Characterization of a Novel Megabirnavirus from \u3cem\u3eSclerotinia sclerotiorum\u3c/em\u3e Reveals Horizontal Gene Transfer from Single-Stranded RNA Virus to Double-Stranded RNA Virus

Mycoviruses have been detected in all major groups of filamentous fungi, and their study represents an important branch of virology. Here, we characterized a novel double-stranded RNA (dsRNA) mycovirus, Sclerotinia sclerotiorum megabirnavirus 1 (SsMBV1), in an apparently hypovirulent strain (SX466) of Sclerotinia sclerotiorum. Two similarly sized dsRNA segments (L1- and L2-dsRNA), the genome of SsMBV1, are packaged in rigid spherical particles purified from strain SX466. The full-length cDNA sequence of L1-dsRNA/SsMBV1 comprises two large open reading frames (ORF1 and ORF2), which encode a putative coat protein and an RNA-dependent RNA polymerase (RdRp), respectively. Phylogenetic analysis of the RdRp domain clearly indicates that SsMBV1 is related to Rosellinia necatrix megabirnavirus 1 (RnMBV1). L2-dsRNA/SsMBV1 comprises two nonoverlapping ORFs (ORFA and ORFB) encoding two hypothetical proteins with unknown functions. The 5′-terminal regions of L1- and L2-dsRNA/SsMBV1 share strictly conserved sequences and form stable stem-loop structures. Although L2-dsRNA/SsMBV1 is dispensable for replication, genome packaging, and pathogenicity of SsMBV1, it enhances transcript accumulation of L1-dsRNA/SsMBV1 and stability of virus-like particles (VLPs). Interestingly, a conserved papain-like protease domain similar to a multifunctional protein (p29) of Cryphonectria hypovirus 1 was detected in the ORFA-encoded protein of L2-dsRNA/SsMBV1. Phylogenetic analysis based on the protease domain suggests that horizontal gene transfer may have occurred from a single-stranded RNA (ssRNA) virus (hypovirus) to a dsRNA virus, SsMBV1. Our results reveal that SsMBV1 has a slight impact on the fundamental biological characteristics of its host regardless of the presence or absence of L2-dsRNA/SsMBV1. IMPORTANCE Mycoviruses are widespread in all major fungal groups, and they possess diverse genomes of mostly ssRNA and dsRNA and, recently, circular ssDNA. Here, we have characterized a novel dsRNA virus (Sclerotinia sclerotiorum megabirnavirus 1 [SsMBV1]) that was isolated from an apparently hypovirulent strain, SX466, of Sclerotinia sclerotiorum. Although SsMBV1 is phylogenetically related to RnMBV1, SsMBV1 is markedly distinct from other reported megabirnaviruses with two features of VLPs and conserved domains. Our results convincingly showed that SsMBV1 is viable in the absence of L2-dsRNA/SsMBV1 (a potential large satellite-like RNA or genuine genomic virus component). More interestingly, we detected a conserved papain-like protease domain that commonly exists in ssRNA viruses, including members of the families Potyviridae and Hypoviridae. Phylogenetic analysis based on the protease domain suggests that horizontal gene transfer might have occurred from an ssRNA virus to a dsRNA virus, which may provide new insights into the evolutionary history of dsRNA and ssRNA viruses

China's power supply chain sustainability: an analysis of performance and technology gap

The power industry is a major source of carbon emissions in China and it is vital, therefore, to address the industry to promote carbon emission reduction. This study takes the power supply chain (PSC) in China, composed of coal-fired thermal power plants and downstream power grid enterprises as its primary research object. From the perspective of sustainable development, the study explores and analyzes the sustainable performance and technology heterogeneity of China’s provinces’ PSCs, proposing the two-system model to evaluate the sustainable performance, generation performance and sale performance of PSCs. In addition, to understand the technology level of PSC, this study applies the meta-frontier technique to analyze the technology heterogeneity of all PSCs across different regions. The proposed models are then applied to analyze the sustainable performance of China’s provincial PSCs. The empirical results demonstrate the market-oriented reform of the power industry in China played a role in promoting the development of power generation enterprises in China’s PSCs but had a limited effect on the power grid enterprises in the PSC. The study also shows that there are significant regional differences in the sustainable performance and technology of China’s PSC. Generally, PSCs in Eastern China have a high level of sustainable performance and technology, while the sustainable performance and technology of the PSCs in Central and Northeast China are relatively poor. Based on these empirical results, specific policy recommendations are presented to improve PSC’s sustainable performance and technology levels at government and enterprise levels

Evaluation of decision-making units based on the weight-optimized DEA model

summary:Data envelopment analysis (DEA) is a methodology for measuring best relative efficiencies of a group of peer decision-making units (DMUs) that take multiple inputs to produce multiple outputs. However, the traditional DEA model only aims to maximize the efficiency of the DMU under evaluation. This usually leads to very small weights (even zero weights) being assigned to some inputs or outputs. Correspondingly, these inputs or outputs have little or even no contribution to efficiency, which is unfair and irrational. The purpose of this paper is to address this problem. Two new weight-optimized models are proposed based upon the perspective of cross evaluation. Using the results of an Advanced Manufacturing Technology (AMT) example, it is found that all AMTs are fully sorted. The decision maker can easily choose the best AMT. In addition, unreasonable weights of AMTs are effectively avoided

Allocating fixed resources for DMUs with interval data

Conventional DEA models tend to allocate the fixed resources to multiple decision-making units (DMUs) and treat the allocated resource as an extra input for every single DMU. However, the existing DEA resource allocation (DEA-RA) methods are applicable exclusively to the DMUs with exact values of inputs and outputs. A lack of precision for the input or output data of DMUs, such as the interval data, would cause a failure of the existing methods to allocate resources to DMUs. In order to resolve this problem, three DEA-RA models are proposed in this paper for different scenarios of decision-making. All of the proposed DEA-RA models are based on a set of common weights. Finally, the proposed models are empirically tested and validated through three examples. As revealed by the results, our proposed models are capable of providing a more fair and practical initial allocation scheme for decision makers

Improved interval DEA models with common weight

summary:The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values. But it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and exact data. In order to solve this problem, this study proposes three new interval DEA models from different points of view. Two examples are presented to illustrate and validate these models

Study on window-opening habit of urban residential buildings in Liaoning province

Residents' window-opening behavior is the most basic method to obtain fresh air and improve the indoor environment, and is also an important factor affecting building energy consumption. The behavior of opening and closing windows of urban residents will be different due to regional differences. However, there are less of the thorough enough research on the time and seasonal habit of residents’ window-opening behavior in Liaoning. This paper investigated the window-opening behavior and its influencing factors of 28 typical urban households in natural ventilation and mechanical ventilation houses in Liaoning province by questionnaire survey and on-site testing. The opening and closing behavior of residents’ windows were continuously monitored online for one year by using magnetic sensors of XIAOMI brand. The long-term monitoring results showed that the window-opening behavior of residents in Liaoning has strong seasonality. In summer, the window-opening time is relatively longer, and often more than 24 hours. What’s more, most local residents have the habit of opening windows for ventilation when they get up in the morning, even in the cold winter. The understanding of the window-opening behavior of residents in this area can provide strong support for architectural design or renovation strategy

Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities

10.1016/j.apenergy.2015.04.034Applied Energy15185-9

Dea-based models for best partner selection for merger

Mergers and Acquisitions (M&A) is a process whereby two or more companies merge into one company to improve their efficiency and strengthen their market positions. Previous studies about best partner selection for M&A simply consider one factor independently among several relevant factors. In this paper, DEA is applied to support decision making for best partner selection in M&A for decision making units (DMUs), i.e., the companies. According to the different perspectives of efficiency, revenue, and cost, three models based on DEA approach are firstly introduced to select the best partner for M&A. By compositing these different perspectives, we further propose a new DEA model, which has comprehensively considered input cost, output revenue and efficiency to select the best partner among many candidates. 0–1 integer linear programming models are built to implement the process. Finally, an example is given to verify the applicability to this model