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Bovine viral diarrhoea virus (BVDV) is the causative agent of bovine viral diarrhoea/mucocutaneous disease (BVD-MD). Its associated co-infections pose a threat to the cattle industry, which is becoming a key breakthrough in the global system of prevention in the cattle industry. In recent years, cases of co-infection have occurred and been reported from time to time, and this situation not only poses certain difficulties in controlling the outbreak and in treatment in the farming industry, but also poses considerable challenges in detection and diagnosis. In this review, by systematically integrating studies on BVDV co-infection, we firstly compared and analysed the characteristics of BVDV co-infection with viruses, bacteria and other pathogens in in vivo/in vitro models in terms of synergism, host immune response and epidemiological transmission. Then we systematically constructed a BVDV Co-infection Impact Map, which demonstrates a paradigm of pathogen–host–immune interactions in the transmission of BVDV and provides a theoretical framework for breaking through the current precision diagnostic strategies and showcasing the effectiveness of integrated prevention and control.

Angiogenesis is involved in ischemic heart disease as well as the prognosis of heart failure (HF), and endothelial cells are the main participants in angiogenesis. In this study, we found that miR-221-3p is highly expressed in vascular tissue, especially in endothelial cells, and increased miR-221-3p was observed in heart tissue of HF patients and transverse aortic constriction (TAC)-induced HF mice. To explore the role of miR-221-3p in endothelial cells, microRNA (miRNA) mimics and inhibitors were employed in vitro. Overexpression of miR-221-3p inhibited endothelial cell proliferation, migration, and cord formation in vitro, while inhibition of miR-221-3p showed the opposite effect. Anti-argonaute 2 (Ago2) coimmunoprecipitation, dual-luciferase reporter assay, and western blotting were performed to verify the target of miR-221-3p. Hypoxia-inducible factor-1α (HIF-1α) was identified as a miR-221-3p target, and the adverse effects of miR-221-3p on endothelial cells were alleviated by HIF-1α re-expression. In vivo, a mouse model of hindlimb ischemia (HLI) was developed to demonstrate the effect of miR-221-3p on angiogenesis. AntagomiR-221-3p increased HIF-1α expression and promoted angiogenesis in mouse ischemic hindlimbs. Using the TAC model, we clarified that antagomiR-221-3p improved cardiac function in HF mice by promoting cardiac angiogenesis. Furthermore, serum miR-221-3p was detected to be negatively correlated with heart function in chronic heart failure (CHF) patients. Our results conclude that miR-221-3p inhibits angiogenesis of endothelial cells by targeting HIF-1α and that inhibition of miR-221-3p improves cardiac function of TAC-induced HF mice. Furthermore, miR-221-3p might be a potential prognostic marker of HF. This study describes how miR-221-3p in endothelial cells reduces angiogenesis by inhibiting hypoxia-inducible factor-1α. Because antagonism of miR-221-3p significantly improves the cardiac function of mice with heart failure it may be a new and effective molecular target for progressing and treatment of heart failure.

In this paper, a novel robust distributed consensus control scheme based on event-triggered adaptive sliding mode control is proposed for multiagent systems with unknown disturbances in a leader-follower framework. First, an adaptive multivariate disturbance observer is utilized to compensate for the disturbance of each agent. Next, a distributed consensus control protocol is constructed via integral sliding mode control, in which a novel adaptive law is designed for the switching gain to overcome the unknown perturbations. An event-triggered strategy is designed to update the control input. Furthermore, the feasibility of the proposed scheme is rigorously analyzed by Lyapunov theory, and a lower bound expression for the inter-event time is derived to guarantee that Zeno behavior can be excluded. The proposed nonlinear consensus algorithm is remarkable in that it does not require any information about the bounds of the disturbances. Finally, compared with existing methods, the proposed algorithm is validated through detailed numerical simulations. In addition, the proposed algorithm is applied to a group of UAVs in this paper, and the results show that it has more application value.

Dedicated to meeting the growing demand for multi-agent collaboration in complex scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning and the following approach. Current multi-agent path planning predominantly relies on grid-based maps, whereas our proposed approach utilizes laser scan data as input, providing a closer simulation of real-world applications. In this approach, the unmanned aerial vehicle (UAV) uses the soft actor–critic (SAC) algorithm as a planner and trains its policy to converge. This policy enables end-to-end processing of laser scan data, guiding the UAV to avoid obstacles and reach the goal. At the same time, the planner incorporates paths generated by a sampling-based method as following points. The following points are continuously updated as the UAV progresses. Multi-UAV path planning tasks are facilitated, and policy convergence is accelerated through sharing experiences among agents. To address the challenge of UAVs that are initially stationary and overly cautious near the goal, a reward function is designed to encourage UAV movement. Additionally, a multi-UAV simulation environment is established to simulate real-world UAV scenarios to support training and validation of the proposed approach. The simulation results highlight the effectiveness of the presented approach in both the training process and task performance. The presented algorithm achieves an 80% success rate to guarantee that three UAVs reach the goal points.

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 104 t) and 78% of the annual oil production (738.2 × 104 t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 104 t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 104 t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields.

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements.

Satellite and reanalysis precipitation estimates of high quality are widely used for hydrological modeling, especially in ungauged or data-scarce regions. To improve flood simulations by merging different precipitation inputs or directly merging streamflow outputs, this study comprehensively evaluates the accuracy and hydrological utility of nine corrected and uncorrected precipitation products (TMPA-3B42V7, TMPA-3B42RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, and PERSIANN-CCS) from 2006 to 2018 on a daily timescale using the Coupled Routing and Excess Storage (CREST) hydrological model in two flood-prone tributaries, the Beijiang and Dongjiang Rivers, of the Pearl River Basin, China. The results indicate that (1) all the corrected precipitation products had better performance (higher CC, CSI, KGE’, and NSCE values) than the uncorrected ones, particularly in the Beijiang River, which has a larger drainage area; (2) after re-calibration under Scenario II, the two daily merged precipitation products (NSCE values: 0.73–0.87 and 0.69–0.82 over the Beijiang and Dongjiang Rivers, respectively) outperformed their original members for hydrological modeling in terms of BIAS and RMSE values; (3) in Scenario III, four evaluation metrics illustrated that merging multi-source streamflow simulations achieved better performance in streamflow simulation than merging multi-source precipitation products; and (4) under increasing flood levels, almost all the performances of streamflow simulations were reduced, and the two merging schemes had a similar performance. These findings will provide valuable information for improving flood simulations and will also be useful for further hydrometeorological applications of remote sensing data.

Urban color represents the visual skin of a city, embodying regional culture, historical memory, and the contemporary spirit. However, while the existing studies focus on pedestrian-level facade colors, the “fifth facade” from a bird’s-eye view has been largely overlooked. Moreover, color distortions in traditional remote sensing imagery hinder precise analysis. This study targeted 56 Chinese coastal cities, decoding the spatiotemporal patterns of their fifth facade color (FFC). Through developing an innovative natural color optimization algorithm, the oversaturation and color bias of Sentinel-2 imageries were addressed. Several color indicators, including dominant colors, hue–saturation–value, color richness, and color harmony, were developed to analyze the spatial variations of FFC. Results revealed that FFC in Chinese coastal cities is dominated by gray, black, and brown, reflecting the commonality of cement jungles. Among them, northern warm grays exude solidity, as in Weifang, while southern cool grays convey modern elegance, as in Shenzhen. Blue PVC rooftops (e.g., Tianjin) and red-brick villages (e.g., Quanzhou) serve as symbols of industrial function and cultural heritage. Economically advanced cities (e.g., Shanghai) lead in color richness, linking vitality to visual diversity, while high-harmony cities (e.g., Lianyungang) foster livability through coordinated colors. The study also warns of color pollution risks. Cities like Qingdao exposed planning imbalances through color clashes. This research pioneers a systematic and large-scale decoding of urban fifth facade color from a remote sensing perspective, quantitatively revealing the dilemma of “identical cities” in modernization development. The findings inject color rationality into urban planning and create readable and warm city images.

Background Leaf coloration in plants, attributed to anthocyanin compounds, plays a crucial role in various physiological functions, and also for pharmaceutical and horticultural uses. However, the molecular mechanisms governing leaf coloration and the physiological significance of anthocyanins in leaves remain poorly understood. Results In this study, we investigated leaf color variation in two closely related mulberry genotypes, one with purplish-red young leaves (EP) and another with normal leaf color (EW). We integrated transcriptomic and metabolomic approaches to gain insights into the metabolic and genetic basis of purplish-red leaf development in mulberry. Our results revealed that flavonoid biosynthesis, particularly the accumulation of delphinidin-3-O-glucoside, is a key determinant of leaf color. Additionally, the up-regulation of CHS genes and transcription factors, including MYB family members, likely contributes to the increased flavonoid content in purplish-red leaves. Conclusion These findings enhance our understanding of the molecular mechanisms responsible for the purplish coloration observed in mulberry leaves and also offer supporting evidence for the hypothesis that anthocyanins serve a protective function in plant tissues until the processes of light absorption and carbon fixation reach maturity, thereby ensuring a balanced equilibrium between energy capture and utilization.

The basic leucine zipper (bZIP) gene family constitutes one of the most abundant and conserved transcription factor families in plants, which participates in diverse physiological processes including response to abiotic stress, anthocyanin accumulation, and the regulation of plant growth and development. Although bZIP genes play an important role in plants, comparable studies in mulberry are lacking, particularly regarding their response under abiotic stress conditions. In this study, we identified 56 mulberry bZIP transcription factors and divided these members into 12 groups by phylogenetic analysis. The coding genes of these bZIPs harbor a large number of segmental duplications and are unevenly distributed on 12 chromosomes. We further identified numerous stress responsive elements in the promoter regions of bZIP genes. Furthermore, by analysis of the expression profiles from RNA-seq data, we identified MabZIP43 and MabZIP24 that respond to heat, salt–alkaline, and high light stress. We also found that the gene expression of MabZIP16 was closely related to anthocyanin biosynthesis. As described, we systematically explored the structures and functions of the bZIP gene family in Morus alba. The results imply that the bZIP gene family plays significant roles in stress response and anthocyanin biosynthesis. Three bZIP candidate genes are suggested for genetic engineering to improve the resistance of mulberry to stress and for high-anthocyanin-producing lines.