Adaptive Double Chain Quantum Genetic Algorithm for Constrained Optimization Problems

Optimization problems are often highly constrained and evolutionary algorithms (EAs) are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA) for solving constrained optimization problems. ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions. Fitness (or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution (SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions. To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process (AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Recuperating Lung Decoction Attenuates the Oxidative Stress State of Chronic Obstructive Pulmonary Disease by Inhibiting the MAPK/AP-1 Signal Pathway and Regulating γ-GCS

Purpose/Objective. To evaluate the effects of Recuperating Lung Decoction (RLD) on the indices of oxidative stress in a rat model of COPD and detect the indices of the MAPK/AP-1/γ-GCS signal pathway for a further survey of the possible targeting site of RLD. Methods/Materials. The rats of COPD were treated with RLD. The protein levels of glutathione (GSH), oxidized glutathione (GSSG), 8-hydroxy-2-deoxyguanosine (8-OHdG), and 4-hydroxynonenal (4-HNE) were measured. In addition, the levels of key signaling molecules (extracellular signal-regulated kinases [ERK], the c-jun N-terminal kinase [JNKs signal pathway], and p38 MAP kinase [p38MAPK], AP-1 proteins [C-fos, C-jun], and γ-glutamyl-cysteine synthetase [γ-GCS-h]) of the MAPK/AP-1/γ-GCS-h signal pathway were assessed. Results. After treatment, the protein level of GSH and the ratio of GSH/GSSG were increased and the amounts of 8-OHdG and 4-HNE were decreased significantly in lung tissues when compared with the nontreated COPD group. Further results showed that the RLD could effectively inhibit the MAPK pathway by inactivation of p38MAPK and ERK and could also downregulate the AP-1 and the γ-GCS-h genes expressions in both protein and mRNA levels. Conclusion. RLD might improve the state of oxidative stress by downregulation of the expression of γ-GCS-h gene by inhibition of the MAPK/AP-1 pathway, thereafter enhancing the ability of antioxidation in COPD

An Antibacterial Peptide with High Resistance to Trypsin Obtained by Substituting d-Amino Acids for Trypsin Cleavage Sites

The poor stability of antibacterial peptide to protease limits its clinical application. Among these limitations, trypsin mainly exists in digestive tract, which is an insurmountable obstacle to orally delivered peptides. OM19R is a random curly polyproline cationic antimicrobial peptide, which has high antibacterial activity against some gram-negative bacteria, but its stability against pancreatin is poor. According to the structure-activity relationship of OM19R, all cationic amino acid residues (l-arginine and l-lysine) at the trypsin cleavage sites were replaced with corresponding d-amino acid residues to obtain the designed peptide OM19D, which not only maintained its antibacterial activity but also enhanced the stability of trypsin. Proceeding high concentrations of trypsin and long-time (such as 10 mg/mL, 8 h) treatment, it still had high antibacterial activity (MIC = 16–32 µg/mL). In addition, OM19D also showed high stability to serum, plasma and other environmental factors. It is similar to its parent peptide in secondary structure and mechanism of action. Therefore, this strategy is beneficial to improve the protease stability of antibacterial peptides

Antibiotic resistance of probiotics isolated from Chinese corn stover silage

ABSTRACTOne of the most important drivers of the emergence of antimicrobial resistance is the irrational usage of antibiotics. But alongside some indirect sources such as feeding Chinese corn stover silage could also transmit resistant genes to animals. This study was aimed at assessing the antibiotic resistance phenotypes, drug resistance genes and mediated drug resistance mechanisms. A total of 37 isolates were obtained by selective medium and identified by 16S rDNA sequencing including Lactobacillus acidophilus (n = 3), Lactobacillus amylovorus (n = 4), Weissella confusa (n = 3), Acetobacter pasteurianus (n = 9), Lactobacillus buchneri (n = 6), Enterococcus faecium (n = 8) and Lactobacillus reuteri (n = 4). Antimicrobial resistance of all isolates to 12 antibiotics was determined using the agar dilution method. Widespread resistance to ampicillin, erythromycin, clindamycin, kanamycin, streptomycin, levofloxacin and ciprofloxacin was observed. The presence of relevant resistance genes was examined by PCR, and the genes pbp5, blaZ, bla TEM, mecA, mexI, tetW, ermB, msrA, msrC, ermC, vatE, aacA-aphD, aphA1, aadA1, aadA2 and vanX were detected. All the isolated strains had multiple drug resistance, and harboured related drug-resistant genes; therefore, the use of probiotics in animal feed should be standardized to reduce the risks of horizontal transmission of drug resistance genes to animal products and human population

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life.

A drug's biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives

Image Quality Assessment for Deep Learning Image Reconstruction Algorithm: A Phantom Study

Objective: To compare the image quality between the deep learning image reconstruction (DLIR) and iterative reconstruction (IR) algorithms via the dedicated phantom. Method: ACR quality assurance phantom (Gammex 464) was scanned by GE Revolution Apex. The CT value accuracy, low contrast resolution, image uniformity and high contrast resolution of five substances from module 1 to module 4 were measured respectively. Through the above indicators, the image quality of three levels (DL, DM and DH) of Truefedelitytm (TFI) and three levels (30%, 60% and 90%) of adaptive statistical iterative reconstruction-V (asir-V, hereinafter referred to as AV) under high dose (20 mgy) were compared. The comparison between the two algorithms for each parameter was tested by One-Way Anova. Results: The high/low-contrast resolution of the six image series were consistent (high-contrast resolution: 10 lp/cm; low-contrast resolution: 6 mm). The two algorithms both slightly overestimated the CT value of polyethylene, air and acrylic, and no statistically significant difference was found among the difference of CT values of the substances. Both algorithms underestimated the CT value of bone and the solid water; TFI showed better performance in evaluating the solid water which was closer to the real value, though statistical difference was not found between each group of images. Among the 6 groups of images, TFI DH showed the best image uniformity and at the same reconstruction level, TFI showed better uniformity than AV. Conclusion: DLIR can further reduced image noise while maintaining image spatial resolution and CT value accuracy at high dose level

An integrated method for the identification of novel genes related to oral cancer

<div><p>Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (<i>e</i>.<i>g</i>., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (<i>e</i>.<i>g</i>., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.</p></div