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Pepper (Capsicum annuum L.) is a herbaceous plant species in the family Solanaceae. Capsicum anthracnose is caused by the genus Colletotrichum. spp., which decreases pepper production by about 50% each year due to anthracnose. In this study, we evaluated the resistance of red ripe fruits from 17 pepper varieties against anthracnose fungus Colletotrichum capsici. We assessed the size of the lesion diameter and conducted significance analysis to identify the resistant variety of B158 and susceptible variety of B161. We selected a resistant cultivar B158 and a susceptible cultivar B161 of pepper and used a transcription to investigate the molecular mechanisms underlying the plant’s resistance to C. capsici, of which little is known. The inoculated fruit from these two varieties were used for the comparative transcription analysis, which revealed the anthracnose-induced differential transcription in the resistant and susceptible pepper samples. In the environment of an anthrax infection, we found that there were more differentially expressed genes in resistant varieties compared to susceptible varieties. Moreover, the response to stimulus and stress ability was stronger in the KANG. The transcription analysis revealed the activation of plant hormone signaling pathways, phenylpropanoid synthesis, and metabolic processes in the defense response of peppers against anthracnose. In addition, ARR-B, AP2-EREBP, bHLH, WRKY, and NAC are associated with disease resistance to anthracnose. Notably, WRKY and NAC were found to have a potentially positive regulatory role in the defense response against anthracnose. These findings contribute to a more comprehensive understanding of the resistance mechanisms of red pepper fruit to anthracnose infection, providing valuable molecular insights for further research on the resistance mechanisms and genetic regulations during this developmental stage of pepper.

Lettuce (Lactuca sativa L.), which belongs to the large Asteraceae (Compositae) family, breeds by sexual reproduction and produces seeds. Actually, lettuce seeds are achenes, which are defined as fruits. However, few studies have described the morphological characteristics of the lettuce achenes, and genes essential for achene development are largely unknown in lettuce. To investigate the gene activity during achene development and determine the possible mechanisms that influence achene development in lettuce, we performed a time-course transcriptome analysis of lettuce achenes. A total of 27,390 expressed genes were detected at the five achene development stages. We investigated the gene expression patterns during achene development and identified the enriched biological processes at the corresponding stages. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed a variety of transcriptomic similarities and differences at different achene development stages. Further, transcription factors and phytohormones were found to play important roles during achene development. Finally, we proposed a working model to illustrate the gene expression modules and possible molecular mechanisms underlying achene development. Our time-course transcriptome data also provide a foundation for future functional studies to reveal the genetic control of achene development in lettuce.

A sequencing batch reactor (SBR) with an intermittent aeration mode was established to achieve partial nitrification (PN) and the short-term effects of C/N ratios were investigated. Stable nitrite accumulation was achieved after 107 cycles, about 56d, with the average ammonia nitrogen removal efficiency (ARE) and nitrite accumulation rate (NAR) of 96.92% and 82.49%, respectively. When the C/N ratios decreased from 4.64 to 3.87 and 2.32, ARE and NAR still kept a stable and high level. However, when the C/N ratio further decreased to 0.77, nitrite accumulation became fluctuation, and ARE, total nitrogen (TN), and chemical oxygen demand (COD) removal performance declined obviously. Except for four common phyla (Proteobacteria, Bacteroidetes, Chloroflexi, and Actinobacteria) in the wastewater treatment system, Patescibacteria, the newly defined superphylum, was found and became the most dominant phylum in the PN sludge for their ultra-small cell size. The only ammonia oxidation bacteria (AOB), Nitrosomonas, and nitrite oxidation bacteria (NOB), Nitrospira, were detected. The relative abundance of NOB was low at different C/N ratios, showing the stable and effective inhibition effects of intermittent aeration on NOB growth.

This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space, fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions, creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks (CE-GAN). The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions, and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution, initial reservoir performance and time-varying well control, thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions. The experimental results in basic (egg model) and actual water-flooding reservoirs show that the model predictions align well with numerical simulations. In the basic reservoir model validation, the median relative residuals for pressure and oil saturation are 0.5% and 9.0%, respectively. In the actual reservoir model validation, the median relative residuals for both pressure and oil saturation are 4.0%. Regarding time efficiency, the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models, respectively, compared with traditional numerical simulations. The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.