Search Results (1,156)

Leptobotia rotundilobus is a newly described species in the subfamily Leptobotinae (Teleostei: Cypriniformes), which is endemic to China. Research on this recently discovered species is preliminary, characterized by limited baseline data and the absence of a fully sequenced mitochondrial genome. To elucidate the structural features of the mitochondrial genome of L. rotundilobus, we performed whole-genome sequencing using next-generation sequencing technology and analyzed its genomic composition, gene content, and structural variation through genome assembly and bioinformatics. The complete circular sequence, spanning 16,593 bp, comprises 13 protein-coding genes (PCGs), two ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA) genes, and a typical control region (D-loop), all arranged in the canonical order. The overall base composition of the genome was determined to be 30.8% adenine (A), 24.4% thymine (T), 28.6% cytosine (C), and 16.2% guanine (G). This A+T bias (55.2%) is consistent with the mitochondrial genomes of other Leptobotia, which may affect secondary structure. The ratio of non-synonymous (Ka) to synonymous substitutions (Ks) of 13 PCGs of 16 Leptobotinae species is far less than 1 (0.012–0.063), indicating strong negative or purifying selection on the mitogenome in these species. Moreover, to investigate the phylogenetic relationships within the subfamily Leptobotinae, particularly within the genus Leptobotia, we constructed multiple phylogenetic trees of the mitogenome and concatenated 13 PCGs of 39 sequences with Sinibotia superciliaris as an outgroup. The phylogentic trees using the maximum likelihood (ML) and Bayesian inference (BI) methods consistently indicate that: (1) after correcting the species identification error, L. rotundilobus is closely related to L. micra; and (2) the species of Leptobotia and Parabotia each form a monophyletic group. This study provides new insights into the taxonomy and phylogenetic relationships of Leptobotinae, with a particular focus on the genus Leptobotia, thereby contributing to the clarification of the systematics, origin, and evolution of Botiidae. Full article

DNA methylation is a fundamental epigenetic regulator in hepatocellular carcinoma (HCC). However, a key paradox remains: how do ubiquitously expressed enzymes like DNMTs and TETs achieve locus-specific regulation without intrinsic sequence specificity? This review aims to elucidate the “lncRNA–DNA methylation axis,” examining how long non-coding RNAs (lncRNAs) confer specificity and plasticity to methylation machinery. We synthesized current literature focusing on the structural mechanisms (e.g., R-loops, DNA:RNA triplexes) by which lncRNAs interact with DNMTs and TETs. We further analyzed the bidirectional regulation between lncRNAs and methylation enzymes and their impact on HCC phenotypes. lncRNAs function as modular scaffolds and guides, directing methylation machinery to specific genomic loci. Rather than binary switches, they act as an “epigenetic rheostat,” fine-tuning methylation intensity to balance stability with plasticity. Crucially, a reciprocal feedback loop exists: aberrant DNA methylation suppresses tumor-suppressive lncRNAs, which in turn unleashes DNMT activity, locking cells into a malignant state. This axis drives proliferation, metastasis, metabolic reprogramming, and therapeutic resistance. The lncRNA–DNA methylation axis is a central determinant of epigenetic heterogeneity in HCC. Moving beyond descriptive cataloging to a mechanistic understanding of this network offers new perspectives for developing targeted epigenetic therapies and biomarkers. Full article

This paper proposes an advanced sensorless control strategy for Permanent Magnet Synchronous Motors (PMSMs) aimed at enhancing dynamic performance, robustness, and reliability while eliminating the need for mechanical sensors. The core contribution lies in a novel hybrid speed regulation framework that combines a terminal sliding mode control scheme with a high-order super-twisting algorithm (HOT-STA-SMC), ensuring finite-time convergence, effective chattering suppression, and strong disturbance rejection under varying operating conditions. For the inner current loop, an Exponential Reaching Law Sliding Mode Controller (ERL-SMC) is implemented to guarantee fast current response and precise current tracking, even in the presence of parameter uncertainties. Furthermore, the conventional Model Reference Adaptive System (MRAS) observer is embedded within the proposed control architecture, resulting in more accurate speed estimation and enhanced stability during load fluctuations. The complete control system is rigorously modeled and tested in MATLAB R2024b/Simulink, capturing the full interaction between machine dynamics, control loops, and observer mechanisms. The simulation results verify that the proposed design achieves superior torque smoothness, minimal current ripples, and fast transient response compared to conventional sensorless methods. By integrating high-order sliding modes with advanced adaptive observation, this work offers a robust and cost-effective solution for high-performance PMSM drives, suitable for demanding applications such as electric vehicles, renewable energy conversion, and industrial motion control. Full article

Local path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution. While Nonlinear Model Predictive Control (NMPC) has emerged as a leading approach, many existing methods decouple velocity planning from tracking, lack formal stability guarantees, or do not demonstrate feasibility on embedded platforms. We present a unified NMPC framework that integrates curvature-aware velocity adaptation directly into the cost function. The controller makes use of cubic spline paths, recursive feasibility constraints, and Lyapunov-based terminal costs to ensure stability. The nonlinear optimization problem is implemented in CasADi and solved using IPOPT, with warm-starting and efficient discretization techniques enabling real-time performance. Our approach has been validated in the CARLA simulator across a variety of urban scenarios, including straight roads, intersections, and roundabouts. The controller achieves a mean cross-track error of 0.10 m on straight roads, 0.44 m on roundabouts, and 1.36 m on tight intersections, while maintaining smooth control inputs and bounded actuator effort. A curvature-aware cost term yields a 14.4% reduction in lateral tracking error compared to the curvature-unaware baseline. Benchmarking results indicate that the Raspberry Pi 5 outperforms the NVIDIA Xavier AGX by 1.5–1.6×, achieving mean execution times of 38–45 ms across all scenarios. This demonstrates that advanced NMPC can run in real time on low-cost consumer hardware ($80 vs. $700). Systematic ablation studies reveal the critical role of state weighting (Q) and input regularization (R): removing Q degrades tracking by 10% and destabilizes control effort (+54% acceleration, +477% steering), while omitting R induces oscillatory behavior with +907% acceleration effort. Euler integration provides no computational benefit (+8% solver time) while degrading accuracy by 25%, confirming RK4 as strictly superior. Sensitivity analysis via Latin Hypercube Sampling identifies the prediction horizon (N) and discretization timestep ($\mathrm{\Delta }t$) as dominant parameters. Per-scenario Pareto analysis yields a balanced operating point ($N=15, \mathrm{\Delta }t=0.055$ s) used for all primary evaluations, while a global sweep identifies a robust alternative ($N=12, \mathrm{\Delta }t=0.086$ s) suitable for general deployment tuning. This framework bridges the gap between spline-based local planning and stability-guaranteed NMPC, offering a simulation-validated, real-time solution for embedded autonomous driving research. Future work will focus on hardware-in-the-loop and full-vehicle deployment, integration with high-level decision-making, and learning-enhanced MPC. Full article

In grid-connected inverter systems, the Phase-Locked Loop (PLL) is fundamental for achieving and maintaining precise synchronization between the inverter and the electrical grid. Developing an efficient and robust PLL is essential to ensure reliable operation, particularly in the presence of abnormal grid conditions. Among the existing synchronization methods, the Synchronous Reference Frame-based PLL (SRF-PLL) is widely adopted due to its robust performance; however, it suffers from degraded accuracy under unbalanced voltage conditions. To address this limitation, the Second-Order Generalized Integrator-Quadrature Signal Generator (SOGI-QSG) was proposed in previous studies as an alternative approach. Despite its advantages, the SOGI-PLL exhibits weak filtering capability for lower-order harmonics and remains sensitive to DC offset, both of which can affect synchronization quality. As a result, numerous advanced PLLs based on SOGI-QSG have been proposed in the literature to address SOGI-QSG limitations by enhancing DC offset rejection, filtering capability, and dynamic response. This article provides a comprehensive assessment of various three-phase PLLs based on SOGI-QSG under unbalanced grid conditions, focusing on peak-to-peak frequency error, filtering performance, and DC offset rejection. The operational principles and mathematical models of each technique are discussed, and their performances are validated using MATLAB/Simulink (R2025b). The results show that the SRF-PLL exhibits oscillatory behavior under unbalanced conditions, whereas the PLLs based on SOGI-QSG demonstrate stable synchronization with different trade-offs between filtering strength and dynamic response. Therefore, the selection of the appropriate PLLs based on SOGI-QSG depends on the priorities of the specific application. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

Background/Objectives: The study investigated the anti-inflammatory potential of neolignan derivatives of dehydrodieugenol (CP1–CP5), focusing on the inhibition of secretory phospholipase A2 (sPLA2), a key enzyme in inflammation. Methods: Comprehensive quantitative docking analysis using four independent algorithms (PLP, ASP, ChemScore, GoldScore) revealed exceptional multitarget binding profiles for CP1 and CP2, with scores consistently above activity thresholds for acetylcholinesterase (AChE), cyclooxygenase-2 (COX-2), and sPLA2 from Crotalus durissus terrificus in both monomeric (Mcdt) and quaternary (Tcdt) forms. Results: Among the compounds, CP1 demonstrated the highest predicted affinity (AChE: 78.5, COX-2: 83.8, sPLA2: 82.7–83.4) and most potent experimental activity, reducing sPLA2 catalytic velocity through mixed-type inhibition involving the active site (His47, Asp48) and Ca²⁺ binding loop. In vivo assays in sPLA2-induced paw edema demonstrated that CP1 and CP2 achieved remarkable anti-inflammatory effects (up to 68.3% reduction), significantly exceeding their protective potential by direct enzyme inhibition, confirming the multitarget mechanism. The strong correlation between predicted docking scores and paw edema reduction (R² = 0.89, p < 0.01) creates a firm foundation for establishing structure–activity relationship explanations. Conclusions: These findings highlight an integrated mechanism involving: (1) partial sPLA2 modulation, (2) neuroimmune regulation via AChE inhibition, and (3) prostaglandin synthesis blockade through COX-2 inhibition. This multitarget approach, combined with the natural origin of the compounds, positions dehydrodieugenol derivatives as promising candidates for developing therapies against complex inflammatory diseases, offering significant advantages over single-target strategies. Full article

R-loops, three-stranded nucleic acid structures formed by an RNA-DNA hybrid, have emerged as important regulators of transcription and genome stability. Although advances in high-throughput sequencing have revealed widespread R-loop landscapes, platform-specific biases hinder the identification of conserved R-loops in specific cell types. Mouse embryonic stem cells, which are transcriptionally active, provide an ideal system for investigating the potential roles of stable R-loops in RNA biology. Here, we integrated 13 independent R-loop profiling datasets from four experimental platforms to define 27,950 Common R-loop regions in mouse embryonic stem cells and characterized their chromatin environment and associated biological functions. Common R-loop regions were reproducibly detected across methods and were preferentially localized to promoter-proximal and genic regions enriched in CpG islands. Genes associated with Common R-loops were highly and stably expressed, showing strong functional enrichment in RNA metabolic processes such as mRNA processing, RNA splicing, and ribonucleoprotein complex biogenesis. Chromatin state analysis revealed that Common R-loops are enriched in transcriptionally active and regulatory contexts. Sequence feature analysis further identified GC skew as a prominent signature of Common R-loops, particularly within transcribed chromatin states. Transcription factor motif analyses have identified distinct regulatory environments in Common R-loop regions, including pluripotency-associated OCT4-SOX2-TCF-NANOG motifs in enhancers, CTCF motifs in open chromatin, and YY1 motifs in promoters. Together, this study provides the first integrated analysis of conserved R-loop regions in mouse embryonic stem cells, revealing their preferential localization at regulatory loci linked to RNA metabolism and highlighting R-loops as structural and functional nodes in RNA biology. Full article

Fluoride (F⁻) contamination in deep groundwater threatens drinking water security, yet its enrichment is commonly governed by coupled nonlinear hydrogeochemical feedbacks that are difficult to resolve with linear diagnostics alone. Here, we integrate an explainable deep learning framework (HydroAttentionNet + SHAP) with thermodynamic and mass-conservative inverse modeling (PHREEQC) to quantitatively link data-driven thresholds to mineral water processes in a multi-aquifer system. Using 258 deep-well samples, we delineate a robust evolution pathway from background to ultra-high-fluoride (Ultra-High F⁻, ≥1.5 mg/L) waters. HydroAttentionNet achieves strong predictive skill (R² = 0.77) and reveals a clear mechanistic tipping behavior: alkalinity (HCO₃⁻/CO₃²⁻) is the primary trigger for F⁻ activation, while progressive Na+ enrichment and Ca²⁺ depletion act as amplifiers by suppressing a(Ca²⁺) and weakening fluorite precipitation capacity. PHREEQC simulations confirm a coupled “salinization–decalcification–fluoridation” loop in which (i) evaporite dissolution elevates ionic strength (salt effect) and supplies Na⁺ to promote Na–Ca exchange, and (ii) carbonate re-equilibration drives calcite precipitation as an efficient Ca sink, offsetting ~45.8% of Ca²⁺ inputs; together, these processes maintain fluorite undersaturation and sustain net fluorite dissolution, contributing 56.6% of newly added dissolved F⁻ in evolved end-members. Monte Carlo health risk assessment (10,000 iterations) indicates substantial intergenerational inequity: 67.9% of children exceed the non-carcinogenic risk threshold (HQ > 1), compared with 29.3% of adults. Sensitivity analysis identifies source-water fluoride concentration as the dominant driver (Spearman r = 0.93), implying that supply-side interventions (defluoridation, well-screen optimization, and blending with low-F sources) are substantially more effective than behavioral measures. Full article

Nme1Cas9 is an encouraging genome-editing tool with high fidelity and compactness, but its applications are limited by poor catalytic efficiency compared with SpyCas9. Understanding the dynamic activation mechanism of the HNH nuclease domain is the key to breaking the kinetic bottleneck. Here, we integrated Steered Molecular Dynamics (SMD) with the Traveling-Salesman-based automated Path Searching (TAPS) algorithm to reconstruct the atomic-level activation landscape of the L1-HNH module. The simulations suggest a complex “Lifting-Rearrangement-Sliding” pathway, revealing the critical role of a “Backbone Sliding” conformation; in this step, the HNH domain rotates across the R-loop surface. A thermodynamic analysis using free energy decomposition by MM/PBSA indicates that the intrinsic instability of the wild-type HNH/R-loop interface constitutes the predominant energetic barrier. Hyperactive variants (S593Q/W596K and S593Q/W596R) can overcome this barrier by substantially increasing binding affinity to the R-loop through a “Geometry–Electrostatics Synergism”: S593Q improves interfacial proximity, whereas W596K/R acts as an “Electrostatic Anchor.” The results of unbiased MD simulations demonstrate that strengthened interfacial interactions effectively promote spontaneous conformational drift toward the activated state. This computational study proposes a novel in silico model for “Dynamic Interface Engineering” in which reinforcing transient interfacial contacts during conformational sliding can be an effective strategy in developing high-efficiency CRISPR-Cas effectors. Full article

Biological soil crusts (BSCs) are critical ecological components in arid lands. Their formation and stability hinge on the assembly and interactive networks of cyanobacteria-led bacterial communities. Yet, how different functional cyanobacteria shape the underlying microbial structure and assembly rules is poorly understood. Here, we cultivated artificial algal crusts using two representative cyanobacteria: the nitrogen-fixing Leptolyngbya sp. and the non-nitrogen-fixing Microcoleus vaginatus (M. vaginatus CM01). A total of six treatments were established based on the presence or absence of spraying with in situ BSCs leachate: a control group without inoculation of algae or bacteria (soil, S); a treatment group sprayed only with bacterial suspension (soil + bacteria, SB); a treatment group sprayed only with M. vaginatus CM01 (soil + M. vaginatus CM01, SM); a treatment group co-inoculated with both BSCs leachate and M. vaginatus CM01 (soil + M. vaginatus CM01 + bacteria, SMB); a treatment group inoculated only with Leptolyngbya sp. CT01 (soil + Leptolyngbya sp. CT01, SL); and a treatment group co-inoculated with Leptolyngbya sp. CT01 and biocrust leachate (soil + Leptolyngbya sp. CT01 + bacteria, SLB). By integrating 16S rRNA gene sequencing, neutral community modeling (NCM), and structural equation modeling (SEM), we dissected differences in Cyano-BSCs development, bacterial community composition, co-occurrence networks, and assembly mechanisms. Inoculation with M. vaginatus CM01 (SM, SMB) superiorly promoted Cyano-BSCs development: the SM group achieved the highest coverage (23.33%), while the SMB group showed marked increases in organic matter (OM, 4.10 g·kg⁻¹) and chlorophyll a (Chla, 13.40 μg·g⁻¹), alongside a >5-fold rise in bacterial, cyanobacterial, and nitrogen-fixation gene abundances versus controls. The mechanism centers on extracellular polymeric substances (EPS) secreted by M. vaginatus, which homogenized the microenvironment, suppressed stochastic bacterial dispersal (NCM, SM: R² = 0.698), and enhanced deterministic selection. This process forged a highly cooperative network (89.74% positive links, average degree 34.71) that directionally enriched Cyanobacteria (relative abundance 40.40%). The Shannon index of Cyano-BSCs from the group (SMB) reached 7.72 ± 0.09, reflecting high microbial community diversity. SEM confirmed M. vaginatus directly regulated bacterial assembly (path coefficient = 0.59, p < 0.05) and indirectly improved the soil environment (path coefficient = 0.64, p < 0.05), establishing a “cyanobacteria-community-environment” feedback loop. Conversely, the Leptolyngbya sp. groups (SL, SLB), despite enriching nitrogen-fixing bacteria and fungi, exhibited low carbon fixation efficiency (notably 1.26 g·kg⁻¹ OM in SL) and lack of EPS; communities remained stochastic (NCM, SL: R² = 0.751) with no effective regulatory pathway—a pattern mirrored in S and SB groups. Our findings demonstrate that M. vaginatus acts as a core engineer of biological soil Cyano-BSCs formation via an “EPS-mediated habitat filtering—functional group enrichment—cooperative network assembly” cascade, enforcing deterministic community construction. Leptolyngbya sp., with limited niche-constructing ability, fails to exert comparable control. This work provides a targeted framework for the artificial restoration of Cyano-BSCs in arid zones. Full article

Background/Objectives: Recently, studies have emerged raising ergotrauma as a new explanation for ventilator-induced lung injury development, with the concept of inspiratory Mechanical Power (MP) as a single physical variable to estimate the contribution of different ventilatory parameters to lung damage. A high value of inspiratory MP is associated with risk of harming the respiratory system. Furthermore, we propose the concept of effective MP as the energy per minute that dissipates in the lungs, subtracting expiratory MP from inspiratory MP. Our objective is to validate the equations proposed for adults to estimate inspiratory MP in children, and to develop and validate equations to estimate effective MP. Methods: Prospective convenience sampling of 18 children undergoing mechanical ventilation in volume-controlled ventilation mode, admitted to the pediatric intensive care unit of a tertiary university hospital. Data of ventilation parameters, loops and curves were obtained electronically with the automated procedure provided by the device software and compared with the theoretical results of the equations. Results: Among the available equations for calculating inspiratory MP in adults under volume-controlled ventilation, the simplified Gattinoni and generalized Giosa equations provided the best estimates in children (R² > 0.99). For expiratory MP, the extended equation proposed in this study showed the best agreement with experimental results (R² = 0.9954). Finally, for effective MP, the simplified equation was the most accurate (R² = 0.9950). Conclusions: This study validated existing inspiratory MP equations in pediatric patients and introduced the concept of effective MP, together with a bedside equation for its estimation. Future studies should determine effective MP thresholds associated with ventilator-induced injury. Full article

Accurate measurement of liquid flow velocity is crucial for pipeline management in the petroleum industry. Traditional flowmeters, such as ultrasonic, electromagnetic, and pressure-based devices, provide only point measurements and often require intrusive installation. Distributed Acoustic Sensing (DAS) offers a non-intrusive alternative by converting optical fibers into continuous acoustic sensors with meter-scale resolution. In this study, a High-Definition DAS system was applied in a laboratory flow loop to monitor single-phase liquid flow. The recorded signals were analyzed in both time and frequency domains. Results showed that the pump operating frequency dominated the spectral energy, accompanied by dispersive features. Power spectral density (PSD) increased linearly with flow rate, while Doppler-induced frequency shifts in the dominant component enabled velocity prediction. A regression model achieved a high coefficient of determination (R² = 0.9928), confirming the strong predictive capability. These findings highlight DAS as a reliable and scalable solution for non-intrusive liquid flow monitoring in pipelines. Full article

High-precision thermal regulation in semiconductor process equipment is critical for product quality, yet it is challenged by actuator transport delays, limited actuator bandwidth due to hardware dynamics, and broadband inlet disturbances in temperature-controlled process fluids. This paper presents a systematic solution integrating architecture optimization with a physics-informed hybrid prediction model to enable effective feedforward compensation. Frequency-domain analysis justifies placing the temperature fluctuation attenuator (TFA) upstream of the heater to filter mid-to-high-frequency disturbances without compromising feedback stability. To address actuation delays, a Physics-CNN-LSTM predictor is developed using a residual learning strategy. This framework employs a mechanism model for baseline estimation and a deep learning network to correct persistent low-frequency residuals caused by unmodeled dynamics. Comparative experiments on industrial data demonstrate that the model achieves a Root Mean Square Error (RMSE) of $3.56\times {10}^{-5}$ K under low-to-mid-frequency inlet disturbances, reducing error by approximately 51.8% compared to a standard LSTM. The model also exhibits strong robustness against disturbance frequency shifts ($R^2>0.996$ on unseen data). Furthermore, closed-loop simulations confirm that the proposed feedforward compensation enhances temperature stability in high-precision thermal control. Full article

The purpose of this study was to assess the suitability of tubular polymeric ultrafiltration membranes for use in a closed-loop water system within a rubber manufacturing plant. This research focused on determining the transport and separation properties of polymeric tubular membranes during the ultrafiltration of wastewater generated from washing vulcanised rubber hoses. The tests were conducted using the installation of the UF-1 membrane supplied by APEKO Sp. z o.o. This study evaluated the performance of modified PES membranes with a molecular weight cut-off (MWCO) of 4 kDa and PVDF membranes with MWCO of 100 kDa in the wastewater treatment process, as well as the effectiveness of membrane regeneration. Given the characteristics of wastewater, the key parameters for evaluating ultrafiltration performance included the determination of contaminant separation coefficients (R, %) for non-ionic surfactants (NIS) and chemical oxygen demand (COD), as well as turbidity reduction. The results demonstrated that the tested membranes substantially improved the visual quality of the wastewater by reducing turbidity by more than 95% and exhibited high separation efficiency for the analysed contaminants, with initial values of R_NIS = 95% and R_COD = 85% at the beginning of the ultrafiltration cycle, decreasing to R_NIS < 10% and R_COD < 10% after several hours of operation. During closed-loop filtration, when a twentyfold concentration of contaminants in the retentate was reached, membrane fouling occurred, significantly reducing filtration performance. Chemical cleaning enabled the recovery of approximately 70% of the initial performance for modified PES membranes and 60% for PVDF membranes. Full article