Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data

We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution. To address this issue, we theoretically analyze and experimentally prove that appropriately boosting uncertainty on unlabeled data can help minimize the distribution gap, which benefits the generalization of the model. We propose two strategies and design an uncertainty booster algorithm, specially for semi-supervised semantic segmentation. Extensive experiments are carried out based on these theories, and the results confirm the efficacy of the algorithm and strategies. Our plug-and-play uncertainty booster is tiny, efficient, and robust to hyperparameters but can significantly promote performance. Our approach achieves state-of-the-art performance in our experiments compared to the current semi-supervised semantic segmentation methods on the popular benchmarks: Cityscapes and PASCAL VOC 2012 with different train settings

A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation

In this paper, we strive to answer the question "how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and exchanging the reliable knowledge between them for semantic segmentation?" Accordingly, we propose an online knowledge distillation (KD) framework that can simultaneously learn compact yet effective CNN-based and ViT-based models with two key technical breakthroughs to take full advantage of CNNs and ViT while compensating their limitations. Firstly, we propose heterogeneous feature distillation (HFD) to improve students' consistency in low-layer feature space by mimicking heterogeneous features between CNNs and ViT. Secondly, to facilitate the two students to learn reliable knowledge from each other, we propose bidirectional selective distillation (BSD) that can dynamically transfer selective knowledge. This is achieved by 1) region-wise BSD determining the directions of knowledge transferred between the corresponding regions in the feature space and 2) pixel-wise BSD discerning which of the prediction knowledge to be transferred in the logit space. Extensive experiments on three benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art online distillation methods by a large margin, and shows its efficacy in learning collaboratively between ViT-based and CNN-based models.Comment: ICCV 202

Autophagy genes of ATG5-ATG12 complex in response to exogenous stimulations in Litopenaeus vannamei

Autophagy plays an important role in resisting pathogens infection and environmental stress. However, there are few studies on autophagy and its regulation in Litopenaeus vannamei. In this study, the autophagy-related genes of ATG5-ATG12 complex (ATG5, ATG7, ATG10 and ATG12) were cloned and investigated on the response to exogenous stimulations in L. vannamei. Multiple sequence alignment and phylogenetic analysis of different species showed that four autophagy genes were conserved among different species. Tissue detection showed that the four autophagy genes were expressed in all tissues, and the expression level was the highest in the hepatopancreas in L. vannamei. Furthermore, the expression levels of the four autophagy genes were up-regulated significantly after stimulation with Vibrio harveyi and the virus analog poly(I:C) (p<0.05), and their peak values occurred at 24-48h. These results indicated that ATG5, ATG7, ATG10 and ATG12 may be involved in resisting pathogen infection in L.vannamei, which provided a basis for studying the molecular mechanism of autophagy in resistance to pathogen infection of L. vannamei

Multi‐gram scale synthesis of chiral 3‐methyl‐2,5‐trans‐tetrahydrofurans

In this article, we report the rapid and facile synthesis of chiral 3‐methyl‐2,5‐trans‐tetrahydrofurans. This reaction utilizes cheap and easily available starting materials. A domino hydrolysis and intramolecular Michael‐type ring closure reaction was the key step. As a result, synthesis of the desired 3‐methyl‐2,5‐trans‐tetrahydrofurans could be achieved in gram‐scale over seven linear steps with high chemical yield and high diastereoselectivity

Identification of Coxiella burnetii Type IV Secretion Substrates Required for Intracellular Replication and Coxiella-Containing Vacuole Formation

Coxiella burnetii, the etiological agent of acute and chronic Q fever in humans, is a naturally intracellular pathogen that directs the formation of an acidic Coxiella-containing vacuole (CCV) derived from the host lysosomal network. Central to its pathogenesis is a specialized type IVB secretion system (T4SS) that delivers effectors essential for intracellular replication and CCV formation. Using a bioinformatics-guided approach, 234 T4SS candidate substrates were identified. Expression of each candidate as a TEM-1 β-lactamase fusion protein led to the identification of 53 substrates that were translocated in a Dot/Icm-dependent manner. Ectopic expression in HeLa cells revealed that these substrates trafficked to distinct subcellular sites, including the endoplasmic reticulum, mitochondrion, and nucleus. Expression in Saccharomyces cerevisiae identified several substrates that were capable of interfering with yeast growth, suggesting that these substrates target crucial host processes. To determine if any of these T4SS substrates are necessary for intracellular replication, we isolated 20 clonal T4SS substrate mutants using the Himar1 transposon and transposase. Among these, 10 mutants exhibited defects in intracellular growth and CCV formation in HeLa and J774A.1 cells but displayed normal growth in bacteriological medium. Collectively, these results indicate that C. burnetii encodes a large repertoire of T4SS substrates that play integral roles in host cell subversion and CCV formation and suggest less redundancy in effector function than has been found in the comparative Legionella Dot/Icm model

Comprehensive Identification of Protein Substrates of the Dot/Icm Type IV Transporter of Legionella pneumophila

A large number of proteins transferred by the Legionella pneumophila Dot/Icm system have been identified by various strategies. With no exceptions, these strategies are based on one or more characteristics associated with the tested proteins. Given the high level of diversity exhibited by the identified proteins, it is possible that some substrates have been missed in these screenings. In this study, we took a systematic method to survey the L. pneumophila genome by testing hypothetical orfs larger than 300 base pairs for Dot/Icm-dependent translocation. 798 of the 832 analyzed orfs were successfully fused to the carboxyl end of β-lactamase. The transfer of the fusions into mammalian cells was determined using the β-lactamase reporter substrate CCF4-AM. These efforts led to the identification of 164 proteins positive in translocation. Among these, 70 proteins are novel substrates of the Dot/Icm system. These results brought the total number of experimentally confirmed Dot/Icm substrates to 275. Sequence analysis of the C-termini of these identified proteins revealed that Lpg2844, which contains few features known to be important for Dot/Icm-dependent protein transfer can be translocated at a high efficiency. Thus, our efforts have identified a large number of novel substrates of the Dot/Icm system and have revealed the diverse features recognizable by this protein transporter