Whole-Cell MALDI-ToF MS Coupled with Untargeted Metabolomics Facilitates Investigations of Microbial Chemical Interactions

The emergence of drug-resistant pathogens necessitates the development of new countermeasures. In this regard, the introduction of probiotics to directly attack or competitively exclude pathogens presents a useful strategy. Application of this approach requires an understanding of how a probiotic and its target pathogen interact. A key means of probiotic-pathogen interaction involves the production of small molecules called natural products (NPs). Here, we report the use of whole-cell matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry to characterize NP production by candidate probiotics (mouse airway microbiome isolates) when co-cultured with the respiratory pathogen Burkholderia. We found that a Bacillus velezensis strain inhibits growth of and elicits NP production by Burkholderia thailandensis. Dereplication of known NPs detected in the metabolome of this B. velezensis strain suggests that a previously unannotated bioactive compound is involved. Thus, we present the use of whole-cell MALDI as a broadly applicable method for screening the NP composition of microbial co-cultures; this can be combined with other -omics methods to characterize probiotic-pathogen and other microbe-microbe interactions.     

Graphitic Carbon Nitride Decorated with Iron Oxide Nanoparticles as a Novel High-Performance Biomimetic Electrochemical Sensing Platform for Paracetamol Detection

Design and development of new generation smart sensors for medical applications have gained considerable interest of research community in the recent past. In this work, we propose the fabrication of highly sensitive paracetamol sensors-based iron oxide nanoparticles intercalated with graphitic carbon nitride (g-C₃N₄) (GCN) via insitu chemical synthesis. Structural features of the composites were analyzed through SEM, EDX, XRD, FTIR, and UV-Visible spectroscopic techniques. Presence of iron oxide nanoparticles in GCN, significantly improved the conductivity bare GCN from 16 to 125 S cm^?1 due to extended π–π conjugation and large surface area in the composite system. The GCN-Iron oxide (GCN-FO) nanocomposite has been employed as an electrochemical sensing platform for non-enzymatic detection of paracetamol. The electrochemical studies and cyclic voltammetry (CV) results shows that the GCN-FO composite exhibit superior electrochemical properties due to their lower values of the oxidation and reduction potentials. Electrochemical impedance spectroscopy (EIS) studies indicate decreased charge-transfer resistance for iron oxide doped GCN composite in compare to base GCN. The improved electrochemical sensing performance of modified GCN-FO composite electrode is attributed to the formation heterojunctions between iron oxide nanoparticles and GCN. The modified GCN-FO electrodes were employed for non-enzymatic electrochemical detection of PR. The GCN-FO composite electrode shows excellent sensitivity towards PR with a LOD 0.3 μM. Furthermore, the modified GCN-FO electrodes show excellent reproducibility, selectivity, stability and anti-interference performance. Due to its low-cost fabrication, superior electrochemical sensing performance, these modified GCN-FO electrodes could be a promising material for the detection of paracetamol at low concentrations.

     

Ossa cordis and os aorta in the one-humped camel: Computed tomography,light microscopy and morphometric analysis

The present study describes the morphological characteristics of the camel heart Ossa cordis, and os aorta using computed tomography soft tissue window (CT) alongside 3D render volume reconstructions and light microscopy. The current study techniques demonstrated the Ossa cordis and os aorta in the cardiac window with more precision than the black and white (ghost), and angiography images. Transverse and sagittal CT images additionally demonstrated the presence of Ossa cordis and os aorta. This study is the first to record two small Ossa cordis sinistrum and one os aorta in the camel heart, in addition to the more commonly observed singular, large, os cordis dextrum. The os cordis dextrum was always located in the upper part of the interventricular septum, near to its junction with the atrium, forming an elongated rectangular shape when observed transversally. The wider cranial part was composed from bone, whereas the caudal aspect was narrow and contained both bone and cartilage. Light microscopy identified that the os cordis dextrum consisted of trabecular bone, marrow spaces, and hyaline cartilage. Two Ossa cordis sinistrum were detected on the left side of the heart, one in the right fibrous ring and another in the interventricular septum, microscopy showed that both contained only trabecular bone with osteocytes, osteoblasts, and osteoclasts. At the level of ascending aorta, there was also trabecular bone containing osteocytes, an os aorta.     

Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.     

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated considerable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications. The difficulty stems from features other than mobile ad-hoc network (MANET), namely aerial mobility in three-dimensional space and often changing topology. In the UAV network, a single node serves as a forwarding, transmitting, and receiving node at the same time. Typically, the communication path is multi-hop, and routing significantly affects the network’s performance. A lot of effort should be invested in performance analysis for selecting the optimum routing system. With this motivation, this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication (COAER-UAVC) technique. The presented COAER-UAVC technique establishes effective routes for communication between the UAVs. It is primarily based on the coati characteristics in nature: if attacking and hunting iguanas and escaping from predators. Besides, the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay. A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system. The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.     

Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model

Autism Spectrum Disorder (ASD) refers to a neuro-disorder where an individual has long-lasting effects on communication and interaction with others. Advanced information technology which employs artificial intelligence (AI) model has assisted in early identify ASD by using pattern detection. Recent advances of AI models assist in the automated identification and classification of ASD, which helps to reduce the severity of the disease. This study introduces an automated ASD classification using owl search algorithm with machine learning (ASDC-OSAML) model. The proposed ASDC-OSAML model majorly focuses on the identification and classification of ASD. To attain this, the presented ASDC-OSAML model follows min-max normalization approach as a pre-processing stage. Next, the owl search algorithm (OSA)-based feature selection (OSA-FS) model is used to derive feature subsets. Then, beetle swarm antenna search (BSAS) algorithm with Iterative Dichotomiser 3 (ID3) classification method was implied for ASD detection and classification. The design of BSAS algorithm helps to determine the parameter values of the ID3 classifier. The performance analysis of the ASDC-OSAML model is performed using benchmark dataset. An extensive comparison study highlighted the supremacy of the ASDC-OSAML model over recent state of art approaches.