A Coarse-to-fine Framework for Automated Kidney and Kidney Tumor Segmentation from Volumetric CT Images

Automatic semantic segmentation of kidney and kidney tumor is a promising tool for the treatment of kidney cancer. Due to the wide variety in kidney and kidney tumor morphology, it is still a great challenge to complete accurate segmentation of kidney and kidney tumor. We propose a new framework based on our previous work accepted by MICCAI2019, which is a coarse-to-fine segmentation framework to realize accurate and fast segmentation of kidney and kidney tumor

MoNuSAC2020:A Multi-Organ Nuclei Segmentation and Classification Challenge

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public

Sparse Representation for Classification of Tumors Using Gene Expression Data

Personalized drug design requires the classification of cancer patients as accurate as possible. With advances in genome sequencing and microarray technology, a large amount of gene expression data has been and will continuously be produced from various cancerous patients. Such cancer-alerted gene expression data allows us to classify tumors at the genomewide level. However, cancer-alerted gene expression datasets typically have much more number of genes (features) than that of samples (patients), which imposes a challenge for classification of tumors. In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is computed by the l1-regularized least square method. To investigate its performance, the proposed method is applied to six tumor gene expression datasets and compared with various support vector machine (SVM) methods. The experimental results have shown that the performance of the proposed method is comparable with or better than those of SVMs. In addition, the proposed method is more efficient than SVMs as it has no need of model selection

www.omicsonline.com Research Article JCSB/Vol.2 March-April 2009 L 1 Least Square for Cancer Diagnosis using Gene Expression Data

Copyright: © 2009 Hang X, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The performance of most methods for cancer diagnosis using gene expression data greatly depends on careful model selection. Least square for classification has no need of model selection. However, a major drawback prevents it from successful application in microarray data classification: lack of robustness to outliers. In this paper we cast linear regression as a constrained l 1-norm minimization problem to greatly alleviate its sensitivity to outliers, and hence the name l 1 least square. The numerical experiment shows that l 1 least square can match the best performance achieved by support vector machines (SVMs) with careful model selection

Transcriptomic and metabolomic analyses revealed regulation mechanism of mixotrophic Cylindrotheca sp. glycerol utilization and biomass promotion

Abstract Background Diatoms have been viewed as ideal cell factories for production of some high-value bioactive metabolites, such as fucoxanthin, but their applications are restrained by limited biomass yield. Mixotrophy, by using both CO2 and organic carbon source, is believed effective to crack the bottleneck of biomass accumulation and achieve a sustainable bioproduct supply. Results Glycerol, among tested carbon sources, was proved as the sole that could significantly promote growth of Cylindrotheca sp. with illumination, a so-called growth pattern, mixotrophy. Biomass and fucoxanthin yields of Cylindrotheca sp., grown in medium with glycerol (2 g L−1), was increased by 52% and 29%, respectively, as compared to the autotrophic culture (control) without compromise in photosynthetic performance. As Cylindrotheca sp. was unable to use glycerol without light, a time-series transcriptomic analysis was carried out to elucidate the light regulation on glycerol utilization. Among the genes participating in glycerol utilization, GPDH1, TIM1 and GAPDH1, showed the highest dependence on light. Their expressions decreased dramatically when the alga was transferred from light into darkness. Despite the reduced glycerol uptake in the dark, expressions of genes associating with pyrimidine metabolism and DNA replication were upregulated when Cylindrotheca sp. was cultured mixotrophically. Comparative transcriptomic and metabolomic analyses revealed amino acids and aminoacyl-tRNA metabolisms were enhanced at different timepoints of diurnal cycles in mixotrophic Cylindrotheca sp., as compared to the control. Conclusions Conclusively, this study not only provides an alternative for large-scale cultivation of Cylindrotheca, but also pinpoints the limiting enzymes subject to further metabolic manipulation. Most importantly, the novel insights in this study should aid to understand the mechanism of biomass promotion in mixotrophic Cylindrotheca sp

A Targeted and Stable Polymeric Nanoformulation Enhances Systemic Delivery of mRNA to Tumors

The high vulnerability of mRNA necessitates the manufacture of delivery vehicles to afford adequate protection in the biological milieu. Here, mRNA was complexed with a mixture of cRGD-poly(ethylene glycol) (PEG)-polylysine (PLys) (thiol) and poly(N-isopropylacrylamide) (PNIPAM)-PLys(thiol). The ionic complex core consisting of opposite-charged PLys and mRNA was crosslinked though redox-responsive disulfide linkage, thereby avoiding structural disassembly for exposure of mRNA to harsh biological environments. Furthermore, PNIPAM contributed to prolonged survival in systemic circulation by presenting a spatial barrier in impeding accessibility of nucleases, e.g., RNase, due to the thermo-responsive hydrophilic-hydrophobic transition behavior upon incubation at physiological temperature enabling translocation of PNIPAM from shell to intermediate barrier. Ultimately, the cRGD ligand attached to the formulation demonstrated improved tumor accumulation and potent gene expression, as manifested by virtue of facilitated cellular uptake and intracellular trafficking. These results indicate promise for the utility of mRNA as a therapeutic tool for disease treatment

Coordination as inference in multi-agent reinforcement learning

The Centralized Training and Decentralized Execution (CTDE) paradigm, where a centralized critic is allowed to access global information during the training phase while maintaining the learned policies executed with only local information in a decentralized way, has achieved great progress in recent years. Despite the progress, CTDE may suffer from the issue of Centralized–Decentralized Mismatch (CDM): the suboptimality of one agent’s policy can exacerbate policy learning of other agents through the centralized joint critic. In contrast to centralized learning, the cooperative model that most closely resembles the way humans cooperate in nature is fully decentralized, i.e. Independent Learning (IL). However, there are still two issues that need to be addressed before agents coordinate through IL: (1) how agents are aware of the presence of other agents, and (2) how to coordinate with other agents to improve joint policy under IL. In this paper, we propose an inference-based coordinated MARL method: Deep Motor System (DMS). DMS first presents the idea of individual intention inference where agents are allowed to disentangle other agents from their environment. Secondly, causal inference was introduced to enhance coordination by reasoning each agent’s effect on others’ behavior. The proposed model was extensively experimented on a series of Multi-Agent MuJoCo and StarCraftII tasks. Results show that the proposed method outperforms independent learning algorithms and the coordination behavior among agents can be learned even without the CTDE paradigm compared to the state-of-the-art baselines including IPPO and HAPPO

Fabrication and Properties of Polyethylene Glycol-Modified Wood Composite for Energy Storage and Conversion

Green fir wood (Pseudotsuga menziesii) was modified with polyethylene glycol (PEG) to produce wood composites for energy storage and conversion. The PEG-modified wood composites were evaluated based on their dimensional stability, durability, and thermal properties by various analytical methods. The differential scanning calorimetry (DSC) results showed the melting temperature and the latent heat of the phase change material (PCM) composite were 26.74 °C and 73.59 J/g, respectively. Thermal cycling tests and thermogravimetric analysis confirmed the composite exhibited good thermal stability, reliability, and chemical stability. All treated specimens were free from noticeable defects, and the addition of a surface varnish coating prevented PEG from leaching. The PEG-modified composites exhibited improved dimensional and thermal performance, which makes this material a potential candidate for economical and green, lightweight building materials

Cloning, Expression Analysis, and Subcellular Localization of the TLR13 Gene of Golden Pompano (Trachinotus ovatus)

The innate immune response serves as the first line of defense and is initiated through the sensing of pathogen-associated molecular patterns (PAMPs). Toll-like receptors (TLRs) are ancient innate immune receptors involved in pathogen-related molecular pattern recognition, which is essential for immune homeostasis and the prevention of infection. As a member of the TLR11 family, TLR13 has been identified in several teleost fishes, including Larimichthys crocea, Oreochromis niloticus, and Epinephelus coioides. These studies have mainly focused on the function of TLR13 in protecting the body from bacterial or viral invasion. To further investigate the immune function of TLR13, the gene of open reading frame (ORF) sequence of TLR13 (ToTLR13) from golden pompano (Trachinotus ovatus) was cloned and characterized in this study. The expression pattern of ToTLR13 was determined in healthy tissues and infected immune-related tissues in golden pompano by real-time fluorescence quantitative PCR (RT-qPCR). Moreover, subcellular localization of ToTLR13 in A549 cells was determined. The results showed that the ORF sequence of ToTLR13 was 1 269 bp, encoding 422 amino acids with an isoelectric point of 8.13. ToTLR13 was classified as a hydrophilic protein by hydrophilic prediction. In addition, ToTLR13 contains a 15-amino-acid-coded signal peptide. Conservative structure domain analysis showed that ToTLR13 contains a transmembrane (TM) domain, a leucine-rich repeat (LRR) domain involved in ligand recognition and binding, and a conserved Toll/interleukin-1 receptor (TIR) domain involved in signal transduction, which is in line with the typical characteristics of the TLR family. The TIR domain exists in almost all transmembrane TLRs and its sequence is highly conserved. By establishing the tertiary structure of the conserved domain of TLR13, ToTLR13 has a high spatial structure that overlaps with the LRR and TIR domains of Mus musculus and L. crocea TLR13, which shows that the TLR13 structure and function in different species have a certain similarity. Multiple sequence alignment showed that ToTLR13 had a high similarity with other teleost fish TLR13 (82.52%~84.58%), while with other classes of species, sequence similarity was low (33.30%~46.11%). Furthermore, according to the phylogenetic tree analysis, we found that the relationship between ToTLR13 and other teleost fish TLR13 is relatively close, among which Epinephelus lanceolatus is the closest evolutionary position. While it is distant from other species, mammals are grouped into one branch; Xenopus tropicalis and Cyclina sinensis are in another branch. RT-qPCR results revealed that ToTLR13 was constitutively expressed, with the highest expression level in the gill and spleen, followed by the brain, liver, and kidney, and expression was lower in the heart, head kidney, and muscle. The mRNA expression of TLR13 is slightly different in different fish, which indicates that TLR13 has species specificity and tissue specificity in normal fish tissues. Moreover, TLR13 is generally highly expressed in fish immune-related tissues, suggesting that TLR13 may play different roles in different fish species and plays an important immunomodulatory role. When stimulated by pathogens or viruses, the mRNA expression of TLR13 in immune-related tissues of different fish varies. In this study, after infection with Streptococcus agalactiae and Vibrio alginolyticus, there were significant changes in the mRNA expression of ToTLR13 in different tissues. The ToTLR13 mRNA expression level in the gill suddenly reached a peak at 72 h after infection with S. agalactiae, but showed significant differences at 12 h and 96 h in the V. alginolyticus experimental group. In the spleen, the mRNA expression of ToTLR13 increased in a time-dependent manner after infection with S. agalactiae and V. alginolyticus, peaking at 24 h and 96 h, respectively. The mRNA expression level of ToTLR13 in the liver showed a regular trend of increasing and then decreasing from 0 h to 48 h after S. agalactiae infection and reached a peak at 72 h. In the V. alginolyticus experimental group, the mRNA expression level of ToTLR13 in the liver decreased to below the original level at first and then increased, reaching a peak at 48 h. In the kidney, the ToTLR13 mRNA expression level in the S. agalactiae group reached a double peak at 12 h and 72 h after infection, respectively. ToTLR13 mRNA expression level reached a peak at 6 h after V. alginolyticus infection, and then decreased to the level before challenge. These results suggest that ToTLR13 plays an important role in the immune response against pathogenic bacteria. According to their intracellular localization, TLRs can be divided into two categories: Those expressed on the surface of the cell membrane and those localized in the cytoplasm. In this study, subcellular localization showed that ToTLR13 was localized in the cytoplasm of A549 cells, and this phenomenon was also found in other teleost fish TLR13. The results of this study showed that ToTLR13 might be involved in the innate immunity of golden pompano against pathogenic bacteria, which lays a foundation for studies on the function of TLR13 in teleost