Impact of SARS-CoV-2 ORF6 and its variant polymorphisms on host responses and viral pathogenesis

: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes several proteins that inhibit host interferon responses. Among these, ORF6 antagonizes interferon signaling by disrupting nucleocytoplasmic trafficking through interactions with the nuclear pore complex components Nup98-Rae1. However, the roles and contributions of ORF6 during physiological infection remain unexplored. We assessed the role of ORF6 during infection using recombinant viruses carrying a deletion or loss-of-function (LoF) mutation in ORF6. ORF6 plays key roles in interferon antagonism and viral pathogenesis by interfering with nuclear import and specifically the translocation of IRF and STAT transcription factors. Additionally, ORF6 inhibits cellular mRNA export, resulting in the remodeling of the host cell proteome, and regulates viral protein expression. Interestingly, the ORF6:D61L mutation that emerged in the Omicron BA.2 and BA.4 variants exhibits reduced interactions with Nup98-Rae1 and consequently impairs immune evasion. Our findings highlight the role of ORF6 in antagonizing innate immunity and emphasize the importance of studying the immune evasion strategies of SARS-CoV-2

Vaccinia Virus Strain MVA Expressing a Prefusion-Stabilized SARS-CoV-2 Spike Glycoprotein Induces Robust Protection and Prevents Brain Infection in Mouse and Hamster Models

24 Pág.The COVID-19 pandemic has underscored the importance of swift responses and the necessity of dependable technologies for vaccine development. Our team previously developed a fast cloning system for the modified vaccinia virus Ankara (MVA) vaccine platform. In this study, we reported on the construction and preclinical testing of a recombinant MVA vaccine obtained using this system. We obtained recombinant MVA expressing the unmodified full-length SARS-CoV-2 spike (S) protein containing the D614G amino-acid substitution (MVA-Sdg) and a version expressing a modified S protein containing amino-acid substitutions designed to stabilize the protein a in a pre-fusion conformation (MVA-Spf). S protein expressed by MVA-Sdg was found to be expressed and was correctly processed and transported to the cell surface, where it efficiently produced cell-cell fusion. Version Spf, however, was not proteolytically processed, and despite being transported to the plasma membrane, it failed to induce cell-cell fusion. We assessed both vaccine candidates in prime-boost regimens in the susceptible transgenic K18-human angiotensin-converting enzyme 2 (K18-hACE2) in mice and in golden Syrian hamsters. Robust immunity and protection from disease was induced with either vaccine in both animal models. Remarkably, the MVA-Spf vaccine candidate produced higher levels of antibodies, a stronger T cell response, and a higher degree of protection from challenge. In addition, the level of SARS-CoV-2 in the brain of MVA-Spf inoculated mice was decreased to undetectable levels. Those results add to our current experience and range of vaccine vectors and technologies for developing a safe and effective COVID-19 vaccine.This research was funded by Instituto de Salud Carlos III, Fondo COVID-19 de proyectos de investigación sobre SARS-CoV-2 y la enfermedad COVID-19 grant COV20/00901, and grant PID2021-128466OR-I00 funded by funded by MCIN/AEI/10.13039/501100011033 as part of Plan Estatal de Investigación Científica, Desarrollo e Innovación. This research work was also funded by the European Commission—NextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global). All experiments using bioluminescent imaging approach were supported by NIH grant to WM. Research on SARS-CoV-2 in L.M-S laboratory was partially supported by the San Antonio Partnership for Precision Therapeutics, the San Antonio Medical Foundation, and the Texas Biomedical Research Institute Forum Foundation.Peer reviewe

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin

Reliability Equivalence to Symmetrical UHVDC Transmission Systems Considering Redundant Structure Configuration

In recent years, the ultra-high voltage direct current (UHVDC) transmission system has been developed rapidly for its significant long-distance, high-capacity and low-loss properties. Equipment failures and overall outages of the UHVDC system have increasingly vital influence on the power supply of the receiving end grid. To improve the reliability level of UHVDC systems, a quantitative selection and configuration approach of redundant structures is proposed in this paper, which is based on multi-state reliability equivalence. Specifically, considering the symmetry characteristic of an UHVDC system, a state space model is established as a monopole rather than a bipole, which effectively reduces the state space dimensions to be considered by deducing the reliability merging operator of two poles. Considering the standby effect of AC filters and the recovery effect of converter units, the number of available converter units and corresponding probability are expressed with in universal generating function (UGF) form. Then, a sensitivity analysis is performed to quantify the impact of component reliability parameters on system reliability and determine the most specific devices that should be configured in the redundant structure. Finally, a cost-benefit analysis is utilized to help determine the optimal scheme of redundant devices. Case studies are conducted to demonstrate the effectiveness and accuracy of the proposed method. Based on the numerical results, configuring a set of redundant transformers is indicated to be of the greatest significance to improve the reliability level of UHVDC transmission systems

A data-driven bottom-up approach for spatial and temporal electric load forecasting

With the rapid urbanization, electrical infrastructure spreads to raw areas without existing loads. How to achieve accurate long-term load forecasts based on land use plans is a realistic problem. On the other hand, load forecasting (LF) should be extended to high spatial resolutions to guide middle- or low-voltage planning and time domain to consider the impacts of distribution generations and diversified users on multi-period system demands. A data-driven bottom-up spatial and temporal LF approach is proposed in this paper to solve these challenges. Land plots are treated as basic LF resolution to describe available multi-attribute data in smart grids and modern cities. Kernel density estimation and adaptive k-means are adopted to aggregate typical load densities and profiles of different land use types. Stacked auto-encoders are utilized to forecast the unknown plot load quantities. The neighbor plot loads are summed up to obtain the estimated loads of larger areas based on clustered load profiles. Case studies demonstrate that the proposed LF is more applicable than benchmark methods both in accuracy and application potential. The estimated hierarchical spatial and temporal results are of great significance to guide load balancing, power system planning, and user integration in different voltage levels.. This work was supported in part by the National Natural Science Foundation of China under Grant 51807173 and in part by the State Grid Corporation of China (5211JY17000L)

Allocation of centrally switched fault current limiters enabled by 5G in transmission system

The allocation of fault current limiters (FCLs) is increasingly challenging in transmission systems these days. Specifically, the utilized deterministic expected short-circuit fault (SCF) scenarios are prone to cause over-configuration of FCLs. Moreover, the well-established local switching framework (LSF) renders inappropriate FCL switching and may further harm the system safe operation. Aiming at the above deficiencies, a novel 5G-based centralized switch FCL (CSF) framework as well as a method to allocate such flexible FCLs optimally is proposed in this paper. In the proposed CSF, the FCLs are switched by a FCL dispatching (FD) model considering system security constraints of both fault current and voltage sags. By exploiting the fast communication capability of 5G network as well as an off-line fault scanning strategy, the FD model is enabled to give online FCL switching schemes to meet the fast requirement of power system protection. Moreover, considering the probabilistic characteristic of SCFs, a bi-level FCL allocation model is established, in which the upper-level model sites and sizes FCLs considering the installation and expected switching costs while the lower-level model determines the optimal switched FCLs under each specific SCF scenario. Finally, numerical results are provided to verify the proposed allocation model, including its defending effect against SCFs in terms of fault current limiting, voltage sags relieving, as well as its cost-effectiveness.This work was supported in part by the China NSFC under Grant 51807173

Deep Reinforcement Learning Based Charging Scheduling for Household Electric Vehicles in Active Distribution Network

With the booming of electric vehicles (EVs) across the world, their increasing charging demands pose challenges to urban distribution networks. Particularly, due to the further implementation of time-of-use prices, the charging behaviors of household EVs are concentrated on low-cost periods, thus generating new load peaks and affecting the secure operation of the medium- and low-voltage grids. This problem is particularly acute in many old communities with relatively poor electricity infrastructure. In this paper, a novel two-stage charging scheduling scheme based on deep reinforcement learning is proposed to improve the power quality and achieve optimal charging scheduling of household EVs simultaneously in active distribution network (ADN) during valley period. In the first stage, the optimal charging profiles of charging stations are determined by solving the optimal power flow with the objective of eliminating peak-valley load differences. In the second stage, an intelligent agent based on proximal policy optimization algorithm is developed to dispatch the household EVs sequentially within the low-cost period considering their discrete nature of arrival. Through powerful approximation of neural network, the challenge of imperfect knowledge is tackled effectively during the charging scheduling process. Finally, numerical results demonstrate that the proposed scheme exhibits great improvement in relieving peak-valley differences as well as improving voltage quality in the ADN

Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders

With the development of advanced metering infrastructure (AMI), electrical data are collected frequently by smart meters. Consequently, the load data volume and length increase dramatically, which aggravates the data storage and transmission burdens in smart grids. On the other hand, for event detection or market-based demand response applications, load service entities (LSEs) want smart meter readings to be classified in specific and meaningful types. Considering these challenges, a stacked auto-encoder (SAE)-based load data mining approach is proposed. First, an innovative framework for smart meter data flow is established. On the user side, the SAEs are utilized to compress load data in a distributed way. Then, centralized classification is adopted at remote data center by softmax classifier. Through the layer-wise feature extracting of SAE, the sparse and lengthy raw data are expressed in compact forms and then classified based on features. A global fine-tuning strategy based on a well-defined labeled subset is embedded to improve the extracted features and the classification accuracy. Case studies in China and Ireland demonstrate that the proposed method is more capable to achieve the minimum of error and satisfactory compression ratios (CR) than benchmark compressors. It also significantly improves the classification accuracy on both appliance and house level datasets