Boosting Few-Shot Semantic Segmentation Via Segment Anything Model

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects

Graph based Label Enhancement for Multi-instance Multi-label learning

Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, in practical applications in MIML, significance of each label for multiple instances per bag (such as an image) is significant different. Ignoring labeling significance will greatly lose the semantic information of the object, so that MIML is not applicable in complex scenes with a poor learning performance. To this end, this paper proposed a novel MIML framework based on graph label enhancement, namely GLEMIML, to improve the classification performance of MIML by leveraging label significance. GLEMIML first recognizes the correlations among instances by establishing the graph and then migrates the implicit information mined from the feature space to the label space via nonlinear mapping, thus recovering the label significance. Finally, GLEMIML is trained on the enhanced data through matching and interaction mechanisms. GLEMIML (AvgRank: 1.44) can effectively improve the performance of MIML by mining the label distribution mechanism and show better results than the SOTA method (AvgRank: 2.92) on multiple benchmark datasets.Comment: 7 pages,2 figure

Growth of tomato and cucumber seedlings under different light environments and their development after transplanting

Selecting suitable light conditions according to the plant growth characteristics is one of the important approaches to cultivating high-quality vegetable seedlings. To determine the more favorable LED light conditions for producing high-quality tomato and cucumber seedlings in plant factories with artificial light (PFALS), the growth characteristics of tomato and cucumber seedlings under seven LED light environments (CK, B, UV-A, FR, B+UV-A, UV-A+FR, and B+FR) and the development of these seedlings after transplanting into a plastic greenhouse were investigated. The results showed that the seedling height and hypocotyl length increased in treatments with far-red light supplementation (FR, UV-A+FR, and B+FR), but decreased in the B treatment, in both varieties. The seedling index of tomato seedlings increased in the B+UV-A treatment, while that of cucumber seedlings increased in the FR treatment. After transplanting into a plastic greenhouse, tomato plants that radiated with UV-A had greater flower numbers on the 15th day after transplanting. In cucumber plants of the FR treatment, the flowering time was significantly delayed, and the female flower exhibited at a lower node position. By using a comprehensive scoring analysis of all detected indicators, light environments with UV-A and FR were more beneficial for improving the overall quality of tomato and cucumber seedlings, respectively

Production of human blood group B antigen epitope conjugated protein in Escherichia coli and utilization of the adsorption blood group B antibody

Additional file 1: Table S1. List of constructed plasmids, strains and primers used in the study. Figure S1. MALDI-TOF detection of MBPmut (a) and MBPmut-OPS (b)

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Pre-Reinforcement Mechanism and Effect Analysis of Surface Infiltration Grouting in Shallow Buried Section of Long-Span Tunnel

In order to solve the problem that the hole-forming rate of boreholes is low and it is difficult to reach the designed length when supporting a long pipe shed in loose stratum in a shallow buried section of a long-span tunnel, it is necessary to pre-reinforce the loose stratum in order to improve the strength and integrity of the surrounding rock. Relying on the grouting project of the shallow buried section at the exit of Botanggou tunnel, it is assumed that the grouting material is Newtonian fluid and the steel floral tube shows cylindrical infiltration and diffusion. Through the analysis of the structural characteristics of the injected stratum, the conceptual model of infiltration grouting is established. Twelve groups of test slurry were prepared with ordinary Portland cement and ultra-fine cement, and through the analysis of the slurry parameters of each group, ordinary Portland cement slurry was selected with a water–cement ratio of 1:1 plus 3% water glass to strengthen the gravel layer, and ultra-fine cement slurry with a water–cement ratio of 1:1 plus 3% water glass and 0.3% polycarboxylate superplasticizer to strengthen the fully and strongly weathered porphyritic granite layer. Through the on-site single-hole grouting test and combining with the empirical formula, the maximum diffusion radius of single-hole infiltration grouting is calculated, and the sliding width of the sidewall is deduced using Terzaghi theory. To ensure the grouting effect, the 5 m expansion of the excavation profile is taken as the grouting range. Grouting construction adopts the overall order of periphery and then interior, and three-sequence opening and grouting are adopted in the same row of grouting holes, which can effectively prevent grouting running and grouting. For the strata treated by surface grouting, the construction of the long pipe shed is smooth and reaches the designed length, and there is no large deformation of the surrounding rock when excavated using the CD method. The treatment effect is analyzed by the P-Q-t control method, excavation observation method, and deformation monitoring method. The results show that the injected stratum is fully infiltrated and gelled, forms an obvious grouting stone body, the integrity and strength of surrounding rock are obviously improved, and the convergence values of the tunnel surface, vault subsidence, and clearance do not exceed the alarm value of 60 mm. The research results provide some awareness and understanding of the grouting pre-reinforcement of loose stratum in a shallow buried section of a long-span tunnel in the future

Pre-Harvest Supplemental Blue Light Enhanced Antioxidant Activity of Flower Stalk in Chinese Kale during Storage

For 10 days before harvest, supplemental 50 μmol m−2 s−1 blue light (430 nm) was applied in greenhouse conditions in order to evaluate the influences of pre-harvest supplemental blue light on both antioxidants and nutrition of the flower stalk of Chinese kale during storage. The weight loss of the flower stalk of Chinese kale treated with supplemental blue light was generally lower than control during storage. Higher antioxidant activity was maintained during storage by supplemental blue light. Meanwhile, supplemental blue light derived higher contents of vitamin C, soluble protein, free amino acids, and chlorophyll at harvest. The samples exposed to supplemental blue light possessed both higher nutrition and antioxidant values. Thus, pre-harvest supplemental blue light treatment might be a promising strategy to enhance the antioxidant activity and nutritional values and extend the shelf-life of the flower stalk of Chinese kale

Growth, Nutritional Quality and Health-Promoting Compounds in Chinese Kale Grown under Different Ratios of Red:Blue LED Lights

Chinese kale (Brassica alboglabra Bailey) is one of the healthiest vegetables which is rich in health-promoting phytochemicals, including carotenoids, vitamin C, amino acid, glucosinolates, anthocyanin, flavonoids and phenolic compounds. The effects of different LEDs (white LED, 8R1B (red:blue = 8:1), 6R3B (red:blue = 6:3)) on nutritional quality in flower stalks and leaves of Chinese kale were investigated in this study. 8R1B and 6R3B were more effective than white LED light for improvement of growth and quality of Chinese kale. Flower stalk contained a higher content of nutritional compounds than leaves in Chinese kale. 8R1B significantly promoted plant growth, accumulation of biomass and soluble sugar content in flower stalks. In contrast, 6R3B significantly reduced plant dry matter, but it promoted nutritional compounds accumulation in flower stalks, such as soluble proteins, total glucosinolate, total anthocyanin, flavonoid, antioxidant activity. In addition, 6R3B enable to increase the amount of sourness and umami tasty amino acids, as well as precursor amino acids of glucosinolate. Accumulation balance of biomass and nutritional compounds is related to the ratio of red to blue light. Generally, 6R3B was more conducive to the enrichment of health-promoting compounds, as well as umami in Chinese kale

Fish Face Identification Based on Rotated Object Detection: Dataset and Exploration

At present, fish farming still uses manual identification methods. With the rapid development of deep learning, the application of computer vision in agriculture and farming to achieve agricultural intelligence has become a current research hotspot. We explored the use of facial recognition in fish. We collected and produced a fish identification dataset with 3412 images and a fish object detection dataset with 2320 images. A rotating box is proposed to detect fish, which avoids the problem where the traditional object detection produces a large number of redundant regions and affects the recognition accuracy. A self-SE module and a fish face recognition network (FFRNet) are proposed to implement the fish face identification task. The experiments proved that our model has an accuracy rate of over 90% and an FPS of 200