1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track

This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All the original labels are projected to a 256-class unified label space, and the model is trained using a cross-entropy loss. Without significant hyperparameter tuning or any specific loss weighting, our solution ranks the first place on all the testing semantic segmentation benchmarks from multiple domains (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposed method can serve as a strong baseline for the multi-domain segmentation task and benefit future works. Code will be available at https://github.com/lambert-x/RVC_Segmentation.Comment: The Winning Solution to The Robust Vision Challenge 2022 Semantic Segmentation Trac

Label-Free Liver Tumor Segmentation

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.Comment: CVPR 202

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size and partially labeled problem of each dataset, as well as a limited investigation of diverse types of tumors, the resulting models are often limited to segmenting specific organs/tumors and ignore the semantics of anatomical structures, nor can they be extended to novel domains. To address these issues, we propose the CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models. This CLIP-based label encoding captures anatomical relationships, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors. The proposed model is developed from an assembly of 14 datasets, using a total of 3,410 CT scans for training and then evaluated on 6,162 external CT scans from 3 additional datasets. We rank first on the Medical Segmentation Decathlon (MSD) public leaderboard and achieve state-of-the-art results on Beyond The Cranial Vault (BTCV). Additionally, the Universal Model is computationally more efficient (6x faster) compared with dataset-specific models, generalized better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio

Using chromosome introgression lines to map quantitative trait loci for photosynthesis parameters in rice (Oryza sativa L.) leaves under drought and well-watered field conditions

Photosynthesis is fundamental to biomass production, but sensitive to drought. To understand the genetics of leaf photosynthesis, especially under drought, upland rice cv. Haogelao, lowland rice cv. Shennong265, and 94 of their introgression lines (ILs) were studied at flowering and grain filling under drought and well-watered field conditions. Gas exchange and chlorophyll fluorescence measurements were conducted to evaluate eight photosynthetic traits. Since these traits are very sensitive to fluctuations in microclimate during measurements under field conditions, observations were adjusted for microclimatic differences through both a statistical covariant model and a physiological approach. Both approaches identified leaf-to-air vapour pressure difference as the variable influencing the traits most. Using the simple sequence repeat (SSR) linkage map for the IL population, 1–3 quantitative trait loci (QTLs) were detected per trait–stage–treatment combination, which explained between 7.0% and 30.4% of the phenotypic variance of each trait. The clustered QTLs near marker RM410 (the interval from 57.3 cM to 68.4 cM on chromosome 9) were consistent over both development stages and both drought and well-watered conditions. This QTL consistency was verified by a greenhouse experiment under a controlled environment. The alleles from the upland rice at this interval had positive effects on net photosynthetic rate, stomatal conductance, transpiration rate, quantum yield of photosystem II (PSII), and the maximum efficiency of light-adapted open PSII. However, the allele of another main QTL from upland rice was associated with increased drought sensitivity of photosynthesis. These results could potentially be used in breeding programmes through marker-assisted selection to improve drought tolerance and photosynthesis simultaneously

Determination of free amino acids, organic acids, and nucleotides in 29 elegant spices

Abstract Spices can be used in cooking to enhance the flavor of food. In order to systematically summarize and discuss the flavor components of 29 elegant spices, the free amino acids, nucleotides, and organic acids in these spices were detected by high‐performance liquid chromatography. Cluster analysis was carried out to classify the 29 elegant spices based on similar data. The results showed considerable variations in the total free amino acids (1.12‒31.59 g/kg), organic acids (9.63‒71.90 g/kg), and nucleotides (0.03‒2.72 g/kg) in the elegant spices. Nine of the amino acids, especially glutamic acid and arginine, were found to have a taste active value (TAV) greater than 1. The TAVs of the 5′‐nucleotides, succinic acid, oxalic acid, tartaric acid, and ascorbic acid were all >1. The equivalent umami concentration (EUC) of sweet marjoram was 83.69 g MSG/100 g. The 29 elegant spices were divided into two categories according to cluster analysis of the EUC. Oregano fell into one category, and the remaining 28 spices fell into the other category

Optimization of beef broth processing technology and isolation and identification of flavor peptides by consecutive chromatography and LC‐QTOF‐MS/MS

To investigate the flavor peptides of beef broth obtained under optimized stewing conditions, separation procedures such as ultrafiltration, Sephadex G-15 column chromatography, and reversed-phase high-performance liquid chromatography were employed to isolate the umami taste peptides. Sensory evaluation was combined with liquid chromatography–mass spectrometry to detect the flavor peptides. The optimization of the stewing process conditions was studied using the orthogonal method, which indicated that time had the most significant effect on the taste efficiency of sensory evaluation, followed by the mixed spices, sucrose, and salt. The optimized cooking conditions included 3.5 hr of cooking time, 1.800 g of sucrose, 2.125 g of salt, and 1.500 g of mixed spices. The results showed that six peptides, including SDEEVEH, AEVPEVH, GVDNPGHP, GSDGSVGPVGP, SDGSVGPVGP, and DEAGPSIVH, were detected in sample X1M1; and seven peptides, including VAPEEHPT, VVSNPVDIL, VGGNVDYK, PFGNTHN, EAGPSIVHR, VDFDDIQK, and DEAGPSIVH, were detected in sample X2M2. This study compared the flavor peptides in stewed beef before and after the optimization, and thus provided a basis for the improvement of beef processing technology

Optimization of beef broth processing technology and isolation and identification of flavor peptides by consecutive chromatography and LC-QTOF-MS/MS

To investigate the flavor peptides of beef broth obtained under optimized stewing conditions, separation procedures such as ultrafiltration, Sephadex G-15 column chromatography, and reversed-phase high-performance liquid chromatography were employed to isolate the umami taste peptides. Sensory evaluation was combined with liquid chromatography–mass spectrometry to detect the flavor peptides. The optimization of the stewing process conditions was studied using the orthogonal method, which indicated that time had the most significant effect on the taste efficiency of sensory evaluation, followed by the mixed spices, sucrose, and salt. The optimized cooking conditions included 3.5 hr of cooking time, 1.800 g of sucrose, 2.125 g of salt, and 1.500 g of mixed spices. The results showed that six peptides, including SDEEVEH, AEVPEVH, GVDNPGHP, GSDGSVGPVGP, SDGSVGPVGP, and DEAGPSIVH, were detected in sample X1M1; and seven peptides, including VAPEEHPT, VVSNPVDIL, VGGNVDYK, PFGNTHN, EAGPSIVHR, VDFDDIQK, and DEAGPSIVH, were detected in sample X2M2. This study compared the flavor peptides in stewed beef before and after the optimization, and thus provided a basis for the improvement of beef processing technology

Lipopolysaccharide and palmitic acid synergistically induced MCP-1 production via MAPK-meditated TLR4 signaling pathway in RAW264.7 cells

Abstract Background Obesity increases the risk of developing diabetes mellitus. Clinical studies suggest that risk factors like palmitic acid (PA) and lipopolysaccharide (LPS) exist simultaneously in diabetes with obesity. Combination of PA and LPS even at low concentration can induce strong inflammatory reaction. Monocyte chemoattractant protein-1 (MCP-1) is an important inflammatory chemokine related to insulin resistance and type II diabetes. Our previous study using PCR array revealed that LPS and PA synergistically induce MCP-1 mRNA expression in macrophage cells RAW264.7, while the protein expression of MCP-1 in this case was not investigated. Moreover, the underling mechanism in the synergistic effect of MCP-1 expression or production induced by treatment of LPS and PA combination remains unclear. Methods Protein secretion of MCP-1 was measured by the enzyme-linked immunosorbent assay (ELISA) and mRNA levels of MCP-1 and Toll-like receptor 4 (TLR4) were measured by real-time PCR. Statistical analysis was conducted using SPSS software. Results LPS could increase MCP-1 transcription as well as secretion in RAW264.7, and PA amplified this effect obviously. Meanwhile, combination of LPS with PA increased TLR4 mRNA expression while LPS alone or PA alone could not, TLR4 knockdown inhibited MCP-1 transcription/secretion induced by LPS plus PA. Moreover, not NF-κB inhibitor but inhibitors of mitogen-activated protein kinase (MAPK) signaling pathways, including c-Jun NH2-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 MAPK were found to block MCP-1 generation stimulated by LPS plus PA. Conclusion LPS and PA synergistically induced MCP-1 secretion in RAW264.7 macrophage cells, in which MCP-1 transcription mediated by MAPK/TLR4 signaling pathways was involved. Combined treatment of PA and LPS in RAW264.7 cells mimics the situation of diabetes with obesity that has higher level of PA and LPS, MAPK/TLR4/ MCP-1 might be potential therapeutic targets for diabetes with obesity