Ocimum kilimandscharicum 4CL11 negatively regulates adventitious root development via accumulation of flavonoid glycosides.

4-Coumarate-CoA Ligase (4CL) is an important enzyme in the phenylpropanoid biosynthesis pathway. Multiple 4CLs are identified in Ocimum species; however, their in planta functions remain enigmatic. In this study, we independently overexpressed three Ok4CL isoforms from Ocimum kilimandscharicum (Ok4CL7, -11, and -15) in Nicotiana benthamiana. Interestingly, Ok4CL11 overexpression (OE) caused a rootless or reduced root growth phenotype, whereas overexpression of Ok4CL15 produced normal adventitious root (AR) growth. Ok4CL11 overexpression in N. benthamiana resulted in upregulation of genes involved in flavonoid biosynthesis and associated glycosyltransferases accompanied by accumulation of specific flavonoid-glycosides (kaempferol-3-rhamnoside, kaempferol-3,7-O-bis-alpha-l-rhamnoside [K3,7R], and quercetin-3-O-rutinoside) that possibly reduced auxin levels in plants, and such effects were not seen for Ok4CL7 and -15. Docking analysis suggested that auxin transporters (PINs/LAXs) have higher binding affinity to these specific flavonoid-glycosides, and thus could disrupt auxin transport/signaling, which cumulatively resulted in a rootless phenotype. Reduced auxin levels, increased K3,7R in the middle and basal stem sections, and grafting experiments (intra and inter-species) indicated a disruption of auxin transport by K3,7R and its negative effect on AR development. Supplementation of flavonoids and the specific glycosides accumulated by Ok4CL11-OE to the wild-type N. benthamiana explants delayed the AR emergence and also inhibited AR growth. While overexpression of all three Ok4CLs increased lignin accumulation, flavonoids, and their specific glycosides were accumulated only in Ok4CL11-OE lines. In summary, our study reveals unique indirect function of Ok4CL11 to increase specific flavonoids and their glycosides, which are negative regulators of root growth, likely involved in inhibition of auxin transport and signaling.

Three-dimensional integral imaging-based image descattering and recovery using physics informed unsupervised CycleGAN.

Image restoration and denoising has been a challenging problem in optics and computer vision. There has been active research in the optics and imaging communities to develop a robust, data-efficient system for image restoration tasks. Recently, physics-informed deep learning has received wide interest in scientific problems. In this paper, we introduce a three-dimensional integral imaging-based physics-informed unsupervised CycleGAN algorithm for underwater image descattering and recovery using physics-informed CycleGAN (Generative Adversarial Network). The system consists of a forward and backward pass. The base architecture consists of an encoder and a decoder. The encoder takes the clean image along with the depth map and the degradation parameters to produce the degraded image. The decoder takes the degraded image generated by the encoder along with the depth map and produces the clean image along with the degradation parameters. In order to provide physical significance for the input degradation parameter w.r.t a physical model for the degradation, we also incorporated the physical model into the loss function. The proposed model has been assessed under the dataset curated through underwater experiments at various levels of turbidity. In addition to recovering the original image from the degraded image, the proposed algorithm also helps to model the distribution under which the degraded images have been sampled. Furthermore, the proposed three-dimensional Integral Imaging approach is compared with the traditional deep learning-based approach and 2D imaging approach under turbid and partially occluded environments. The results suggest the proposed approach is promising, especially under the above experimental conditions.

Underwater object detection and temporal signal detection in turbid water using 3D-integral imaging and deep learning.

Underwater scattering caused by suspended particles in the water severely degrades signal detection performance and poses significant challenges to the problem of object detection. This paper introduces an integrated dual-function deep learning-based underwater object detection and classification and temporal signal detection algorithm using three-dimensional (3D) integral imaging (InIm) under degraded conditions. The proposed system is an efficient object classification and temporal signal detection system for degraded environments such as turbidity and partial occlusion and also provides the object range in the scene. A camera array captures the underwater objects in the scene and the temporally encoded binary signals transmitted for the purpose of communication. The network is trained using a clear underwater scene without occlusion, whereas test data is collected in turbid water with partial occlusion. Reconstructed 3D data is the input to a You Look Only Once (YOLOv4) neural network for object detection and a convolutional neural network-based bidirectional long short-term memory network (CNN-BiLSTM) is used for temporal optical signal detection. Finally, the transmitted signal is decoded. In our experiments, 3D InIm provides better image reconstruction in a degraded environment over 2D sensing-based methods. Also, reconstructed 3D images segment out the object of interest from occlusions and background which improves the detection accuracy of the network with 3D InIm. To the best of our knowledge, this is the first report that combines deep learning with 3D InIm for simultaneous and integrated underwater object detection and optical signal detection in degraded environments.

Tyramine-Mediated Hyperactivity Modulates the Dietary Habits in Helicoverpa armigera.

Helicoverpa armigera exhibits extensive variability in feeding habits and food selection. Neuronal regulation of H. armigera feeding behavior is primarily influenced by biogenic amines such as Tyramine (TA) and Octopamine (OA). The molecular responses of H. armigera to dietary challenges in the presence of TA or OA have yet to be studied. This investigation dissects the impact of OA and TA on H. armigera feeding choices and behaviors under non-host nutritional stress. It has been observed that feeding behavior remains unaltered during the exogenous administration of OA and TA through an artificial diet (AD). Ingestion of higher OA or TA concentrations leads to increased mortality. OA and TA treatment in combination with host and non-host diets results in the induction of feeding and higher locomotion toward food, particularly in the case of TA treatment. Increased expression of markers, prominin-like, and tachykinin-related peptide receptor-like transcripts further assessed increased locomotion activity. Insects subjected to a non-host diet with TA treatment exhibited increased feeding and overexpression of the feeding indicator, the Neuropeptide F receptor, and the feeding regulator, Sulfakinin, compared with other conditions. Expression of sensation and biogenic amine synthesis genesis elevated in insects fed a non-host diet in combination with OA or TA. Metabolomics analysis revealed a decreased concentration of the feeding behavior elicitor, dopamine, in insects fed a non-host diet containing TA. This work highlights the complex interplay between biogenic amine functions during dietary stress and suggests the role of tyramine in feeding promotion under stressed conditions.

Catalyzing epoxy oxygen migration on the basal surface of graphene oxide using strong hydrogen-bond donors.

High-level double-hybrid DFT simulations reveal that strong hydrogen-bond-donor catalysts (e.g., ethylene glycol, guanidine, and thiourea) significantly accelerate the migration of epoxy oxygen on the surface of graphene oxide, enhancing the reaction rate by 6-12 orders of magnitude. These results shed light on previously puzzling experimental observations.

Trehalase inhibition in Helicoverpa armigera activates machinery for alternate energy acquisition.

Trehalose serves as a primary circulatory sugar in insects which is crucial in energy metabolism and stress recovery. It is hydrolyzed into two glucose molecules by trehalase. Silencing or inhibiting trehalase results in reduced fitness, developmental defects, and insect mortality. Despite its importance, the molecular response of insects to trehalase inhibition is not known. Here, we performed transcriptomic analyses of Helicoverpa armigera treated with validamycin A (VA), a trehalase inhibitor. VA ingestion resulted in increased mortality, developmental delay, and reduced ex vivo trehalase activity. Pathway enrichment and gene ontology analyses suggest that key genes involved in carbohydrate, protein, fatty acid, and mitochondria-related metabolisms are deregulated. The activation of protein and fat degradation may be necessary to fulfil energy requirements, evidenced by the dysregulated expression of critical genes in these metabolisms. Co-expression analysis supports the notion that trehalase inhibition leads to putative interaction with key regulators of other pathways. Metabolomics correlates with transcriptomics to show reduced levels of key energy metabolites. VA generates an energy-deficient condition, and insects activate alternate pathways to facilitate the energy demand. Overall, this study provides insights into the molecular mechanisms underlying the response of insects to trehalase inhibition and highlights potential targets for insect control.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication.

BACKGROUND: Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment. OBJECTIVES: This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication. METHODS: A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (ß-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation. RESULTS: The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability. CONCLUSION: By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

Chitinase inhibition induces transcriptional dysregulation altering ecdysteroid-mediated control of Spodoptera frugiperda development.

Chitinases and ecdysteroid hormones are vital for insect development. Crosstalk between chitin and ecdysteroid metabolism regulation is enigmatic. Here, we examined chitinase inhibition effect on Spodoptera frugiperda ecdysteroid metabolism. In vitro studies suggested that berberine inhibits S. frugiperda chitinase 5 (SfCht5). The Berberine feeding resulted in defective S. frugiperda development. Berberine-fed insects showed higher SfCht5 and Chitinase 7 expression and cumulative chitinase activity. Chitinase inhibition led to overexpression of chitinases, ecdysteroid biosynthesis, and responsive genes. SfCht5 silencing and overexpression resulted in ecdysone receptor deregulation. Transcription factors, like Broad Complex Z4, regulate the ecdysteroid metabolism and showed high expression upon berberine ingestion. Broad Complex Z4 binding in 5' UTR of Ecdysone receptor, SfCht5, Chitinase 7, Phantom, Neverland, and other ecdysteroid biosynthesis genes might lead to their upregulation in berberine-fed insects. As a result, berberine-fed insects showed ecdysone overaccumulation. These findings underscore chitinase activity's impact on ecdysone biosynthesis and its transcriptional crosstalk.

An Adsorption Based Downstream Processing Approach for Penicillin V from a Penicillium chrysogenum BIONCL I22 Culture Filtrate.

Penicillin V (phenoxy methyl penicillin) is highly sought after among natural penicillins because of its exceptional acid stability and effectiveness against common skin and respiratory infections. Given its wide-ranging therapeutic uses, there is a need to establish a greener method for its maximum recovery to reduce the carbon footprint. Here, we have identified and validated optimized operational conditions for resin-based penicillin V recovery. It was observed that Amberlite XAD4 had the highest penicillin V hydrophobic adsorption capacity among the other screened resins. Kinetic and isothermal studies using linear and nonlinear regression analysis showed that the adsorption process well fitted with pseudo-second-order kinetics (R 2 = 0.9816) and the Freundlich adsorption isotherm model (R 2 = 0.9871). Adsorption equilibrium was attained within 4 h, while maximum adsorption was observed at 3 mg/mL penicillin V concentration. Furthermore, the optimized extraction protocol was compared with the conventional butyl acetate-based downstream processing. Under optimum conditions resin-based penicillin V recovery was 2-fold higher as compared to the solvent extraction method and the resin could be reused for over six cycles without compromising the yield. These findings signify substantial progress toward the development of an environmentally sustainable approach for penicillin V recovery and a potentially viable method for extractive fermentation.

Comparing artificial intelligence guided image assessment to current methods of burn assessment.

Appropriate identification of burn depth and size is paramount. Despite the development of burn depth assessment aids [e.g., laser doppler imaging (LDI)], clinical assessment, which assesses partial thickness burn depth with 67% accuracy, currently remains the most consistent standard of practice. We sought to develop an image-based artificial intelligence system that predicts burn severity and wound margins for use as a triaging tool in thermal injury management. Modified EfficientNet architecture trained by 1684 mobile-device-captured images of different burn depths were previously utilized to create a convoluted neural network (CNN). The CNN was modified to a novel Boundary-Attention Mapping (BAM) algorithm using elements of saliency mapping, which was utilized to recognize the boundaries of burns. For validation, 144 patient charts that included clinical assessment, burn location, total body surface area, and LDI assessment were retrieved for a retrospective study. The clinical images underwent CNN-BAM assessment and were directly compared with the LDI assessment. CNN using a four-level burn severity classification achieved an accuracy of 85% (micro/macro-averaged ROC scores). The CNN-BAM system can successfully highlight burns from surrounding tissue with high confidence. CNN-BAM burn area segmentations attained a 91.6% accuracy, 78.2% sensitivity, and 93.4% specificity, when compared to LDI methodology. Results comparing the CNN-BAM outputs to clinical and LDI assessments have shown a high degree of correlation between the CNN-BAM burn severity predictions to those extrapolated from LDI healing potential (66% agreement). CNN-BAM algorithm gives equivalent burn-depth detection accuracy as LDI with a more economical and accessible application when embedded in a mobile device.

Using Computer Vision and Artificial Intelligence to Track the Healing of Severe Burns.

Burn care management includes assessing the severity of burns accurately, especially distinguishing superficial partial-thickness burns from deep partial-thickness burns, in the context of providing definitive, downstream treatment. Moreover, the healing of the wound in the subacute care setting requires continuous tracking to avoid complications. Artificial intelligence (AI) and computer vision (CV) provide a unique opportunity to build low-cost and accessible tools to classify burn severity and track changes in wound parameters, both in the clinic by physicians and nurses and asynchronously in the remote setting by the patient themselves. Wound assessments can be achieved by AI-CV using the principles of image-guided therapy using high-quality 2D color images. Wound parameters can include wound 2D spatial dimension and the characterization of wound color changes, which demonstrates physiological changes such as the presentation of eschar/necrotic tissue, pustulence, granulation tissue, and scabbing. Here we present the development of AI-CV-based Skin Abnormality Tracking Algorithm pipeline. Additionally, we provide the results on a single localized burn tracked for a 6-week period in the clinic and an additional 2-week period of home monitoring.

Investigating the Neuroprotective and Cognitive-Enhancing Effects of Bacopa monnieri: A Systematic Review Focused on Inflammation, Oxidative Stress, Mitochondrial Dysfunction, and Apoptosis.

The aging of the global population has increased the prevalence of neurodegenerative conditions. Bacopa monnieri (BM), an herb with active compounds, such as bacosides A and B, betulinic acid, loliolide, asiatic acid, and quercetin, demonstrates the potential for brain health. Limited research has been conducted on the therapeutic applications of BM in neurodegenerative conditions. This systematic review aims to project BM's beneficial role in brain disorders. BM has anti-apoptotic and antioxidant actions and can repair damaged neurons, stimulate kinase activity, restore synaptic function, improve nerve transmission, and increase neuroprotection. The included twenty-two clinical trials demonstrated that BM can reduce Nuclear Factor-κB phosphorylation, improve emotional function, cognitive functions, anhedonia, hyperactivity, sleep routine, depression, attention deficit, learning problems, memory retention, impulsivity, and psychiatric problems. Moreover, BM can reduce the levels of pro-inflammatory biomarkers and oxidative stress. Here, we highlight that BM provides notable therapeutic benefits and can serve as a complementary approach for the care of patients with neurodegenerative conditions associated with brain disorders. This review adds to the growing interest in natural products and their potential therapeutic applications by improving our understanding of the mechanisms underlying cognitive function and neurodegeneration and informing the development of new therapeutic strategies for neurodegenerative diseases.

Synergistic barium titanate/MXene composite as a high-performance piezo-photocatalyst for efficient dye degradation.

Piezo-photocatalysis combines photocatalysis and piezoelectric effects to enhance catalytic efficiency by creating an internal electric field in the photocatalyst, improving carrier separation and overall performance. This study presents a high-performance piezo-photocatalyst for efficient dye degradation using a synergistic barium titanate (BTO)-MXene composite. The composite was synthesized via a facile method, combining the unique properties of BTO nanoparticles with the high conductivity of MXene. The structural and morphological analysis confirmed the successful formation of the composite, with well-dispersed BTO nanoparticles on the MXene surface. The piezo-photocatalytic activity of the composite was evaluated using a typical dye solution (Rhodamine B: RhB) under ultraviolet irradiation and mechanical agitation. The results revealed a remarkable enhancement in dye degradation (90 % in 15 min for piezo-photocatalysis) compared to individual stimuli (58.2 % for photocatalysis and 95.8 % in 90 min for piezocatalysis), highlighting the synergistic effects between BTO and MXene. The enhanced catalytic performance was attributed to the efficient charge separation and transfer facilitated by the composite's structure, leading to increased reactive species generation and dye molecule degradation. Furthermore, the composite exhibited excellent stability and reusability, showcasing its potential for practical applications in wastewater treatment. Overall, this work represents a promising strategy for designing high-performance synergistic catalysts, addressing the pressing need for sustainable solutions in environmental remediation.