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Recent studies have shown that the present limited antifungal arsenal and the high toxicity of the chemicals toward the high rates of morbidity and death driven on by fungal infections. Furthermore, because human and fungal cells have many similarities, it might be difficult to find new therapeutic targets. Mycoses are fungal illnesses that can affect the skin, nails, body hair, internal organs including the lungs, and bodily systems like the neurological system. In the current investigation, a molecule called 4-aminobenzoic acid 4-nitroaniline that was identified through certain FT-IR and UV spectral studies. Suitable basis sets were used to study the molecular geometry. Based on the PED results, vibrational assignments have been created for all vibrational modes. The NBO analysis was used to investigate the molecular stability and bond strength, which are relevant for bioactivities. Through NCI and IRI investigations, the chemical interactions inside the molecule were examined. Molecular docking research was carried out using the anti-fungal proteins after NABA was screened for its anti-fungal activity to validate the substance's powerful anti-fungal action against pathogenic fungi. The pharmokinetic properties were expected to be determined by drug likeness and the ADMET parameters. Accordingly, the NABA molecule is recommended for the anti-fungal medications. [ABSTRACT FROM AUTHOR]

This study addresses challenges in road extraction from remote sensing imagery by introducing a novel approach leveraging Dilated Convolution-based layers and Vision Transformer (ViT). ViT extracts features from image patches, capturing relationships via self-attention and refining with a feedforward neural network. The model incorporates dilated convolution for an enlarged receptive field without increased parameters, enhancing road extraction. Fusion of Dilated Convolution-based layers with ViT forms feature representation, enabling the encoder-decoder architecture to predict roads pixel-wise. Experimental results on the DGR dataset showcase the approach’s high performance (F1 score: 98%, recall: 96.5%, specificity: 96.7%, accuracy: 98.8%, precision: 97.75%), demonstrating resilience across dataset sizes and geographical regions. This approach holds promise for real-world applications and future advancements in remote sensing research. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of social media addiction that pose a major threat to the human population especially children and teenagers. It is well known that Cognitive Behavioral Therapy (CBT) is an effective treatment to treat the addict individuals and delay in the treatment leads the patient to worst stage even to death. Therefore, it is important to identify the individuals who has addiction symptoms at early stage and to provide proper counseling. We propose and analyze a nonlinear mathematical model for social media addiction problem using case detection strategy to reduce the addiction. The basic reproduction number and equilibria of the model are computed. Further, the deterministic model is extended to delay differential equation model by incorporating transmission delay and treatment delay in the system. The local stability of different equilibria is discussed in detail. Additionally, the model is converted to stochastic model and numerical simulation is carried out to compare the results of both deterministic and stochastic model. Numerical result shows that the introduction of time delays can destabilize the model system and Hopf bifurcation occurs due to periodic oscillations when certain equilibrium point crosses the delay threshold limit. Our results of stochastic model show a smaller number of social media users and addict population when compared with deterministic model. Also, our results reveal that detection and counseling parameters play a vital role in reducing addiction population. Presented results clearly suggest that there is a need to use effective detection strategy and suitable counseling program to reduce the social media addiction level. [ABSTRACT FROM AUTHOR]

The defect classification of knitted fabrics is a challenging area of research. Most of the defect detection works in India; Bangladesh is being carried by manually trained inspectors. The long working hours and the working environment at the company induces the fatigue, lack of concentration, and triteness to the workers due to this they may not able to detect the defects on the clothes after it is manufactured. To overcome this problem, a computer-aided defect detection system is being developed using digital image processing and artificial neural Network methods. The two types of artificial neural networks were applied to compare the results obtained. The networks were: a back propagation based feed forward neural network and the other was Levenberg–Marquardt (LM) algorithm based back propagation network. Experimental results predicted detection of a high degree of variety of fabric defects. [ABSTRACT FROM AUTHOR]

TargetingM. tuberculosis(Mtb) in latent form of infection is a major obstacle and big challenge in tuberculosis (TB) eradication by current chemotherapy. A better understanding of the physiology of Mtb and of the metabolic state of the bacillus during the dormancy, is a primary need in finding drug targets against non replicating persistent (NRP) form of the Mtb. Identification of potential drug targets against dormant state of Mtb is critically important to achieve the goal of complete eradication for shortening of anti-TB therapy. The metabolic processes functioning in dormant Mtb in providing cell viability and protecting Mtb from sterilization by the hostile environment can be explored as drug targets. This review discusses about the potential drug targets in dormant tubercle bacilli, anti-dormancy inhibitors and the strategies which can be pursued for maximizing success if these targets are exploited for killing the dormant bacilli. This has important implications as the currently available arsenal of drugs suffers from the demerit of achieving sterilizing killing of mycobacteria. This lacuna can be overcome if the proposed strategies for eliminating dormant bacteria are combined along with the existing treatment modalities for the extirpation of bacilli. [ABSTRACT FROM PUBLISHER]

Context:Traditionally used plants for treating chest-related problems/tuberculosis (TB) have not been evaluated in detail and hence a thorough study is needed in this regard. This knowledge may find application in developing new anti-TB drugs. Objective:This article elaborates on studying the activity of medicinal plants against different forms ofMycobacterium tuberculosis(Mtb) using different model strains,in vitroandex vivoassays for studying the tuberculocidal activity and discusses the results from different studies on the activity against different forms ofMtband human immunodeficiency virus-tuberculosis (HIV-TB) co-infection. Methods:Scientific databases such as PubMed, Elsevier, Scopus, Google scholar, were used to retrieve the information from 86 research articles (published from 1994 to 2016) related to the topic of this review. Results:Twenty-three plant species have been reported to possess active molecules against multi-drug resistant (MDR) isolates ofMtb. Seven plants were found to be active against intracellularMtband six against dormant bacilli. Seven plants were synergistically effective when combined with anti-TB drugs. Six studies suggest that the beneficial effects of plant extracts are due to their wide array of immuno-modulatory effects manifested by the higher expression of cytokines. Some studies have also shown the dual activity (anti-HIV and anti-TB) of plants. Conclusion:We emphasize on identifying plants based on traditional uses and testing their extracts/phytomolecules against MDR strains, intracellularMtbas well as against dormantMtb. This will help in future to shorten the current therapeutic regimens for TB and also for treating HIV-TB co-infection. [ABSTRACT FROM PUBLISHER]