Cloud Image Retrieval for Sea Fog Recognition (CIR-SFR) Using Double Branch Residual Neural Network

Sea fog is a common weather phenomenon at sea, which reduces visibility and causes tremendous hazards to marine transportation, marine fishing, and other maritime operations. Traditional sea fog monitoring methods have enormous difficulties in characterizing the diversity of sea fog and distinguishing sea fog from low-level clouds. Thus, we propose a cloud image retrieval method for sea fog recognition (CIR-SFR) in a deep learning (DL) framework by combining the advantages of metric learning. CIR-SFR includes the feature extraction module and the retrieval-based SFR module. The feature extraction module adopts the double branch residual neural network (DBRNN) to comprehensively extract the global and local features of cloud images. By introducing local branches and using activation masks, DBRNN can focus on regions of interest in cloud images. Moreover, cloud image features are projected into the semantic space by introducing multisimilarity loss, which effectively improves the discrimination ability of sea fog and low-level clouds. For the retrieval-based SFR module, similar cloud images are retrieved from the cloud image dataset according to the distance in the feature space, and accurate SFR results are obtained by counting the percentage of various cloud image types in the retrieval results. To evaluate the SFR system, we establish a dataset of 2544 cloud images including clear sky, low-level cloud, medium high cloud, and sea fog. Experimental results show that the proposed method outperforms the traditional methods in SFR, which provides a new way for SFR

In-Silico Prediction and Modeling of the Quorum Sensing LuxS Protein and Inhibition of AI-2 Biosynthesis in Aeromonas hydrophila

luxS is conserved in several bacterial species, including A. hydrophila, which causes infections in prawn, fish, and shrimp, and is consequently a great risk to the aquaculture industry and public health. luxS plays a critical role in the biosynthesis of the autoinducer-2 (AI-2), which performs wide-ranging functions in bacterial communication, and especially in quorum sensing (QS). The prediction of a 3D structure of the QS-associated LuxS protein is thus essential to better understand and control A. hydrophila pathogenecity. Here, we predicted the structure of A. hydrophila LuxS and characterized it structurally and functionally with in silico methods. The predicted structure of LuxS provides a framework to develop more complete structural and functional insights and will aid the mitigation of A. hydrophila infection, and the development of novel drugs to control infections. In addition to modeling, the suitable inhibitor was identified by high through put screening (HTS) against drug like subset of ZINC database and inhibitor ((−)-Dimethyl 2,3-O-isopropylidene-l-tartrate) molecule was selected based on the best drug score. Molecular docking studies were performed to find out the best binding affinity between LuxS homologous or predicted model of LuxS protein for the ligand selection. Remarkably, this inhibitor molecule establishes agreeable interfaces with amino acid residues LYS 23, VAL 35, ILE76, and SER 90, which are found to play an essential role in inhibition mechanism. These predictions were suggesting that the proposed inhibitor molecule may be considered as drug candidates against AI-2 biosynthesis of A. hydrophila. Therefore, (−)-Dimethyl 2,3-O-isopropylidene-l-tartrate inhibitor molecule was studied to confirm its potency of AI-2 biosynthesis inhibition. The results shows that the inhibitor molecule had a better efficacy in AI-2 inhibition at 40 μM concentration, which was further validated using Western blotting at a protein expression level. The AI-2 bioluminescence assay showed that the decreased amount of AI-2 biosynthesis and downregulation of LuxS protein play an important role in the AI-2 inhibition. Lastly, these experiments were conducted with the supplementation of antibiotics via cocktail therapy of AI-2 inhibitor plus OXY antibiotics, in order to determine the possibility of novel cocktail drug treatments of A. hydrophila infection

Application of three-dimensional visualization modeling technology of ore bodies in metallogenic mode analysis

Objective Copper is an important strategic metal resource for national economic and social development. Therefore, the exploration and resource assessment of copper mines are of great significance. Methods Based on collected geological profiles and drilling data, this study constructed a three-dimensional geological model for a copper ore deposit in western Yunnan, which was applied to estimate the resource reserve in the mining area. Results The ore resource is estimated to be 48.93 million tons, including 0.543 million tons of copper. Through comparative analysis, the model and resource estimation established by our newly proposed three-dimensional geological modelling system shows high credibility, in which multiple analysis modules and dynamic update function have a wide range of applications and it can be used for future drilling engineering and resources estimation. Conclusion This study provides the basis for further exploration work in this region, and it can also be applied to the exploration and mining of related polymetallic deposits

Radiomic Analysis of Quantitative T2 Mapping and Conventional MRI in Predicting Histologic Grade of Bladder Cancer

We explored the added value of a radiomic strategy based on quantitative transverse relaxation (T2) mapping and conventional magnetic resonance imaging (MRI) to evaluate the histologic grade of bladder cancer (BCa) preoperatively. Patients who were suspected of BCa underwent pelvic MRI (including T2 mapping and diffusion-weighted imaging (DWI) before any treatment. All patients with histological-proved urothelial BCa were included. We constructed different prediction models using the mean signal values and radiomic features from both T2 mapping and apparent diffusion coefficient (ADC) maps. The diagnostic performance of each model or parameter was assessed using receiver operating characteristic curves. In total, 92 patients were finally included (training cohort, n = 64; testing cohort, n = 28); among these, 71 had high-grade BCa. In the testing cohort, the T2-mapping radiomic model achieved the highest prediction performance (area under the curve (AUC), 0.87; 95% confidence interval (CI), 0.73–1.0) compared with the ADC radiomic model (AUC, 0.77; 95%CI, 0.56–0.97), and the joint radiomic model of 0.78 (95%CI, 0.61–0.96). Our results demonstrated that radiomic mapping could provide more information than direct evaluation of T2 and ADC values in differentiating histological grades of BCa. Additionally, among the radiomic models, the T2-mapping radiomic model outperformed the ADC and joint radiomic models

Development of Paroxetine Hydrochloride Single Layer Controlled-Release Tablets Based on 3² Factorial Design

Major depressive disorder (MDD) is one of the main contributors to disability and suicide mortality globally. Paroxetine hydrochloride (PHH) is the most potent antidepressant used for MDD treatment. Due to its reduced side effects PAXIL® CR is a widely-used controlled-release formulation of PHH. However, the complicated double-layer production of PAXIL® CR faces the risk of layer separation. In this study, PHH enteric coating single layer controlled-release tablets (PHH-EC-SLTs) were designed as a simplified substitution of PAXIL® CR through a rational formulation screening. The optimized PHH-EC-SLTs showed similar release behaviors in vitro to PAXIL® CR and the release profiles corresponded to a zero-order release model (R2 = 0.9958). Polymer matrix erosion was the main release mechanism, according to the fitting exponents n > 1 in the Korsmeyer-Pappas model. Crucial pharmacokinetic parameters including peak-reaching time (Tmax), peak concentration (Cmax) and the area under the blood level-time curve (AUC0-48) of PHH-EC-SLTs and PAXIL® CR had no significant difference (p > 0.05) and the relative bioavailability (F = 97.97%) of PHH-EC-SLTs demonstrated their similar pharmacokinetic profiles in vivo. In view of avoiding layer separation risk and simplifying the preparation processing, the self-made PHH-EC-SLTs could be considered as a safe and economic alternative to PAXIL® CR

Germacrone Inhibits Cell Proliferation and Induces Apoptosis in Human Esophageal Squamous Cell Carcinoma Cells

Germacrone, a natural 10-membered monocyclic sesquiterpene with three double bonds and a ketone, was isolated from the roots of traditional Chinese medicine Saussurea costus (SC). The pharmacological value and intrinsic mechanism of germacrone in the treatment of esophageal squamous cell carcinoma (ESCC) are still unclear. Therefore, in this study, we further explored the internal molecular mechanism by which germacrone exerts its antiproliferation and antimigration ability against ESCC. 3-(4,5-Dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) assays showed that germacrone dose-dependently inhibited the proliferation of ESCC cells. Flow cytometry analysis (FACS) and wound healing experiments on germacrone treated ESCC cells showed that germacrone could induce apoptosis and inhibit the migration of ESCC cells in a dose-dependent manner. In the study on the mechanism of action of germacrone in antiesophageal cancer, we found that germacrone increased the ratio of Bax/Bcl-2 in the cytoplasm of ESCC, resulting in the activation of Caspase-9 and Caspase-3 and decreased the expression of Grp78, thereby reducing the inhibition of Caspase-12 and Caspase-7. In addition, we found that germacrone also inhibited STAT3 phosphorylation in a dose-dependent manner. In conclusion, we determined that germacrone exerted an antiesophageal effect through intrinsic apoptotic signaling pathways and by inhibiting STAT3 activity in ESCC cells

Additional file 1 of Alamandine attenuates ovariectomy-induced osteoporosis by promoting osteogenic differentiation via AMPK/eNOS axis

Supplementary Material

Proteomics Analysis Reveals a Potential Antibiotic Cocktail Therapy Strategy for Aeromonas hydrophila Infection in Biofilm

Antibiotic fitness and acquired resistance are the two critical factors when bacteria respond to antibiotics, and the correlations and mechanisms between these two factors remain largely unknown. In this study, a TMT-labeling-based quantitative proteomics method was used to compare the differential expression of proteins between the fitness and acquired resistance to chlortetracycline in Aeromonas hydrophila biofilm. Bioinformatics analysis showed that translation-related ribosomal proteins, such as 30s ribosome subunits, increased in both factors; fatty acid biosynthesis related proteins, such as FabB, FabD, FabG, AccA, and AccD, increased in biofilm fitness, and some pathways (including propanoate-metabolism-related protein, such as PrpB, AtoB, PflB, AcsA, PrpD, and GabT) displayed decreased abundance in acquired resistance biofilm. The varieties of selected proteins involved in fatty acid biosynthesis and propanoate metabolism were further validated by q-PCR assay or Western blotting. Furthermore, the antibiotic-resistance-function assays showed that fatty-acid biosynthesis should be a protective antibiotics-resistance mechanism and a cocktail of chlortetracycline and triclosan, a fatty-acid-biosynthesis inhibitor, exhibited more efficient antimicrobial capability than did each antibiotic individually on biofilm, specifically on chlortetracycline-sensitive biofilm. We therefore demonstrate that the up-regulation of fatty acid biosynthesis may play an important role in antibiotic resistance and suggest that a cocktail of chlortetracycline and triclosan may be a potential cocktail therapy for pathogenic infections in biofilm