Search Results (24)

Scenario analysis and the modeling of typhoons are fundamental prerequisites for effective emergency decision-making. However, current studies on typhoon scenario modeling lack analyses of cascading effects and users’ concerns, failing to represent cascading disaster impacts and user adaptability. This study constructs a scenario evolution model for typhoons and their cascading disasters through typhoon-related public opinion mining and an analysis of disaster evolution characteristics to address these limitations. Specifically, this study analyzes and extracts information about users’ sentiments and concerns based on public opinion data. Then, public opinion and typhoon evolution progression analyses are conducted, identifying cascading disaster evolution characteristics to determine scenario elements. The scenario model is constructed by calculating scenario node probability distributions using dynamic Bayesian networks (DBNs). In this study, Typhoon Bebinca is selected to verify the proposed scenario model; the results demonstrate that the model is reliable and its evolution process aligns with the impacts of typhoon cascading disasters. This study also reveals two critical insights: (1) Users’ concerns will change with typhoon evolution. (2) Emergency measures for dealing with typhoons and their cascading disasters are fragmented. It is essential to consider their cascading effects when enacting these measures. These findings provide novel insights that could aid government agencies in their decision making. Full article

This study presents a novel approach for enhancing the survivability of Lactiplantibacillus plantarum BXM2 using bamboo shoot-derived nanocellulose hydrogels. Nanocellulose hydrogels, composed of cellulose nanofibers (CNFs), cellulose nanocrystals (CNCs), and polyvinyl alcohol (PVA), were developed as protective matrices for probiotics. Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) confirmed the successful formation of hydrogen-bonded networks between PVA and nanocelluloses, while scanning electron microscopy (SEM) revealed that the ternary PVA-CNF-CNC hydrogel exhibited a dense, hierarchical porous structure, effectively encapsulating probiotics with an encapsulation efficiency of 92.56 ± 0.53%. Under simulated gastrointestinal digestion, the encapsulated probiotics maintained 8.04 log CFU/g viability, significantly higher than that of free bacteria (3.54 log CFU/mL). The hydrogel also enhanced heat tolerance (6.58 log CFU/mL at 70 °C) and freeze-drying survival (86.92% viability), outperforming binary systems. During 60-day storage at 4 °C and 25 °C, encapsulated probiotics retained viability above the critical threshold (≥6 log CFU/unit), whereas free cells declined rapidly. These findings highlight the potential of PVA-CNF-CNC hydrogel as an efficient delivery system to improve probiotic stability in food applications. Full article

Heat stress has a significant negative impact on plant growth, development, and yield. The cell wall, a key structural feature that sets plants apart from animals, not only acts as the first physical barrier against heat stress but also plays an active role in the heat stress (HS) response through signaling pathways. The plant cell wall has a complex structural composition, including cellulose, hemicellulose, lignin, and pectin. These components not only provide mechanical support for cell growth but also constitute the material basis for plant response to environmental changes. This review summarizes recent research on how the cell wall’s structural composition affects its mechanical properties in response to stresses. It examines changes in plant cell walls under HS and the adaptive mechanisms leading to cell wall thickening. Additionally, it explores the role of cell wall integrity in sensing and transmitting HS, along with the molecular mechanisms that maintain this integrity. Finally, it addresses unresolved scientific questions regarding plant cell wall responses to HS. This review aims to provide a theoretical foundation and research direction for enhancing plant thermotolerance through genetic improvement of the cell wall. Full article

In response to the inadequate color-matching effectiveness and the difficulty of restoring color intentions in cultural heritage recreation, a Cultural Color Interactive Genetic Algorithm (Cultural Color IGA) is proposed, which combines a color network model and a color harmony prediction model. First, the role of the color network model in providing color genes for subsequent design is emphasized. Then, a dataset of 10,743 color and color rating data points is used to train 12 color harmony prediction models, with the most efficient stacking model selected to improve the efficiency of user evaluation of color schemes. A prototype system for color regeneration is built in Python, and a user interface is designed. The example analysis is conducted using the Yungang Grottoes as the source of color imagery, and image colorization is tested. Independent experiments compare the proposed method with traditional IGA in terms of average fitness, maximum fitness, and evaluation time. Fuzzy evaluation is applied to assess the effectiveness of cultural heritage color regeneration design. The results show that the trained stacking model achieves an accuracy of 65.52% in color harmony prediction, outperforming previous methods. Compared to the traditional IGA algorithm, Cultural Color IGA reduces the number of user evaluations by 67.4%, improves the average fitness by 22.68%, and increases the maximum fitness by approximately 13.37%. Regarding cultural heritage color regeneration effectiveness, 80.6% of respondents considered the generated color schemes to be of good or higher quality. This method not only generates design solutions with high cultural representation and color harmony but also improves the efficiency and sustainability of the design process by reducing trial numbers and manual evaluation workload. It demonstrates the potential of digital technologies in the protection and sustainable application of cultural heritage color, offering valuable references for the digital preservation and innovative design of cultural heritage. Full article

The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R² value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods. Full article

In recent years, the problem of ecosystem degradation caused by human activities has become increasingly serious. Vegetation restoration is a key means to solve this problem, which has increased. To address the suitability and structural optimization of vegetation restoration in the Loess Plateau (China), the MaxEnt model was used to quantify the impacts of environmental and human activities on the planting suitability of vegetation restoration species at the raster scale. Three layers of trees, shrubs, and herbs with 12 common vegetation restoration species were selected. The factor index system was constructed by combining climatic, ecological, and socio-economic data, and the MaxEnt model predicted land suitability. It was found that human activities significantly increased the unsuitable planting area. This especially affected Robinia pseudoacacia in the tree layer and Amorpha fruticosa in the shrub layer. High and medium suitable areas were mainly sparsely populated areas with close water sources. Through maximum suitability optimization, it was identified that the overall spatial distribution of the three layers in the study area was relatively consistent, and the structural dominance of trees + shrubs + herbs and single herbs in the vertical structure was obvious; these were concentrated in the southwestern and northeastern parts of the study area, respectively. In addition, organic content (OC) and distance from the road to woodland (RW) were the dominant factors affecting land suitability, with a contribution rate of more than 50% and up to 80%. These results provide a scientific basis for optimizing planting structures. They are of significant theoretical and practical significance in guiding vegetation restoration work. Full article

As a crucial enzyme for cellulose degradation, β-glucosidase finds extensive applications in food, feed, and bioethanol production; however, its potential is often limited by inadequate thermal stability and glucose tolerance. In this study, a functional gene (lq-bg5) for a GH1 family β-glucosidase was obtained from the metagenomic DNA of a hot spring sediment sample and heterologously expressed in E. coli and the recombinant enzyme was purified and characterized. The optimal temperature and pH of LQ-BG5 were 55 °C and 4.6, respectively. The relative residual activity of LQ-BG5 exceeded 90% at 55 °C for 9 h and 60 °C for 6 h and remained above 100% after incubation at pH 5.0–10.0 for 12 h. More importantly, LQ-BG5 demonstrated exceptional glucose tolerance with more than 40% activity remaining even at high glucose concentrations of 3000 mM. Thus, LQ-BG5 represents a thermophilic β-glucosidase exhibiting excellent thermal stability and remarkable glucose tolerance, making it highly promising for lignocellulose development and utilization. Full article

Micro/nanoplastics are widespread in the environment and may cause severe damage to creatures and human beings. Micro/nanoplastic pollution has become a global focus issue; hence, the rapid and accurate detection of micro/nanoplastics is an essential step to ensure health. Herein, we report a surface-enhanced Raman scattering (SERS) technique to sensitively and quantitatively identify micro/nanoplastics in environmental water samples. A three-dimensional hierarchical Au@Ag nanostar (NSs) was synthesized and employed as an efficient SERS substrate. The “lightning rod effect” generated by tip branches of the nanostars and the coupling effect of the neighboring branches of the nanostar array enabled the ultra-trace detection of crystal violet (CV) down to 10⁻⁹ M, even with a portable Raman device. Moreover, the hydrophobic property of the SERS substrate endowed it with a desirable enrichment effect, which meant an increase in the concentration or quantity of the micro/nanoplastic particles. And thereafter, the SERS sensor achieved a highly sensitive detection of polystyrene (PS) particle standard solution at a low concentration of 25 μg/mL or 2.5 μg/mL. Importantly, the detected concentration and the SERS intensity followed a nearly linear relationship, indicating the capability of quantitative analysis of micro/nanoplastics. In addition, the SERS sensor was successfully extended to detect PS particles in environmental water samples, including tap water, sea water, and soil water, and the detection concentration was determined to be 25 μg/mL, 2.5 μg/mL, and 25 μg/mL, respectively. The present Au@AgNSs array substrate with a two-order magnitude signal amplification further exhibited significant advantages in the label-free analysis of micro/nanoplastics in real water samples. Full article

Given its high biological and pharmacological activities, curcumin (CUR) offers promising applications in functional foods. However, its low stability and bioavailability have greatly hindered its application in the food industry. The present study prepared cellulose nanofiber (CNF) from bamboo shoot processing byproducts and investigated its potential as a low-cost carrier. Our results showed that CUR was immobilized on CNF surfaces mainly through hydrogen bonding and eventually encapsulated in CNF matrices, forming a CNF–CUR complex with an encapsulation efficiency of 88.34% and a loading capacity of 67.95%. The CUR encapsulated in the complex showed improved stability after thermal and UV light treatments. Moreover, a slow and extended release pattern of CUR in a simulated gastrointestinal tract was observed, which could be appropriately described using the Korsmeyer–Peppas model. These results revealed that CNF is a promising protective carrier for the slow release of CUR, making it a better candidate for functional foods. Full article

Postoperative cognitive dysfunction (POCD) is a clinical syndrome characterizing by cognitive impairments in the elderly after surgery. There is limited effective treatment available or clear pathological mechanisms known for this syndrome. In this study, a Connectivity Map (CMap) bioinformatics model of POCD was established by using differently expressed landmark genes in the serum samples of POCD and non-POCD patients from the only human transcriptome study. The predictability and reliability of this model were further supported by the positive CMap scores of known POCD inducers and the negative CMap scores of anti-POCD drug candidates. Most retinoic acid receptor (RAR) agonists were negatively associated with POCD in this CMap model, suggesting that RAR might be a novel target for POCD. Most importantly, acitretin, a clinically used RAR agonist, significantly inhibited surgery-induced cognitive impairments and prevented the reduction in RARα and RARα-target genes in the hippocampal regions of aged mice. The study denotes a reliable CMap bioinformatics model of POCD for future use and establishes that RAR is a novel therapeutic target for treating this clinical syndrome. Full article

The strawberry (Fragaria × ananassa Duch.) is an important horticultural crop that is widely grown all over the world. Its sweetness, aroma, nutritional value and bright color make it popular. The woodland strawberry (Fragaria vesca) is a model plant for studying non-climacteric fruits because its respiration rate does not change significantly during fruit ripening. The B-box (BBX) protein family is made up of zinc-finger transcription factors important in plant growth and development. In this study, we identified 22 FveBBX genes from the newly released woodland strawberry genome database by comprehensive bioinformatics analysis. Phylogenetic analysis divided these FveBBX genes into five subfamilies. A promoter cis-acting element analysis detected 29 elements related to plant development, light response, abiotic stress and hormone response in the promoter of FveBBX genes. According to transcriptome data, relatively few BBX genes had tissue-specific expression, with examples including FveBBX12, which was expressed only in pre-fertilization cortex and pitch, and FveBBX19, which was specifically expressed in mature anthers. During fruit ripening, the expressions of eight FveBBX genes decreased by more than two-fold, and three FveBBX gene expressions increased more than two-fold both in “Ruegen” and “Yellow Wonder”. After cold and heat stresses, around half of the FveBBX genes displayed altered expression, especially FveBBX16 which showed an 8.3-fold increase in expression after heat treatment, while FveBBX14 showed at least an 11-fold decrease in expression after cold treatment. According to the result of quantitative real-time PCR (qRT-PCR), FveBBX genes’ expression differed depending on the photoperiod. Notably, FveBBX7 gene expression was the opposite during the first 16 h of the long-day (LD) and short-day (SD) conditions. This study provides helpful information for further research on BBX gene activity of the woodland strawberry in plant growth and development and adaptation to temperature and photoperiod environmental conditions. Full article

Deoxyribonucleic Acid (DNA) computing has demonstrated great potential in data encryption due to its capability of parallel computation, minimal storage requirement, and unbreakable cryptography. Focusing on high-dimensional image data for encryption with DNA computing, we propose a novel hash encoding-based DNA computing algorithm, which consists of a DNA hash encoding module and content-aware encrypting module. Inspired by the significant properties of the hash function, we build a quantity of hash mappings from image pixels to DNA computing bases, properly integrating the advantages of the hash function and DNA computing to boost performance. Considering the correlation relationship of pixels and patches for modeling, a content-aware encrypting module is proposed to reorganize the image data structure, resisting the crack with non-linear and high dimensional complexity originating from the correlation relationship. The experimental results suggest that the proposed method performs better than most comparative methods in key space, histogram analysis, pixel correlation, information entropy, and sensitivity measurements. Full article

Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets and do not involve scientific principles or laws during the model training process, which may lead to predictions that do not conform to physical laws. To this end, we propose a flood prediction method based on data-driven and knowledge-guided heterogeneous graphs and temporal convolutional networks (DK-HTAN). In the data preprocessing stage, a low-rank approximate decomposition algorithm based on a time tensor was designed to interpolate the input data. Adding an attention mechanism to the heterogeneous graph module is beneficial for introducing prior knowledge. A self-attention mechanism with temporal convolutional network was introduced to dynamically calculate spatiotemporal correlation characteristics of flood data. Finally, we propose physical mechanism constraints for flood processes, adjusted and optimized data-driven models, corrected predictions that did not conform to physical mechanisms, and quantified the uncertainty of predictions. The experimental results on the Qijiang River Basin dataset show that the model has good predictive performance in terms of interval prediction index (PI), RMSE, and MAPE. Full article

Essential oils (EO) are promising feed additives for their antibacterial, antioxidant, and immune-enhancing abilities with low toxicity. Carvacrol, thymol, and cinnamaldehyde are commonly used to synthesize EO. However, few studies focus on combining these three EO in early-weaned piglets. In the present study, 24 piglets weaned at 21 d of age were randomly divided into 2 groups (6 replicate pens per group, 2 piglets per pen). The piglets were fed a basal diet (the control group) and a basal diet supplemented with 400 mg/kg EO (a blend consisting of carvacrol, thymol, and cinnamaldehyde, the EO group) for 28 days. At the end of the experiment, one piglet per pen was randomly chosen to be sacrificed. Growth performance, hematology, plasma biochemical indices, antioxidant capacity, intestinal epithelial development and immunity, colonic volatile fatty acids (VFA), and microbiota were determined. The results indicated that the diet supplemented with EO significantly improved average daily feed intake (ADFI, p < 0.01) and average daily gain (ADG, p < 0.05) in the day 0 to 28 period. EO supplementation led to a significant decrease in plasma lysozyme (p < 0.05) and cortisol levels (p < 0.01). Additionally, EO significantly promoted jejunal goblet cells in the villus, jejunal mucosa ZO-1 mRNA expression, ileal villus height, and ileal villus height/crypt depth ratio in piglets (p < 0.05). The ileal mucosal TLR4 and NFκB p-p65/p65 protein expression were significantly inhibited in the EO group (p < 0.05). Colonic digesta microbiota analysis revealed that bacteria involving the Erysipelotrichaceae family, Holdemanella genus, Phascolarctobacterium genus, and Vibrio genus were enriched in the EO group. In conclusion, these findings indicate that the EO blend improves ADG and ADFI in the day 0 to 28 period, as well as intestinal epithelial development and intestinal immunity in early-weaned piglets, which provides a theoretical basis for the combined use of EO in weaned piglets. Full article