110023 research outputs found
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Whole-genome-sequence-based characterization of an NDM-5-producing uropathogenic Escherichia coli EC1390
No full text[[abstract]]Background
Urinary tract infection (UTI) is one of the most common outpatient bacterial infections. In this study, we isolated and characterized an extensively-drug resistant (XDR) NDM-5-producing Escherichia coli EC1390 from a UTI patient by using whole-genome sequencing (WGS) in combination with phenotypic assays.
Methods
Antimicrobial susceptibility to 23 drugs was determined by disk diffusion method. The genome sequence of EC1390 was determined by Nanopore MinION MK1C platform. Conjugation assays were performed to test the transferability of EC1390 plasmids to E. coli recipient C600. Phenotypic assays, including growth curve, biofilm formation, iron acquisition ability, and cell adhesion, were performed to characterize the function of EC1390 plasmids.
Results
Our results showed that EC1390 was only susceptible to tigecycline and colistin, and thus was classified as XDR E. coli. A de novo genome assembly was generated using Nanopore 73,050 reads with an N50?value of 20,936 bp and an N90?value of 7,624 bp. WGS analysis showed that EC1390 belonged to the O101-H10 serotype and phylogenetic group A E. coli. Moreover, EC1390 contained 2 conjugative plasmids with a replicon IncFIA (pEC1390-1 with 156,286 bp) and IncFII (pEC1390-2 with 71,840 bp), respectively. No significant difference was observed in the bacterial growth rate in LB broth and iron acquisition ability between C600, C600 containing pEC1390-1, C600 containing pEC1390-2, and C600 containing pEC1390-1 and pEC1390-2. However, the bacterial growth rate in nutrition-limited M9 broth was increased in C600 containing pEC1390-2, and the cell adhesion ability was increased in C600 containing both pEC1390-1 and pEC1390-2. Moreover, these plasmids modulated the biofilm formation under different conditions.
Conclusions
In summary, we characterized the genome of XDR-E. coli EC1390 and identified two plasmids contributing to the antimicrobial resistance, growth of bacteria in a nutrition-limited medium, biofilm formation, and cell adhesion
Exploring Perceived Stress in Mothers with Singleton and Multiple Preterm Infants: A Cross-Sectional Study in Taiwan
No full text[[abstract]]Objective: The aim of this study was to explore mothers' perceived level of stress one month after hospital discharge following the birth of singleton and multiple preterm infants.
Design: A cross-sectional design was used to compare mother's perceived stress in two groups of postpartum mothers and the relationship of the theoretical antecedents and these variables.
Setting: A neonatal intensive care unit in a medical center in Taiwan.
Participants: Mothers of 52 singletons and 38 multiple premature infants were recruited. One month after the infant was discharged, the participants completed a self-reported questionnaire that included demographic data about the mother and infant, the 21-item Social Support Scale, and the 15-item Perceived Stress Scale. This was returned by email or completed at the outpatient unit.
Analysis: Descriptive and inferential analysis.
Results: The mean social support scores were 76.6 and 76.5 (out of 105) for mothers with singleton and multiple birth infants, respectively. The most important supporter was the husband. The mean perceived stress scores of 25.8 and 31.0 for mothers with singleton and multiple birth infants, respectively, were significantly different (p = 0.02). Sleep deprivation and social support were predictive indicators of perceived stress in mothers with preterm infants.
Conclusions: We suggest that the differences in stress and needs of mothers with singleton and multiple births should be recognized and addressed in clinics. The findings of this study serve as a reference for promoting better preterm infant care
Factors Related to Psychological Distress of Multiparous Women in the First Trimester: A Cross-Sectional Study
No full text[[abstract]]Background: The birth rate in Taiwan has declined rapidly; thus, encouraging women to give birth is an important issue in the country. Pregnant women may experience psychological distress, which may negatively impact the health of children and mothers. Prenatal psychological distress is more common in multiparous women than in primiparous women. In addition, compared with that in the second and third trimesters, psychological distress in the first trimester is relatively high. Understanding psychological distress and the associated factors for multiparous women in the first trimester is important to providing early interventions and preventing subsequent maternal and child health problems.
Purpose: This study was designed to examine the important predictive factors related to depression, anxiety, and stress among Taiwanese multiparous women in the first trimester.
Methods: A cross-sectional design was used. In all, 216 multiparous women at 16 weeks of pregnancy were recruited from three hospitals in Taiwan. Self-reported questionnaires were used to gather data on demographic characteristics, the parenting stress of motherhood, spousal support, and psychological distress. A multiple logistic regression analysis was conducted to examine the factors associated with psychological distress.
Results: The prevalence of depression, anxiety, and stress in multiparous women was found to be 31.9%, 42.6%, and 11.1%, respectively. The multiple logistic regression analysis indicated that the stress related to parent-child interaction was a significant predictor of depression and anxiety, the stress related to child-rearing was a significant predictor of anxiety, and low spousal instrumental support was a significant predictor of stress. The model respectively explained 30%, 27%, and 23% of the variance in depression, anxiety, and stress.
Conclusions/implications for practice: Reducing the stress related to parent-child interaction and child-rearing and encouraging spousal instrumental support should be considered during prenatal care when designing interventions to reduce the psychological distress of multiparous women in their first trimester
Predicting exercise behaviors and intentions of Taiwanese urban high school students using the theory of planned behavior
No full text[[abstract]]Purpose: This study applied the Theory of Planned Behavior to predict exercise behaviors and intentions of teenagers and analyzed sex differences. Design and methods: A prospective study design was employed to survey tenth-grade students in Taipei, Taiwan. The 951 participants reported their exercise attitudes, subjective norms, perceived behavioral control (PBC), and intentions, and their exercise behaviors were tracked 6 months later. Results: Results revealed that 22.1% of all students and more male students than female students exercised for ≥30 min/day on 5 or more days/week. Hierarchical multiple regression analyses demonstrated that intentions, PBC, attitudes, and subjective norms explained 32.5% of the variation in exercise behavior (p < .001). Intentions, attitudes, and PBC were related to exercise behavior regardless of sex. Attitudes, subjective norms, and PBC explained 67.0% of the variation in intentions (p < .001). Attitudes and PBC were related to intentions regardless of sex. Conclusions: The findings support that the main constructs of the Theory of Planned Behavior can effectively predict regular exercise intentions and behaviors among adolescents. Practice implications: The results can serve as a reference for nurses and other healthcare professionals when formulating effective strategies to encourage adolescents to engage in exercise practices
A local diagnosis algorithm for hypercube-like networks under the BGM diagnosis model
No full text[[abstract]]System diagnosis is process of identifying faulty nodes in a system. An efficient diagnosis is crucial for a multiprocessor system. The BGM diagnosis model is a modification of the PMC diagnosis model, which is a test-based diagnosis. In this paper, we present a specific structure and propose an algorithm for diagnosing a node in a system under the BGM model. We also give a polynomial-time algorithm that a node in a hypercube-like network can be diagnosed correctly in three test rounds under the BGM diagnosis model
A method for the process of collagen modified polyester from fish scales waste
No full text[[abstract]]In this study, we introduced a novel polymerization method of polyester using collagen peptides derived from fish scale waste. After the extraction process of collagen peptide from fish scales, putting collagen peptide, ethylene glycol and Benzenedicarboxylic acid into a container, and mixing them to form a mixture; heating the mixture for executing an esterification reaction, to product esters and water; heating the esters, and stirring the esters via a mixer; in a specific period, decreasing the pressure in the container for executing a polycondensation reaction; decreasing the pressure in the container to a second pressure, and stirring the esters via the mixer, to produce a collagen modified polyester.
Collagen peptides are rich in glycine, proline, and hydroxyproline, and by forming a triple helix structure, such as that of the copolyester, gain better hydrophilicity, antistaticity, and ductility. As a result, the produced collagen modified polyester fiber keeps the characteristics of the traditional polyethylene terephthalate fibers including strength, durability, and resistance to wrinkle and shrink. However, the supramolecular collagen modified polyester containing animal collagen peptides has naturally a soft touch and champagne-like color. Consequently, it can be used as a suitable material for skin-friendly functional clothes with or without additional dying. In brief
A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals
No full text[[abstract]]In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided
An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning
No full text[[abstract]]Alzheimer's disease (AD) is the most common type (>60%) of dementia and can wreak havoc on the psychological and physiological development of sufferers and their carers, as well as the economic and social development. Attributed to the shortage of medical staff, automatic diagnosis of AD has become more important to relieve the workload of medical staff and increase the accuracy of medical diagnoses. Using the common MRI scans as inputs, an AD detection model has been designed using convolutional neural network (CNN). To enhance the fine-tuning of hyperparameters and, thus, the detection accuracy, transfer learning (TL) is introduced, which brings the domain knowledge from heterogeneous datasets. Generative adversarial network (GAN) is applied to generate additional training data in the minority classes of the benchmark datasets. Performance evaluation and analysis using three benchmark (OASIS-series) datasets revealed the effectiveness of the proposed method, which increases the accuracy of the detection model by 2.85-3.88%, 2.43-2.66%, and 1.8-40.1% in the ablation study of GAN and TL, as well as the comparison with existing works, respectively
Deep active reinforcement learning for privacy preserve data mining in 5G environments
No full text[[abstract]]Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches
Enhanced risk of osteoporotic fracture in patients with sarcopenia: A national population-based study in Taiwan
No full text[[abstract]]Sarcopenia is a progressive and generalized skeletal muscle disorder associated with poor health outcomes in older adults. However, its association with the risk of fracture risk is yet to be clarified. Therefore, this study aimed to assess the incidence and consequence of osteoporosis-related fractures among patients with sarcopenia in Taiwan. A retrospective, population-based study on 616 patients with sarcopenia, aged >40 years, and 1232 individuals without sarcopenia was conducted to evaluate claims data from Taiwan's National Health Insurance Research Database collected in the period January 2000-December 2013. The incidence rate of osteoporosis-related fracture was 18.13 and 14.61 per 1000 person years in the patients with sarcopenia and comparison cohort, respectively. Patients with sarcopenia had a greater osteoporotic fracture risk (adjusted hazard ratio [HR] 2.11; 95% confidence interval [CI] 1.47-3.04) after correcting for possible confounding. Additionally, females showed statistically significant correlations of sarcopenia with osteoporosis-related fracture risk (HR 1.53; CI 0.83-2.8 for males and HR 2.40, CI 1.51-3.81 for females). During this retrospective study on the fracture risk in Taiwan, an adverse impact of sarcopenia was observed, which substantiates the need to work toward sarcopenia prevention and interventions to reverse fracture susceptibility in patients with sarcopenia