Dynamic description of temporal changes of gut microbiota in broilers.

The diversity of bacteria and fungi in the gut microbiota of commercial broilers that raised in cages from hatch to the end of the production cycle were examined by an analysis of 3,592 and 3,899 amplicon sequence variants (ASVs), respectively. More than 90% sequences in bacterial communities were related to Firmicutes and Proteobacteria. More than 90% sequences in fungal communities were related to Ascomycota, Basidiomycota, and Glomeromycota. A statistical analysis of the microbiota composition succession showed that age was one of the main factors affecting the intestinal microbial communities of broilers. The increasingly complex community succession of transient microbiota occurred along with an increase of age. This dynamic change was observed to be similar between bacteria and fungi. The gut microbiota had a special structure in the first 3 d after birth of broiler. The microbiota structure was quite stable in the period of rapid skeletal growth (d 14-21), and then changed significantly in the period of rapid gaining weight (d 35-42), thus indicating the composition of gut microbiota in broilers had unique structures at different developmental stages. We observed that several bacteria and fungi occupied key functions in the gut microbiota of broilers, suggesting that the gut homeostasis of broilers might be affected by losses of bacteria and fungi via altering interactions between microbiota. This study aimed to provide a data basis for manipulating the microbiota at different developmental stages, in order to improve production and the intestinal health of broilers.

A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images.

Accurate assessment of pulmonary nodules can help to diagnose the serious degree of lung cancer. In most computed aided diagnosis (CADx) systems, the feature extraction module plays quite an important role in classifying pulmonary nodules based on different attributes of them. To precisely evaluate the malignancy of an unknown pulmonary nodule, this paper first proposes a novel pixel value space statistics map (PVSSM) for pulmonary nodules classification. By means of PVSSM this study can transform an original two-dimensional (2D) or three-dimensional (3D) pulmonary nodule into a 2D feature matrix, which contributes to better classifying a pulmonary nodule. To validate the proposed method, this study assembled 5385 valid 3D nodules from 1006 cases in LIDC-IDRI database. This study extracts sets of features from the created feature matrixes by singular value decomposition (SVD) method. Using several popular classifiers including KNN, random forest and SVM, we acquire the classification accuracies of 77.29%, 80.07% and 84.21%, respectively. Moreover, this study also utilizes the convolutional neural network (CNN) to assess the malignancy of nodules and the sensitivity, specificity and area under the curve (AUC) reach up to 86.0%, 88.5% and 0.913, respectively. Experiments demonstrate that the PVSSM has a benefit for nodules classification.

Content-based retrieval for lung nodule diagnosis using learned distance metric.

Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a vector of 26 texture features. We then propose a content-based image retrieval (CBIR) scheme to classify between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then computed to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.

The crystal water affect in the interaction between the tenebrio molitor alpha-amylase and its inhibitor.

Molecular dynamics simulation of the interaction between the Tenebrio molitor alpha-amylase and its inhibitor at different proportion of crystal water was carried out with OPLS force field by hyperchem 7.5 software. In the correlative study, the optimal temperature of wheat monomeric and dimeric protein inhibitors was from 273 K to 318 K. The the average temperature of experimentation is 289 K. (1) The optimal temperature of interaction between alpha-amylase and its inhibitors was 280 K without crystal water that was close to the results of experimentation. The forming of enzyme-water and inhibitor-water was easy, but incorporating third monomer was impossible. (2) Having analyzed the potential energy data, the optimal temperature of interaction energy between alpha-amylase and its inhibitors covering 9 : 1, 5 : 5, 4 : 6, and 1 : 9 proportion crystal water was 290 K. (3) We compared the correlative QSAR properties. The proportion of crystal water was close to the data of polarizability (12.4%) in the QSAR properties. The optimal temperature was 280 K. This result was close to 289 K. These findings have theoretical and practical implications.