Fingerprinting Technique Research Articles

Tooth as a vital source of DNA in forensic odontology: Recent perspective

DNA has been the hereditary material of almost all organisms, including humans. In recent years, DNA fingerprinting technique has been the most renowned method used in forensics for human identification. Extracting DNA from routinely used samples becomes very challenging in degraded and fragmented human remains, i.e., in cases of incineration, mutilation, and fire. Due to the unique placement of teeth in the jaw and their composition, DNA material present gets additional protection compared to bones or other sources of DNA. Hence, it frequently becomes the only source of DNA that would be available in such situations for identification. DNA fingerprinting helps unravel the mysteries within the mouth, including the manifestations of diseases in the oral cavity. There have been highly improved methods described for analyzing various forensic scenarios and DNA extractions. Identifying the deceased victims, the missing person, the masked culprits involved in crime, the paternity issues, interpretation of genetic diseases, and determining the ancestry account for some uses of DNA fingerprinting. This review briefly summarizes recent literature and current knowledge of forensic odontology, DNA content, and distribution in teeth and its preservation. This also intends to emphasize the role of the tooth being a valuable source of DNA and thereby discusses some of the methods of recovery of DNA from teeth, ways of DNA analysis, and highlights the benefits and challenges regarding the DNA extraction protocol in the field of forensic odontology.

1426. Integrating artificial intelligence and dynamic programming to identify microbial clusters through antimicrobial susceptibility sequence analysis

Abstract Background The spread of infectious diseases and antimicrobial resistance poses a significant threat, especially in developing countries where traditional DNA fingerprinting techniques are often not available. We propose a novel approach using dynamic programming algorithms to compare antimicrobial susceptibility sequences and identify clusters of related microbes. Methods We selected pathogens from specific infections and tested them with a range of antimicrobials, recording the test results as either sensitive (S), resistant (R), or unknown (U) to each drug. These results were combined into a sequence, or ATB string, for each pathogen (Fig. 1). To compare the sequences, we used dynamic programming (Fig. 2) to calculate the minimum edit distance between each pair of sequences. The edit distance reflects the number of atomic changes needed to transform one sequence into the other. In this case, atomic changes include insertion, deletion, substitution, and matching of individual test results (Fig. 3). We created a matrix of pairwise similarities by comparing all ATB strings. A cluster of similar strains was identified based on a cutoff in the edit distance matrix, such as a distance of less than 1. The method, which is available in the SACIH+ system (https://plus.sacihweb.com), was used to analyze the same strain of specific healthcare infections from a public hospital in Belo Horizonte, Brazil, diagnosed between Jan 2022 and Mar 2023.Figure 1.Antimicrobial testing and ATB string generation for selected pathogens. Antimicrobial testing and ATB string generation for selected pathogens.Figure 2.Sequence comparison using dynamic programming. Sequence comparison using dynamic programming. Results We analyzed S. aureus strains from surgical site infection - SSI (23 cases), pneumonia - PNEU (29 cases), and bloodstream infection - BSI (13 cases). Identified 4 SSI, 10 PNEU clusters (Fig. 4 and 5), and 4 BSI clusters using edit distance matrix. For K. pneumoniae strains from 21 BSI, 22 PNEU, and 34 urinary tract infection - UTI cases, found 5 clusters for PNEU and UTI, and 7 clusters for BSI (Fig. 6).Figure 3.Minimum edit distance calculation for sequence comparison. Minimum edit distance calculation for sequence comparison.Figure 4.Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Figure 5.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Conclusion Our novel approach using dynamic programming to compare antimicrobial susceptibility sequences has shown promise in identifying clusters of related microorganisms, even without traditional DNA fingerprinting methods. The method represents a valuable tool for tracking the spread of infectious diseases, especially in settings where traditional methods are not available.Figure 6.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases. Disclosures All Authors: No reported disclosures

FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery

Antibiotic resistance continues to be a growing concern for global health, accentuating the need for novel antibiotic discoveries. Traditional methodologies in this field have relied heavily on extensive experimental screening, which is often time-consuming and costly. Contrastly, computer-assisted drug screening offers rapid, cost-effective solutions. In this work, we propose FIAMol-AB, a deep learning model that combines graph neural networks, text convolutional networks and molecular fingerprint techniques. This method also combines an attention mechanism to fuse multiple forms of information within the model.The experiments show that FIAMol-AB may offer potential advantages in antibiotic discovery tasks over some existing methods. We conducted some analysis based on our model's results, which help highlight the potential significance of certain features in the model's predictive performance. Compared to different models, ours demonstrate promising results, indicating potential robustness and versatility. This suggests that by integrating multi-view information and attention mechanisms, FIAMol-AB might better learn complex molecular structures, potentially improving the precision and efficiency of antibiotic discovery. We hope our FIAMol-AB can be used as a useful method in the ongoing fight against antibiotic resistance.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity.

In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm's resilience under varying temperatures.

Dynamic of Phenolic Compounds in Guadua angustifolia Kunth under Chemical, Organic, and Biological Fertilization

Guadua angustifolia produces phenolic compounds, and this production may be influenced by the application of chemical, organic, and biological fertilizers. Currently, the effect of such fertilizers on the synthesis dynamics of this group of metabolites in bamboo is unknown. In this study, the total phenolic content (TPC) and total flavonoid content (TFC) in the leaves of plants fertilized with diammonium phosphate (DAP) and humus in combination with the biofertilizers Promofort®, Azospirillum brasilense, Pseudomonas fluorescens, and Stenotrophomonas sp. were determined using colorimetric techniques across three sampling events (four, five, and seven months after planting). Additionally, an approximation of the bacterial profile of G. angustifolia roots was performed using the DGGE-PCR fingerprint technique. Through repeated measures ANOVA (rmANOVA), it was determined that there is no statistically significant three-way interaction between humus or DAP application, biological fertilizers, and time for either TPC or TFC. However, there were interactions between the sampling event and the application of biological fertilizers for both TPC and TFC, with the latter being promoted by the application of Promofort®. Finally, NMDS analyses and heatmaps with hierarchical clustering showed that the composition and abundance of OTUs in the bacterial profile varied with fertilization type and increased over time.

An improved indoor positioning based on crowd-sensing data fusion and particle filter

Due to the lack of global positioning system (GPS) signals in some enclosed areas, indoor localisation has recently gained significant importance for academics. However, indoor localisation has a number of challenges and defects, including accuracy, cost, coverage, and ease of use. This paper explores the integration between the inertial measurement unit (IMU) and Wi-Fi-based received signal strength indicator (RSSI) measurements, demonstrating their combined potential for robust indoor localisation. IMUs excel at capturing precise short-term motion dynamics, offering insights into an object’s acceleration and orientation. Conversely, RSSI measurements serve as valuable indicators for relative positioning within indoor environments. By fusing data from these sources, our approach compensates for the inherent weaknesses of each sensor type. To achieve accurate indoor positioning, we employ techniques such as sensor fusion, Wi-Fi fingerprinting, and dead reckoning. Wi-Fi fingerprinting allows us to create a database that maps RSSI measurements to specific locations, while dead reckoning helps mitigate drift and inaccuracies. By combining these methods, we estimate a device’s position with increased precision. Through experimental evaluation, we assess the performance and efficiency of our integrated approach, comparing the estimated path or new location with a predefined reference path. The findings emphasise a significant improvement in accuracy, with the integration of crowd-sensing, particle filtering, and magnetic fingerprinting techniques resulting in a notable increase from 80.49% to 96.32% accuracy.

Liquid chromatographic fingerprinting technique for quality control of Angelica sinensis and its preparation

Abstract In this paper, the fingerprints of 10 batches of Angelica sinensis herbs and 5 batches of commercially available Angelica sinensis preparations originating from different parts of China were determined by high-performance liquid chromatography (HPLC) fingerprinting using a methanol-acetonitrile solution system with linear gradient elution, and the similarity analyses were applied for the classification. Derringer’s satisfaction function evaluated the acceptable limit values for processing multiple characteristic peaks of Angelica sinensis herbs. The results showed that under selected chromatographic conditions, HPLC fingerprints of Angelica sinensis were obtained, and four characteristic peaks were calibrated. The similarities of 10 batches of Angelica sinensis herbs ranged from 0.953 to 0.998, and those of 5 batches of Angelica sinensis preparations ranged from 0.969 to 0.999. The method established in this paper is stable, reliable and reproducible, and it can be used to provide a reference for quality control of the herbs of Angelica sinensis and the drinkable tablets of Angelica sinensis body and to provide a reference for quality control of the classic prescriptions containing Angelica sinensis or the It can provide a reference for the quality control of Angelica sinensis herb and Angelica sinensis body tablets, and provide a basis for the development and research of classical prescriptions containing Angelica sinensis or Angelica sinensis.

Provenance analyses of silted sediments in Shenzhen Bay: Insights based on rare earth elements and stable isotopes

Shenzhen Bay (SZB) in southern China is a typical eutrophic area, with internal pollution from its sediments representing an important nutrient source. However, the transport paths and sources of sediments in SZB remain unclear, making it difficult to analyze the nutritional budget and propose effective sediment management strategies. To address this, we linked a sediment fingerprinting technique to a Bayesian mixing model (MixSIAR) and conducted provenance analyses. We collected particle samples from SZB sediment and surrounding areas, including the Shenzhen River (SZR), Pearl River Estuary (PRE), and the northern South China Sea (SCS). Two groups of natural tracers were measured to trace different phases of sediments: (1) C and N parameters for the fates of the organic phase of sediments, and (2) rare earth element (REE) patterns for the provenance of mineral fragments. The results showed that the concentrations of total organic C and total N were 0.89–1.44 % and 0.05–0.13 %, respectively. MixSIAR suggested that fluvial inputs from SZR and PRE contributed 46.6 % and 30.3 % of organic matter, respectively. The organic matter in the PRE mainly originated from sewage and the upper reaches of the Pearl River. The concentration range of REEs in SZB sediments was 153.12–480.09 mg/kg with clear enrichment for light REE. MixSIAR results showed that the mineral fragments mainly originated from the outer bay (SCS and PRE, which contributed 57.2 % and 32.7 %, respectively). These results indicate that organic pollution follows a different path from the inorganic base, which is mainly related to anthropogenic input from land. This study highlights that complex sediment transport processes and pollution intrusions from the Pearl River are the issues that must be considered for eutrophication restoration in SZB.

Source identification of morainic materials in soils of the Three Lakes region (Switzerland) using the fingerprinting technique

In regions of mid- to high-latitudes, soil development patterns are intricately associated with the Quaternary glaciations, which have reworked sediments and deposited a layer of heterogeneous superficial moraines having both autochthonous and allochthonous components. While the allochtony of the morainic material in the Swiss Plateau is widely accepted, the specific proportion of various geologic origins of the soil parent material remains largely unexplored. Fingerprinting techniques have emerged as valuable tools for accurately estimating the origin of the parent material composition. They may unveil the distinct contributions of different rock types to the overall soil composition. In total, 166 sites were analysed for their geochemical composition and the relative contribution of several rock components to them, such as carbonate, sandstone and three groups of rock granite (incl. granite, granulite and gneiss), gabbro (incl. gabbro, Allalin-gabbro and diorite) and serpentinite (incl. serpentinite and eclogite). For each site, an optimal set of elements (between 11 and 17) was selected. Thereby, a three-step statistical selection process was employed that included the range tests, the Kruskal-Wallis tests and a discrimination function. We identified eight key elements – Al, Ca, P, Fe, Mn, Sb, La, and Sm – which consistently appeared across all sites during the selection process. The geochemical composition of the soils of the Three Lakes Region was mainly determined by a mixture between granitic material (53.6%) and sandstone (21.6%) with a smaller contribution of serpentinite, gabbroic and carbonate material. The findings highlight that the FingerPro model can effectively identify the sources of geochemical composition in soils. One major difficulty in such calculations was, however, that sandstones had a relatively variable composition owing to their different origins.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy.

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Pyricularia's Capability of Infecting Different Grasses in Two Regions of Mexico.

The genus Pyricularia includes species that are phytopathogenic fungi, which infect different species of Poaceae, such as rice and sorghum. However, few isolates have been genetically characterized in North America. The current study addresses this lack of information by characterizing an additional 57 strains of three grasses (Stenotaphrum secundatum, Cenchrus ciliaris and Digitaria ciliaris) from two distant regions of Mexico. A Pyricularia dataset with ITS sequences retrieved from GenBank and the studied sequences were used to build a haplotype network that allowed us to identify a few redundant haplotypes highly related to P. oryzae species. An analysis considering only the Mexican sequences allowed us to identify non-redundant haplotypes in the isolates of C. ciliaris and D. ciliaris, with a high identity with P. pennisetigena. The Pot2-TIR genomic fingerprinting technique resulted in high variability and allowed for the isolates to be grouped according to their host grass, whilst the ERIC-PCR technique was able to separate the isolates according to their host grass and their region of collection. Representative isolates from different host grasses were chosen to explore the pathogenic potential of these isolates. The selected isolates showed a differential pathogenic profile. Cross-infection with representative isolates from S. secundatum and C. ciliaris showed that these were unable to infect D. ciliaris grass and that the DY1 isolate from D. ciliaris was only able to infect its host grass. The results support the identification of pathogenic strains of Pyricularia isolates and their cross-infection potential in different grasses surrounding important crops in Mexico.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Repeatability of Quantitative Knee Cartilage T1, T2, and T1ρ Mapping With 3D-MRI Fingerprinting.

Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. To assess the intra-day and inter-day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. Prospective. Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. 3 T/3D-MRF, T1, T2, and T1ρ maps. The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. 1 TECHNICAL EFFICACY: Stage 1.

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation

Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q 2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design. Communicated by Ramaswamy H. Sarma

Revealing soil erosion and sediment sources using 137Cs and fingerprinting in an agroforestry catchment

Soil erosion and accompanying sediment issues have a significant impact on environmental sustainability. However, there is not currently a clear understanding of the relationship between soil erosion and sediment sources at the catchment scale, which has led some researchers to regard areas with severe soil erosion as the main sediment source. This view does not apply in all circumstances.•This paper uses 137Cs to study the average annual erosion rate of different land use types. The results show that the erosion rates of woodland, sparse woodland and farmland areas were 400, 1756, and 2771 t·km−2·a−1, respectively, multiplied by the corresponding area. Surface soil erosion levels in the catchments of the three types accounted for 11%, 26% and 25%, respectively, and the total accounted for 62% when the gully bank accounted for 38%.•Fingerprinting technique results show that the relative source contributions of woodland, farmland, sparse woodland and gully bank areas to sediment at the small catchment outlet were 4%, 10%, 48% and 38%, respectively. The results reveal that the detachment process of soil and the transport process of sediment with runoff were internally related and independent of each other.•By comparing on-site erosion rates with downstream source contributions, we have a clearer understanding of soil erosion and subsequent sediment transport process in the agroforestry catchment in mountainous areas.We emphasize the importance of governance and management in areas with severe soil erosion, mainly targeting types such as hillslope framland. In addition, sparse woodland and gully banks should be given more attention, as they account for a large proportion of sediment in the downstream catchment.

Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.

Soil erosion and sediment sourcing in the Hyrcanian forests, Northern Iran: an integration approach of the G2loss model and sediment fingerprinting technique

Soil erosion and sediment sourcing in the Hyrcanian forests, Northern Iran: an integration approach of the G2loss model and sediment fingerprinting technique

Molecular characterization of multidrug resistant (MDR) clinical isolates of Pseudomonas aeruginosa from Nsukka, South Eastern Nigeria.

P. aeruginosa is an important nosocomial pathogen with increasing resistance to antibiotics. Objective: This study was performed to evaluate the genetic relatedness of MDR clinical isolates of P. aeruginosa. A total of 1000 samples were analysed in the study. Antibiotic resistance profiles of the isolates were determined using Kirby Bauer disk diffusion method. Polymerase chain reaction (PCR) and sequencing were simultaneously used to detect the consensus region of 16S rRNA. Genetic relatedness of the isolates was determined using restriction patterns from ALU 1 digest and random amplified polymorphic DNA. Out of the 192 P. aeruginosa isolates recovered, 136 (78.83%) were multidrug resistant. Sequence analysis of the confirmed isolates (80.68%) revealed that all the isolates shared homology with each other and also showed sequence similarity to known strains of P. aeruginosa (ATCC 27853; KT 315654; KU 321274 and KT894767). The PCR-Restriction fragment length polymorphism (PCR-RFLP) analysis revealed that there was a lot of genetic relatedness among the isolates. The RFLP finger printing technique detected seven distinct RFLP types among the isolates. Thus, study shows that there is high prevalence of MDRPA and high degree of genetic relatedness among the MDRPA isolates circulating in Nsukka area.

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.