Natural selection and functional diversification of the epidermal growth factor receptorEGFR family in vertebrates

AbstractBackgroundGenes that have been subject to adaptive evolution can produce varying degrees of pathology or differing symptomatology. ErbB family receptor activation will initiate a number of downstream signaling pathways, such as mitogen-activated protein kinase (MAPK), activator of transcription (STAT), the modulation of calcium channels, and so on, all of which lead to aggressive tumor behavior. However, the evolutionary mechanisms operating in the retention of ErbB family genes and the changes in selection pressures are not clear.ResultsSixty-two full-length cDNA sequences from 27 vertebrate species were extracted from the UniProt protein database, NCBI's GenBank and the Ensembl database. The result of phylogenetic analysis showed that the four ErbB family members in vertebrates might be formed by gene duplication. In order to determine the mode of evolution in vertebrates, selection analysis and functional divergence analysis were combined to explain the relationship of the site-specific evolution and functional divergence in the vertebrate ErbB family. Our results indicate that the acceleration of asymmetric evolutionary rates and purifying selection together were the main force for the production of ErbBs, and positive selections were detected in the ErbB family.ConclusionAn evolutional phylogeny of 27 vertebrates was presented in our study; the tree showed that the genes have evolved through duplications followed by purifying selection, except for seven sites, which evolved by positive selection. There was one common site with positive selection and functional divergence. In the process of functional differentiation evolving through gene duplication, relaxed selection may play an important part

Local Readjustment for High-Resolution 3D Reconstruction

Global bundle adjustment usually converges to a non-zero residual and produces sub-optimal camera poses for local areas, which leads to loss of details for high- resolution reconstruction. Instead of trying harder to optimize everything globally, we argue that we should live with the non-zero residual and adapt the camera poses to local areas. To this end, we propose a segment-based approach to readjust the camera poses locally and improve the reconstruction for fine geometry details. The key idea is to partition the globally optimized structure from motion points into well-conditioned segments for re-optimization, reconstruct their geometry individually, and fuse everything back into a consistent global model. This significantly reduces severe propagated errors and estimation biases caused by the initial global adjustment. The results on several datasets demonstrate that this approach can significantly improve the reconstruction accuracy, while maintaining the consistency of the 3D structure between segments

Joint Affinity Propagation for Multiple View Segmentation

A joint segmentation is a simultaneous segmentation of registered 2D images and 3D points reconstructed from the multiple view images. It is fundamental in structuring the data for subsequent modeling applications. In this paper, we treat this joint segmentation as a weighted graph labeling problem. First, we construct a 3D graph for the joint 3D and 2D points using a joint similarity measure. Then, we propose a hierarchical sparse affinity propagation algorithm to automatically and jointly segment 2D images and group 3D points. Third, a semi-supervised affinity propagation algorithm is proposed to refine the automatic results with the user assistance. Finally, intensive experiments demonstrate the effectiveness of the proposed approaches. 1

Validity of the International HIV Dementia Scale as Assessed in a Socioeconomically Underdeveloped Region of Southern China: Assessing the Influence of Educational Attainment

In 2012, more than 80,000 cases of HIV infection were recorded in the Southern Chinese minority autonomous region of Guangxi Zhuang, where the occurrence of HIV-associated dementia remains high. The International HIV Dementia Scale is a relatively simple-to-administer screening scale for HIV-associated neurocognitive disorders. However, clinical experience in utilizing the scale with large Chinese samples is currently lacking, especially among individuals with limited formal schooling. In this study, a full neuropsychological evaluation the gold standard was conducted to identify the incidence/prevalence of HIV-associated neurocognitive disorders in a socioeconomically underdeveloped region of Southern China and to locate the optimal cut-off scale value using receiver operating characteristic curves. The highest Youden index of the scale was 0.450, with a corresponding cut-off point of 7.25. The sensitivity and specificity were 0.737 and 0.713, respectively. These results suggest that the scale is an effective and feasible screening tool for HIV-associated neurocognitive disorders in poorer regions of China with fewer well-educated residents

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve highprecision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value