AFM-based technologies as the way towards the reverse Avogadro number

Achievement of the concentration detection limit for proteins at the level of the reverse Avogadro number determines the modern development of proteomics. In this review, the possibility of approximating the reverse Avogadro number by using nanotechnological methods (AFM-based fishing with mechanical and electrical stimulation, nanowire detectors, and other methods) are discussed. The ability of AFM to detect, count, visualize and characterize physico-chemical properties of proteins at concentrations up to 10-17-10-18 M is demonstrated. The combination of AFM-fishing with mass-spectrometry allows the identification of proteins not only in pure solutions, but also in multi-component biological fluids (serum). The possibilities to improve the biospecific fishing efficiency by use of SOMAmers in both AFM and nanowire systems are discussed. The paper also provides criteria for evaluation of the sensitivity of fishing-based detection systems. The fishing efficiency depending on the detection system parameters is estimated. The practical implementation of protein fishing depending on the ratio of the sample solution volume and the surface of the detection system is discussed. The advantages and disadvantages of today's promising nanotechnological protein detection methods implemented on the basis of these schemes.

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The State of the Art in Biodefense Related Bacterial Pathogen Detection Using Bacteriophages: How It Started and How It’s Going

Viruses ◽

10.3390/v12121393 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1393

Author(s):

Shanmuga Sozhamannan ◽

Edward R. Hofmann

Keyword(s):

Pathogen Detection ◽

Detection System ◽

Clinical Success ◽

Genetic Material ◽

Bacterial Pathogen ◽

Detection Methods ◽

Predator Prey ◽

Advantages And Disadvantages ◽

Sensitivity And Selectivity ◽

Detection And Diagnosis

Accurate pathogen detection and diagnosis is paramount in clinical success of treating patients. There are two general paradigms in pathogen detection: molecular and immuno-based, and phage-based detection is a third emerging paradigm due to its sensitivity and selectivity. Molecular detection methods look for genetic material specific for a given pathogen in a sample usually by polymerase chain reaction (PCR). Immuno-methods look at the pathogen components (antigens) by antibodies raised against that pathogen specific antigens. There are different variations and products based on these two paradigms with advantages and disadvantages. The third paradigm at least for bacterial pathogen detection entails bacteriophages specific for a given bacterium. Sensitivity and specificity are the two key parameters in any pathogen detection system. By their very nature, bacteriophages afford the best sensitivity for bacterial detection. Bacteria and bacteriophages form the predator-prey pair in the evolutionary arms race and has coevolved over time to acquire the exquisite specificity of the pair, in some instances at the strain level. This specificity has been exploited for diagnostic purposes of various pathogens of concern in clinical and other settings. Many recent reviews focus on phage-based detection and sensor technologies. In this review, we focus on a very special group of pathogens that are of concern in biodefense because of their potential misuse in bioterrorism and their extremely virulent nature and as such fall under the Centers for Disease and Prevention (CDC) Category A pathogen list. We describe the currently available phage methods that are based on the usual modalities of detection from culture, to molecular and immuno- and fluorescent methods. We further highlight the gaps and the needs for more modern technologies and sensors drawing from technologies existing for detection and surveillance of other pathogens of clinical relevance.

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Analytical Methods for Determination of Phytic Acid and Other Inositol Phosphates: A Review

Molecules ◽

10.3390/molecules26010174 ◽

2020 ◽

Vol 26 (1) ◽

pp. 174

Author(s):

Gregor Marolt ◽

Mitja Kolar

Keyword(s):

Phytic Acid ◽

Inositol Phosphates ◽

Detection System ◽

Chemical Properties ◽

Detection Methods ◽

Specific Chemical ◽

Reliable Determination ◽

Sensor Applications ◽

Nano Sensors

From the early precipitation-based techniques, introduced more than a century ago, to the latest development of enzymatic bio- and nano-sensor applications, the analysis of phytic acid and/or other inositol phosphates has never been a straightforward analytical task. Due to the biomedical importance, such as antinutritional, antioxidant and anticancer effects, several types of methodologies were investigated over the years to develop a reliable determination of these intriguing analytes in many types of biological samples; from various foodstuffs to living cell organisms. The main aim of the present work was to critically overview the development of the most relevant analytical principles, separation and detection methods that have been applied in order to overcome the difficulties with specific chemical properties of inositol phosphates, their interferences, absence of characteristic signal (e.g., absorbance), and strong binding interactions with (multivalent) metals and other biological molecules present in the sample matrix. A systematical and chronological review of the applied methodology and the detection system is given, ranging from the very beginnings of the classical gravimetric and titrimetric analysis, through the potentiometric titrations, chromatographic and electrophoretic separation techniques, to the use of spectroscopic methods and of the recently reported fluorescence and voltammetric bio- and nano-sensors.

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URBAN PASSENGER TRANSPORT ROAD NETWORKS: PROSPECTS OF METHODS’ OPTIMIZATION

The Russian Automobile and Highway Industry Journal ◽

10.26518/2071-7296-2019-3-241-255 ◽

2019 ◽

Vol 16 (3) ◽

pp. 241-255

Author(s):

S. M. Mochalin ◽

J. A. Koleber

Keyword(s):

Economic Effect ◽

Practical Implementation ◽

Large Set ◽

Passenger Transport ◽

Advantages And Disadvantages ◽

Route Network ◽

Transport Services ◽

Modern Development ◽

Metaheuristic Methods ◽

The City

Introduction.City passenger transport plays an important role in life of the population of the city and in ensuring the efficient, uninterrupted operation of the entire urban system as a whole. However, currently in the field of urban passenger transport in many cities of Russia a number of significant problems have accumulated. In particular, these are problems of the development of the urban route network, the performance indicators of which determine the level of quality of transport services for the population and the economic effect of the operation of urban passenger transport. In this connection, the study of the prospects for the development of methods for optimizing the urban route network becomes relevant.Materials and methods.The article presents a chronological analysis of methods for optimizing route networks of urban passenger transport. It reflects the specifics of their use, shows the advantages and disadvantages. The authors also reflect the trends in the development of modern methods of optimization of route networks of urban passenger transport. The existing numerous methods for optimizing urban route networks could be divided into two types: heuristic, which have become classical today, and qualitatively new ones – metaheuristic, allowing managing tasks that contain nonlinear functions in the process of optimizing urban route networks. As modern science, software and computing facilities in the studied area have been developing very fast, metaheuristic methods are becoming a promising direction.Results.It had been revealed that over time, methods for optimizing the route networks of urban passenger transport had been improved and made it possible to take into account the opposing interests of the participants in the passenger transportation process in the city, as well as to set a large set of initial parameters and constraints for a mathematical model for optimizing the urban route network. The authors revealed the main features of the optimization of the route networks of urban passenger transport in the conditions of the modern development of science and software and computing facilities in the studied area. To date, there were no exact optimization methods for optimizing urban route networks. The task of optimizing the route network appeared to be combinatorial.Discussion and conclusions.The research is useful not only for the further development of science in the area under study, but also for the practical implementation of the process of optimizing the route networks of urban passenger transport.

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How to Improve Physico-Chemical Properties of Silk Fibroin Materials for Biomedical Applications?—Blending and Cross-Linking of Silk Fibroin—A Review

Materials ◽

10.3390/ma14061510 ◽

2021 ◽

Vol 14 (6) ◽

pp. 1510

Author(s):

Sylwia Grabska-Zielińska ◽

Alina Sionkowska

Keyword(s):

Silk Fibroin ◽

Biomedical Applications ◽

Magnetic Particles ◽

Chemical Properties ◽

Ternary Mixtures ◽

Cross Linking ◽

Advantages And Disadvantages ◽

Physico Chemical ◽

Binary And Ternary Mixtures ◽

To Receive

This review supplies a report on fresh advances in the field of silk fibroin (SF) biopolymer and its blends with biopolymers as new biomaterials. The review also includes a subsection about silk fibroin mixtures with synthetic polymers. Silk fibroin is commonly used to receive biomaterials. However, the materials based on pure polymer present low mechanical parameters, and high enzymatic degradation rate. These properties can be problematic for tissue engineering applications. An increased interest in two- and three-component mixtures and chemically cross-linked materials has been observed due to their improved physico-chemical properties. These materials can be attractive and desirable for both academic, and, industrial attention because they expose improvements in properties required in the biomedical field. The structure, forms, methods of preparation, and some physico-chemical properties of silk fibroin are discussed in this review. Detailed examples are also given from scientific reports and practical experiments. The most common biopolymers: collagen (Coll), chitosan (CTS), alginate (AL), and hyaluronic acid (HA) are discussed as components of silk fibroin-based mixtures. Examples of binary and ternary mixtures, composites with the addition of magnetic particles, hydroxyapatite or titanium dioxide are also included and given. Additionally, the advantages and disadvantages of chemical, physical, and enzymatic cross-linking were demonstrated.

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Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽

10.3390/s21072538 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2538

Author(s):

Shuang Zhang ◽

Feng Liu ◽

Yuang Huang ◽

Xuedong Meng

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spread Spectrum ◽

High Efficiency ◽

Detection System ◽

Direct Sequence Spread Spectrum ◽

Detection Performance ◽

Detection Methods ◽

Direct Sequence ◽

Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

SN Applied Sciences ◽

10.1007/s42452-021-04579-4 ◽

2021 ◽

Vol 3 (6) ◽

Author(s):

R. Sekhar ◽

K. Sasirekha ◽

P. S. Raja ◽

K. Thangavel

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Missing Values ◽

Detection System ◽

Radial Basis Function Network ◽

Detection Methods ◽

Support Vector ◽

Parameter Method ◽

List Type ◽

Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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A Synthetic Biology Approach Using Engineered Bacteria to Detect Perfluoroalkyl Substance (PFAS) Contamination in Water

Military Medicine ◽

10.1093/milmed/usaa367 ◽

2021 ◽

Vol 186 (Supplement_1) ◽

pp. 801-807

Author(s):

Nathaniel A Young ◽

Ryan L Lambert ◽

Angela M Buch ◽

Christen L Dahl ◽

Jackson D Harris ◽

...

Keyword(s):

Synthetic Biology ◽

Dna Sequences ◽

Detection System ◽

The United States ◽

Detection Methods ◽

Health Concern ◽

Contaminated Water ◽

Engineered Bacteria ◽

Synthetic Biology Approach ◽

Rhodococcus Jostii

ABSTRACT Introduction Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic compounds used industrially for a wide variety of applications. These PFAS compounds are very stable and persist in the environment. The PFAS contamination is a growing health issue as these compounds have been reported to impact human health and have been detected in both domestic and global water sources. Contaminated water found on military bases poses a potentially serious health concern for active duty military, their families, and the surrounding communities. Previous detection methods for PFAS in contaminated water samples require expensive and time-consuming testing protocols that limit the ability to detect this important global pollutant. The main objective of this work was to develop a novel detection system that utilizes a biological reporter and engineered bacteria as a way to rapidly and efficiently detect PFAS contamination. Materials and Methods The United States Air Force Academy International Genetically Engineered Machine team is genetically engineering Rhodococcus jostii strain RHA1 to contain novel DNA sequences composed of a propane 2-monooxygenase alpha (prmA) promoter and monomeric red fluorescent protein (mRFP). The prmA promoter is activated in the presence of PFAS and transcribes the mRFP reporter. Results The recombinant R. jostii containing the prmA promoter and mRFP reporter respond to exposure of PFAS by activating gene expression of the mRFP. At 100 µM of perfluorooctanoic acid, the mRFP expression was increased 3-fold (qRT-PCR). Rhodococcus jostii without exposure to PFAS compounds had no mRFP expression. Conclusions This novel detection system represents a synthetic biology approach to more efficiently detect PFAS in contaminated samples. With further refinement and modifications, a similar system could be readily deployed in the field around the world to detect this critical pollutant.

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Electrokinetic Formation of “Microbridges” for Protein Biomarkers as Sensors

JALA Journal of the Association for Laboratory Automation ◽

10.1016/j.jala.2007.06.001 ◽

2007 ◽

Vol 12 (5) ◽

pp. 311-317 ◽

Cited By ~ 2

Author(s):

Vindhya Kunduru ◽

Shalini Prasad

Keyword(s):

Electric Fields ◽

Microelectrode Array ◽

Protein Detection ◽

High Specificity ◽

Detection Methods ◽

Label Free ◽

Protein Biomarkers ◽

Detection Scheme ◽

Polystyrene Beads ◽

Conducting Path

We demonstrate a technique to detect protein biomarkers contained in vulnerable coronary plaque using a platform-based microelectrode array (MEA). The detection scheme is based on the property of high specificity binding between antibody and antigen similar to most immunoassay techniques. Rapid clinical diagnosis can be achieved by detecting the amount of protein in blood by analyzing the protein's electrical signature. Polystyrene beads which act as transportation agents for the immobile proteins (antigen) are electrically aligned by application of homogenous electric fields. The principle of electrophoresis is used to produce calculated electrokinetic movement among the anti-C-reactive protein (CRP), or in other words antibody funtionalized polystyrene beads. The electrophoretic movement of antibody-functionalized polystyrene beads results in the formation of “Microbridges” between the two electrodes of interest which aid in the amplification of the antigen—antibody binding event. Sensitive electrical equipment is used for capturing the amplified signal from the “Microbridge” which essentially behaves as a conducting path between the two electrodes. The technique circumvents the disadvantages of conventional protein detection methods by being rapid, noninvasive, label-free, repeatable, and inexpensive. The same principle of detection can be applied for any receptor—ligand-based system because the technique is based only on the volume of the analyte of interest. Detection of the inflammatory coronary disease biomarker CRP is achieved at concentration levels spanning over the lower microgram/milliliter to higher order nanogram/milliliter ranges.

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Partial Discharge Detection Using Acoustic and Optical Methods in High Voltage Power Equipments: A Review

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.845.283 ◽

2013 ◽

Vol 845 ◽

pp. 283-286 ◽

Cited By ~ 3

Author(s):

Malik Abdul Razzaq Al Saedi ◽

Mohd Muhridza Yaacob

Keyword(s):

Acoustic Emission ◽

High Voltage ◽

Power Transformer ◽

Partial Discharge ◽

Optical Detection ◽

Detection Threshold ◽

Optical Methods ◽

Detection Methods ◽

Detection Techniques ◽

Advantages And Disadvantages

There is a high risk of insulation system dielectric instability when partial discharge (PD) occurs. Therefore, measurement and monitoring of PD is an important preventive tool to safeguard high-voltage equipment from wanton damage. PD can be detected using optical method to increase the detection threshold and to improve the performance of on-line measurement of PD in noise environment. The PD emitted energy as acoustic emission. We can use this emitted energy to detect PD signal. The best method to detect PD in power transformer is by using acoustic emission. Optical sensor has some advantages such as; high sensitivity, more accuracy small size. Furthermore, in on-site measurements and laboratory experiments, it isoptical methodthat gives very moderate signal attenuations. This paper reviews the available PD detection methods (involving high voltage equipment) such as; acoustic detection and optical detection. The advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages from the consideration of accuracy and suitability for the applications when compared to other techniques.

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Hybrid classification model to detect advanced intrusions using data mining techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.4.10031 ◽

2018 ◽

Vol 7 (2.4) ◽

pp. 10

Author(s):

V Mala ◽

K Meena

Keyword(s):

Data Mining ◽

Learning Algorithm ◽

Detection System ◽

Classification Model ◽

Detection Methods ◽

Data Mining Techniques ◽

Detection Systems ◽

Intruder Detection ◽

Hybrid Classification ◽

Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

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