Tea's Characteristic Components Eliminate Acrylamide in the Maillard Model System.

This study investigated the effects of various characteristic components of tea-theaflavins, catechins, thearubigins, theasinensins, theanine, catechin (C), catechin gallate (CG), epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), epigallocatechin gallate (EGCG), gallocatechin (GC), and gallocatechin gallate (GCG)-on acrylamide formation. The results revealed that most of tea's characteristic components could significantly eliminate acrylamide, ranked from highest to lowest as follows: GC (55.73%) > EC (46.31%) > theaflavins (44.91%) > CG (40.73%) > thearubigins (37.36%) > ECG (37.03%) > EGCG (27.37%) > theabrownine (22.54%) > GCG (16.21%) > catechins (10.14%) > C (7.48%). Synergistic elimination effects were observed with thearubigins + EC + GC + CG, thearubigins + EC + CG, thearubigins + EC + GC, theaflavins + GC + CG, and thearubigins + theaflavins, with the reduction rates being 73.99%, 72.67%, 67.62%, 71.03%, and 65.74%, respectively. Tea's components reduced the numbers of persistent free radicals to prevent acrylamide formation in the model system. The results provide a theoretical basis for the development of low-acrylamide foods and the application of tea resources in the food industry.

Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM.

Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficient µ ' s and absorption coefficient µ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and µ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for µ a and less than 10% for µ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

Ultra-Tough Waterborne Polyurethane-Based Graft-Copolymerized Piezoresistive Composite Designed for Rehabilitation Training Monitoring Pressure Sensors.

Effective training is crucial for patients who need rehabilitation for achieving optimal recovery and reducing complications. Herein, a wireless rehabilitation training monitoring band with a highly sensitive pressure sensor is proposed and designed. It utilizes polyaniline@waterborne polyurethane (PANI@WPU) as a piezoresistive composite material, which is prepared via the in situ grafting polymerization of PANI on the WPU surface. WPU is designed and synthesized with tunable glass transition temperatures ranging from -60 to 0 °C. Dipentaerythritol (Di-PE) and ureidopyrimidinone (UPy) groups are introduced, endowing the material with good tensile strength (14.2 MPa), toughness (62 MJ-1 m-3 ), and great elasticity (low permanent deformation: 2%). Di-PE and UPy enhance the mechanical properties of WPU by increasing the cross-linking density and crystallinity. Combining the toughness of WPU and the high-density microstructure derived by hot embossing technology, the pressure sensor exhibits high sensitivity (168.1 kPa-1 ), fast response time (32 ms), and excellent stability (10 000 cycles with 3.5% decay). In addition, the rehabilitation training monitoring band is equipped with a wireless Bluetooth module, which can be easily applied to monitor the rehabilitation training effect of patients using an applet. Therefore, this work has the potential to significantly broaden the application of WPU-based pressure sensors for rehabilitation monitoring.

Aggregating-agent-assisted surface-enhanced Raman spectroscopy-based detection of acrylamide in fried foods: A case study with potato chips.

This study developed a simple, rapid, stable, and reliable technique for acrylamide (AAm) detection through surface-enhanced Raman scattering (SERS) on an AgNPs substrate with an aggregating agent. Specifically, the agglomeration effects of five types of salt solutions (NaCl, KCl, MgCl2, Na2SO4, and MgSO4) were investigated at different concentrations and optimized using an orthogonal experiment. The optimal amounts of the aggregating agent, analytes, and AgNPs were 4, 4, and 12 µL, respectively. A linear relationship (peak area I1449 = 7.4197x + 5984.8, R2 = 0.9971) between the characteristic peak area and AAm concentration was established in the range of 10 to 500 µg/L, and the LOD was 2.5 µg/L. The recoveries and relative standard deviations in the analysis of potato chips samples were 94.67 %-117.50 % and 8.43 %-12.29 %, respectively. The results of the proposed method were consistent with those obtained by LC-MS/MS method. This study demonstrated that SERS has excellent potential for application in the qualitative and quantitative analyses of AAm in fried foods.

Hexachloropentadiene in Soil, Air, and Biota Around an Agrochemical Factory: Concentrations, Distribution, and Risk Evaluation.

Hexachloropentadiene (HCPD) is a highly toxic compound that is mainly used for preparation of organochlorine insecticides. To investigate HCPD contamination of the environment during pesticide processing, 153 air, soil, and biota samples were collected around an agrochemical factory in different seasons of 1 year and analyzed for HCPD. The HCPD concentrations were 0.01-12.7 ng/m3 (average 2.60 ng/m3) in the air samples and 0.14-51.5 ng/g (average 4.11 ng/g) in the soil samples. HCPD concentrations were highest within 1 km north of the production site, which was in the downwind direction of the factory and storage tanks, especially in autumn and winter. Soil-air exchange analysis showed that HCPD was deposited from air to soil with a flux of 0.003 to 0.20 ng/(m2 d) throughout the year. The dismantling of obsolete equipment accelerated the release of HCPD into the air and increased the amount of HCPD deposited in the soil. HPCD concentration ranges were 0.44-55.7 ng/g dry weight [d.w.] (average 22.2 ng/g d.w.) and 6.69-91.4 ng/g d.w. (average 26.2) in locally grown rice and wheat, respectively. The concentration range was 12.1-1596 ng/g lipid weight (average 560 ng/g lipid weight) in local organisms, except for chicken. In tissues from locally raised chicken, the HCPD concentrations decreased in the order of gizzard, liver, heart, and meat. HCPD was amplified through a short food chain (soil, Vigna unguiculata leaves, larvae of Pieris rapae, and chicken), and the bioaccumulation factor gradually increased over a range of 1.19-25.1 (mean 9.81).

Enhanced Contactless Salt-Collecting Solar Desalination.

Solar desalination is expected to solve the problem of global water shortage. Yet its stability is plagued by salt accumulation. Here, a paper-based thermal radiation-enabled evaporation system (TREES) is demonstrated to achieve sustainable and highly efficient salt-collecting desalination, featuring a dynamic evaporation front based on the accumulated salt layer where water serves as its own absorber via energy down-conversion. When processing 7 wt % brine, it continuously evaporates water at a high rate─2.25 L m-2 h-1 under 1 sun illumination─which is well beyond the input solar energy limit for over 366 h. It is revealed that such enhanced evaporation arises from the unique vertical evaporation wall of the paper-TREES, which captures the thermal energy from the heated bottom efficiently and gains extra energy from the warmer environment. These findings provide novel insights into the design of next-generation salt-harvesting solar evaporators and take a step further to advance their applications in green desalination.

A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins.

Geographic-label is a remarkable feature for Chinese tea products. In this study, the UHPLC-Q/TOF-MS-based metabolomics approach coupled with chemometrics was used to determine the five narrow-geographic origins of Keemun black tea. Thirty-nine differentiated compounds (VIP > 1) were identified, of which eight were quantified. Chemometric analysis revealed that the linear discriminant analysis (LDA) classification accuracy model is 91.7%, with 84.7% cross-validation accuracy. Three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF) and support vector machine (SVM), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under the curve (AUC). The recognition of RF, SVM and FNN for Keemun black tea from five narrow-geographic origins were 87.5%, 94.44%, and 100%, respectively. Importantly, FNN exhibited an excellent classification effect with 100% accuracy. The results indicate that metabolomics fingerprints coupled with chemometrics can be used to authenticate the narrow-geographic origins of Keemun black teas.

Wireless Rehabilitation Training Sensor Arrays Made with Hot Screen-Imprinted Conductive Hydrogels with a Low Percolation Threshold.

Herein, we propose a highly sensitive wireless rehabilitation training ball with a piezoresistive sensor array for patients with Parkinson's disease (PD). The piezoresistive material is a low percolation threshold conductive hydrogel which is formed with polypyrrole (PPy) nanofibers (NFs) as a conductive filler derived from a polydopamine (PDA) template. The proton acid doping effect and molecular template of PDA are essential for endowing PPy NFs with a high aspect ratio, leading to a low percolation threshold (∼0.78 vol %) and a low Young's 004Dodulus of 37.69 kPa and hence easy deformation. The piezoresistive sensor exhibited a static and dynamic stability of 10,000 s and 15,000 cycle times, respectively. This stability could be attributed to the increased hydrophilicity of conductive fillers, enhancing the interfacial strength between the conductive filler and the matrix. The interaction between the PDA-PPy NFs and the hydrogel matrix endows the hydrogel with toughness and ensures the stability of the device. Additionally, the microdome structure of the conductive hydrogel, produced by hot screen-imprinting, dramatically improves the sensitivity of the piezoresistive sensor (∼856.14 kPa-1). The microdome conductive hydrogel can distinguish a subtle pressure of 15.40 Pa compared to the control hydrogel without a microstructure. The highly sensitive piezoresistive sensor has the potential to monitor the hand-grip force, which is not well controlled by patients with PD. The rehabilitation training ball assembled with a sensor array on the surface and a wireless chip for communication inside is built and used to monitor the pressure in real time through the WeChat applet. Thus, this work has significantly broadened the application of hydrogel-based flexible piezoresistive sensors for human activity monitoring, which provides a promising strategy to realize next-generation electronics.

Evaluation of Rearing Factors Affecting Clanis bilineata tsingtauica Mell Larvae Fed by Susceptible Soybean Variety NN89-29 in Spring and Autumn Sowing.

Clanis bilineata tsingtauica Mell is a nutritious edible insect. In the present study, soybean variety NN89-29 susceptible to leaf-feeding insects was used as the experiment material to evaluate the rearing factors affecting the production of C. bilineata tsingtauica in spring and autumn sowing. We artificially inoculated C. bilineata tsingtauica eggs to soybean plants, and the relevant indexes of larvae and soybean plants were recorded. The main results from spring sowing were as follows: (1) Larval number, single larval weight and plot-larval weight were all higher in the V6 stage (sixth trifoliolate) than those in the R3 stage (beginning pod) of inoculated eggs. (2) Larval number, single larval weight and plot-larval weight significantly decreased under higher planting density. Meanwhile, the soybean plant height and internode length increased, while the main stem node number of soybean decreased under higher planting density. (3) Single larval weight and plot-larval weight were not significantly affected by different numbers of eggs inoculated, but the larval number was significantly affected. Under autumn sowing conditions: (1) The larvae production and soybean plant growth were not significantly affected by covering the top of the net house with plastic film to isolate rainwater. (2) More eggs inoculated were associated with higher plot-larval weight. Conversely, fewer eggs inoculated resulted in a higher proportion of single larvae weight more than or equal to 6 g (≥6 g). Compared to spring sowing, the low biomass of autumn-sown soybean plants did not provide sufficient food for C. bilineata tsingtauica growth, and competition for food resulted in lower larval number, single larval weight and plot-larval weight.

Efficient isolation and purification of tissue-specific protoplasts from tea plants (Camellia sinensis (L.) O. Kuntze).

BACKGROUND: Plant protoplasts constitute unique single-cell systems that can be subjected to genomic, proteomic, and metabolomic analysis. An effective and sustainable method for preparing protoplasts from tea plants has yet to be established. The protoplasts were osmotically isolated, and the isolation and purification procedures were optimized. Various potential factors affecting protoplast preparation, including enzymatic composition and type, enzymatic hydrolysis duration, mannitol concentration in the enzyme solution, and iodixanol concentration, were evaluated. RESULTS: The optimal conditions were 1.5% (w/v) cellulase and 0.4-0.6% (w/v) macerozyme in a solution containing 0.4 M mannitol, enzymatic hydrolysis over 10 h, and an iodixanol concentration of 65%. The highest protoplast yield was 3.27 × 106 protoplasts g-1 fresh weight. As determined through fluorescein diacetate staining, maximal cell viability was 92.94%. The isolated protoplasts were round and regularly shaped without agglomeration, and they were less than 20 µm in diameter. Differences in preparation, with regard to yield and viability in the tissues (roots, branches, and leaves), cultivars, and cultivation method, were also observed. CONCLUSIONS: In summary, we reported on a simple, efficient method for preparing protoplasts of whole-organ tissue from tea plant. The findings are expected to contribute to the rapid development of tea plant biology.

Prevalence of Comorbid Personality Disorder in Psychotic and Non-psychotic Disorders.

Introduction: The burden of personality disorders (PDs) in China is large and the focus on mental health services is increasing. However, there is a lack of sufficient evidence regarding the prevalence of comorbid PD in psychotic and non-psychotic disorders, and whether PDs have different distributions. We aimed to investigate the PD comorbidity distribution pattern between psychotic and non-psychotic disorders using a clinical population-based study. Materials and Methods: We conducted a cross-sectional study of 1,497 patients in Shanghai. PDs were screened using the Personality Diagnostic Questionnaire Fourth Edition Plus (PDQ-4+). All patients were interviewed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) Axis II (SCID-II). We compared the differences in PD comorbidities classified as the 10 types of PDs in the DSM-IV, in 531 patients with psychosis and 966 patients with non-psychotic disorders. Results: More than one-third (37%) of patients with psychotic disorders met the criteria of at least one PD. Approximately half (46%) of patients with non-psychotic disorders met the criteria of at least one PD. Patients with non-psychotic disorders were more likely to meet the criteria of borderline (χ2 = 20.154, p < 0.001) and obsessive-compulsive PD (χ2 = 21.164, p < 0.001) diagnoses compared to those with psychotic disorders. In contrast, patients with psychotic disorders were more likely to meet the criteria of paranoid (χ2 = 11.144, p = 0.001) and schizotypal PD (χ2 = 14.004, p < 0.001) diagnoses than those with non-psychotic disorders. Discussion: PD comorbidity is common and comorbidity distribution pattern is varied in patients with psychotic and non-psychotic disorders, implicating the development of specific strategies that could screen and assess PDs in psychiatric clinical practice.