissions in Hunan’s construction industry from 2023 to 2040. The results indicate that carbon emissions from Hunan’s construction industry showed an overall upward trend during the study period, with indirect emissions constituting the primary component. Through variable optimization, the core positive drivers and negative restraints of carbon emissions in the construction industry were identified. The constructed hybrid model demonstrated excellent fitting performance, with prediction accuracy significantly higher than that of traditional machine learning and single deep learning models. Carbon emission trends varied significantly across different development scenarios, with the low-carbon development scenario identified as the optimal path for achieving the industry’s carbon peak target. These findings provide a theoretical basis and data support for the low-carbon transition of Hunan Province’s construction sector, as well as for the formulation and optimization of carbon peaking implementation plans. Full article

Background/Objectives: Preservation of critical white matter (WM) pathways is essential for maximizing surgical safety in neuro-oncology and functional neurosurgery. Constrained spherical deconvolution (CSD) offers superior modeling of complex fiber architecture compared to diffusion tensor imaging (DTI). This case series evaluates the clinical utility of CSD in surgical planning and intraoperative navigation. Methods: A retrospective review of 20 patients (15 brain tumors, 5 functional disorders) treated between September 2022, and September 2024 was performed. All patients underwent preoperative MRI with CSD-based reconstruction of eloquent WM tracts. Clinical presentation, tract involvement, surgical strategy, and postoperative outcomes were analyzed. Results: CSD reliably reconstructed CST, AF, IFOF, OT, and DRTT depending on tumor location or DBS target. Compared with standard DTI, CSD provided improved delineation of tract extent and tumor–tract interfaces. Gross total resection (GTR) was achieved in all tumor patients without new neurological deficits. DBS cases showed precise correlation between stimulation thresholds, side effects, and CSD-predicted distances to critical WM tracts. DRTT targeting resulted in marked clinical improvement in Holmes tremor. Conclusions: CSD enhances anatomical accuracy in WM tract visualization, supporting safer resections in eloquent areas and improving DBS targeting. Its integration into routine workflow may optimize neurosurgical outcomes. Full article

This study addresses the critical need for accurate sea wind speed predictions to support ocean wind farm operations, equipment maintenance, and maritime navigation safety. To enhance prediction accuracy for any location within target sea areas, we propose a short-to-medium-term wind speed prediction method that effectively explores spatiotemporal correlations in ocean reanalysis grid data. The method involves collecting and reanalyzing data, as well as spatial processing, to reconstruct the historical wind speed sequence at the target point. Finally, a future wind speed time series is generated using an LSTM network and a Transformer encoder. Test results validated against NOAA buoy data demonstrate the effectiveness of our spatiotemporal prediction model, achieving RMSE values of 1.161 m/s, 1.500 m/s, and 1.854 m/s for 1 h, 6 h, and 12 h predictions, respectively, outperforming comparative methods. The conclusions are threefold: (1) The proposed hybrid model effectively captures spatiotemporal dependencies and achieves more accurate spatiotemporal predictions compared to the benchmark model; (2) taking into account seasonal factors and forecasting time periods, the method proposed in this paper maintains good stability; (3) this framework provides a reliable technical approach for generating operational references in maritime navigation and wind power maintenance, with potential applications in wind farm siting and resource assessment. Full article