电位型无标记IgG免疫探针测人IgG

以Pt为基体电极,用戊二醛交联法在Pt电极上固定兔抗人IgG抗体,制成IgG免疫探针.根据人免疫球蛋白(IgG)能与兔抗人IgG抗体发生定量的特异性免疫反应,利用电化学方法,找到了测定人血清中IgG的简单方法.人IgG的含量在0.2～1.2цg/L之间,与兔抗人IgG免疫探针的电位变化值(△E)呈良好的线性关系.工作曲线的回归方程△E=23.0 c 9.733,相关系数r=0.998 6,检测下限小于0.2μg/L.用于正常人血清中IgG含量的测定,加标回收率为95%～103%,结果满意.     

Automated serum peptide profiling using novel magnetic C18 beads off-line coupled to MALDI-TOF-MS

Serum peptide profiling by MS is an emerging approach for disease diagnosis and biomarker discovery. A magnetic bead‐based method for off‐line serum peptide capture coupled to MALDI‐TOF‐MS has been recently introduced. However, the reagents are not available to the general scientific community. Here, we developed a protocol for serum peptide capture using novel magnetic C18 beads, and automated the procedure on a high‐throughput magnetic particle processor. We investigated bead equilibration, peptide binding and peptide elution conditions. The method is evaluated in terms of peaks counts and reproducibility of ion intensities in control serum. Overall, the DynaBead‐RPC18‐based serum sample processing protocol reported here is reproducible, robust and allows for the detection of ?200 peptides at m/z 800–4000 of serum that was allowed to clot for 1 h. The average intra‐experiment %CV of normalized ion intensities for crude serum and 0.5% TFA/0.15% n‐octyl glucoside‐treated serum, respectively, were 12% (range 2–38%) and 10% (3–21%) and the inter‐experiment %CVs were 24% (10–53%) and 31% (10–59%). Importantly, this method can be used for serum peptide profiling by anyone in possession of a MALDI‐TOF instrument. In conjunction with the KingFisher® 96, the whole serum peptide capture procedure is high‐throughput (?20 min per isolation of 96 samples in parallel), thereby facilitating large‐scale disease profiling studies.     

Evaluation of a type 1 diabetes serum cohort by SELDI-TOF MS protein profiling

Proteomics analysis of serum from patients with type 1 diabetes (T1D) may lead to novel biomarkers for prediction of disease and for patient monitoring. However, the serum proteome is highly sensitive to sample processing and before proteomics biomarker research serum cohorts should preferably be examined for potential bias between sample groups. SELDI‐TOF MS protein profiling was used for preliminary evaluation of a biological‐bank with 766 serum samples from 270 patients with T1D, collected at 18 different paediatric centers representing 15 countries in Europe and Japan over 2 years (2000–2002). Samples collected 1 (n = 270), 6 (n = 248), and 12 (n = 248) months after T1D diagnosis were grouped across centers and compared. The serum protein profiles varied with collection site and day of analysis; however, markers of sample processing were not systematically different between samples collected at different times after diagnosis. Three members of the apolipoprotein family increased with time in patient serum collected 1, 6, and 12 months after diagnosis (ANOVA, p<0.001). These results support the use of this serum cohort for further proteomic studies and illustrate the potential of high‐throughput MALDI/SELDI‐TOF MS protein profiling for evaluation of serum cohorts before proteomics biomarker research.     

Kalman filtering from POP-based diagonalization of ARH(1)

The autoregressive Hilbertian model of order one (ARH(1)) is considered to represent the dynamics of a sequence of spatial functional data. Spatiotemporal interaction is defined in terms of the autocorrelation operator. A diagonalization of ARH(1) models is derived based on the functional principal oscillation pattern (POP) decomposition of such an operator. The results are applied to implement the Kalman filter for spatiotemporal prediction from the information provided by the observation of a finite sequence of spatial functional data.     

Endogeneous peptide profiling of cerebrospinal fluid by MALDI-TOF mass spectrometry: Optimization of magnetic bead-based peptide capture and analysis of preanalytical variables

Cerebrospinal fluid (CSF) perfuses the brain and spinal cord. CSF contains peptides and proteins important for brain physiology and potentially also relevant to brain pathology. High-throughput endogeneous peptide profiling by MS is an emerging approach for disease diagnosis and biomarker discovery. A magnetic bead-based method for off-line serum peptide capture coupled to MALDI-TOF-MS has been introduced recently. In this study, we optimize the peptide capture method for profiling of CSF and investigate the effect of a number of preanalytical variables. The CSF profiles contain ～100 reliably detected peptides at m/z 800-4000 with reproducible ion intensities (average 7% CV). The investigated preanalytical variables include: time at room temperature (RT) before storage, storage temperature, freeze-thawing cycles, and blood contamination. The CSF peptidome (<20?kDa) is relatively stable and can withstand a few hours at RT and several freeze-thaw cycles. Several peptides sensitive to storage at -20°C, including Cystatin C, were assigned based on mass or identified by MS/MS. Hemoglobin α and β chains were detected in blood contaminated samples, at levels invisible to the eye (0.01%). These peptides may be used for quality control in a MALDI-TOF-MS screening strategy to select high quality samples for in-depth proteomics analysis in disease studies.     

LL(1) grammars and Sub-LL(1) grammars

LL(1) grammars play an important role in top-down parsing. In order to improve their efficiency, this paper presents a new type of grammar, Sub-LL(1), based on LL(1), and shows that there is a grammar which is Sub-LL(1) and not LL(1) though LL(1)- and Sub-LL(1)-languages are identical.     

Parallel construction of SLR (1) and LALR (1) parsers

The generation of an LR parser consists of constructing a parse table, with one row per state (in a push-down automaton), and one column per terminal symbol. Traditionally, this is carried out row by row, with the computation of one row depending (potentially) on all the others. We present a technique for carrying out the lookahead computation of SLR (1) and LALR (1) parsers in a completely parallel fashion. Our technique performs the computation by column, rather than by row. We show that the computation is totally independent for each column, making it ideal for parallelization. The speedup factor of the technique is min (N, T), whereN is the number of processors andT is the number of terminal symbols in the user's grammar.     

Homology modeling,molecular dynamics,and site-directed mutagenesis study of AlkB human homolog 1 (ALKBH1)

The ability to repair DNA is important for the conservation of genetic information of living organisms. Cells have a number of ways to restore damaged DNA, such as direct DNA repair, base excision repair, and nucleotide excision repair. One of the proteins that can perform direct repair of DNA bases is Escherichia coli AlkB. In humans, there are 9 identified AlkB homologs, including AlkB homolog 1 (ALKBH1). Many of these proteins catalyze the direct oxidative dealkylation of DNA and RNA bases and, as such, have an important role in repairing DNA from damage induced by alkylating agents. In addition to the dealkylase activity, ALKBH1 can also function as an apyrimidinic/apurinic lyase and was proposed to have a distinct lyase active site. To our knowledge, no crystal structure or complete homology model of ALKBH1 protein is available. In this study, we have used homology modeling to predict the structure of ALKBH1 based on AlkB and Duffy-binding-like domain crystal structures as templates. Molecular dynamics simulations were subsequently performed on the predicted structure of ALKBH1. The positions of two disulfide bonds or a zinc-finger motif and a disulfide bond were predicted and the importance of these features was tested by mutagenesis. Possible locations for the lyase active site are proposed based on the analysis of our predicted structures and previous experimental results.     

SLR(1) and LALR(1) parsing for unrestricted grammars

Summary Simple LR(1) and lookahead LR(1) phrase structure grammars are defined and corresponding deterministic two-pushdown automata which parse all sentences are given. These grammars include a wide variety of grammars for non context-free languages. A given phrase structure grammar is one of these types if the parse table for the associated automaton has no multiple entries. A technique for construction of this parse table is given which in the lookahead case involves elimination of inverses in a grammar for lookahead strings for LR(0) items and computation of first sets for strings of symbols in the given grammar.     

MAX(1)和MARG(1)中公式改名的复杂性

改名是一个将变元映射到变元本身或它的补的函数,变元改名是公式变元集合上的一个置换,文字改名是一个改名和一个变元改名的组合.研究CNF公式的改名有助于改进DPLL算法.考虑判定问题"对于给定的CNF公式H和F是否存在一个变元(或文字)改名ψ使得ψ(H)=F?"的计算复杂性.MAX(1)和MARG(1)是极小不可满足公式的两个子类,这两个子类中的公式可以用树表示.树同构的判定问题在线性时间内是可解的.证明了对于MAX(1)和MARG(1)中的公式,文字改名问题在线性时间内可解,变元改名问题在平方次时间内可解.     

Detection of 3,4-methylenedioxymethamphetamine (MDMA, ecstasy) by displacement of antibodies

A molecular layer with low non-specific binding enabling determination of low concentrations of 3,4-methylenedioxymethamphetamine (MDMA) by the displacement of antibodies has been developed. Antibody Fab′-fragments at various concentrations have been site-directly immobilised on gold and intercalated with a hydrophilic non-ionic polymer that reduces non-specific binding. Bovine serum albumin conjugated with MDMA and various concentrations of anti-MDMA antibodies were bound to the layer. The amount of conjugates and antibodies bound was dependent on the amount of Fab′-fragments in the layer. Antibodies were also bound to the conjugates physisorbed directly onto the gold surface and in mixtures with the polymer or with a lipoamide. A high displacement of antibodies was observed by surface plasmon resonance (SPR) on interaction of MDMA with the different layers in buffer solution. No displacement could, however, be observed in saliva with the pure conjugate layer because of a high non-specific binding of proteins. When the conjugates were coupled to the surface through the antibody Fab-fragment/polymer layer, MDMA concentrations as low as 0.02 ng mL⁻¹ (0.14 nM) could easily be detected in buffer. In diluted saliva the lowest limit of detection was 0.4 ng mL⁻¹ enabling determination of drugs from saliva with a cut-off concentration of 2 ng mL⁻¹. The molecular layer of antibody Fab′-fragments and polymer thus shows great potential for binding conjugates and antibodies that can be displaced on the interaction with very low concentrations of small-sized molecules. A low non-specific binding is guaranteed by the presence of the hydrophilic polymer.     

Process automation toward ultra-high-throughput screening of combinatorial one-bead-one-compound (OBOC) peptide libraries

With an aim to develop peptide-based protein capture agents that can replace antibodies for in vitro diagnosis, an ultra-high-throughput screening strategy has been investigated by automating labor-intensive, time-consuming processes that are the construction of peptide libraries, sorting of positive beads, and peptide sequencing through analysis of tandem mass spectrometry data. Although instruments for automation, such as peptide synthesizers and automatic bead sorters, have been used in some groups, the overall process has not been well optimized to minimize time, cost, and efforts, as well as to maximize product quality and performance. Herein we suggest and explore several solutions to the existing problems with the automation of the key processes. The overall process optimization has been done successfully in orchestration with the technologies such as rapid cleavage of peptides from beads and semiautomatic peptide sequencing that we have developed previously. This optimization allowed one-round screening, from peptide library construction to peptide sequencing, to be completed within 4 to 5 days. We also successfully identified a 6-mer ligand for carcinoembryonic antigen-cell adhesion molecule 5 (CEACAM 5) through three-round screenings, including one-round screening of a focused library.     

Urine proteomics for profiling of human disease using high accuracy mass spectrometry

Knowledge of the biologically relevant components of human tissues has enabled the invention of numerous clinically useful diagnostic tests, as well as non-invasive ways of monitoring disease and its response to treatment. Recent use of advanced MS-based proteomics revealed that the composition of human urine is more complex than anticipated. Here, we extend the current characterization of the human urinary proteome by extensively fractionating urine using ultra-centrifugation, gel electrophoresis, ion exchange and reverse-phase chromatography, effectively reducing mixture complexity while minimizing loss of material. By using high-accuracy mass measurements of the linear ion trap-Orbitrap mass spectrometer and LC-MS/MS of peptides generated from such extensively fractionated specimens, we identified 2362 proteins in routinely collected individual urine specimens, including more than 1000 proteins not described in previous studies. Many of these are biomedically significant molecules, including glomerularly filtered cytokines and shed cell surface molecules, as well as renally and urogenitally produced transporters and structural proteins. Annotation of the identified proteome reveals distinct patterns of enrichment, consistent with previously described specific physiologic mechanisms, including 336 proteins that appear to be expressed by a variety of distal organs and glomerularly filtered from serum. Comparison of the proteomes identified from 12 individual specimens revealed a subset of generally invariant proteins, as well as individually variable ones, suggesting that our approach may be used to study individual differences in age, physiologic state and clinical condition. Consistent with this, annotation of the identified proteome by using machine learning and text mining exposed possible associations with 27 common and more than 500 rare human diseases, establishing a widely useful resource for the study of human pathophysiology and biomarker discovery.     

The IELR(1) algorithm for generating minimal LR(1) parser tables for non-LR(1) grammars with conflict resolution

There has been a recent effort in the literature to reconsider grammar-dependent software development from an engineering point of view. As part of that effort, we examine a deficiency in the state of the art of practical LR parser table generation. Specifically, LALR sometimes generates parser tables that do not accept the full language that the grammar developer expects, but canonical LR is too inefficient to be practical particularly during grammar development. In response, many researchers have attempted to develop minimal LR parser table generation algorithms. In this paper, we demonstrate that a well known algorithm described by David Pager and implemented in Menhir, the most robust minimal LR(1) implementation we have discovered, does not always achieve the full power of canonical LR(1) when the given grammar is non-LR(1) coupled with a specification for resolving conflicts. We also detail an original minimal LR(1) algorithm, IELR(1) (Inadequacy Elimination LR(1)), which we have implemented as an extension of GNU Bison and which does not exhibit this deficiency. Using our implementation, we demonstrate the relevance of this deficiency for several real-world parser specifications, and we demonstrate the feasibility of IELR(1). Finally, we demonstrate that, if canonical LR(1) were employed instead, grammar development would be severely impeded regardless of the power of the computer hardware.     

Large-scale antibody profiling of human blood sera: The future of molecular diagnosis

Despite the progress in cancer diagnosis the timely detection of many cancer types is still a grand challenge. For various human cancer types including lung cancer, prostate cancer, and breast cancer, several groups recently demonstrated that autoantibody profiling might be a promising approach towards earlier and more accurate cancer diagnosis. In this paper, we confirm the ability of autoantibody profiling as a diagnostic test by providing evidence that not only cancer sera can be distinguished well from normal controls, but also from sera of patients with noncancerous diseases. Altogether, we screened blood sera of 191 cancer patients, 60 physiologically unaffected controls, and 177 sera of patients with noncancerous diseases for more than 1800 immunogenic clones. The measured autoantibody fingerprints were evaluated using a novel image analysis pipeline. For 13 antigens, statistically significant (p<0.05) and at least two-fold elevated immuno-reactivity in cancer sera compared to normal sera could be observed. Nine of these antigens also showed increased reactivity compared to sera of patients with other diseases, including the tumor marker vimentin. Supervised discrimination between cancer and normal sera by using linear Support Vector Machines was possible with an accuracy of 94.04%, a specificity of 83.38%, and a sensitivity of 97.44%. Here, our so-called MIMM (minimally invasive multiple marker) approach showed no significant difference in the classification accuracy between low and higher tumor grades. The classification in healthy and diseased sera showed an even higher accuracy of 96.12% while the discrimination in cancer sera and diseased controls revealed an accuracy of 69.58%. These results demonstrate that autoantibody profiling offers the possibility of cancer screening for a variety of different cancer types as well as inflammatory diseases at an early disease stage.