Docking control for probe-drogue refueling: An additive-state-decomposition-based output feedback iterative learning control method

Designing a controller for the docking maneuver in Probe-Drogue Refueling (PDR) is an important but challenging task, due to the complex system model and the high precision requirement. In order to overcome the disadvantage of only feedback control, a feedforward control scheme known as Iterative Learning Control (ILC) is adopted in this paper. First, Additive State Decomposition (ASD) is used to address the tight coupling of input saturation, nonlinearity and the property of NonMinimum Phase (NMP) by separating these features into two subsystems (a primary system and a secondary system). After system decomposition, an adjoint-type ILC is applied to the Linear Time-Invariant (LTI) primary system with NMP to achieve entire output trajectory tracking, whereas state feedback is used to stabilize the secondary system with input saturation. The two controllers designed for the two subsystems can be combined to achieve the original control goal of the PDR system. Furthermore, to compensate for the receiver-independent uncertainties, a correction action is proposed by using the terminal docking error, which can lead to a smaller docking error at the docking moment. Simulation tests have been carried out to demonstrate the performance of the proposed control method, which has some advantages over the traditional derivative-type ILC and adjoint-type ILC in the docking control of PDR

Fully Automated Multidimensional Reversed-Phase Liquid Chromatography with Tandem Anion/Cation Exchange Columns for Simultaneous Global Endogenous Tyrosine Nitration Detection, Integral Membrane Protein Characterization, and Quantitative Proteomics Mapping in Cerebral Infarcts

Protein tyrosine nitration (PTN) is a signature hallmark of radical-induced nitrative stress in a wide range of pathophysiological conditions, with naturally occurring abundances at substoichiometric levels. In this present study, a fully automated four-dimensional platform, consisting of high-/low-pH reversed-phase dimensions with two additional complementary, strong anion (SAX) and cation exchange (SCX), chromatographic separation stages inserted in tandem, was implemented for the simultaneous mapping of endogenous nitrated tyrosine-containing peptides within the global proteomic context of a <i>Macaca fascicularis</i> cerebral ischemic stroke model. This integrated RP–SA­(C)­X–RP platform was initially benchmarked through proteomic analyses of <i>Saccharomyces cerevisiae</i>, revealing extended proteome and protein coverage. A total of 27 144 unique peptides from 3684 nonredundant proteins [1% global false discovery rate (FDR)] were identified from <i>M. fascicularis</i> cerebral cortex tissue. The inclusion of the S­(A/C)­X columns contributed to the increased detection of acidic, hydrophilic, and hydrophobic peptide populations; these separation features enabled the concomitant identification of 127 endogenous nitrated peptides and 137 transmembrane domain-containing peptides corresponding to integral membrane proteins, without the need for specific targeted enrichment strategies. The enhanced diversity of the peptide inventory obtained from the RP–SA­(C)­X–RP platform also improved analytical confidence in isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analyses

Fully Automated Multidimensional Reversed-Phase Liquid Chromatography with Tandem Anion/Cation Exchange Columns for Simultaneous Global Endogenous Tyrosine Nitration Detection, Integral Membrane Protein Characterization, and Quantitative Proteomics Mapping in Cerebral Infarcts

Protein tyrosine nitration (PTN) is a signature hallmark of radical-induced nitrative stress in a wide range of pathophysiological conditions, with naturally occurring abundances at substoichiometric levels. In this present study, a fully automated four-dimensional platform, consisting of high-/low-pH reversed-phase dimensions with two additional complementary, strong anion (SAX) and cation exchange (SCX), chromatographic separation stages inserted in tandem, was implemented for the simultaneous mapping of endogenous nitrated tyrosine-containing peptides within the global proteomic context of a <i>Macaca fascicularis</i> cerebral ischemic stroke model. This integrated RP–SA­(C)­X–RP platform was initially benchmarked through proteomic analyses of <i>Saccharomyces cerevisiae</i>, revealing extended proteome and protein coverage. A total of 27 144 unique peptides from 3684 nonredundant proteins [1% global false discovery rate (FDR)] were identified from <i>M. fascicularis</i> cerebral cortex tissue. The inclusion of the S­(A/C)­X columns contributed to the increased detection of acidic, hydrophilic, and hydrophobic peptide populations; these separation features enabled the concomitant identification of 127 endogenous nitrated peptides and 137 transmembrane domain-containing peptides corresponding to integral membrane proteins, without the need for specific targeted enrichment strategies. The enhanced diversity of the peptide inventory obtained from the RP–SA­(C)­X–RP platform also improved analytical confidence in isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analyses

Data for "Nitrogen enrichment induces more plant species loss under drier conditions"

Nitrogen (N) deposition is a major driver of plant species loss worldwide. However, what regulates N-driven species loss remains unclear. Based on a 7-year field experiment on the Qinghai-Tibetan Plateau, we found that the impact of N addition on plant species richness strongly depended on precipitation. During experimental years with lower precipitation, N addition induced more species loss. The main underlying mechanism was that lower precipitation stimulated soil inorganic N accumulation under N addition, resulting in stronger competitive exclusion and ammonium toxicity in plant communities. These site observations were complemented by a global synthesis derived from 45 N addition experiments, showing N-induced more species loss in dry than in wet ecosystems. Given the importance of plant species richness in supporting ecosystem functioning and stability, our findings suggest that ecosystems during drought periods or in arid areas are particularly sensitive to N deposition, having important implications for their management and conservation.</p

Online Two-Dimensional Porous Graphitic Carbon/Reversed Phase Liquid Chromatography Platform Applied to Shotgun Proteomics and Glycoproteomics

A novel fully automatable two-dimensional liquid chromatography (2DLC) platform has been integrated into a modified commercial off-the-shelf LC instrument, incorporating porous graphitic carbon (PGC) separation and conventional low-pH reversed-phase (RP) separation for both proteomics and <i>N</i>-glycomics analyses; the dual-trap column configuration of this platform offers desirable high-throughput analyses with almost no idle time, in addition to a miniaturized setup and simplified operation. The total run time per analysis was only 19 h when using eight PGC fractions for unattended large-scale qualitative and quantitative proteomic analyses; the identification of 2678 nonredundant proteins and 11 984 unique peptides provided one of the most comprehensive proteome data sets for primary cerebellar granule neurons (CGNs). The effect of pH on the PGC column was investigated for the first time to improve the hydrophobic peptide coverage; the performance of the optimized system was first benchmarked using tryptic digests of Saccharomyces cerevisiae cell lysates and then evaluated through duplicate analyses of Macaca fascicularis cerebral cortex lysates using isobaric tags for relative and absolute quantitation (iTRAQ) technology. An additional plug-and-play PGC module functioned in a complementary manner to recover unretained hydrophilic solutes from the low-pH RP column; synchronization of the fractionations between the PGC-RP system and the PGC module facilitated simultaneous analyses of hydrophobic and hydrophilic compounds from a single sample injection event. This methodology was applied to perform, for the first time, detailed glycomics analyses of Macaca fascicularis plasma, resulting in the identification of a total 130 <i>N</i>-glycosylated plasma proteins, 705 <i>N</i>-glycopeptides, and 254 <i>N</i>-glycosylation sites