Crop pest image classification based on improved densely connected convolutional network

IntroductionCrop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.MethodsTo address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.ResultsExperimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality

Tightly-bound and room-temperature-stable excitons in van der Waals degenerate-semiconductor Bi4O4SeCl2 with high charge-carrier density

Excitons, which represent a type of quasi-particles consisting of electron-hole pairs bound by the mutual Coulomb interaction, were often observed in lowly-doped semiconductors or insulators. However, realizing excitons in the semiconductors or insulators with high charge carrier densities is a challenging task. Here, we perform infrared spectroscopy, electrical transport, ab initio calculation, and angle-resolved-photoemission spectroscopy studies of a van der Waals degenerate-semiconductor Bi4O4SeCl2. A peak-like feature (i.e., alpha peak) is present around ~ 125 meV in the optical conductivity spectra at low temperature T = 8 K and room temperature. After being excluded from the optical excitations of free carriers, interband transitions, localized states and polarons, the alpha peak is assigned as the exciton absorption. Moreover, assuming the existence of weakly-bound excitons--Wannier-type excitons in this material violates the Lyddane-Sachs-Teller relation. Besides, the exciton binding energy of ~ 375 meV, which is about an order of magnitude larger than those of conventional semiconductors, and the charge-carrier concentration of ~ 1.25 * 10^19 cm^-3, which is higher than the Mott density, further indicate that the excitons in this highly-doped system should be tightly bound. Our results pave the way for developing the optoelectronic devices based on the tightly-bound and room-temperature-stable excitons in highly-doped van der Waals degenerate semiconductors.Comment: Accepted by Communications Material

Regulation of serotonin production by specific microbes from piglet gut

Abstract Background Serotonin is an important signaling molecule that regulates secretory and sensory functions in the gut. Gut microbiota has been demonstrated to affect serotonin synthesis in rodent models. However, how gut microbes regulate intestinal serotonin production in piglets remains vague. To investigate the relationship between microbiota and serotonin specifically in the colon, microbial composition and serotonin concentration were analyzed in ileum-cannulated piglets subjected to antibiotic infusion from the ileum when comparing with saline infusion. Microbes that correlated positively with serotonin production were isolated from piglet colon and were further used to investigate the regulation mechanisms on serotonin production in IPEC-J2 and a putative enterochromaffin cell line RIN-14B cells. Results Antibiotic infusion increased quantities of Lactobacillus amylovorus (LA) that positively correlated with increased serotonin concentrations in the colon, while no effects observed for Limosilactobacillus reuteri (LR). To understand how microbes regulate serotonin, representative strains of LA, LR, and Streptococcus alactolyticus (SA, enriched in feces from prior observation) were selected for cell culture studies. Compared to the control group, LA, LR and SA supernatants significantly up-regulated tryptophan hydroxylase 1 (TPH1) expression and promoted serotonin production in IPEC-J2 cells, while in RIN-14B cells only LA exerted similar action. To investigate potential mechanisms mediated by microbe-derived molecules, microbial metabolites including lactate, acetate, glutamine, and γ-aminobutyric acid were selected for cell treatment based on computational and metabolite profiling in bacterial supernatant. Among these metabolites, acetate upregulated the expression of free fatty acid receptor 3 and TPH1 while downregulated indoleamine 2,3-dioxygenase 1. Similar effects were also recapitulated when treating the cells with AR420626, an agonist targeting free fatty acid receptor 3. Conclusions Overall, these results suggest that Lactobacillus amylovorus showed a positive correlation with serotonin production in the pig gut and exhibited a remarkable ability to regulate serotonin production in cell cultures. These findings provide evidence that microbial metabolites mediate the dialogue between microbes and host, which reveals a potential approach using microbial manipulation to regulate intestinal serotonin biosynthesis

Ileum terminal antibiotic infusion affects jejunal and colonic specific microbial population and immune status in growing pigs

Abstract Background Compared with oral antibiotics (primarily disrupt foregut microbiota), the present study used antibiotics with ileum terminal infusion to disrupt the hindgut microbiota, and investigated the changes in specific bacterial composition and immune indexes in the jejunum and colon, and serum of growing pigs. Twelve barrows (45 d of age, 12.08 ± 0.28 kg) fitted with a T-cannula at the terminal ileum, were randomly assigned to two groups and infused either saline without antibiotics (Control) or with antibiotics (Antibiotic) at the terminal ileum. After 25 d experiment, all pigs were euthanized for analyzing bacterial composition and immune status. Results Ileum terminal antibiotic infusion (ITAI) altered dominant bacteria counts, with a decrease in Bifidobacterium, Clostridium cluster IV and Clostridium cluster IV in the colon (P < 0.05), and an increase in Escherichia coli in the jejunum (P < 0.05). ITAI decreased (P < 0.05) short-chain fatty acids concentrations in the colon. ITAI decreased interleukin-8 (IL-8), IL-10 and secretory immunoglobulin A (sIgA) concentrations, and down-regulated IL-10, Mucin-1 (MUC1), Mucin-2 (MUC2) and zonula occludens-1 (ZO-1) mRNA expression in the colonic mucosa (P < 0.05). In the jejunal mucosa, ITAI decreased interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), sIgA and IgG levels together with down-regulation of IFN-γ, TNF-α, MUC2 and ZO-1 mRNA expression (P < 0.05). Furthermore, ITAI decreased IL-10, INF-γ, TNF-α, IgA and IgG concentrations in serum (P < 0.05). Correlation analysis revealed that the change in intestinal microbiota was correlated with alterations of Ig and cytokines. Conclusions ITAI affected jejunal and colonic specific bacteria counts, and altered some immune markers levels in the jejunal and colonic mucosa and serum. These findings implicate the potential contribution of hindgut bacteria to immune response in the intestinal mucosa and serum of growing pigs

An efficient and facile synthesis of flavanones catalyzed by N-methylimidazole

N-Methylimidazole used as an efficient catalyst for the cyclization of 2’- hydroxychalcones to the corresponding flavanones in DMSO was investigated. The scope of this process has been studied and various flavanones were obtained exclusively in good yields

Joint optimization of cascades in Yalong River and the middle and downstream of Jinsha River

The main tributaries of the upper reaches of the Yangtze River are not only the strategic base for China’s water resources, but also an important hydropower base for the “West-East Power Transmission Strategy”. With the completion of these reservoir groups, a large-scale mixed reservoir system across the different basins has been formed, which makes the requirements for joint optimization and scheduling of large-scale hydropower system getting higher and higher. This paper focuses on the key problems faced by the joint optimization of large-scale hydropower system in the basin. Taking the Yalong River and the middle and downstream of the Jinsha River as the research area, the hybrid optimization method is introduced herein to solve the joint optimal scheduling model. The results reveal that the power generation by joint optimal scheduling is much more than separately scheduling, and the total power generation increased by 2.84% on average. As Mid-Jinsha cascade and Yalong River cascade has a 690 million kW·h and 190 million kW·h decrease in power generation respectively, the downstream Jinsha River cascade has a power generation increase of 4.31 billion kW·h

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level

Joint optimization of cascades in Yalong River and the middle and downstream of Jinsha River

The main tributaries of the upper reaches of the Yangtze River are not only the strategic base for China’s water resources, but also an important hydropower base for the “West-East Power Transmission Strategy”. With the completion of these reservoir groups, a large-scale mixed reservoir system across the different basins has been formed, which makes the requirements for joint optimization and scheduling of large-scale hydropower system getting higher and higher. This paper focuses on the key problems faced by the joint optimization of large-scale hydropower system in the basin. Taking the Yalong River and the middle and downstream of the Jinsha River as the research area, the hybrid optimization method is introduced herein to solve the joint optimal scheduling model. The results reveal that the power generation by joint optimal scheduling is much more than separately scheduling, and the total power generation increased by 2.84% on average. As Mid-Jinsha cascade and Yalong River cascade has a 690 million kW·h and 190 million kW·h decrease in power generation respectively, the downstream Jinsha River cascade has a power generation increase of 4.31 billion kW·h

Additional file 1: of Ileum terminal antibiotic infusion affects jejunal and colonic specific microbial population and immune status in growing pigs

Figure S1. Scatterplots demonstrating correlations of some immune markers levels in the jejunal and colonic mucosa and serum, dominant microbial communities counts in the jejunum and colon and butyrate concentration in the colon. IL-8 = interleukin-8; IL-10 = interleukin-10; IFN-γ = interferon-γ; TNF-α = tumor necrosis factor-α; sIgA = secretory immunoglobulin A; IgG = immunoglobulin G. (DOCX 1289 kb