Insights into "wheat aroma": Analysis of volatile components in wheat grains cultivated in saline-alkali soil.

The wheat grains that are cultivated in saline-alkali soil exhibit a richer "wheat aroma" compared to their counterparts. This study characterized the composition and content of volatiles in five wheat kernel varieties, harvested from two fields with varying pH levels and total salt content in the soil. The wheat grown in soil with high pH and total salt content had significantly lower levels (p < 0.05) of ethyl 3-methylbutanoate and 1-octen-3-one and significantly higher levels (p < 0.05) of 1-butanol and 1-octen-3-ol. Among all factors, plant site contributed the highest F-value contribution rate (more than 77 %) for these four volatile compounds. Six e-nose sensors responsive to these four compounds exhibited consistent trends. Therefore, the lower of ethyl 3-methylbutanoate and 1-octen-3-one, the higher of 1-butanol and 1-octen-3-ol in wheat, grown on saline-alkali soil, served as characteristic markers for "wheat aroma".

Potential ocular indicators to distinguish posterior cortical atrophy and typical Alzheimer's disease: a cross-section study using optical coherence tomography angiography.

BACKGROUND: Posterior cortical atrophy (PCA) is a form of dementia that frequently displays significant visual dysfunction and relatively preserved cognitive and executive functions, thus hindering early diagnosis and treatment. This study aimed to investigate possible fundus markers in PCA patients and compare them with those of typical Alzheimer's disease (AD) patients to seek potential diagnostic patterns. METHODS: Age-matched PCA and AD patients and healthy controls (HC) completed optometry, intraocular pressure measurement, neuropsychologic assessments, optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA) examination in one visit. Overall, six outcomes of thicknesses of various retinal layers and seven outcomes of the retinal microvascular network were calculated. After adjusting for age, sex, and years of education, the OCT and OCTA results were analyzed using analysis of covariance and generalized linear models. Correlation analyses were performed using Spearman correlation, and ROC curves were plotted. RESULTS: Twelve PCA patients, nineteen AD patients, and thirty HC, aged 45-80 years were included. Fifty HC, thirty AD, and twenty PCA eyes were available for foveal avascular zone (FAZ) area analysis; forty-nine HC, thirty-four AD, and eighteen PCA eyes were available for OCT and OCTA assessments. PCA patients had thinner retinal nerve fiber layer and ganglion cell layer + inner plexiform layer than HC in the 0-3 mm circle and 1-3 mm ring. Few structural differences were observed between the AD group and the other two groups. The flow area of the superficial capillary plexus and the intermediate capillary plexus was smaller in the PCA group than in the HC group in the 0-1 mm circle, 0-3 mm circle. MMSE performed better than any combination of optical parameters in identifying AD and PCA from HC (AUC = 1), while the combination of MoCA, retinal thickness and vascular density of ICP in the 1-3 mm ring, with flow area of ICP in the 0-1 mm circle showed the strongest ability to distinguish PCA from AD (AUC = 0.944). CONCLUSIONS: PCA patients exhibited similar impairment patterns to AD patients in the fundus structure and microvascular network. OCTA may aid in the non-invasive detection of AD and PCA, but still remains to be substantiated.

Evaluation of a novel deep learning based screening system for pathologic myopia.

AIM: To evaluate the clinical application value of the artificial intelligence assisted pathologic myopia (PM-AI) diagnosis model based on deep learning. METHODS: A total of 1156 readable color fundus photographs were collected and annotated based on the diagnostic criteria of Meta-pathologic myopia (PM) (2015). The PM-AI system and four eye doctors (retinal specialists 1 and 2, and ophthalmologists 1 and 2) independently evaluated the color fundus photographs to determine whether they were indicative of PM or not and the presence of myopic choroidal neovascularization (mCNV). The performance of identification for PM and mCNV by the PM-AI system and the eye doctors was compared and evaluated via the relevant statistical analysis. RESULTS: For PM identification, the sensitivity of the PM-AI system was 98.17%, which was comparable to specialist 1 (P=0.307), but was higher than specialist 2 and ophthalmologists 1 and 2 (P<0.001). The specificity of the PM-AI system was 93.06%, which was lower than specialists 1 and 2, but was higher than ophthalmologists 1 and 2. The PM-AI system showed the Kappa value of 0.904, while the Kappa values of specialists 1, 2 and ophthalmologists 1, 2 were 0.968, 0.916, 0.772 and 0.730, respectively. For mCNV identification, the AI system showed the sensitivity of 84.06%, which was comparable to specialists 1, 2 and ophthalmologist 2 (P>0.05), and was higher than ophthalmologist 1. The specificity of the PM-AI system was 95.31%, which was lower than specialists 1 and 2, but higher than ophthalmologists 1 and 2. The PM-AI system gave the Kappa value of 0.624, while the Kappa values of specialists 1, 2 and ophthalmologists 1 and 2 were 0.864, 0.732, 0.304 and 0.238, respectively. CONCLUSION: In comparison to the senior ophthalmologists, the PM-AI system based on deep learning exhibits excellent performance in PM and mCNV identification. The effectiveness of PM-AI system is an auxiliary diagnosis tool for clinical screening of PM and mCNV.

Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning.

Purpose: To apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic maculopathy (MM) based on macular optical coherence tomography (OCT) images using transfer learning (TL). Method: In this retrospective study, a total of 3,945 macular OCT images from 2,866 myopic patients were recruited from the ophthalmic outpatients of three hospitals. After culling out 545 images with poor quality, a dataset containing 3,400 macular OCT images was manually classified according to the ATN system, containing four types of MM with high OCT diagnostic values. Two DL classification algorithms were trained to identify the targeted lesion categories: Algorithm A was trained from scratch, and algorithm B using the TL approach initiated from the classification algorithm developed in our previous study. After comparing the training process, the algorithm with better performance was tested and validated. The performance of the classification algorithm in the test and validation sets was evaluated using metrics including sensitivity, specificity, accuracy, quadratic-weighted kappa score, and the area under the receiver operating characteristic curve (AUC). Moreover, the human-machine comparison was conducted. To better evaluate the algorithm and clarify the optimization direction, the dimensionality reduction analysis and heat map analysis were also used to visually analyze the algorithm. Results: Algorithm B showed better performance in the training process. In the test set, the algorithm B achieved relatively robust performance with macro AUC, accuracy, and quadratic-weighted kappa of 0.986, 96.04% (95% CI: 0.951, 0.969), and 0.940 (95% CI: 0.909-0.971), respectively. In the external validation set, the performance of algorithm B was slightly inferior to that in the test set. In human-machine comparison test, the algorithm indicators were inferior to the retinal specialists but were the same as the ordinary ophthalmologists. In addition, dimensionality reduction visualization and heatmap visualization analysis showed excellent performance of the algorithm. Conclusion: Our macular OCT image classification algorithm developed using the TL approach exhibited excellent performance. The automatic diagnosis system for macular OCT images of MM based on DL showed potential application prospects.

AI-Model for Identifying Pathologic Myopia Based on Deep Learning Algorithms of Myopic Maculopathy Classification and "Plus" Lesion Detection in Fundus Images.

Background: Pathologic myopia (PM) associated with myopic maculopathy (MM) and "Plus" lesions is a major cause of irreversible visual impairment worldwide. Therefore, we aimed to develop a series of deep learning algorithms and artificial intelligence (AI)-models for automatic PM identification, MM classification, and "Plus" lesion detection based on retinal fundus images. Materials and Methods: Consecutive 37,659 retinal fundus images from 32,419 patients were collected. After excluding 5,649 ungradable images, a total dataset of 32,010 color retinal fundus images was manually graded for training and cross-validation according to the META-PM classification. We also retrospectively recruited 1,000 images from 732 patients from the three other hospitals in Zhejiang Province, serving as the external validation dataset. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and quadratic-weighted kappa score were calculated to evaluate the classification algorithms. The precision, recall, and F1-score were calculated to evaluate the object detection algorithms. The performance of all the algorithms was compared with the experts' performance. To better understand the algorithms and clarify the direction of optimization, misclassification and visualization heatmap analyses were performed. Results: In five-fold cross-validation, algorithm I achieved robust performance, with accuracy = 97.36% (95% CI: 0.9697, 0.9775), AUC = 0.995 (95% CI: 0.9933, 0.9967), sensitivity = 93.92% (95% CI: 0.9333, 0.9451), and specificity = 98.19% (95% CI: 0.9787, 0.9852). The macro-AUC, accuracy, and quadratic-weighted kappa were 0.979, 96.74% (95% CI: 0.963, 0.9718), and 0.988 (95% CI: 0.986, 0.990) for algorithm II. Algorithm III achieved an accuracy of 0.9703 to 0.9941 for classifying the "Plus" lesions and an F1-score of 0.6855 to 0.8890 for detecting and localizing lesions. The performance metrics in external validation dataset were comparable to those of the experts and were slightly inferior to those of cross-validation. Conclusion: Our algorithms and AI-models were confirmed to achieve robust performance in real-world conditions. The application of our algorithms and AI-models has promise for facilitating clinical diagnosis and healthcare screening for PM on a large scale.

Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.

Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.

Analyzing fundus images to detect diabetic retinopathy (DR) using deep learning system in the Yangtze River delta region of China.

BACKGROUND: This study aimed to establish and evaluate an artificial intelligence-based deep learning system (DLS) for automatic detection of diabetic retinopathy. This could be important in developing an advanced tele-screening system for diabetic retinopathy. METHODS: A DLS with a convolutional neural network was developed to recognize fundus images of referable diabetic retinopathy. A total data set of 41,866 color fundus images were obtained from 17 cities in the Yangtze River Delta Urban Agglomeration (YRDUA). Five experienced retinal specialists and 15 ophthalmologists were recruited to verify images. For training, 80% of the data set was used, and the other 20% served as the validation data set. To effectively understand the learning process, the DLS automatically superimposed a heatmap on the original image. The regions utilized by the DLS were highlighted for diagnosis. RESULTS: Using the local validation data set, the DLS achieved an area under the curve of 0.9824. Based on the manual screening criteria, an operating point was set at about 0.9 sensitivity to evaluate the DLS. Specificity was recorded at 0.9609 and sensitivity was 0.9003. The DLSs showed excellent reliability, repeatability, and high efficiency. After analyzing the misclassification, it was found that 88.6% of the false-positives were mild non-proliferative diabetic retinopathy (NPDR) whereas, 81.6% of the false-negatives were intraretinal microvascular abnormalities. CONCLUSIONS: The DLS efficiently detected fundus images from complex sources in the real world. Incorporating DLS technology in tele-screening will advance the current screening programs to offer a cost-effective and time-efficient solution for detecting diabetic retinopathy.

Clinical features of simple hemorrhage and myopic choroidal neovascularization associated with lacquer cracks in pathologic myopia.

PURPOSE: To investigate the clinical features of simple hemorrhage (SH) and myopic choroidal neovascularization (mCNV) lesions in pathologic myopia (PM) accompanied with lacquer cracks (LCs). METHODS: Altogether 105 PM subjects were recruited with fifty-eight eyes categorized as group LC + SH and sixty eyes as group LC + mCNV. LCs were categorized into stellate and linear subtypes. Eye fundus photography, optical coherence tomography, fluorescein angiography, and indocyanine green angiography were performed. Clinical demographic data, PM maculopathy, peripapillary atrophy, and macular choroidal thickness (mCT) were documented. RESULTS: Significant differences in age, gender, BCVA, and PM atrophies were observed between LC + SH and LC + mCNV groups. The stellate LC was more common in elder subjects with more severe chorioretinal atrophy and thinner mCT compared with linear LCs (P < 0.05). The mCT in group LC + SH was significantly larger than group LC + CNV (P < 0.001), especially in temporal, inferior, and superior locations of macula. The mCT showed correlation with age (P < 0.001)with a decreasing rate of 0.696 µm/year. CONCLUSIONS: SH tended to initially occur in younger subjects with linear LCs. mCNV was more common in elder subjects with severe chorioretinal atrophy. Stellate LCs were associated with the worse PM lesions.

Efficient Biofilm-Based Fermentation Strategies by eDNA Formation for l-Proline Production with Corynebacterium glutamicum.

Biofilms could provide favorable conditions for the growth of cells during industrial fermentation. However, biofilm-immobilized fermentation has not yet been reported in Corynebacterium glutamicum (C. glutamicum), one of the main strains for amino acid production. This is mainly because C. glutamicum has a poor capability of adsorption onto materials or forming an extracellular polymeric substance (EPS). Here, an engineered strain, C. glutamicum Pro-ΔexeM, was created by removing the extracellular nuclease gene exeM, which effectively increased extracellular DNA (eDNA) in the EPS and cell adhesiveness onto carrier materials. In repeated-batch fermentation using the biofilm, l-proline production increased from 10.2 to 17.1 g/L. In summary, this research demonstrated that a synthetic C. glutamicum biofilm could be favorable for l-proline production, which could be extended to other industrial applications of C. glutamicum, and the strategy may also be applicable to the engineering of other strains.

Efficient Biofilm-Based Fermentation Strategies for L-Threonine Production by Escherichia coli.

Biofilms provide cells favorable growth conditions, which have been exploited in industrial biotechnological processes. However, industrial application of the biofilm has not yet been reported in Escherichia coli, one of the most important platform strains, though the biofilm has been extensively studied for pathogenic reasons. Here, we engineered E. coli by overexpressing the fimH gene, which successfully enhanced its biofilm formation under industrial aerobic cultivation conditions. Subsequently, a biofilm-based immobilized fermentation strategy was developed. L-threonine production was increased from 10.5 to 14.1 g/L during batch fermentations and further to 17.5 g/L during continuous (repeated-batch) fermentations with enhanced productivities. Molecular basis for the enhanced biofilm formation and L-threonine biosynthesis was also studied by transcriptome analysis. This study goes beyond the conventional research focusing on pathogenic aspects of E. coli biofilm and represents a successful application case of engineered E. coli biofilm to industrial processes.

[Emission Characteristics of VOCs from Typical Restaurants in Beijing].

Using the EPA method, emission of volatile organic compounds (VOCs) , sampled from barbecue, Chinese and Western fast-food, Sichuan cuisine and Zhejiang cuisine restaurants in Beijing was investigated. VOCs concentrations and components from different cuisines were studied. The results indicated that based on the calibrated baseline ventilation volume, the VOCs emission level from barbecue was the highest, reaching 12.22 mg · m(-3), while those from fast-food of either Chinese or Western, Sichuan cuisine and Zhejiang cuisine were about 4 mg · m(-3). The components of VOCs from barbecue were different from those in the other cuisines, which were mainly propylene, 1-butene, n-butane, etc. The non-barbecue cuisines consisted of high concentration of alcohols, and Western fast-food contained relatively high proportion of aldehydes and ketones organic compounds. According to emission concentration of baseline ventilation volume, barbecue released more pollutants than the non-barbecue cuisines at the same scale. So, barbecue should be supervised and controlled with the top priority.

Two kinds of ocular trauma score for paediatric traumatic cataract in penetrating eye injuries.

PURPOSE: To compare the ocular trauma score (OTS) and the paediatric penetrating ocular trauma score (POTS) as prognostic model for visual outcome in paediatric traumatic cataract cases after penetrating eye injuries. METHODS: All children younger than 16 years with unilateral traumatic cataract following penetrating trauma between 2007 and 2012 were retrospectively reviewed. Univariate chi-square analysis was conducted to identify the variables associated with profound visual loss. The area under the receiver-operating characteristic curves (AUROC) was used to assess the predictive ability of the two models. RESULTS: The study group comprised 65 boys and 37 girls. The variables associated with profound visual loss were: a relative afferent papillary defect (RAPD) (P<0.001), poor initial vision (P=0.01), vitreous haemorrhage (P<0.001), retinal detachment (P<0.001), posterior penetrating site (P<0.001), hyphema (P<0.001), no intraocular len implantation (P<0.001) and endophthalmitis (P=0.001). OTS could not be calculated in 21 patients (20.6%) without clinical data on initial visual acuity and RAPD. For the patients with complete clinical data, POTS was similar to OTS in predicting poor vision (AUROC 0.904 vs 0.924) and in predicting good vision (AUROC 0.766 vs 0.736). For all the samples, POTS was a robust predictor of poor vision (AUROC 0.910) and had a moderate ability to predict good vision (AUROC 0.764). CONCLUSION: OTS has high ability to predict visual outcome for paediatric traumatic cataract following penetrating ocular trauma. POTS is also a reliable prognostic model for very young child without initial vision or RAPD, but is only for penetrating eye injuries.

Gas/particle partitioning of polycyclic aromatic hydrocarbons in coastal atmosphere of the north Yellow Sea, China.

Samples of gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs) were collected at three sampling stations (Xiaomai Island, Laohutan, and Zhangzi Island) in the north Yellow Sea, China during November 2008 and September 2009 to study their atmospheric transport potential and the gas/particle distributions. The composition of PAHs was dominated by gaseous compounds. The percentages of the particle-phase PAHs to the total concentrations were found to be higher during the heating period than the non-heating period. The ratios of naphthalene and acenaphthene to phenanthrene, chrysene and dibenzo(a,h)anthracene showed an increasing trend from Xiaomai Island to Zhangzi Island, which can be called as the local atmospheric distillation of PAHs. Gas/particle partitioning coefficients (K p) and their relationship with the sub-cooled liquid vapor pressures (pºL) of PAHs were investigated. The regressions of logK p versus logpºL gave significant correlations for all samples of the three sites with r (2) values in the range 0.56-0.66 (p<0.01). Both Junge-Pankow adsorption model and octanol-air partition coefficient absorption model tended to underestimate the sorption for most PAHs, but the absorption model appeared to be more suitable for predicting the particle fraction of PAHs than the Junge-Pankow model.