Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer.

Rice serves as a fundamental food staple for humans. Its production process, however, unavoidably exposes it to pesticides which may detrimentally impact its quality due to residues. Therefore, it is extremely necessary to monitor pesticide residues on rice during storage. In this research, the Quatformer model, which considers the effects of temperature and humidity on pesticide residues in rice grains, was utilized to forecast the amount of pesticide residues in rice grains during the storage process, and the predicted results were combined with actual observations to form a quality assessment index. By applying the K-Means algorithm, the quality of rice grains was graded and assessed. The findings indicated that the model had high prediction accuracy, and the MAE, MSE, MAPE, RMSE and SMAPE indexes were calculated to be 0.0112, 0.0814, 0.1057, 0.1055 and 0.0204, respectively. These findings provide valuable technical and theoretical support for planning storage conditions, enhancing pesticide residue decomposition, and monitoring rice quality during storage.

Nuclear deformation and cell division of single cell on elongated micropatterned substrates fabricated by DMD lithography.

Cells sense mechanical signals from the surrounding environment and transmit them to the nucleus through mechanotransduction to regulate cellular behavior. Microcontact printing, which utilizes elastomer stamps, is an effective method for simulating the cellular microenvironment and manipulating cell morphology. However, the conventional fabrication process of silicon masters and elastomer stamps requires complex procedures and specialized equipment, which restricts the widespread application of micropatterning in cell biology and hinders the investigation of the role of cell geometry in regulating cell behavior. In this study, we present an innovative method for convenient resin stamp microfabrication based on digital micromirror device planar lithography. Using this method, we generated a series of patterns ranging from millimeter to micrometer scales and validated their effectiveness in controlling adhesion at both collective and individual cell levels. Additionally, we investigated mechanotransduction and cell behavior on elongated micropatterned substrates. We then examined the effects of cell elongation on cytoskeleton organization, nuclear deformation, focal adhesion formation, traction force generation, nuclear mechanics, and the growth of HeLa cells. Our findings reveal a positive correlation between cell length and mechanotransduction. Interestingly, HeLa cells with moderate length exhibit the highest cell division and proliferation rates. These results highlight the regulatory role of cell elongation in mechanotransduction and its significant impact on cancer cell growth. Furthermore, our methodology for controlling cell adhesion holds the potential for addressing fundamental questions in both cell biology and biomedical engineering.

Deep-Learning-Based Nanomechanical Vibration for Rapid and Label-Free Assay of Epithelial Mesenchymal Transition.

Cancer is a profound danger to our life and health. The classification and related studies of epithelial and mesenchymal phenotypes of cancer cells are key scientific questions in cancer research. Here, we investigated cancer cell colonies from a mechanical perspective and developed an assay for classifying epithelial/mesenchymal cancer cell colonies using the biomechanical fingerprint in the form of "nanovibration" in combination with deep learning. The classification method requires only 1 s of vibration data and has a classification accuracy of nearly 92.5%. The method has also been validated for the screening of anticancer drugs. Compared with traditional methods, the method has the advantages of being nondestructive, label-free, and highly sensitive. Furthermore, we proposed a perspective that subcellular structure influences the amplitude and spectrum of nanovibrations and demonstrated it using experiments and numerical simulation. These findings allow internal changes in the cell colony to be manifested by nanovibrations. This work provides a perspective and an ancillary method for cancer cell phenotype diagnosis and promotes the study of biomechanical mechanisms of cancer progression.

EHD2, a novel HIF target gene, is a promising biomarker in clear cell renal cell carcinoma.

OBJECTIVES: The aim of the present study was to determine the clinical value of a novel hypoxia-inducible factor (HIF) target EH domain-containing protein 2 (EHD2) for predicting the outcome of patients with clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: GEPIA public database was searched to determine a possible association between HIF2Α and EHD protein family members, and kidney renal clear cell carcinoma data were used to find the expression profile of EHD proteins in ccRCC samples. A tissue microarray from 70 ccRCC samples was used for immunohistochemical analysis to determine the specific expression pattern of EHD2 in ccRCC samples. In addition, univariate and multivariate analyses were performed to assess the utility of EHD2 as an independent prognostic factor for ccRCC. RESULTS: EHD protein family members were all found to be significantly correlated with HIF2Α expression in ccRCC. However, EHD2 was the only protein that was observed to be overexpressed in ccRCC cancer tissues compared with normal tissues. EHD2 and HIF2Α mRNA expression levels were found to be higher in cancer tissues compared with those in adjacent normal tissue according to reverse transcription-quantitative PCR analysis. Among the 70 patients with ccRCC, EHD2 was overexpressed in 52.8% (37/70). Subsequently, EHD2 was found to be significantly associated with both overall survival (P=0.016) and disease-free survival (P=0.029). Furthermore, by multivariate analysis, EHD2 was an independent prognostic factor for patients with ccRCC. CONCLUSION: EHD2 is a novel HIF target, based on a relatively large sample of EHD2 research in patients with ccRCC. Furthermore, our study provided evidence that EHD2 can serve as a promising biomarker for predicting ccRCC outcome.

Universal method using a pre-deformed reference subset to eliminate the interpolation bias in digital image correlation.

The high measurement accuracy of the digital image correlation (DIC) method is derived from the sub-pixel registration algorithm, which interpolates the intensities at the sub-pixel position in the image. The displacement error caused by the interpolation is a systematic bias in the DIC method, known as the sinusoidal bias in the sub-pixel translation experiment. Although the interpolation bias has been well researched, there is a lack of a universal method to eliminate interpolation bias. In this work, we propose a universal method to eliminate the interpolation bias using a pre-deformed reference subset; pixel points in the pre-deformed subset are deviated from the integer-pixel location. The purpose of the adjustment is to set the deformed pixel points at a specific position, so that the interpolation bias of all deformed pixel points cancels each other out, close to zero. The adjustment of the pre-deformed reference subset is related with the subset size and subset deformation. Numerical experiments including DIC challenge data and a real uniaxial tensile test were conducted to verify the effectiveness and universality of the proposed method, contributing to improved measurement accuracy. Considering the effect of pixel point location on the interpolation bias, this work proposes a universal method to eliminate the interpolation bias and provides a perspective to study DIC errors.

Dynamic response of the cell traction force to osmotic shock.

Osmotic pressure is vital to many physiological activities, such as cell proliferation, wound healing and disease treatment. However, how cells interact with the extracellular matrix (ECM) when subjected to osmotic shock remains unclear. Here, we visualize the mechanical interactions between cells and the ECM during osmotic shock by quantifying the dynamic evolution of the cell traction force. We show that both hypertonic and hypotonic shocks induce continuous and large changes in cell traction force. Moreover, the traction force varies with cell volume: the traction force increases as cells shrink and decreases as cells swell. However, the direction of the traction force is independent of cell volume changes and is always toward the center of the cell-substrate interface. Furthermore, we reveal a mechanical mechanism in which the change in cortical tension caused by osmotic shock leads to the variation in traction force, which suggests a simple method for measuring changes in cell cortical tension. These findings provide new insights into the mechanical force response of cells to the external environment and may provide a deeper understanding of how the ECM regulates cell structure and function. Traction force exerted by cells under hypertonic and hypotonic shocks. Scale bar, 200 Pa. Color bar, Pa. The black arrows represent the tangential traction forces.

Umami-BERT: An interpretable BERT-based model for umami peptides prediction.

Umami peptides have received extensive attention due to their ability to enhance flavors and provide nutritional benefits. The increasing demand for novel umami peptides and the vast number of peptides present in food call for more efficient methods to screen umami peptides, and further exploration is necessary. Therefore, the purpose of this study is to develop deep learning (DL) model to realize rapid screening of umami peptides. The Umami-BERT model was devised utilizing a novel two-stage training strategy with Bidirectional Encoder Representations from Transformers (BERT) and the inception network. In the pre-training stage, attention mechanisms were implemented on a large amount of bioactive peptides sequences to acquire high-dimensional generalized features. In the re-training stage, umami peptide prediction was carried out on UMP789 dataset, which is developed through the latest research. The model achieved the performance with an accuracy (ACC) of 93.23% and MCC of 0.78 on the balanced dataset, as well as an ACC of 95.00% and MCC of 0.85 on the unbalanced dataset. The results demonstrated that Umami-BERT could predict umami peptides directly from their amino acid sequences and exceeded the performance of other models. Furthermore, Umami-BERT enabled the analysis of attention pattern learned by Umami-BERT model. The amino acids Alanine (A), Cysteine (C), Aspartate (D), and Glutamicacid (E) were found to be the most significant contributors to umami peptides. Additionally, the patterns of summarized umami peptides involving A, C, D, and E were analyzed based on the learned attention weights. Consequently, Umami-BERT exhibited great potential in the large-scale screening of candidate peptides and offers novel insight for the further exploration of umami peptides.

Deep learning-based method for analyzing the optically trapped sperm rotation.

Optical tweezers exert a strong trapping force on cells, making it crucial to analyze the movement of trapped cells. The rotation of cells plays a significant role in their swimming patterns, such as in sperm cells. We proposed a fast deep-learning-based method that can automatically determine the projection orientation of ellipsoidal-like cells without additional optical design. This method was utilized for analyzing the planar rotation of trapped sperm cells using an optical tweezer, demonstrating its feasibility in extracting the rotation of the cell head. Furthermore, we employed this method to investigate sperm cell activity by examining variations in sperm rotation rates under different conditions, including temperature and laser output power. Our findings provide evidence for the effectiveness of this method and the rotation analysis method developed may have clinical potential for sperm quality evaluation.

Food safety in health: a model of extraction for food contaminants.

Contaminants are the critical targets of food safety supervision and risk assessment. In existing research, food safety knowledge graphs are used to improve the efficiency of supervision since they supply the relationship between contaminants and foods. Entity relationship extraction is one of the crucial technologies of knowledge graph construction. However, this technology still faces the issue of single entity overlap. This means that a head entity in a text description may have multiple corresponding tail entities with different relationships. To address this issue, this work proposes a pipeline model with neural networks for multiple relations enhanced entity pairs extraction. The proposed model can predict the correct entity pairs in terms of specific relations by introducing the semantic interaction between relation identification and entity extraction. We conducted various experiments on our own dataset FC and on the open public available data set DuIE2.0. The results of experiments show our model reaches the state-of-the-art, and the case study indicates our model can correctly extract entity-relationship triplets to release the problem of single entity overlap.

Prediction of Safety Risk Levels of Benzopyrene Residues in Edible Oils in China Based on the Variable-Weight Combined LSTM-XGBoost Prediction Model.

To assess and predict the food safety risk of benzopyrene (BaP) in edible oils in China, this study collected national sampling data of edible oils from 20 Chinese provinces and their prefectures in 2019, and constructed a risk assessment model of BaP in edible oils with consumption data. Initially, the k-means algorithm was used for risk classification; then the data were pre-processed and trained to predict the data using the Long Short-Term Memory (LSTM) and the eXtreme Gradient Boosting (XGBoost) models, respectively, and finally, the two models were combined using the inverse error method. To test the effectiveness of the prediction model, this study experimentally validated the model according to five evaluation metrics: root mean square error (RMSE), mean absolute error (MAE), precision, recall, and F1 score. The variable-weight combined LSTM-XGBoost prediction model proposed in this paper achieved a precision of 94.62%, and the F1 score value reached 95.16%, which is significantly better than other neural network models; the results demonstrate that the prediction model has certain stability and feasibility. Overall, the combined model used in this study not only improves the accuracy but also enhances the practicality, real-time capabilities, and expandability of the model.

Real-time profile measurement method for a large-scale satellite antenna.

To improve the detection capability of satellite-based synthetic aperture radar, a large antenna array with a length scale of 100 meters is urgently needed. However, the structural deformation of the large antenna leads to phase errors, which will significantly reduce the antenna gain; hence, real-time and high-precision profile measurements of the antenna are essential for active compensation of the phase and thus improving the antenna gain. Nevertheless, the conditions of antenna in-orbit measurements are rather severe because of limited installation locations of measurement instruments, large areas, and long distance to be measured, and unstable measurement environments. To deal with the issues, we propose a three-dimensional displacement measurement method for the antenna plate based on laser distance measuring and digital image correlation (DIC). The proposed method uses the DIC method to retrieve the in-plane displacement information in combination with a laser range finder to provide depth information. A Scheimpflug camera is used to overcome the limitation of the depth of field of traditional cameras and enable clear imaging of the full field. Moreover, a vibration compensation scheme is proposed to eliminate the measurement error of the target displacement caused by the random vibration (within 0.01°) of the camera support rod. The results of the experiment in a laboratory setting show that the proposed method can effectively eliminate the measurement error caused by camera vibration (50 mm) and reduce the displacement measurement error to within 1 mm with a measurement range of 60 m, which can meet the measurement requirements of next-generation large satellite antennas.

Autoformer-Based Model for Predicting and Assessing Wheat Quality Changes of Pesticide Residues during Storage.

Proper grain storage plays a critical role in maintaining food quality. Among a variety of grains, wheat has emerged as one of the most important grain reserves globally due to its short growing period, high yield, and storage resistance. To improve the quality assessment of wheat during storage, this study collected and analyzed monitoring data from more than 20 regions in China, including information on storage environmental parameters and changes in wheat pesticide residue concentrations. Based on these factors, an Autoformer-based model was developed to predict the changes in wheat pesticide residue concentrations during storage. A comprehensive wheat quality assessment index Q was set for the predicted and true values of pesticide residue concentrations, then combined with the K-means++ algorithm to assess the quality of wheat during storage. The results of the study demonstrate that the Autoformer model achieved the optimal prediction results and the smallest error values. The mean absolute error (MAE) and the other four error values are 0.11017, 0.01358, 0.04681, 0.11654, and 0.13005. The findings offer technical assistance and a scientific foundation for enhancing the quality of stored wheat.

Prediction of Food Safety Risk Level of Wheat in China Based on Pyraformer Neural Network Model for Heavy Metal Contamination.

Heavy metal contamination in wheat not only endangers human health, but also causes crop quality degradation, leads to economic losses and affects social stability. Therefore, this paper proposes a Pyraformer-based model to predict the safety risk level of Chinese wheat contaminated with heavy metals. First, based on the heavy metal sampling data of wheat and the dietary consumption data of residents, a wheat risk level dataset was constructed using the risk evaluation method; a data-driven approach was used to classify the dataset into risk levels using the K-Means++ clustering algorithm; and, finally, on the constructed dataset, Pyraformer was used to predict the risk assessment indicator and, thus, the risk level. In this paper, the proposed model was compared to the constructed dataset, and for the dataset with the lowest risk level, the precision and recall of this model still reached more than 90%, which was 25.38-4.15% and 18.42-5.26% higher, respectively. The model proposed in this paper provides a technical means for hierarchical management and early warning of heavy metal contamination of wheat in China, and also provides a scientific basis for dynamic monitoring and integrated prevention of heavy metal contamination of wheat in farmland.

FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments.

The storage environment can significantly impact paddy quality, which is vital to human health. Changes in storage can cause growth of fungi that affects grain quality. This study analyzed grain storage monitoring data from over 20 regions and found that five factors are essential in predicting quality changes during storage. The study combined these factors with the FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) model and k-medoids algorithm to construct a paddy quality change prediction model and a grading evaluation model, which showed the highest accuracy and lowest error in predicting quality changes during paddy storage. The results emphasize the need for monitoring and controlling the storage environment to preserve grain quality and ensure food safety.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes.

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.

Tumor tissue microorganisms are closely associated with tumor immune subtypes.

Studies have found that different immune subtypes are present in the same tumor. Different tumor subtypes have different tumor microenvironments (TME). This means that the efficacy of immunotherapy in actual applications will, therefore, have different results. Existing tumor immune subtype studies have mostly focused on immune cells, stromal cells, genes and molecules without considering the presence of microbes. Some studies have shown that microflora can strongly promote many gastrointestinal cancers. The microbiome has, therefore, become an important biomarker and regulatory factor of cancer progression and therapeutic responses. In addition, the presence of microflora can strongly regulate the host immune system, indirectly affecting tumor growth. Taken together, it is important to study the relationships that develop among tumor tissue microorganisms, tumor immune subtype, and the TME. In this study, correlations between microbial abundance, immune cell infiltration, immune gene expression and tumor immune subtype were studied. To accomplish this, tissue microorganisms and immune cell ratios with significant differences between the different cancers were obtained by comparing 203 gastric cancer and intestinal cancer samples. Two immune subtypes of intestinal samples were obtained by K-means clustering algorithm and tissue microorganisms, immune cell ratios and immune-related genes with significant differences between different immune subtypes were screened through Wilcoxon rank sum test. The results showed that Clostridioides difficile, Aspergillus fumigatus, Yarrowia lipolytica, and Fusarium pseudograminearum were all closely associated with the identified tumor immune subtypes. Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/IMM-subtype.git.

A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments.

Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models.

Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China.

Focused supervision and early warning of heavy metal (HM)-contaminated rice areas can effectively protect people's livelihood security and maintain social stability. To improve the accuracy of risk prediction, an Informer-based safety risk prediction model for HMs in rice is constructed in this paper. First, based on the national sampling data and residential consumption statistics of rice, we construct a dataset of evaluation indicators that can characterize the level of rice safety risk so as to form a safety risk space. Second, based on the K-medoids clustering algorithm, we classify the rice safety risk space into levels. Finally, we use the Informer neural network model to predict the safety risk indicators of rice in each province so as to predict the safety risk level. This study compares the prediction accuracy of a self-constructed dataset of rice safety risk assessment indicators. The experimental results show that the prediction precision of the method proposed in this paper reaches 99.17%, 91.77%, and 91.33% for low, medium, and high risk levels, respectively. The model provides technical support and a scientific basis for screening the time and area of HM contamination of rice, which needs focus.

Nanomechanical assay for ultrasensitive and rapid detection of SARS-CoV-2 based on peptide nucleic acid.

The massive global spread of the COVID-19 pandemic makes the development of more effective and easily popularized assays critical. Here, we developed an ultrasensitive nanomechanical method based on microcantilever array and peptide nucleic acid (PNA) for the detection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) RNA. The method has an extremely low detection limit of 0.1 fM (105 copies/mL) for N-gene specific sequence (20 bp). Interestingly, it was further found that the detection limit of N gene (pharyngeal swab sample) was even lower, reaching 50 copies/mL. The large size of the N gene dramatically enhances the sensitivity of the nanomechanical sensor by up to three orders of magnitude. The detection limit of this amplification-free assay method is an order of magnitude lower than RT-PCR (500 copies/mL) that requires amplification. The non-specific signal in the assay is eliminated by the in-situ comparison of the array, reducing the false-positive misdiagnosis rate. The method is amplification-free and label-free, allowing for accurate diagnosis within 1 h. The strong specificity and ultra-sensitivity allow single base mutations in viruses to be distinguished even at very low concentrations. Also, the method remains sensitive to fM magnitude lung cancer marker (miRNA-155). Therefore, this ultrasensitive, amplification-free and inexpensive assay is expected to be used for the early diagnosis of COVID-19 patients and to be extended as a broad detection tool. Electronic Supplementary Material: Supplementary material (experimental section, N gene sequences and all nucleic acid sequences used in the study, Figs. S1-S6, and Tables S1-S3) is available in the online version of this article at 10.1007/s12274-022-4333-3.

Fabrication and characterization of starch-lipid complexes using chain-elongated waxy corn starches as substrates.

In this study, waxy corn starch (WCS) was enzymatically modified by amylosucrase, followed by complexation with lauric acid (LA) to produce starch-lipid complexes. Compared to the native WCS with average chain length (CL¯) of 25.4, the amylosucrease-modified WCSs showed a significantly higher CL¯ ranging from 29.3 to 52.5. The complexation with lauric acid inhibited the reassociation of starch chains, producing V-type complexes with crystallinity reached as much as 42.4 %. Besides, the melting of V-type complexes presented endothermic peaks at Tp of 55.1-60.4 °C, and thermal stability of V-type complexes had a negative correlation with the V-type crystallinity. In vitro digestion implies that the formation of V-type complexes gradually increased the content of rapidly digestible starch and accordingly decreased the content of resistant starch. This study may provide an efficient technology to produce V-type starch-lipid complexes with controllable physical and digestion properties using waxy starch as substrate.