Effect of habitat attributes on the abundance of Sargassum’s epifauna and the feeding habit of an amphipod epifauna from the coast of Western India

432-439The Sargassum bed along the rocky intertidal region is highly productive and serves as a habitat for numerous associated species. In this study, the effect of habitat attributes (algal morphology) on the abundance and diversity of epifauna in Sargassum tenerrimum was investigated. Qualitative and quantitative analysis showed that the overall species diversity was high, and Hyale nuda, Gammaropsis sp. and Modiolus adriaticus were the most abundant epifaunal species observed. The abundance of epifauna varied significantly with time. Compared to pre-monsoon months (February to May), post-monsoon (October to January) had a greater diversity of S. tenerrimum associated epifauna. The diversity was particularly high in the month of November. Epifauna showed a positive correlation with the characteristic of algal morphology, indicating its influence on abundance and diversity. Hyale nuda being the most ubiquitous and abundant epifauna observed, the biotic interaction between S. tenerrimum and H. nuda was studied further. Close examination of the gut contents of H. nuda revealed that they graze and consume the epiphytic microalgae of S. tenerrimum, instead of the macroalgal fronds. This suggests that through this relationship, epibiosis in Sargassum may remain in control, while the amphipod derives food, shelter and protection from the predators

Bioactive molecules from haloarchaea: Scope and prospects for industrial and therapeutic applications

Marine environments and salty inland ecosystems encompass various environmental conditions, such as extremes of temperature, salinity, pH, pressure, altitude, dry conditions, and nutrient scarcity. The extremely halophilic archaea (also called haloarchaea) are a group of microorganisms requiring high salt concentrations (2–6 M NaCl) for optimal growth. Haloarchaea have different metabolic adaptations to withstand these extreme conditions. Among the adaptations, several vesicles, granules, primary and secondary metabolites are produced that are highly significant in biotechnology, such as carotenoids, halocins, enzymes, and granules of polyhydroxyalkanoates (PHAs). Among halophilic enzymes, reductases play a significant role in the textile industry and the degradation of hydrocarbon compounds. Enzymes like dehydrogenases, glycosyl hydrolases, lipases, esterases, and proteases can also be used in several industrial procedures. More recently, several studies stated that carotenoids, gas vacuoles, and liposomes produced by haloarchaea have specific applications in medicine and pharmacy. Additionally, the production of biodegradable and biocompatible polymers by haloarchaea to store carbon makes them potent candidates to be used as cell factories in the industrial production of bioplastics. Furthermore, some haloarchaeal species can synthesize nanoparticles during heavy metal detoxification, thus shedding light on a new approach to producing nanoparticles on a large scale. Recent studies also highlight that exopolysaccharides from haloarchaea can bind the SARS-CoV-2 spike protein. This review explores the potential of haloarchaea in the industry and biotechnology as cellular factories to upscale the production of diverse bioactive compounds.</jats:p

Polymicrobial Infections and Biofilms: Clinical Significance and Eradication Strategies

Biofilms are population of cells growing in a coordinated manner and exhibiting resistance towards hostile environments. The infections associated with biofilms are difficult to control owing to the chronicity of infections and the emergence of antibiotic resistance. Most microbial infections are contributed by polymicrobial or mixed species interactions, such as those observed in chronic wound infections, otitis media, dental caries, and cystic fibrosis. This review focuses on the polymicrobial interactions among bacterial-bacterial, bacterial-fungal, and fungal-fungal aggregations based on in vitro and in vivo models and different therapeutic interventions available for polymicrobial biofilms. Deciphering the mechanisms of polymicrobial interactions and microbial diversity in chronic infections is very helpful in anti-microbial research. Together, we have discussed the role of metagenomic approaches in studying polymicrobial biofilms. The outstanding progress made in polymicrobial research, especially the model systems and application of metagenomics for detecting, preventing, and controlling infections, are reviewed

Interpretable deep learning approach for oral cancer classification using guided attention inference network

Significance: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network's attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. Aim: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. Approach: We utilized Selvaraju et al.'s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.'s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. Results: The network's attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. Conclusions: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification. © The Authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Mobile-based oral cancer classification for point-of-care screening

Significance: Oral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings. Aim: To develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection. Approach: The dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is 1/416.3 MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images. Results: We investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes 1/4300 ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists. Conclusions: Our study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]