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Magnetorquers and thrusters are usually employed independently to dump the accumulated momentum of reaction wheels installed in a satellite to control the spacecraft attitude. Momentum dumping using magnetorquers can be slow and restricted by Earth magnetic field strength. Hybrid use of thrusters and magnetorquers brings the benefit of minimizing the thruster fuel dissipation and maintaining the required dumping cycle. This paper focuses on investigating and analyzing hybrid control strategies of onboard thrusters and magnetorquers for dumping reaction wheel momentum on a 3-axis stabilized Earthpointing satellite. Both one-phase and two phase combined algorithms are established. Based on optimal dumping torque profile from an optimal dumping controller by using PWM thrusters only, two sub-optimal combined dumping algorithms based on instantaneous cost function minimization on energy and fuel at each sampling interval are explored and derived. In addition, the boundary constraints due to actuator control limits for practical application concerns are analyzed and their impacts on control algorithms are discussed. Extensive simulation demonstrations revealed that a large mount of thruster propellant can be saved through hybrid control for momentum dumping. Those hybrid dumping methods do not rely on complex geomagnetic field model and can be practically implemented onboard satellites.

Mutations in the TSC1 and TSC2 genes lead to tuberous sclerosis complex (TSC), which is characterized clinically by mental retardation, epilepsy, and benign tumors affecting multiple tissues. Numerous components of the TSC protein complex remain uncharacterized. Here we report the purification of the TSC1 complex under physiological conditions using a proteomic strategy. We purified the TSC1 protein complex using a tandem affinity purification method and identified a protein complex containing 139 components. Two known binding proteins of TSC1 (TSC2 and DOCK7) were identified along with other new potential partners, which cover reported and novel TSC1 functional categories. Bioinformatics and biochemical methods were used to evaluate the observed protein – protein interactions. A comparative analysis with a published expression proteomics/genomics study of TSC1 revealed more than 20 common candidates that might be functionally relevant. The data set provides new directions in which to expand our knowledge of the functions of TSC1 and the mechanisms of TSC. The results are highly reliable, which is reflected by the identification of a few reported partners of TSC1 and many TSC1/2regulated proteins. Interestingly, many new functional categories were identified, such as DNA repair, which provide novel hints to the function of TSC1. Moreover, a few neuronal disease-related proteins that might regulate the normal functions of neurons were identified. Thus, the results suggest that many of the new interactions should be biologically significance. It will be interesting to further investigate the regulatory mechanisms of these components.

Background Codon bias is believed to play an important role in the control of gene expression. In Escherichia coli, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in Escherichia coli. Results In Escherichia coli, we defined the "rare codon pairs" by calculating the frequency of occurrence of all codon pairs in coding sequences. Rare codons which are disliked in genes could make great contributions to forming rare codon pairs. Meanwhile our investigation showed that many of these rare codon pairs contain termination codons and the recognized sites of restriction enzymes. Furthermore, a new index (Frare) was developed. Through comparison with the classical indices we found a significant negative correlation between Frare and the indices which depend on reference datasets. Conclusions Our approach suggests that we can identify rare codons by studying the context in which a codon lies. Also, the frequency of rare codons (Frare) could be a useful index of codon bias regardless of the lack of expression abundance information.

To search for potential protein markers of colorectal cancer (CRC), the changes in protein expression levels between microdissected tumor cells and normal mucosa epithelia were analyzed by an acetylation stable isotopic labeling method coupled with linear quadrupole ion trap fourier transform mass spectrometry (LTQ-FTMS). In total, 137 proteins were up-regulated or down-regulated significantly in cancer by at least two-fold. Based on gene ontology analysis, the largest part of differential proteins were unknown for both subcellular localization and biological process. In particular, the significant up-regulation of transgelin-2 (TAGLN2) in CRC was validated by Western blot analysis and further evaluated by immunohistochemistry in paired tumor and normal mucosa samples from 120 consecutive CRC patients, 20 adenomas, and eight synchronous hepatic metastases of CRC. TAGLN2 expression was frequently observed in cancer cells, precancerous lesions, and hepatic metastases, whereas in normal epithelia expression was rarely observed. The overexpression of TAGLN2 was associated with lymph node and distant metastasis, advanced clinical stage (P < 0.001), and shorter overall survival in CRCs. Cox regression analysis indicated that high tumor-TAGLN2 expression represents an independent prognostic factor. Consequently, over-expression of TAGLN2 may serve as a new biomarker for predicting progression and prognosis of CRC.

Peptides Count (SC) was widely used for protein abundance estimation in proteomics. On the basis of that, Mann and co-workers corrected the SC by dividing spectrum counts by the number of observable peptides per protein and named it PAI. Here we present modified spectral count index (mSCI) for protein abundance estimation, which was defined as the number of observed peptides divided by protein relative identification possibility (RIPpro). RIPpro was derived from 6788 mRNA and protein expression data (collected from human liver samples) and related to proteins' three physical and chemical properties (MW/pI/Hp). For 46 proteins in mouse neuro2a cells, mSCI shows a linear relationship with the actual protein concentration, similar or better than PAI abundance. Also, multiple linear regressions were performed to quantitative assess several factors' impact on the mRNA/protein abundance correlation. Our results shown that the primary factor affecting protein levels was mRNA abundance (32-37%), followed by variability in protein measurement, MW and protein turnover (7-12%,7-9% and 2-3%, respectively). Interestingly, we found that the concordance between mRNA transcripts and protein expression was not consistent among all protein functional categories. This correlation was lower for signaling proteins as compared to metabolism genes. It was determined that RIPpro was the primary factor affecting signaling protein abundance (23% on average), followed by mRNA abundance (17%). In contrast, only 5% (on average) of the variability of metabolic protein abundance was explained by RIPpro, much lower than mRNA abundance (40%). These results provide the impetus for further investigation of the biological significance of mechanisms regulating the mRNA/protein abundance correlation and provide additional insight into the relative importance of the technological parameter (RIPpro) in mRNA/protein correlation research.

The relationship between sample loading amount and peptide identification is crucial for the optimization of proteomics experiments, but few studies have addressed this matter. Herein, we present a systematic study using a replicate run strategy to probe the inherent influence of both peptide physicochemical properties and matrix effects on the relationship between peptide identification and sample loading amounts, as well as its applications in protein quantification. Ten replicate runs for a series of laddered loading amounts (ranging between 0.01 approximately 10 microg) of total digested proteins from Saccharomyces cerevisiae were performed with nanoscale liquid chromatography coupled with linear ion trap/Fourier transform ion cyclotron resonance (nanoLC-LTQ-FT) to obtain a nearly saturated peptide identification. This permitted us to differentiate the linear correlativity of peptide identification by the commonly used peptide quantitative index, the area of constructed ion chromatograms (XIC) (SA, from MS and tandem MS data) in the given experiments. The absolute loading amount of a given complex sample affected the final qualitative identification result; thus, optimization of the sample loading amount before every proteomics study was essential. Peptide physicochemical properties had little effect on the linear correlativity between SA-based peptide quantification and loading amount. The matrix effects, rather than the static physicochemical properties of individual peptides, affect peptide measurability. We also quantified the target protein by selecting peptides with good parallel linear correlativity based upon SA as signature peptides and revised the data by multiplying by the reciprocal of the slope coefficient. We found that this optimized the linear protein abundance relativity at every amount range and thus extended the linear dynamic range of label-free quantification. This empirical rule for linear peptide selection (ERLPS) can be adopted to correct comparison results in proteolytic peptide-based quantitative proteomics, such as accurate mass tag (AMT) and targeted quantitative proteomics, as well as in tag-labeled comparative proteomics.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

The purpose of this study was to describe radiologic anatomy of the left atrium diverticulum. There were 20 patients with 27 left atrium diverticulums in 120 consecutive patients who underwent CT of coronary angiography. The presence probability of left atrium diverticulum was 16.7%, male of it was 13.0%, female was 17.6%. There was no difference on gender (P0.05). There were four patients accompanying with variation of pulmonary vein at one time. The diverticulum might be single or multiple, cystiform or tubiform. It could locate anterior wall or posterior wall or superior wall of left atrium. Left atrium diverticulums which was single, cystiform, and located in anterior wall were common. The cervix width of diverticulum was 4.9 +/- 3.2 mm, the body height of them was 5.4 +/- 2.0 mm. The ratio of body height to cervix width was from 0.47 to 4.08 (median 1.16). Ten patients of them undertook cardiac ultrasound examination at same time. There were five patients who left atrial diastolic function decreased, four patients who left ventricular systolic function decreased. Three of them both existed left atrial diastolic function decreasing and left ventricular systolic function decreasing, accompanied with mitral or aortic regurgitation. No patient was found that left atrium pressure or left ventricle diastolic pressure was increasing. The left atrium diverticulums of ten patients were probably congenital because their hemodynamical status cannot lead to diverticulum formation. It can be proved by reexamination after therapy or autopsy at last. In conclusion, multi-detector row computed tomography could provide anatomy details of left atrium diverticulum to help to finish heart and chest surgery successfully.

The liver is the largest organ in the body, with many complex, essential functions, such as metabolism, deintoxication, and secretion, often regulated via post-translational modifications, especially phosphorylation. Thus, the detection of phosphoproteins and phosphorylation sites is important to comprehensively explore human liver biological function. The human Chang liver cell line is among the first derived from non-malignant tissue, and its phosphoproteome profile has never been globally analyzed. To develop the complete phosphoproteome and probe the roles of protein phosphorylation in normal human liver, we adopted a shotgun strategy based on strong cation exchange chromatograph, titanium dioxide and LC-MS/MS to isolate and identify phosphorylated proteins. Two types of MS approach, Q-TOF and IT, were used and compared to identify phosphosites from complex protein mixtures of these cells. A total of 1035 phosphorylation sites and 686 phosphorylated peptides were identified from 607 phosphoproteins. A search using the public database of PhosphoSite showed that approximately 344 phosphoproteins and 760 phosphorylation sites appeared to be novel. In addition, N-terminal phosphorylated peptides were a greater fraction of all identified phosphopeptides. With GOfact analysis, we found that most of the identified phosphoproteins are involved in regulating metabolism, consistent with the liver's role as a key metabolic organ.

Background Analysis of complex samples with tandem mass spectrometry (MS/MS) has become routine in proteomic research. However, validation of database search results creates a bottleneck in MS/MS data processing. Recently, methods based on a randomized database have become popular for quality control of database search results. However, a consequent problem is the ignorance of how to combine different database search scores to improve the sensitivity of randomized database methods. Results In this paper, a multivariate nonlinear discriminate function (DF) based on the multivariate nonparametric density estimation technique was used to filter out false-positive database search results with a predictable false positive rate (FPR). Application of this method to control datasets of different instruments (LCQ, LTQ, and LTQ/FT) yielded an estimated FPR close to the actual FPR. As expected, the method was more sensitive when more features were used. Furthermore, the new method was shown to be more sensitive than two commonly used methods on 3 complex sample datasets and 3 control datasets. Conclusion Using the nonparametric model, a more flexible DF can be obtained, resulting in improved sensitivity and good FPR estimation. This nonparametric statistical technique is a powerful tool for tackling the complexity and diversity of datasets in shotgun proteomics.

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.

Background Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. Results We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. Conclusion SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

In order to improve the control performance of DC/DC buck-boost converters, the control problem of the output voltage is transformed to the inductor current control, and a cascade controller is presented. The outer loop of the cascade controller adopts a conventional PI control algorithm and takes the capacitor voltage as the controlled variable; the inner one adopts a particular PI control algorithm and takes the inductor current as the controlled variable. This kind of controller not only can make the controlled system achieve excellent dynamic characteristics, but also has strong robustness to the variations of system parameters, such as inductance, capacitance, input voltage, load resistance. Simulation results of a certain DC/DC buck-boost power converter indicate that the controller presented above is feasible.

Electrospray ionization in proteomic mass spectrometry is one of important methods of soft ionization or vaporization for the widest range of polar biomolecules. The number of charges attached onto peptides in ESI can extend the detection limit of mass spectrometer and can be used to estimate the location of mass signal of peptides in the mass spectrum. We present an approach to predict the charge state of peptides according to the composition of peptides, and it has been tested and verified on different datasets. It shows sufficient performance.