Sustainable polysaccharide and protein hydrogel-based packaging materials for food products: A review

Sustainable food packaging is a necessary element to ensure the success of a food system, the accomplishment of which is weighed in terms of quality retention and ensured products safety. Irrespective of the raised environmental concerns regarding petroleum-based packaging materials, a sustainable analysis and a lab to land assessment should be a priority to eliminate similar fates of new material. Functionalized bio-based hydrogels are one of the smartest packaging inventions that are expected to revolutionize the food packaging industry. Although in this review, the focus relies on recent developments in the sustainable bio-based hydrogel packaging materials, natural biopolymers such as proteins and polysaccharides from which hydrogels could be obtained, the challenges encountered in hydrogel-based packaging materials and the future prospects of hydrogel-based food packaging materials are also discussed. Moreover, the need for 'Life Cycle Assessment' (LCA), stress on certifications and a sustainable waste management system is also suggested which can bring both food and packaging into the same recycling bins.Authors (SS and RB) acknowledge the research support from the ongoing project ERA-Chair in VALORTECH at the Estonian University of Life Sciences, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 810630.Authors (SS and RB) acknowledge the research support from the ongoing project ERA-Chair in VALORTECH at the Estonian University of Life Sciences, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 810630

Sustainable polysaccharide and protein hydrogel-based packaging materials for food products: A review

Sustainable food packaging is a necessary element to ensure the success of a food system, the accomplishment of which is weighed in terms of quality retention and ensured products safety. Irrespective of the raised environmental concerns regarding petroleum-based packaging materials, a sustainable analysis and a lab to land assessment should be a priority to eliminate similar fates of new material. Functionalized bio-based hydrogels are one of the smartest packaging inventions that are expected to revolutionize the food packaging industry. Although in this review, the focus relies on recent developments in the sustainable bio-based hydrogel packaging materials, natural biopolymers such as proteins and polysaccharides from which hydrogels could be obtained, the challenges encountered in hydrogel-based packaging materials and the future prospects of hydrogel-based food packaging materials are also discussed. Moreover, the need for 'Life Cycle Assessment' (LCA), stress on certifications and a sustainable waste management system is also suggested which can bring both food and packaging into the same recycling bins.Horizon 2020, (810630); Eesti Maaülikool, EMÜEuropean Union [810630

Engineered Microbes for Pigment Production Using Waste Biomass

Received: February 04, 2020; Revised: March 08, 2020; Accepted: March 16, 2020.Agri-food waste biomass is the most abundant organic waste and has high valorisation potential for sustainable bioproducts development. These wastes are not only recyclable in nature but are also rich sources of bioactive carbohydrates, peptides, pigments, polyphenols, vitamins, natural antioxidants, etc. Bioconversion of agri-food waste to value-added products is very important towards zero waste and circular economy concepts. To reduce the environmental burden, food researchers are seeking strategies to utilize this waste for microbial pigments production and further biotechnological exploitation in functional foods or value-added products. Microbes are valuable sources for a range of bioactive molecules, including microbial pigments production through fermentation and/or utilisation of waste. Here, we have reviewed some of the recent advancements made in important bioengineering technologies to develop engineered microbial systems for enhanced pigments production using agrifood wastes biomass/by-products as substrates in a sustainable way.MS, VKG and RB acknowledge ERA Chair for Food (By-) Products Valorization Technologies of the Estonian University of Life Sciences (VALORTECH) which has received funding from the European Union’s Horizon 2020 research and innovation program (under grant agreement No. 810630)

NEXT-GENERATION SEQUENCING IN CLINICAL PRACTICE: IMPLICATIONS FOR DISEASE DIAGNOSIS AND MANAGEMENT

The main aim of this research is to identify the impact of next-generation sequencing in clinical practice for disease diagnosis and management. This process also has the capability to identify the response of individuals to using the drugs. Moreover, this information has the potential to optimize drug selection and dosages. Literature Review: Accuracy of the diagnosis has to be fostered by this process. Moreover, with the aid of this process, genetic disorders become identified and diagnosed. Trust of the patients in the treatment process becomes fostered, and it helps to develop the diagnosis process. The personalized medicine process becomes facilitated by this process. Methodology: This research study is based on the “theoretical analysis”, therefore, researchers are capable of collecting data from different online sources. This data collection process helps to get a deep conceptual understanding. After that, better knowledge about this study has to be collected with the aid of this data collection process Findings: Next-generation sequencing allows for the sequencing of the entire genome of an individual. Therefore, this has revolutionized precision medicine for personalized treatment. Better disease diagnosis, as well as genetic profile of patients is detected carefully with the aid of this process. Moreover, rare genetic diseases are also identified critically with the aid of this NGS process. Discussion: Overall knowledge about this study has to be identified by this study. This study helps to understand that, “Next-generation sequencing has a significant impact on clinical practices. Therefore, it has a remarkable impact on disease diagnosis and management Conclusion:  Genetic mutation is an important factor that has to be facilitated by this process. Moreover, different types of heredity disease and infectious diseases are detected carefully with the aid of this advanced disease diagnosis proces

Sustainable Phenylalanine-Derived SAILs for Solubilization of Polycyclic Aromatic Hydrocarbons

The solubilization capacity of a series of sustainable phenylalanine-derived surface-active ionic liquids (SAILs) was evaluated towards polycyclic aromatic hydrocarbons—naphthalene, anthracene and pyrene. The key physico-chemical parameters of the studied systems (critical micelle concentration, spectral properties, solubilization parameters) were determined, analyzed and compared with conventional cationic surfactant, CTABr. For all studied PAH solubilization capacity increases with extension of alkyl chain length of PyPheOCn SAILs reaching the values comparable to CTABr for SAILs with n = 10–12. A remarkable advantage of the phenylalanine-derived SAILs PyPheOCn and PyPheNHCn is a possibility to cleave enzymatically ester and/or amide bonds under mild conditions, to separate polycyclic aromatic hydrocarbons in situ. A series of immobilized enzymes was tested to determine the most suitable candidates for tunable decomposition of SAILs. The decomposition pathway could be adjusted depending on the choice of the enzyme system, reaction conditions, and selection of SAILs type. The evaluated systems can provide selective cleavage of the ester and amide bond and help to choose the optimal decomposition method of SAILs for enzymatic recycling of SAILs transformation products or as a pretreatment towards biological mineralization. The concept of a possible practical application of studied systems for PAHs solubilization/separation was also discussed focusing on sustainability and a green chemistry approach

A Novel Approach for Rice Plant Disease Detection, classification and localization using Deep Learning Techniques

Background. This Thesis addresses the critical issue of disease management in ricecrops, a key factor in ensuring both food security and the livelihoods of farmers. Objectives. The primary focus of this research is to tackle the often-overlooked challenge of precise disease localization within rice plants by harnessing the power of deep learning techniques. The primary goal is not only to classify diseases accurately but also to pinpoint their exact locations, a vital aspect of effective disease management. The research encompasses early disease detection, classification, andthe precise identification of disease locations, all of which are crucial components of a comprehensive disease management strategy. Methods. To establish the reliability of the proposed model, a rigorous validation process is conducted using standardized datasets of rice plant diseases. Two fundamental research questions guide this study: (1) Can deep learning effectively achieve early disease detection, accurate disease classification, and precise localizationof rice plant diseases, especially in scenarios involving multiple diseases? (2) Which deep learning architecture demonstrates the highest level of accuracy in both disease  diagnosis and localization? The performance of the model is evaluated through the application of three deep learning architectures: Masked RCNN, YOLO V8, and SegFormer. Results. These models are assessed based on their training and validation accuracy and loss, with specific metrics as follows: For Masked RCNN, the model achieves a training accuracy of 91.25% and a validation accuracy of 87.80%, with corresponding training and validation losses of 0.3215 and 0.4426. YOLO V8 demonstrates a training accuracy of 85.50% and a validation accuracy of 80.20%, with training andvalidation losses of 0.4212 and 0.5623, respectively. SegFormer shows a training accuracy of 78.75% and a validation accuracy of 75.30%, with training and validation losses of 0.5678 and 0.6741, respectively. Conclusions. This research significantly contributes to the field of agricultural disease management, offering valuable insights that have the potential to enhance crop yield, food security, and the overall well-being of farmer

A Novel Approach for Rice Plant Disease Detection, classification and localization using Deep Learning Techniques

Background. This Thesis addresses the critical issue of disease management in ricecrops, a key factor in ensuring both food security and the livelihoods of farmers. Objectives. The primary focus of this research is to tackle the often-overlooked challenge of precise disease localization within rice plants by harnessing the power of deep learning techniques. The primary goal is not only to classify diseases accurately but also to pinpoint their exact locations, a vital aspect of effective disease management. The research encompasses early disease detection, classification, andthe precise identification of disease locations, all of which are crucial components of a comprehensive disease management strategy. Methods. To establish the reliability of the proposed model, a rigorous validation process is conducted using standardized datasets of rice plant diseases. Two fundamental research questions guide this study: (1) Can deep learning effectively achieve early disease detection, accurate disease classification, and precise localizationof rice plant diseases, especially in scenarios involving multiple diseases? (2) Which deep learning architecture demonstrates the highest level of accuracy in both disease  diagnosis and localization? The performance of the model is evaluated through the application of three deep learning architectures: Masked RCNN, YOLO V8, and SegFormer. Results. These models are assessed based on their training and validation accuracy and loss, with specific metrics as follows: For Masked RCNN, the model achieves a training accuracy of 91.25% and a validation accuracy of 87.80%, with corresponding training and validation losses of 0.3215 and 0.4426. YOLO V8 demonstrates a training accuracy of 85.50% and a validation accuracy of 80.20%, with training andvalidation losses of 0.4212 and 0.5623, respectively. SegFormer shows a training accuracy of 78.75% and a validation accuracy of 75.30%, with training and validation losses of 0.5678 and 0.6741, respectively. Conclusions. This research significantly contributes to the field of agricultural disease management, offering valuable insights that have the potential to enhance crop yield, food security, and the overall well-being of farmer

Synthesis, DFT, ADME and docking studies of Homoegonol and Egonol as potential inhibitors of COVID-19 main protease (6LU7)

To produce Homoegonol and Egonol with high yields via a divergent synthetic method, we describe a novel and simple synthesis strategy. This method, which makes use of the adaptable Heck reaction, starts with commercially available 2-hydroxy-3-methoxybenzaldehyde, (3,4-dimethoxy-phenyl)methanol, and (benzo[d][1,3]dioxol-5-yl)methanol. When synthesising these important molecules, our approach offers improved efficiency, simplicity, and ease. The chemical structures of all the newly synthesized intermediates and products were elucidated by their IR, 1H & 13C NMR and mass spectral data. Further DFT calculation was carried out at B3LYP/6–31 g++(d,p) level theory. ADME analysis represents synthesized drugs that show oral bioavailability and drug-like nature. Docking studies carried out against 6LU7, docking results represent the good interaction with Egonol and Homoegonol, binding energy is −10.16 kcal/mol for both Homoegonol and egonol