Kinematic analysis and process optimization of root-cutting systems in field harvesting of garlic based on computer simulation technology.

Introduction: Root cutting is an important process in garlic field harvesting but is the weakest link in the full mechanization of garlic production. To improve the current situation of technological backwardness and poor operational quality of mechanized garlic root-cutting in the main garlic-producing regions of China, this study combined the physical characteristics and agronomic requirements of garlic plants, and proposed an innovative floating root-cutting technology for garlic combine harvesters that enables the top alignment of bulb, adaptive profiling floating of cutter, and embedded cutting of roots. Methods: Through the kinematic analysis of the floating cutting process, the coordinate equations of the initial contact point of the bulb, the mathematical model of the floating displacement of the cutting component. Using computer simulation techniques, the dynamic simulation study of the floating cutting process was carried out in the rigid-flexible coupling numerical simulation model of root-cutting mechanism and garlic plant. The influence law of garlic conveying speed, extension spring preload force and stiffness on the floating displacement of the cutting component and the angular velocity of swing arm reset and its formation causes were analyzed by a single-factor simulation test. The key operating parameters of the root-cutting mechanism were optimized through the computerized virtual orthogonal test and fuzzy comprehensive evaluation. Results and discussion: The significance of the factors affecting the floating cutting performance decreased in the following order: extension spring preload force, garlic conveying speed and extension spring stiffness. The optimal parameter combination of the root cutting mechanism obtained from the optimization were as follow: extension spring preload force was 16 N, garlic conveying speed was 0.8 m/s, and extension spring stiffness was 215 N/m. Tests conducted with the optimal parameter combination yielded a root excision rate of 92.72%, which meets the requirements of Chinese garlic field harvesting quality. This study provides computer simulation optimization methods for the optimal design of the root-cutting mechanism, and also provides technical and equipment support for the full mechanization of garlic production in China.

Improving the Autofluorescence of Lophira alata Woody Cells via the Removal of Extractives.

The autofluorescence phenomenon is an inherent characteristic of lignified cells. However, in the case of Lophira alata (L. alata), the autofluorescence is nearly imperceptible during occasional fluorescence observations. The aim of this study is to investigate the mechanism behind the quenching of lignin's autofluorescence in L. alata by conducting associated experiments. Notably, the autofluorescence image of L. alata observed using optical microscopy appears to be quite indistinct. Abundant extractives are found in the longitudinal parenchyma, fibers, and vessels of L. alata. Remarkably, when subjected to a benzene-alcohol extraction treatment, the autofluorescence of L. alata becomes progressively enhanced under a fluorescence microscope. Additionally, UV-Vis absorption spectra demonstrate that the extractives derived from L. alata exhibit strong light absorption within the wavelength range of 200-500 nm. This suggests that the abundant extractives in L. alata are probably responsible for the autofluorescence quenching observed in the cell walls. Moreover, the presence and quantity of these extractives have a significant impact on the fluorescence intensity of lignin in wood, resulting in a significant decrease therein. In future studies, it would be interesting to explore the role of complex compounds such as polyphenols or terpenoids, which are present in the abundant extractives, in interfering with the fluorescence quenching of lignin in L. alata.

Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning.

Premature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein-protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI.

STKD: Distilling Knowledge From Synchronous Teaching for Efficient Model Compression.

Knowledge distillation (KD) transfers discriminative knowledge from a large and complex model (known as teacher) to a smaller and faster one (known as student). Existing advanced KD methods, limited to fixed feature extraction paradigms that capture teacher's structure knowledge to guide the training of the student, often fail to obtain comprehensive knowledge to the student. Toward this end, in this article, we propose a new approach, synchronous teaching knowledge distillation (STKD), to integrate online teaching and offline teaching for transferring rich and comprehensive knowledge to the student. In the online learning stage, a blockwise unit is designed to distill the intermediate-level knowledge and high-level knowledge, which can achieve bidirectional guidance of the teacher and student networks. Intermediate-level information interaction provides more supervisory information to the student network and is useful to enhance the quality of final predictions. In the offline learning stage, the STKD approach applies a pretrained teacher to further improve the performance and accelerate the training process by providing prior knowledge. Trained simultaneously, the student learns multilevel and comprehensive knowledge by incorporating online teaching and offline teaching, which combines the advantages of different KD strategies through our STKD method. Experimental results on the SVHN, CIFAR-10, CIFAR-100, and ImageNet ILSVRC 2012 real-world datasets show that the proposed method achieves significant performance improvements compared with the state-of-the-art methods, especially with satisfying accuracy and model size. Code for STKD is provided at https://github.com/nanxiaotong/STKD.

Integrated metabolomics and transcriptomics to reveal biomarkers and mitochondrial metabolic dysregulation of premature ovarian insufficiency.

Background: The metabolic characteristics of premature ovarian insufficiency (POI), a reproductive endocrine disease characterized by abnormal sex hormone metabolism and follicle depletion, remain unclear. Metabolomics is a powerful tool for exploring disease phenotypes and biomarkers. This study aims to identify metabolic markers and construct diagnostic models, and elucidate the underlying pathological mechanisms for POI. Methods: Non-targeted metabolomics was utilized to characterize the plasma metabolic profile of 40 patients. The metabolic markers were identified through bioinformatics and machine learning, and constructed an optimal diagnostic model by classified multi-model analysis. Enzyme-linked immunosorbent assay (ELISA) was used to verify antioxidant indexes, mitochondrial enzyme complexes, and ATP levels. Finally, integrated transcriptomics and metabolomics were used to reveal the dysregulated pathways and molecular regulatory mechanisms of POI. Results: The study identified eight metabolic markers significantly correlated with ovarian reserve function. The XGBoost diagnostic model was developed based on six machine learning models, demonstrating its robust diagnostic performance and clinical applicability through the evaluation of receiver operating characteristic (ROC) curve, decision curve analysis (DCA), calibration curve, and precise recall (PR) curve. Multi-omics analysis showed that mitochondrial respiratory chain electron carrier (CoQ10) and enzyme complex subunits were down-regulated in POI. ELISA validation revealed an elevation in oxidative stress markers and a reduction in the activities of antioxidant enzymes, CoQ10, and mitochondrial enzyme complexes in POI. Conclusion: Our findings highlight that mitochondrial dysfunction and energy metabolism disorders are closely related to the pathogenesis of POI. The identification of metabolic markers and predictive models holds significant implications for the diagnosis, treatment, and monitoring of POI.

Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment.

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning-a convolutional neural network-is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.

Direct measuring of single-heterogeneous bubble nucleation mediated by surface topology.

Heterogeneous bubble nucleation is one of the most fundamental interfacial processes ranging from nature to technology. There is excellent evidence that surface topology is important in directing heterogeneous nucleation; however, deep understanding of the energetics by which nanoscale architectures promote nucleation is still challenging. Herein, we report a direct and quantitative measurement of single-bubble nucleation on a single silica nanoparticle within a microsized droplet using scanning electrochemical cell microscopy. Local gas concentration at nucleation is determined from finite element simulation at the corresponding faradaic current of the peak-featured voltammogram. It is demonstrated that the criteria gas concentration for nucleation first drops and then rises with increasing nanoparticle radius. An optimum nanoparticle radius around 10 nm prominently expedites the nucleation by facilitating the special topological nanoconfinements that consequently catalyze the nucleation. Moreover, the experimental result is corroborated by our theoretical calculations of free energy change based on the classic nucleation theory. This study offers insights into the impact of surface topology on heterogenous nucleation that have not been previously observed.

COVID-19 lockdown introduces human mobility pattern changes for both Guangdong-Hong Kong-Macao greater bay area and the San Francisco bay area.

In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.

Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing.

Garlic root cutting is generally performed manually; it is easy for the workers to sustain hand injuries, and the labor efficiency is low. However, the significant differences between individual garlic bulbs limit the development of an automatic root cutting system. To address this problem, a deep learning model based on transfer learning and a low-cost computer vision module was used to automatically detect garlic bulb position, adjust the root cutter, and cut garlic roots on a garlic root cutting test bed. The proposed object detection model achieved good performance and high detection accuracy, running speed, and detection reliability. The visual image of the output layer channel of the backbone network showed the high-level features extracted by the network vividly, and the differences in learning of different networks clearly. The position differences of the cutting lines predicted by different backbone networks were analyzed through data visualization. The excellent and stable performance indicated that the proposed model had learned the correct features in the data of different brightness. Finally, the root cutting system was verified experimentally. The results of three experiments with 100 garlic bulbs each indicated that the mean qualified value of the system was 96%. Therefore, the proposed deep learning system can be applied in garlic root cutting which belongs to food primary processing.

Structural basis of Q-dependent transcription antitermination.

Bacteriophage Q protein engages σ-dependent paused RNA polymerase (RNAP) by binding to a DNA site embedded in late gene promoter and renders RNAP resistant to termination signals. Here, we report a single-particle cryo-electron microscopy (cryo-EM) structure of an intact Q-engaged arrested complex. The structure reveals key interactions responsible for σ-dependent pause, Q engagement, and Q-mediated transcription antitermination. The structure shows that two Q protomers (QI and QII) bind to a direct-repeat DNA site and contact distinct elements of the RNA exit channel. Notably, QI forms a narrow ring inside the RNA exit channel and renders RNAP resistant to termination signals by prohibiting RNA hairpin formation in the RNA exit channel. Because the RNA exit channel is conserved among all multisubunit RNAPs, it is likely to serve as an important contact site for regulators that modify the elongation properties of RNAP in other organisms, as well.

Comparative pharmacokinetics of florfenicol in healthy and Pasteurella multocida-infected Gaoyou ducks.

Pasteurella multocida is the causative agent of fowl cholera, and florfenicol (FF) has potent antibacterial activity against P. multocida and is widely used in the poultry industry. In this study, we established a P. multocida infection model in ducks and studied the pharmacokinetics of FF in serum and lung tissues after oral administration of 30 mg/kg bodyweight. The maximum concentrations reached (Cmax) were lower in infected ducks (13.88 ± 2.70 µg/ml) vs. healthy control animals (17.86 ± 1.57 µg/ml). In contrast, the mean residence time (MRT: 2.35 ± 0.13 vs. 2.27 ± 0.18 hr) and elimination half-life (T½ß : 1.63 ± 0.08 vs. 1.57 ± 0.12 hr) were similar for healthy and diseased animals, respectively. As a result, the area under the concentration curve for 0-12 hr (AUC0-12 hr ) for FF in healthy ducks was significantly greater than that in infected ducks (49.47 ± 5.31 vs. 34.52 ± 8.29 µg hr/ml). The pharmacokinetic differences of FF in lung tissues between the two groups correlated with the serum pharmacokinetic differences. The Cmax and AUC0-12 hr values of lung tissue in healthy ducks were higher than those in diseased ducks. The concentration of FF in lung tissues was approximately 1.2-fold higher than that in serum both in infected and healthy ducks indicating that FF is effective in treating respiratory tract infections in ducks.

Delivery of miR-375 and doxorubicin hydrochloride by lipid-coated hollow mesoporous silica nanoparticles to overcome multiple drug resistance in hepatocellular carcinoma.

Multidrug resistance (MDR) due to overexpression of P-glycoprotein (P-gp) is a major obstacle that hinders the treatment of hepatocellular carcinoma (HCC). It has been shown that miR-375 inhibits P-gp expression via inhibition of astrocyte elevated gene-1 (AEG-1) expression in HCC, and induces apoptosis in HCC cells by targeting AEG-1 and YAP1. In this study, we prepared lipid-coated hollow mesoporous silica nanoparticles (LH) containing doxorubicin hydrochloride (DOX) and miR-375 (LHD/miR-375) to deliver the two agents into MDR HCC cells in vitro and in vivo. We found that LHD/miR-375 overcame drug efflux and delivered miR-375 and DOX into MDR HepG2/ADR cells or HCC tissues. MiR-375 delivered by LHD/miR-375 was taken up through phagocytosis and clathrin- and caveolae-mediated endocytosis. Following release from late endosomes, it repressed the expression of P-gp in HepG2/ADR cells. The synergistic effects of miR-375 and hollow mesoporous silica nanoparticles (HMSN) resulted in a profound increase in the uptake of DOX by the HCC cells and prevented HCC cell growth. Enhanced antitumor effects of LHD/miR-375 were also validated in HCC xenografts and primary tumors; however, no significant toxicity was observed. Mechanistic studies also revealed that miR-375 and DOX exerted a synergistic antitumor effect by promoting apoptosis. Our study illustrates that delivery of miR-375 using HMSN is a feasible approach to circumvent MDR in the management of HCC. It, therefore, merits further development for potential clinical application.

Cherubism With Bilateral Mandible and Maxilla Involvement: A Case Report.

Cherubism is a rare, nonneoplastic, self-limiting fibro-osseous that occurs in children. Affected children usually appear normal at birth. Lesions are characterized by the replacement of bone with fibrovascular tissue containing many multinucleated giant cells. Most studies have reported cherubism to be familial and with bilateral involvement of the mandibles. The authors describe a nonfamilial case of cherubism, involving both the mandible and the maxilla, in a 4-year-old female child with slowly enlarging, painless, symmetrical swelling of both cheeks.Cherubism is a rare disease that is usually limited to the mandible, but the maxilla may be involved. Computed tomography scan and biopsy are helpful for early diagnosis.

Nanocrystal-constructed mesoporous single-crystalline Co3O4 nanobelts with superior rate capability for advanced lithium-ion batteries.

In this paper, one-dimensional (1D) mesoporous single-crystalline Co3O4 nanobelts are synthesized by a facile hydrothermal method followed by calcination treatment. The as-prepared nanobelts have unique mesoporous structures, which are constructed by many interconnected nanocrystals with sizes of about 20-30 nm. And typical size of the nanobelts is in the range of 100-300 nm in width and up to several micrometers in length. The BET surface area of Co3O4 nanobelts is determined to be about 36.5 m² g⁻¹ with dominant pore diameter of 29.2 nm. Because of the 1D structure, mesoporous morphologies and scrupulous nanoarchitectures, the Co3O4 nanobelts show excellent electrochemical performances such as high storage capacity and superior rate capability. The specific capacity of Co3O4 nanobelts could remains over 614 mA h g⁻¹ at a current density of 1 A g⁻¹ after 60 cycles. Even at a high current density of 3 A g⁻¹, these Co3O4 nanobelts still could deliver a remarkable discharge capacity of 605 mA h g⁻¹ with good cycling stability.

[The accuracy of 3D-CT volume rendering for craniofacial linear measurements].

PURPOSE: This study was designed to determine the consistency of craniofacial measurements using spiral computed tomography volume rendering by computer systems and sliding caliper. METHODS: The study population consisted of 12 cadaver heads which were examined with spiral CT. The archived CT data were transferred to a workstation, and 3D-CT volume rendered images were generated using computer graphics tools. Linear measurements (n = 20), based upon conventional craniometric anatomical landmarks (n = 16) were made, by Display Tools and sliding caliper respectively. The consistency between the two measurements was analyzed by paired t test using SAS9.0 software package. RESULTS: The results demonstrated no statistically significant difference between imaging measurement and physical measurement, P > 0.05. CONCLUSIONS: There is significant consistency between spiral CT measurement and physical measurement for craniofacial linear distance. 3D-CT volume rendering images using craniometric measurements can be used for clinical and basic studies in stomatology.