Fish species-specific TRIM gene FTRCA1 negatively regulates interferon response through attenuating IRF7 transcription

In mammals and fish, emerging evidence highlights that TRIM family members play important roles in the interferon (IFN) antiviral immune response. Fish TRIM family has undergone an unprecedented expansion leading to generation of finTRIM subfamily, which is exclusively specific to fish. Our recent results have shown that FTRCA1 (finTRIM C. auratus 1) is likely a fish species-specific finTRIM member in crucian carp C. auratus and acts as a negative modulator to downregulate fish IFN response by autophage-lysosomal degradation of protein kinase TBK1. In the present study, we found that FTRCA1 also impedes the activation of crucian carp IFN promoter by IRF7 but not by IRF3. Mechanistically, FTRCA1 attenuates IRF7 transcription levels likely due to enhanced decay of IRF7 mRNA, leading to reduced IRF7 protein levels and subsequently reduced fish IFN expression. E3 ligase activity is required for FTRCA1 to negatively regulate IRF7-mediated IFN response, because ligase-inactive mutants and the RING-deleted mutant of FTRCA1 lose the ability to block the activation of crucian carp IFN promoter by IRF7. These results together indicate that FTRCA1 is a multifaceted modulator to target different signaling factors for shaping fish IFN response in crucian carp.</p

Small molecule-mediated tribbles homolog 3 promotes bone formation induced by bone morphogenetic protein-2.

Although bone morphogenetic protein-2 (BMP2) has demonstrated extraordinary potential in bone formation, its clinical applications require supraphysiological milligram-level doses that increase postoperative inflammation and inappropriate adipogenesis, resulting in well-documented life-threatening cervical swelling and cyst-like bone formation. Recent promising alternative biomolecular strategies are toward promoting pro-osteogenic activity of BMP2 while simultaneously suppressing its adverse effects. Here, we demonstrated that small molecular phenamil synergized osteogenesis and bone formation with BMP2 in a rat critical size mandibular defect model. Moreover, we successfully elicited the BMP2 adverse outcomes (i.e. adipogenesis and inflammation) in the mandibular defect by applying high dose BMP2. Phenamil treatment significantly improves the quality of newly formed bone by inhibiting BMP2 induced fatty cyst-like structure and inflammatory soft-tissue swelling. The observed positive phenamil effects were associated with upregulation of tribbles homolog 3 (Trib3) that suppressed adipogenic differentiation and inflammatory responses by negatively regulating PPARγ and NF-κB transcriptional activities. Thus, use of BMP2 along with phenamil stimulation or Trib3 augmentation may be a promising strategy to improve clinical efficacy and safety of current BMP therapeutics

Fuzzy ARTMAP Ensemble Based Decision Making and Application

Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs’ ensemble can classify the different category reliably and has a better classification performance compared with single FAM

Fuzzy ARTMAP Ensemble Based Decision Making and Application

Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs&apos; ensemble can classify the different category reliably and has a better classification performance compared with single FAM

Diagnosis Analysis of 4 TCM Patterns in Suboptimal Health Status: A Structural Equation Modelling Approach

Background. We illustrated an example of structure equation modelling (SEM) in the research on SHS to explore the diagnosis of the Sub optimal health status (SHS) and provide evidence for the standardization of traditional Chinese medicine (TCM) patterns in SHS. And the diagnosis of 4 TCM patterns in SHS was evaluated in this analysis. Methods. This study assessed data on 2807 adults (aged 18 to 49) with SHS from 6 clinical centres. SEM was used to analyze the patterns of SHS in TCM. Parameters in the introduced model were estimated by the maximum likelihood method. Results. The discussed model fits the SHS data well with CFI = 0.851 and RMSEA = 0.075. The direct effect of Qi deficiency pattern on dampness pattern had the highest magnitude (value of estimate is 0.822). With regard to the construct of “Qi deficiency pattern”, “fire pattern”, “stagnation pattern” and “dampness pattern”, the indicators with the highest load were myasthenia of limbs, vexation, deprementia, and dizziness, respectively. It had been shown that estimate factor should indicate the important degree of different symptoms in pattern. Conclusions. The weights of symptoms in the respective pattern can be statistical significant and theoretical meaningful for the 4 TCM patterns identification in SHS research. The study contributed to a theoretical framework, which has implications for the diagnosis points of SHS

From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model

Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design. Despite recent advance in data-driven methods in affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally depicted by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, we curated an MD dataset containing 3,218 different protein-ligand complexes, and further developed Dynaformer, which is a graph-based deep learning model. Dynaformer was able to accurately predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that our model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, we performed a virtual screening on the heat shock protein 90 (HSP90) using Dynaformer that identified 20 candidates and further experimentally validated their binding affinities. We demonstrated that our approach is more efficient, which can identify 12 hit compounds (two were in the submicromolar range), including several newly discovered scaffolds. We anticipate this new synergy between large-scale MD datasets and deep learning models will provide a new route toward accelerating the early drug discovery process.Comment: totally reorganize the texts and figure

The LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in the Southern Galactic Cap I. The Spectroscopic Redshift Catalog

We present a spectroscopic redshift catalog from the LAMOST Complete Spectroscopic Survey of Pointing Area (LaCoSSPAr) in the Southern Galactic Cap (SGC), which is designed to observe all sources (Galactic and extra-galactic) by using repeating observations with a limiting magnitude of $r=18.1~mag$ in two $20~deg^2$ fields. The project is mainly focusing on the completeness of LAMOST ExtraGAlactic Surveys (LEGAS) in the SGC, the deficiencies of source selection methods and the basic performance parameters of LAMOST telescope. In both fields, more than 95% of galaxies have been observed. A post-processing has been applied to LAMOST 1D spectrum to remove the majority of remaining sky background residuals. More than 10,000 spectra have been visually inspected to measure the redshift by using combinations of different emission/absorption features with uncertainty of $\sigma_{z}/(1+z)<0.001$. In total, there are 1528 redshifts (623 absorption and 905 emission line galaxies) in Field A and 1570 redshifts (569 absorption and 1001 emission line galaxies) in Field B have been measured. The results show that it is possible to derive redshift from low SNR galaxies with our post-processing and visual inspection. Our analysis also indicates that up to 1/4 of the input targets for a typical extra-galactic spectroscopic survey might be unreliable. The multi-wavelength data analysis shows that the majority of mid-infrared-detected absorption (91.3%) and emission line galaxies (93.3%) can be well separated by an empirical criterion of $W2-W3=2.4$. Meanwhile, a fainter sequence paralleled to the main population of galaxies has been witnessed both in $M_r$/$W2-W3$ and $M_*$/$W2-W3$ diagrams, which could be the population of luminous dwarf galaxies but contaminated by the edge-on/highly inclined galaxies ($\sim30\%$).Comment: 19 pages, 14 figures, 2 MRT, accepted by ApJ