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Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in an unprecedented global crisis. Although primarily a respiratory illness, dysregulated immune responses may lead to multi-organ dysfunction. Prior data showed that the resident microbial communities of gastrointestinal and respiratory tracts act as modulators of local and systemic inflammatory activity (the gut–lung axis). Evolving evidence now signals an alteration in the gut microbiome, brought upon either by cytokines from the infected respiratory tract or from direct infection of the gut, or both. Dysbiosis leads to a “leaky gut”. The intestinal permeability then allows access to bacterial products and toxins into the circulatory system and further exacerbates the systemic inflammatory response. In this review, we discuss the available data related to the role of the gut microbiome in the development and progression of COVID-19. We provide mechanistic insights into early data with a focus on immunological crosstalk and the microbiome’s potential as a biomarker and therapeutic target.

Purpose Climate change and global warming are acknowledged to be growing issues caused by emissions of greenhouse gases. Fuel-efficient or green cars offer a technological solution to reduce CO2 emissions from car exhaust. This study aims to draw on value–belief–norm (VBN) theory to provide an explanation of eco-socially conscious consumer behavior (ESCCB) related to the choice and use of personal cars. Design/methodology/approach The data was collected from 1,372 customers of the automobile industry of Pakistan, randomly drawn from 8 metropolitan cities. Partial least squares structural equation modeling is used to verify the hypotheses of the study. Findings By using variance-based structural equation modeling, this study showed that the VBN theory is fully supported in the cultural context of Pakistan and behavioral context of ESCCB. More specifically, personal introjected norms of the customers proved to be strong predictors of ESCCB while personal integrated norms predicted eco-social purchase intentions well. Originality/value To the best of the authors’ knowledge, this is the only study of its kind that examines the VBN model in the context of a developing country, Pakistan, and for a category of behaviors rarely tested before, i.e. ESCCBs related to purchase and use of personal cars.

Maintenance of plant physiological functions under drought stress is normally considered a positive feature as it indicates sustained plant health and growth. This study was conducted to investigate whether plant growth-promoting rhizobacteria (PGPR) Bacillus subtilis HAS31 has potential to maintain potato growth and yield under drought stress. We analyzed trends of chlorophyll concentration, photosynthesis process, relative water content, osmolytes, antioxidants enzymes and oxidative stress, relative growth rate, tuber and aboveground biomass production in two potato varieties, Santae (drought-tolerant) and PRI-Red (drought-sensitive). Plants of both genotypes were treated with 100 g of HAS31 inoculant at 10 days after germination and exposed to different soil relative water contents (SRWC), including 80 ± 5% (well watered), 60 ± 5% (moderate stress) and 40 ± 5% SRWC (severe stress) for 7 days at tuber initiation stage (30 days after germination). The drought stress reduced plant relative growth rate, biomass production, leaf area, number of leaves and tubers, tuber weight, and final yield. The drought-stressed plants showed decline in chlorophyll contents, membrane stability, leaf relative water contents and photosynthetic rate. Under drought stress, enzymatic activity of catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD), contents of total soluble sugars, soluble proteins and proline increased. The application of PGPR reduced the impact of drought and maintained higher growth and physio-chemical traits of the plants. The plants with PGPR application showed higher relative growth rate, dry matter production, leaf area, number of tubers, tuber weight and yield as compared to plants without PGPR. The PGPR-HAS31 treated plants maintained higher photosynthetic process, contents of chlorophyll, soluble proteins, total soluble sugars, and enzymatic activities of CAT, POD and SOD as compared to plants without PGPR. The results of the study suggest that plant growth regulators have ability to sustain growth and yield of potato under drought stress by maintaining physiological functions of the plants.

Electrohydrodynamic (EHD) printing is an additive manufacturing technique that allows ultra-high-resolution printing. It is a non-vacuum, direct-writing, non-contact, and mask-less process. EHD printing Reduces the number of fabrication steps, making it more cost-effective and material efficient. EHD printing technology is used to fabricate micro and nanoelectronic devices. EHD printing can be operated in two different printing styles; drop-on-demands (DOD) and continuous jet. DOD utilizes the microdripping and dripping jet characteristic, while continuous jet utilizes cone-jet for patterning and deposition of materials. This technology performs various coating and printing of a wide range of printable materials and substrates. Past results showed that the EHD printing technique has advantages in aspect ratio, performance improvement, and ink materials flexibility compared to other manufacturing techniques. However, researchers have identified production outputs and limited working space as limitations of this printing technology. This paper presents a comprehensive literature review on the EHD research activities related to the EHD system research and developments, studies utilizing EHD fabrication for modern electronic devices and potential future works related to EHD printing technology.

Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.

Farmers’ willingness and ability to adapt agricultural systems depend on their knowledge about changes in climate and perceived risks of extreme events. Using cross-sectional data of 450 farmers collected from three agro-ecological zones of Punjab, Pakistan, this study investigates farmer perceptions of climate change and their agreement with observed climatic trends. In addition, this study explores the correlation between different adaptation stages (perceptions, intentions, and adaptation) and their key drivers using a Multivariate Probit Model. This study also explores the adaptation measures adopted by farmers. The results of the study show that the perceptions of increasing mean temperature match well with locally recorded data. However, a discrepancy is found in some cases between farmer perceptions of rainfall changes and local climate records. Moreover, education, experience, land tenure, land holdings, extension, cooperation, access to weather forecasting, and marketing information are the factors influencing the three adaptation stages. A strong association is found among the three adaptation stages. Particularly, the study confirms the hypothesis that accurate perceptions lead to stronger adaptation intentions compared to underestimated or no perceptions. Further, farmers prefer basic adaptation measures including changing crop varieties, input use and planting dates over advanced measures, such as planting shade trees, soil conservation, and crop diversification. The study recommends providing farmers, especially small landholders and tenants, easy access to information, institutional services and training on the use of advanced measures to reduce negative impacts of climate change at the farm level.

The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.

Consumption of heavy metals, especially lead (Pb) contaminated food is a serious threat to human health. Higher Pb uptake by the plant affects the quality, growth and yield of crops. However, inoculation of plant growth-promoting rhizobacteria (PGPR) along with a mixture of organic amendments and biochar could be an effective way to overcome the problem of Pb toxicity. That’s why current pot experiment was conducted to investigate the effect of compost mixed biochar (CB) and ACC deaminase producing PGPR on growth and yield of spinach plants under artificially induced Pb toxicity. Six different treatments i.e., control, Alcaligenes faecalis (PGPR1), Bacillus amyloliquefaciens (PGPR2), compost + biochar (CB), PGPR1 + CB and PGPR2 + CB were applied under 250 mg Pb kg -1 soil. Results showed that inoculation of PGPRs ( Alcaligenes faecalis and Bacillus amyloliquefaciens ) alone and along with CB significantly enhanced root fresh (47%) and dry weight (31%), potassium concentration (11%) in the spinach plant. Whereas, CB +  Bacillus amyloliquefaciens significantly decreased (43%) the concentration of Pb in the spinach root over control. In conclusion, CB +  Bacillus amyloliquefaciens has the potential to mitigate the Pb induced toxicity in the spinach. The obtained result can be further used in the planning and execution of rhizobacteria and compost mixed biochar-based soil amendment.