Search Results (3,555)

Clostridium butyricum Argonaute (CbAgo) can achieve DNA-guided DNA recognition and cleavage at physiological temperatures (~37 °C), making it a promising tool for gene editing. However, its significant off-target effects, particularly associated with the seed region (sites 2–8), pose challenges for precise gene therapy. This study focuses on enhancing the specificity of the seed region recognition to mitigate these off-target effects. We investigated the molecular recognition process between the CbAgo-gDNA complex and the seed region of the target DNA using molecular dynamics simulations and automated path searching. Our findings reveal that positively charged residues located in an α-helix domain at the DNA–protein interface (R279, H285, K287, K288, K291, K298) facilitate rapid binding to the DNA phosphate backbone. Such interaction enhances the pre-formation of the DNA double helix, reducing the reliance on base complementarity during duplex pairing. Further simulations showed that alanine replacement of these positively charged residues led to significantly improved sequence specificity for the target DNA seed region. Collectively, these results offered critical insights into the origin of off-target recognition by CbAgo in its seed region, shedding lights on its fidelity enhancement. Full article

Pectin is a dynamic and complex polysaccharide that forms a substantial proportion of the primary plant cell wall and middle lamella of forage ingested by grazing ruminants. Pectin methylesterases (PMEs) are enzymes that belongs to the carbohydrate esterase family 8 (CE8) and catalyze the demethylesterification of pectin, a key polysaccharide in cell walls. Here we present the crystal structure of the catalytic domain of PmeC5 that is associated with a gene from Butyrivibrio fibrisolvens D1^T that encodes a large secreted pectinesterase family protein (2089 aa) determined to a resolution of 1.33 Å. Protein in silico modelling of the secreted pectinesterase confirmed the presence of an additional pectate lyase (PL9) and adhesin-like domains. The structure of PmeC5 was the characteristic right-handed parallel β-helical topology and active site residues of Asp231, Asp253, and Arg326 typical of the enzyme class. PmeC5 is a large modular enzyme that is characteristic of rumen B. fibrisolvens megaplasmids and plays a central role in degrading plant cell wall components and releasing methanol in the rumen environment. Such secreted PMEs are significant contributors to plant fiber digestion and methane production, making them attractive targets for both methane mitigation strategies and livestock productivity enhancement. Full article

China’s natural gas demand is growing under the “dual carbon” goal. However, the peaking capacity of gas storage remains insufficient. Oil reservoir-based underground gas storage (UGS) has, thus, emerged as a critical research focus due to its potential for efficient capacity expansion. The complexity of seepage and phase change characteristics during the multi-cycle injection–production process has not been systematically elucidated. This study combines experimental and numerical simulations to examine the seepage and phase change characteristics. This study innovatively reveals the synergistic mechanism of permeability, pressure, and cycle. The control law of multi-factor coupling on the dynamic peaking capacity of UGS is first expounded. Oil–water mutual drive reduced oil displacement efficiency by 2.5–4.7%. Conversely, oil–gas mutual drive improved oil displacement efficiency by 3.0–4.5% and storage capacity by 4.7–6.5%. The fifth-cycle oil–gas mutual displacement in high-permeability cores (74 mD) under high pressure (22 MPa) exhibited reductions in irreducible water saturation (7.06 percentage points) and residual oil saturation (6.38 percentage points) compared with the first-cycle displacement in low-permeability cores (8.36 mD) under low pressure (16 MPa). Meanwhile, the gas storage capacity increased by 13.44 percentage points, and the displacement efficiency improved by 10.62 percentage points. Multi-cycle huff-and-puff experiments and numerical simulations revealed that post-depletion multi-cycle huff-and-puff operations can enhance the oil recovery factor by 2.74–4.22 percentage points compared to depletion. After five-cycle huff-and-puff, methane content in the produced gas increased from 80.2% to 87.3%, heavy components (C₈₊) in the remaining oil rose by 2.7%, and the viscosity of the remaining oil increased from 2.0 to 4.6 mPa·s. The deterioration of the physical properties of the remaining oil leads to a reduction in the recovery factor in the cycle stage. This study elucidates seepage mechanisms and phase evolution during multi-cycle injection–production, demonstrating the synergistic optimization of high-permeability reservoirs and high-pressure injection techniques for enhanced gas storage design and efficiency. Full article

Forecasting demand for newly introduced products presents substantial challenges within high-mix, low-volume manufacturing contexts, primarily due to cold-start conditions and unpredictable order behavior. This research proposes the Dynamic Dual-Phase Forecasting Framework (DDPFF) that amalgamates machine learning-based classification, similarity-driven analogous forecasting, ARMA-based residual compensation, and statistical process control for adaptive model refinement. The framework underwent evaluation through five real-world case studies conducted by a Taiwanese semiconductor tray manufacturer, encompassing a variety of scenarios characterized by high volatility, seasonality, and structural drift. The results indicate that DDPFF consistently outperformed conventional ARIMA and analogous forecasting methodologies, yielding an average reduction of 35.7% in mean absolute error and a 41.8% enhancement in residual stability across all examined cases. In one representative instance, the forecast error decreased by 44.9% compared to established benchmarks. These findings underscore the framework’s resilience in cold-start situations and its capacity to adapt to evolving demand patterns, providing a viable solution for data-scarce and dynamic manufacturing environments. Full article

Methylene blue (MB), a widely used industrial dye, is a persistent pollutant with documented toxicity to aquatic organisms and potential health risks to humans, even at ultra-trace levels. Conventional monitoring techniques such as UV–Vis spectroscopy and fluorescence emission suffer from limited sensitivity, typically failing to detect MB below ~10⁻⁷ M. In this study, we introduce a surface-enhanced Raman spectroscopy (SERS) platform based on silver nanowire (AgNW) substrates that enables MB detection over an unprecedented dynamic range—from 1.5 × 10⁻⁴ M down to 1.5 × 10⁻¹⁶ M. Raman mapping confirmed the presence of individual signal hot spots at the lowest concentration, consistent with the theoretical number of analyte molecules in the probed area, thereby demonstrating near-single-molecule detection capability. The calculated enhancement factors reached up to 1.90 × 10¹², among the highest reported for SERS-based detection platforms. A semi-quantitative calibration curve was established spanning twelve orders of magnitude, and this platform was successfully applied to monitor MB degradation during two advanced oxidation processes (AOPs): TiO₂ nanotube-mediated photocatalysis under UV irradiation and atmospheric-pressure dielectric barrier discharge (DBD) plasma treatment. While UV–Vis and fluorescence techniques rapidly lost sensitivity during the degradation process, the SERS platform continued to detect the characteristic MB Raman peak at ~1626 cm⁻¹ throughout the entire treatment duration. These persistent SERS signals revealed the presence of residual MB or partially degraded aromatic intermediates that remained undetectable by conventional optical methods. The results underscore the ability of AgNW-based SERS to provide ultra-sensitive, molecular-level insights into pollutant transformation pathways, enabling time-resolved tracking of degradation kinetics and validating treatment efficiency. This work highlights the importance of integrating SERS with AOPs as a powerful complementary strategy for advanced environmental monitoring and water purification technologies. By delivering an ultra-sensitive, low-cost sensor (<USD 0.16 per test) and promoting reagent-free treatment methods, this study directly advances SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Full article

Traditional guidance and control systems often treat guidance and control systems separately, leading to reduced interception accuracy and responsiveness, especially during high-speed terminal trajectories. These limitations are further exacerbated in autonomous underwater vehicles (AUVs) due to unknown wave/current disturbances, harsh underwater acoustic conditions, and limited sensor capabilities. To address these challenges, this paper studies an integrated guidance and control (IGC) design for AUVs intercepting maneuvering targets with unknown disturbances and unmeasurable system states. The IGC model is derived based on the relative motion equations between the AUV and the target, incorporating the lateral dynamics of the AUV. A model transformation is introduced to synthesize external disturbances with unmeasurable states, extending the resultant disturbance to a new system state. A finite-time convergent extended state observer (ESO) is thus designed for the transformed system to estimate the unknown signals. Using these estimates from the observer, a finite-time event-triggered sliding mode controller is developed, ensuring finite-time convergence of system errors to an adjustable residual set, as rigorously proven through Lyapunov stability analysis. Simulation results demonstrate the superiority of the proposed method in achieving higher interception accuracy and faster response compared to traditional guidance and control approaches with unknown disturbances and unmeasurable states. Full article

In recent years, frequent open biomass burning (OBB) activities such as agricultural residue burning and forest fires have led to severe air pollution and carbon emissions across South and Southeast Asia (SSEA). We selected this area as our study area and divided it into two sub-regions based on climate characteristics and geographical location: the South Asian Subcontinent (SEAS), which includes India, Laos, Thailand, Cambodia, etc., and Equatorial Asia (EQAS), which includes Indonesia, Malaysia, etc. However, existing methods—primarily emission inventories relying on burned area, fuel load, and emission factors—often lack accuracy and temporal resolution for capturing fire dynamics. Therefore, in this study, we employed high-resolution fire point data from China’s Feng Yun-4A (FY-4A) geostationary satellite and the Fire Radiative Power (FRP) method to construct a daily OBB emission inventory at a 5 km resolution in this region for 2020–2022. The results show that the average annual emissions of carbon (C), carbon dioxide (CO₂), carbon monoxide (CO), methane (CH₄), non-methane organic gases (NMOGs), hydrogen (H₂), nitrogen oxide (NO_X), sulfur dioxide (SO₂), fine particulate matter (PM_2.5), total particulate matter (TPM), total particulate carbon (TPC), organic carbon (OC), black carbon (BC), ammonia (NH₃), nitric oxide (NO), nitrogen dioxide (NO₂), non-methane hydrocarbons (NMHCs), and particulate matter ≤ 10 μm (PM₁₀) are 178.39, 598.10, 33.11, 1.44, 4.77, 0.81, 1.02, 0.28, 3.47, 5.58, 2.29, 2.34, 0.24, 0.58, 0.43, 0.99, 1.87, and 3.84 Tg/a, respectively. Taking C emission as an example, 90% of SSEA’s emissions come from SEAS, especially concentrated in Laos and western Thailand. Due to the La Niña climate anomaly in 2021, emissions surged, while EQAS showed continuous annual growth at 16.7%. Forest and woodland fires were the dominant sources, accounting for over 85% of total emissions. Compared with datasets such as the Global Fire Emissions Database (GFED) and the Global Fire Assimilation System (GFAS), FY-4A showed stronger sensitivity and regional adaptability, especially in SEAS. This work provides a robust dataset for carbon source identification, air quality modeling, and regional pollution control strategies. Full article

Background/Objectives: Globally, antibiotic contamination has become an emerging issue in agricultural lands. The presence of antibiotic residues in farmlands, especially through the application of manure fertilizers containing veterinary antibiotics, e.g., tetracycline (TC), can cause severe toxicity, which inhibits crop growth and performance, subsequently threatening human health via consumption of contaminated products. This study was conducted to evaluate the phytotoxicity of TC on Kimchi cabbage (Brassica campestris L.) during seed germination, seedling, and vegetative growth stages, along with its physiological responses and bioaccumulation under TC stress. Methods: The responses of cabbage plants to TC stress were assessed through a germination test and a pot experiment, conducted for three days and six weeks, respectively, under different doses of TC (0, 5, 10, 25, and 50 mg/L). Results: As a result of the germination test, higher TC doses (25 and 50 mg/L) tended to delay seed germination, but all treatments achieved a 100% germination percentage by Day 3 after sowing. Eight days after sowing, the length of shoots and roots of seedlings exhibited a TC dose-dependent decline, specifically under 50 mg TC/L, showing a considerable decrease of 24% and 77%, respectively, compared to control. Similar results were observed in the plants transitioning from the seedling to vegetative stages in the pot experiment. Four and six weeks after sowing, the 50 mg TC/L dose showed the strongest phytotoxicity in cabbage plants with physiological parameters, such as the maximum photosystem II quantum yield (F_v/F_m), pigment content (chlorophyll and carotenoid), biomass, and leaf number, significantly reduced by 26 to 60% compared to control. Interestingly, at lower TC doses (5 and 10 mg/L), a hormesis effect was observed in the phenotype and biomass of the plants. In addition, the degree of TC accumulation in the plants was highly dose-dependent at Week 4 and Week 6, but a temporal decline in TC accumulation was noted between these time points in all TC treatments. This phenomenon might affect the value of the bio-concentration factor (BCF) as an indicator of the plant’s tendency to uptake TC. That is, in Week 6, the dose-dependent reduction in BCF for TC in the plants was likely attributed to a dilution effect caused by plant biomass increase or a degradation mechanism within the plant. Conclusions: Overall, our findings suggest that tetracycline toxicity induces seed germination delay and influences seedling elongation and photosynthetic functions, ultimately impairing crop growth and performance. Also, the antibiotic dynamics related to accumulation and degradation in plants were identified. These results will not only suggest the toxicity threshold of TC for cabbage but also provide insights into effective soil management strategies for food production safety and agroecosystem sustainability in antibiotic-contaminated soils. Full article

The classification of high-frequency marine mammal vocalizations often faces challenges due to the limitations of acoustic features, which are sensitive to mid-to-low frequencies but offer low resolution in high-frequency ranges. Additionally, single-channel networks can restrict overall classification performance. To tackle these challenges, we introduce MT-Resformer, an innovative dual-channel model with a multi-scale framework designed for classifying marine mammal vocalizations. Our approach introduces a feature fusion strategy that combines the constant-Q spectrogram with Mel filter-based spectrogram features, effectively overcoming the low resolution of Mel spectrograms in high frequencies. The MT-Resformer model incorporates two key components: a multi-scale parallel residual network (MResNet) and a Transformer network channel. The model employs a multi-level neural perceptron (MLP) to dynamically regulate the weighting of the two channels, enabling flexible feature fusion. Experimental findings validate the proposed approach, yielding classification accuracies of 99.17% on the Watkins dataset and 95.22% on the ChangLong dataset. These results emphasize its outstanding performance. Full article

Soil organic carbon (SOC) plays a vital role in maintaining soil fertility and ecosystem sustainability, with crop residues serving as a key carbon input. However, how different maize residue components influence SOC stabilization across aggregate sizes and fertility levels remains poorly understood. This study investigated the effects of maize roots, stems, and leaves on SOC dynamics and aggregate-associated carbon under low- and high-fertility Brown Earth soils through a 360-day laboratory incubation. Results revealed that residue incorporation induced an initial increase in SOC, followed by a gradual decline due to microbial mineralization, yet maintained net carbon retention. In low-fertility soil, leaf residues led to the highest SOC content (12.08 g kg⁻¹), whereas root residues were most effective under high-fertility conditions (18.93 g kg⁻¹). Residue addition enhanced macroaggregate (>0.25 mm) formation while reducing microaggregate fractions, with differential patterns of SOC distribution across aggregate sizes. SOC initially accumulated in 0.25–2 mm aggregates but gradually shifted to >2 mm and <0.053 mm fractions over time. Root residues favored stabilization in high-fertility soils via mineral association, while stem and leaf residues promoted aggregate-level carbon protection in low-fertility soils. These findings highlight the interactive roles of residue type and soil fertility in regulating SOC sequestration pathways. Full article

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield reduction due to the temperature and environment. In this study, a lightweight pepper disease identification method, DD-YOLO, based on the YOLOv8n model, is proposed. First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. Secondly, the DySample sampling operator (Dynamic Sample) is integrated into the head network to reduce the amount of data and the complexity of computation. Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. Compared with the base network YOLOv8n, it improves 6.2, 2.3, and 2.8 percentage points, respectively. The model weight is reduced by 22.6%, and the number of floating-point operations per second is improved by 11.1%. This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases. Full article

This study investigates the socioeconomic determinants governing biomass energy transitions in rural areas of Eastern China through a multiregional spatial analysis. Drawing on time-series data from national and local statistical yearbooks, screened and processed to ensure consistency, the research analyzes evolving rural energy consumption patterns across nine cities in Heilongjiang, Jiangsu, and Guangdong provinces. Biomass energy potential was estimated by integrating crop production and domestic waste data with region-specific residue-to-product ratios, calorific values, and conversion efficiencies. These estimates were further spatialized through GIS-based surplus–deficit modeling to reveal regional disparities in supply–demand balance. The analysis identifies a critical income threshold, whereby lower-income regions exhibit rapid growth in energy consumption until reaching a saturation point around RMB 13,000, while higher-income areas experience continued increases in energy demand beyond the capacity of biomass resources to supply. The findings emphasize that an integrated approach, incorporating agricultural residue and domestic waste utilization, is essential for facilitating sustainable energy transitions, particularly in economically advanced regions. Furthermore, the study develops a scalable framework that integrates socioeconomic and spatial variables into biomass energy planning, underscoring the need for regional transition strategies to address not only resource endowments but also demographic mobility, urbanization dynamics, and income-driven consumption behaviors. Full article

Implant–prosthetic rehabilitation of the posterior edentulous maxilla is challenging due to inadequate bone volume resulting from alveolar ridge resorption and maxillary sinus pneumatization. This study explores the use of hyaluronic acid (HA) as a biomaterial in maxillary sinus elevation, particularly in combination with a fluid dynamic approach, as an alternative to traditional lateral approaches and granular biomaterials. Methods: A prospective study was conducted on 58 patients with posterior maxillary edentulism. Preoperative CBCT scans assessed residual bone height and sinus width. A minimally invasive surgical protocol utilizing a device for fluid-dynamic membrane elevation and injection of 2% cross-linked hyaluronic acid was employed, followed by simultaneous implant placement. Postoperative follow-up included a CBCT scan at 12 months to evaluate new bone height, measured mesially and distally. Implant stability was assessed using resonance frequency analysis at second-stage surgery. Results: A significant increase in bone height was observed at 12 months post-surgery, with an average bone gain of 7.5 mm. All 58 implants achieved primary stability, and no implant failures or signs of peri-implantitis were noted during the follow-up period. Higher bone gain was observed in wider sinuses. Conclusions: The fluid-dynamic transcrestal sinus floor elevation technique combined with hyaluronic acid appears to be a minimally invasive and effective method for achieving significant bone regeneration in the posterior maxilla, facilitating implant–prosthetic rehabilitation with potentially low risks and morbidity. Further large-scale studies are warranted to validate these findings across diverse clinical scenarios. Full article

Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. To address these issues, we propose an Anisotropic Dynamic-aware Multi-scale Network for Infrared Small Target Detection (IR-ADMDet). The core of IR-ADMDet is a Dual-Path Hybrid Feature Extractor Network (DPHFENet). This network effectively synergizes local residual learning with global context modeling. It enhances faint target signatures while suppressing interference. Additionally, a Hierarchical Adaptive Fusion Framework (HAFF) is utilized. HAFF integrates bidirectional gating, recursive graph enhancement, and interlink fusion. This framework optimally refines features across multiple scales. The entire architecture is optimized for efficiency using dynamic feature recalibration. Extensive experiments were conducted on benchmark datasets including SIRSTv2, IRSTD-1k, and NUDT-SIRST. These experiments demonstrate the superiority of IR-ADMDet. It achieves state-of-the-art (SOTA) results, such as 0.96 AP50 and 0.95 F1-score on SIRSTv2. This performance is achieved with significantly fewer parameters, only 5.77 M, compared to existing methods. This shows remarkable robustness in low-contrast, high-noise scenarios. IR-ADMDet also outperforms contemporary segmentation-based approaches. Full article

The residual magnetism of the iron core of power transformers can cause an excitation inrush current, posing a threat to the safe and stable operation of the power grid. This paper proposes a transformer remanence measurement method based on a demagnetization hysteresis loop to address the problems of large errors, complex operation, and poor universality in existing remanence measurement methods. This method is designed for off-grid transformers to avoid potential interference to the power grid caused by current pulses during the measurement process. This method constructs an RLC oscillation circuit that utilizes capacitor energy storage and iron core magnetic field energy conversion, combined with the dynamic characteristics of hysteresis loops, to achieve accurate measurement of residual magnetism and synchronous demagnetization. The effectiveness of this method has been verified through residual magnetism measurement experiments on ring transformers and large converter transformers, and it can be applied in specific engineering practice operations. Theoretical analysis shows that the charging range of energy storage capacitors is affected by the hysteresis characteristics of the iron core and the saturation magnetic flux, and the residual magnetization value can be directly calculated based on the difference in the intersection point of the longitudinal axis of the demagnetization hysteresis loop. Simulation and experimental results show that the measurement error of the proposed method is less than 5%—significantly better than traditional methods. This method does not require complex control strategies, has high precision and efficiency, and can provide reliable technical support for residual magnetism detection and suppression of off-grid power transformers. Full article