The evolution of BIR domain and its containing proteins

AbstractBIR domain and its containing proteins play critical roles in cell apoptosis and cell division. Here several lines of novelty were revealed based on a comprehensive evolutionary analysis of BIR domains in 11 representative organisms. First, the type II BIR domains in Survivin and Bruce showed more conservation compared with the type I BIR domains in the inhibitors of apoptosis proteins (IAPs). Second, cIAP was derived from a XIAP duplicate and emerged just after the divergence of invertebrates and vertebrates. Third, the three BIR domains of NAIP displayed significantly elevated evolutionary rates compared with the BIR domains in other IAPs

Harmonized neonatal brain MR image segmentation model for cross-site datasets

Accurate segmentation of white matter, gray matter and cerebrospinal fluid from neonatal brain MR images is of great importance in characterizing early brain development. Deep-learning-based methods have been successfully applied to neonatal brain MRIs with superior performance if testing subjects were acquired with the same imaging protocols/scanners as training subjects. However, for the testing subjects acquired with different imaging protocols/scanners, they cannot achieve accurate segmentation results due to large appearance/pattern differences between the testing and training subjects. Besides, imaging artifacts, like head motion, which are inevitable during the imaging acquisition process, also pose a challenge for the segmentation methods. To address these issues, in this paper, we propose a harmonized neonatal brain MR image segmentation model that harmonizes testing images acquired by different protocols/scanners into the domain of training images through a cycle-consistent generative adversarial network (CycleGAN). Meanwhile, the artifacts can be largely alleviated during the harmonization. Then, a densely-connected U-Net based segmentation model trained in the domain of training images can be applied robustly for segmenting the harmonized testing images. Comparisons with existing methods illustrate the better performance of the proposed method on neonatal brain MR images from cross-sites, a grand segmentation challenge, as well as images with artifacts

Novel Word Learning in Multilingual Children with and without Autism Spectrum Disorder: Roles of Social Cognition, Multilingualism and Vocabulary Proficiency

While the impact of social cognition on novel word learning has been extensively studied in monolingual populations, limited research has investigated its role in multilingual children with and without autism spectrum disorder. This study examined the role of multilingualism on the acquisition of novel English words under directly addressed and overhearing conditions. Participants included four groups of children with different language status (multilingual and monolingual and diagnostic status (typically developing and autistic). The results revealed that the learning preferences vary across participant groups depending on their language and diagnostic statuses. Additionally, dynamic patterns of novel word learning were unveiled, demonstrating the influence of English vocabulary proficiency on multilingual children's learning process. The findings highlighted the complex role of multilingualism on driving the formation of learning preference for typical developing and autistic children

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China

Negative curvature fiber for suppressing high-order radial OAM modes transmission

The combination of terahertz (THz) wave and the orbital angular momentum (OAM) multiplexing technology can further improve the communication capacity. Compared to other outer cladding structures, the negative curvature structure has been proven to provide stronger confinement effect on electromagnetic waves. Here, we propose a novel polymer (COC TOPAS) fiber which consists a central hollow-core, annular region and single layer negative curvature circle tubes as outer cladding for terahertz OAM modes transmission. The mode map is established by mathematical analysis of cut-off conditions for the vector modes, and the excitation of high-order radial modes in the fiber is successfully suppressed. In addition, the effective refractive index, confinement loss, effective mode area, mode purity and dispersion characteristics of the fiber are investigated under the condition of ‘single mode’ operation in the frequency range of 2.0–3.0 THz

Image Recommendation Algorithm Combined with Deep Neural Network Designed for Social Networks

In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms

Study on the Basic Characteristics of Iron Ore Powder with Different Particle Sizes

In order to study in depth the differences in basic characteristics between iron ore fines commonly used by a steel company, and guide the sintering performance plant to choose the best ore allocation method, experimental studies on the basic characteristics of seven iron ore powders of three sizes were carried out using micro-sintering equipment, mainly including assimilation properties, liquid phase fluidity, and bonding phase strength. The results of the research showed that with the increase of the iron ore powder particle size, the assimilation of the seven iron ore powders showed an overall decreasing trend, deteriorating fluidity and decreasing bonding phase strength. Among them, the overall fluidity of iron ore powder A was poor, and the fluidity of iron ore powder B varied greatly between different particle grades, and the fluidity of iron ore powder C was more balanced and its bonding phase strength was high, while the overall bonding phase strength of iron ore powders B and E was low. The results of the study provide a theoretical basis for optimal ore allocation in sintering plants