Major Research Interest Research Articles

Effects of Different Non-Saccharomyces Strains in Simultaneous and Sequential Co-Fermentations with Saccharomyces cerevisiae on the Quality Characteristics of Kiwi Wine.

With the increasing awareness of health, more people have shown a preference for low-alcohol beverages. Seeking various methods to improve the quality of kiwi wine is now a major research interest in the wine industry. In this study, kiwi wine was fermented by Saccharomyces cerevisiae and different non-Saccharomyces strains (Torulaspora delbrueckii, Kluyveromyces thermotolerans, Pichia fermentans) in three methods (pure fermentation, simultaneous, and sequential co-fermentation). The physicochemical characteristics, color parameters, phenolic profiles, total phenolic content (TPC), antioxidant activities, organic acids, and taste sense of the different wines were evaluated to determine the effects of different yeasts and fermentation methods on the quality of the kiwi wine. Results indicated that co-fermentation reduced the contents of alcohol while enhancing the lightness of the kiwi wine. The TPC of sequential co-fermentation with K. thermotolerans/S. cerevisiae was significantly higher than that of their simultaneous co-fermentation. Compared to K. thermotolerans/S. cerevisiae, the antioxidant activities were increased by co-fermentation of T. delbrueckii/S. cerevisiae and P. fermentans/S. cerevisiae. Principal component analysis showed that kiwi wines fermented by different yeasts and inoculation methods could be separated and grouped. Correlation analysis presented positive correlations of phenolic composition, antioxidant activities, and color intensity. This study provided theoretical guidance for co-fermentation of non-Saccharomyces/S. cerevisiae and accelerated the industrialization process of kiwi wine.

ChaQra: a cellular unit of the Indian quantum network

Major research interests on quantum key distribution (QKD) are primarily focused on increasing 1. Point-to-point transmission distance (1000 km). 2. Secure key rate (Mbps). 3. Security of quantum layer (device-independence). It is great to push the boundaries in these fronts but these isolated approaches are neither scalable nor cost-effective due to requirements of specialised hardware and different infrastructure. Current and future QKD network requires addressing different set of challenges apart from distance, key rate and quantum security. In this regard, we present ChaQra—a sub quantum network with core features as 1. Crypto agility (integration in the already deployed telecommunication fibres). 2. Software defined networking (SDN paradigm for routing different nodes). 3. reliability (addressing denial-of-service with hybrid quantum safe cryptography). 4. upgradability (modules upgradation based on scientific and technological advancements). 5. Beyond QKD (using QKD network for distributed computing, multi-party computation etc). Our results demonstrate a clear path to create and accelerate quantum secure Indian subcontinent under national quantum mission.

Can Doped-MXene-Based Supercapacitors Be the Game-Changer for Future Energy Landscape? A Critical Perspective.

MXene has attracted considerable attention for supercapacitor applications in the past decade owing to its exceptional electrochemical properties. Although major research interests are focused on composite-based MXene, doping engineering of MXene has recently emerged as a promising alternative. This work unveils the potential of doped MXene for supercapacitor applications with a critical perspective. Various doping engineering strategies and synthesis methods adopted are explicitly delineated. Detailed discussions on the optimization of lattice, functionalization, substitution, and interface modification are provided. Further, it sheds light on recent developments with the asssociated mechanism of doped MXene supercapacitors, followed by the associated challenges. Finally, a roadmap for further progress of doped MXene for the realization of advanced and high-performing energy storage systems has been described. We envision that this Perspective will open up new avenues for the further exploration of this domain.

Angiotensin II Type 1 receptor blockade attenuates the neuropathological changes in the spinal cords of diabetic rats with modulation of nuclear factor erythroid 2-related factor 2/ heme oxygenase 1 system

Peripheral and central neuropathies frequently complicate worldwide diabetes. Compared to peripheral neuropathy, central neuropathy didn`t gain a major research interest. Angiotensin II is reported to be involved in diabetic neuropathic pain but its role in the central pathological changes in the spinal cord is not clear. Here, we study the role of Losartan; an Angiotensin II receptor 1 (AT1) antagonist in suppression of the diabetes-induced changes in the spinal cord. Three groups of rats were applied; a negative control group, a streptozotocin (STZ) diabetic group, and a group receiving STZ and Losartan. After two months, the pathological alteration in the spinal cord was investigated, and an immunohistochemical study was performed for neuronal, astrocytic, and microglial markers; nuclear protein (NeuN), Glial fibrillary acidic protein (GFAP), and Ionized calcium-binding adaptor molecule 1 (Iba1), respectively, and for an apoptosis marker; caspase-3, and the inflammatory marker; nuclear factor kappa B (NF-kB) signaling, heme oxygenase-1 (HO-1) and nuclear factor erythroid 2-related factor 2 (Nrf2); physiological antioxidant system. The results showed that Losartan caused recovery of spinal cord changes, by inhibiting the microglial and astrocytic activation, suppressing neuronal apoptosis and NF-kB expression with activation of Nrf2/HO-1 (P<0.0005). It is suggested, herein, that Losartan can suppress diabetes-induced glial activation, inflammation, neuronal apoptosis, and oxidative stress in the spinal cord; the mechanisms that may underlie the role of AT1 antagonism in suppressing diabetic neuropathic pain.

Research Paper on Emotion Recognition

As a recognizing in machine learning algorithm a significant amount of in different various field has been done in many technologies field of machines through which speech has a major impact research interest, especially in the affective computing domain. Increasing potential, algorithmic advancements, and applications in real-world. This human speech contains para-linguistic information that can be represented using different various quantitative features such as pitch, intensity for its deltaic result. It is commonly achieved following three key steps: data processing, feature extraction, and classification based on the underlying emotional features. The nature of these steps, help with the distinct features of human speech, to get the exact result through the underpin with the use of ML methods. Many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Emotion recognition the review covers databases used, emotions extracted, contributions made toward emotion recognition and limitations related to it. signals are an important but challenging component of Human-Computer Interaction (HCI) in machine learning aspect in computer machines through various different perspective and given signals

Geographic Information System (GIS) for ecology: a bibliometric network analysis

Introduced in 1960s to store, analyse, and manipulate data collected for the Canada Land Inventory by mapping information about soils, agriculture, recreation, wildlife, waterfowl, forestry and land use, since their origins Geographic Information Systems (GISs) have had a fundamental role in the study of the environment. As computers systems for capturing, storing, checking, and displaying data related to positions on Earth's surface, GISs have quickly become an effective and powerful tool for addressing ecological issues, analysing the relationship between living things and their habitats, assessing the environmental impact of man-made transformations of a territory. This article aims to show and discuss the results of a study concerning quantitative distribution of GIS for ecology in literature from Scopus database using bibliometric analysis. Bibliometric analysis is a scientific computer assisted review methodology to explore the major research interests in scientific literature, so to identify core research authors, as well as their relationship, by covering articles, conference papers, book chapters and reviews related to a given topic or field. The high number of scientific manuscripts published on the subject (5,204) in the analysed period (1979-2021), remarked the soundness of this topic. While China shows the greatest number of published documents, USA is the country with the most cited documents. Specifically, “remote sensing”, “spatial analysis” and “land use” are the keywords most frequently linked to GIS and ecology, so to underline respectively three aspects: the relevance of environment monitoring by satellite, airplane or drone, the utility of relating ecological questions to the geo-localization, the necessity of produce thematic maps for describing the economic and cultural activities (e.g., agricultural, residential, industrial, and recreational uses) that are practiced at a given place. Since the study shows that trend of publications focusing on the application of GIS for ecology is increasing, further growing is expected in the next future, also considering the ductility that these computer systems provide in different fields.

The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis

The widespread interest towards a sustainable and effective monitoring of the environment is increasingly demanding the development of modern and more affordable technologies to support or even replace the traditional time-consuming, high-cost sampling surveys at a multi-scale level. Researchers are highly benefitting from the recent enormous progresses achieved in the Artificial Intelligence (AI) field, with Machine/Deep Learning (ML/DL) applications increasing at sight. This gives a remarkable contribution to the environmental monitoring at sea, further allowing to develop efficient, smart and low-cost solutions to support the wide variety of tasks dealing with this objective. This study explores the global scientific literature on AI and ML/DL applications for the environmental monitoring over the last years. The VOSviewer software has been used to create maps based on the bibliographic network data: this allowed to display the relationships among scientific journals, researchers, and countries and to analyze the co-occurrence of different terms connected to the research. The resulting bibliometric analysis aims at verifying the major research interests and at providing the community with interesting findings and new perspectives on this very important topic, highlighting the great potential and flexibility of these methodologies and the excellent achievements they obtained in the last years.

Early interferon lambda production is induced by double-stranded RNA in iPS-derived hepatocyte-like cells.

Hepatotropic viruses are amongst the most ubiquitous pathogens worldwide, causing significant morbidity and mortality. As hepatocytes are among the primary targets of these viruses, their ability to mount early effective innate defence responses is of major research interest. Interferon lambda (IFNL) is produced early in response to viral stimulation in other cell types, but hepatocyte production of this interferon is little investigated. Due to the difficulty and significant costs in obtaining and culturing human primary hepatocytes, surrogate systems are widely sought. Here we used induced pluripotent stem (iPS)-derived hepatocyte-like cells (HLCs) to investigate hepatic IFNL expression in response to viral-like ligands. We demonstrate that hepatocytes rely on cytoplasmic pattern recognition receptors (PRRs) such as Protein Kinase RNA-dependent (PKR) and retinoic acid-inducible gene-I (RIG-I)-like receptors (RLR) for the detection of double stranded RNA. Stimulation of HLCs by viral-like RNA ligands activating cytosolic RNA sensors resulted in thousand fold increase of type III interferon gene expression. These results are in contrast with type I IFN expression, which was induced to a lower extent. Concomitant induction of interferon stimulated genes, such as interferon-stimulated gene 15 (ISG15) and CXCL10, indicated the ability of HLCs to activate interferon-dependent activity. These results demonstrate that HLCs mount an innate antiviral response upon stimulation with viral-like RNA characterized by the induction of type III IFN.

Development of Switchable Transformer Research at University of South Australia

This paper describes research development focusing on the designs of switchable transformers and their control system at University of South Australia. Research development of switchable transformer has become one of the major research interests in school of Electrical and Information Engineering, University of South Australia since year 2000. Research in this area has been carried out through undergraduate to post graduate studies. Several transformer designs, starting from the rating of 1kVA to 10kVA, have been introduced. Testing results from the designs have shown the improvement of the transformer efficiency during low or high loads. As the switchable transformers utilize several switching devices that need to be synchronously controlled, research on the development of the switchable transformer control systems has also been carried out. Now, in this school, a study of utilizing controller area network (CAN) as media to integrate modules in the control system is done. The study also focused on the remote monitoring and control system of the switchable transformer through a local area network (LAN).

Salicylic acid in plant immunity and beyond.

As the most widely used herbal medicine in human history and a major defence hormone in plants against a broad spectrum of pathogens and abiotic stresses, salicylic acid (SA) has attracted major research interest. With applications of modern technologies over the past 30 years, studies of the effects of SA on plant growth, development, and defence have revealed many new research frontiers and continue to deliver surprises. In this review, we provide an update on recent advances in our understanding of SA metabolism, perception, and signal transduction mechanisms in plant immunity. An overarching theme emerges that SA executes its many functions through intricate regulation at multiple steps: SA biosynthesis is regulated both locally and systemically, while its perception occurs through multiple cellular targets, including metabolic enzymes, redox regulators, transcription cofactors, and, most recently, an RNA-binding protein. Moreover, SA orchestrates a complex series of post-translational modifications of downstream signaling components and promotes the formation of biomolecular condensates that function as cellular signalling hubs. SA also impacts wider cellular functions through crosstalk with other plant hormones. Looking into the future, we propose new areas for exploration of SA functions, which will undoubtedly uncover more surprises for many years to come.

Electrochemically decoupled reduction of CO2 to formate over a dispersed heterogeneous bismuth catalyst enabled via redox mediators.

Electrochemical CO2 reduction is a topic of major interest in contemporary research as an approach to use renewably-derived electricity to synthesise useful hydrocarbons from waste CO2. Various strategies have been developed to optimise this challenging reaction at electrode interfaces, but to-date, decoupled electrolysis has not been demonstrated for the reduction of CO2. Decoupled electrolysis aims to use electrochemically-derived charged redox mediators - electrical charge and potential vectors - to separate catalytic product formation from the electrode surface. Utilising an electrochemically generated highly reducing redox mediator; chromium propanediamine tetraacetate, we report the first successful application of decoupled electrolysis to electrochemical CO2 reduction. A study of metals and metal composites found formate to be the most accessible product, with bismuth metal giving the highest selectivity. Copper, tin, gold, nickel and molybdenum carbide heterogeneous catalysts were also investigated, in which cases H2 was found to be the major product, with minor yields of two-electron CO2 reduction products. Subsequent optimisation of the bismuth catalyst achieved a high formate selectivity of 85%. This method represents a radical new approach to CO2 electrolysis, which may be coupled directly with renewable energy storage technology and green electricity.

Synthesis and characterization of calcium-iron-chromium nanocomposites for electromagnetic radiation shielding application.

Over a century, shielding harmful electromagnetic radiations (EMR) and finding a suitable material, which can replace lead has become the major interest of researchers in this field. Herein, calcium-iron-chromium oxide nanocomposites with the different atomic ratios are synthesized using the solution combustion method. The as-obtained nanoparticles (NPs) are subjected to several structural and surface characteristics such as powder X-ray diffraction, scanning electron microscopy, elemental diffraction X-ray analysis, Fourier Transform Infrared Spectroscopy and UV-visible spectroscopy analysis were performed to confirm the successful synthesis. Furthermore, the EMR shielding of as-procured NPs is investigated and observed that the obtained NPs show good shielding properties.

A Deep Stacked Ensemble Model for Microarray Data Classification with Boosted Meta Classifier

Classification of microarray data is one of the major research interests in the biomedical field. It allows physicians for early detection of cancer through analysis of the Deoxyribonucleic acid (DNA). Classification of these sensitive data is still challenging due to the small sample and more feature size. In this paper, the authors have used an ensemble model for classifying two types of leukemia as Acute lymphocytic leukemia (ALL) and Acute myelocytic leukemia (AML). The work is carried out with other types of genetic data such as Leukemia, lung tumor, liver cancer, and liver Cirrhosis. The biomedical data are imbalanced. The ensemble classifier is based on a stacked approach where deep neural network (DNN) classifiers are used as the base classifier. The structure of each DNN is chosen as the homogenous type for the same training process for all classifiers. Because of the adaptive nature and random weight initialization, it provides different results for each classifier. The outputs of the base classifiers are again fed to a gradient boosting ensemble model termed a meta classifier. The meta classifier provides the final classification output. For comparison purposes, two types of meta-classifiers such as support vector machine (SVM) and ensemble gradient boosting are used in the proposed work. The performance of the model is verified well and the results are provided in the result section. From the experimental result, it is observed that the classification accuracy is 96% with SVM and 98% with boosted meta classifier for leukemia data, whereas 99.04% for lung tumor and 99.03% for liver Cirrhosis.

Identification of Mir-532-5p As a Reliable miRNA Reference in Acute Lymphoblastic Leukemia

In acute lymphoblastic leukemia (ALL) cure rates have reached &amp;gt;90%, however, therapy resistance and relapses occur. Hence, the major interest in leukemia research is the understanding of ALL biology and, based on that, the development of new targeted therapies. As patient-derived xenograft (PDX) mouse models, such as the NOD/SCID/human ALL xenotransplantation model, are systems resembling the human disease with leukemia engraftment and manifestation in bone marrow (BM), central nervous system (CNS), and spleen, they provide reliable models to study ALL biology and preclinically evaluate new treatment strategies. MicroRNAs (miRNAs) are described to post-transcriptionally regulate the expression of genes coding for critical regulators of leukemogenesis either functioning as tumor-suppressors or oncomirs. Moreover, ALL miRNA profiles can serve as biomarkers indicating disease stages and distribution and can be used for patient risk stratification. To reliably compare miRNA data across different samples and, most importantly, across various cohorts, data normalization with a constantly expressed miRNA reference in the respective samples is required. We aimed at identifying a reliable miRNA reference which shows a stable and constant expression in different ALL samples in a series of cell lines and PDX ALL specimens derived from different organ compartments. We analyzed the expression of 6 frequently used miRNA references (RNU6, RNU1A1, SNORD44, 5sRNA, miR-103a-3p and miR-532-5p) by real-time quantitative PCR. All together 9 ALL cell lines (NALM-6, REH, RS4;11, KOPN-8, UoCB6, RCH-ACV, MHH-CALL4, EU3, and HAL-01), 22 PDX specimens including CNS-, BM-, and spleen-derived ALL cells, and peripheral blood mononuclear cells of 6 healthy controls (HC-PBMCs) were investigated. Expression stabilities of the respective miRNA references were assessed applying four different algorithms (geNorm, BestKeeper, Normfinder, ∆CT). MiRNA scores were ranked according to their performance using RefFinder. Finally, the impact of using various miRNA references on data normalization was evaluated in different ALL and control samples. Analyzing the stability of miRNAs within the ALL cohort, we identified miR-532-5p in combination with miR-103a-3p (geNorm), miR-532-5p (BestKeeper and NormFinder), and miR-103a-3p followed by miR-532-5p (∆CT; mean standard deviation of miR-103a-3p and miR-532-5p: 2.215 and 2.220, respectively) as the most stably expressed miRNAs. Importantly, the RefFinder algorithm identified miR-532-5p as the most reliable reference. Next, we assessed the stability of miRNA expression in HC-PBMCs, and identified SNORD44 (NormFinder, BestKeeper, ∆CT) or SNORD44 in combination with RNU6 (geNorm) as the miRNAs showing the lowest variation. These data were further substantiated by using RefFinder which identified SNORD44 as the most reliable miRNA reference in HC-PBMCs. Interestingly, when we combined the cohorts of ALL and HC samples and applied RefFinder, miR-532-5p in combination with miR-103a-3p were identified as the most stable miRNAs. Last, we analyzed the influence of using expression levels of different miRNAs for data normalization. We assessed the expression of miR-181a-5p, a miRNA described to be frequently overexpressed in ALL cells as compared to HC-PBMCs and used expression data of different miRNAs for normalization: miR-532-5p (most stably expressed in ALL cohort), miR-532-5p/miR-103a-3p (combining the most stable miRNA references in ALL cohort and HCs), or SNORD44 (most stably expressed in HC-PBMCs). Interestingly and in line with its described upregulation in ALL, we identified a significant overexpression of miR-181a-5p in BM-derived PDX samples as compared to HC-PBMCs when using miR-532-5p or miR-532-5p/miR-103a-3p for data normalization. However, when SNORD44 was used as a reference, no differential expression of miR-181a-5p in HC-PBMCs and BM-ALL cells was found indicating a critical impact of the normalization reference. In summary, we identified miR-532-5p to be constantly expressed across different leukemia samples providing a reliable miRNA reference for data normalization in ALL miRNA expression studies. Moreover, these data clearly emphasize that the chosen reference critically depends on the cellular background of the specific samples and requires careful selection and verification within the respective sample types.

Regulation of pDC fate determination by histone deacetylase 3.

Dendritic cells (DCs), the key antigen-presenting cells, are primary regulators of immune responses. Transcriptional regulation of DC development had been one of the major research interests in DC biology; however, the epigenetic regulatory mechanisms during DC development remains unclear. Here, we report that Histone deacetylase 3 (Hdac3), an important epigenetic regulator, is highly expressed in pDCs, and its deficiency profoundly impaired the development of pDCs. Significant disturbance of homeostasis of hematopoietic progenitors was also observed in HDAC3-deficient mice, manifested by altered cell numbers of these progenitors and defective differentiation potentials for pDCs. Using the in vitro Flt3L supplemented DC culture system, we further demonstrated that HDAC3 was required for the differentiation of pDCs from progenitors at all developmental stages. Mechanistically, HDAC3 deficiency resulted in enhanced expression of cDC1-associated genes, owing to markedly elevated H3K27 acetylation (H3K27ac) at these gene sites in BM pDCs. In contrast, the expression of pDC-associated genes was significantly downregulated, leading to defective pDC differentiation.

Influence of ZnO Nanoparticles on the Properties of Ibuprofen-Loaded Alginate-Based Biocomposite Hydrogels with Potential Antimicrobial and Anti-Inflammatory Effects.

Hydrogels are a favorable alternative to accelerate the burn wound healing process and skin regeneration owing to their capability of absorbing contaminated exudates. The bacterial infections that occur in burn wounds might be treated using different topically applied materials, but bacterial resistance to antibiotics has become a major problem worldwide. Therefore, the use of non-antibiotic treatments represents a major interest in current research. In this study, new antibiocomposite hydrogels with anti-inflammatory and antimicrobial properties based on hyaluronic acid (HA) and sodium alginate (AG) were obtained using 4-(4,6-dimethoxy-1,3,5-triazinyl-2)-4-methylmorpholinium chloride as an activator. The combination of Ibuprofen, a non-steroidal anti-inflammatory drug commonly used to reduce inflammation, fever and pain in the body, with zinc oxide nanoparticles (ZnO NPs) was used in this study aimed at creating a complex hydrogel with anti-inflammatory and antimicrobial action and capable of improving the healing process of wounds caused by burns. FTIR spectra confirmed the cross-linking of AG with HA as well as the successful incorporation of ZnO NPs. Using electronic microscopy, it was noticed that the morphology of hydrogels is influenced by the incorporation of ZnO nanoparticles. Moreover, the incorporation of ZnO nanoparticles into hydrogels also has an influence on the swelling behavior at both pH 7.4 and 5.4. In fact, the swelling rate is lower when the amounts of the activator, HA and ZnO NPs are high. A drug release rate of almost 100% was observed for hydrogels without ZnO NPs, whereas the addition of nanoparticles to hydrogels led to a decrease in the release rate to 68% during 24 h. Cellular viability tests demonstrated the non-cytotoxic behavior of the hydrogels without the ZnO NPs, whereas a weak to moderate cytotoxic effect was noticed for hydrogels with ZnO NPs. The hydrogels containing 4% and 5% ZnO NPs, respectively, showed good antimicrobial activity against the S. aureus strain. These preliminary data prove that these types of hydrogels can be of interest as biomaterials for the treatment of burn wounds.

Motivation to use digital educational content – differences between science and other STEM students in higher education

Abstract Digitalization leads to an increased importance of digital educational content for learning in higher education as well as in the sciences. The investigation of individual factors (e.g., motivation, self-efficacy, attitudes) influencing the intention to use digital educational content is a major research interest in design and implementation of suitable content (Hsu, J.-Y., Chen, C.-C., &amp; Ting, P.-F. (2018). Understanding MOOC continuance: An empirical examination of social support theory. Interactive Learning Environments, 26(8), 1100–1118), yet to date without differentiation by discipline. A questionnaire following the Theory of Planned Behavior (Fishbein, M., &amp; Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. Psychology Press. http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10462474) was developed to analyze relationships between STEM students’ individual beliefs and their personal motivation to use digital educational content. In November 2021, a total of 662 STEM students from 8 universities in Germany participated in the study. Analyses showed differences within the group of STEM students: science students rated their digital skills lower and expected more difficulties compared to other STEM students, but did not differ in terms of their motivation. For science students, unlike other STEM students, motivation was predicted only by attitudes and digital skills. Moreover, the present results suggest that, first, the focus of the design should be on learning environment rather than on digital tools, and second, the potential of collaboration is largely unrecognized by students.

Meet the First Authors.

Meet the First Authors.

Nuclear genome organization in fungi: from gene folding to Rabl chromosomes.

Comparative genomics has recently provided unprecedented insights into the biology and evolution of the fungal lineage. In the postgenomics era, a major research interest focuses now on detailing the functions of fungal genomes, i.e. how genomic information manifests into complex phenotypes. Emerging evidence across diverse eukaryotes has revealed that the organization of DNA within the nucleus is critically important. Here, we discuss the current knowledge on the fungal genome organization, from the association of chromosomes within the nucleus to topological structures at individual genes and the genetic factors required for this hierarchical organization. Chromosome conformation capture followed by high-throughput sequencing (Hi-C) has elucidated how fungal genomes are globally organized in Rabl configuration, in which centromere or telomere bundles are associated with opposite faces of the nuclear envelope. Further, fungal genomes are regionally organized into topologically associated domain-like (TAD-like) chromatin structures. We discuss how chromatin organization impacts the proper function of DNA-templated processes across the fungal genome. Nevertheless, this view is limited to a few fungal taxa given the paucity of fungal Hi-C experiments. We advocate for exploring genome organization across diverse fungal lineages to ensure the future understanding of the impact of nuclear organization on fungal genome function.

Synthesis, Characterization and Biological Activities of Zinc Oxide Nanoparticles Derived from Secondary Metabolites of Lentinula edodes.

Zinc oxide nanoparticles (ZnO NPs) are the second most prevalent metal oxide, owing to their characteristics of low cost, safe, and easily prepared. ZnO NPs have been found to exhibit unique properties which show their potential to be used in various therapies. Numerous techniques have been devised for the manufacture of zinc oxide because it is one of the nanomaterials that has received major research interest. Mushroom sources are proven to be efficient, ecologically friendly, inexpensive, and safe for humankind. In the current study, an aqueous fraction of methanolic extract of Lentinula edodes (L. edoes) was used to synthesize ZnO NPs. The biosynthesis of ZnO NPs was achieved by using the reducing and capping capability of an L. edodes aqueous fraction. Bioactive compounds from mushroom, such as flavonoids and polyphenolic compounds, are used in the green synthesis process to biologically reduce metal ions or metal oxides to metal NPs. Biogenically synthesized ZnO NPs were further characterized by using UV-Vis, FTIR, HPLC, XRD, SEM, EDX, zeta sizer and zeta potential analyses. The FTIR showed the functional group at the spectra in the range 3550-3200 cm-1 indicated the presence of the hydroxyl (OH) group, while bands in the range 1720-1706 cm-1 indicated C=O carboxylic stretches bonds. Furthermore, the XRD pattern of ZnO NPs created in the current study was found to be nanocrystals which are hexagonal. The SEM analysis of ZnO NPs showed spherical shapes and size distributions in the range 90-148 nm. Biologically synthesized ZnO NPs have substantial biological activities including antioxidant, antimicrobial, antipyretic, antidiabetic and anti-inflammatory potential. Biological activities showed significant antioxidant (65.7 ± 1.09), antidiabetic (85.18 ± 0.48), and anti-inflammatory potential (86.45 ± 0.60) at 300 µg inhibition in paw inflammation of (1.1 ± 0.06) and yeast-induced pyrexia (97.4 ± 0.51) at 10 mg in a dose-dependent manner. The outcomes of this research indicated that ZnO NPs significantly reduced inflammation and have the ability to scavenge free radicals and prevent protein denaturation, while also indicating their possible use in food and nutraceutical applications to treat various ailments.