Search Results (318)

Cyclin-dependent kinases (CDKs) and cyclins are master regulators of the cell cycle, playing critical roles in plant growth, development, and stress responses. While these gene families have been extensively studied in model plants, a comprehensive analysis in soybean remains underexplored. To address this gap, we performed a genome-wide identification and systematic analysis of these families in soybean using bioinformatic approaches. Expression profiles and protein interactions of selected GmCDK and GmCyclin candidates were tested by qRT-PCR and BiFC assays. A total of 28 GmCDK and 101 GmCyclin genes were identified, revealing a significant expansion compared to Arabidopsis, rice, and maize, primarily driven by whole-genome and segmental duplications. Phylogenetic analysis classified GmCDKs into seven conserved clades (CDKA-CDKG) and GmCyclins into ten distinct subfamilies. Expression profiling demonstrated dynamic, tissue-specific patterns, with distinct modules active during seed development and in tissues. Promoter analysis further linked these genes to hormonal and stress-responsive pathways. Crucially, BiFC assay identified specific interactions between GmCDKA2, GmCDKA3, GmCDKB1 and GmCYCA3-3, suggesting evolutionary divergence in soybean CDK-Cyclin regulatory networks. This study provides a foundational resource for the soybean cell cycle regulome, highlighting its evolutionary plasticity and implicating specific CDK-Cyclin pairs as potential targets for manipulating agronomic traits such as seed development and stress resilience. Full article

The impact of force field models on the thermal and mechanical characteristics of polyimides was comprehensively examined for the first time. Polyimides (PI) are heterocyclic polymers with outstanding thermal and chemical stabilities and excellent dielectric properties. In this study, we used all-atom molecular dynamics (MD) simulations to examine how the flexibility of the dianhydride fragment affects the thermal and mechanical properties of three polyimides: PMDA-ODA, ODPA-ODA, and R-ODA. The considered polyimides have different dianhydride fragments based on pyromellitic acid (PMDA), tetracarboxylic acid diphenyl oxide (ODPA) and 1,3-bis(3′,4-dicarboxyphenoxy)benzene acid (R), with a constant diamine: 4,4′-oxydianiline (ODA). Models were built using five classical force fields (OPLS-AA, Amber/GAFF, Gromos, Charmm/CGenFF, and UFF). For each polyimide, eight models were generated using different force fields and charge schemes: (i) OPLS-AA with 1.14*CM1A charges, (ii) OPLS-AA with HF/6-31G* (RESP) charges, (iii) GAFF with AM1-BCC charges, (iv) GAFF with HF/6-31G* (RESP) charges, (v) CGenFF (version 4.6) with native charges, (vi) CGenFF (version 5.0) with native charges, (vii) Gromos54a7 with native charges, and (viii) UFF with QEq charges. The difference in the chemical structures of the polyimide repeating unit leads to differences in the thermodynamic and kinetic flexibilities that affect the thermal and mechanical properties. Simulations of glass transition temperatures (T_g) for three polyimides PMDA-ODA, ODPA-ODA, and R-ODA mostly replicate the experimental order T_g(PMDA-ODA) > T_g(ODPA-ODA) > T_g(R-ODA), except for the CGenFF (version 4.6) force field. The experimental density ratio ρ(PMDA-ODA) > ρ(ODPA-ODA) > ρ(R-ODA) is most accurately replicated by OPLS-AA (RESP) and CGenFF (version 5.0) polyimide models. The coefficients of thermal expansion (CTE) correspond with the experimental trend, exhibiting an increase in the following order: PMDA-ODA < ODPA-ODA < R-ODA. Gromos54a7 precisely delineates both the ratio and absolute values CTE for all polymers. OPLS-AA (RESP), OPLS-AA (CM1A), CGenFF (version 4.6), and UFF (QEq) models replicate PMDA-ODA’s CTE, while GAFF (RESP) and GAFF (AM1-BCC) models replicate ODPA-ODA and R-ODA CTE values. The ratio between the simulated values of Young’s modulus, yield strength, and strain-hardening modulus followed the sequence PMDA-ODA > ODPA-ODA > R-ODA for the OPLS-AA (RESP) and CGenFF (version 5.0) models. Full article

Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-level collaborative framework integrating Particle Swarm Optimization (PSO) with the Bidirectional Rapidly Exploring Random Tree (Bi-RRT). The framework unifies adaptive sampling, online parameter optimization, and trajectory smoothing within a single planning architecture. Specifically, the framework constructs a five-dimensional particle encoding that includes the expansion step size and multi-level strategy switching thresholds. During the Bi-RRT expansion process, an expansion-failure-driven adaptive sampling mechanism is introduced to enhance search performance in cluttered environments, while local-density-based suppression and directional dispersion are employed to reduce redundant exploration. In addition, a lightweight PSO-based monitoring mechanism enables online adaptive parameter adjustment. For multi-target scheduling, a greedy heuristic based on a hybrid weighted graph determines the visitation sequence. Trajectory smoothness is further improved using cubic B-spline interpolation combined with bounded perturbation optimization. Experimental results demonstrate that the proposed framework improves planning efficiency while maintaining stable performance across environments with different obstacle densities. These results demonstrate the effectiveness of the proposed framework for multi-target AGV path planning in complex warehouse environments. Full article

Precision Livestock Farming (PLF) increasingly relies on edge-deployed sensors to monitor bovine behaviors, fostering improved welfare and management. However, behavioral data naturally expands over time and presents severe class imbalances due to animals’ predominantly sedentary routines. When continuous sequential updates are required without access to historical datasets, deep learning methods frequently succumb to catastrophic forgetting. This study introduces an ultra-lightweight (∼0.85 MB) Continual Learning (CL) architecture built upon a CNN-BiLSTM feature extractor, tailored to process multivariate Inertial Measurement Unit (IMU) streams. We exhaustively evaluated baseline Naïve Fine-Tuning against Elastic Weight Consolidation (EWC), Learning without Forgetting (LwF), and episodic Replay under three rigorous real-world paradigms: Class Incremental, Subject Incremental (domain shift), and Imbalanced Realistic scenarios. Our empirical findings expose the fragility of static paradigms: in Class Incremental expansions, Naïve Fine-Tuning collapsed to an Average Accuracy of 33.33%. Conversely, Experience Replay emerged as the most robust defense, achieving a statistically significant Average Accuracy of 74.64 ± 6.77% across multiple random seeds. Furthermore, LwF effectively mitigated structural variations across unseen animal domains (Subject Incremental) without requiring raw data buffers. Notably, under severe biological class imbalances (Imbalanced Cumulative), the architecture proved highly resilient, maintaining 98.46% Average Accuracy and retaining perfect minority class recall. This research validates the operational feasibility of deploying adaptive, privacy-preserving CL frameworks directly on low-power wearable devices for lifelong livestock monitoring. Full article

Endogenous gene tagging using CRISPR has changed the understanding of the role played by different proteins due to the ability to track and study proteins in their natural state. With CRISPR-based gene tagging, it is possible to insert fluorescent, luminescent, epitope, affinity, and proximity labels into the target protein at its endogenous genomic location without affecting its physiological expression and dynamics. Here, we discuss the DNA-repair mechanisms employed in endogenous gene tagging, including homology-dependent repair, NHEJ-based integration, and alternative approaches that can be used with challenging cell types. Key aspects of efficient CRISPR tagging experiments are also described. Additionally, we review recent advances in the increasing array of protein tag technologies, including fluorescent proteins, split-reporter technologies, NanoLuc/HiBiT, peptide epitopes, and proximity biotinylation enzymes. Lastly, we review the scalability of endogenous tagging approaches using multiplex editing, atlas-scale proteome tagging, iPSC-based disease modeling, and drug discovery platforms for assessing target engagement, protein degradation, phenotype screening, and mechanism of action of compounds. Although difficult in primary and pluripotent cells, new methods based on avoiding double-strand breaks, such as prime editing, PASTE, and CRISPR associated transposases, will drive the future expansion of endogenous tagging approaches. Such developments firmly set up CRISPR gene tagging as a fundamental technology in quantitative cell biology and translational pharmacology. Full article

To integrate high chromaticity with visible-light-driven antibacterial functionality in yellow inorganic pigments, carbon-doped BiVO₄ (C-BiVO₄) pigments were synthesized via a one-step self-propagating combustion synthesis (SCS) using citric acid as a fuel and carbon source. The effects of citric acid dosage on phase composition, morphology, chromatic performance, and antibacterial activity were systematically investigated. The results indicate that carbon doping induces lattice expansion and oxygen vacancy formation, modulates the electronic band structure, and significantly suppresses photogenerated electron-hole recombination. At an optimal citric acid to BiVO₄ molar ratio of 1.2, the pigment exhibits excellent yellow chromaticity (b* = 79.71). Under visible-light irradiation, C-BiVO₄ achieves a methylene blue photodegradation rate of 96.63% and an E. coli inactivation efficiency of 99.99%, substantially outperforming undoped BiVO₄. Moreover, the C-BiVO₄ yellow pigment shows good dispersibility and thermal stability in PMMA and glass matrices and passes acute skin irritation and dermal toxicity tests, confirming its low toxicity and non-irritating nature. This work provides a new strategy for developing environmentally friendly inorganic pigments that combine high chromaticity with photocatalytic antibacterial functionality. Full article

Remote sensing image change detection is widely used in urban expansion analysis, land-use monitoring, and disaster assessment. Nevertheless, it still faces significant challenges due to pseudo-change interference in high-resolution imagery, the large-scale variation in small changed objects, and the need for lightweight models in real-world engineering applications. To address these issues, this paper proposes LDST-ChangeNet, a lightweight dual spatiotemporal attention network for change detection. The network adopts a Siamese EfficientNet-B1 as its dual-branch encoder and employs a differential bi-temporal feature fusion strategy (Diff) to explicitly model temporal discrepancies, enabling efficient feature extraction while significantly reducing model complexity. A Position Attention Module (PAM) is introduced at the encoder bottleneck to suppress pseudo changes caused by non-structural factors. Meanwhile, a lightweight Pyramid Pooling Module (PPM-lite) is incorporated at the entrance of the deepest decoder features to enhance multi-scale contextual representation. Furthermore, a Boundary Attention Module (BAM) is applied in the decoder output stage to improve boundary delineation and small-object change detection. Experimental results on the LEVIR-CD and WHU-CD datasets show that LDST-ChangeNet outperforms other state-of-the-art methods, achieving F1-scores of 90.67% and 91.08%, respectively. The model maintains a lightweight design, requiring only 11.72 M parameters and 10.03 GFLOPs on LEVIR-CD, and 11.77 M parameters and 9.12 GFLOPs on WHU-CD. Full article

Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration. Full article

In the present work, we introduce and study a new two-parameter generalization of Legendre-based Appell polynomials, defined through an explicit representation that unifies classical Legendre structures with the Appell polynomial framework. Starting from a generating function, we derive a three-term recurrence relation, a degree-lowering operator, an integro-partial degree-raising operator, and a corresponding integro-partial differential equation satisfied by the new family. A determinant representation is established via Cramer’s rule applied to the Cauchy-product expansion of the generating function. Several subfamilies of independent interest arise naturally as special cases, namely, Legendre-based Hermite–Frobenius–Euler polynomials, Legendre-based Miller–Lee polynomials, and both the probabilist’s and physicist’s variants of Legendre-based bi-variate Hermite polynomials. For each subfamily we record the corresponding recurrence relations, shift operators, differential equations, and determinant forms, and we illustrate the behavior of selected members through three-dimensional surface plots and real-root distribution diagrams. The framework presented here extends several constructions available in the recent literature and points to natural directions for future work, including connections with q-series, combinatorial identities, and symbolic-computation methods, which are outlined in the concluding section. Full article

We investigated the structural, magnetic, magnetocaloric, and magnetotransport properties of Ni₅₀Mn₃₅In₁₅ Heusler alloys via partial substitution of Ni with 3 at.% Bi (Ni₄₇Bi₃Mn₃₅In₁₅) and 3 at.% Si (Ni₄₇Si₃Mn₃₅In₁₅) synthesized by arc melting. X-ray diffraction confirms a predominantly L2₁ cubic structure (space group Fm-3m), while SEM/EDX analysis verifies compositional homogeneity. Temperature-dependent magnetization measurements reveal that the Bi-substituted alloy exhibits a first-order magnetostructural transition associated with the martensitic transformation, followed by a second-order magnetic phase transition from ferromagnetic to paramagnetic behavior near the Curie temperature. In contrast, the Si-substituted alloy shows a single second-order transition with negligible thermal hysteresis, indicating suppression of the martensitic phase. The Curie temperature decreases from 324 K for the parent alloy to 313 K and 286 K for the Bi- and Si-substituted alloys, respectively. A maximum magnetic entropy change of 6.0 Jkg⁻¹K⁻¹ and 4.5 Jkg⁻¹K⁻¹ is observed for the Bi- and Si-substituted alloys, respectively, under an applied magnetic field change of 50 kOe, with corresponding relative cooling power values of 303 Jkg⁻¹ and 345 Jkg⁻¹. These results demonstrate that lattice expansion (Bi) and contraction (Si) distinctly modify Mn–Mn exchange interactions, enabling tunable magnetocaloric performance in Ni–Mn–In Heusler alloys. Full article

To address the challenges posed by the diversification of electric vehicle charging demand and the low-carbon economic operation of distribution networks, this paper proposes a bi-level low-carbon distribution network expansion planning method considering the integration of multi-type EV charging facilities. The planning layer of the model aims to minimize the annual total system cost and performs coordinated decision-making for multi-type charging facilities, new line construction, and distributed generation. By introducing a coordinated configuration mechanism for multi-type charging facilities, the model effectively matches diverse user charging demands. In the operation layer, the Aumann–Shapley value method is employed to fairly and accurately quantify carbon emission responsibilities, based on which system carbon allowances are determined. An integrated green certificate-tiered carbon trading mechanism is then established. Meanwhile, a low-carbon demand response model considering dynamic carbon emission factors is introduced to enable low-carbon optimal operation of the distribution network. Finally, simulations are conducted on a modified IEEE 33-bus system. The results demonstrate that the proposed method can effectively reduce total system cost and carbon emissions while satisfying diverse charging demands. Full article

The rapid expansion of Internet of Things (IoT) infrastructures in smart city environments has increased the demand for reliable intrusion detection systems (IDS). However, many existing studies rely on single-dataset evaluations and inconsistent experimental settings, which can lead to overly optimistic performance estimates. In this study, we propose a standardized benchmarking framework for evaluating artificial intelligence-based IDS across heterogeneous IoT datasets, including CIC-IoT 2023, BoT-IoT, and N-BaIoT. Multiple classical machine learning and deep learning models are evaluated under a unified preprocessing pipeline and a consistent evaluation protocol. A hybrid CNN–BiLSTM–Attention architecture is also implemented as a reference model within this framework. While several models achieve near-perfect performance under intra-dataset evaluation, cross-dataset experiments reveal substantial performance degradation and unstable metric behavior under distribution shifts. These results highlight the limitations of dataset-specific optimization and emphasize the necessity of cross-dataset validation for realistic IoT intrusion detection evaluation. All experiments are conducted under a binary intrusion detection setting (benign vs. attack) to enable consistent comparison across datasets. Consequently, the reported results reflect binary detection performance and do not capture attack-type discrimination. Full article

With the rapid expansion of rail transit networks and increasing operational density, foreign object intrusion on tracks has emerged as a critical threat to train safety. Conventional manual inspection methods suffer from low efficiency, high miss rates, and inadequate real-time performance, failing to meet the stringent requirements of modern intelligent railway maintenance. While deep learning offers a promising paradigm shift, existing models often struggle with complex background interference and multi-scale target detection in railway scenarios. To address these challenges, this paper proposes CMF-Net, a unified detection framework for railway track foreign object detection. The CGG module serves as a lightweight feature extraction unit in the backbone, mitigating gradient vanishing and overfitting. The MSAF module enables adaptive multi-scale feature fusion via dual attention (CBAM), enhancing small-object detectability. The FGAF module captures fine-grained edges and textures through a four-branch decomposed convolution and fine-grained attention, suppressing complex background interference. The BiFPN module restructures the neck for efficient bidirectional cross-scale feature fusion. Furthermore, the TPSA module injects explicit railway-domain prior knowledge by fusing a learnable rail-centerline distance-decay field with the CBAM spatial attention map, guiding the detector to focus on operational danger zones and reducing false positives. Experiments on the OFBDs dataset demonstrate that CMF-Net achieves a mean Average Precision (mAP50) of 89.2% and an mAP50:95 of 64.5%, surpassing the baseline YOLOv5s by 4.8 pp and 5.3 pp, respectively. The model maintains a compact parameter size of 5.4 M, a computational cost of 15.2 GFLOPs, and real-time inference capability (56.2 FPS). Edge-deployment feasibility is validated via on-device benchmarking on three Jetson platforms (Nano, Xavier NX, and Orin Nano), where INT8 TensorRT inference achieves 16.2, 108.7, and 153.8 FPS, respectively, under one-hour continuous-inference soak tests with peak power below 16 W and steady-state junction temperatures within safe thermal margins. Statistical significance testing (p < 0.05) confirms the stability of these performance gains. These results indicate that CMF-Net provides rapid and accurate detection of various track intrusions, enabling robust real-time monitoring in dynamic railway environments and enhancing operational safety and intelligence. Full article

A series of Mg-Al LDH-based photocatalysts were synthesized via a one-pot steam-assisted method, including pure Mg-Al LDH (MA), Zn-In ion-exchange-modified Mg-Al LDH (MAZ), BiOCl-loaded pristine Mg-Al LDH (MAB), and Zn-In-modified Mg-Al LDH co-loaded with TiO₂ and BiOCl (MA/Zn-In/TiO₂/BiOCl, MAZB). The one-pot synthesis facilitated the in situ intercalation and uniform loading of BiOCl/TiO₂/Zn-In, while Zn²⁺/In³⁺ modified the MA layers via ion exchange, leading to an expansion of the interlayer spacing. The innovation of this work is reflected in two aspects: first, all raw materials are added via a one-pot strategy to achieve in situ preparation of modified hydrotalcite; second, this synthetic route features simple post-treatment without complicated washing, pressure filtration, and other tedious operations. The samples were characterized by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and N₂ adsorption–desorption isotherms. The bismuth chloride oxide/TiO₂/LDHs exhibited a layered structure, with the active components uniformly distributed between the layers and on the MA surface. Under simulated sunlight irradiation, MAZB achieved 97.5% degradation of 20 mg/L MB within 120 min, with an apparent rate constant of 0.0297 min⁻¹, which is 7.2 times, 2.4 times, and 2.9 times that of MA, MAZ, and MAB, respectively. The degradation rate of MAZB still remained at 89.5% after five cycles, demonstrating excellent stability and reusability. Compared with traditional hydrothermal methods, this steam-assisted system features mild reaction conditions (180 °C, atmospheric pressure), sodium-free raw materials, no washing requirement, and zero waste discharge, showing prominent green advantages. Full article

Aims: Coronary heart disease (CHD) associated with rheumatoid arthritis (RA) is a primary driver of mortality in RA patients. In this study, we sought to establish a combined rat model of CHD and RA by integrating cardiac ischemia/reperfusion (I/R), high-fat diet (HFD), and intradermal administration of bovine type II collagen emulsified in complete Freund’s adjuvant. The aim of constructing this model is to investigate and analyze the pathogenesis of RA-induced CHD under the modulation of HFD and cardiac I/R exposure. Methods and Results: Sixty-four male Sprague–Dawley rats were randomly categorized into eight groups (n = 8 per group): control, I/R, HFD, collagen-induced arthritis (CIA), I/R + CIA, HFD + CIA, I/R + HFD, and I/R + HFD + CIA groups (n = 8 per group). We applied Synchrotron radiation-based X-ray micro-computed tomography (micro-CT) to observe the structural changes within the model over time. To further elucidate molecular mechanisms, transcriptome RNA-seq analysis was carried out to identify key signaling pathways, with particular emphasis on the homeostasis of Toll-like receptor 4 (TLR4)/Myd88 signaling in the ischemic myocardium. Furthermore, we conducted in vivo shRNA-mediated knockdown of polymerase I and transcription release factor (PTRF) and evaluated the co-localization of PTRF and TLR4 through immunofluorescence experiments. It is worth mentioning that our rat model of RA-induced (CHD) under a high-fat diet effectively manifested the relevant pathological features that align with the Traditional Chinese Medicine (TCM) definition of “bi” syndrome. The results indicate that the combined stimulation of HFD and CIA significantly elevated cardiac injury markers (CK-MB, LDH, CRP, and c-TNT) and was accompanied by a more severe expansion of the infarct area and increased cardiomyocyte apoptosis compared to the I/R group alone. In addition, the histopathological evaluation revealed significantly aggravated myocardial inflammation and fibrosis deposition, accompanied by extensive areas of tissue damage, further indicating a state of heightened inflammation and severe cardiac degenerative changes. Consistently, myocardial tissues from rats in the I/R + CIA + HFD group exhibited robust activation of the TLR4/MyD88 signaling pathway and a pronounced elevation in the p-JNK/JNK ratio. Moreover, pronounced co-localization between PTRF and TLR4 was evident in small vessels surrounding the infarcted myocardium. Importantly, AAV-mediated knockdown of PTRF attenuated the HFD- and CIA-induced exacerbation of myocardial injury in I/R rats. Conclusions: We successfully established a rat model of CHD with rheumatic syndrome using I/R in combination with RA and HFD. The present findings suggest that the PTRF-related TLR4/MyD88-JNK signaling pathway may act as an important regulatory mechanism underlying myocardial injury aggravated by combined HFD and CIA stimulation. Full article