Fast Mode Decision for 3D-HEVC Depth Intracoding

The emerging international standard of high efficiency video coding based 3D video coding (3D-HEVC) is a successor to multiview video coding (MVC). In 3D-HEVC depth intracoding, depth modeling mode (DMM) and high efficiency video coding (HEVC) intraprediction mode are both employed to select the best coding mode for each coding unit (CU). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs the 3D-HEVC from practical application. In this paper, a fast mode decision algorithm based on the correlation between texture video and depth map is proposed to reduce 3D-HEVC depth intracoding computational complexity. Since the texture video and its associated depth map represent the same scene, there is a high correlation among the prediction mode from texture video and depth map. Therefore, we can skip some specific depth intraprediction modes rarely used in related texture CU. Experimental results show that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC depth intracoding while maintaining coding efficiency

An optimized algorithm for optimal power flow based on deep learning

With the increasing requirements for power system transient stability assessment, the research on power system transient stability assessment theory and methods requires not only qualitative conclusions about system transient stability but also quantitative analysis of stability and even development trends. Judging from the research and development process of this direction at home and abroad in recent years, it is mainly based on the construction of quantitative index models to evaluate its transient stability and development trend. Regarding the construction theories and methods of quantitative index models, a lot of results have been achieved so far. The research ideas mainly focus on two categories: uncertainty analysis methods and deterministic analysis methods. Transient stability analysis is one of the important factors that need to be considered. Therefore, this paper proposed an optimized algorithm based on deep learning for preventive control of the transient stability in power systems. The proposed algorithm accurately fits the generator’s power and transient stability index through a deep belief network (DBN) by unsupervised pre-training and fine-tuning. The non-linear differential–algebraic equation and complex transient stability are determined using the deep neural network. The proposed algorithm minimizes the control cost under the constraints of the contingency by realizing the data-driven acquisition of the optimal preventive control. It also provides an efficient solution to stability and reliability rules with similar safety into the corresponding control model. Simulation results show that the proposed algorithm effectively improved the accuracy and reduces the complexity as compared with existing algorithms.National Research Foundation of Korea [2019R1C1C1007277]

Global patterns of species richness of the holarctic alpine herb Saxifraga: The role of temperature and habitat heterogeneity

Postponed access: the file will be available after 2022-08-03The effects of contemporary climate, habitat heterogeneity and long-term climate change on species richness are well studied for woody plants in forest ecosystems, but poorly understood for herbaceous plants, especially in alpine–arctic ecosystems. Here, we aim to test if the previously proposed hypothesis based on the richness–environment relationship could explain the variation in richness patterns of the typical alpine–arctic herbaceous genus Saxifraga. Using a newly compiled distribution database of 437 Saxifraga species, we estimated the species richness patterns for all species, narrow- and wide-ranged species. We used generalized linear models and simultaneous autoregressive models to evaluate the effects of contemporary climate, habitat heterogeneity and historical climate on species richness patterns. Partial regressions were used to determine the independent and shared effects of different variables. Four widely used models were tested to identify their predictive power in explaining patterns of species richness. We found that temperature was negatively correlated with the richness patterns of all and wide-ranged species, and that was the most important environmental factor, indicating a strong conservatism of its ancestral temperate niche. Habitat heterogeneity and long-term climate change were the best predictors of the spatial variation of narrow-ranged species richness. Overall, the combined model containing five predictors can explain ca. 40%–50% of the variation in species richness. We further argued that additional evolutionary and biogeographical processes might have also played an essential role in shaping the Saxifraga diversity patterns and should be considered in future studies.acceptedVersio

A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians.MethodsThree COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity.ResultsPatients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively.ConclusionOverall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics

Generation of H7N9-Specific Human Polyclonal Antibodies from a Transchromosomic Goat (caprine) System

To address the unmet needs for human polyclonal antibodies both as therapeutics and diagnostic reagents, building upon our previously established transchromosomic (Tc) cattle platform, we report herein the development of a Tc goat system expressing human polyclonal antibodies in their sera. In the Tc goat system, a human artificial chromosome (HAC) comprising the entire human immunoglobulin (Ig) gene repertoire in the germline configuration was introduced into the genetic makeup of the domestic goat. We achieved this by transferring the HAC into goat fetal fibroblast cells followed by somatic cell nuclear transfer for Tc goat production. Gene and protein expression analyses in the peripheral blood mononuclear cells (PBMC) and the sera, respectively, of Tc caprine demonstrated the successful expression of human Ig genes and antibodies. Furthermore, immunization of Tc caprine with inactivated influenza A (H7N9) viruses followed by H7N9 Hemagglutinin 1 (HA1) boosting elicited human antibodies with high neutralizing activities against H7N9 viruses in vitro. As a small ungulate, Tc caprine offers the advantages of low cost and quick establishment of herds, therefore complementing the Tc cattle platform in responses to a range of medical needs and diagnostic applications where small volumes of human antibody products are needed

Effects of redox mediators on the catalytic activity of iron porphyrins towards oxygen reduction in acidic media

The effects of different redox mediators on the oxygen reduction reaction (ORR) catalyzed by an iron porphyrin complex, iron(III) meso-tetra(N-methyl-4-pyridyl)porphine chloride [FeIIITMPyP], in 0.1 M triflic acid were investigated by cyclic voltammetry (CV) and spectroelectrochemistry in conjunction with density functional theory (DFT) calculations. The formal potentials of the FeIIITMPyP catalyst and the redox mediators, as well as the half-wave potentials for the ORR, were determined by CV in the absence and presence of oxygen in acidic solutions. UV/Vis spectroscopic and spectroelectrochemical studies confirmed that only the 2,2′-azino-bis(3-ethylbenzothiazioline-6-sulfonic acid)diammonium salt (C18H24N6O6S4) showed effective interactions with FeIIITMPyP during the ORR. DFT calculations suggested strong interaction between FeIIITMPyP and the C18H24N6O6S4 redox mediator. The redox mediator caused lengthening of the dioxygen iron bond, which thus suggested easier dioxygen reduction. Consistent results were observed in electrochemical impedance spectroscopic measurements for which the electron-transfer kinetics were also evaluated

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Quality assessment metric of stereo images considering cyclopean integration and visual saliency

This paper was accepted for publication in the journal Information Sciences and the definitive published version is available at http://dx.doi.org/10.1016/j.ins.2016.09.004.In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called “cyclopean saliency”, which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with “cyclopean saliency” in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality