Disentangling stochastic signals superposed on short localized oscillations

We introduce a procedure for separating periodic oscillations superposed on a stochastic signal. The procedure combines empirical mode decomposition (EMD) of a signal with tools of data analysis based on stochastic differential equations, namely nonlinear Langevin equations. Taking the set of modes retrieved from the EMD of the signal, our procedure is able to separate them into two groups, one composing the periodic signal and another composing the stochastic signal. The framework is robust for a broad family of localized oscillations, in the range of large frequencies. In particular, we show that, in this context, the EMD method outperforms a low-pass filter and is robust for a wide interval of different frequency ranges and amplitudes of the periodic oscillation, as well as for a broad family of different non-linear Langevin processes.     

Signal amplification strategies for DNA and protein detection based on polymeric nanocomposites and polymerization: A review

Demand is increasing for ultrasensitive bioassays for disease diagnosis, environmental monitoring and other research areas. This requires novel signal amplification strategies to maximize the signal output. In this review, we focus on a series of significant signal amplification strategies based on polymeric nanocomposites and polymerization. Some common polymers are used as carriers to increase the local concentration of signal probes and/or biomolecules on their surfaces or in their interiors. Some polymers with special fluorescence and optical properties can efficiently transfer the excitation energy from a single site to the whole polymer backbone. This results in superior fluorescence signal amplification due to the resulting collective effort (integration of signal). Recent polymerization-based signal amplification strategies that employ atom transfer radical polymerization (ATRP) and photo-initiated polymerization are also summarized. Several distinctive applications of polymers in ultrasensitive bioanalysis are highlighted.     

Dual gold nanoparticle lateflow immunoassay for sensitive detection of Escherichia coli O157:H7

Two patterns of signal amplification lateral flow immunoassay (LFIA), which used anti-mouse secondary antibody-linked gold nanoparticle (AuNP) for dual AuNP-LFIA were developed. Escherichia coli O157:H7 was selected as the model analyte. In the signal amplification direct LFIA method, anti-mouse secondary antibody-linked AuNP (anti-mouse-Ab-AuNP) was mixed with sample solution in an ELISA well, after which it was added to LFIA, which already contained anti-E. coli O157:H7 monoclonal antibody-AuNP (anti-E. coli O157:H7-mAb-AuNP) dispersed in the conjugate pad. Polyclonal antibody was the test line, and anti-mouse secondary antibody was the control line in nitrocellulose (NC) membrane. In the signal amplification indirect LFIA method, anti-mouse-Ab-AuNP was mixed with sample solution and anti-E. coli O157:H7-mAb-AuNP complex in ELISA well, creating a dual AuNP complex. This complex was added to LFIA, which had a polyclonal antibody as the test line and secondary antibody as the control line in NC membrane. The detection sensitivity of both LFIAs improved 100-fold and reached 1.14 × 10³ CFU mL⁻¹. The 28 nm and 45 nm AuNPs were demonstrated to be the optimal dual AuNP pairs. Signal amplification LFIA was perfectly applied to the detection of milk samples with E. coli O157:H7 via naked eye observation.     

自振荡凝胶的仿生运动

定向运动是生命体最基本的功能,是其进化、生存和繁衍的前提。近年来为了研究生命体的运动机制,许多人工系统被相继开发并用于模拟部分生命体的运动行为。在诸多人工仿生系统里,自振荡凝胶由于同时具有内部驱动产生动能、运动定向性、无缆化和环境自适应等性能而备受瞩目。本文介绍了自振荡凝胶仿生运动的化学-机械能转换的理论根源并综述了仿生运动模式研究近期的进展,在此基础上展望了自振荡凝胶运动研究面临的机遇、挑战和未来发展方向。     

基于杂交链式反应辅助多重信号放大的端粒酶灵敏检测

端粒酶是真核细胞维持端粒长度的关键逆转录酶,其生物活性的高低可以为多种癌症的临床诊断和预后治疗提供有价值的信息.本研究以人宫颈癌细胞(HeLa细胞)裂解液中的端粒酶为研究对象,通过借助杂交链式反应辅助多重信号放大策略,提出了一种新颖、灵敏的检测端粒酶电化学方法.首先将端粒酶的延伸引物自组装在金电极表面,当端粒酶存在时,端粒酶能够催化引物的延伸,产生与发卡环探针H1部分互补的序列,进而引发杂交链式反应,形成由两个发卡环探针(H1和H2)交替杂交而形成的DNA长链.由于H1和H2末端均修饰有生物素,加入链霉亲和素修饰辣根过氧化物酶后,辣根过氧化物酶被被连接到电极表面,催化邻苯二胺氧化生成2,3-二氨基吩嗪,产生显著的电化学信号.实验结果表明,本研究建立的端粒酶电化学检测方法高效、可行,线性范围宽,灵敏度高,可以检测每毫升10个HeLa细胞裂解液中的端粒酶.本方法具有较好的选择性,能有效区分端粒酶和对照蛋白.     

Comparative and evolutionary studies of mammalian arylsulfatase and sterylsulfatase genes and proteins encoded on the X-chromosome

At least 19 sulfatase genes have been reported on the human genome, including four arylsulfatase (ARS) genes (ARSD; ARSE; ARSF; ARSH) and a sterylsulfatase (STS) gene located together on the X-chromosome. Bioinformatic analyses of mammalian genomes were undertaken using known human STS and ARS amino acid sequences to study the evolution of these genes and proteins encoded on eutherian and marsupial genomes. Several domain regions and key residues were conserved including signal peptides, active site residues, metal (Ca²⁺) and substrate binding sequences, transmembranes and N-glycosylation sites. Phylogenetic analyses describe the relationships and potential origins of these genes during mammalian evolution. Primate ARSH enzymes lacked signal peptide sequences which may influence their biological functions. CpG117 and CpG92 were detected within the 5′ region of the human STS and ARSD genes, respectively, and miR-205 within the 3′-UTR for the human STS gene, using bioinformatic methods A proposal is described for a primordial invertebrate STS-like gene serving as an ancestor for unequal cross over events generating the gene complex on the eutherian mammalian X-chromosome.     

MicroRNAs电化学生物传感器在乳腺癌检测中的研究进展

MicroRNAs是一类内源性非编码小RNA分子,可调控靶基因的表达。特异性microRNAs的失调在诸如癌症、心血管疾病、免疫疾病、神经退行性疾病和皮肤疾病等的发展过程中起着关键作用,常作为疾病早期诊断和预后的生物标志物。电化学生物传感器由于其灵敏、快速、成本低等优势,已经成为传统microRNAs检测方法的一种很有前途的替代方法。本文综述了基于microRNA电化学生物传感器检测乳腺癌的研究进展,并对其所构建的电化学生物传感器的检测方法进行了分析探讨。     

The Mechanism of Biochemical NO-Sensing: Insights from Computational Chemistry

The binding of small gas molecules such as NO and CO plays a major role in the signaling routes of the human body. The sole NO-receptor in humans is soluble guanylyl cyclase (sGC) – a histidine-ligated heme protein, which, upon NO binding, activates a downstream signaling cascade. Impairment of NO-signaling is linked, among others, to cardiovascular and inflammatory diseases. In the present work, we use a combination of theoretical tools such as MD simulations, high-level quantum chemical calculations and hybrid QM/MM methods to address various aspects of NO binding and to elucidate the most likely reaction paths and the potential intermediates of the reaction. As a model system, the H-NOX protein from Shewanella oneidensis (So H-NOX) homologous to the NO-binding domain of sGC is used. The signaling route is predicted to involve NO binding to form a six-coordinate intermediate heme-NO complex, followed by relatively facile His decoordination yielding a five-coordinate adduct with NO on the distal side with possible isomerization to the proximal side through binding of a second NO and release of the first one. MD simulations show that the His sidechain can quite easily rotate outward into solvent, with this motion being accompanied in our simulations by shifts in helix positions that are consistent with this decoordination leading to significant conformational change in the protein.     

应用相位移法的二维光栅测量

本文介绍了二种二维位移测量的光栅方法。测试系统采用CCD摄象系统记录正交光栅图象,通过调整摄象机变焦镜头的放大倍数来控制采样的空间频率。采样后用数字信号处理的方法,通过软件手段实现水平向和垂直向光栅信号的分离,然后运用了相位移技术及载波—相位移技术得到二维位移。所开发的测试系统除具有高精度、高灵敏度的特点外,还具有较高的空间分辨率。     

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.