Estimation of salt intake assessed by 24-h urinary sodium level among adults speaking different dialects from the Chaoshan region of southern China.

OBJECTIVE: Dietary salt intake may vary depending on different lifestyles. We aimed to estimate the different salt intakes and evaluate the knowledge and self-awareness about salt among people speaking the Teochew, Teochew-Hakka and Hakka dialects in the Chaoshan region of southern China. DESIGN: The study followed a cluster sampling of residents in Chaoshan region. General characteristics, lifestyles, health status as well as knowledge and self-awareness related to salt intake were investigated using a questionnaire. Anthropometric variables as well as Na and K excretion in a 24-h urine collection were measured. SETTING: Chaoshan region of China. PARTICIPANTS: Four hundred fifteen adults who spoke only one of these three dialects. RESULTS: The salt intake of adults who spoke the Teochew, Teochew-Hakka and Hakka dialects was 7·19 (interquartile range (IQR) 5·29-10·17), 9·03 (IQR 6·62-11·54) and 10·12 (IQR 7·61-12·82) g/d, respectively, with significant differences between Teochew and Teochew-Hakka speakers and between Teochew and Hakka speakers (both P < 0·05). The Na:K ratio for adults who spoke the three dialects was 3·00 (IQR 2·00-4·11), 3·50 (IQR 2·64-4·82) and 4·52 (IQR 3·35-5·97), respectively, and differed significantly among the groups (all P < 0·05). Multiple linear regression analysis showed increased Na:K ratio associated with hypertension (ß = 0·71, P = 0·043) in Hakka speakers. Knowledge and self-awareness about salt intake were poor in this population. CONCLUSIONS: Salt intake was closely related to lifestyles and was higher than the upper limit (5 g/d) recommended by the WHO in adults of Chaoshan, especially those speaking the Hakka dialect.

Years of life lost with premature death due to ambient temperatures in a southwest plateau region of China: a cause-specific and individual characteristics stratified mortality study.

We aimed to explore whether there were cold and heat temperature adverse effects on years of life lost (YLL) for non-accidental mortality in Yuxi, a southwest plateau region of China. From data for 89,467 non-accidental deaths over an 8-year study period, we used a general linear regression model combined with a distributed lag non-linear model to assess the burden of disease non-accidental mortality due to ambient temperature with the YLL indicator. We estimated the mean YLL change per 1 °C decrease from the 25th to 1st percentile mean temperature as the cold effect and per 1 °C increase from the 75th to 99th percentile as the heat effect. The 95% empirical confidence intervals (eCIs) were calculated by using a bootstrap simulation method. The exposure-response curve between average temperature and YLL was U-shaped. The cold effect peaked at the first day after exposure and disappeared at 2 weeks, and the heat effect only lasted for the first 3 days. A per 1 °C decrease from the 25th to 1st mean temperature percentile was associated with an increase of 15.6 (95% eCI: 2.4, 22.9) in YLL for non-accidental diseases, and the cumulative effects due to cold were stronger in contrast to that attributed by heat. Cold temperature had a significant impact on YLL among the subgroups, with higher YLL in cardiovascular disease, stroke, males, Han nationality, married, and those engaged in agriculture than their corresponding categories. An increasing death burden of non-accidental in Yuxi of China due to cold temperature was demonstrated, and the association was also modified by specific disease causes and individual features.

Integrated analysis identifying new lncRNA markers revealed in ceRNA network for tumor recurrence in papillary thyroid carcinoma and build of nomogram.

OBJECT: The risk of tumor recurrence is currently the focus of clinical attention in the treatment of papillary thyroid carcinoma (PTC). This study focuses on the identification of novel prognostic long noncoding RNA (lncRNA) signatures for tumor recurrence in PTC. METHOD: RNA sequencing profiling of patients with PTC was obtained from the TCGA and Gene Expression Omnibus databases. Differently expressed lncRNA, microRNA (miRNA), and messenger RNA (mRNA) signatures between patients with and without tumor recurrence were selected. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network for tumor recurrence in PTC was constructed to identify lncRNAs associated with tumor relapse in papillary carcinoma. Functional enrichment analysis was performed. Adjusted odds ratios were estimated to identify candidate prognostic lncRNAs considering clinical covariates. Validation analysis was conducted. Nomogram was built based on the verified prognostic lncRNAs and clinical features. RESULTS: The lncRNA-miRNA-mRNA ceRNA network for tumor recurrence in PTC was constructed. Functional enrichment analysis suggested that the identified lncRNAs were associated with PTC. Adjusted odds ratios indicated that 5 of the 16 selected lncRNAs were candidate biomarkers predicting tumor recurrence of thyroid carcinoma. Among which, TTTY10 was verified as novel prognostic markers in validation analysis. Nomogram was built based on the newly identified lncRNA biomarker and clinical covariates. CONCLUSION: In this study, lncRNA TTTY10 was identified as potential novel prognostic markers predicting tumor recurrence in PTC. It may provide useful information for future molecular and cohort studies focusing on the prognosis of PTC.

Integration analysis for novel lncRNA markers predicting tumor recurrence in human colon adenocarcinoma.

BACKGROUND: Numerous evidence has suggested that long non-coding RNA (lncRNA) acts an important role in tumor biology. This study focuses on the identification of novel prognostic lncRNA biomarkers predicting tumor recurrence in human colon adenocarcinoma. METHODS: We obtained the research data from The Cancer Genome Atlas (TCGA) database. The interaction among different expressed lncRNA, miRNA and mRNA markers between colon adenocarcinoma patients with and without tumor recurrence were verified with miRcode, starBase and miRTarBase databases. We established the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on the verified association between the selected markers. We performed the functional enrichment analysis to obtain better understanding of the selected lncRNAs. Then we use multivariate logistic regression to identify the prognostic lncRNA markers with covariates. We also generated a nomogram predicting tumor recurrence risk based on the identified lncRNA biomarkers and clinical covariates. RESULTS: We included 12,727 lncRNA, 1881 miRNA and 47,761 mRNA profiling and clinical features for 113 colon adenocarcinoma patients obtained from the TCGA database. After filtration, we used 37 specific lncRNAs, 60 miRNAs and 148 mRNAs in the ceRNA network analysis. We identified five lncRNAs as prognostic lncRNA markers predicting tumor recurrence in colon adenocarcinoma, in which four of them were identified for the first time. Finally, we generated a nomogram illustrating the association between the identified lncRNAs and the tumor recurrence risk in colon adenocarcinoma. CONCLUSIONS: The four newly identified lncRNA biomarkers might be potential prognostic biomarkers predicting tumor recurrence in colon adenocarcinoma. We recommend that further clinical and fundamental researches be conducted on the identified lncRNA markers.

Preliminary Results on Preparation and Performance of a Self-Emulsifying Waterborne Epoxy Curing Agent at Room Temperature.

Polyethylene glycol 1000 (PEG1000) and epoxy resin E20 were used to synthesize the E20/PEG1000 polymer (EP1K), which was later transformed into a self-emulsifying water-based epoxy curing agent by reacting with m-Xylylenediamine (MXDA). The effects of molecular weight, the molar ratio of the raw materials, the catalyst dosage, and the different co-solvents on the properties of the prepared curing agent were systematically explored. The infrared absorption spectra of E20, EP1K, and the water-based epoxy curing agent were compared and analyzed. The coating properties of the waterborne epoxy varnish, which was based on water-based epoxy curing agents to emulsify and cure the resin E44, were systematically tested. The results demonstrated that with a molar ratio of 1:1:4 of PEG1000, E20, and MXDA, the boron trifluoride etherate (BF3·Et2O) as catalyst accounts for 0.3% of the total mass of E20 and PEG1000, and an applicable period of 3 h for the prepared varnish, the anti-corrosion performance, and mechanical properties of the coatings were excellent.

Synthesis of a Reactive Cationic/Nonionic Waterborne Polyurethane Dye Fixative and Its Application Performance on Viscose Fiber Fabrics.

A series of cationic waterborne polyurethane (CWPU) emulsions was synthesized with isophorone diisocyanate (IPDI) and hexamethylene diisocyanate (HDI) as hard segments; polyol (N210) and polyethylene glycol (PEG-2000) as soft segments; N-methyldiethanolamine (MDEA) as a hydrophilic chain extender; and trimethylolpropane (TMP) as a crosslinker. Then, the effects of the R-value, MDEA content, and TMP content on the properties of the CWPU emulsion, film, and fabric treatment were investigated. The results indicated that when the R-value was 3.0, the MEDA content accounted for 4.0% of the solid and the TMP content accounted for 1.0% of the solid. CWPU has excellent storage stability. Applying it to the fixing treatment of the viscose fiber fabrics can effectively improve the color fastness to rubbing, elasticity, surface smoothness, and anti-static properties.

A generic battery-cycling optimization framework with learned sampling and early stopping strategies.

Battery optimization is challenging due to the huge cost and time required to evaluate different configurations in experiments or simulations. Optimizing the cycling performance is especially costly since battery cycling is extremely time consuming. Here, we introduce an optimization framework building on recent advances in machine learning, which optimizes battery parameters efficiently to significantly reduce the total cycling time. It consists of a pruner and a sampler. The pruner, using the Asynchronous Successive Halving Algorithm and Hyperband, stops unpromising cycling batteries to save the budget for further exploration. The sampler, using Tree of Parzen Estimators, predicts the next promising configurations based on query history. The framework can deal with categorical, discrete, and continuous parameters and can run in an asynchronously parallel way to allow multiple simultaneous cycling cells. We demonstrated the performance by a parameter-fitting problem for calendar aging. Our framework can foster both simulations and experiments in the battery field.

Self-directed online machine learning for topology optimization.

Topology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly complicated problems. However, with hundreds of design variables or more involved, solving such problems would require millions of Finite Element Method (FEM) calculations whose computational cost is huge and impractical. Here we report Self-directed Online Learning Optimization (SOLO) which integrates Deep Neural Network (DNN) with FEM calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN's prediction of the optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. The optimum predicted by the DNN is proved to converge to the true global optimum through iterations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 ~ 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. This approach enables solving large multi-dimensional optimization problems.

Integrating Machine Learning with Human Knowledge.

Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.

Association of body mass index with serum alanine aminotransferase in Chinese adolescents: a school-based cross-sectional study.

BACKGROUND: Numerous studies have consistently demonstrated that high body mass index (BMI) is related to elevated serum alanine aminotransferase (ALT) among adults, but little is known about the association regarding adolescents, especially in China. In this study, we aimed to investigate the association between BMI and ALT activity among Chinese adolescents. METHODS: A school-based cross-sectional study was performed among nine high schools in Shenzhen, China between February 2017 and June 2018. A generalized linear-regression model adjusting for age and gender was conducted, and bivariate correlation analysis between ALT and BMI was also performed. RESULTS: A total of 7,271 adolescents aged from 9 years to 17 years were enrolled. Height, weight, BMI, and ALT were higher among boys than among girls (all P < 0.001). The mean (standard deviation) of serum ALT levels was 14.26 (14.77) U/L. In the entire BMI range, the BMI-ALT correlation was stronger for boys (Spearman's r = 0.396, P < 0.001) and adolescents of 14 years-17 years (r = 0.356, P < 0.001) than for girls (r = 0.203, P < 0.001) and adolescents of 9 years-13 years (r = 0.221, P < 0.001), respectively. Serum ALT increased rapidly and followed a linear pattern from the point of BMI ≥20.5 kg/m2, and each increase of 1 kg/m2 in BMI range above 20.5 kg/m2 was averagely correlated with an increase of 2.71 U/L in ALT levels (P < 0.001). CONCLUSIONS: We found a significant BMI-ALT relationship. BMI at 20.5 kg/m2 may be a cut-off for evaluating serum ALT. BMIs ≥27.1 kg/m2 for boys and ≥24.9 kg/m2 for girls were linked to an elevated ALT activity for Shenzhen adolescents.

Burden of non-accidental mortality attributable to ambient temperatures: a time series study in a high plateau area of southwest China.

OBJECTIVE: To examine the total non-accidental mortality burden attributable to ambient temperatures and assess the effect modification of the burden by specific causes of death and individual characteristics in a high plateau area in southwest China. METHODS: Using daily mortality and meteorological data from 2009 to 2016, we applied a quasi-Poisson model combined with a distributed lag non-linear model to estimate the temperature-mortality association with the assessment of attributable fraction and number. We calculated attributable fractions and deaths with 95% empirical CIs (eCIs), that were due to cold and heat, defined as temperatures below and above the median temperature, and for mild and extreme temperatures, defined by cut-offs at the 2.5th and 97.5th temperature percentiles. RESULTS: We analysed 89 467 non-accidental deaths; 4131 were attributable to overall temperatures, with an attributable fraction of 4.75% (95% eCI 2.33% to 6.79%). Most of the mortality burden was caused by cold (4.08%; 0.86% to 7.12%), whereas the burden due to heat was low and non-significant (0.67%; -2.44% to 3.64%). Extreme cold (1.17%; 0.58% to 1.69%) was responsible for 24.6% (ie, 1.17% divided by 4.75%) of the total death burden. In the stratification analyses, attributable risk due to cold was higher for cardiovascular than respiratory disease (6.18% vs 3.50%). We found a trend of risk of increased death due to ambient temperatures with increasing age, with attributable fractions of 1.83%, 2.27% and 6.87% for age ≤64, 65-74 and ≥75 years old, respectively. The cold-related burden was slightly greater for females, farmers, ethnic minorities and non-married individuals than their corresponding categories. CONCLUSIONS: Most of the burden of death was attributable to cold, and specific causes and individual characteristics might modify the mortality burden attributable to ambient temperatures. The results may help make preventive measures to confront climate change for susceptible population in this region.

Short-term associations of ambient air pollution and cause-specific emergency department visits in Guangzhou, China.

BACKGROUND: Evidence of association of ambient air pollution with cause-specific emergency department visits in China is still limited. This study aimed to investigate short-term associations between exposures to air pollutants and daily cause-specific emergency department visits using a large-scale multicenter database involving a total of 65 sentinel hospitals in Guangzhou, the most densely-populated city in south China, during 2013-2015. MATERIAL AND METHODS: We obtained data on 162,771 emergency department visits from 65 hospitals from the Emergency Medical Command Center in Guangzhou between January 1, 2013 and December 31, 2015. Daily air pollution data on particulate matter (PM) of aerodynamic diameter<10 and 2.5µm (PM10, and PM2.5, respectively), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) were collected from the Daily Quality Report of the Guangzhou Environmental Protection Bureau during the study period. Visits for neurologic, respiratory and circulatory diseases were assessed in relation to air pollutants using Poisson generalized additive models. RESULTS: Mean daily number of emergency department visits for neurologic, respiratory and circulatory diseases was 89, 24 and 35, respectively. After adjustment for other pollutants (PM2.5, PM10, NO2 and O3), meteorological factors and time-varying confounders, a 7.98-µg/m3 (interquartile range) increment in 2-day moving average of same-day and previous-day SO2 concentrations was associated with the statistically significant increase of 4.89% (95% confidence interval: 2.86, 6.95) in neurologic emergency department visits; elevation in SO2 level (per 7.98µg/m3) was linked to a 5.19% (95% confidence interval: 2.03, 8.44) increase in circulatory emergency department visits. Most positive links were seen during the cold season. CONCLUSIONS: The results of this study contribute to the evidence of the significant associations between SO2 and specific neurologic and circulatory conditions, and also provide insight into the planning of clinical services and emergency contingency response for air pollution exposures in Guangzhou.

Can internet search queries be used for dengue fever surveillance in China?

China experienced an unprecedented outbreak of dengue fever in 2014, and the number of cases reached the highest level over the past 25 years. Traditional sentinel surveillance systems of dengue fever in China have an obvious drawback that the average delay from receipt to dissemination of dengue case data is roughly 1-2 weeks. In order to exploit internet search queries to timely monitor dengue fever, we analyzed data of dengue incidence and Baidu search query from 31 provinces in mainland China during the period of January 2011 to December 2014. We found that there was a strong correlation between changes in people's online health-seeking behavior and dengue fever incidence. Our study represents the first attempt demonstrating a strong temporal and spatial correlation between internet search trends and dengue epidemics nationwide in China. The findings will help the government to strengthen the capacity of traditional surveillance systems for dengue fever.

Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model.

Seasonal influenza epidemics cause serious public health problems in China. Search queries-based surveillance was recently proposed to complement traditional monitoring approaches of influenza epidemics. However, developing robust techniques of search query selection and enhancing predictability for influenza epidemics remains a challenge. This study aimed to develop a novel ensemble framework to improve penalized regression models for detecting influenza epidemics by using Baidu search engine query data from China. The ensemble framework applied a combination of bootstrap aggregating (bagging) and rank aggregation method to optimize penalized regression models. Different algorithms including lasso, ridge, elastic net and the algorithms in the proposed ensemble framework were compared by using Baidu search engine queries. Most of the selected search terms captured the peaks and troughs of the time series curves of influenza cases. The predictability of the conventional penalized regression models were improved by the proposed ensemble framework. The elastic net regression model outperformed the compared models, with the minimum prediction errors. We established a Baidu search engine queries-based surveillance model for monitoring influenza epidemics, and the proposed model provides a useful tool to support the public health response to influenza and other infectious diseases.

Effects of ambient temperature on stroke hospital admissions: Results from a time-series analysis of 104,432 strokes in Guangzhou, China.

BACKGROUND: Stroke is a main cause of death and public health burden in China. The evidence on the burden of different strokes attack attribute to ambient temperature in China is limited. This study aimed to show the characteristics of stroke attack and the attributable risk due to temperature based on hospital admission data in Guangzhou, one of the most developed cities in China. MATERIAL AND METHODS: From January 1, 2013 to December 31, 2015, 104,432 stroke hospitalizations in Guangzhou residents from 67 hospitals for stroke sentinel surveillance were registered. Characteristics of hospital admissions by gender, age group, calendar year and stroke subtype were analyzed, and distributed lag non-linear models were applied to evaluate the effects of temperature on stroke attack admissions. RESULTS: Stroke attack admissions increased from 31,851 to 36,755 through 2013 to 2015, increasing by 15.4%. An increasing trend in the risk of stroke attack with age was observed, irrespectively of stroke subtype and calendar year. People with hypertension were more likely to have an associated stroke than people without that. The effects of cold temperature on attack admissions for CBI and ICH strokes were significant. Overall, the percentages of CBI and ICH attack admissions attribute to cold temperature were 9.06% (95% CI: 1.84, 15.00) and 15.09% (95% CI: 5.86, 21.96), respectively. Besides, elderly people were more vulnerable to cold temperature than the young. CONCLUSIONS: Measures should be taken to increase public awareness about the ill effects of cold temperature on stroke attack, and educate the public about self-protection.