Transcriptomic Analysis of Shiga Toxin-Producing Escherichia coli FORC_035 Reveals the Essential Role of Iron Acquisition for Survival in Canola Sprouts and Water Dropwort

Enterohemorrhagic Escherichia coli (EHEC) is a foodborne pathogen that poses a serious threat to humans. Although EHEC is problematic mainly in food products containing meat, recent studies have revealed that many EHEC-associated foodborne outbreaks were attributable to spoiled produce such as sprouts and green leafy vegetables. To understand how EHEC adapts to the environment in fresh produce, we exposed the EHEC isolate FORC_035 to canola spouts (Brassica napus) and water dropwort (Oenanthe javanica) and profiled the transcriptome of this pathogen at 1 and 3 h after incubation with the plant materials. Transcriptome analysis revealed that the expression of genes associated with iron uptake were down-regulated during adaptation to plant tissues. A mutant strain lacking entB, presumably defective in enterobactin biosynthesis, had growth defects in co-culture with water dropwort, and the defective phenotype was complemented by the addition of ferric ion. Furthermore, gallium treatment to block iron uptake inhibited bacterial growth on water dropwort and also hampered biofilm formation. Taken together, these results indicate that iron uptake is essential for the fitness of EHEC in plants and that gallium can be used to prevent the growth of this pathogen in fresh produce

Prevalence and Determinants of Metabolic Syndrome among Women in Chinese Rural Areas

BACKGROUND AND AIMS: Metabolic syndrome (MS) is prevalent in recent years but few data is reported in the rural areas in China. The aim of this study was to examine MS prevalence and its risk factors among women in rural China. METHODS AND RESULTS: The Nantong Metabolic Syndrome Study (NMSS), a population based cross-sectional study, was conducted during 2007-2008 in Nantong, China. In person interviews, blood glucose and lipid measurements were completed for 13,505 female participants aged 18-74 years. The International Diabetes Federation (IDF), the US Third Report of the National Cholesterol Education Program, the Adult Treatment Panel (ATPIII) and modified ATPIII for Asian population has determined three criteria of MS. These criteria for MS were used and compared in this study. The prevalence of MS was 22.0%, 16.9% and 23.3% according to IDF, ATPIII and ATPIII-modified criteria, respectively. Levels of agreement of these criteria for MS were above 0.75. We found that vigorous-intensity of occupational physical activity was associated with a low prevalence of MS with OR of 0.76 (95% confidence interval (CI): 0.63-0.91). Rice wine drinkers (alcohol >12.8 g/day) had about 34% low risks of developing MS with OR of 0.66 (95% CI: 0.48-0.91), compared with non-drinkers. Odds ratio of MS was 1.81 (95% CI: 1.15-2.84) in women who smoked more than 20 pack-years, compared to non-smokers. Odds ratio of MS was 1.56 (95% CI: 1.25-1.95) in women who had familial history of diseases, including hypertension, diabetes and stroke, compared to women without familial history of those diseases. CONCLUSION: MS is highly prevalent among women in rural China. Both physical activity and rice wine consumption play a protective role, while family history and smoking are risk factors in MS development. Educational programs should be established for promoting healthy lifestyles and appropriate interventions in rural China

Regeneration of Pancreatic Non-β Endocrine Cells in Adult Mice following a Single Diabetes-Inducing Dose of Streptozotocin

The non-β endocrine cells in pancreatic islets play an essential counterpart and regulatory role to the insulin-producing β-cells in the regulation of blood-glucose homeostasis. While significant progress has been made towards the understanding of β-cell regeneration in adults, very little is known about the regeneration of the non-β endocrine cells such as glucagon-producing α-cells and somatostatin producing δ-cells. Previous studies have noted the increase of α-cell composition in diabetes patients and in animal models. It is thus our hypothesis that non-β-cells such as α-cells and δ-cells in adults can regenerate, and that the regeneration accelerates in diabetic conditions. To test this hypothesis, we examined islet cell composition in a streptozotocin (STZ)-induced diabetes mouse model in detail. Our data showed the number of α-cells in each islet increased following STZ-mediated β-cell destruction, peaked at Day 6, which was about 3 times that of normal islets. In addition, we found δ-cell numbers doubled by Day 6 following STZ treatment. These data suggest α- and δ-cell regeneration occurred rapidly following a single diabetes-inducing dose of STZ in mice. Using in vivo BrdU labeling techniques, we demonstrated α- and δ-cell regeneration involved cell proliferation. Co-staining of the islets with the proliferating cell marker Ki67 showed α- and δ-cells could replicate, suggesting self-duplication played a role in their regeneration. Furthermore, Pdx1+/Insulin− cells were detected following STZ treatment, indicating the involvement of endocrine progenitor cells in the regeneration of these non-β cells. This is further confirmed by the detection of Pdx1+/glucagon+ cells and Pdx1+/somatostatin+ cells following STZ treatment. Taken together, our study demonstrated adult α- and δ-cells could regenerate, and both self-duplication and regeneration from endocrine precursor cells were involved in their regeneration

Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world

Microporous 3D Graphene-like Zeolite-Templated Carbons for Preferential Adsorption of Ethane

Microporous 3D graphene-like carbons were synthesized in Faujasite (FAU)-, EMT-, and beta-zeolite templates using the recently developed Ca2+ ion-catalyzed synthesis method. The microporous carbons liberated from these large-pore zeolites (0.7-0.9 nm) retain the structural regularity of zeolite. FAU-, EMT-, and beta zeolite-templated carbons (ZTCs) with faithfully constructed pore diameters of 1.2, 1.1, and 0.9 nm, respectively, and very large Brunauer-Emmet-Teller areas (2700-3200 m2 g-1) were obtained. We have discovered that these schwarzite-like carbons exhibit preferential adsorption of ethane over ethylene at pressures in the range of 1-10 bar. The curved graphene structure, consisting of a diverse range of carbon polygons with a narrow pore size of ∼1 nm, provides abundant adsorption sites in micropores and retains its ethane selectivity at pressures up to 10 bar. After varying the oxygen content in the beta ZTC, the ethane and ethylene adsorption isotherms show that the separation ability is not significantly affected by surface oxygen groups. Based on these adsorption results, a breakthrough separation procedure using a C2H4/C2H6 gas mixture (9:1 molar ratio) is demonstrated to produce ethylene with a purity of 99.9%11Nsciescopu

Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases