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Water lilies belong to the angiosperm order Nymphaeales. Amborellales, Nymphaeales and Austrobaileyales together form the so-called ANA-grade of angiosperms, which are extant representatives of lineages that diverged the earliest from the lineage leading to the extant mesangiosperms 1 – 3 . Here we report the 409-megabase genome sequence of the blue-petal water lily ( Nymphaea colorata ). Our phylogenomic analyses support Amborellales and Nymphaeales as successive sister lineages to all other extant angiosperms. The N. colorata genome and 19 other water lily transcriptomes reveal a Nymphaealean whole-genome duplication event, which is shared by Nymphaeaceae and possibly Cabombaceae. Among the genes retained from this whole-genome duplication are homologues of genes that regulate flowering transition and flower development. The broad expression of homologues of floral ABCE genes in N. colorata might support a similarly broadly active ancestral ABCE model of floral organ determination in early angiosperms. Water lilies have evolved attractive floral scents and colours, which are features shared with mesangiosperms, and we identified their putative biosynthetic genes in N. colorata . The chemical compounds and biosynthetic genes behind floral scents suggest that they have evolved in parallel to those in mesangiosperms. Because of its unique phylogenetic position, the N. colorata genome sheds light on the early evolution of angiosperms. The genome of the tropical blue-petal water lily Nymphaea colorata and the transcriptomes from 19 other Nymphaeales species provide insights into the early evolution of angiosperms.

Clematis florida is a popular ornamental vine species known for diverse colors and shapes of its flowers but not for scent. Here we investigated the composition and biosynthesis of floral scent in ‘Kaiser’, a fragrant cultivar of C. florida that has sterile flowers. Volatile profiling revealed that flowers of ‘Kaiser’ emit more than 20 compounds, with monoterpenes being most abundant. Among the three floral organs, namely sepals, transformed-petals, and ovaries, ovaries had the highest rates of total volatile emission. To determine the molecular mechanism underlying floral scent biosynthesis in ‘Kaiser’, we sequenced a flower transcriptome and searched the transcriptome for terpene synthase genes (TPSs), which are key genes for terpene biosynthesis. Among the TPS genes identified, three were putative intact full-length genes and were designated CfTPS1, CfTPS2, and CfTPS3. Phylogenetic analysis placed CfTPS1, CfTPS2, and CfTPS3 to the TPS-g, TPS-b, and TPS-a subfamily, respectively. Through in vitro enzyme assays with Escherichia coli-expressed recombinant proteins, both CfTPS1 and CfTPS2 were demonstrated to catalyze the conversion of geranyl diphosphate to linalool, the most abundant constituent of C. florida floral scent. In addition, CfTPS1 and CfTPS2 produced the sesquiterpene nerolidol from (E,E)-farnesyl diphosphate. CfTPS3 showed sesquiterpene synthase activity and produced multiple products in vitro. All three CfTPS genes showed higher levels of expression in sepals than those in transformed-petals and ovaries. Our results show that despite being sterile, the flowers of ‘Kaiser’ have normal mechanisms for floral scent biosynthesis that make the flowers fragrant.

Users and edge servers are not fully mutually trusted in mobile edge computing (MEC), and hence blockchain can be introduced to provide trustable MEC. In blockchain-based MEC, each edge server functions as a node in both MEC and blockchain, processing users’ tasks and then uploading the task related information to the blockchain. That is, each edge server runs both users’ offloaded tasks and blockchain tasks simultaneously. Note that there is a trade-off between the resource allocation for MEC and blockchain tasks. Therefore, the allocation of the resources of edge servers to the blockchain and the MEC is crucial for the processing delay of blockchain-based MEC. Most of the existing research tackles the problem of resource allocation in either blockchain or MEC, which leads to unfavorable performance of the blockchain-based MEC system. In this paper, we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aim to minimize the total system processing delay. For the problem, we propose a computing resource Allocation algorithm for Blockchain-based MEC (ABM) which utilizes the Slater’s condition, Karush-Kuhn-Tucker (KKT) conditions, partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution. Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

By the addition of a carbon-based electromagnetic absorbing agent during the foaming process, a novel electromagnetic absorbent polymethacrylimide (PMI) foam was obtained. The proposed foam exhibits excellent electromagnetic wave-absorbing properties, with absorptivity exceeding 85% at a large frequency range of 4.9–18 GHz. However, its poor mechanical properties would limit its application in load-carrying structures. In the present study, a novel enhancement approach is proposed by inserting metallic tubes into pre-perforated holes of PMI foam blocks. The mechanical properties of the tube-enhanced PMI foams were studied experimentally under compressive loading conditions. The elastic modulus, compressive strength, energy absorption per unit volume, and energy absorption per unit mass were increased by 127.9%, 133.8%, 54.2%, and 46.4%, respectively, by the metallic tube filling, and the density increased only by 5.3%. The failure mechanism of the foams was also explored. We found that the weaker interfaces between the foam and the electromagnetic absorbing agent induced crack initiation and subsequent collapses, which destroyed the structural integrity. The excellent mechanical and electromagnetic absorbing properties make the novel structure much more competitive in electromagnetic wave stealth applications, while acting simultaneously as load-carrying structures.

Mixed-integer linear programming (MILP) is one of the most popular mathematical formulations with numerous applications. In practice, improving the performance of MILP solvers often requires a large amount of high-quality data, which can be challenging to collect. Researchers thus turn to generation techniques to generate additional MILP instances. However, existing approaches do not take into account specific block structures -- which are closely related to the problem formulations -- in the constraint coefficient matrices (CCMs) of MILPs. Consequently, they are prone to generate computationally trivial or infeasible instances due to the disruptions of block structures and thus problem formulations. To address this challenge, we propose a novel MILP generation framework, called Block Structure Decomposition (MILP-StuDio), to generate high-quality instances by preserving the block structures. Specifically, MILP-StuDio begins by identifying the blocks in CCMs and decomposing the instances into block units, which serve as the building blocks of MILP instances. We then design three operators to construct new instances by removing, substituting, and appending block units in the original instances, enabling us to generate instances with flexible sizes. An appealing feature of MILP-StuDio is its strong ability to preserve the feasibility and computational hardness of the generated instances. Experiments on the commonly-used benchmarks demonstrate that using instances generated by MILP-StuDio is able to significantly reduce over 10% of the solving time for learning-based solvers.

The present paper described an experimental investigation of separation control of an Unmanned Aerial Vehicle （UAV） at high wind speeds. The plasma actuator was based on Dielectric Barrier Discharge （DBD） and operated in a steady manner. The flow over a wing of UAV was performed with smoke flow visualization in the φ0.75 m low speed wind tunnel to reveal the flow structure over the wing so that the locations of plasma actuators could be optimized. A full model of the UAV was experimen- tally investigated in the φ3.2 m low speed wind tunnel using a six-component internal strain gauge balance. The effects of the key parameters, including the locations of the plasma actuators, the applied voltage amplitude and the operating frequency, were obtained. The whole test model was made of aluminium and acted as a cathode of the actuator. The results showed that the plasma acting on the surface of UAV could obviously suppress the boundary layer separation and reduce the model vibra- tion at the high wind speeds. It was found that the maximum lift coefficient of the UAV was increased by 2.5% and the lift/drag ratio was increased by about 80% at the wind speed of 100 m/s. The control mechanism of the plasma actuator at the test configuration was also analyzed.

The outbreak of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health, calling for the development of safe and effective prophylactics and therapeutics against infection of its causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as 2019 novel coronavirus (2019-nCoV). The CoV spike (S) protein plays the most important roles in viral attachment, fusion and entry, and serves as a target for development of antibodies, entry inhibitors and vaccines. Here, we identified the receptor-binding domain (RBD) in SARS-CoV-2 S protein and found that the RBD protein bound strongly to human and bat angiotensin-converting enzyme 2 (ACE2) receptors. SARS-CoV-2 RBD exhibited significantly higher binding affinity to ACE2 receptor than SARS-CoV RBD and could block the binding and, hence, attachment of SARS-CoV-2 RBD and SARS-CoV RBD to ACE2-expressing cells, thus inhibiting their infection to host cells. SARS-CoV RBD-specific antibodies could cross-react with SARS-CoV-2 RBD protein, and SARS-CoV RBD-induced antisera could cross-neutralize SARS-CoV-2, suggesting the potential to develop SARS-CoV RBD-based vaccines for prevention of SARS-CoV-2 and SARS-CoV infection.

Developing versatile, scalable, and durable coatings that resist the accretion of matters (liquid, vapor, and solid phases) in various operating environments is important to industrial applications, yet has proven challenging. Here, we report a cellular coating that imparts liquid-repellence, vapor-imperviousness, and solid-shedding capabilities without the need for complicated structures and fabrication processes. The key lies in designing basic cells consisting of rigid microshells and releasable nanoseeds, which together serve as a rigid shield and a bridge that chemically bonds with matrix and substrate. The durability and strong resistance to accretion of different matters of our cellular coating are evidenced by strong anti-abrasion, enhanced anti-corrosion against saltwater over 1000 h, and maintaining dry in complicated phase change conditions. The cells can be impregnated into diverse matrixes for facile mass production through scalable spraying. Our strategy provides a generic design blueprint for engineering ultra-durable coatings for a wide range of applications. The design and scalable fabrication of durable superhydrophobic coatings that prevent accretion of matter is challenging. Here the authors developed a generic cellular design for ultra-durable coatings that provides liquid-repellence, vapor imperviousness, and solid-shedding capabilities.

Since the 18th National Congress of the Communist Party of China in 2012, green and low-carbon development has become a national strategic priority. This study constructs a 39-indicator evaluation system grounded in the DPSIRM framework, which includes six interlinked subsystems. A key innovation lies in incorporating the Digital Inclusive Finance Index as a driver of green transitions and using Baidu search indices for “environmental protection” and “carbon dioxide” as proxies for public awareness. Using a projection pursuit model optimized by simulated annealing, we assess green low-carbon development across 30 Chinese provinces from 2011 to 2021. Temporal and spatial patterns are analyzed via kernel density estimation and Moran’s I, while Theil Index decomposition quantifies regional disparities. Results: First, substantial variations exist among Chinese provinces in both subsystem performance and integrated green low-carbon development levels, and response subsystems have the greatest influence on the overall development level. Second, over time, the gaps in green, low-carbon development between provinces have become more pronounced. Third, geographically, a distinct east-to-west declining gradient characterizes the regional clustering patterns of green low-carbon development. Fourth, the Theil Index for green, low-carbon development exhibits an overall trend of fluctuating increase, indicating that the overall gap in green, low-carbon development is gradually widening, with within-group disparities as the primary cause. This research enhances understanding of China’s green and low-carbon development, actively promoting global sustainable development and environmental improvement.

Precise and real-time measurements of economic prosperity are vital to a country’s economic system. This study aims to identify news topics that promoted economic prosperity in China from 2011–2021. By extracting economic topics from news text data, we construct a news coincidence index with comprehensive information and strong timeliness and reveal the trend of topic contribution. The Latent Dirichlet Allocation (LDA) topic model is applied to extract economic topics from the news. We use a mixed-frequency dynamic factor model to track rapid economic development without using high-frequency weekly and daily data. We identify the six most influential topics and investigate their evolution, which may serve as a reference for economic construction and regulation.