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The functional foods market represents one of the fastest growing and most fascinating areas of investigation and innovation in the food sector. This new volume focuses on recent findings, new research trends, and emerging technologies in bioprocessing: making use of microorganisms in the production of food with health and nutritional benefits.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

Calendula officinalis Linn. (CO) is a popular medicinal plant from the plant kingdom’s Asteraceae family that has been used for millennia. This plant contains flavonoids, triterpenoids, glycosides, saponins, carotenoids, volatile oil, amino acids, steroids, sterols, and quinines. These chemical constituents confer multifaceted biological effects such as anti-inflammatory, anti-cancer, antihelminthic, antidiabetes, wound healing, hepatoprotective, and antioxidant activities. Additionally, it is employed in cases of certain burns and gastrointestinal, gynecological, ocular, and skin conditions. In this review, we have discussed recent research from the last five years on the therapeutic applications of CO and emphasized its myriad capabilities as a traditional medicine. We have also elucidated CO’s molecular mechanisms and recent clinical studies. Overall, this review intends to summarize, fill in the gaps in the existing research, and provide a wealth of possibilities for researchers working to validate traditional claims and advance the safe and effective use of CO in treating various ailments.

The present study explores the SARS-CoV-2 drugable target inhibition efficacy of phytochemicals from Indian medicinal plants using molecular docking, molecular dynamics (MD) simulation, and MM-PBSA analysis. A total of 130 phytochemicals were screened against SARS-CoV-2 Spike (S)-protein, RNA-dependent RNA polymerase (RdRp), and Main protease (M pro ). Result of molecular docking showed that Isoquercetin potentially binds with the active site/protein binding site of the Spike, RdRP, and Mpro targets with a docking score of -8.22, -6.86, and -9.73 kcal/mole, respectively. Further, MS 3, 7-Hydroxyaloin B, 10-Hydroxyaloin A, showed -9.57, -7.07, -8.57 kcal/mole docking score against Spike, RdRP, and M pro targets respectively. The MD simulation was performed to study the favorable confirmation and energetically stable complex formation ability of Isoquercetin and 10-Hydroxyaloin A phytochemicals in M pro -unbound/ligand bound/standard inhibitor bound system. The parameters such as RMSD, RMSF, Rg, SASA, Hydrogen-bond formation, energy landscape, principal component analysis showed that the lead phytochemicals form stable and energetically stabilized complex with the target protein. Further, MM-PBSA analysis was performed to compare the Gibbs free energy of the M pro -ligand bound and standard inhibitor bound complexes. The analysis revealed that the His-41, Cys145, Met49, and Leu27 amino acid residues were majorly responsible for the lower free energy of the complex. Drug likeness and physiochemical properties of the test compounds showed satisfactory results. Taken together, the study concludes that that the Isoquercetin and 10-Hydroxyaloin A phytochemical possess significant efficacy to bind SARS-Cov-2 M pro active site. The study necessitates further in vitro and in vivo experimental validation of these lead phytochemicals to assess their anti-SARS-CoV-2 potential.

Taro is a tropical plant and an underutilized root crop that has a good source of carbohydrate. Taro tuber contains 70%–80% of starch on dry basis. This review highlights the extraction of taro starch, latest advancements in the modification such as physical, chemical and enzymatic modification of taro starch. Furthermore, after modification of taro starch, molecular weight and amylopectin branch chain length distribution, granular shape, percentage crystallinity, swelling and solubilization, pasting and thermal properties and in vitro digestibility of taro starch were significantly affected. Additionally, researchers have explored novel methods to modify the physicochemical characteristics of taro starch, enhancing its functionality as a thickening, gelling, and stabilizing agent in various food formulations. However, fabrication of nanoparticles from taro starch was also studies. Various health benefits of taro starch have been reported in this study. One significant health benefit of taro starch is its potential to improve blood sugar management. Furthermore, the versatility of taro starch in food applications has expanded, ranging from traditional staples to modern convenience foods. Its gluten‐free nature makes it an attractive option for individuals with gluten sensitivity or celiac disease. Taro starch is increasingly incorporated into bakery products, snacks, noodles, and as a thickening agent in soups and sauces. The unique sensory attributes and nutritional profile of taro starch contribute to the development of novel, health‐conscious food products that cater to evolving consumer preferences. Taro is an underutilized root crop in many countries, and it is a good source of carbohydrate. So in this article, we have summarized the extraction, modification, characterization, and application of taro starch.

The migration of chemical compounds from packaging polymers to food presents a multifaceted challenge with implications for food safety and public health. This review explores the interaction between packaging materials and food products, focusing on permeation, migration, and sorption processes. The different migration mechanisms of contact migration, gas phase migration, penetration migration, set-off migration, and condensation/distillation migration have been discussed comprehensively. The major migrating compounds are plasticizers, nanoparticles, antioxidants, light stabilizers, thermal stabilizers, monomers, oligomers, printing inks, and adhesives, posing potential health risks due to their association with endocrine disruption and carcinogenic effects. Advanced analytical methods help in the monitoring of migrated compounds, facilitating compliance with regulatory standards. Regulatory agencies enforce guidelines to limit migration, prompting the development of barrier coatings and safer packaging alternatives. Furthermore, there is a need to decipher the migration mechanism for mitigating it along with advancements in analytical techniques for monitoring the migration of compounds.

The human immunodeficiency virus type 1 (HIV-1) genome encodes 15 proteins that perform structural, enzymatic, regulatory, and accessory functions. The capsid protein (CA) is the primary structural protein of HIV-1 and plays multiple functions during infection. Early studies predicted that HIV-1 CA mainly protected and delivered the viral genome to the target cell. However, it is now well established that CA plays a critical role after the cellular entry steps of infection. During the early stages, CA promotes reverse transcription of the RNA genome into a DNA copy and the nuclear import/entry step of HIV-1 infection. Emerging evidence also supports the functional role of CA in the post-nuclear entry steps of infection, such as HIV-1 integration. During the late stages of infection, CA coordinates hexameric lattice formation for the assembly of immature virion. CA also regulates theformation of the mature capsid that encases the viral genome and associated factors in the infectious progeny virion. Because of these indispensable roles, HIV-1 CA has emerged as a new and validated target for antiviral drug development. Accordingly, the first CA-targeting drug, lenacapavir (GS-6207), was recently approved to treat certain HIV-1-infected individuals whose viral load cannot be controlled by other antiviral drugs. Still, new and superior CA inhibitors are needed to qualify as part of the front-line antiviral therapy regimens for all infected individuals. The development of such inhibitors requires a clear understanding of CA’s role in HIV-1 infection. In this review, we will describe CA’s role during the early stages of HIV-1 infection, with particular emphasis on post-nuclear entry steps. HIV-1 capsid protein (CA)—independently or by recruiting host factors—mediates several key steps of virus replication in the cytoplasm and nucleus of the target cell. Research in the recent years have established that CA is multifunctional and genetically fragile of all the HIV-1 proteins. Accordingly, CA has emerged as a validated and high priority therapeutic target, and the first CA-targeting antiviral drug was recently approved for treating multi-drug resistant HIV-1 infection. However, development of next generation CA inhibitors depends on a better understanding of CA’s known roles, as well as probing of CA’s novel roles, in HIV-1 replication. In this timely review, we present an updated overview of the current state of our understanding of CA’s multifunctional role in HIV-1 replication—with a special emphasis on CA’s newfound post-nuclear roles, highlight the pressing knowledge gaps, and discuss directions for future research.

Identifying and characterizing young populations of star-forming regions are crucial to unraveling their properties. In this regard, Gaia-DR3 data and machine-learning tools are very useful for studying large star-forming complexes. In this work, we analyze the ∼7.1 deg2 area of one of our Galaxy’s dominant feedback-driven star-forming complexes, i.e., the region around Trumpler 37. Using the Gaussian mixture and random-forest classifier methods, we identify 1243 highly probable members in the complex, of which ∼60% are new members and are complete down to the mass limit of ∼0.1–0.2 M ⊙. The spatial distribution of the stars reveals multiple clusters toward the complex, where the central cluster around the massive star HD 206267 reveals two subclusters. Of the 1243 stars, 152 have radial velocity, with a mean value of −16.41 ± 0.72 km s−1. We investigate stars’ internal and relative movement within the central cluster. The kinematic analysis shows that the cluster’s expansion is relatively slow compared to the whole complex. This slow expansion is possibly due to newly formed young stars within the cluster. We discuss these results in the context of hierarchical collapse and feedback-induced collapse mode of star formation in the complex.