Abstract TVB-N is one of the important indexes of the freshness of chilled pork. To present the freshness more accurately and completely, a visualization technology of chemical composition was applied and optimized for pork freshness detection. Two hyperspectral cameras covering visible and near-infrared wavebands were used to detect the TVB-N content. GA, SPA, iPLSR, and siPLSR were employed to select characteristic wavebands from the spectra data extracted from the hyperspectral images, and PLSR models were established on full wavebands and characteristic wavebands. According to the models established on the spectral data, TVB-N distribution maps were obtained, and URV and ICV were employed to evaluate the performance of distribution maps. The results showed that, both PLSR models established on the characteristic wavebands and full wavebands achieved good accuracy, and that the characteristic wavebands cluster was in the spectral range of 600-750 nm, 1020-1120 nm, and 1450-1570 nm. To optimize the performance of distribution maps, prediction models were reconstructed under the guidance of URV and ICV. This study provided a method of predicting TVB-N content of pork in both spectral and imaging aspect for online testing the freshness of chilled pork, which improves the efficiency and quality of testing. TVBN TVB N completely detection nearinfrared near infrared GA SPA iPLSR images obtained accuracy 600750 600 750 600-75 nm 10201120 1020 1120 1020-112 14501570 1450 1570 1450-157 60075 60 75 600-7 1020112 102 112 1020-11 1450157 145 157 1450-15 6007 6 7 600- 102011 10 11 1020-1 145015 14 15 1450-1 10201 1 1020- 14501 1450-

Abstract Ginkgo biloba leaf is a kind of food raw material with high concentration of total flavonoid content (TFC). Since conventional measurements of TFC are time-consuming and destructive, the approach of hyperspectral imaging-based prediction was investigated in this study. Hyperspectral images were collected in visual near-infrared (VIS-NIR) and short-wave near-infrared (SW-NIR). Initial comparative analysis on the full spans of the two wavelengths using partial least squares regression (PLSR) and support vector regression (SVR) showed that the TFC of the leaves could be barely captured only when PLSR working on short-wave infrared, with R p 2 and RMSE p on testing set being respectively 0.5496 and 0.6384 mg/g. To further improve prediction performance meeting industrial requirements, another comparative study on feature selection was conducted to fine-tune the PLSR on SW-NIR using a genetic algorithm (GA) and successive projections algorithm (SPA). Results showed that GA-PLSR with 50 characteristic wavelengths mostly from the ranges of 1100-1200 nm and 1400-1500 nm had a significant improvement in performance, giving 0.8482 and 0.2967 mg/g for R p 2 and RMSE p, respectively. Therefore, an approach, GA-PLSR modeling on SW-NIR hyperspectral images, was established for the ginkgo leaves industry to rapidly and non-invasively predict the leaf concentration from the total flavonoid of ginkgo biloba seedling leaf. TFC. . (TFC) timeconsuming time consuming destructive imagingbased imaging based nearinfrared near infrared VISNIR VIS NIR (VIS-NIR shortwave short wave SWNIR. SWNIR SW (SW-NIR) (PLSR SVR (SVR 05496 0 5496 0.549 06384 6384 0.638 mgg mg g requirements finetune fine tune GA (GA SPA. SPA (SPA) GAPLSR 5 11001200 1100 1200 1100-120 14001500 1400 1500 1400-150 08482 8482 0.848 02967 2967 0.296 Therefore noninvasively non invasively (TFC (SW-NIR 0549 549 0.54 0638 638 0.63 (SPA 1100120 110 120 1100-12 1400150 140 150 1400-15 0848 848 0.84 0296 296 0.29 054 54 0.5 063 63 0.6 110012 11 12 1100-1 140015 14 15 1400-1 084 84 0.8 029 29 0.2 05 0. 06 6 11001 1 1100- 14001 1400- 08 8 02

Abstract Objective This study aimed to identify the predictors and threshold of failure in neonatal acute respiratory distress syndrome. Methods Newborns with severe acute respiratory distress syndrome aged 0-28 days and gestational age ≥36 weeks were included in the study if their cases were managed with non-extra corporal membrane oxygenation treatments. Patients were divided into two groups according to whether they died before discharge. Predictors of non-extra corporal membrane oxygenation treatment failure were sought, and the threshold of predictors was calculated. Results A total of 103 patients were included in the study. A total of 77 (74.8%) survived hospitalization and were discharged, whereas 26 (25.2%) died. Receiver operating characteristic analysis of oxygen index, pH, base excess, and combinations of these indicators demonstrated the advantage of the combination of oxygen index and base excess over the others variables regarding their predictive ability. The area under the curve for the combination of oxygen index and base excess was 0.865. When the cut-off values of oxygen index and base excess were 30.0 and −7.4, respectively, the sensitivity and specificity for predicting death were 77.0% and 84.0%, respectively. The model with base excess added a net reclassification improvement of 0.090 to the model without base excess. Conclusion The combination of oxygen index and base excess can be used as a predictor of outcomes in neonates receiving non-extra corporal membrane oxygenation treatment for acute respiratory distress syndrome. In neonates with acute respiratory distress syndrome, if oxygen index >30 and base excess <−7.4, non-extra corporal membrane oxygenation therapy is likely to lead to death.

Abstract Chemometric modeling concerns both accuracy and computational expense for the prediction of quality-indicating attributes of food materials. Modeling approaches were explored with the hyperspectral images with pH and Brix values of greengages. A two-phase architecture was applied for modeling. Firstly, waveband selection was performed using two approaches, i.e., succession projection algorithm (SPA) and its combination with genetic algorithm (SPA+GA). Secondly, multispectral models based on the two feature sets of wavebands were built via a total of six different modeling methods, i.e., partial least squares regression (PLSR) and extreme learning machine (ELM) in their respective stand-alone versions, their applications combined with genetic algorithm (GA), and their ensemble enhancements with modified Adaboost.RT (MAdaboost.RT). Analysis of accuracy and computational expense showed that supervised feature selection with SPA+GA was superior to unsupervised SPA for better modeling accuracy. MAdaboost.RT-ELM showed high accuracy at low computational expense. ELM models were the better base models than the PLSR ones, for being more randomized and diverse. It indicates that MAdaboost.RT-ELM on SPA is the best choice for a quick test on a newly available dataset, while switching the dimensionality reduction from SPA to SPA+GA may yield more accurate models with added, but well worthy, computational expense.

RESUMO Objetivo Há evidências crescentes de que o estresse oxidativo e a inflamação estão envolvidos na perda de memória induzida pelo peptídeo beta-amilóide (βA). O ácido ursólico (AU), um composto triterpenóide, apresenta atividades anti-inflamatórias e antioxidantes potentes. Entretanto, não se sabe ainda se o AU atenua a neurotoxicidade induzida pelo βA. Método O agregado βA 25-35 foi administrado aos ratos por via intracerebroventricular. Resultados Observou-se que o AU reverteu significativamente os déficits de aprendizado e de memória induzidos pelo βA 25-35. Portanto, um dos potenciais mecanismos do efeito neuroprotetor seria a atenuação do acúmulo de malondialdeído e a depleção de glutationa no hipocampo induzidos pelo βA 25-35. Além disso, o AU suprimiu significativamente a supra regulação dos níveis de IL-1β, IL-6 e do fator de necrose tumoral α no hipocampo dos ratos tratados com βA 25-35. Conclusão Esses achados sugerem que o AU previne a perda de memória através da melhora do estresse oxidativo e da resposta inflamatória, podendoproporcionar uma nova estratégia terapêutica para o tratamento da doença de Alzheimer.

ABSTRACT Objective Increasing evidence demonstrates that oxidative stress and inflammatory are involved in amyloid β (Aβ)-induced memory impairments. Ursolic acid (UA), a triterpenoid compound, has potent anti-inflammatory and antioxidant activities. However, it remains unclear whether UA attenuates Aβ-induced neurotoxicity. Method The aggregated Aβ25-35 was intracerebroventricularly administered to mice. Results We found that UA significantly reversed the Aβ25-35-induced learning and memory deficits. Our results indicated that one of the potential mechanisms of the neuroprotective effect was attenuating the Aβ25-35-induced accumulation of malondialdehyde (MDA) and depletion of glutathione (GSH) in the hippocampus. Furthermore, UA significantly suppressed the upregulation of IL-1β, IL-6, and tumor necrosis-α factor levels in the hippocampus of Aβ25-35-treated mice. Conclusion These findings suggest that UA prevents memory impairment through amelioration of oxidative stress, inflammatory response and may offer a novel therapeutic strategy for the treatment of Alzheimer’s disease.

The association between T174M polymorphism of angiotensinogen gene and essential hypertension risk remains controversial. We herein performed a meta-analysis to achieve a reliable estimation of their relationship. All the studies published up to May 2013 on the association between T174M polymorphism and essential hypertension risk were identified by searching the electronic repositories PubMed, MEDLINE and EMBASE, Springer, Elsevier Science Direct, Cochrane Library and Google Scholar. Data were extracted and pooled odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. Ultimately, nine eligible studies, including 2188 essential hypertension cases and 2459 controls, were enrolled in this meta-analysis. No significant associations were found under the overall ORs for M-allele comparison (M vs. T, pooled OR 0.92, 95% CI 0.62-1.37), MM vs. TT (pooled OR 0.86, 95% CI 0.29-2.51), TM vs. TT n (pooled OR 0.91, 95% CI 0.63-1.32), recessive model (MM vs. TT+TM, pooled OR 0.89, 95% CI 0.35-2.30), dominant model (MM+TM vs. TT, pooled OR 0.91, 95% CI 0.60-1.38) between T174M polymorphism and risk for essential hypertension. This meta-analysis suggested that the T174M polymorphism of the angiotensinogen gene might not be associated with the susceptibility of essential hypertension in Asian or European populations.

The mechanisms of exercise-induced fatigue have not been investigated using proteomic techniques, an approach that could improve our understanding and generate novel information regarding the effects of exercise. In this study, the proteom alterations of rat skeletal muscle were investigated during exercise-induced fatigue. The proteins were extracted from the skeletal muscle of SD rat thigh, and then analyzed by two-dimensional electrophoresis and PDQuest software. Compared to control samples, 10 significantly altered proteins were found in exercise samples, two of them were upregulated and eight of them were downregulated. These proteins were identified by MALDI TOF-MS. The two upregulated proteins were identified as MLC1 and myosin L2 (DTNB) regulatory light-chain precursors. The eight decreased proteins are Glyceraldehyde-3-phosphate Dehydrogenas (GAPDH); Beta enolase; Creatine kinase M chain (M-CK); ATP-AMP Transphosphorylase (AK1); myosin heavy chain (MHC); actin; Troponin I, fast-skeletal muscle (Troponin I fast-twitch isoform), fsTnI; Troponin T, fast-skeletal muscle isoforms (TnTF). In these proteins, four of the eight decreased proteins are related directly or indirectly to exercise induced fatigue. The other proteins represent diverse sets of proteins including enzymyes related to energy metabolism, skeletal muscle fabric protein and protein with unknown functions. They did not exhibit evident relationship with exercise-induced fatigue. Whereas the two identified increased proteins exhibit evident relationship with fatigue. These findings will help in understanding the mechanisms involved in exercise-induced fatigue.