Characterization of volatile aroma compounds after in-vial cooking of foxtail millet porridge with gas chromatography-mass spectrometry

Foxtail millet has become popular over recent years for its nutritional value and ecological functions. The aroma of foxtail millet is not well characterized, which is critical for its eating quality and understanding the biochemistry and genetics of aroma is important for molecular breeding of millets rich in aroma. In this study, the volatile aroma compounds of the elite millet variety Jingu 21 were investigated at different cooking times, pH, processing methods, and compared with 3 other varieties. An in-vial cooking method was developed which combined solid phase micro-extraction and gas chromatography-mass spectrometry for the detection and identification of volatile compounds. The main findings were: a) Twelve aroma compounds were identified during cooking, which were hexanal, heptanal, octanal, (E)-2-heptenal, nonanal, trans-2-octenal, trans-2-nonenal, 2,4-nonadienal, (E,E)-2,4-decadienal, 1-octen-3-ol, 2-pentylfuran and 6-methyl-5- hepten-2-one. b) Longer cooking times produced higher concentrations of aroma compounds. c) Variations in cooking pH (from 6 to 8) had no obvious impact on the aroma of the millet porridge. d) More volatile compounds were released from millet flour compared to millet grain. e) There were significant differences among varieties and Jingu 21 millet showed the highest abundance of most aroma compounds, explaining partly why it is strongly favored by consumers for decades

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}

Characterization of volatile aroma compounds after in-vial cooking of foxtail millet porridge with gas chromatography-mass spectrometry

Foxtail millet has become popular over recent years for its nutritional value and ecological functions. The aroma of foxtail millet is not well characterized, which is critical for its eating quality and understanding the biochemistry and genetics of aroma is important for molecular breeding of millets rich in aroma. In this study, the volatile aroma compounds of the elite millet variety Jingu 21 were investigated at different cooking times, pH, processing methods, and compared with 3 other varieties. An in-vial cooking method was developed which combined solid phase micro-extraction and gas chromatography-mass spectrometry for the detection and identification of volatile compounds. The main findings were: a) Twelve aroma compounds were identified during cooking, which were hexanal, heptanal, octanal, (E)-2-heptenal, nonanal, trans-2-octenal, trans-2-nonenal, 2,4-nonadienal, (E,E)-2,4-decadienal, 1-octen-3-ol, 2-pentylfuran and 6-methyl-5- hepten-2-one. b) Longer cooking times produced higher concentrations of aroma compounds. c) Variations in cooking pH (from 6 to 8) had no obvious impact on the aroma of the millet porridge. d) More volatile compounds were released from millet flour compared to millet grain. e) There were significant differences among varieties and Jingu 21 millet showed the highest abundance of most aroma compounds, explaining partly why it is strongly favored by consumers for decades

Normalized lactate load is associated with development of acute kidney injury in patients who underwent cardiopulmonary bypass surgery.

Cardiac surgery associated acute kidney injury is a major postoperative complication and has long been associated with adverse outcomes. However, the association of lactate and AKI has not been well established. The study aimed to explore the association of normalized lactate load with AKI in patients undergoing cardiac surgery.This was a prospective observational cohort study conducted in a 47-bed ICU of a tertiary academic teaching hospital from July 2012 to January 2014. All patients undergoing cardiopulmonary bypass surgery were included. Normalized lactate load (L) was calculated by the equation: [Formula: see text], where ti was time point for lactate measurement and vi was the value of lactate. L was transformed by natural log (Lln) to improve its normality. Logistic regression model was fitted by using stepwise method. Scale of Lln was examined by using fractional polynomial approach and potential interaction terms were explored.A total of 117 patients were included during study period, including 17 AKI patients and 100 non-AKI patients. In univariate analysis Lln was significantly higher in AKI as compared with non-AKI group (1.43±0.38 vs 1.01±0.45, p = 0.0005). After stepwise selection of covariates, the main effect logistic model contained variables of Lln (odds ratio: 11.1, 95% CI: 1.22-101.6), gender, age, baseline serum creatinine and fluid balance on day 0. Although the two-term fractional polynomial model was the best-fitted model, it was not significantly different from the linear model (Deviance difference = 6.09, p = 0.107). There was no significant interaction term between Lln and other variables in the main effect model.Our study demonstrates that Lln is independently associated with postoperative AKI in patients undergoing CPB. There is no significant interaction with early postoperative fluid balance

Prediction model for critically ill patients with acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is a major cause respiratory failure in intensive care unit (ICU). Early recognition of patients at high risk of death is of vital importance in managing them. The aim of the study was to establish a prediction model by using variables that were readily available in routine clinical practice.The study was a secondary analysis of data obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. Patients were enrolled between August 2007 and July 2008 from 33 hospitals. Demographics and laboratory findings were extracted from dataset. Univariate analyses were performed to screen variables with p<0.3. Then these variables were subject to automatic stepwise forward selection with significance level of 0.1. Interaction terms and fractional polynomials were examined for variables in the main effect model. Multiple imputations and bootstraps procedures were used to obtain estimations of coefficients with better external validation. Overall model fit and logistic regression diagnostics were explored.A total of 282 ARDS patients were included for model development. The final model included eight variables without interaction terms and non-linear functions. Because the variable coefficients changed substantially after exclusion of most poorly fitted and influential subjects, we estimated the coefficient after exclusion of these outliers. The equation for the fitted model was: g(Χ)=0.06×age(in years)+2.23(if on vasopressor)+1.37×potassium (mmol/l)-0.007×platelet count (×109)+0.03×heart rate (/min)-0.29×Hb(g/dl)-0.67×T(°C)+0.01×PaO_2+13, and the probability of death π(Χ)=eg(Χ)/(1+eg(Χ)).The study established a prediction model for ARDS patients requiring mechanical ventilation. The model was examined with rigorous methodology and can be used for risk stratification in ARDS patients

Graphical presentation of PEEP and FiO2 in survivors and non-survivors at the onset of ARDS.

<p>There was no significant difference on PEEP (9.1±3.7 vs.9.3±3.3 cmH2O; p = 0.71) and FiO2 (0.58±0.19 vs.0.57±0.17; p = 0.69) between survivors and non-survivors.</p

Graphical presentation of the fitted two-term (3 3) fractional polynomial logistic regression model.

<p>In the upper panel, the y-axis is in logit scale with the advantage of better distribution for model fit. Y-axis is transformed to the probability of AKI in lower panel which is more comprehensible to subject matter audience. The plots show that the probability of AKI increases progressively with increasing normalized lactate load, reaching its peak at L_ln≈1.4.</p

Comparisons of fractional polynomial models.

<p>‡P-value from deviance difference comparing reported model with m = 2 model.</p><p>Abbreviations: L_ln, log transformed normalized lactate load.</p><p>Comparisons of fractional polynomial models.</p

Main effect model.

<p>Abbreviations: L_ln, log transformed normalized lactate load; Scr, serum creatinine.</p><p>Main effect model.</p