Experimental Study on the Mechanisms of Soil Water-Solute- Heat Transport and Nutrient Loss Control

The release and migration of nutrients, pesticides, and other chemicals in the runoff from agricultural lands is not only an economic loss but a threat to the quality of our surface and groundwater. In contrast to pollution from point sources, pollution from non-point sources is often low in intensity but high in volume. The development of a physically based model to simulate the transport of soil solutes would provide a better understanding of transport mechanisms and assist in the development of effective methods to control the loss of nutrients from soils and the pollution of waterways. As a result, numerous studies have been conducted in this area. But due to the soil genesis and human activity, the process is very complex, which can have a great impact on soil water movement, solute transport, as well as nutrient loss. In this study, we determined water movement and solute and heat transport through columns of disturbed soil samples. We also carried out simulated rainfall experiments on an artificial slope to study the nutrient loss

Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Acknowledgments This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.Peer reviewedPostprin

COVID-19 vaccination and the risk of autoimmune diseases: a Mendelian randomization study

BackgroundIn recent times, reports have emerged suggesting that a variety of autoimmune disorders may arise after the coronavirus disease 2019 (COVID-19) vaccination. However, causality and underlying mechanisms remain unclear.MethodsWe collected summary statistics of COVID-19 vaccination and 31 autoimmune diseases from genome-wide association studies (GWAS) as exposure and outcome, respectively. Random-effects inverse variance weighting (IVW), MR Egger, weighted median, simple mode, and weighted mode were used as analytical methods through Mendelian randomization (MR), and heterogeneity and sensitivity analysis were performed.ResultsWe selected 72 instrumental variables for exposure (p < 5 × 10−6; r2 < 0.001, genetic distance = 10,000 kb), and MR analyses showed that COVID-19 vaccination was causally associated with an increased risk of multiple sclerosis (MS) (IVW, OR: 1.53, 95% CI: 1.065–2.197, p = 0.026) and ulcerative colitis (UC) (IVW, OR: 1.00, 95% CI: 1.000–1.003, p = 0.039). If exposure was refined (p < 5 × 10−8; r2 < 0.001, genetic distance = 10,000 kb), the associations became negative. No causality was found for the remaining outcomes. These results were robust to sensitivity and heterogeneity analyses.ConclusionOur study provided potential evidence for the impact of COVID-19 vaccination on the risk of MS and UC occurrence, but it lacks sufficient robustness, which could provide a new idea for public health policy

Segmentation of field grape bunches via an improved pyramid scene parsing network

With the continuous expansion of wine grape planting areas, the mechanization and intelligence of grape harvesting have gradually become the future development trend. In order to guide the picking robot to pick grapes more efficiently in the vineyard, this study proposed a grape bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic segmentation network for different varieties of grapes in the natural field environments. To this end, the Convolutional Block Attention Module (CBAM) attention mechanism and the atrous convolution were first embedded in the backbone feature extraction network of the PSPNet model to improve the feature extraction capability. Meanwhile, the proposed model also improved the PSPNet semantic segmentation model by fusing multiple feature layers (with more contextual information) extracted by the backbone network. The improved PSPNet was compared against the original PSPNet on a newly collected grape image dataset, and it was shown that the improved PSPNet model had an Intersection-over-Union (IoU) and Pixel Accuracy (PA) of 87.42% and 95.73%, respectively, implying an improvement of 4.36% and 9.95% over the original PSPNet model. The improved PSPNet was also compared against the state-of-the-art DeepLab-V3+ and U-Net in terms of IoU, PA, computation efficiency and robustness, and showed promising performance. It is concluded that the improved PSPNet can quickly and accurately segment grape bunches of different varieties in the natural field environments, which provides a certain technical basis for intelligent harvesting by grape picking robots

A new p-terphenyl derivative from the insect-derived fungus Aspergillus candidus Bdf-2 and the synergistic effects of terphenyllin

A new p-terphenyl derivative 4″-deoxy-2′-methoxyterphenyllin (1), along with six known p-terphenyl derivatives (2–7), a known flavonoid derivative dechlorochlorflavonin (8) and a known fellutanine A (9), were isolated from the insect-derived strain of the fungus Aspergillus candidus Bdf-2, associated with Blaptica dubia. The structure of 1 was established by the analysis of the 1D and 2D NMR and HR-ESI-MS spectra. Compounds 1–9 were evaluated for antibacterial activities against Staphylococcus aureus ATCC29213, Escherichia coli ATCC25922 and Ralstonia solanacearum, and for antioxidant activities. Synergistic effects of compound 2 with the other compounds were also investigated. As a result, compound 6 displayed the best antibacterial activities in all single compound with MIC value of 32 µg/mL against S. aureus ATCC29213 and R. solanacearum, respectively. However, no antibacterial effect against E. coli ATCC25922 was detected from any single compound. The combination of 2 + 6 exhibited obvious synergistic effect against S. aureus ATCC29213 and the MIC value was 4 µg/mL. Compound 6 also showed the best antioxidant activity as a single compound with an IC50 value of 17.62 µg/mL. Combinations of 5 + 6, 2 + 4 + 5 and 2 + 4 + 5 + 6 displayed synergistic effect and their antioxidant activities were better than that of any single compound

Interplay between moment-dependent and field-driven unidirectional magnetoresistance in CoFeB/InSb/CdTe heterostructures

Magnetoresistance effects are crucial for understanding the charge/spin transport as well as propelling the advancement of spintronic applications. Here we report the coexistence of magnetic moment-dependent (MD) and magnetic field-driven (FD) unidirectional magnetoresistance (UMR) effects in CoFeB/InSb/CdTe heterostructures. The strong spin-orbital coupling of InSb and the matched impedance at the CoFeB/InSb interface warrant a distinct MD-UMR effect at room temperature, while the interaction between the in-plane magnetic field and the Rashba effect at the InSb/CdTe interface induces the marked FD-UMR signal that dominates the high-field region. Moreover, owning to the different spin transport mechanisms, these two types of nonreciprocal charge transport show opposite polarities with respect to the magnetic field direction, which further enable an effective phase modulation of the angular-dependent magnetoresistance. Besides, the demonstrations of both the tunable UMR response and two-terminal spin-orbit torque-driven magnetization switching validate our CoFeB/InSb/CdTe system as a suitable integrated building block for multifunctional spintronic device design

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG

Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging

A Siamese Network-Based Method for Improving the Performance of Sleep Staging with Single-Channel EEG

Sleep staging is of critical significance to the diagnosis of sleep disorders, and the electroencephalogram (EEG), which is used for monitoring brain activity, is commonly employed in sleep staging. In this paper, we propose a novel method for improving the performance of sleep staging models based on Siamese networks, based on single-channel EEG. Our proposed method consists of a Siamese network architecture and a redesigned loss with distance metrics. Two encoders are used in the Siamese network to generate latent features of the EEG epochs, and the contrastive loss, which is also a distance metric, is used to compare the similarity or differences between EEG epochs from the same or different sleep stages. We evaluated our method on single-channel EEGs from different channels (Fpz-Cz and F4-EOG (left)) from two public datasets SleepEDF and MASS-SS3 and achieved the overall accuracies MF1 and Cohen’s kappa coefficient of 85.2%, 78.3% and 0.79 on SleepEDF and 87.2%, 82.1% and 0.81 on MASS-SS3. The results show that our method can significantly improve the performance of sleep staging models and outperform the state-of-the-art sleep staging methods. The performance of our method also confirms that the features captured by Siamese networks and distance metrics are useful for sleep staging