In a human neutrophil cDNA library, an orphan G-protein-coupled receptor, HNFAG09, with 37% nucleotide identity to the C5a receptor (C5a-R, CD88) was identified. A novel feature of this gene, unlike C5a-R and other G-protein-coupled receptors, is the presence of an extraordinarily large predicted extracellular loop comprised of in excess of 160 amino acid residues between transmembrane domains 4 and 5. Northern blot analysis revealed that expression of mRNA for this receptor in human tissues, while similar, was distinct from C5a-R expression. Although there were differences in expression, transcripts for both receptors were detected in tissues throughout the body and the central nervous system. Mammalian cells stably expressing HNFAG09 specifically bound 125I-C3a and responded to a C3a carboxyl-terminal analogue synthetic peptide and to human C3a but not to rC5a with a robust calcium mobilization response. HNFAG09 encodes the human anaphylatoxin C3a receptor. In a human neutrophil cDNA library, an orphan G-protein-coupled receptor, HNFAG09, with 37% nucleotide identity to the C5a receptor (C5a-R, CD88) was identified. A novel feature of this gene, unlike C5a-R and other G-protein-coupled receptors, is the presence of an extraordinarily large predicted extracellular loop comprised of in excess of 160 amino acid residues between transmembrane domains 4 and 5. Northern blot analysis revealed that expression of mRNA for this receptor in human tissues, while similar, was distinct from C5a-R expression. Although there were differences in expression, transcripts for both receptors were detected in tissues throughout the body and the central nervous system. Mammalian cells stably expressing HNFAG09 specifically bound 125I-C3a and responded to a C3a carboxyl-terminal analogue synthetic peptide and to human C3a but not to rC5a with a robust calcium mobilization response. HNFAG09 encodes the human anaphylatoxin C3a receptor.
Autonomous driving related researches require the analysis and usage of massive amounts of driving scenario data. Compared to raw data collected by sensors, scenario data provide a preliminary abstraction of driving tasks and processes, explicitly integrate information about the road environment and the dynamic and static attributes of traffic participants, making it easier to conduct task understanding and decision making. However, many existing driving scenario datasets have the following two problems. First, it is not clear which data fields need to be recorded for driving scenarios. The data storage formats and organization standards are inconsistent. Second, the datasets cannot establish driving scenario indexing effectively. Existing datasets are sparsely annotated and difficult to index, which is detrimental to data sampling and extraction for machine learning process, thus hindering efficient fusion and reuse. In this paper, we propose MetaScenario, a framework for driving scenario data. We describe driving scenarios and design the centralized and unified data framework for the storage, processing, and indexing of scenario data based on relational database. The concept of atom scenario is proposed and characterized using semantic graphs. We also annotate and classify behaviors and interactions of traffic participants in atom scenarios by extracting the spatiotemporal evolution of semantic information. The annotation facilitates the indexing and extraction of data. The scenario datasets are further evaluated via the data distribution and annotation statistics. MetaScenario can provide researchers with convenient tools for scenario data extraction and important analytical references.
Tumour necrosis factor (TNF)-related apoptosis inducing ligand (TRAIL) is a promising anti-cancer agent that rapidly induces apoptosis in cancer cells. Unfortunately, the clinical application of recombinant TRAIL (rTRAIL) has been hampered by its common cancer resistance. Naturally TRAIL is delivered as a membrane-bound form by extracellular vesicles (EV-T) and is highly efficient for apoptosis induction. SCH727965 (dinaciclib), a potent cyclin-dependent kinase (CDK) inhibitor, was shown to synergize with other drugs to get better efficacy. However, it has never been investigated if dinaciclib coordinates with EV-T to enhance therapeutic results. This study explores the potential of combination therapy with EV-T and dinaciclib for cancer treatment. EV-T was successfully derived from human TRAIL transduced cells and shown to partially overcome resistance of A549 cells. Dinaciclib was shown to drastically enhance EV-T killing effects on cancer lines that express good levels of death receptor (DR) 5, which are associated with suppression of CDK1, CDK9 and anti-apoptotic proteins. Combination therapy with low doses of EV-T and dinaciclib induced strikingly enhanced apoptosis and led to complete regression in A549 tumors without any adverse side effects observed in a subcutaneous xenograft model. Tumor infiltration of mass NK cells and macrophages was also observed. These observations thus indicate that the combination of EV-T with dinaciclib is a potential novel therapy for highly effective and safe cancer treatment.
TGFβ1 and Smad3 play an important role in the process of EMT. TGFβ1 regulates the expression of Jagged1 by modulating Notch signaling. Jagged1 is related to tumor invasion, metastasis, chemotherapy resistance, and tumor immune escape. The aims of this study are to examine deregulation of TGFβ1-Smad3-Jagged1-Notch1-Slug signaling in TSCC and to investigate its roles in TSCC progression.Notch1, Smad3, Jagged1 and Slug proteins and mRNA expression were detected in specimens from 89 cases of patients. We analyzed the correlation between their expressions and histological grade, clinical stage and lymph node metastasis.Notch1, Smad3, Jagged1 and Slug mRNA expressions in TSCC were higher than normal tissue (P <0.05). The protein expression of Notch1 and Smad3 in TSCC were higher (χ(2) =7.30, P <0.01 and χ(2) = 5.84, P <0.05). Notch1 and Smad3 expressions were correlated with clinical stage (χ(2) =18.81, P<0.01; χ(2) =22.29, P<0.01), but not Jagged1 (χ(2) =0.53, P>0.05). The Slug protein expression was correlated with clinical stage. The positive rate of Notch1 was higher in lymph node metastases positive cases (χ(2) =7.30, P<0.01). Moreover, higher expression of Jagged1 was found in lymph node positive cases (χ(2) =10.82, P<0.01).The key protein expression in TGFβ1-Smad3-Jagged1-Notch1-Slug signaling pathway significantly correlated with the clinicopathological features of TSCC patients. It's potential as a biomarker and a novel therapeutic target for TSCC patients at risk of metastasis. It may play an irreplaceable role in TSCC progression which may attribute to promoting EMT which enhances cell migration, invasion and metastasis.
Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. To address this issue, we propose a supervised contrastive learning strategy that is tailored to train model-agnostic deep networks for reliable chromosome classification. This method enables extracting fine-grained chromosomal embeddings in latent space. These embeddings effectively expand inter-class boundaries and reduce intra-class variations, enhancing their distinctiveness in predicting chromosome types. On top of two large-scale chromosome datasets, we comprehensively validate the power of our contrastive learning strategy in boosting cutting-edge deep networks such as Transformers and ResNets. Extensive results demonstrate that it can significantly improve models' generalization performance, with an accuracy improvement up to +4.5%. Codes and pretrained models will be released upon acceptance of this work.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
