Identification of tumor mutation burden-associated molecular and clinical features in cancer by analyzing multi-omics data

BackgroundTumor mutation burden (TMB) has been recognized as a predictive biomarker for immunotherapy response in cancer. Systematic identification of molecular features correlated with TMB is significant, although such investigation remains insufficient.MethodsWe analyzed associations of somatic mutations, pathways, protein expression, microRNAs (miRNAs), long non-coding RNAs (lncRNAs), competing endogenous RNA (ceRNA) antitumor immune signatures, and clinical features with TMB in various cancers using multi-omics datasets from The Cancer Genome Atlas (TCGA) program and datasets for cancer cohorts receiving the immune checkpoint blockade therapy.ResultsAmong the 32 TCGA cancer types, melanoma harbored the highest percentage of high-TMB (≥ 10/Mb) cancers (49.4%), followed by lung adenocarcinoma (36.9%) and lung squamous cell carcinoma (28.1%). Three hundred seventy-six genes had significant correlations of their mutations with increased TMB in various cancers, including 11 genes (ARID1A, ARID1B, BRIP1, NOTCH2, NOTCH4, EPHA5, ROS1, FAT1, SPEN, NSD1,and PTPRT) with the characteristic of their mutations associated with a favorable response to immunotherapy. Based on the mutation profiles in three genes (ROS1, SPEN, and PTPRT), we defined the TMB prognostic score that could predict cancer survival prognosis in the immunotherapy setting but not in the non-immunotherapy setting. It suggests that the TMB prognostic score’s ability to predict cancer prognosis is associated with the positive correlation between immunotherapy response and TMB. Nine cancer-associated pathways correlated positively with TMB in various cancers, including nucleotide excision repair, DNA replication, homologous recombination, base excision repair, mismatch repair, cell cycle, spliceosome, proteasome, and RNA degradation. In contrast, seven pathways correlated inversely with TMB in multiple cancers, including Wnt, Hedgehog, PI3K-AKT, MAPK, neurotrophin, axon guidance, and pathways in cancer. High-TMB cancers displayed higher levels of antitumor immune signatures and PD-L1 expression than low-TMB cancers in diverse cancers. The association between TMB and survival prognosis was positive in bladder, gastric, and endometrial cancers and negative in liver and head and neck cancers. TMB also showed significant associations with age, gender, height, weight, smoking, and race in certain cohorts.ConclusionsThe molecular and clinical features significantly associated with TMB could be valuable predictors for TMB and immunotherapy response and therefore have potential clinical values for cancer management

Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol

Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification

Generalizable person re-identification (Re-ID) is a very hot research topic in machine learning and computer vision, which plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. However, previous methods mainly focus on the visual representation learning, while neglect to explore the potential of semantic features during training, which easily leads to poor generalization capability when adapted to the new domain. In this paper, we propose a Multi-Modal Equivalent Transformer called MMET for more robust visual-semantic embedding learning on visual, textual and visual-textual tasks respectively. To further enhance the robust feature learning in the context of transformer, a dynamic masking mechanism called Masked Multimodal Modeling strategy (MMM) is introduced to mask both the image patches and the text tokens, which can jointly works on multimodal or unimodal data and significantly boost the performance of generalizable person Re-ID. Extensive experiments on benchmark datasets demonstrate the competitive performance of our method over previous approaches. We hope this method could advance the research towards visual-semantic representation learning. Our source code is also publicly available at https://github.com/JeremyXSC/MMET

Comparative expression profiles of carboxylesterase orthologous CXE14 in two closely related tea geometrid species, Ectropis obliqua Prout and Ectropis grisescens Warren

Insect carboxylesterases (CXEs) can be expressed in multiple tissues and play crucial roles in detoxifying xenobiotic insecticides and degrading olfactory cues. Therefore, they have been considered as an important target for development of eco-friendly insect pest management strategies. Despite extensive investigation in most insect species, limited information on CXEs in sibling moth species is currently available. The Ectropis obliqua Prout and Ectropis grisescens Warren are two closely related tea geometrid species, which share the same host of tea plant but differ in geographical distribution, sex pheromone composition, and symbiotic bacteria abundance, providing an excellent mode species for studies of functional diversity of orthologous CXEs. In this study, we focused on EoblCXE14 due to its previously reported non-chemosensory organs-biased expression. First, the EoblCXE14 orthologous gene EgriCXE14 was cloned and sequence characteristics analysis showed that they share a conserved motif and phylogenetic relationship. Quantitative real-time polymerase chain reaction (qRT-PCR) was then used to compare the expression profiles between two Ectropis spp. The results showed that EoblCXE14 was predominately expressed in E. obliqua larvae, whereas EgriCXE14 was abundant in E. grisescens at multiple developmental stages. Interestingly, both orthologous CXEs were highly expressed in larval midgut, but the expression level of EoblCXE14 in E. obliqua midgut was significantly higher than that of EgriCXE14 in E. grisescens midgut. In addition, the potential effect of symbiotic bacteria Wolbachia on the CXE14 was examined. This study is the first to provide comparative expression profiles of orthologous CXE genes in two sibling geometrid moth species and the results will help further elucidate CXEs functions and identify a potential target for tea geometrid pest control

Shrinkage properties of porous materials during drying: a review

The shrinkage characteristic of porous materials is an important consideration in the drying process, as it can significantly impact the texture of the dried product and energy utilization. This phenomenon is influenced by numerous factors, including the structure of the cells, drying conditions, and the glass transition temperature. To gain a deeper understanding of the drying process, it is necessary to develop theoretical models that account for the simultaneous heat and mass transfer processes at the cellular level, as well as simulation tools to analyze the associated changes in drying morphology. In this paper, we highlight several key factors affecting shrinkage during the drying of porous materials, and also outline drying modeling, morphological simulation, and drying technology design considerations to provide guidance for improving the drying quality of porous materials as well as energy conversion efficiency

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

Background: H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. Results: We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI. Conclusions: The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies

A highly selective electrochemical assay based on the Sakaguchi reaction for the detection of protein arginine methylation state

Protein arginine methylation is a common form of post-translational modification that plays an important role in many bioprocesses. However, research advances in this field have been severely hampered by the lack of a quick and sensitive method for detecting the arginine methylation state of a protein. In this work we propose a direct and sensitive electrochemical method for identifying the arginine methylation state. This novel assay combines an electrochemical technique with the Sakaguchi reaction, which is highly selective towards the arginine methylation state. We show that the presence of a methyl group on the arginine residue of a protein prevents the Sakaguchi reaction, while the unmethylated arginine residue selectively reacts with 8-hydroxyquinoline; the electrical signal of the reaction product is used for electrochemical detection. From this, a highly selective and simple electrochemical sensor has been developed based on (1) the high selectivity of the Sakaguchi reaction towards the arginine methylation state, and (2) the sensitive electrochemical signal generated by the linked 8-hydroxyquinoline. The assay described in this work thus provides a convenient tool for detection of protein arginine methylation, which may facilitate studies of the biological functions of protein arginine methylases and demethylases

A fluorescent bisboronic acid compound that selectively labels cells expressing oligosaccharide Lewis X

Two fluorescent diboronic acid compounds (6a and 6b) with a dipeptide linker were synthesized as potential sensors for cell surface saccharide Lewis X (Le(X)). Compound 6a with a dipeptide (H-Asp-Ala-) as the linker was found to selectively label CHOFUT4 cells, which express Le(x), at micromolar concentrations, while non-Le(x)-expressing control cells were not labeled