Molecular Docking of Potential Inhibitors for Influenza H7N9

As a new strain of virus emerged in 2013, avian influenza A (H7N9) virus is a threat to the public health, due to its high lethality and pathogenicity. Furthermore, H7N9 has already generated various mutations such as neuraminidase R294K mutation which could make the anti-influenza oseltamivir less effective or ineffective. In this regard, it is urgent to develop new effective anti-H7N9 drug. In this study, we used the general H7N9 neuraminidase and oseltamivir-resistant influenza virus neuraminidase as the acceptors and employed the small molecules including quercetin, chlorogenic acid, baicalein, and oleanolic acid as the donors to perform the molecular docking for exploring the binding abilities between these small molecules and neuraminidase. The results showed that quercetin, chlorogenic acid, oleanolic acid, and baicalein present oseltamivir-comparable high binding potentials with neuraminidase. Further analyses showed that R294K mutation in neuraminidase could remarkably decrease the binding energies for oseltamivir, while other small molecules showed stable binding abilities with mutated neuraminidase. Taken together, the molecular docking studies identified four potential inhibitors for neuraminidase of H7N9, which might be effective for the drug-resistant mutants

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.Comment: 21 pages, 11 figure

Transcriptome Profiling of Testis during Sexual Maturation Stages in Eriocheir sinensis Using Illumina Sequencing

The testis is a highly specialized tissue that plays dual roles in ensuring fertility by producing spermatozoa and hormones. Spermatogenesis is a complex process, resulting in the production of mature sperm from primordial germ cells. Significant structural and biochemical changes take place in the seminiferous epithelium of the adult testis during spermatogenesis. The gene expression pattern of testis in Chinese mitten crab (Eriocheir sinensis) has not been extensively studied, and limited genetic research has been performed on this species. The advent of high-throughput sequencing technologies enables the generation of genomic resources within a short period of time and at minimal cost. In the present study, we performed de novo transcriptome sequencing to produce a comprehensive transcript dataset for testis of E. sinensis. In two runs, we produced 25,698,778 sequencing reads corresponding with 2.31 Gb total nucleotides. These reads were assembled into 342,753 contigs or 141,861 scaffold sequences, which identified 96,311 unigenes. Based on similarity searches with known proteins, 39,995 unigenes were annotated based on having a Blast hit in the non-redundant database or ESTscan results with a cut-off E-value above 10−5. This is the first report of a mitten crab transcriptome using high-throughput sequencing technology, and all these testes transcripts can help us understand the molecular mechanisms involved in spermatogenesis and testis maturation

Characterization of a novel Wx-A1b′ null allele and PCR markers for allele identification at three Wx loci

Amylose synthesis needs the important enzyme granule-bound starch synthase I (Wx protein), and mutants with a complete or partial lack of the Wx protein result in waxy or partial waxy wheat. In this study, a novel Wx-A1b′ null allele was identified from a waxy wheat variety Miannuomai 829. The 19 bp InDel of Wx-A1b was also detected at the junction of the first exon and the first intron of Wx-A1b′, and a non-synonymous substitution from G to T at position 2,041 bp of the eighth exon was identified between Wx-A1b′ and Wx-A1b, which resulted in Glu being replaced by a stop codon. By amplifying the flanking sequence, the Wx-B1 of Miannuomai 829 was confirmed to be completely deleted, and the Wx-D1 of Miannuomai 829 was Wx-D1b based on pedigree analysis. Phylogenetic analysis suggested that the Wx-A1b′ allele was more closely related to Wx-A1a′ and Wx-A1d of tetraploid wheat and evolved independently from Wx-A1a and Wx-A1c of non-waxy hexaploid wheat. A multiplex PCR-based marker simultaneously valid for null and normal alleles of Wx-A1 and Wx-D1 and a codominant marker for discriminating the null and normal alleles of Wx-B1 were developed. These two markers were verified in 22 F2:3 family lines from the Miannuomai 829/Chuanmai 68 cross. The results showed that these two markers can be used in breeding for waxy characteristics in wheat

CHSY3 promotes proliferation and migration in gastric cancer and is associated with immune infiltration

Abstract Background The glycosyltransferase CHSY3 is a CHSY family member, yet its importance in the context of gastric cancer development remains incompletely understood. The present study was thus developed to explore the mechanistic importance of CHSY3 as a regulator of gastric cancer. Methods Expression of CHSY3 was verified by TCGA, GEO and HPA databases. Kaplan–Meier curve, ROC, univariate cox, multivariate cox, and nomogram models were used to verify the prognostic impact and predictive value of CHSY3. KEGG and GO methods were used to identify signaling pathways associated with CHSY3. TIDE and IPS scores were used to assess the immunotherapeutic value of CHSY3. WGCNA, Cytoscape constructs PPI networks and random forest models to identify key Hub genes. Finally, qRT-PCR and immunohistochemical staining were performed to verify CHSY3 expression in clinical specimens. The ability of CHSY3 to regulate tumor was further assessed by CCK-8 assay and cloning assay, EDU assay, migration assay, invasion assay, and xenograft tumor model analysis. Results The expression of CHSY3 was discovered to be abnormally upregulated in GC tissues through TCGA, GEO, and HPA databases, and the expression of CHSY3 was associated with poor prognosis in GC patients. Correlation analysis and Cox regression analysis revealed higher CHSY3 expression in higher T staging, an independent prognostic factor for GC. Moreover, elevated expression of CHSY3 was found to reduce the benefit of immunotherapy as assessed by the TIDE score and IPS score. Then, utilizing WGCNA, the PPI network constructed by Cytoscape, and random forest model, the Hub genes of COL5A2, POSTN, COL1A1, and FN1 associated with immunity were screened. Finally, the expression of CHSY3 in GC tissues was verified by qRT-PCR and immunohistochemical staining. Moreover, the expression of CHSY3 was further demonstrated by in vivo and in vitro experiments to promote the proliferation, migration, and invasive ability of GC. Conclusions The results of this study suggest that CHSY3 is an important regulator of gastric cancer progression, highlighting its promise as a therapeutic target for gastric cancer

High proportion strontium-doped micro-arc oxidation coatings enhance early osseointegration of titanium in osteoporosis by anti-oxidative stress pathway

The excessive accumulation of reactive oxygen species (ROS) under osteoporosis precipitates a microenvironment with high levels of oxidative stress (OS). This could significantly interfere with the bioactivity of conventional titanium implants, impeding their early osseointegration with bone. We have prepared a series of strontium (Sr)-doped titanium implants via micro-arc oxidation (MAO) to verify their efficacy and differences in osteoinduction capabilities under normal and osteoporotic (high OS levels) conditions. Apart from the chemical composition, all groups exhibited similar physicochemical properties (morphology, roughness, crystal structure, and wettability). Among the groups, the low Sr group (Sr25%) was more conducive to osteogenesis under normal conditions. In contrast, by increasing the catalase (CAT)/superoxide dismutase (SOD) activity and decreasing ROS levels, the high Sr-doped samples (Sr75% and Sr100%) were superior to Sr25% in inducing osteogenic differentiation of MC3T3-E1 cells and the M2 phenotype polarization of RAW264.7 cells, thus enhancing early osseointegration. Furthermore, the results of both in vitro cell co-culture and in vivo studies also showed that the high Sr-doped samples (especially Sr100%) had positive effects on osteoimmunomodulation under the OS microenvironment. Ultimately, the collated findings indicated that the high proportion Sr-doped MAO coatings were more favorable for osteoporosis patients in implant restorations

Osteoinduction Evaluation of Fluorinated Hydroxyapatite and Tantalum Composite Coatings on Magnesium Alloys

Magnesium (Mg) alloys have a wide range of biomaterial applications, but their lack of biocompatibility and osteoinduction property impedes osteointegration. In order to enhance the bioactivity of Mg alloy, a composite coating of fluorinated hydroxyapatite (FHA) and tantalum (Ta) was first developed on the surface of the alloy through thermal synthesis and magnetron sputtering technologies in this study. The samples were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), energy dispersive spectroscopy (EDS) mapping, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and water contact angle measurement (WCA), which characterized the surface alternation and confirmed the deposition of the target FHA/Ta coating. The results of cell morphology showed that the MC3T3-E1 cells on the surface of Mg/FHA/Ta samples had the largest spreading area and lamellipodia. Moreover, the FHA coating endowed the surface with superior cell viability and osteogenic properties, while Ta coating played a more important role in osteogenic differentiation. Therefore, the combination of FHA and Ta coatings could synergistically promote biological functions, thus providing a novel strategy for implant design