Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

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

MicroRNAs in Pathogenesis of Acute Kidney Injury

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression mainly by repressing their target gene translation. A large spectrum of human diseases is associated with significant changes in miRNAs. Many miRNAs are induced in diseases, whereas some others are downregulated. The significance of miRNAs has been demonstrated in renal development and physiology, and in major kidney diseases such as acute kidney injury (AKI). Recent studies have further implicated specific miRNAs in the pathogenesis of AKI. miRNAs also have the potential to become new diagnostic biomarkers of AKI. Further investigation will identify the key pathogenic miRNAs in various types of AKI and test miRNA-based therapeutics and diagnosis. © 2016 S. Karger AG, Basel

Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

Author Contributions ZM designed and performed the experiment, selected algorithm, analyzed data, and wrote the manuscript. XZ trained algorithms and analyzed data. JS analyzed data and wrote the manuscript. DH collected data and monitored data analysis. BS conceived the study and participated in its design. All authors contributed to the article and approved the submitted version. Funding This work was funded by the Fundamental Research Funds for the Central Universities (No.2452019028).Peer reviewedPublisher PD

A novel synergistic covalence and complexation bridging strategy based on multi-functional biomass-derived aldehydes and Al(III) for engineering high-quality eco-leather

8 figures.-- Supplementary material available.-- © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/To get rid of the chrome pollution faced by the leather industry, we explored a novel engineering high-quality eco-leather technology based on the synergistic interactions between biomass-based aldehydes and Al(III). Firstly, dialdehyde xanthan gum (DXG) was prepared to covalently crosslink with the collagen fibers (CFs) via Schiff-base linkages under alkaline conditions, endowing the leather with a shrinkage temperature (Ts) of 80 °C and opening channels for the subsequent penetration of Al species (AL). Secondly, and for this latter purpose, the DXG-tanned leather was acidified to release part of the DXG from the leather according to the dynamic nature of the Schiff-base. Containing suitable oxygen-containing groups (OGs) with excellent complexation capabilities, the released DXG served as masking agents for AL, facilitating the penetration of AL into the inner CFs network for further complexation crosslinking. Consequently, a denser crosslinking network was constructed in the leather, and the crust leather exhibited higher Ts (82.2 °C), improved mechanical (tensile strength: 13.4 N/mm2, tear strength: 53.3 N/mm) and organoleptic properties than those of the DXG crust or AL crust leathers. This demonstrates that this synergistic covalence and complexation bridging strategy is a sustainable option to substitute highly restricted chrome tanning agent for eco-leather production.This work was financially supported by the National Natural Science Foundation of China (22108297), the National Key Research and Development Program (2020YFE0203800), and the Science and Technology Innovation Key Project of Sinolight Corporation (ZQ2021YY05). Javier Remón is grateful to the Spanish Ministry of Science, Innovation and Universities for the awarded Juan de la Cierva (JdC) fellowship (IJC2018-037110-I).Peer reviewe

Synthesis of Large-Area Highly Crystalline Monolayer Molybdenum Disulfide with Tunable Grain Size in a H₂ Atmosphere

Large-area and highly crystalline monolayer molybdenum disulfide (MoS₂) with a tunable grain size was synthesized in a H₂ atmosphere. The influence of introduced H₂ on MoS₂ growth and grain size, as well as the corresponding mechanism, was tentatively explored by controlling the H₂ flow rate. The as-grown monolayer MoS₂ displays excellent uniformity and high crystallinity evidenced by Raman and high-resolution transmission electron microscopy. The Raman results also give an indication that the quality of the monolayer MoS₂ synthesized in a H₂ atmosphere is comparable to that synthesized by using seed or mechanical exfoliation. In addition, the electronic properties and dielectric inhomogeneity of MoS₂ monolayers were also detected <i>in situ</i> via scanning microwave microscopy, with measurements on impedance and differential capacitance (d<i>C</i>/d<i>V</i>). Back-gated field-effect transistors based on highly crystalline monolayer MoS₂ shows a field-effect mobility of ∼13.07 cm² V^–1 s^–1 and an <i>I</i>_on/<i>I</i>_off ratio of ∼1.1 × 10⁷, indicating that the synthesis of large-area and high-quality monolayer MoS₂ with H₂ is a viable method for electronic and optoelectronic applications