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Image processing is an essential first step towards fully utilizing robotics, deep learning, and machine learning techniques. Using techniques like image enhancement, restoration, and segmentation are able to extract pertinent information from images and use it for task execution and decision-making. However, the hardware implementation of these algorithms demands more delay, area, and power. This work proposes a new mantissa bit-size reduced half-precision floating-point format for processing and characterizing image pixels for machine learning algorithms. In the realm of imaging, lowering the mantissa bit-size in floating-point can conserve area and power when utilized for internal calculations. Together with the area-power reduction, there is also a progressive reduction in image quality. For any image application, it becomes necessary to monitor the area and power trade-offs related to the amount of bits used to process the raw data. This work assists in selecting the bit-size for internal computations based on the accuracy requirements of the application by reporting the area, and power for various bit size reductions. The updated pixel values after applying the mantissa bit-size reduction are displayed in this study, along with a theoretical explanation of its inaccuracy. Since multipliers and adders are needed for the majority of mathematical equations in machine learning image algorithms, they are developed later in this work to process the image. The processed image is based on various adjusted pixel values, and the experimental findings demonstrate 75.2% to 21.3% area optimization, and 66.43% to 20.4% power optimization. However, determining the PSNR and MSE values of the processed image allows for quality validation.

Chicken anaemia virus (CAV) is the aetiological agent of chicken infectious anaemia (CIA), an immunosuppressive disease, which brings a huge economic burden to the poultry industry globally. Concurrent infections and vaccination failures further aggravate the impact of the disease on the sector. The epidemiology of the disease and virulence of the circulating strains is to be known while formulating prevention strategies for any infectious disease. So far, there are no reports regarding the presence of CAV among poultry flocks in Kerala. In this scenario, the present study contemplated the investigation of the chicken anaemia virus using ELISA and PCR techniques. Indirect ELISA revealed the presence of CAV antibodies in 80 samples out of the 92 serum samples screened. Among the 100 samples collected from suspected cases, 29 were found to be positive for CAV infection with detection primers targeting the VP2 gene. This implies widespread distribution of the virus in Kerala.

Alzheimer's disease (AD) is a neurological disorder characterized by cognitive decline and memory loss. An early and precise diagnosis of Alzheimer's disease is critical for effective therapy and management. The electroencephalogram (EEG) has shown promise as a non-invasive and cost-effective tool for Alzheimer's disease (AD) categorization. The capacity to diagnose Alzheimer's disease at an early stage is one of the benefits of EEG (electroencephalogram) over other methodologies in Alzheimer's disease research. Traditional EEG analysis approaches, such as estimating coherence between various pairs of electrodes, necessitate a significant amount of human labor. We introduce a novel strategy for AD classification based on EEG by combining deep generative adversarial networks (GANs) and the Marine Predators Algorithm (MPA). GANs are powerful deep-learning models that can be trained to generate realistic samples via negative generator and discriminator training. MPA is a nature-inspired optimization method well-known for its ability to solve complicated optimization problems. The proposed system generates synthetic EEG samples using GANs' ability to learn meaningful representations from raw EEG data. The MPA is then employed to enhance classification performance by optimizing the discriminative features extracted by GANs. The MPA simulates the hunting behavior of marine predators, facilitating the exploration of the feature space and identifying significant characteristics for AD categorization. We evaluated the efficacy of the proposed strategy using a publicly accessible EEG database of AD patients and healthy controls. The results of classification precision, sensitivity, and specificity demonstrate that deep GANs with MPA are superior to state-of-the-art techniques.

Intro: The putative TolC-derived antigenic epitope may trigger an effective immune response and prove to be a novel vaccine that protects against all rickettsial infections. Methods: A total of 421 healthy volunteers were recruited and tested for the Typhus group-, Spotted Fever group- and Scrub Typhus- specific IgG ELISA. Epitopes that are highly immunogenic and conserved across groups of rickettsial species were identified through in silico approaches. An in-house ELISA was developed using identified B cell peptide as antigen and the serum was tested for peptide-specific pre-existing antibodies. The MHC I-presented peptides are screened for their T-cell stimulation using human PBMCs isolated from the buffy coat using Ficoll Paque. After seeding, the cells were stimulated in RPMI media with TolC-peptides and phytohemagglutinin as control. Following incubation at 37°C with 5% CO2, the supernatant was measured for an antigen-specific relative increase in IFN-γ using real-time RT-PCR and ELISA. Findings: Among the peptide epitopes screened, B-cell epitope IYPEGGAQYSRIRSAKNQTRNSA/VVQ and T-cell epitope KLYEAKITR were conserved across three rickettsial groups. Of the 415 samples tested, 266 were positive for any of the three rickettsial IgG. In-house peptide-specific ELISA indicated the presence of pre-existing antibodies in 167 samples against identified TolC B-cell peptide epitopes. In TolC T-cell peptide epitope-treated PBMC cultures, a significant relative increase in IFN-γ was observed by real-time RT-PCR and ELISA compared to mock-treated cultures. The ELISA method yielded a high number of readouts, however, the two methods showed a significant correlation. Discussion: Rickettsial pathogens need a combination of B- cell and T-cell immunity for considerable protection against them. The peptide epitopes induced antibody response and IFN-γ by human PBMCs. Conclusion: The B- and T-cell epitopes identified from the TolC protein were highly conserved and immunogenic. This could serve as a potential vaccine for protection against both the spotted fever group and typhus group of rickettsial pathogens.

Intro: Streptococcus mutans are considered to be the primary etiologic agents of dental caries and it is one of the major public health problems worldwide. The production of virulence traits that are regulated through a Quorum Sensing system confers resistance to antibiotics. This includes exopolysaccharide (EPS) production, acidurity and acidogenicity and biofilm formation. Methods: The antibiofilm activity of 4-hydroxy-3-methoxybenzaldehyde (4-H-3- MB) was evaluated against the S. mutans UA159 (control strain) and clinical isolate of S. mutans isolated from patients with dental caries. The 4-H-3-MB (Sigma-Aldrich, USA) was procured from commercial sources. Crystal violet staining assay was performed to assess antibiofilm activity. Bacterial growth curve analysis, quantification of EPS and inhibition of acid production were performed. The ADME properties of the compound were predicted using Molinspiration tool. Molecular docking was performed using glucan binding protein, glucansucrase and antigen I/II active site of S. mutans as proteins and 4- H-3-MB as ligand using Autodock Program. Findings: The compound 4-H-3-MB showed antibiofilm activity on S. mutans UA159 at low concentrations of 0.2, 0.1, 0.05 mg/ml (79.81%, 67.76% and 56.31% respectively) compared to the clinical strain (77.00%, 59.48% and 48.22% respectively). The 4-H-3-MB inhibited the EPS production and acid production on S. mutans UA159 and clinical isolates of S. mutans in a dose- dependent manner. The microscopic imaging revealed a dramatic reduction in the treated biofilm group as compared to the control. The 4-H-3-MB did not show any growth inhibition even at concentrations of 0.2–0.05 mg/ml. However, the 4- H-3-MB significantly reduced the biofilm thickness of both the control and the clinical strain. Autodock showed the lowest binding energy (-6.12 kcal/mol) with glucansucrase with six hydrogen bonds followed by GPBC (-5.32 kcal/mol) and antigen I/II (-4.87 kcal/mol). Conclusion: The present study indicates the potent anti-biofilm activity of 4-H- 3-MB compound for potential use against S. mutans.

This article presents the investigation of dielectric and impedance spectroscopic properties of an organic product of 3-nitrophenol -2,4,6-tri amino-1,3,5- triazine (3NPTAT) single crystal, synthesized from melamine and m-nitrophenol. Comprehensive dielectric studies and charge transportation properties of the grown 3NPTAT crystal are given. The dielectric characteristics of the specimen were carried out in the frequency range of 50 Hz and 5 MHz at different temperatures, namely, 313 K, 333 K, 353 K, and 373 K. From the spectra, it was observed that the slowdown occurs at low temperatures, and the hopping mechanism takes place based on localized charge carriers. The impedance spectroscopic results indicate that there is a single relaxation process that occurs at high frequencies. The variation detected in the material properties of 3NPTAT corresponding to the temperature and frequency has been discussed in detail.

With the motivation of materializing a high-performance electrode material for the high-energy supercapacitor, ultrathin mesoporous graphitic‑carbon was synthesized from biomass green-tea wastes via the KOH activation process combined with either of the water or the hydrochloric acid treatment. The water-treated graphitic‑carbon showed an interconnected ultrathin-nanoflake structure with a high porosity, while the hydrochloric acid-treated graphitic carbon exhibited an aggregated structure of irregular nanoparticles. The supercapacitor with an electrode of water-treated graphitic‑carbon nanoflakes displayed an enhanced specific capacitance of 162 F/g at 0.5 A/g. Furthermore, the device revealed an excellent cycle stability after multiple cyclic charge-discharge operations (i.e., 121% cyclic capacitance retention over 5000 cycles). These may open up a new avenue toward the recycling of biomass carbonaceous resources (e.g., green tea wastes) for inexpensive high-performance electrochemical energy-storage devices such as high-energy supercapacitors. Keywords: Biomass resource, Green tea waste, Graphitic carbon, Nanoflakes, Electrode, Supercapacitor

A convenient synthesis of novel isoxazole-substituted 9-anilinoacridine derivatives 5a–j was reported. The compounds were confirmed by physical and analytical data and screened for in vitro antioxidant activity by DPPH method, reducing power assay and total antioxidant capacity method. The cytotoxic activity of the compounds was also studied in HEp-2 cell line. The docking studies of the synthesized compounds were performed towards the key nucleoside dsDNA by using AutoDock vina 4.0 programme. All the isoxazole-substituted compounds have significant activities.

The significant goal of this article is to highlight the utilization of digital imaging techniques for pathogenic disease detection in banana crops (Musa). The disease occurs in a variety of geographical climatic ranges, posing a threat to banana cultivators worldwide. The inevitable pathogenic invasions in banana plants remarkably influence crop yield loss and consequently agriculture. Subsequently, to enhance cultivation quality and quantity aspects, we involve computer vision models that have been established for automated disease prediction. Moreover, we interpreted the growth evaluation and understanding of different varieties of biotic disease properties that cause damage to the crops. Hence this review aims to address the major challenges in disease detection for identifying disease patterns in stressed plants. It involves classifying infection regions based on different factors such as the type of infectious neighboring plants, and the geometric illusions on the leaf surface area. Additionally, the review addressed the recent strategies of cloud-based computer vision in agriculture which will aid researchers in understanding how digital imaging technologies can be used for pathogen detection in banana plants. Further, this survey ensures a comprehensive review of pathogen diagnosing techniques through artificial intelligence (AI) approaches like convolutional neural networks (CNN), and deep learning (DL) to visually analyze disease patterns based on digital image processing. [ABSTRACT FROM AUTHOR]