Probiotic Characteristics of Human-Residential Bifidobacterium longum subsp. longum Strains

This study was conducted to isolate and identify Bifidobacterium from the feces of Kazakh school-age children in Yining, Xinjiang and evaluate the in vitro probiotic characteristics of B. longum subsp. longum isolates. By groEL gene sequencing and repetitive element sequence-based polymerase chain reaction (rep-PCR) fingerprinting, 416 Bifidobacterium strains were identified to belong to B. longum, B. bifidum, B. pseudocatenulatum, B. catenulatum and B. breve. According to the fingerprints of B. longum subsp. longum, 27 genotypes showed genetic differences between individual strains and the coexistence of multiple strains in the gut was found. The in vitro experimental results showed that out of 27 representative strains, strains 2B3-21, 1B23-11, 2B33-3, and 1B68-16 were optimal in acid and bile salt tolerance, strains 1B68-16, 2B13-5, 2B33-3, and 1B39-2 had broad-spectrum antibacterial properties, and strains 1B38-1, 2B33-3, 1B68-16, and 2B13-28 showed a strong antioxidant capacity. Considering the antibiotic resistance of all strains and their ability to utilize plant-derived glycans, strains 1B38-1 and 2B13-28 were selected to assess their in vivo probiotic potentials. This study may lay the foundation for the development of excellent probiotics and related products for populations from specific areas

Preparation and Photocatalytic Activity of Ag Modified Ti-Doped-Bi 2

Ti doped Bi2O3 (TDB) and Ag ion modified Ti doped Bi2O3 (Ag@TDB) photocatalysts were prepared by framework replacement synthesis method with different Ag loadings (0.05, 0.3, 0.75, and 1.0 mol/L AgNO3). The structural properties of the prepared catalysts were studied by scanning electron microscope (SEM), X-ray diffraction (XRD), BET surface area, and UV/Vis diffuse reflectance (DRS). The XRD spectra of the Ti doped Bi2O3 calcined at 650°C showed the diffraction peaks of a mixture of Bi12TiO20 and Bi4Ti3O12, with bits of mixed crystallite consisting of TiO2 and B2O3. A high blue shift in the range 650–550 nm was detected in the DRS band. This blue shift increased with the decreasing Ag content. The photocatalytic activities of the catalysts were evaluated for the degradation of crystal violet (CV) under UV light irradiation. The results indicated that the degradation rate of CV by using 1.0 mol/L AgNO3 doped bismuth titanate composite photocatalyst (1.0 Ag@TDB) was 1.9 times higher than that by using the bare Ti doped Bi2O3 photocatalyst. The higher activity of Ag@TDB is due to the enhancement of electron-hole pair separation by the electron trapping of silver particles

The metagenome of the female upper reproductive tract

publishedVersio

lenstronomy II: A gravitational lensing softwareecosystem

peer reviewe

The Chronic Effect of Transgenic Maize Line with <i>mCry1Ac</i> or <i>maroACC</i> Gene on Ileal Microbiota Using a Hen Model

The experiment was to determine the chronic effects of two transgenic maize lines that contained the mCry1Ac gene from the Bacillus thuringiensis strain (BT) and the maroACC gene from Agrobacterium tumefaciens strain (CC), respectively, on ileal microbiota of laying hens. Seventy-two laying hens were randomly assigned to one of the three dietary treatments for 12 weeks, as follows: (1) nontransgenic near-isoline maize-based diet (CT diet), (2) BT maize-based diet (BT diet), and (3) CC maize-based diet (CC diet). Ileum histological examination did not indicate a chronic effect of two transgenic maize diets. Few differences were observed in any bacterial taxa among the treatments that used high-throughput 16S rRNA gene sequencing. The only differences that were observed for bacterial genera were that Bifidobacterium belong within the Bifidobacteriaceae family tended to be greater (p = 0.114) abundant in hens fed the transgenic maize-based diet than in hens fed the CT diet. Birds that consumed the CC maize diet tended to have less abundance (p = 0.135) of Enterobacteriaceae family in the ileum than those that consumed the CT maize diet. These results indicate the lack of adverse effects of the BT maize and the CC maize lines on the ileal microbiota of hens for long term and provide important data regarding biosafety assessment of the transgenic maize lines

An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets

The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products

Design of Flow Big Data System Based on Smart Pipeline Theory

As telecom operators to build intelligent pipe more and more, analysis and processing of big data technology to deal the huge amounts of data intelligent pipeline generated has become an inevitable trend. Intelligent pipe describes operational data, sales data; operator’s pipe flow data make the value for e-commerce business form and business model in mobile e-business environment. Intelligent pipe is the third dimension of 3 D pipeline mobile electronic commerce system. Intelligent operation dimensions make the mobile e-business three-dimensional artifacts. This paper discusses the smart pipeline theory, smart pipeline flow big data system, their system framework and core technology

Design of Flow Big Data System Based on Smart Pipeline Theory

As telecom operators to build intelligent pipe more and more, analysis and processing of big data technology to deal the huge amounts of data intelligent pipeline generated has become an inevitable trend. Intelligent pipe describes operational data, sales data; operator’s pipe flow data make the value for e-commerce business form and business model in mobile e-business environment. Intelligent pipe is the third dimension of 3 D pipeline mobile electronic commerce system. Intelligent operation dimensions make the mobile e-business three-dimensional artifacts. This paper discusses the smart pipeline theory, smart pipeline flow big data system, their system framework and core technology