Ligand-binding properties of XaffOBP9, a Minus-C odorant-binding protein from Xyleborus affinis (Coleoptera: Curculionidae: Scolytinae)

Xyleborus affinis, one of the most important pests of rubber trees, has caused severe damage to the natural rubber industry in Hainan province. The ability to detect host plants through a sensitive and specific olfactory system is crucial for Xyleborus affinis. Odorant binding proteins (OBPs) are believed to bind and carry hydrophobic active compounds from the environment to the surface of olfactory receptor neurons. To investigate the potential functional role of the highly expressed XaffOBP9 in binding with semiochemicals, we cloned and analyzed the cDNA sequence of XaffOBP9. The results showed that XaffOBP9 contains a 411bp open reading frame that encodes 136 amino acids. Then XaffOBP9 was expressed in Escherichia coli. The binding affinity of the recombinant OBP to 15 different ligands (14 host plant volatiles and 1 aggregation pheromone) was then examined using a fluorescence competitive binding approach. The results demonstrated that XaffOBP9 exhibited broad binding capabilities and strong affinities for 14 ligands. The structure of XaffOBP9 and its interactions with fourteen ligands were further analyzed by modeling and molecular docking, respectively. Based on the docking result, we found hydrophobic interactions are important between XaffOBP9 to these ligands and three amino acid residues (L71, Y106, and L114) were highly overlapped and contributed to the interaction with ligands. Mutation functional assays confirmed that the mutant L114A showed significantly reduced binding capacity to these ligands. This study suggested that XaffOBP9 may be involved in the chemoreception of semiochemicals and that it is helpful for the integrated management of X. affinis

Data Descriptor: A global multiproxy database for temperature reconstructions of the Common Era

Reproducible climate reconstructions of the Common Era (1 CE to present) are key to placing industrial-era warming into the context of natural climatic variability. Here we present a community-sourced database of temperature-sensitive proxy records from the PAGES2k initiative. The database gathers 692 records from 648 locations, including all continental regions and major ocean basins. The records are from trees, ice, sediment, corals, speleothems, documentary evidence, and other archives. They range in length from 50 to 2000 years, with a median of 547 years, while temporal resolution ranges from biweekly to centennial. Nearly half of the proxy time series are significantly correlated with HadCRUT4.2 surface temperature over the period 1850-2014. Global temperature composites show a remarkable degree of coherence between high-and low-resolution archives, with broadly similar patterns across archive types, terrestrial versus marine locations, and screening criteria. The database is suited to investigations of global and regional temperature variability over the Common Era, and is shared in the Linked Paleo Data (LiPD) format, including serializations in Matlab, R and Python.(TABLE)Since the pioneering work of D'Arrigo and Jacoby1-3, as well as Mann et al. 4,5, temperature reconstructions of the Common Era have become a key component of climate assessments6-9. Such reconstructions depend strongly on the composition of the underlying network of climate proxies10, and it is therefore critical for the climate community to have access to a community-vetted, quality-controlled database of temperature-sensitive records stored in a self-describing format. The Past Global Changes (PAGES) 2k consortium, a self-organized, international group of experts, recently assembled such a database, and used it to reconstruct surface temperature over continental-scale regions11 (hereafter, ` PAGES2k-2013').This data descriptor presents version 2.0.0 of the PAGES2k proxy temperature database (Data Citation 1). It augments the PAGES2k-2013 collection of terrestrial records with marine records assembled by the Ocean2k working group at centennial12 and annual13 time scales. In addition to these previously published data compilations, this version includes substantially more records, extensive new metadata, and validation. Furthermore, the selection criteria for records included in this version are applied more uniformly and transparently across regions, resulting in a more cohesive data product.This data descriptor describes the contents of the database, the criteria for inclusion, and quantifies the relation of each record with instrumental temperature. In addition, the paleotemperature time series are summarized as composites to highlight the most salient decadal-to centennial-scale behaviour of the dataset and check mutual consistency between paleoclimate archives. We provide extensive Matlab code to probe the database-processing, filtering and aggregating it in various ways to investigate temperature variability over the Common Era. The unique approach to data stewardship and code-sharing employed here is designed to enable an unprecedented scale of investigation of the temperature history of the Common Era, by the scientific community and citizen-scientists alike

Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of “Low carbon”, a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers’ average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains

Reversible flash-bonding of zirconia and nickel alloys

A electric field-assisted flash-joining technique of ceramic and metal was shown here. It was demonstrated that when the voltage and current density were higher than critical values, the zirconia ceramic and Ni based superalloy were strongly bonded in as fast as 1 s at 800 °C. Another important advantage of the technique is that the resultant joint can be readily de-bonded by reversing the electric field, which provides significant manufacturing flexibility. The underlying bonding mechanism follows an electric field-induced internal reaction, and the novel method could be extended to a wide range of ceramic-metal systems in principle