Towards a target label-free suboptimum oligonucleotide displacement-based detection system

A novel method for the future development of label-free DNA sensors is proposed here. The approach is based on the displacement of a labelled suboptimum mutated oligonucleotide hybridised with the immobilised biotin-capture probe. The target fully complementary to the biotin-capture probe can displace the labelled oligonucleotide causing a subsequent decrease of the signal that verifies the presence of the target. The decrease of signal was demonstrated to be proportional to the target concentration. A study of the hybridisation of mutated and complementary labelled oligonucleotides with an immobilised biotin-capture probe was carried out. Different kinetic and thermodynamic behaviour was observed for heterogeneous hybridisation of biotin-capture probe with complementary or suboptimum oligonucleotides. The displacement method evaluated colourimetrically achieved the objective of decreasing the response time from 1 h for direct hybridisation of 19-mer oligonucleotides in the direct enzyme-linked oligonucleotide assay (ELONA) to 5 min in the case of displacement detection in the micromolar concentration range

The Aspartate-Semialdehyde Dehydrogenase of Edwardsiella ictaluri and Its Use as Balanced-Lethal System in Fish Vaccinology

asdA mutants of Gram-negative bacteria have an obligate requirement for diaminopimelic acid (DAP), which is an essential constituent of the peptidoglycan layer of the cell wall of these organisms. In environments deprived of DAP, i.e., animal tissues, they will undergo lysis. Deletion of the asdA gene has previously been exploited to develop antibiotic-sensitive strains of live attenuated recombinant bacterial vaccines. Introduction of an Asd+ plasmid into a ΔasdA mutant makes the bacterial strain plasmid-dependent. This dependence on the Asd+ plasmid vector creates a balanced-lethal complementation between the bacterial strain and the recombinant plasmid. E. ictaluri is an enteric Gram-negative fish pathogen that causes enteric septicemia in catfish. Because E. ictaluri is a nasal/oral invasive intracellular pathogen, this bacterium is a candidate to develop a bath/oral live recombinant attenuated Edwardsiella vaccine (RAEV) for the catfish aquaculture industry. As a first step to develop an antibiotic-sensitive RAEV strain, we characterized and deleted the E. ictaluri asdA gene. E. ictaluri ΔasdA01 mutants exhibit an absolute requirement for DAP to grow. The asdA gene of E. ictaluri was complemented by the asdA gene from Salmonella. Several Asd+ expression vectors with different origins of replication were transformed into E. ictaluri ΔasdA01. Asd+ vectors were compatible with the pEI1 and pEI2 E. ictaluri native plasmids. The balanced-lethal system was satisfactorily evaluated in vivo. Recombinant GFP, PspA, and LcrV proteins were synthesized by E. ictaluri ΔasdA01 harboring Asd+ plasmids. Here we constructed a balanced-lethal system, which is the first step to develop an antibiotic-sensitive RAEV for the aquaculture industry

Efficacy of Major Plant Extracts/Molecules on Field Insect Pests

Insect pests are considered the major hurdle in enhancing the production and productivity of any farming system. The use of conventional synthetic pesticides has led to the emergence of pesticide-resistant insects, environmental pollution, and negative effects on natural enemies, which have caused an ecological imbalance of the predator-prey ratio and human health hazards; therefore, eco-friendly alternative strategies are required. The plant kingdom, a rich repertoire of secondary metabolites, can be tapped as an alternative for insect pest management strategies. A number of plants have been documented to have insecticidal properties against various orders of insects in vitro by acting as antifeedants, repellents, sterilant and oviposition deterrents, etc. However, only a few plant compounds are applicable at the field level or presently commercialised. Here, we have provided an overview of the broad-spectrum insecticidal activity of plant compounds from neem, Annona, Pongamia, and Jatropha. Additionally, the impact of medicinal plants, herbs, spices, and essential oils has been reviewed briefl

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping

Grasping force estimation detecting slip by tactile sensor adopting machine learning techniques

Abstract— Adequate grasping force estimation and slip detection is a vital problem in wider applications of robots and manipulators in industries as well as in our everyday life. In this paper, a new methodology for slip detection during grasping by robot grippers/end-effectors using tactile sensor has been presented. During the object slippage, the tactile sensor in touch with the object surface travels along the peaks and valleys of surface texture of the object which creates vibratory motions in the tactile. A newly developed mathematical model is used to compute the scattered energy of vibrations, which contains parameters of surface texture geometry as well as trial grasping force, and other relevant parameters. Using the scattered energy of vibrations predicted by soft computing techniques, an attempt to instantly estimate the adequate grasping force has been reasonably successful. Surface texture data, for experimental estimation of grasping force, were collected from a huge number of machined specimens and were used to build four different machine learning estimation techniques. Experimental results using Linear Regression (LR), Simple Linear Regression (SLR), Pace Regression (PR) and Support Vector Machine (SVM) demonstrate a relatively better technique for industrial applications

Opto-tactile sensor for surface texture pattern identification using support vector machine

Experimental application of a recently developed opto-tactile sensor in object surface texture pattern recognition using soft computational techniques has been successfully demonstrated in this article. Design and working principles of a number of optical type sensors have been illustrated and explained. Using the opto-tactile sensor multiple surface texture patterns of a number of objects like a carpet, stone, rough sheet metal, paper carton and a table surface have been captured and saved in MATLAB environment. The captured data have been adopted to soft computational techniques like Support Vector Machine (SVM) technique, Decision Tree (DT) C4.5 algorithm, and Naive Bayes (NB) algorithm for their learning. Testing with unknown surfaces using these techniques shows promising results at this stage and demonstrates its potential industrial use with further development. Results suggest that the methodology and procedures presented here are well suited for applications in intelligent robotic grasping

Grasping force estimation detecting slip by tactile sensor adopting machine learning techniques

Abstract— Adequate grasping force estimation and slip detection is a vital problem in wider applications of robots and manipulators in industries as well as in our everyday life. In this paper, a new methodology for slip detection during grasping by robot grippers/end-effectors using tactile sensor has been presented. During the object slippage, the tactile sensor in touch with the object surface travels along the peaks and valleys of surface texture of the object which creates vibratory motions in the tactile. A newly developed mathematical model is used to compute the scattered energy of vibrations, which contains parameters of surface texture geometry as well as trial grasping force, and other relevant parameters. Using the scattered energy of vibrations predicted by soft computing techniques, an attempt to instantly estimate the adequate grasping force has been reasonably successful. Surface texture data, for experimental estimation of grasping force, were collected from a huge number of machined specimens and were used to build four different machine learning estimation techniques. Experimental results using Linear Regression (LR), Simple Linear Regression (SLR), Pace Regression (PR) and Support Vector Machine (SVM) demonstrate a relatively better technique for industrial applications

Input space reduction for Rule Based Classification

... There are number of algorithms for rule based classification. C4.5 and Partial Decision Tree (PART) are very popular algorithms among them and both have many empirical features such as continuous number categorization, missing value handling, etc. However in many cases these algorithms takes more processing time and provides less accuracy rate for correctly classified instances. One of the main reasons is high dimensionality of the databases. A large dataset might contain hundreds of attributes with huge instances. We need to choose most related attributes among them to obtain higher accuracy. It is also a difficult task to choose a proper algorithm to perform efficient and perfect classification. With our proposed method, we select the most relevant attributes from a dataset by reducing input space and simultaneously improve the performance of these two rule based algorithms. The improved performance is measured based on better accuracy and less computational complexity. We measure Entropy of Information Theory to identify the central attribute for a dataset. Then apply correlation coefficient measure namely, Pearson’s, Spearman and Kendall correlation utilizing the central attribute of the same dataset. We have conducted a comparative study using these three most popular correlation coefficient measures to choose the best method. We have picked datasets from well known data repository UCI (University of California Irvine) database. We have used box plot to compare experimental results. Our proposed method has showed better performance in most of the individual experiment