Strains of Acinetobacter baumannii are commensal and opportunistic pathogens that have emerged as problematic hospital pathogens due to its biofilm formation ability and multiple antibiotic resistances. The biofilm-associated pathogens usually exhibit dramatically decreased susceptibility to antibiotics. This study was aimed to investigate the correlation of biofilm-forming ability, antibiotic resistance and biofilm-related genes of 154 A. baumannii isolates which were collected from a teaching hospital in Taiwan. Biofilm-forming ability of the isolates was evaluated by crystal violet staining and observed by scanning electron microscopy. Antibiotic susceptibility was determined by disc diffusion method and minimum inhibitory concentration; the biofilm-related genes were screened by polymerase chain reaction. Results showed that among the 154 tested isolates, 15.6% of the clinical isolates were weak biofilm producers, while 32.5% and 45.4% of them possessed moderate and strong biofilm formation ability, respectively. The experimental results revealed that the multiple drug resistant isolates usually provided a higher biofilm formation. The prevalence of biofilm related genes including bap, bla_PER-1, csuE and ompA among the isolated strains was 79.2%, 38.3%, 91.6%, and 68.8%, respectively. The results indicated that the antibiotic resistance, the formation of biofilm and the related genes were significantly correlated. The results of this study can effectively help to understand the antibiotic resistant mechanism and provides the valuable information to the screening, identification, diagnosis, treatment and control of clinical antibiotic-resistant pathogens.

The antioxidant and antibacterial activities of wood vinegar from Litchi chinensis, and its components have been studied. The chemical compositions of wood vinegar were analyzed by gas chromatography-mass spectrometry (GC-MS). A total of 17 chemical compounds were identified, representing 83.96% of the compositions in the wood vinegar. Three major components, included 2,6-dimethoxyphenol (syringol, 29.54%), 2-methoxyphenol (guaiacol, 12.36%), and 3,5-dimethoxy-4-hydroxytoluene (11.07%), were found in the wood vinegar. Antioxidant activities of the acids were investigated from the aspects of 1,1-Diphyl-2-picrylhydrazyl (DPPH) free radicals scavenging capacity, superoxide anion radical scavenging capacity, and reducing power. The pyroligneous acid exhibited high antioxidant activity which was comparable to the reference standards (vitamin C and butylated hydroxyl toluene) at the same dose with IC₅₀ values of 36.5 ppm calculated by the DPPH radical scavenging assay, 38.38 g Trolox equivalent/100 g DW by the trolox equivalent antioxidant capacity (TEAC) assay, and 67.9 by the reducing power analysis. Antibacterial activity was evaluated using the disc diffusion and microdilution methods against a group of clinically antibiotic resistant isolates. The major components exhibited broad spectrum inhibition against all the bacterial strains with a range of disc inhibition zoon between 15–19 mm. The minimum inhibition concentration and minimum bactericide concentration against the test strains was ranging in 0.95–3.80 μL/100 μL and 1.90–3.80 μL/100 μL, respectively. Most of the antibiotic resistant strains were more susceptible to the wood vinegar than the non-antibiotic resistant strain except the strain of ornithine resistant Staphylococcus aureus. Based on the chemical profile, it was considered that the strongest antioxidant and antibacterial activity of Litchi chinensis wood vinegar was due to its highly phenolic compositions. This study revealed that the Litchi chinensis wood vinegar is valuable to develop as alternative food antioxidant and antibiotics.

Accurate vessel trajectory prediction is essential for maritime traffic control and management. In addition to collision avoidance, accurate vessel trajectory prediction can help in planning navigation routes, shortening the sailing distance, and increasing navigation efficiency. Vessel trajectory prediction with automatic identification system (AIS) data has thus attracted considerable attention in the maritime industry. Original AIS data may contain noise, which limits their application in real-world maritime traffic management. To overcome this problem, this study proposes a vessel trajectory prediction method that combines data denoising and a deep learning prediction model. In this method, data denoising is realized in three steps: trajectory separation, data denoising, and standardization. First, outliers from the original AIS data samples are removed, after which the moving average model is employed to further clean up the data; finally the denoised data are standardized into uniformly distributed time-series data. Bidirectional long short-term memory (Bi-LSTM) is then applied for vessel trajectory prediction. The performance of the proposed prediction model was verified using data on the trajectories of ten vessels and comparing the results obtained with those obtained using other prediction models (exponential smoothing, autoregressive integrated moving average, support vector regression, recurrent neural network, and LSTM models); the trajectory data were downloaded from a public AIS database. The experimental results revealed that model prediction accuracy increased after the data denoising process. Specifically, the Bi-LSTM model had the lowest mean absolute error, mean absolute percentage error, and root-mean-square error, demonstrating that the proposed method is highly efficient for trajectory prediction and can help vessel traffic controllers predict accurate vessel tracks; this would enable them to take early preventive measures to avoid collisions and thus improve the efficiency and safety of maritime traffic.

Recently, drug resistance due to the extensive abuse and over-use of antibiotics has become an increasingly serious problem, making the development of alternative antibiotics a very urgent issue. In this study, the Chinese herbal medicine, Polygonum cuspidatum, was extracted with 95% ethanol and the crude extracts were further purified by partition based on solvent polarity. The antimicrobial activities of the extracts and fractions were determined by the disk diffusion and minimum inhibitory concentration (MIC) methods. The results showed that the ethyl ether fraction (EE) of the ethanol extracts possesses a broader antimicrobial spectrum and greater antimicrobial activity against all of the tested clinical drug-resistant isolates, with a range of MIC values between 0.1–3.5 mg/mL. The active extract showed complete inhibition of pathogen growth and did not induce resistance to the active components. In addition, according to scanning electron microscope observations, EE resulted in greater cell morphological changes by degrading and disrupting the cell wall and cytoplasmic membrane, whereby ultimately this cell membrane integrity damage led to cell death. In conclusion, the EE extracts from Polygonum cuspidatum may provide a promising antimicrobial agent for therapeutic applications against nosocomial drug-resistant bacteria.

Possible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP–SNP interactions. In this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification. Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.