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.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

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.

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.

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.

Constructing a 3D Bi2WO6/ZnIn2S4 direct Z-scheme heterostructure for improved photocatalytic CO2 reduction performance.

Developing efficient heterojunction photocatalysts with enhanced charge transfer and reduced recombination rates of photogenerated carriers is crucial for harnessing solar energy in the photocatalytic CO2 reduction into renewable fuels. This study employed electrostatic self-assembly techniques to construct a 3D Bi2WO6/ZnIn2S4 direct Z-scheme heterojunctions. The unique 3D structure provided abundant active sites and facilitated CO2 adsorption. Moreover, the optimized Bi2WO6/ZnIn2S4 composite demonstrated an impressive CH4 yield of 19.54 µmol g-1 under 4 h of simulated sunlight irradiation, which was about 8.73 and 16.30-fold higher than pure ZnIn2S4 and Bi2WO6. The observed enhancements in photocatalytic performance are attributed to forming a direct Z-scheme heterojunction, which effectively promotes charge transport and migration. This research introduces a novel strategy for constructing photocatalysts through the synergistic effect of morphological interface modifications.

Transcription factor FOXP4 inversely governs tumor suppressor genes and contributes to thyroid cancer progression.

Objective: In recent decades, thyroid cancer (TC) has exhibited a rising incidence pattern. Elevated levels of the transcription factor FOXP4 have been strongly linked to the progression of diverse tumors; nevertheless, its specific role in thyroid cancer remains underexplored. The primary objective of this study was to elucidate the functions of FOXP4 and its associated target gene, FBXW7, in the context of thyroid cancer. Methods: FOXP4 and FBXW7 expression levels in TC tissues and cell lines were assessed through immunohistochemistry and RT-qPCR analyses. The functional aspects of FOXP4, including its effects on cell proliferation, migration capabilities, cell cycle regulation, and epithelial-mesenchymal transition (EMT), were investigated. Furthermore, the interaction between FOXP4 and FBXW7 was confirmed using chromatin immunoprecipitation (ChIP) assays. The impact of FBXW7 on FOXP4-mediated cellular phenotypes was subsequently examined. Additionally, the in vivo role of FOXP4 and FBXW7 in tumor growth was elucidated through the establishment of a murine tumor model. Results: Elevated levels of FOXP4 were observed in papillary carcinoma tissues, and patients exhibiting high FBXW7 levels showed a more favorable prognosis. KTC-1 cells displayed a concomitant increase in FOXP4 expression and decrease in FBXW7 expression. FOXP4 overexpression in these cells enhanced cell proliferation, migration capabilities, and EMT. The interaction between the FOXP4 protein and the FBXW7 promoter was confirmed, and the effects of FOXP4 were mitigated upon overexpression of FBXW7. Furthermore, knockdown of FOXP4 led to decelerated growth of transplanted tumors and increased FBXW7 levels within the tumors. Conclusion: The findings of the current study underscore the regulatory role of FOXP4 in the transcription of FBXW7 and establish a clear link between aberrations in FBXW7 expression and the manifestation of malignant phenotypes in highly aggressive TC cells.

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.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy. Current computational methods can predict ATGs from primary protein sequences, but owing to the limitations of algorithms, significant room for improvement still exists. In this research, we propose EnsembleDL-ATG, an ensemble deep learning framework that aggregates multiple deep learning models to predict ATGs from protein sequence and evolutionary information. We first evaluated the performance of individual networks for various feature descriptors to identify the most promising models. Then, we explored all possible combinations of independent models to select the most effective ensemble architecture. The final framework was built and maintained by an organization of four different deep learning models. Experimental results show that our proposed method achieves a prediction accuracy of 94.5 % and MCC of 0.890, which are nearly 4 % and 0.08 higher than ATGPred-FL, respectively. Overall, EnsembleDL-ATG is the first ATG machine learning predictor based on ensemble deep learning. The benchmark data and code utilized in this study can be accessed for free at https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/EnsembleDL-ATG.

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.

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.

Research on microstructural-mechanical and shearing properties of castor seed during mechanical extraction.

Castor seed oil, as an important biomass fuel, has attracted extensive attention worldwide due to inclusive applications. Castor seed screw mechanical extraction is in fact seed shear damage and oil output. Seed shearing mechanism has been investigated with a developed tribometer. Influences of pressing load, shearing speed, roller roughness were analyzed. Castor seed structural damage was in-situ observed with optical microscope, and in-depth analyzed with Scanning Electron Microscopy and Energy Dispersive Spectroscopy. The results reveal that shear interaction can be divided into three stages: coat damage, transition shearing and endosperm oil output. Seed shear mechanism includes coat peeling, endosperm plowing, tissue transferring and oil lubrication. High pressing load leads to more damage of coat and endosperm, causing more oil to flow out. With shearing speed increasing, coat is easily peeled, obvious endosperm shear plowing and oil lubrication happened in contact area. Coat damage by high roughness leads more oil output. Castor oil enters the contact area and work as lubricant, leading to the decrease of friction resistance.

Targeting of cold-inducible RNA-binding protein alleviates sepsis via alleviating inflammation, apoptosis and oxidative stress in heart.

A systemic inflammatory response is observed in patients undergoing shock and sepsis. This study aimed to explore the effects of cold-inducible RNA-binding protein (CIRP) on sepsis-associated cardiac dysfunction and the underlying mechanism. In vivo and in vitro lipopolysaccharide (LPS)-induced sepsis models were established in mice and neonatal rat cardiomyocytes (NRCMs), respectively. CRIP expressions were increased in the mouse heart and NRCMs treated with LPS. CIRP knockdown alleviated LPS-induced decreases of left ventricular ejection fraction and fractional shortening. CIRP downregulation attenuated the increases of inflammatory factors in the LPS-induced septic mouse heart, and NRCMs. The enhanced oxidative stress in the LPS-induced septic mouse heart and NRCMs was suppressed after CIRP knockdown. By contrast, CIRP overexpression yielded the opposite results. Our current study indicates that the knockdown of CIRP protects against sepsis-induced cardiac dysfunction through alleviating inflammation, apoptosis and oxidative stress of cardiomyocytes.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m5U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m5U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m5U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m5U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.

Fast and Efficient Design of Deep Neural Networks for Predicting N7-Methylguanosine Sites Using autoBioSeqpy.

N7-Methylguanosine (m7G) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying m7G sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process. Recently, computational approaches, especially deep learning (DL) techniques, have gained popularity in achieving this objective. Convolutional neural networks and recurrent neural networks are examples of DL algorithms that have emerged as versatile tools for modeling biological sequence data. However, developing an efficient network architecture with superior performance remains a challenging task, requiring significant expertise, time, and effort. To address this, we previously introduced a tool called autoBioSeqpy, which streamlines the design and implementation of DL networks for biological sequence classification. In this study, we utilized autoBioSeqpy to develop, train, evaluate, and fine-tune sequence-level DL models for predicting m7G sites. We provided detailed descriptions of these models, along with a step-by-step guide on their execution. The same methodology can be applied to other systems dealing with similar biological questions. The benchmark data and code utilized in this study can be accessed for free at http://github.com/jingry/autoBioSeeqpy/tree/2.0/examples/m7G.

ATP-Binding Cassette Exporter PDR11-Mediated Terpenoid Secretion in Engineered Yeast.

The metabolic burden caused by terpenoid accumulation limits the development of highly efficient microbial cell factories, which can be circumvented using exporter-mediated product secretion. Although our previous study showed that the pleiotropic drug resistance exporter (PDR11) mediates the export of rubusoside in Saccharomyces cerevisiae, the underlying mechanism is still unclear. Herein, we used GROMACS software to simulate PDR11-mediated rubusoside recruitment and found six residues (D116, D167, Y168, P521, R663, and L1146) on PDR11 that are critical for this process. We also explored the exportation potential of PDR11 for 39 terpenoids by calculating their binding affinity using batch molecular docking. Then, we verified the accuracy of the predicted results by conducting experiments with squalene, lycopene, and ß-carotene as examples. We found that PDR11 can efficiently secrete terpenoids with binding affinities lower than -9.0 kcal/mol. Combining the computer-based prediction and experimental verification, we proved that binding affinity is a reliable parameter to screen exporter substrates and might potentially enable rapid screening of exporters for natural products in microbial cell factories.

A novel method for predicting DNA N4-methylcytosine sites based on deep forest algorithm.

N4-methyladenosine (4mC) methylation is an essential epigenetic modification of deoxyribonucleic acid (DNA) that plays a key role in many biological processes such as gene expression, gene replication and transcriptional regulation. Genome-wide identification and analysis of the 4mC sites can better reveal the epigenetic mechanisms that regulate various biological processes. Although some high-throughput genomic experimental methods can effectively facilitate the identification in a genome-wide scale, they are still too expensive and laborious for routine use. Computational methods can compensate for these disadvantages, but they still leave much room for performance improvement. In this study, we develop a non-NN-style deep learning-based approach for accurately predicting 4mC sites from genomic DNA sequence. We generate various informative features represented sequence fragments around 4mC sites, and subsequently implement them into a deep forest (DF) model. After training the deep model using 10-fold cross-validation, the overall accuracies of 85.0%, 90.0%, and 87.8% were achieved for three representative model organisms, A. thaliana, C. elegans, and D. melanogaster, respectively. In addition, extensive experiment results show that our proposed approach outperforms other existing state-of-the-art predictors in the 4mC identification. Our approach stands for the first DF-based algorithm for the prediction of 4mC sites, providing a novel idea in this field.

Honokiol alleviates sepsis-associated cardiac dysfunction via attenuating inflammation, apoptosis and oxidative stress.

OBJECTIVE: Honokiol, a natural active compound extracted from Chinese herbal medicine, can ameliorate acute lung and kidney injury of sepsis. This study was to explore the effects of honokiol on sepsis-associated cardiac dysfunction and the underlying mechanism. METHODS: Septic mice were induced by cecal ligation and puncture (CLP) or lipopolysaccharide (LPS), and septic HL-1 or AC16 cells were induced by LPS. RESULTS: Honokiol improved the survival and alleviated cardiac dysfunction in mice with CLP-induced sepsis. Honokiol inhibited the increased interleukin (IL) 1-ß, IL-6 and tumour necrosis factor (TNF)-α in the serum and heart of CLP- and LSP-induced septic mice. Honokiol treatment reversed the increased levels of IL1-ß, IL-6 and TNF-α in LPS-induced HL-1 cells. Honokiol treatment also decreased the elevated levels of IL1-ß, IL-6 and TNF-α in LPS-induced AC16 cells. The increased cardiac apoptosis in CLP- and LPS-induced septic mice was alleviated by honokiol. The enhancement of oxidative stress in the heart of CLP- and LPS-induced septic mice was suppressed after honokiol administration. CONCLUSION: These results showed that honokiol could ameliorate sepsis-associated cardiac dysfunction via attenuating inflammation, apoptosis, and oxidative stress. Honokiol is a prospective drug for sepsis-associated heart damage in the future.

Research on Damage Properties of Apples Based on Static Compression Combined with the Finite Element Method.

Apples are easily damaged during transportation due to extrusion and collision, resulting in structural damage and deterioration. To better understand apples' mechanical-structural damage behavior, a texture analyzer platform combined with in situ observation was established. The effects of extrusion distance, speed, working temperature, and typical kinds of apple were considered for damage mechanisms. Apple damage was analyzed via the finite element method (FEM). The results indicated that the apple extrusion behavior can be divided into elastic interaction and plastic damage. Compression displacement effects were obviously significant in terms of structural damage, and apple samples were in an elastic stage with displacement of less than 2.3 mm, and no structural damage. The peak force energy-displacement mathematical model was established, showing an "s" shape and upward parabolic shape. The critical compression energy was around 100N·mm during elastic interaction. The damaged area was positively correlated with the compression energy. The FEM simulation results were consistent with the damage distribution of apples. The effects of speed on the three apple types were different. Red Fuji apples with a bruised area were not sensitive to pressure speed. The effect on the crack forming of Ralls apples was significant. Golden Delicious apples with a bruised area and crack formation showed an intermediate effect. The peak force-temperature fitting curve showed a downward parabolic shape and an R2 determination factor of 0.99982. Apple squeeze damage mechanisms provide theoretical guidance for apple damage control.

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.