Transformer Transforms Salient Object Detection and Camouflaged Object Detection

The transformer networks are particularly good at modeling long-range dependencies within a long sequence. In this paper, we conduct research on applying the transformer networks for salient object detection (SOD). We adopt the dense transformer backbone for fully supervised RGB image based SOD, RGB-D image pair based SOD, and weakly supervised SOD within a unified framework based on the observation that the transformer backbone can provide accurate structure modeling, which makes it powerful in learning from weak labels with less structure information. Further, we find that the vision transformer architectures do not offer direct spatial supervision, instead encoding position as a feature. Therefore, we investigate the contributions of two strategies to provide stronger spatial supervision through the transformer layers within our unified framework, namely deep supervision and difficulty-aware learning. We find that deep supervision can get gradients back into the higher level features, thus leads to uniform activation within the same semantic object. Difficulty-aware learning on the other hand is capable of identifying the hard pixels for effective hard negative mining. We also visualize features of conventional backbone and transformer backbone before and after fine-tuning them for SOD, and find that transformer backbone encodes more accurate object structure information and more distinct semantic information within the lower and higher level features respectively. We also apply our model to camouflaged object detection (COD) and achieve similar observations as the above three SOD tasks. Extensive experimental results on various SOD and COD tasks illustrate that transformer networks can transform SOD and COD, leading to new benchmarks for each related task. The source code and experimental results are available via our project page: https://github.com/fupiao1998/TrasformerSOD.Comment: Technical report, 18 pages, 22 figure

MCL1 gene co-expression module stratifies multiple myeloma and predicts response to proteasome inhibitor-based therapy

Multiple myeloma (MM) is the second most common hematologic cancer, characterized by abnormal accumulation of plasma cells in the bone marrow. The extensive biological and clinical heterogeneity of MM hinders effective treatment and etiology research. Several molecular classification systems of prognostic impact have been proposed, but they do not predict the response to treatment nor do they correlate to plasma cell development pathways. Here we describe the classification of MM into two distinct subtypes based on the expression levels of a gene module coexpressed with MCL1 (MCL1-M), a regulator of plasma cell survival. The classification system enabled prediction of the prognosis and the response to bortezomib-based therapy. Moreover, the two MM subtypes were associated with two different plasma cell differentiation pathways (enrichment of a preplasmablast signature versus aberrant expression of B cell genes). 1q gain, harboring 63 of the 87 MCL1-M members including MCL1, was found in about 80% of the MM with upregulated MCL1-M expression. Clonal analysis showed that 1q gain tended to occur as an early clonal event. Members of MCL1-M captured both MM cell-intrinsically acting signals and the signals regulating the interaction between MM cells with bone marrow microenvironment. MCL1-M members were co-expressed in mouse germinal center B cells. Together, these findings indicate that MCL1-M may play previously inadequately recognized, initiating role in the pathogenesis of MM. Our findings suggest that MCL1-M signature-based molecular clustering of MM constitutes a solid framework toward understanding the etiology of this disease and establishing personalized care. Article Summary: A pathogenic mechanism-guided molecular classification would facilitate treatment decision and etiology research of multiple myeloma. On the basis of the expression levels of a gene module coexpressed with MCL1, w

Towards Deeper Understanding of Camouflaged Object Detection

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to survival. To detect and segment the whole scope of a camouflaged object, camouflaged object detection (COD) is introduced as a binary segmentation task, with the binary ground truth camouflage map indicating the exact regions of the camouflaged objects. In this paper, we revisit this task and argue that the binary segmentation setting fails to fully understand the concept of camouflage. We find that explicitly modeling the conspicuousness of camouflaged objects against their particular backgrounds can not only lead to a better understanding about camouflage, but also provide guidance to designing more sophisticated camouflage techniques. Furthermore, we observe that it is some specific parts of camouflaged objects that make them detectable by predators. With the above understanding about camouflaged objects, we present the first triple-task learning framework to simultaneously localize, segment and rank camouflaged objects, indicating the conspicuousness level of camouflage. As no corresponding datasets exist for either the localization model or the ranking model, we generate localization maps with an eye tracker, which are then processed according to the instance level labels to generate our ranking-based training and testing dataset. We also contribute the largest COD testing set to comprehensively analyse performance of the camouflaged object detection models. Experimental results show that our triple-task learning framework achieves new state-of-the-art, leading to a more explainable camouflaged object detection network. Our code, data and results are available at: https://github.com/JingZhang617/COD-Rank-Localize-and-Segment

Sesquiterpenoids from the Florets of Carthamus tinctorius (Safflower) and Their Anti-Atherosclerotic Activity

(1) Background: The florets of Carthamus tinctorius L. are traditionally used as a blood-activating drug and can be used for the treatment of atherosclerosis, but no compounds with anti-atherosclerotic activity have been reported. (2) Methods: This study investigated the chemical compounds from the florets of C. tinctorius. Comprehensive spectroscopic techniques revealed their structures, and ECD calculations established their absolute configurations. Nile Red staining, Oil Red O staining, and cholesterol assessment were performed on these compounds and their aglycones for the inhibitory activity against the formation of foam cells induced by oxidized low-density lipoprotein (ox-LDL) in RAW264.7 macrophages. In addition, RAW264.7 macrophages were tested for their anti-inflammatory activity by measuring the inhibition of NO production caused by LPS. (3) Results: Five new sesquiterpenoids (1–5) isolated from the florets of C. tinctorius were identified as (–)-(1R,4S,9S,11R)-caryophyll-8(13)-en-14-ol-5-one (1), (+)-(1R,4R,9S,11R)-caryophyll-8(13)-en-14-ol-5-one (2), (–)-(3Z,1R,5S,8S,9S,11R)-5,8-epoxycaryophyll-3-en-14-O-β-D-glucopyranoside (3), (+)-(1S,7R,10S)-guai-4-en-3-one-11-O-β-D-fucopyranoside (4), and (–)-(2R,5R,10R)-vetispir-6-en-8-one-11-O-β-D-fucopyranoside (5). All compounds except for compound 3 reduced the lipid content in ox-LDL-treated RAW264.7 cells. Compounds 3 and 4 and their aglycones were found to reduce the level of total cholesterol (TC) and free cholesterol (FC) in ox-LDL-treated RAW264.7 cells. However, no compounds showed anti-inflammatory activity. (4) Conclusion: Sesquiterpenoids from C. tinctorius help to decrease the content of lipids, TC and FC in RAW264.7 cells, but they cannot inhibit NO production, which implies that their anti-atherogenic effects do not involve the inhibition of inflammation

Lipid-Based Liquid Crystalline Nanoparticles Facilitate Cytosolic Delivery of siRNA via Structural Transformation

RNA interference (RNAi) technology has shown great promise for the treatment of cancer and other genetic disorders. Despite the efforts to increase the target tissue distribution, the safe and effective delivery of siRNA to the diseased cells with sufficient cytosolic transport is another critical factor for successful RNAi clinical application. Here, the constructed lipid-based liquid crystalline nanoparticles, called nano-Transformers, can transform thestructure in the intracellular acidic environment and perform high-efficient siRNA delivery for cancer treatment. The developed nano-Transformers have satisfactory siRNA loading efficiency and low cytotoxicity. Different from the traditional cationic nanocarriers, the endosomal membrane fusion induced by the conformational transition of lipids contributes to the easy dissociation of siRNA from nanocarriers and direct release of free siRNA into cytoplasm. We show that transfection with cyclin-dependent kinase 1 (CDK1)-siRNA-loaded nano-Transformers causes up to 95% reduction of relevant mRNA <i>in vitro</i> and greatly inhibits the tumor growth without causing any immunogenic response <i>in vivo</i>. This work highlights that the lipid-based nano-Transformers may become the next generation of siRNA delivery system with higher efficacy and improved safety profiles

Lipid-Based Liquid Crystalline Nanoparticles Facilitate Cytosolic Delivery of siRNA via Structural Transformation

RNA interference (RNAi) technology has shown great promise for the treatment of cancer and other genetic disorders. Despite the efforts to increase the target tissue distribution, the safe and effective delivery of siRNA to the diseased cells with sufficient cytosolic transport is another critical factor for successful RNAi clinical application. Here, the constructed lipid-based liquid crystalline nanoparticles, called nano-Transformers, can transform thestructure in the intracellular acidic environment and perform high-efficient siRNA delivery for cancer treatment. The developed nano-Transformers have satisfactory siRNA loading efficiency and low cytotoxicity. Different from the traditional cationic nanocarriers, the endosomal membrane fusion induced by the conformational transition of lipids contributes to the easy dissociation of siRNA from nanocarriers and direct release of free siRNA into cytoplasm. We show that transfection with cyclin-dependent kinase 1 (CDK1)-siRNA-loaded nano-Transformers causes up to 95% reduction of relevant mRNA <i>in vitro</i> and greatly inhibits the tumor growth without causing any immunogenic response <i>in vivo</i>. This work highlights that the lipid-based nano-Transformers may become the next generation of siRNA delivery system with higher efficacy and improved safety profiles

Lipid-Based Liquid Crystalline Nanoparticles Facilitate Cytosolic Delivery of siRNA via Structural Transformation

RNA interference (RNAi) technology has shown great promise for the treatment of cancer and other genetic disorders. Despite the efforts to increase the target tissue distribution, the safe and effective delivery of siRNA to the diseased cells with sufficient cytosolic transport is another critical factor for successful RNAi clinical application. Here, the constructed lipid-based liquid crystalline nanoparticles, called nano-Transformers, can transform thestructure in the intracellular acidic environment and perform high-efficient siRNA delivery for cancer treatment. The developed nano-Transformers have satisfactory siRNA loading efficiency and low cytotoxicity. Different from the traditional cationic nanocarriers, the endosomal membrane fusion induced by the conformational transition of lipids contributes to the easy dissociation of siRNA from nanocarriers and direct release of free siRNA into cytoplasm. We show that transfection with cyclin-dependent kinase 1 (CDK1)-siRNA-loaded nano-Transformers causes up to 95% reduction of relevant mRNA <i>in vitro</i> and greatly inhibits the tumor growth without causing any immunogenic response <i>in vivo</i>. This work highlights that the lipid-based nano-Transformers may become the next generation of siRNA delivery system with higher efficacy and improved safety profiles