Increased recruitment of endogenous stem cells and chondrogenic differentiation by a composite scaffold containing bone marrow homing peptide for cartilage regeneration

Even small cartilage defects could finally degenerate to osteoarthritis if left untreated, owing to the poor self-healing ability of articular cartilage. Stem cell transplantation has been well implemented as a common approach in cartilage tissue engineering but has technical complexity and safety concerns. The stem cell homing-based technique emerged as an alternative promising therapy for cartilage repair to overcome traditional limitations. In this study, we constructed a composite hydrogel scaffold by combining an oriented acellular cartilage matrix (ACM) with a bone marrow homing peptide (BMHP)-functionalized self-assembling peptide (SAP). We hypothesized that increased recruitment of endogenous stem cells by the composite scaffold could enhance cartilage regeneration. Methods: To test our hypothesis, in vitro proliferation, attachment and chondrogenic differentiation of rabbit mesenchymal stem cells (MSCs) were tested to confirm the bioactivities of the functionalized peptide hydrogel. The composite scaffold was then implanted into full-thickness cartilage defects on rabbit knee joints for cartilage repair, in comparison with microfracture or other sample groups. Stem cell recruitment was monitored by dual labeling with CD29 and CD90 under confocal microcopy at 1 week after implantation, followed by chondrogenic differentiation examined by qRT-PCR. Repaired tissue of the cartilage defects was evaluated by histological and immunohistochemistry staining, microcomputed tomography (micro-CT) and magnetic resonance imaging (MRI) at 3 and 6 months post-surgery. Macroscopic and histological scoring was done to evaluate the optimal in vivo repair outcomes of this composite scaffold. Results: The functionalized SAP hydrogels could stimulate rabbit MSC proliferation, attachment and chondrogenic differentiation during in vitro culture. At 7 days after implantation, increased recruitment of MSCs based on CD29(+)/CD90(+) double-positive cells was found in vivo in the composite hydrogel scaffold, as well as upregulation of cartilage-associated genes (aggrecan, Sox9 and type II collagen). After 3 and 6 months post-surgery, the articular cartilage defect in the composite scaffold-treated group was fully covered with cartilage-like tissue with a smooth surface, which was similar to the surrounding native cartilage, according to the results of histological and immunohistochemistry staining, micro-CT and MRI analysis. Macroscopic and histological scoring confirmed that the quality of cartilage repair was significantly improved with implantation of the composite scaffold at each timepoint, in comparison with microfracture or other sample groups. Conclusion: Our findings demonstrated that the composite scaffold could enhance endogenous stem cell homing and chondrogenic differentiation and significantly improve the therapeutic outcome of chondral defects. The present study provides a promising approach for in vivo cartilage repair without cell transplantation. Optimization of this strategy may offer great potential and benefits for clinical application in the future

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Research on Calibration Methods of Long-Wave Infrared Camera and Visible Camera

Long-wave infrared (LWIR) and visible (VIS) cameras can image information at different dimensions, but the way to calibrate these two types of cameras while registering and fusing the acquired images is difficult. We propose a calibration plate and a calibration method for thermal imaging and visible imaging to solve three problems: (1) the inability of the existing calibration plates to address LWIR and VIS cameras simultaneously; (2) severe heat interference in the calibration images of LWIR cameras; (3) difficulty in finding feature points for registration due to the different imaging spectra between thermal imaging and visible imaging. Simulation tests and error analysis show the error of outline central point computation is less than 0.1 pixel. Average errors of Euclidean distances from the margin outline scattered point sets of the closed circle and closed ellipse to the outline central points decrease by 10% and 9.9%, respectively. The Mean Reprojection Error in the calibration of LWIR and VIS cameras are 0.1 and 0.227 pixels, respectively. Through image registration design and fusion experiments, the FMIdct, MS-SSIM, Qabf, SCD, and SSIM of the images fused after distortion correction are all higher than those of the images fused before distortion correction, with the highest increases being 4.6%, 0.3%, 3.1%, 7.2%, and 1.4%. These results prove the effectiveness and feasibility of our method

Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN–SE–BiLSTM model

Abstract Enhancing flood forecasting accuracy, promoting rational water resource utilization and management, and mitigating river disasters all hinge on the crucial role of improving the accuracy of daily flow prediction. The coupled model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Bidirectional Long Short-Term Memory (BiLSTM) demonstrates higher stability when faced with nonlinear and non-stationary data, stronger adaptability to various types and lengths of time series data by utilizing sample entropy, and significant advantages in processing sequential data through the BiLSTM network. In this study, in the context of predicting daily flow at the Huayuankou Hydrological Station in the lower reaches of the Yellow River, a coupled CEEMDAN–SE–BiLSTM model was developed and utilized. The results showed that the CEEMDAN–SE–BiLSTM coupled model achieved the utmost accuracy in prediction and optimal fitting performance. Compared with the CEEMDAN–SE–LSTM, CEEMDAN–BiLSTM, and BiLSTM coupled models, the root mean square error (RMSE) of this model is reduced by 42.77, 182.02, and 193.71, respectively; the mean absolute error (MAE) is reduced by 37.62, 118.60, and 126.67, respectively; and the coefficient of determination (R2) is increased by 0.0208, 0.1265, 0.1381

Construction and application of urban water system connectivity evaluation index system based on PSR-AHP-Fuzzy evaluation method coupling

The connectivity of urban water systems can enhance the connectivity of regional rivers, lakes, and water systems, which can improve the regional water environment and water ecology to a certain extent, and enhance the water disaster prevention capability. To scientifically evaluate the effect of water system connectivity in the comprehensive urban water system management project. In this study, a regional waterlogging model based on the coupling of ArcGis and SWMM is established. At the same time, the evaluation index system of water system connectivity effect of regional town water system comprehensive management project was constructed regarding PSR (“Pressure-Status-Response”) theory with structural connectivity, hydraulic connectivity and ecological and environmental improvement as the criteria, and the fuzzy evaluation method was used for analysis and evaluation. The results show that the improvement in the water environment is obvious after the water system is connected. The total length and area of rivers in the study area increased by 68.98% and 57.51%, respectively. River network density increased from 0.66 km/km2 to 0.69 km/km2, an increase of 4.55%; the regional water surface rate increased from 2.22% to 3.51%, an increase of 58.11%; the river frequency increased from 0.15/km2 to 0.29/km2, an increase of 93.33%. The water exchange capacity increased by 37.5% and the flow rate increased by 30%. All water systems have a better degree of connectivity and a better structure of river network water system. The decay rate of point source pollution increased by 40.61%, the water quality of rivers reached V standard, and the area covered by green areas increased to 32.29%. The evaluation grade of hydraulic characteristics and ecological environment indexes was “excellent”. The total evaluation set D=[0.5417,0.0791,0.2125,0] for the effect of water system connection in the study area. According to the principle of maximum affiliation, the improvement effect of the water system connection project in the study area is “excellent”

Monthly runoff prediction based on a coupled VMD-SSA-BiLSTM model

Abstract The accurate prediction of monthly runoff in the lower reaches of the Yellow River is crucial for the rational utilization of regional water resources, optimal allocation, and flood prevention. This study proposes a VMD-SSA-BiLSTM coupled model for monthly runoff volume prediction, which combines the advantages of Variational Modal Decomposition (VMD) for signal decomposition and preprocessing, Sparrow Search Algorithm (SSA) for BiLSTM model parameter optimization, and Bi-directional Long and Short-Term Memory Neural Network (BiLSTM) for exploiting the bi-directional linkage and advanced characteristics of the runoff process. The proposed model was applied to predict monthly runoff at GaoCun hydrological station in the lower Yellow River. The results demonstrate that the VMD-SSA-BiLSTM model outperforms both the BiLSTM model and the VMD-BiLSTM model in terms of prediction accuracy during both the training and validation periods. The Root-mean-square deviation of VMD-SSA-BiLSTM model is 30.6601, which is 242.5124 and 39.9835 lower compared to the BiLSTM model and the VMD-BiLSTM model respectively; the mean absolute percentage error is 5.6832%, which is 35.5937% and 6.3856% lower compared to the other two models, respectively; the mean absolute error was 19.8992, which decreased by 136.7288 and 25.7274 respectively; the square of the correlation coefficient (R 2 ) is 0.93775, which increases by 0.53059 and 0.14739 respectively; the Nash–Sutcliffe efficiency coefficient was 0.9886, which increased by 0.4994 and 0.1122 respectively. In conclusion, the proposed VMD-SSA-BiLSTM model, utilizing the sparrow search algorithm and bidirectional long and short-term memory neural network, enhances the smoothness of the monthly runoff series and improves the accuracy of point predictions. This model holds promise for the effective prediction of monthly runoff in the lower Yellow River

Assessing effect of best management practices in unmonitored watersheds using the coupled SWAT-BiLSTM approach

Abstract In order to enhance the simulation of BMPs (Best Management Practices) reduction effects in unmonitored watersheds, in this study, we combined the physically-based hydrological model Soil & Water Assessment Tool (SWAT) and the data-driven model Bi-directional Long Short-Term Memory (Bi-LSTM), using the very-high-resolution (VHR) Land Use and Land Cover (LULC) dataset SinoLC-1 as data input, to evaluate the feasibility of constructing a water environment model for the Ba-River Basin (BRB) in central China and improving streamflow prediction performance. In the SWAT-BiLSTM model, we calibrated the top five SWAT parameters sorted by P-Value, allowing SWAT to act as a transfer function to convert meteorological data into base flow and storm flow, serving as the data input for the Bi-LSTM model. This optimization improved the Bi-LSTM's learning process for the relationship between the target and explanatory variables. The daily streamflow prediction results showed that the hybrid model had 9 regions rated as "Very good," 2 as "Good," 2 as "Satisfactory," and 1 as "Unsatisfactory" among the 14 regions. The model achieved an NSE of 0.86, R2 of 0.85, and PBIAS of −2.71% for the overall daily streamflow prediction performance during the verification period of the BRB. This indicates that the hybrid model has high predictive accuracy and no significant systematic bias, providing a sound hydrodynamic environment for water quality simulation. The simulation results of different BMPs scenarios showed that in the scenarios with only one BMP measure, stubble mulch had the best reduction effect, with average reductions of 17.83% for TN and 36.17% for TP. In the scenarios with a combination of multiple BMP measures, the combination of stubble mulch, soil testing and formula fertilization, and vegetative filter strip performed the best, achieving average reductions of 42.71% for TN and 50.40% for TP. The hybrid model provides a novel approach to simulate BMPs' reduction effects in regions without measured hydrological data and has the potential for wide application in BMP-related decision-making

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Abstract Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB