Characterization of Diversity and Probiotic Efficiency of the Autochthonous Lactic Acid Bacteria in the Fermentation of Selected Raw Fruit and Vegetable Juices

The diversity of indigenous lactic acid bacteria (LAB) in fermented broccoli, cherry, ginger, white radish, and white-fleshed pitaya juices was analyzed using culture-independent and -dependent approaches. The major properties of selected probiotic strains, including dynamic variations in pH, viable cell counts, antibiotic resistance, bacterial adhesion to hydrophobic compounds, and survivability during simulated gastrointestinal transit, were investigated using broccoli as the fermentation substrate. In broccoli and ginger juices, the genus Lactobacillus occupied the dominant position (abundances of 79.0 and 30.3%, respectively); in cherry and radish juices, Weissella occupied the dominant position (abundances of 78.3 and 83.2%, respectively); and in pitaya juice, Streptococcus and Lactococcus occupied the dominant positions (52.2 and 37.0%, respectively). Leuconostoc mesenteroides, Weissella cibaria/soli/confusa, Enterococcus gallinarum/durans/hirae, Pediococcus pentosaceus, Bacillus coagulans, and Lactococcus garvieae/lactis subspecies were identified by partial 16S rRNA gene sequencing. In general, the selected autochthonous LAB isolates displayed no significant differences in comparison with commercial strains with regard to growth rates or acidification in fermented broccoli juice. Among all the isolates, L. mesenteroides B4-25 exhibited the highest antibiotic resistance profile (equal to that of L. plantarum CICC20265), and suitable adhesion properties (adhesion of 13.4 ± 5.2% ∼ 36.4 ± 3.2% and 21.6 ± 1.4% ∼ 69.6 ± 2.3% to ethyl acetate and xylene, respectively). Furthermore, P. pentosaceus Ca-4 and L. mesenteroides B-25 featured the highest survival rates (22.4 ± 2.6 and 21.2 ± 1.4%, respectively), after simulated gastrointestinal transit. These results indicated a high level of diversity among the autochthonous bacterial community in fermented fruit and vegetable juices, and demonstrated the potential of these candidate probiotics for applications in fermentation

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively

Strategies for the economic transformation of a port city: A case study of Ningbo

This article describes the development of a long-term economic strategy for Ningbo, which is the largest non-container and fourth-largest container port in China. During the period of ‘reform and opening up’, the port expanded rapidly and there was large-scale growth of port-related industries. Since the turn of the century there has been a move to a more diversified economy emphasizing service industries. Following a review of the experience of other first- and second-tier port cities around the world, a strategy for Ningbo’s economic transformation has been drawn up, with three main strands: achieving industrial transformation (the core objective), advancing the regional position of Ningbo among adjacent cities (the major programmes), and urbanization (the driving force).China; economic development; industrial transformation; Ningbo; port city; regional planning; urbanization

Prediction of bacterial type IV secreted effectors by C-terminal features

Article deposited according to publisher policies: http://www.biomedcentral.com/about/copyright June 13, 2014YesFunding provided by the Open Access Authors Fund

Summary of the total genome-encoding proteins, T3_MM predicted T3S effectors and BPBAac predicted T3S effectors in <i>Salmonella</i>.

<p>The total protein number for each <i>Salmonella</i> strain was depicted and linked with a line in red, while the number of T3S effectors predicted by T3_MM and BPBAac was shown in blue and purple, respectively. The patterns of these three lines were generally similar with moderate difference.</p

Inter-species cross validation of the T3S effector predictions.

<p>The sensitivity (Sn) and specificity (Sp) of classification were shown in blue and purple, respectively. The T3S effector recall of each representative genera or subgroup was also indicated. Genus names are listed below each series of dots.</p

Summary of <i>Salmonella</i> effectors predicted by T3_MM and BPBAac.

<p>Summary of <i>Salmonella</i> effectors predicted by T3_MM and BPBAac.</p

The classifying performance of different models on T3S and non-T3S training data.

<p>The parameters were calculated based on training-reclassifying results for training dataset.</p

Predicted strain-specific T3S effectors.

<p>Predicted strain-specific T3S effectors.</p