9 research outputs found
Prognostics and health management technology for radar system
Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future
Prognostics and health management technology for radar system
Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future
Pepsin-Containing Membranes for Controlled Monoclonal Antibody Digestion Prior to Mass Spectrometry Analysis
Pepsin-Containing Membranes for Controlled Monoclonal Antibody Digestion Prior to Mass Spectrometry Analysis
Monoclonal antibodies (mAbs) are
the fastest growing class of therapeutic
drugs, because of their high specificities to target cells. Facile
analysis of therapeutic mAbs and their post-translational modifications
(PTMs) is essential for quality control, and mass spectrometry (MS)
is the most powerful tool for antibody characterization. This study
uses pepsin-containing nylon membranes as controlled proteolysis reactors
for mAb digestion prior to ultrahigh-resolution Orbitrap MS analysis.
Variation of the residence times (from 3 ms to 3 s) of antibody solutions
in the membranes yields “bottom-up” (1–2 kDa)
to “middle-down” (5–15 kDa) peptide sizes within
less than 10 min. These peptides cover the entire sequences of Trastuzumab
and a Waters antibody, and a proteolytic peptide comprised of 140
amino acids from the Waters antibody contains all three complementarity
determining regions on the light chain. This work compares the performance
of “bottom-up” (in-solution tryptic digestion), “top-down”
(intact protein fragmentation), and “middle-down” (in-membrane
digestion) analysis of an antibody light chain. Data from tandem MS
show 99%, 55%, and 99% bond cleavage for “bottom-up”,
“top-down”, and “middle-down” analyses,
respectively. In-membrane digestion also facilitates detection of
PTMs such as oxidation, deamidation, N-terminal pyroglutamic acid
formation, and glycosylation. Compared to “bottom-up”
and “top-down” approaches for antibody characterization,
in-membrane digestion uses minimal sample preparation time, and this
technique also yields high peptide and sequence coverage for the identification
of PTMs
Investigating drought tolerance in chickpea using genome-wide association mapping and genomic selection based on whole-genome resequencing data
Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs
Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data
Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs
Experiments on formation mechanism of root humping in high-power laser autogenous welding of thick plates with stainless steels
Table_1.DOCX
<p>Drought tolerance is a complex trait that involves numerous genes. Identifying key causal genes or linked molecular markers can facilitate the fast development of drought tolerant varieties. Using a whole-genome resequencing approach, we sequenced 132 chickpea varieties and advanced breeding lines and found more than 144,000 single nucleotide polymorphisms (SNPs). We measured 13 yield and yield-related traits in three drought-prone environments of Western Australia. The genotypic effects were significant for all traits, and many traits showed highly significant correlations, ranging from 0.83 between grain yield and biomass to -0.67 between seed weight and seed emergence rate. To identify candidate genes, the SNP and trait data were incorporated into the SUPER genome-wide association study (GWAS) model, a modified version of the linear mixed model. We found that several SNPs from auxin-related genes, including auxin efflux carrier protein (PIN3), p-glycoprotein, and nodulin MtN21/EamA-like transporter, were significantly associated with yield and yield-related traits under drought-prone environments. We identified four genetic regions containing SNPs significantly associated with several different traits, which was an indication of pleiotropic effects. We also investigated the possibility of incorporating the GWAS results into a genomic selection (GS) model, which is another approach to deal with complex traits. Compared to using all SNPs, application of the GS model using subsets of SNPs significantly associated with the traits under investigation increased the prediction accuracies of three yield and yield-related traits by more than twofold. This has important implication for implementing GS in plant breeding programs.</p