Impact of vector richness on the risk of vector-borne disease: The role of vector competence.

A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two-vector, one-host SIR-SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects. Secondly, we extended the above model to the case of N vectors and assumed that all vectors were homogeneous to obtain analytic expressions for disease risk. It was found that in the two-vector model, disease risk declined more rapidly as interspecific competition of the high-competence vector increased. When vector richness increases, the positive effects of adding a high-competence vector species on disease transmission may outweigh the negative effects of feeding interference due to increased vector richness, making an amplification effect more likely to occur. While the addition of a highly competitive vector species may exacerbate the negative effects of feeding interference, making a dilution effect more likely to occur. In the N-vector model, the effect of increased vector richness on disease risk was fully driven by the strength of feeding interference and interspecific competition, and changes in vector competence only quantitatively but not qualitatively altered the vector richness-disease risk relationship. This work clarifies the role of vector competence in the relationship between vector richness and disease risk and provides a new perspective for studying the diversity-disease relationship. It also provides theoretical guidance for vector management and disease prevention strategies.

Shape recovery from fusion of polarization binocular vision and shading.

The shape from polarization can recover the fine texture of the target surface. However, the gradient field for shape recovery by polarization is ambiguous, which is caused by the multi-value of the azimuth angle. In response to the problem, a method of correcting the ambiguity by the fusion of polarization binocular vision and shading information is proposed in this paper. An iterative optimization algorithm is designed to estimate the direction of the light source, which provides the basis for the shading method to calculate the depth map. Additionally. the low-frequency depth map generated by binocular matching is used to correct the polarization gradient field. The polarization gradient field of the holes and small zenith angle regions in the binocular are corrected by the improved shading method. In the experiment, four different material target objects were used for shape recovery and compared with other methods. The results of the fusion method proposed are better in terms of fine texture. At the camera working distance of  700 mm, the resolving power performs well and demonstrates that changes in the depth of at least 0.1 mm can be distinguished from that recovery result.

Effects of controlled hypotension with restrictive transfusion on intraoperative blood loss and systemic oxygen metabolism in elderly patients who underwent lumbar fusion.

BACKGROUND: The effects of restrictive fluid therapy combined with controlled hypotension in the elderly on systemic oxygen metabolism and renal function are clinical concerns. The aim of this study was to evaluate blood loss, oxygen metabolism, and renal function in different levels of controlled hypotension induced by intravenous nitroglycerin, in combination with limited infusion, in elderly patients undergoing posterior lumbar fusion. METHODS: A total of 40 patients, aged 60-75 with ASA grade II or III, who were planned for posterior lumbar fusion were randomly allocated into two groups: experimental group [target mean arterial pressure 65 mmHg (MAP 65) or control group (MAP 75)]. Indicators for blood loss, hemodynamic, systemic oxygen metabolism, and renal function evaluation index were recorded before operation (T0), 1 h after induced hypotension (T1), 2 h after hypotension (T2), and in recovery (T3). We compared changes in these parameters between groups to evaluate the combined effects of controlled hypotension with restrictive infusion. RESULTS: CI, DO2I, and VO2I were lower in both groups at T1-T3 compared with T0 (p < 0.05). DO2I and VO2I in the MAP 65 group were lower than the MAP 75 group after operation. In both groups, SCysC increased at T1, T2, and T3 (p < 0.05) compared with T0. CONCLUSIONS: Restrictive transfusion and control MAP at 65 mmHg can slightly change in renal function and reduce the risk of insufficient oxygen supply and importantly have no significant effect on blood loss and postoperative complications. TRIAL REGISTRATION: ChiCTR-INR-16008153 . Registered on 25 March 2016.

Improved calibration method based on the RANSAC approach and an improved gray centroid method for a laser-line-based structured light system.

This paper proposes an improved calibration method for a structured light system by using the random sample consensus (RANSAC) method with nonlinear optimization and an improved gray centroid method. The proposed method is composed of two steps: calibrating intrinsic and extrinsic parameters for the camera, exploiting the improved gray centroid method to extract the centerline, and fitting the structured light plane by the RANSAC approach with the three-dimensional (3D) points obtained from different positions. The error function caused by the extracted centerline is deduced based on the pixel error perturbation method. The error results of the 3D points are simulated and analyzed. An imaging system is built to realize the 3D imaging. The experimental results show that the calibration error is within 0.08 mm and the reconstruction error is less than 0.45 mm. Moreover, it performs better for the reconstruction of complex objects compared with traditional methods.

Rapid identification of human-infecting viruses.

Viruses have caused much mortality and morbidity to humans and pose a serious threat to global public health. The virome with the potential of human infection is still far from complete. Novel viruses have been discovered at an unprecedented pace as the rapid development of viral metagenomics. However, there is still a lack of methodology for rapidly identifying novel viruses with the potential of human infection. This study built several machine learning models to discriminate human-infecting viruses from other viruses based on the frequency of k-mers in the viral genomic sequences. The k-nearest neighbor (KNN) model can predict the human-infecting viruses with an accuracy of over 90%. The performance of this KNN model built on the short contigs (≥1 kb) is comparable to those built on the viral genomes. We used a reported human blood virome to further validate this KNN model with an accuracy of over 80% based on very short raw reads (150 bp). Our work demonstrates a conceptual and generic protocol for the discovery of novel human-infecting viruses in viral metagenomics studies.

RAEdb: a database of enhancers identified by high-throughput reporter assays.

High-throughput reporter assays have been recently developed to directly and quantitatively assess enhancer activity for thousands of regulatory elements. However, there is still no database to collect these enhancers. We developed RAEdb, the first database to collect enhancers identified by high-throughput reporter assays. RAEdb includes 538 320 enhancers derived from eight studies, most of which were from six human cell lines. An activity score was assigned to each enhancer based on reporter assays. Based on these enhancers, 7658 epromoters (promoters with enhancer activity) were identified and stored in the database. RAEdb provides two ways of searches: the first is to search studies by species and cell line; the other is to search enhancers or epromoters by position, activity score, sequence and gene. RAEdb also provides a genome browser to query, visualize and compare enhancers. All data in RAEdb is freely available for download.

Cell membrane proteins with high N-glycosylation, high expression and multiple interaction partners are preferred by mammalian viruses as receptors.

MOTIVATION: Receptor mediated entry is the first step for viral infection. However, the question of how viruses select receptors remains unanswered. RESULTS: Here, by manually curating a high-quality database of 268 pairs of mammalian virus-host receptor interaction, which included 128 unique viral species or sub-species and 119 virus receptors, we found the viral receptors are structurally and functionally diverse, yet they had several common features when compared to other cell membrane proteins: more protein domains, higher level of N-glycosylation, higher ratio of self-interaction and more interaction partners, and higher expression in most tissues of the host. This study could deepen our understanding of virus-receptor interaction. AVAILABILITY AND IMPLEMENTATION: The database of mammalian virus-host receptor interaction is available at http://www.computationalbiology.cn: 5000/viralReceptor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification.

In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods.

Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification.

In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a ℓ 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation. The proposed algorithm not only exploits the discrimination ability of multiple features but also greatly reduces the interference of atoms that are irrelevant to the test sample, thus effectively improving classification performance. Conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) public SAR database, experimental results show that the proposed approach is effective and superior to many state-of-the-art methods.

Predicting the host of influenza viruses based on the word vector.

Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences. The results show that the length of the word within the word vector, the sequence type (DNA or protein) and the species from which the sequences were derived for generating the word vector all influence the performance of models in predicting the host of influenza viruses. In nearly all cases, the models built on the surface proteins hemagglutinin (HA) and neuraminidase (NA) (or their genes) produced better results than internal influenza proteins (or their genes). The best performance was achieved when the model was built on the HA gene based on word vectors (words of three-letters long) generated from DNA sequences of the influenza virus. This results in accuracies of 99.7% for avian, 96.9% for human and 90.6% for swine influenza viruses. Compared to the method of sequence homology best-hit searches using the Basic Local Alignment Search Tool (BLAST), the word vector-based models still need further improvements in predicting the host of influenza A viruses.