Construction of a dense genetic linkage map and mapping quantitative trait loci for economic traits of a doubled haploid population of Pyropia haitanensis (Bangiales, Rhodophyta)

The genotypes of 4550 LP markers that were mapped onto the genetic map. (XLSX 1645 kb

Advances in Fiber-Optic Extrinsic Fabry-Perot Interferometric Physical and Mechanical Sensors: A Review

Fabry-Perot Interferometers Have Found a Multitude of Scientific and Industrial Applications Ranging from Gravitational Wave Detection, High-Resolution Spectroscopy, and Optical Filters to Quantum Optomechanics. Integrated with Optical Fiber Waveguide Technology, the Fiber-Optic Fabry-Perot Interferometers Have Emerged as a Unique Candidate for High-Sensitivity Sensing and Have Undergone Tremendous Growth and Advancement in the Past Two Decades with their Successful Applications in an Expansive Range of Fields. the Extrinsic Cavity-Based Devices, I.e., the Fiber-Optic Extrinsic Fabry-Perot Interferometers (EFPIs), Enable Great Flexibility in the Design of the Sensitive Fabry-Perot Cavity Combined with State-Of-The-Art Micromachining and Conventional Mechanical Fabrication, Leading to the Development of a Diverse Array of EFPI Sensors Targeting at Different Physical Quantities. Here, We Summarize the Recent Progress of Fiber-Optic EFPI Sensors, Providing an overview of Different Physical and Mechanical Sensors based on the Fabry-Perot Interferometer Principle, with a Special Focus on Displacement-Related Quantities, Such as Strain, Force, Tilt, Vibration and Acceleration, Pressure, and Acoustic. the Working Principle and Signal Demodulation Methods Are Shown in Brief. Perspectives on Further Advancement of EFPI Sensing Technologies Are Also Discussed

Experimental Study on the Down-Speed of Conductor Pipe Influenced by Jetting Displacement in Deepwater Drilling

Based on the theory of jet drilling technology and displacement optimization, a set of experimental equipment about jet drilling is devised. The laws of conductor pipe down-speed influenced by pump displacement were studied by laboratory experiments. According to the experimental results and analysis, the following conclusions can be drawn. The down-speed of conductor pipe increases with the increasing of displacement, also the drilling speed is boosted. But the unstableness of borehole wall is augmented as well. And this will result in the increasing of waiting time for borehole formation. In the process of conductor pipe jetting, the conductor pipe down-speed and the waiting time of soil returning to a certain bearing capacity should be considered together in order to shorten the entirety drilling time. The research can provide certain references for expensive offshore operation and have important significance to improve the economic benefits of deepwater drilling

CeO2 based catalysts for elemental mercury capture

The alternative materials to remove Hg 0 from energy utilization sectors is crucial to mercury control in atmosphere. CeO2 based catalysts were prepared by an incipient wetness impregnation (IWI) method. A novel Hg 0 and Hg T temperature-programmed surface reaction (Hg 0 -Hg T -TPSR) was proposed in this study for the investigation of the prepared CeO2 based catalysts with the qualitative and quantitative analyses. The characteristic temperatures, the areas of adsorption region and desorption region, and activation energy with reaction kinetics were investigated to evaluate the performance. It was found that 2wt% Ce catalyst has the best mercury removal performance with the highest Hg 0 removal ability and the lowest Ea. There was also small amount of Hg 2+ detected which indicated the catalytic effect contributed. The results suggested that 2wt% Ce based binary catalysts could be the potential candidates to be investigated in the future study. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy

A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms

We describe a genetic variation map for the chicken genome containing 2.8 million single-nucleotide polymorphisms ( SNPs). This map is based on a comparison of the sequences of three domestic chicken breeds ( a broiler, a layer and a Chinese silkie) with that of their wild ancestor, red jungle fowl. Subsequent experiments indicate that at least 90% of the variant sites are true SNPs, and at least 70% are common SNPs that segregate in many domestic breeds. Mean nucleotide diversity is about five SNPs per kilobase for almost every possible comparison between red jungle fowl and domestic lines, between two different domestic lines, and within domestic lines - in contrast to the notion that domestic animals are highly inbred relative to their wild ancestors. In fact, most of the SNPs originated before domestication, and there is little evidence of selective sweeps for adaptive alleles on length scales greater than 100 kilobases

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Nanotools for Neuroscience and Brain Activity Mapping

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing