Sequential Identification Research Articles

Sequential or multiplex electrochemical detection of miRs based on the p19 function relative to three sandwiches of different structural hybrids on the liposomal sensor

In this work, we designed a liposomal electrochemical sensor with DOTAP-DOPE liposome, chimeric probes, p19 as a caliper molecule, and the competitor structural hybrid (just RNA) for detection of three micro-RNAs in one SPCE/GNP electrode. The sensor is stabled when the cationic spherical DOTAP-DOPE liposomes sandwich with hybrids of the different sandwiched of probes (T-M-linear, Stem) and 21-124a-221miRs. With the addition of P19, in the presence of a sandwiched competitor (T-linear/21miR), the system is stable (ON) and is shut off in the presence of structural sandwiched hydrides of M-linear+124a/Stem+221 miR due to the lack of adequate access to segments of RNA-miRs of chimeric probes. For the first time in this study, three probes were sandwiched on the separate liposome for sequential identification of 21-124a-221 or multiplex detection of miRs (221 or 124a with 21) with high specificity and sensitivity (as low as 0.1 fM). Electrochemical impedance (EIS) were performed for sensing three miRs in PBS containing 1 mM [Fe(CN)6]−3/−4 which DOTAP-DOPE liposome acted as an enhancing intermediate layer in the electrochemical reactions. Transmission Electron Microscopy (TEM)), Atomic Force Microscopy (AFM), Dynamic Light Scattering (DLS) and Ultraviolet–Visible (UV) spectroscopic techniques are used to understand the interactions between the DOPE-DOTAP, AuNP, different probes, miRs and p19.

Abstract 3004: Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression

Abstract Myelodysplastic syndromes (MDS) are a collection of clonal diseases with dysfunctional hematopoietic stem cells, characterized by ineffective hematopoiesis, cytopenias, and dysplasia. MDS is increasingly being recognized as a molecularly heterogeneous disease with variability in clinical phenotype, prognosis, and response to treatment. The clonal evolution in response to therapy remains a challenge in the management of MDS. However, resolution of complex clonal architectures is difficult with current bulk sequencing approaches. High-throughput single-cell genomic profiling enables the resolution of tumor heterogeneity, and may improve clinical diagnosis and treatment monitoring by allowing for characterization and early identification of resistant subclonal populations. To enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cells confined to droplets. The barcodes were then used to reassemble the genetic profiles of cells from next-generation sequencing data. We applied this approach to sequential clinical MDS samples, genotyping the most clinically relevant loci across more than 15,000 individual cells. Additionally, to study effects of subclonal mutations on drug sensitivity, ex vivo functional testing was performed on red blood cell-lysed peripheral blood and/or bone marrow aspirate patient samples. Targeted single-cell sequencing was able to recapitulate bulk sequencing data from both peripheral blood and bone marrow aspirate samples. Furthermore, the single-cell nature of our approach enabled definitive determination of mutational co-occurrence within the same cell. For examples in one sample, bulk sequencing identified mutations in JAK2 and NRAS both at 3% variant allele frequency (VAF), where single-cell analysis suggested that these mutations were mutually exclusive, each defining a distinct subclone. Single-cell sequencing allowed for serial monitoring of clonal evolution, with analysis of sequential samples from this same patient showing increase of NRAS clone from 2.5% to 24.7% at time of disease progression after hypomethylating agent therapy. Furthermore, single-cell analysis was able to identify a distinct subclone characterized by a KRAS mutation (0.4% at initiation of therapy and 6.7% at relapse), missed by serial bulk sequencing. Taken together, our results suggest a greater degree of heterogeneity in MDS samples than suggested with bulk sequencing methods alone, and demonstrate utility of single-cell sequencing for sequential monitoring and identification of resistant clones prior to therapy initiation. We show here that this approach is a feasible, scalable, and effective way to identify and track heterogeneous populations of cells in MDS. Citation Format: Alexey Aleshin, Peter L. Greenberg, Bruno C. Medeiros, Diane Heiser, Marianne Santaguida, Sacha Prashad, Robert Durruthy- Durruthy, Dennis J. Eastburn. Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3004.

A Review of Auditory Prediction and Its Potential Role in Tinnitus Perception.

The precise mechanisms underlying tinnitus perception and distress are still not fully understood. A recent proposition is that auditory prediction errors and related memory representations may play a role in driving tinnitus perception. It is of interest to further explore this. To obtain a comprehensive narrative synthesis of current research in relation to auditory prediction and its potential role in tinnitus perception and severity. A narrative review methodological framework was followed. The key words Prediction Auditory, Memory Prediction Auditory, Tinnitus AND Memory, Tinnitus AND Prediction in Article Title, Abstract, and Keywords were extensively searched on four databases: PubMed, Scopus, SpringerLink, and PsychINFO. All study types were selected from 2000-2016 (end of 2016) and had the following exclusion criteria applied: minimum age of participants <18, nonhuman participants, and article not available in English. Reference lists of articles were reviewed to identify any further relevant studies. Articles were short listed based on title relevance. After reading the abstracts and with consensus made between coauthors, a total of 114 studies were selected for charting data. The hierarchical predictive coding model based on the Bayesian brain hypothesis, attentional modulation and top-down feedback serves as the fundamental framework in current literature for how auditory prediction may occur. Predictions are integral to speech and music processing, as well as in sequential processing and identification of auditory objects during auditory streaming. Although deviant responses are observable from middle latency time ranges, the mismatch negativity (MMN) waveform is the most commonly studied electrophysiological index of auditory irregularity detection. However, limitations may apply when interpreting findings because of the debatable origin of the MMN and its restricted ability to model real-life, more complex auditory phenomenon. Cortical oscillatory band activity may act as neurophysiological substrates for auditory prediction. Tinnitus has been modeled as an auditory object which may demonstrate incomplete processing during auditory scene analysis resulting in tinnitus salience and therefore difficulty in habituation. Within the electrophysiological domain, there is currently mixed evidence regarding oscillatory band changes in tinnitus. There are theoretical proposals for a relationship between prediction error and tinnitus but few published empirical studies.

Nonlinear identification via connected neural networks for unsteady aerodynamic analysis

In the present work, a nonlinear system identification strategy is proposed which is based on the series connection of a recurrent local linear neuro-fuzzy model (NFM) and a multilayer perceptron (MLP) neural network. The NFM with output feedback is initially used for multi-step ahead predictions, whereas the MLP neural network is a posteriori employed to perform a nonlinear quasi-static correction of the NFM's time-series response. The novel identification approach is utilized exemplarily as a reduced-order modeling (ROM) technique to lower the computational effort of unsteady aerodynamic simulations, although the approach is generally applicable to any nonlinear identification task. In order to demonstrate the method's fidelity for unsteady aerodynamic modeling, the NLR 7301 airfoil is investigated at transonic flow conditions, while the motion-induced aerodynamic forces are considered in particular. Therefore, the pitch and plunge degrees of freedom are simultaneously excited via forced motions to obtain the training data for model calibration, while the respective aerodynamic response is computed using a computational fluid dynamics (CFD) solver. The sequential nonlinear identification process as well as the generalization of the resulting model is presented. Besides, a Monte-Carlo-based training procedure, which is novel in the context of aerodynamic reduced-order modeling, is introduced to estimate statistical errors. It is shown that the essential linear and nonlinear system characteristics are accurately reproduced by the new approach compared to the full-order solution. Moreover, by examining the results in comparison to established ROM methods it is indicated that the connected neural network approach leads to an enhanced simulation and generalization performance.

Parameter estimation of a dynamic model of Escherichia coli fed-batch cultures

This work proposes an original sequential parameter identification method of a dynamic model of Escherichia coli BL21(DE3) fed-batch cultures. The proposed macroscopic model is based on the overflow (or bottleneck) metabolism assumption of Sonnleitner and Kappeli, suggesting two metabolic pathways (respirative and respiro-fermentative), and consists of a set of nonlinear mass balance differential equations. Model unknown parameters are estimated from dedicated experimental data collected from a 5-liter pilot bioreactor. Experiments are designed in order to force switches from one metabolic pathway to another. A sequential identification procedure, based on a specific data partitioning, is achieved and results are qualitatively and quantitatively assessed using Fisher information. The resulting dynamic model is in good agreement with the experimental data, and could be used for process optimization and/or control. The identification procedure could also easily be transposed to other processes.

High-order hierarchical nanotwins with superior strength and ductility

Microstructures dominate the mechanical properties of metals because of the interactions of dislocations and other defects. Hierarchical nanotwins (HNTs) in different orientations/orders are more effective in interacting with dislocations, which will enhance the mechanical properties of materials. However, the confirmation of high-order HNTs (>2) has not been formally established to fully explore the potential of mechanical property enhancement of HNT structured materials. In this work, HNTs up to five orders with sequential identification in pure silver (Ag) were fabricated by combining the techniques of surface mechanical attrition treatment and uniaxial pre-loading. The strength of this high-order HNT-structured Ag has been improved by over three times without sacrificing ductility. In addition, compared to other structures, the HNT structure can increase the limit of strength of the materials at high ductility. By performing in situ transmission electron microscopy, the HNT generation mechanism is revealed in real time, and the generation conditions of stable HNTs are established. Molecular dynamic simulations present the structure evolution details of HNTs at atomic scale, and the dislocation-based theory unveils the underlying mechanism. By using this generic approach, high-order HNT (≥5) structures can be achieved in crystalline materials.

Sequential emitter identification method based on D-S evidence theory

This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information.

Sequential identification of nonignorable missing data mechanisms

With nonignorable missing data, likelihood-based inference should be based on the joint distribution of the study variables and their missingness indicators. These joint models cannot be estimated from the data alone, thus requiring the analyst to impose restrictions that make the models uniquely obtainable from the distribution of the observed data. We present an approach for constructing classes of identifiable nonignorable missing data models. The main idea is to use a sequence of carefully set up identifying assumptions, whereby we specify potentially different missingness mechanisms for different blocks of variables. We show that the procedure results in models with the desirable property of being non-parametric saturated.

JIE for a class of MD systems

In multi-detection (MD) systems including over-the-horizon radar (OTHR) systems, the forward-based receivers systems and passive coherent location systems, resolvable multiple detections of one target due to the multi-path propagation will be received, in which both identification (including measurement association and propagation mode/transmitting origin identification) and estimation (including path-conditional state estimation and multi-path track fusion) are deeply coupled and affect each other. Up to present, all corresponding methods fall into the scope of sequential identification orientated or estimation orientated processing, and hence the solutions are not satisfactory. Here the joint identification and estimation scheme for the MD systems (MD-JIE) is developed based on the generalized Bayes risk considering both identification and estimation performance. The likelihood-ratio function and the conditional probability density function as the identification cost and estimation cost are recursively calculated, respectively. Taking the data-association constraints into consideration, the proposed MD-JIE scheme is implemented via online constrained optimization technology. Compared to the identification-then-estimation (ITE) method and the estimation-then-identification (ETI) method, the proposed MD-JIE method prevails in the joint performance measure with the higher correct identification rate than the ETI method and the smaller root MSE than the ITE method. Besides, the robustness of the proposed method is verified.

WITHDRAWN: Heralds of parallel MS: Data-independent acquisition surpassing sequential identification of data dependent acquisition in proteomics.

This article has been withdrawn by the authors. This article did not comply with the editorial guidelines of MCP. Specifically, single peptide based protein identifications of 9-19% were included in the analysis and discussed in the results and conclusions. We wish to withdraw this article and resubmit a clarified, corrected manuscript for review.

Mathematical modeling of randomness and fuzziness phenomena in scientific studies. II. Applications

This paper considers some elements of the optimal fuzzy decision theory that are similar to the optimal statistical decision theory, in particular, the theory of optimal fuzzy identification and optimal fuzzy hypothesis testing, such as Neyman–Pearson statistical hypothesis testing and optimal fuzzy estimation along with a sequential fuzzy identification algorithm similar to the Wald sequential statistical criterion. Some elements of the fuzzy measuring and computing transducer theory and its applications in the problems of the analysis and interpretation of measurement experiment data are given.

A Sequential Algebraic Parametric Identification Approach for Nonlinear Vibrating Mechanical Systems

AbstractA systematic on‐line algebraic parametric identification method in time domain is proposed for active vibration control on nonlinear vibrating mechanical systems. The identification is performed iteratively, to reduce the computational effort of the parameter estimation process, for finite or infinite dimensional large flexible structures into an interest bandwidth. Position signals are only required to algebraically and quickly estimate the parameters of multiple degrees‐of‐freedom nonlinear vibrating mechanical systems. Then, an adaptive‐like output feedback tracking dynamic control scheme is introduced to assess the efficiency and effectiveness of the closed‐loop parameter estimation algorithms. Some simulation results are included to provide evidence of the efficient dynamic performance of the proposed parametric identification approach combined with asymptotic trajectory tracking control.

Sequential Parameter Identification of Fractional-Order Duffing System Based on Differential Evolution Algorithm

Using the dynamic properties of fractional-order Duffing system, a sequential parameter identification method based on differential evolution optimization algorithm is proposed for the fractional-order Duffing system. Due to the step by step parameter identification method, the dimension of parameter identification of each step is greatly reduced and the search capability of the differential evolution algorithm has been greatly improved. The simulation results show that the proposed method has higher convergence reliability and accuracy of identification and also has high robustness in the presence of measurement noise.

Bioinformatics Study of Structural Patterns in Plant MicroRNA Precursors.

According to the RNA world theory, RNAs which stored genetic information and catalyzed chemical reactions had their contribution in the formation of current living organisms. In recent years, researchers studied this molecule diversity, i.a. focusing on small non-coding regulatory RNAs. Among them, of particular interest is evolutionarily ancient, 19–24 nt molecule of microRNA (miRNA). It has been already recognized as a regulator of gene expression in eukaryotes. In plants, miRNA plays a key role in the response to stress conditions and it participates in the process of growth and development. MicroRNAs originate from primary transcripts (pri-miRNA) encoded in the nuclear genome. They are processed from single-stranded stem-loop RNA precursors containing hairpin structures. While the mechanism of mature miRNA production in animals is better understood, its biogenesis in plants remains less clear. Herein, we present the results of bioinformatics analysis aimed at discovering how plant microRNAs are recognized within their precursors (pre-miRNAs). The study has been focused on sequential and structural motif identification in the neighbourhood of microRNA.

Real-time identification of mean retention time and end-of-life rate in cyclic manufacturing

Used in predicting demand and new orders for shelf stock replacement and in inventory planning, the mean retention time (MRT) of consumer products is long established as a key production and marketing parameter. Higher MRTs are also associated with enhanced sustainability, favoring longer production/consumption cycles, waste prevention and resource preservation. The MRT, or the corresponding product stock level and the end-of-life (EoL) outflow to be collected for disassembly and recycling are useful for planning efficient cyclic operations. Determination of stock and EoL level for cyclic products is challenging however, due to random stock losses and to the volatile and distributed nature of returns and EoL exit. Actuary science methods presume specific residual life distributions, usually fitted ex-post, which may become inaccurate when applied to real production/consumption cycles. Anchored on a recent constitutive law in cyclic manufacturing, a method is presented for real-time MRT and EoL identification. The law related mean stock and EoL levels under random losses and random, distributed EoL exit via a simple equation, without involving residual life or EoL distributions or early loss history. One degree of freedom still remained, for, both stock and EoL are random and unobservable. The work herein ties the remaining degree of freedom. It enables sequential identification of stock and EoL level, using reliable and readily acquired data in a key equation obtained for the production/consumption cycle. Adapting to varying early loss, consumer take-back and discard volatility, it may be useful in proactive policy, sizing of disassembly and recycle lines and return inventory control towards cleaner production/consumption cycles.

Electrical detection and quantification of single and mixed DNA nucleotides in suspension.

High speed sequential identification of the building blocks of DNA, (deoxyribonucleotides or nucleotides for short) without labeling or processing in long reads of DNA is the need of the hour. This can be accomplished through exploiting their unique electrical properties. In this study, the four different types of nucleotides that constitute a DNA molecule were suspended in a buffer followed by performing several types of electrical measurements. These electrical parameters were then used to quantify the suspended DNA nucleotides. Thus, we present a purely electrical counting scheme based on the semiconductor theory that allows one to determine the number of nucleotides in a solution by measuring their capacitance-voltage dependency. The nucleotide count was observed to be similar to the multiplication of the corresponding dopant concentration and debye volume after de-embedding the buffer contribution. The presented approach allows for a fast and label-free quantification of single and mixed nucleotides in a solution.

The analysis of iridoid compounds in motherwort herb

The pressing objective for modern pharmacy is the search of new herbal remedies with ensured resource base for successful realization of national programs, prescribed by the Concept of pharmaceutical sector development of healthcare of Ukraine for 2011–2020 years, devoted to development and implementation of efficient, safe, non-toxic, and standardized according to European standards of quality phytopharmaceutical remedies, manufactured in accordance to the modern technologies with verified effect and dosage. Motherwort herb contains alkaloids, flavonoids (quinqueloside, rutin, quercetin, glucorhamnoside, hyperoside, etc.), triterpene saponins, steroids, tannins, essential oil, organic acids, monoterpene (iridoids) and diterpene compounds, choline, vitamins, micro- and macroelements. Such composition of biologically active compounds (BAC) causes sedative, anticonvulsant, antianginal, hypotensive, antispasmodic, and diuretic effect. Besides, motherwort herb provides soft cardiotonic effect. Glucorhamnoside of Motherwort herb slows the heart rate down, reduces blood pressure, and shows sedative action. Flavonoids, iridoids, and steroids cause antispasmodic, sedative, and hypotensive properties. Remedies, containing Motherwort, are related to drugs influencing the nervous system. Aim. The aim of our work was identification and quantitative analysis of iridoids in Motherwort herb and in viscous extract on its basis (MHVE). Methods. Identification of different groups of BAC was carried out using qualitative reactions and thin-layer chromatography method (TLC). Standardization was carried out by means of spectroscopy method. Results. As a result of research, identification of iridoid components by TLC method in Motherwort herb and in MHVE was substantiated, and Motherwort herb and VEMH were standardized by iridoids content. Conclusion. Motherwort herb is herbal source to obtain remedies influencing the nervous system. BAC complex of herb, in particular iridoids, shows sedative effect; therefore, their sequential identification and standardization in herb-remedy line is important

Isolation, purification and characterization of antioxidative peptide of pearl millet (Pennisetum glaucum) protein hydrolysate

Pearl millet (Pennisetum glaucum) is a rich source of protein, used for present study to hydrolyze protein, peptide separation and its functional activity. Antioxidative bioactive peptide was successfully identified from pearl millet using trypsin enzyme. Different antioxidative potential of isolated peptide were assessed based on activity of DPPH radical, ABTS radical, hydroxyl radical, Fe2+ chelating ability and reducing power. Bioactive peptide separated by gel-filtration chromatography, showed the higher antioxidant activity as tested by different free radicals. The activity of pearl millet protein hydrolysate fraction was found for DPPH assay (67.66%), ABTS assay (78.81%), Fe2+ chelating ability (51.20%), hydroxyl assay (60.95%) and reducing power (0.375nm) was further purified using reversed-phase UFLC and subjected to matrix assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry (MALDI-TOF/TOF-MS) for sequential identification of the peptide. The sequence SDRDLLGPNNQYLPK was identified as antioxidant peptide.

A Novel Bias Detection Technique for Partial Discharge Localization in Oil Insulation System

This paper presents a sequential fault detection and identification algorithm for detecting a bias fault in the measurements of partial discharge (PD) in a transformer insulation system using acoustic signals. The algorithm identifies a bias fault in any of the ultrasonic sensors by computing the probability of having that bias fault given a carefully constructed measurement residual. The constructed measurement residual is a function of the measurement noise and the possible measurement fault. A set of bias hypotheses is assumed and initially given equal alarm probability. It is assumed that only one sensor will acquire a bias at any given time. Once the probability of a hypothesis approaches 1, that hypothesis is declared as the correct hypothesis and the bias associated with the hypothesis is removed from the sensors’ reading. Therefore, this will enable high-accuracy estimation of the location of PD. First, the algorithm is verified in a simulation environment. Subsequently, the accuracy of the proposed algorithm is verified using the experimental results.

Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments

Nowadays, advancements in depth imaging technologies have made human activity recognition (HAR) reliable without attaching optical markers or any other motion sensors to human body parts. This study presents a depth imaging-based HAR system to monitor and recognize human activities. In this work, we proposed spatiotemporal features approach to detect, track, and recognize human silhouettes using a sequence of RGB-D images. Under our proposed HAR framework, the required procedure includes detection of human depth silhouettes from the raw depth image sequence, removing background noise, and tracking of human silhouettes using frame differentiation constraints of human motion information. These depth silhouettes extract the spatiotemporal features based on depth sequential history, motion identification, optical flow, and joints information. Then, these features are processed by principal component analysis for dimension reduction and better feature representation. Finally, these optimal features are trained and they recognized activity using hidden Markov model. During experimental results, we demonstrate our proposed approach on three challenging depth videos datasets including IM-DailyDepthActivity, MSRAction3D, and MSRDailyActivity3D. All experimental results show the superiority of the proposed approach over the state-of-the-art methods.