Characterization and genomic analysis of a novel Pseudoalteromonas phage PS_L5.

Pseudoalteromonas is a widely distributed bacterial genus that is associated with marine algae. However, there is still limited knowledge about their bacteriophage. In this study, we reported the isolation of a novel lytic bacteriophage that infects Pseudoalteromonas marina. Transmission electron microscopy revealed that PS_L5 had an icosahedral head of 52.6 ± 2 nm and a non-contractile tail with length of 96.5 ± 2 nm. The genome sequence of this phage was 34, 257 bp and had a GC content of 40.75%. Furthermore, this genome contained 61 predicted open reading frames (ORFs), which involved in various functions such as phage structure, packaging, DNA metabolism, host lysis and other additional functions. Additionally, the phylogenetic analysis based on major capsid protein showed that the phage PS_L5 was closely related to five other Pseudoalteromonas phages, namely PHS3, PHS21, AL, SL25 and Pq0 which also possessed the non-contractile long tail. This study provided the fundamental insights into the evolutionary dynamics of Pseudoalteromonas phages and the interaction between phage and host.

Long short-term memory network of machine learning for compensating temperature error of a fiber optic gyroscope independent of the temperature sensor.

We present an artificial intelligence compensation method for temperature error of a fiber optic gyroscope (FOG). The difference from the existing methods is that the compensation model finally determined by this method only uses the FOG's data to complete the regression prediction of the temperature error and eliminate the dependency on the temperature sensor. In the experimental stage, the proposed method performs temperature experiments with three varying trends of temperature heating, holding, and cooling and obtains sufficient output data sets of the FOG. Taking the output time series of the FOG as the input sample and based on the long short-term memory network of machine learning, the training, validation, and test of the model are completed. From the two perspectives of network learning ability and the improvement degree of the FOG's performance, four indicators, including root mean square error, error cumulative distribution function, FOG bias stability, and Allan variance analysis are selected to evaluate the performance of the compensation model comprehensively. Compared with the existing methods using temperature information for prediction and compensation, the results show that the error compensation method without temperature information proposed can effectively improve the accuracy of the FOG and reduce the complexity of the compensation system. The work can also provide technical references for error compensation of other sensors.

Weakly Supervised Histopathology Image Segmentation With Sparse Point Annotations.

Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervised models are not ideal for handling severe intra-class heterogeneity and inter-class homogeneity in histopathology images. Therefore, we propose a novel end-to-end weakly supervised learning framework named WESUP. With only sparse point annotations, it performs accurate segmentation and exhibits good generalizability. The training phase comprises two major parts, hierarchical feature representation and deep dynamic label propagation. The former uses superpixels to capture local details and global context from the convolutional feature maps obtained via transfer learning. The latter recognizes the manifold structure of the hierarchical features and identifies potential targets with the sparse annotations. Moreover, these two parts are trained jointly to improve the performance of the whole framework. To further boost test performance, pixel-wise inference is adopted for finer prediction. As demonstrated by experimental results, WESUP is able to largely resolve the confusion between histological foreground and background. It outperforms several state-of-the-art weakly supervised methods on a variety of histopathology datasets with minimal annotation efforts. Trained by very sparse point annotations, WESUP can even beat an advanced fully supervised segmentation network.