Potato ß-aminobutyric acid receptor IBI1 manipulates VOZ1 and VOZ2 transcription factor activity to promote disease resistance.

Upon infection with non-pathogenic microorganisms or treatment with natural or synthetic compounds, plants exhibit a more rapid and potent response to both biotic and abiotic stresses. However, the molecular mechanisms behind this phenomenon, known as defense priming, are poorly understood. ß-aminobutyric acid (BABA) is an endogenous stress metabolite that enhances plant tolerance to various abiotic stresses and primes plant defense responses, providing the ability to resist a variety of pathogens (broad-spectrum resistance). In this study, we identified an aspartyl-tRNA synthetase (AspRS), StIBI1 (named after Arabidopsis  IMPAIRED IN BABA-INDUCED IMMUNITY 1; IBI1), as a BABA receptor in Solanum tuberosum. We elucidated the regulatory mechanisms by which StIBI1 interacts with two NAC (NAM, ATAF1, 2, and CUC2) transcription factors (TFs), StVOZ1 and StVOZ2 (VASCULAR PLANT ONE ZINC FINGER, VOZ), to activate BABA-induced resistance (BABA-IR). StVOZ1 represses, whereas StVOZ2 promotes, immunity to the late blight pathogen Phytophthora infestans. Interestingly, BABA and StIBI1 influence StVOZ1- and StVOZ2-mediated immunity. StIBI1 interacts with StVOZ1 and StVOZ2 in the cytoplasm, reducing the nuclear accumulation of StVOZ1 and promoting the nuclear accumulation of StVOZ2. Our findings indicate that StVOZ1 and StVOZ2 finely regulate potato resistance to late blight through distinct signaling pathways. In summary, our study provides insights into the interaction between the potato BABA receptor StIBI1 and the TFs StVOZ1 and StVOZ2, which affects StVOZ1 and StVOZ2stability and nuclear accumulation to regulate late blight resistance during BABA-IR. This research advances our understanding of the primary mechanisms of BABA-IR in potato and contributes to a theoretical basis for the prevention and control of potato late blight using BABA-IR.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Comprehensive genome-wide analysis of wheat xylanase inhibitor protein (XIP) genes: unveiling their role in Fusarium head blight resistance and plant immune mechanisms.

In this comprehensive genome-wide study, we identified and classified 83 Xylanase Inhibitor Protein (XIP) genes in wheat, grouped into five distinct categories, to enhance understanding of wheat's resistance to Fusarium head blight (FHB), a significant fungal threat to global wheat production. Our analysis reveals the unique distribution of XIP genes across wheat chromosomes, particularly at terminal regions, suggesting their role in the evolutionary expansion of the gene family. Several XIP genes lack signal peptides, indicating potential alternative secretion pathways that could be pivotal in plant defense against FHB. The study also uncovers the sequence homology between XIPs and chitinases, hinting at a functional diversification within the XIP gene family. Additionally, the research explores the association of XIP genes with plant immune mechanisms, particularly their linkage with plant hormone signaling pathways like abscisic acid and jasmonic acid. XIP-7A3, in particular, demonstrates a significant increase in expression upon FHB infection, highlighting its potential as a key candidate gene for enhancing wheat's resistance to this disease. This research not only enriches our understanding of the XIP gene family in wheat but also provides a foundation for future investigations into their role in developing FHB-resistant wheat cultivars. The findings offer significant implications for wheat genomics and breeding, contributing to the development of more resilient crops against fungal diseases.

Development and validation of a predictive model for end-stage renal disease in systemic lupus erythematosus patients.

Systemic lupus erythematosus (SLE) affects many populations. This study aims to develop a predictive model and create a nomogram for assessing the risk of end-stage renal disease (ESRD) in patients diagnosed with SLE. Data from electronic health records of SLE patients treated at the Affiliated Hospital of North Sichuan Medical College between 2013 and 2023 were collected. The dataset underwent thorough cleaning and variable assignment procedures. Subsequently, variables were selected using one-way logistic regression and lasso logistic regression methods, followed by multifactorial logistic regression to construct nomograms. The model's performance was assessed using calibration, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves. Statistical significance was set at P < 0.05. The predictive variables for ESRD development in SLE patients included anti-GP210 antibody presence, urinary occult blood, proteinuria, white blood cell count, complement 4 levels, uric acid, creatinine, total protein, globulin, glomerular filtration rate, pH, specific gravity, very low-density lipoprotein, homocysteine, apolipoprotein B, and absolute counts of cytotoxic T cells. The nomogram exhibited a broad predictive range. The ROC area under the curve (AUC) was 0.886 (0.858-0.913) for the training set and 0.840 (0.783-0.897) for the testing set, indicating good model performance. The model demonstrated both applicability and significant clinical benefits. The developed model presents strong predictive capabilities and considerable clinical utility in estimating the risk of ESRD in patients with SLE.

Study on the Correlation Between Patent Foramen Ovale and Migraine Based on Meta Analysis.

Objective: This meta-analysis systematically investigates the association between Patent Foramen Ovale (PFO) and the prevalence of migraine. Our goal is to quantify this relationship and evaluate its implications for clinical practice and future research. Methods: An extensive literature search was carried out in various databases, such as PubMed, Embase, The Cochrane Library, Web of Science, CNKI, VIP, WanFang Data, and CBM, up to November 2023. The search focused on case-control, cross-sectional, and cohort studies examining the link between PFO and migraine. The literature screening and data extraction, based on predefined inclusion and exclusion criteria, were independently conducted by two reviewers. The studies' quality was evaluated using the Newcastle-Ottawa Scale (NOS), and RevMan 5.3 software was employed for the meta-analysis. Results: A total of 27 studies involving 8,875 participants were included in the meta-analysis. The results indicate a statistically significant association between PFO and migraine prevalence. Key findings include: (1) Overall, individuals with migraine had higher rates of PFO compared to healthy controls (OR = 3.22, 95% CI = 2.21 to 4.67, P < .00001). (2) The association was stronger in the Migraine with Aura group (OR = 3.69, 95% CI = 1.93 to 7.04, P < .0001) than in the Non-Migraine with Aura group (OR = 1.48, 95% CI = 1.09 to 2.00, P = .01). (3) The prevalence of PFO was notably higher in the Migraine with Aura group compared to the Non-Migraine with Aura group (OR = 2.32, 95% CI = 1.96 to 2.76, P < .00001). Conclusion: The analysis confirms a noteworthy correlation between PFO and migraine, underscoring the relationship and suggesting additional studies need to elucidate the underlying mechanisms and clinical ramifications.

Bidirectional negative feedback actions of DNMT3A and miR-145 in regulating autophagy in cardiac fibroblasts and affecting myocardial fibrosis.

Epigenetic regulation has crucial implications for myocardial fibrosis. It has been reported that autophagy, regulated by miR-145, is implicated in the proliferation and fibrosis of cardiac fibroblasts (CFs). However, how it works during the process remains unclear. This study explored the exact effects of epigenetic regulation of miR-145 expression on autophagy, proliferation, and fibrosis of CFs. To examine the expression levels of myocardial fibrosis markers (α-SMA and collagen I), autophagy-related proteins (LC3I, LC3II, p62), DNMT3A, and miR-145, qRT-PCR and western blot were employed. And the proliferation of CFs was detected by CCK-8 and ErdU. As for the determination of the binding relationship between DNMT3A and miR-145, dual-luciferase assay was conducted. Next, the detection of the methylation level of the pre-miR-145 promoter region was completed by MSP. And the verification of the effect of the DNMT3A/miR-145 axis on myocardial fibrosis was accomplished by constructing mouse myocardial infarction (MI) models based on the ligation of the left anterior descending method. In TGF-ß1-activated CFs, remarkable up-regulation of DNMT3 and considerable down-regulation of miR-145 were observed. And further experiments indicated that DNMT3A was able to down-regulate miR-145 expression by maintaining the hypermethylation level of the pre-miR-145 promoter region. In addition, DNMT3A expression could be directly targeted and negatively modulated by miR-145. Moreover, in vitro cell experiments and mouse MI models demonstrated that DNMT3A overexpression could inhibit autophagy, and promote cell proliferation and fibrosis of CFs. However, this kind of effect could be reversed by miR-145 overexpression. In summary, myocardial fibroblast autophagy can be regulated by bidirectional negative feedback actions of DNMT3A and miR-145, thus affecting myocardial fibrosis. This finding will provide a potential target for the clinical treatment of myocardial fibrosis.

Cluster-aware channel estimation with deep learning method in deep-water acoustic communications.

In underwater acoustic (UWA) communications, channels often exhibit a clustered-sparse structure, wherein most of the channel impulse responses are near zero, and only a small number of nonzero taps assemble to form clusters. Several algorithms have used the time-domain sparse characteristic of UWA channels to reduce the complexity of channel estimation and improve the accuracy. Employing the clustered structure to enhance channel estimation performance provides another promising research direction. In this work, a deep learning-based channel estimation method for UWA orthogonal frequency division multiplexing (OFDM) systems is proposed that leverages the clustered structure information. First, a cluster detection model based on convolutional neural networks is introduced to detect the cluster of UWA channels. This method outperforms the traditional Page test algorithm with better accuracy and robustness, particularly in low signal-to-noise ratio conditions. Based on the cluster detection model, a cluster-aware distributed compressed sensing channel estimation method is proposed, which reduces the noise-induced errors by exploiting the joint sparsity between adjacent OFDM symbols and limiting the search space of channel delay spread. Numerical simulation and sea trial results are provided to illustrate the superior performance of the proposed approach in comparison with existing sparse UWA channel estimation methods.

Data augmentation aided complex-valued network for channel estimation in underwater acoustic orthogonal frequency division multiplexing system.

In this paper, a data augmentation aided complex-valued network is proposed for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel estimations, wherein empirical mode decomposition based data augmentation is proposed to solve the current dilemma in the deep learning embedded UWA-OFDM communications: data scarcity and data-sampling difficulties in real-world applications. In addition, the significance of high-frequency component augmentation for the UWA channel and how it positively influences the following model training are discussed in detail and demonstrated experimentally in this paper. In addition, the complex-valued network is specially designed for the complex-formatted UWA-OFDM signal, which can fully utilize the relationship between its real and imaginary parts with half of the spatial resources of its real-valued counterparts. The experiments with the at-sea-measured WATERMARK dataset indicate that the proposed method can perform a near-optimal channel estimation, and its low resource requirements (on dataset and model) make it more adaptable to real-world UWA applications.

Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identify TF binding sites and predict their affinities. Recently, deep learning (DL) algorithms have achieved promising results in the prediction of DNA-TF binding, however, various deep learning architectures have not been systematically compared, and the relative merit of each architecture remains unclear. To address this problem, we applied four different deep learning architectures to SELEX-seq and HT-SELEX data, covering three species and 35 families. We evaluated and compared the performance of different deep neural models using 10-fold cross-validation. Our results indicate that the hybrid CNN + DNN model shows the best performances. We expect that our study will be broadly applicable to modeling and predicting TF binding specificity when more high-throughput affinity data are available.

Angiotensin-(3-7) alleviates isoprenaline-induced cardiac remodeling via attenuating cAMP-PKA and PI3K/Akt signaling pathways.

The renin-angiotensin system is involved in the regulation of various heart diseases. The present study aimed to determine the effects of angiotensin (Ang)-(3-7) on cardiac remodeling and its downstream signaling pathways in neonatal rat cardiomyocytes (NRCMs) and neonatal rat cardiac fibroblasts (NRCFs). The administration of Ang-(3-7) alleviated isoprenaline (ISO)-induced cardiac hypertrophy and fibrosis of mice. ISO treatment increased the levels of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP) and beta-myosin heavy chain (ß-MHC) in NRCMs, and reduced the levels of collagen I, collagen III, fibronectin, and alpha-smooth muscle actin (α-SMA) in NRCFs. These changes were inhibited by Ang-(3-7) administration. The levels of protein kinase A (PKA), phosphorylated phosphatidylinositol-3-kinase (p-PI3K), and phosphorylated protein kinase B (p-Akt) were increased in NRCMs and NRCFs treated with ISO. The increase of PKA, but not p-PI3K or p-Akt was attenuated by Ang-(3-7) treatment in NRCMs. The increases of p-PI3K and p-Akt, but not PKA were reversed by Ang-(3-7) treatment in NRCFs. Treatment with cAMP or PKA overexpression reversed the attenuating effects of Ang-(3-7) on ISO-induced hypertrophy of NRCMs. The administration of PI3K inhibitor or Akt inhibitor alleviated ISO-induced fibrosis of NRCFs. These results indicated that Ang-(3-7) could alleviate cardiac remodeling. The administration of Ang-(3-7) attenuated hypertrophy of NRCMs via inhibiting the cAMP/PKA signaling pathway, and alleviated fibrosis of NRCFs via inhibiting PI3K/Akt signaling pathway.

Meta-learning-aided orthogonal frequency division multiplexing for underwater acoustic communications.

In this paper, a meta-learning-based underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system is proposed to deal with the environment mismatch in real-world UWA applications, which can effectively drive the model from the given UWA environment to the new UWA environment with a relatively small amount of data. With meta-learning, we consider multiple UWA environments as multi-UWA-tasks, wherein the meta-training strategy is utilized to learn a robust model from previously observed multi-UWA-tasks, and it can be quickly adapted to the unknown UWA environment with only a small number of updates. The experiments with the at-sea-measured WATERMARK dataset and the lake trial indicate that, compared with the traditional UWA-OFDM system and the conventional machine learning-based framework, the proposed method shows better bit error rate performance and stronger learning ability under various UWA scenarios.

Household Decision-Making Choices: Investment in Children's Education or Self-Consumption.

Analyzing the sustainable decision-making mechanism between household consumption and education investment can theoretically develop education. This study uses the continuous-time utility model to demonstrate the independent characteristics of consumption and education investment, as well as the principle of decision incompatibility in the decision-making process of the utility maximization problem. Then, we establish a three-phase logarithmic utility model to obtain the intertemporal decision-making path of a family. The analysis shows that the investment allocation ratio between the two phases depends on the expected and discounted level of the offsprings' abilities, while the total investment level is related to parental altruism. When parents, with foresight, factor in prospective transfer payments from progeny, the optimal decision is to maximize their children's ultimate human capital within a given total investment. Education investment not only squeezes out consumption but also promotes consumption in various periods due to future transfer payments. The decision-making process of three typical growth stages indicates that as offspring mature and their human capital increases, parents' willingness to invest in education decreases while self-consumption escalates. This study provides a new perspective and theoretical basis for studying household education expenditure, motivation, and related policy formulation.

Study on the Damage Characteristics of Wheat Kernels under Continuous Compression Conditions.

Peeling wheat yields higher-quality flour. During processing in a flaking machine, wheat kernels undergo continuous compression within the machine's chamber. As this compression persists, damage to the kernels intensifies and accumulates, eventually leading to kernel breakage. To study the damage characteristics of wheat kernels during peeling, this study established a continuous damage model based on Hertzian contact theory and continuous damage theory. The model's accuracy was validated through experiments, culminating in the calculation of critical parameters for wheat peeling. This study focused on different wheat varieties (Ningmai 22 and Jichun 1) and kernel sizes (the thicknesses of the small, medium, and large kernels were standardized as follows: Ningmai 22-2.67 ± 0.07 mm, 2.81 ± 0.07 mm, and 2.95 ± 0.07 mm; Jichun 1-2.98 ± 0.11 mm, 3.20 ± 0.11 mm, and 3.42 ± 0.11 mm). Continuous compression tests were conducted using a mass spectrometer, and critical damage parameters were analyzed and calculated by integrating the theoretical model with experimental data. The test results showed that the average maximum crushing force (Fc) for small, medium, and large-sized kernels of Ningmai 22 was 96.71 ± 2.27 N, 110.17 ± 2.68 N, and 128.41 ± 2.85 N, respectively. The average maximum crushing deformation (αc) was 0.65 ± 0.08 mm, 0.68 ± 0.13 mm, and 0.77 ± 0.17 mm, respectively. The average elastic-plastic critical pressure (Fs) was 50.21 N, 60.13 N, and 59.08 N, respectively, and the average critical values of elastic-plastic deformation (αs) were 0.37 mm, 0.38 mm, and 0.39 mm, respectively. For Jichun 1, the average maximum crushing force (Fc) for small-, medium-, and large-sized kernels was 113.34 ± 3.15 N, 125.28 ± 3.64 N, and 136.15 ± 3.29 N, respectively. The average maximum crushing deformation (αc) was 0.75 ± 0.11 mm, 0.83 ± 0.15 mm, and 0.88 ± 0.18 mm, respectively. The average elastic-plastic critical pressure (Fs) was 58.11 N, 64.17 N, and 85.05 N, respectively, and the average critical values of elastic-plastic deformation (αs) were 0.45 mm, 0.47 mm, and 0.52 mm, respectively. The test results indicated that during mechanical compression, if the deformation is less than αs, the continued application of the compression load will not result in kernel crushing. However, if the deformation exceeds αs, continued compression will lead to kernel crushing, with the required number of compressions decreasing as the deformation increases. If the deformation surpasses αc, a single compression load is sufficient to cause kernel crushing. Since smaller wheat kernels are more susceptible to breakage during processing, the peeling pressure (F) within the chamber should be controlled to remain below the average elastic-plastic critical pressure (Fs) of small-sized wheat kernels. Additionally, the kernel deformation (α) induced by the flow rate and loading in the chamber should be kept below the average elastic-plastic critical deformation (αs) of small-sized wheat kernels. This paper provides a theoretical foundation for the structural design and optimization of processing parameters for wheat peeling machines.

ELF5-Regulated lncRNA-TTN-AS1 Alleviates Myocardial Cell Injury via Recruiting PCBP2 to Increase CDK6 Stability in Myocardial Infarction.

Myocardial infarction (MI) seriously threatens the health of elderly people, and reducing myocardial injury is of great significance for the treatment of MI. LncRNA-TTN-AS1 shows protective effects on cardiomyocyte injury, while the role of TTN-AS1 in MI remains unknown. CCK8, flow cytometry, and JC-1 staining assessed cell viability, apoptosis and mitochondrial membrane potential (MMP), respectively. Cellular reactive oxygen species (ROS) and secreted lactate dehydrogenase (LDH) levels were measured. The interactions between ELF5, TTN-AS1, PCBP2 and CDK6 were explored using ChIP, luciferase reporter assay, RIP, and pull-down. The severity of MI in mice was evaluated using TTC, H&E, and TUNEL staining. The data revealed that OGD/R significantly induced ROS, mitochondrial injury and apoptosis in AC16 cells, while overexpression of ELF5 or TTN-AS1 reversed these phenomena. ELF5 transcriptionally activated TTN-AS1 through binding with its promoter. TTN-AS1 increased CDK6 stability via recruiting PCBP2. CDK6 knockdown abolished the inhibitory effects of TTN-AS1 overexpression on OGD/R-induced myocardial injury. Furthermore, overexpression of TTN-AS1 or ELF5 alleviated MI progression in mice by upregulating CDK6. Collectively, TTN-AS1 transcriptionally regulated by ELF5 alleviated myocardial apoptosis and injury during MI via recruiting PCBP2 to increase CDK6 stability, which shed new lights on exploring new strategies against MI.

Characteristics of Damage to Brown Rice Kernels under Single and Continuous Mechanical Compression Conditions.

During the rice milling process, single and continuous compression occurs between brown rice and the processing parts. When the external load exceeds the yield limit of brown rice, brown rice kernels are damaged; with an increase in compression deformation or the extent of compression, the amount of damage to the kernels expands and accumulates, ultimately leading to the fracture and breakage of kernels. In order to investigate the mechanical compression damage characteristics of brown rice kernels under real-world working conditions, this study constructs an elastic-plastic compression model and a continuous damage model of brown rice kernels based on Hertz theory and continuous damage theory; the accuracy of this model is verified through experiments, and the relevant processing critical parameters are calculated. In this study, three varieties of brown rice kernels are taken as the research object, and mechanical compression tests are carried out using a texture apparatus; finally, the test data are analysed and calculated by combining them with the theoretical model to obtain the relevant critical parameters of damage. The results of the single compression crushing test of brown rice kernels showed that the maximum destructive forces Fc in the single compression of Hunan Early indica 45, Hunan Glutinous 28, and Southern Japonica 518 kernels were 134.77 ± 11.20 N, 115.64 ± 4.35 N, and 115.84 ± 5.89 N, respectively; the maximum crushing deformations αc in the single compression crushing test were 0.51 ± 0.04 mm, 0.43 ± 0.01 mm, and 0.48 ± 0.17 mm, respectively; and the critical average deformations αs of elasticity-plasticity deformation were 0.224 mm, 0.267 mm, and 0.280 mm, respectively. The results of the continuous compression crushing test of brown rice kernels showed that the critical deformations αd of successive compression damage formation were 0.224 mm, 0.267 mm, and 0.280 mm, and the deformation ratios δ of compression damage were 12.24%, 14.35%, and 12.84%. From the test results, it can be seen that the continuous application of compression load does not result in the crushing of kernels if the compression deformation is less than αd during mechanical compression. The continuous application of compressive loads can lead to fragmentation of the kernels if the compressive deformation exceeds αd; the larger the compression variant, the less compression is required for crushing. If the compression deformation exceeds αc, then a single compressive load can directly fragment the kernels. Therefore, the load employed during rice milling should be based on the variety of brown rice used in order to prevent brown rice deformation, which should be less than αd, and the maximum load should not exceed Fc. The results of this study provide a theoretical reference for the structure and parameter optimisation of a rice milling machine.

A metric to quantify the time-varying characteristics of underwater acoustic communication channels.

Underwater acoustic communication signals suffer from time dispersion due to time-varying multipath propagation in the ocean. This leads to intersymbol interference, which in turn degrades the performance of the communication system. Typically, the channel correlation functions are employed to describe these characteristics. In this paper, a metric called the channel average correlation coefficient (CACC) is proposed from the correlation function to quantify the time-varying characteristics. It has a theoretical negative relationship with communication performance. Comparative analysis involving simulations and experimental data processing highlights the superior effectiveness of CACC over the traditional metric, the channel coherence time.

Research on rice starch gel preparation and crosslink network structure-rheological property based on direct-writing 3D printing.

Amylopectin and amylose components are natural polymers within rice starch granules, intertwined in specific conditions to form gel polymerized with pore crosslink network, has potential printing properties. In this study, a rice starch gel preparation scheme is proposed for stable properties, and starch granule phase transition mechanism is analyzed based on RVA test during preparation, it can be divided into four-stage, swelling, reacting, homogenizing and self-assembling stages. Gel surface tension and contact angle tested with starch concentration effect, a correlation is developed, reflecting a competition result to gel droplet macro-morphology between the intermolecular cohesion and crosslink network. SEM is used to reveal typical crosslink structures of different starch molecular component proportions, providing objective support for starch gel rheologic property change. Results indicate gel interior crosslink network formed under concentration 12 %, the gel with amylose 4.475 % presents better printing accuracy. Gel shear modulus positively correlated with amylose proportion. Japonica gel under 20 % is of higher viscosity and rapid reassembly ability after interior crosslink network is broken. Max dynamic viscosity is positively correlated with starch concentration. The study aims to provide theoretical and practical support for in-depth analysis of rice starch material application in direct-write 3D printing.

Research on the castor oil pressing extraction mechanism based on multi-physics coupling simulation.

Castor oil has been widely used in various fields due to its properties, leading to large attention for its extraction mechanism. To research the castor oil extraction mechanism during pressing, a self-developed uniaxial compression device combined with an in situ observation is established. The effects of pressure, loading speed, and creep time are investigated, and a finite element model coupling with multi-physics is established for castor oil pressing extraction, verified by the seed cake experimental compression strain matching with numerical simulation under the same condition. Simulation results indicated that the pressing oil extraction process can be divided into two stages, Darcy's speed shows the first sharp decreasing stage and the second gradual increasing stage during porosity and pressure interaction. In the first stage, porosity is dominant on Darcy's speed. With porosity decreasing, the pressure effect on Darcy's speed exceeds porosity in the second stage. With seed thickness increasing, Darcy's speed first increases and then decreases. With loading speed increasing, Darcy's speed increases. Darcy's speed decreases constantly with creep time increasing. This study can provide basic theoretical and practical guidance for oil extraction.

Portraying on-road CO2 concentrations using street view panoramas and ensemble learning.

A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched "street view-CO2 concentration" were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree of bran layer remaining (DOR) datasets is established with image capturing and processing (grain color, texture, and shape features extraction). The mapping relationship between DOR and the DOM is in-depth analyzed. Rice grain DOR typical machine learning and deep learning prediction models are established. The results indicate that the optimized Catboost model can be established with cross-validation and grid search method, with the best accuracy improving from 84.28% to 91.24%, achieving precision 91.31%, recall 90.89%, and F1-score 91.07%. Shapley additive explanations analysis indicates that color, texture, and shape feature affect Catboost prediction accuracy, the feature importance: color > texture > shape. The YCbCr-Cb_ske and GLCM-Contrast features make the most significant contribution to rice milling quality prediction. The feature importance provides theoretical and practical guidance for grain DOM prediction model. PRACTICAL APPLICATION: Rice milling degree prediction and detection are valuable for rice milling process in practical application. In this paper, image processing and machine learning methods provide an automated, nondestructive, and cost-effective way to predict the quality of rice. The study may serve as a valuable reference for improving rice milling methods, retaining rice nutrition, and reducing broken rice yield.