An NMR fingerprint matching approach for the identification and structural re-evaluation of Pseudomonas lipopeptides.

Cyclic lipopeptides (CLiPs) are secondary metabolites secreted by a range of bacterial phyla. CLiPs display diverse structural variations in terms of the number of the amino acid residues, macrocycle size, amino acid identity and stereochemistry (e.g. D- vs. L-amino acids). Reports detailing the discovery of novel or already characterized CLiPs from new sources appear regularly in literature. However, in some cases, the lack of characterization detail threatens to cause considerable confusion, especially if configurational heterogeneity is present for one or more amino acids. The NMR fingerprint matching approach introduced in this work exploits the fact that the 1H and 13C NMR chemical shift fingerprint is sufficiently sensitive to differentiate the diastereomers of a particular CLiP even when they only differ in a single D/L configuration. This provides a means for a fast screening to determine whether an extracted CLiP has been reported before, by simply comparing the fingerprint of a novel CLiP with that of a reference CLiP. Even when the stereochemistry of a particular reference CLiP is unknown, the NMR fingerprint approach still allows to verify whether a CLiP from a novel source is identical to the reference. To facilitate this, we have made a publicly available knowledge base at https://www.rhizoclip.be, where we present an overview of published NMR fingerprint data of characterized CLiPs, together with literature data on the originally determined structures. The latter includes a description of the CLiPs original description, molecular mass, three dimensional structures (if available), and a summary of published antimicrobial activities. Moreover, a detailed protocol will be made available for researchers that wish to record NMR data of their newly extracted lipopeptides to compare them to the publicly available reference data.

Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.

Donor-Acceptor Pair Quantum Emitters in Hexagonal Boron Nitride

Quantum emitters are needed for a myriad of applications ranging from quantum sensing to quantum computing. Hexagonal boron nitride (hBN) quantum emitters are the most promising solid-state platform to date due to its high brightness, stability, and the possibility of spin photon interface. However, the understanding of the physical origins of the single-photon emitters (SPEs) is still limited. Here, we present concrete and conclusive evidence that the dense SPEs in hBN, across entire visible spectrum, can be well explained by donor-acceptor pairs (DAPs). Based on the DAP transition generation mechanism, we have calculated their wavelength fingerprint, matching well with the experimentally observed photoluminescence spectrum. Our work serves as a step forward for the physical understanding of SPEs in hBN and their applications in quantum technologies.

Analysis and comparison of normal and altered fingerprint using artificial neural networks

Fingerprint matching is based on the number of minute matches between two fingerprints. Implementation mainly includes image enhancement, segmentation, orientation histogram, etc., extraction (completeness) and corresponding minutiae. Finally, a matching score is generated that indicates whether two fingerprints coincide with the help of coding with MATLAB to find the matching score and simulation of Artificial Neural Network extending the feedback of the network. Using the artificial neural network tool, an important advantage is the similarity index between the sample data or unknown data. A neural network is a massively parallel distributed processor consisting of simple processing units that have a natural property to store knowledge and computer experiences are available for use. A fingerprint comparison essentially consists of two fingerprints to generate a fingerprint match score the match score is used to determine whether the two impressions they are of the same finger. The decision is made this study shows the comparison of normal and altered fingerprints using MATLAB coding and data used to study in the self-generated data using biometric scanner also the open source data available on the web is used for finding out matching score or similarity index, The study shows that there is hardly any matching between normal and altered fingerprints of the same person.