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We incorporate deep learning (DL) into tiled aperture coherent beam combining (CBC) systems for the first time, to the best of our knowledge. By using a well-trained convolutional neural network DL model, which has been constructed at a non-focal-plane to avoid the data collision problem, the relative phase of each beamlet could be accurately estimated, and then the phase error in the CBC system could be compensated directly by a servo phase control system. The feasibility and extensibility of the phase control method have been demonstrated by simulating the coherent combining of different hexagonal arrays. This DL-based phase control method offers a new way of eliminating dynamic phase noise in tiled aperture CBC systems, and it could provide a valuable reference on alleviating the long-standing problem that the phase control bandwidth decreases as the number of array elements increases.
Maternal supraphysiological estradiol (E2) environment during pregnancy leads to adverse perinatal outcomes. However, the influence of oocyte exposure to high E2 levels on perinatal outcomes remains unknown. Thus, a retrospective cohort study was conducted to explore the effect of high E2 level induced by controlled ovarian stimulation (COH) on further outcomes after frozen embryo transfer (FET). The study included all FET cycles (n = 10,581) between 2014 and 2017. All cycles were categorized into three groups according to the E2 level on the day of the human Chorionic Gonadotropin trigger. Odds ratios (ORs) and their confidence intervals (CIs) were calculated to evaluate the association between E2 level during COH and pregnancy outcomes and subsequent neonatal outcomes. From our findings, higher E2 level was associated with lower percentage of chemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth as well as increased frequency of early miscarriage. Preterm births were more common among singletons in women with higher E2 level during COH (aOR1 = 1.93, 95% CI: 1.22–3.06; aOR2 = 2.05, 95% CI: 1.33–3.06). Incidence of small for gestational age (SGA) was more common in both singletons (aOR1 = 2.01, 95% CI: 1.30–3.11; aOR2 = 2.51, 95% CI: 1.69–3.74) and multiples (aOR1 = 1.58, 95% CI: 1.03–2.45; aOR2 = 1.99, 95% CI: 1.05–3.84) among women with relatively higher E2 level. No association was found between high E2 level during COH and the percentage of macrosomia or large for gestational age. In summary, oocyte exposure to high E2 level during COH should be brought to our attention, since the pregnancy rate decreasing and the risk of preterm birth and SGA increasing following FET.
Evaluation of Cr, Mn, Fe, Zn and Se in humans is challenged by the potentially high within-individual variability of these elements in biological specimens, which are poorly characterised. This study aimed to evaluate their within-day, between-day and between-month variability in spot samples, first-morning voids and 24-h collections. A total of 529 spot urine samples (including eighty-eight first-morning voids and 24-h collections) were collected from eleven Chinese adult men on days 0, 1, 2, 3, 4, 30, 60 and 90 and analysed for these five elements using inductively coupled plasma-MS. Intraclass correlation coefficients (ICC) were utilised to characterise the reproducibility, and their sensitivity and specificity were analysed to assess how well a single measurement classified individuals’ 3-month average exposures. Serial measurements of Zn in spot samples exhibited fair to good reproducibility (creatinine-adjusted ICC = 0·47) over five consecutive days, which became poor when the samples were gathered months apart (creatinine-adjusted ICC = 0·33). The reproducibility of Cr, Mn, Fe and Se in spot samples was poor over periods ranging from days to months (creatinine-adjusted ICC = 0·01–0·12). Two spot samples were sufficient for classifying 60 % of the men who truly had the highest (top 33 %) 3-month average Zn concentrations; for Cr, Mn, Fe and Se, however, at least three specimens were required to achieve similar sensitivities. In conclusion, urinary Cr, Mn, Fe, Zn and Se concentrations showed a strong within-individual variability, and a single measurement is not enough to efficiently characterise individuals’ long-term exposures.
Maternal one-carbon metabolism during pregnancy is crucial for fetal development and programming by DNA methylation. However, evidence on one-carbon biomarkers other than folate is lacking. We, therefore, investigated whether maternal plasma methyl donors, that is, choline, betaine and methionine, are associated with birth outcomes. Blood samples were obtained from 115 women during gestation (median 26·3 weeks, 90 % range 22·7–33·0 weeks). Plasma choline, betaine, methionine and dimethylglycine were measured using HPLC-tandem MS. Multivariate linear and logistic regression models were used to estimate the association between plasma biomarkers and birth weight, birth length, the risk of small-for-gestational-age and large-for-gestational-age (LGA). Higher level of maternal betaine was associated with lower birth weight (–130·3 (95 % CI –244·8, –15·9) per 1 sd increment for log-transformed betaine). Higher maternal methionine was associated with lower risk of LGA, and adjusted OR, with 95 % CI for 1 sd increase in methionine concentration was 0·44 (95 % CI 0·21, 0·89). Stratified analyses according to infant sex or maternal plasma homocysteine status showed that reduction in birth weight in relation to maternal betaine was only limited to male infants or to who had higher maternal homocysteine status (≥5·1 µmol/l). Higher maternal betaine status was associated with reduced birth weight. Maternal methionine was inversely associated with LGA risk. These findings are needed to be replicated in future larger studies.
Civil emergencies occurring with little warning can quickly produce mass casualties. To develop an Emergency Department’s surge capacity, medical student involvement in the disaster response has been advocated. Duke-NUS Medical School in Singapore is located in proximity to Singapore General Hospital (SGH) and represents an untapped manpower resource. With appropriate training, medical students can be leveraged upon as ready and reasonably qualified manpower.
This review provides a snapshot of the conceptualization and setting up of the Disaster Volunteer Corps (DVC) program. We discuss the overall strategy and benefits to stakeholders, emphasizing the close symbiotic relationship between academia and healthcare services.
Duke-NUS medical students will be recruited to receive training from SGH emergency physicians. The frequency of training will be four times yearly, with ad hoc participation in disaster simulation exercises. A call-tree will be employed for DVC activation. The DVC curriculum includes disaster response principles, HAZMAT, crowd control, marshaling, logistics, psychological support, and basic first aid. Teaching methods include didactic lectures, case discussions, involvement in event medical cover, and participation in disaster simulation exercises and response planning.
To date, there are 10 medical students and four emergency physician faculty volunteers involved in the program. Support is provided by adjunct instructors from nursing, nuclear medicine, social work, and security, for training in decontamination, radiological disasters, psychological first aid, and crowd control measures respectively. Assessment by faculty will be conducted to ensure the quality of training and competency of skills.
The DVC provides a unique way of teaching medical students disaster medicine principles in a hands-on experiential format, while simultaneously enhancing the operational readiness of the hospital in times of disaster. This model of close collaboration between university educational and healthcare services provides a feasible model of structured volunteerism that could be replicated in other similar settings.
Nutrition therapy is considered an important treatment of burn patients. The aim of the study was to delineate the nutritional support in severe burn patients and to investigate association between nutritional practice and clinical outcomes. Severe burn patients were enrolled (n 100). In 90 % of the cases, the burn injury covered above 70 % of the total body surface area. Mean interval from injury to nutrition start was 2·4 (sd 1·1) d. Sixty-seven patients were initiated with enteral nutrition (EN) with a median time of 1 d from injury to first feed. Twenty-two patients began with parenteral nutrition (PN). During the study, thirty-two patients developed EN intolerance. Patients received an average of about 70 % of prescribed energy and protein. Patients with EN providing <30 % energy had significantly higher 28- d and in-hospital mortality than patients with EN providing more than 30 % of energy. Mortality at 28 d was 11 % and in-hospital mortality was 45 %. Multiple regression analysis demonstrated that EN providing <30 % energy and septic shock were independent risk factors for 28- d prognosis. EN could be initiated early in severe burn patients. Majority patients needed PN supplementation for energy requirement and EN feeding intolerance. Post-pyloric feeding is more efficient than gastric feeding in EN tolerance and energy supplement. It is difficult for severe burn patients to obtain enough feeding, especially in the early stage of the disease. More than 2 weeks of underfeeding is harmful to recovery.
Principal component analysis (PCA) is used for dimension reduction and data summary. However, principal components (PCs) cannot be easily interpreted. To interpret PCs, this study compares two methods to approximate PCs. One uses the PCA loadings to understand how input variables are projected to PCs. The other uses forward-stepwise regression to determine the proportions of PC variances explained by input variables.
Two data sets derived from the Canadian Health Measures Survey (CHMS) were used to test the concept of PC approximation: a spirometry subset with the measures from the first trial of spirometry; and, full data set that contained representative variables. Variables were centered and scaled. PCA were conducted with 282 and twenty-three variables respectively. PCs were approximated with two methods.
The first PC (PC1) could explain 12.1 percent and 50.3 percent of total variances in respective data sets. The leading variables explained 89.6 percent and 79.0 percent of the variances of PC1 in respective data sets. It required one and two variables to explain more than 80 percent of the variances of PC1, respectively. Measures related to physical development were the leading variables to approximate PC1 and lung function variables were leading to approximate PC2 in the full data set. The leading variable to approximate PC1 of the spirometry subset were forced expiratory volume (FEV) 0.5/forced vital capacity (FVC) (percent) and FEV1/FVC (percent).
Approximating PCs with input variables were highly feasible and helpful for the interpretation of PCs, especially for the first PCs. This method is also useful to identify major or unique sources of variances in data sets. The variables related to physical development are the variables related to the most variations in the full data set. The leading variable in the spirometry subset, FEV0.5/FVC (percent), is not well studied for its application in clinical use.
Composite measures and indices are used in medical research to represent certain concepts that cannot be measured with one variable. They can be used to predict outcomes or serve as outcomes in trials. The creation of innovative indices is important to increase publications and secure research funding. However, some assumptions and problems are prevalent among indices. We aim to develop a reporting guide and an appraisal tool for indices based on the issues we identified.
We reproduced the three frailty indices from a previous publication and 134,689 principal component-based indices. We reviewed the index assumptions, bias introduced by data processing, relationships between input variables. We interpreted the indices with input variables.
We identified four major issues to be addressed in a reporting guide: constraints imposed by index creation on the input variables; data processing without evidence base; indices poorly linked to input variables; and, relatively inferior predictive power. We demonstrated a flow diagram and a checklist to report and review these four issues related to innovative indices.
A reporting and critical appraisal tool for innovative indices is lacking and needed. These four issues that need to be explicitly considered are previously neglected. This guide is the first attempt to improve the quality and generalizability of innovative indices. This guide can be used to lead further discussion with other experts and review committees.
Principal component analysis (PCA) is important to summarize data or reduce dimensionality. However, one disadvantage of using PCA is the interpretability of the principal components (PCs), especially in a high-dimensional database. This study aims to analyze the patterns of variance accumulation according to PCA loadings and to approximate PCs with input variables from sample data sets.
There were three data sets of various sizes used to understand the performance of PC approximation: Hitters; SF-12v2 subset of the 2004 to 2011 Medical Expenditure Panel Survey (MEPS); and, the full set of 1996 to 2011 MEPS data. The variables in three data sets were first centered and scaled before PCA. PCs approximation was studied with two approaches. First, the PC loadings were squared to estimate the variance contribution by variables to PCs. The other method was to use forward-stepwise regression to approximate PCs with all input variables.
The first few PCs represented large portions of total variances in each data set. Approximating PCs using stepwise regression could more efficiently identify the input variables that explain large portions of PC variances than approximating according to PCA loadings in three data sets. It required few numbers of variables to explain more than eighty percent of the PC variances.
Approximating and interpreting PCs with stepwise regression is highly feasible. Approximating PCs can help i) interpret PCs with input variables, ii) understand the major sources of variances in data sets, iii) select unique sources of information and iv) search and rank input variables according to the proportions of PC variance explained. This is an approach to systematically understand databases and search for variables that are highly representative of databases.
Index mining is a new discipline that aims to search for the composite measures or indices most relevant to the contexts or outcomes. After reviewing three frailty indices and principal component (PC)-based indices, we hereby show certain occasions that can lead to ineffective indices, which consist of bias or fail to represent the theories.
We reproduced and reviewed the three frailty indices and the 134,689 PC (principal component) -based indices from previous publications. The impact of aggregating the input variables on the final indices was analyzed using forward stepwise regression.
Several methods to combine the input variables were related to ineffective projection of information onto the indices. The most common causes leading to ineffective summation of input variables were shown in three examples involving different types of input variables, which were positively or negatively correlated or uncorrelated to the outcome. Ineffective indices were created often because of the summation of redundant information or uncorrelated variables.
The creation of ineffective indices can be avoided if the relationships between input variables and outcomes are properly scrutinized. The creation of composite measures and indices is still a discipline under active development. The three examples we identified are the mistakes that may be repeated unintentionally and need to be addressed with explicit rules. A reporting guide for the creation of composite measures has been proposed. A proper review of index objectives, data characteristics, and data limitations before creating composite measures or indices is recommended.
The present study was undertaken to investigate the antiparasitic activity of extracellular products of Streptomyces albus. Bioactivity-guided isolation of chloroform extracts affording a compound showing potent activity. The structure of the compound was elucidated as salinomycin (SAL) by EI-MS, 1H NMR and 13C NMR. In vitro test showed that SAL has potent anti-parasitic efficacy against theronts of Ichthyophthirius multifiliis with 10 min, 1, 2, 3 and 4 h (effective concentration) EC50 (95% confidence intervals) of 2.12 (2.22–2.02), 1.93 (1.98–1.88), 1.42 (1.47–1.37), 1.35 (1.41–1.31) and 1.11 (1.21–1.01) mg L−1. In vitro antiparasitic assays revealed that SAL could be 100% effective against I. multifiliis encysted tomonts at a concentration of 8.0 mg L−1. In vivo test demonstrated that the number of I. multifiliis trophonts on Erythroculter ilishaeformis treated with SAL was markedly lower than that of control group at 10 days after exposed to theronts (P < 0.05). In the control group, 80% mortality was observed owing to heavy I. multifiliis infection at 10 days. On the other hand, only 30.0% mortality was recorded in the group treated with 8.0 mg L−1 SAL. The median lethal dose (LD50) of SAL for E. ilishaeformis was 32.9 mg L−1.
Healthcare-associated infections (HAIs) are a major worldwide public-health problem, but less data are available on the long-term trends of HAIs and antimicrobial use in Eastern China. This study describes the prevalence and long-term trends of HAIs and antimicrobial use in a tertiary care teaching hospital in Hefei, Anhui, China from 2010 to 2017 based on annual point-prevalence surveys. A total of 12 505 inpatients were included; 600 HAIs were recorded in 533 patients, with an overall prevalence of 4.26% and a frequency of 4.80%. No evidence was found for an increasing or decreasing trend in prevalence of HAI over 8 years (trend χ2 = 2.15, P = 0.143). However, significant differences in prevalence of HAI were evident between the surveys (χ2 = 21.14, P < 0.001). The intensive care unit had the highest frequency of HAIs (24.36%) and respiratory tract infections accounted for 62.50% of all cases; Escherichia coli was the most common pathogen (16.67%). A 44.13% prevalence of antimicrobial use with a gradually decreasing trend over time was recorded. More attention should be paid to potential high-risk clinical departments and HAI types with further enhancement of rational antimicrobial use.
Maternal dietary patterns and macronutrients intake have been shown to affect the development of gestational diabetes mellitus (GDM), but the findings are inconsistent. We aimed to identify maternal dietary patterns and examine their associations with GDM risk, and to evaluate the contributions of macronutrients intake to these associations. We included 2755 Chinese pregnant women from the Tongji Maternal and Child Health Cohort. Dietary intakes were assessed using a validated semi-quantitative FFQ 2 weeks before the diagnosis of GDM. GDM (n 248) was diagnosed based on the results of a 75-g, 2-h oral glucose tolerance test at 24–28 weeks gestation. We derived five different dietary patterns from a principal component analysis. The results showed that high fish–meat–eggs scores, which were positively related to protein intake and inversely related to carbohydrate intake, were associated with a higher risk of GDM (adjusted OR for quartile 4 v. quartile 1: 1·83; 95 % CI 1·21, 2·79; Ptrend=0·007) and higher plasma glucose levels. In contrast, high rice–wheat–fruits scores, which were positively related to carbohydrate intake and inversely related to protein intake, were associated with lower risk of GDM (adjusted OR for quartile 3 v. quartile 1: 0·54; 95 % CI 0·36, 0·83; Ptrend=0·010) and lower plasma glucose levels. In addition, dietary protein and carbohydrate intake significantly contributed to the associations between dietary patterns and GDM risk or glucose levels. These findings suggest that a dietary pattern characterised by high protein and low carbohydrate intake in pregnancy was associated with a higher risk of GDM, which may provide important clues for dietary guidance during pregnancy to prevent GDM.
Kawasaki disease is the leading cause of acquired heart disease in children from developed countries. The Interleukin-6/ Interleukin-12 cytokine family has many members, including the paradoxical anti- and pro-inflammatory Interleukin-27. Recent studies have demonstrated that Interleukin-27 plays a role in immune diseases. Given this, we sought to evaluate the association between Interleukin-27 genetic polymorphisms and Kawasaki disease in Chinese children.
Methods and results
Interleukin-27 was genotyped in 100 Kawasaki disease children and 98 healthy children (controls), resulting in the direct sequencing of eight Single-nucleotide Polymorphisms: rs17855750, rs40837, rs26528, rs428253, rs4740, rs4905, rs153109, and rs181206). There were no significant differences in Interleukin-27 genotypes between Kawasaki disease and control groups. Of the eight Single-nucleotide Polymorphisms, there was a significant increase in the risk of Kawasaki disease with coronary arterial lesions in children with the rs17855750 (T>G), rs40837 (A>G), rs4740 (G>A), rs4905 (A>G), rs153109 (T>C), and rs26528 (A>G) Single-nucleotide Polymorphisms. This was particularly true for rs17855750 (T>G), which had a greater frequency in Kawasaki disease children with coronary arterial aneurysm.
These findings may be used as risk factors when assessing a child’s likelihood of developing Kawasaki disease, as well as for the development of future therapeutic treatments for Kawasaki disease.
Sedative–hypnotic medication use has been related to severe adverse events and risks. This study investigated the prevalence of and characteristics associated with the use of sedatives and hypnotics among community-dwelling elderly persons aged 65 years and over in Taiwan.
A representative sample of community-dwelling adults was recruited. Clinical and sociodemographic data were collected for assessing physical, mental, and cognitive functioning and disorders. Sedatives and hypnotics use was determined via both self-reporting and prescription records. Logistic regression modeling was used to evaluate associations between sedative–hypnotic use and demographic and health status.
Among the 3,978 participants aged 65 years and over, the rate of sedative–hypnotic use was 19.7% (n = 785). 4.5% (n = 35) of users reported sedative–hypnotic use without a doctor's prescription. Several sociodemographic characteristics were positively associated with sedative and hypnotic use, including older age, female gender, higher education level, married status, unemployment, and current alcohol consumption. Comorbid chronic and cardiovascular diseases, mental illness, depression, pain, and sleep problems also increased the likelihood of sedative–hypnotic use.
This study is one of the largest pioneer studies to date to survey sedatives–hypnotics use among community-dwelling elderly. One in five community-dwelling older adults reported sedative–hypnotic drugs use in Taiwan, and about 5% of sedative and/or hypnotics usage was without a doctor's prescription. Findings could be helpful for drug-use safety interventions to identify target geriatric patients who are in general at higher risk of downstream harm associated with sedative–hypnotic use in geriatric patients.
We have proposed and demonstrated numerically an ultra-small (4×4μm2) hydrogen sensor based on micro ring resonator. With a palladium or platinum layer coated on the inner surface of the micro ring resonator, the device is highly sensitive to the low hydrogen concentration variation and the sensitivity is at least one magnitude order larger than the optical fiber-based hydrogen sensor. We have also investigated the tradeoff between the portion coverage of palladium/platinum layer and the sensitivity. The width of the hydrogen sensitive layer is also studied and the minimum feature width is determined to be the length of the ring waveguide evanescent wave. This ultra-small optical hydrogen sensor will be promising to realize highly compact sensor with integration capability for applications on hydrogen fuel economy.
This paper examines the relationships of income with education and health using heterogeneous panel cointegration techniques to account for the potential cross-country heterogeneity in the effects of education and health. Our main results are: (i) education and health are, on average, income-enhancing; (ii) for different schooling levels, although primary education lowers income, both secondary and tertiary education raise income with larger impacts for the former than the latter, on average; (iii) there is considerable heterogeneity in the effects of education and health on income across countries; and (iv) the effect of education (health) on income tends to be greater (smaller) in countries with higher levels of development, greater (less) trade openness, less abundant natural resources, less corruption, higher levels of democracy, and a more homogeneous society.
In this paper, we mainly study the moderate deviation principle of sample quantiles and order statistics for stationary m-dependent random variables. The results obtained in this paper extend the corresponding ones for an independent and identically distributed sequence to a stationary m-dependent sequence.