Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. ...Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.
Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic ...classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.
Nowadays, the number of brain tumor cases among people is increasing globally across the world due to several reasons such as obesity, overweight, excess levels of stress in life, exposure to ...ionizing radiation, and many more. In previous years, many investigators have provided a range of solutions and effective tools for the identification and categorization of brain tumors. Nevertheless, the existing developed models for brain tumor identification and categorization have diverse limitations such as minimal accuracy and precision values. In this paper, the authors developed a novel model for the comparative analysis of the Progressive Growing-Generative Adversarial Network (PGGAN) with other data augmentation techniques for brain tumor classification. Because of the availability of finite datasets, the brain tumor classification algorithm along with the convolutional neural networks (CNNs) must be enhanced to be more competent for brain tumor classification and identification in real-time diagnosis. The outcome of the proposed model demonstrates that PGGAN delivers higher accuracy, as well as precision, and the Recall with the F1 score is 99.22%, 98.11%, 98.66%, and 97.45%, respectively. In the future, the developed model performance could be measured with other data augmentation techniques for larger datasets for performance constraints computations for further study and implementation of the model for real-time diagnosis of the patients.
Inter-auricular distance (IAD) has been often used for physical calibration of defects in panicle emergence of rice plants induced by stress-prone environments or cytoplasmic male sterility genetic ...factors slackening male gametogenesis. Information is scant on the physiological relationship inherent between IAD, panicle enclosure, and spikelet filling despite the wide phenotypic diversity of rice cultural types. In the present study, IAD extension and grain filling pattern in the panicle of the main shoot of rice cultivars contrasting for spikelet sterility were assessed concerning physiological factors like assimilate homeostasis, ethylene evolution, and expression of ethylene signal transducers genes at the anthesis stage of development. The panicle retention time was longer and the concentration and expression of ethylene and its transducer protein genes respectively, were higher in high sterile cultivars compared to low sterile ones. Spikelets subjected to a high concentration of ethylene for a longer time interval filled poorly because of infringement of starch synthesis in the post-anthesis stage of development. Alternatively, faster release of panicle by enhanced IAD extension shortened the exposure time and marginalized inhibitory effects of ethylene on grain filling. Rapid IAD extension and attenuation of boot ethylene production could be beneficial for male gametogenesis and grain filling.
Prolonged inflammation and infection are the major factors that promote chronicity of the wound. Despite the substantial advancements in therapeutic modalities, the treatment of chronic wounds still ...represents a major clinical challenge. Conventional remedies are associated with several complications like drug resistance, systemic toxicity, and serious side effects that need to be addressed. This necessitates the development of safe and alternative pro-healing agents for efficient wound repair. In this scenario, the application of phytoconstituents has garnered much attention due to their safety profile and cost-effectiveness. They enhance the wound repair process by modulating various signaling pathways like TGF-β, NFκB, Nrf2, MAPK, etc, and promote cell proliferation, migration, differentiation, angiogenesis, and inhibit inflammation. The topical route of drug administration is the most preferable method of drug delivery for the effective management of chronic wounds. Currently, much emphasis has been given to the formulation of different phytoconstituents-based topical delivery systems like hydrogels, films, foam, sponges, fibers, nanoformulations, etc. These topical formulations improve the pharmacokinetic and physicochemical features of phytoconstituents. The main emphasis of the present review is to discuss the therapeutic potential, pharmacological importance, and current clinical status of various phytoconstituents-based topical formulations as well as their associated molecular mechanisms in chronic wound management.
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Toll-like receptors (TLRs) are a class of innate immune receptors that sense pathogens or their molecular signatures and activate signaling cascades to induce a quick and non-specific immune response ...in the host. Among various types of TLRs, TLR22 is exclusively present in teleosts and amphibians and is expected to play the distinctive role in innate immunity. This report describes molecular cloning, three-dimensional (3D) modeling, and expression analysis of TLR22 in rohu (Labeo rohita), the most commercially important freshwater fish species in the Indian subcontinent. The open reading frame (ORF) of rohu TLR22 (LrTLR22) comprised of 2,838 nucleotides (nt), encoding 946 amino acid (aa) residues with the molecular mass of ∼107.6 kDa. The secondary structure of deduced LrTLR22 exhibited the presence of signal peptide (1–22 aa), 18 leucine-rich repeat (LRR) regions (79–736 aa), and TIR domain (792–935 aa). The 3D model of LrTLR22-LRR regions together elucidated the horse-shoe-shaped structure having parallel β-strands at the concave surface and few α-helices at the convex surface. The TIR domain structure revealed alternate presence of five α-helices and β-sheets. Phylogenetically, LrTLR22 was closely related to common carp and exhibited significant similarity (92.2 %) and identity (86.1 %) in their amino acids. In rohu, TLR22 was constitutively expressed in all embryonic developmental stages, and tissue-specific analysis illustrated its expression in all examined tissues, highest was in liver and lowest in brain. In vivo modulation of TLR22 gene expression was analyzed by quantitative real-time PCR (qRT-PCR) assay following stimulation with lipopolysaccharide (LPS), synthetic double stranded RNA (polyinosinic-polycytidylic acid), and bacterial (Aeromonas hydrophila) RNA. Among these ligands, bacterial RNA most significantly (p < 0.05) induced TLR22 gene expression in most of the tested tissues. In A. hydrophila infection, induction of TLR22 gene expression was also observed in majority of the tested tissues. Together, these data suggested that in addition to sensing other microbial signatures, TLR22 can recognize bacterial RNA and may play the important role in augmenting innate immunity in fish.
•Spin wave dynamics of connected nanodots array.•Role of nanochannels to tune the spin-wave modes of nanodots.•Frequency selective spin wave propagation through nanochannels leading towards ...one-to-four magnonic demultiplexer logic operations.
Patterned magnetic nanostructures are potential candidates for future energy efficient, on-chip communication devices. Here, we have studied experimentally and numerically the role of nanochannels to manipulate spin waves in Ni80Fe20 connected nanodot arrays of varying filling fraction. Rich spin-wave spectra are observed in these sample, where the number of spin-wave modes decreases with increasing filling fraction due to the reduction in the demagnetization field. The nanochannels affect the spin-wave modes of the connected dots by modified magnetic coupling as well as selective channelling of spin-wave modes. For all modes, the vertical nanochannels couple the nanodots, except for the highest frequency mode where all nanochannels act as coupler. This feature is further explored in the simulation, which reveals that only the highest frequency mode can propagate through all the nanochannels, which may be further exploited to construct an electronic demultiplexer. This study will be useful to understand the role of nanochannels in patterned magnetic nanostructures and their applications in spin-wave based communication devices.
Acephate is an insecticide made up of organophosphates. It is applied to food crops, citrus trees, on golf courses, in commercial or institutional buildings, and as a seed treatment. Products ...containing acephate can be purchased as tablets, liquids, granules, powders, and water-soluble packs. Acephate 75% brand name-Asataf insecticide manufactured by TATA RALLIS was used for the test. The solvent used was glass double distilled (g.d.d.) water. Fresh water catfish Clarias batrachus were collected from local water bodies of Cuttack district. All the fishes were acclimatized for fifteen days in laboratory aquaria containing 30L dechlorinated tap water prior to the initiation of the experiment. The peripheral blood smear slides were prepared from the blood collected by caudal incision in accordance with Al-Sabti (1986) and Das and Nanda (1986) with some modifications which were prepared animals were sacrificed after 24, 48 and 72 hours of Exposure and were used for each treatment group in both types of administrations (IP and dermal). The increased concentration of acephate directly affects our biological fish sample i.e. Clarias batrachus. Acephate is causing serious problems in fish as per our genotoxicity study of acephate on Clarias batrachus. Clarias batrachus is a commonly found fish species in fresh water habitat which includes ponds, ditches, wetlands and rice fields of India specially in Odisha.The irrational use of pesticides containing acephate in agriculture cause harmful effects on Clarias batrachus, which is a most important species of fish for maintaining the aquatic diversity.
There are several antibiotic resistance genes (ARG) for the
bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has ...been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in
in gene expression data.
The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models.
The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (
< 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR.
aiGeneR successfully detected the
genes validating our four hypotheses.
Ferromagnetic antidot arrays have emerged as a system of tremendous interest due to their interesting spin configuration and dynamics as well as their potential applications in magnetic storage, ...memory, logic, communications and sensing devices. Here, we report experimental and numerical investigation of ultrafast magnetization dynamics in a new type of antidot lattice in the form of triangular-shaped Ni
Fe
antidots arranged in a hexagonal array. Time-resolved magneto-optical Kerr effect and micromagnetic simulations have been exploited to study the magnetization precession and spin-wave modes of the antidot lattice with varying lattice constant and in-plane orientation of the bias-magnetic field. A remarkable variation in the spin-wave modes with the orientation of in-plane bias magnetic field is found to be associated with the conversion of extended spin-wave modes to quantized ones and vice versa. The lattice constant also influences this variation in spin-wave spectra and spin-wave mode profiles. These observations are important for potential applications of the antidot lattices with triangular holes in future magnonic and spintronic devices.
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