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Fermentation parameters, especially the duration, are important in imparting a peculiar taste and flavor to soy sauce. The main purpose of this research was to monitor metabolic changes occurring during the various time intervals of the fermentation process. NMR-based metabolomics was used to monitor the compositional changes in soy sauce during fermentation. The 1H-NMR spectra of the soy sauce samples taken from the fermentation tanks at 0 to 8 months were analyzed using 1H-NMR spectroscopy, and the obtained spectra were analyzed by multivariate statistical analysis. The Principal Component Analysis (PCA) and Partial Least Square Discriminate analysis (PLSDA) revealed the separation of samples fermented for various time durations under identical conditions. Key metabolites shown by corresponding loading plots exhibited variations in amino acids (lysine, threonine, isoleucine, etc.), acetate, glucose, fructose, sucrose, ethanol, glycerol, and others. The levels of ethanol in soy sauce increased with longer fermentation durations, which can be influenced by both natural fermentation and the intentional addition of ethanol as a preservative. The study shows that the variation in metabolite can be very efficiently monitored using 1H-NMR-based metabolomics, thus suggestion to optimize the time duration to get the soy sauce product with the desired taste and flavor.

Breast cancer is a prevalent and potentially life-threatening disease that affects women worldwide. Natural products have gained attention as potential anticancer agents due to their fewer side effects, low toxicity, and cost effectiveness compared to traditional chemotherapy drugs. In the current study, the network pharmacology approach was used following a molecular docking study to evaluate the therapeutic potential of N. sativa-derived phytochemicals against breast cancer. Specifically, the study aimed to identify potential anticancer agents targeting key proteins implicated in breast cancer progression. Five proteins (i.e., EGFR, MAPK3, ESR1, MAPK1, and PTGS2) associated with breast cancer were selected as receptor proteins. Fourteen phytochemicals from N. sativa were prioritized based on drug-likeness (DL) and oral bioavailability (OB) parameters (with criteria set at DL > 0.18 and OB > 30%, respectively). Subsequent analysis of gene targets identified 283 overlapping genes primarily related to breast cancer pathogenesis. Ten hub genes were identified through topological analysis based on their significance in the KEGG pathway and GO annotations. Molecular docking revealed strong binding affinities between folic acid, betulinic acid, stigmasterol, and selected receptor proteins. These phytochemicals also demonstrated druggability potential. In vitro experiments in the MDA-MB-231 breast cancer cell line revealed that betulinic acid and stigmasterol significantly reduced cell viability after 24 h of treatment, confirming their anticancer activity. Furthermore, in vivo evaluation using a DMBA-induced rat model showed that betulinic acid and stigmasterol contributed to the significant recovery of cancer markers. This study aimed to explore the mechanisms underlying the anticancer potential of N. sativa phytochemicals against breast cancer, with the ultimate goal of identifying novel therapeutic candidates for future drug development. Overall, these results highlight betulinic acid and stigmasterol as promising candidates to develop novel anticancer agents against breast cancer. The comprehensive approach of this study, which integrates network pharmacology and molecular docking study and its experimental validation, strengthens the evidence supporting the therapeutic benefits of N. sativa-derived phytochemicals in breast cancer treatment, making them promising candidates for the development of novel anticancer agents against breast cancer.

Combination therapy and multitarget drugs have recently attracted much attention as promising tools to fight against many challenging diseases and, thus, represent a new research focus area. The aim of the current project was to screen multitarget compounds and to study their individual and combined effects on acetaminophen-induced liver injury. In this study, 2 of the best hepatoprotective multitargeting compounds were selected from a pool of 40 major compounds present in Curcuma longa and Cinnamomum zeylanicum by using molecular docking, ADMET profiling, and Pfizer’s rule of five. The two selected compounds, quercetin and curcumin, showed a high binding affinity for the CYP2E1 enzyme, MAPK, and TLR4 receptors that contribute to liver injury. The candidates caused the decreased viability of cancer cell lines (HepG2 and Huh7) but showed no effect on a normal cell line (Vero). Examination of biochemical parameters (ALT, AST, ALP, and bilirubin) showed the hepatoprotective effect of the candidate drugs in comparison with the control group, which was confirmed by histological findings. Taken together, quercetin and curcumin not only satisfied the drug-like assessment criterion and proved to be multitargeting by preventing liver damage but also showed anticancer activities.

Cadmium (Cd) contamination in the soil potentially hampers microbial biomass and adversely affects their services such as decomposition and mineralization of organic matter. It can reduce nitrogen (N) metabolism and consequently affect plant growth and physiology. Further, Cd accumulation in plants can pose health risks through vegetable consumption. Here, we investigated consequences of Cd contamination on fertilizer value and associated health risks following the application of biogas residues (BGR) to various soil types. Our results indicate that the application of BGR to all soil types significantly increased dry matter (DM) yield and N uptake. However, the Cd contamination negatively affected DM yield and N recovery from BGR in a dose-dependent manner. Organic N mineralization from BGR also decreased in Cd-contaminated soils. The highest DM yield and N recovery were recorded in sandy soil, whereas the lowest values were observed in clay soil. Cadmium was accumulated in spinach, and health risk index (HRI) associated with its dietary intake revealed that consuming spinach grown in Cd-contaminated soil, with or without BGR, is unsafe. Among the soil types, values of daily intake of metals (DIM) and HRI were lowest in clay soil and highest in sandy soil. However, the application of BGR curtailed HRI across all soil types. Notably, the application of BGR alone resulted in HRI values < 1, which are under the safe limit. We conclude that soil contamination with Cd reduces fertilizer value and entails implications for human health. However, the application of BGR to the soil can decrease Cd effects.

Infertility is a well-recognized multifactorial problem affecting the majority of people who struggle with infertility issues. In recent times, among infertility cases, the male factor has acquired importance, and now it contributes to approximately half of the infertility cases because of different abnormalities. In the current study, we used natural phytochemicals as potential drug-lead compounds to target different receptor proteins that are involved in the onset of male infertility. A set of 210 plant phytochemicals were docked counter to active site residues of sex hormone-binding globulin, a disintegrin and metalloproteinase 17, and DNase I as receptor proteins. On the basis of binding scores and molecular dynamics simulation, the phytochemicals tricin, quercetin, malvidin, rhamnetin, isorhamnetin, gallic acid, kaempferol, esculin, robinetin, and okanin were found to be the potential drug candidates to treat male infertility. Molecular dynamics simulation showed tricin as a strong inhibitor of all selected receptor proteins because the ligand–protein complexes remained stabilized during the entire simulation time of 100 ns. Further, an in vivo study was designed to evaluate the effect of tricin in male rats with nicotine-induced infertility. It was explored that a high dose of tricin significantly reduced the levels of alanine transaminase, aspartate transaminase, urea, creatinine, cholesterol, triglyceride, and low-density lipoprotein and raised the level of high-density lipoprotein in intoxicated male rats. A high dose of tricin also increased the reproductive hormones (i.e., testosterone, luteinizing hormone, follicle-stimulating hormone, and prolactin) and reduced the level of DHEA-SO4. The phytochemical (tricin, 10 mg/kg body weight) also showed significant improvement in the histo-architecture after nicotine intoxication in rats. From the current study, it is concluded that the phytochemical tricin could serve as a potential drug candidate to cure male infertility.

Rapid industrialization and urbanization are the two significant issues causing environmental pollution. The polluted water from various industries contains refractory organic materials such as dyes. Heterogeneous photocatalysis using semiconductor metal oxides is an effective remediation technique for wastewater treatment. In this research, we used a co-precipitation-assisted hydrothermal method to synthesize a novel I-FeWO4/GO sunlight-active nanocomposite. Introducing dopant reductive iodine species improved the catalytic activity of FeWO4/GO. I− ions improved the catalytic performance of H2O2 by doping into FeWO4/GO composite. Due to I− doping and the introduction of graphene as a support medium, enhanced charge separation and transfer were observed, which is crucial for efficient heterogeneous surface reactions. Various techniques, like FTIR, SEM-EDX, XRD, and UV–Vis spectroscopy, were used to characterize composites. The Tauc plot method was used to calculate pristine and iodine-doped FeWO4/GO bandgap. Iodine doping reduced the bandgap from 2.8 eV to 2.6 eV. The degradation of methylene blue (MB) was evaluated by optimizing various parameters like catalyst concentration, oxidant dose, pH, and time. The optimum conditions for photocatalysts where maximum degradation occurred were pH = 7 for both FeWO4/GO and I-FeWO4/GO; oxidant dose = 9 mM and 7 mM for FeWO4/GO and I-FeWO4/GO; and catalyst concentration = 30 mg and 35 mg/100 mL for FeWO4/GO and I-FeWO4/GO; the optimum time was 120 min. Under these optimum conditions, FeWO4/GO and I-FeWO4/GO showed 92.0% and 97.0% degradation of MB dye.

The white mango scale (WMS) insect, Aulacaspis tubercularis (Hemiptera: Diaspididae), is a polyphagous, multivoltine pest which is a serious threat to qualitative mango production and export. The WMS insect sucks sap from leaves, branches and fruits. The heavy infestation of this pest may cause the falling of young leaves, drying up of twigs, poor flowering, and, finally, reduce the quality of fruits by producing pink spots on fruits’ surface. This review paper was written to provide comprehensive information about pest biology, ecology and management in different parts of the world. WMS was first reported on the island of Formosa on Mangifera indica in 1929 and later on in the Caribbean Islands, India and Brazil. Now it is found in almost 69 mango-producing countries of the world. The thermal regime may affect the population of pests. In Australia, the life cycle is completed in 35–40 days in summer and 70–85 days in winter. Variety, age of plants, number of trees per acre, canopy size and sunlight penetration affect the density of WMS. Different Coccinellid beetles and parasitoid Encarsia femorosa feed on WMS; however, farmers most commonly use insecticides to get rid of this pest. In Pakistan, WMS is a growing threat to the export of mangoes; hence IPM plan is needed to reduce the pest numbers and enhance qualitative mango production.

Hepatocellular carcinoma (HCC) is the fifth most common and fatal cancer reported, representing 72.5% of malignancies around the world. The majority of HCC incidents have been associated with infections caused by hepatitis B and C viruses. Many first- and second-line conventional drugs, e.g., sorafenib, cabozantinib, or ramucirumab, have been used for the management of HCC. Despite different combinational therapies, there are still no defined biomarkers for an early stage diagnosis of HCC. The current study evaluated the potential of Bergenia stracheyi, Bergenia ciliata, Bergenia pacumbis, and Bergenia purpurascens, which belong to the family Saxifragaceae, to treat HCC using an integrated network pharmacology and molecular docking approach. Four active phytochemicals were selected based on oral bioavailability (OB) and drug likeness (DL) parameters. The criteria of phytochemical selection were set to OB > 30% and DL > 0.18. Similarly, the gene targets related to Bergenia spp. and the genes related to HCC were retrieved from different databases. The integration of these genes revealed 98 most common overlapping genes, which were mainly interrelated with HCC pathogenesis. Ultimately, the 98 Bergenia-HCC associated genes were used for protein–protein interaction (PPI), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) enrichment analyses. Finally, the topological analysis revealed the top ten hub genes with maximum degree rank. From the top ten genes, STAT3, MAPK3, and SRC were selected due to their involvement in GO annotation and KEGG pathway. To confirm the network pharmacology results, molecular docking analysis was performed to target STAT3, MAPK3, and SRC receptor proteins. The phytochemical (+)-catechin 3-gallate exhibited a maximum binding score and strong residue interactions with the active amino acids of MAPK3-binding pockets (S-score: −10.2 kcal/mol), SRC (S-score: −8.9 kcal/mol), and STAT3 (S-score: −8.9 kcal/mol) as receptor proteins. (+)-Catechin 3-gallate and β-sitosterol induced a significant reduction in cell viability in HepG2 after 24 h of treatment in a dose-dependent manner. The results of this study explore the potential of (+)-catechin 3-gallate and β-sitosterol, which can be used in the future as potential drug candidates to suppress HCC.

This paper presents the advanced classes of linear symmetric subdivision schemes for the fitting of data and the creation of geometric shapes. These schemes are derived from the B-spline and Lagrange’s blending functions. The important characteristics of the derived schemes, including continuity, support, and the impact of parameters on the magnitude of the artifact and Gibbs oscillations are discussed. Schemes additionally generalize various subdivision schemes. Linear symmetric subdivision schemes can produce Gibbs oscillations when the initial data is taken from discontinuous functions. Additionally, these schemes may generate unwanted artifacts in the limit curve that do not exist in the original polygon. One solution is to use non-linear schemes, but this approach increases the computational complexity of the scheme. An alternative approach is proposed that involves modifying the linear symmetric schemes by introducing parameters into the linear rules. The suitable values of these parameters reduce or eliminate Gibbs oscillations and artifacts while still using linear symmetric schemes. Our approach provides a balance between reducing or eliminating Gibbs oscillations and artifacts while maintaining computational efficiency.

The genus Capripoxvirus belongs to the Poxviridae family. The sheeppox, goatpox, and lumpy skin disease viruses are three species of this genus with 96% identity in their genomes. These are financially devastating viral infections among cattle, which cause a reduction in animal products and lead to a loss in livestock industries. In the current study, the phylogenetic analysis was carried out to reveal the evolutionary relationships of Capripoxvirus species (i.e., sheeppox virus (SPPV), goatpox virus (GTPV), and lumpy skin disease virus (LSDV)) with other viruses from the Poxviridae family with >96% query coverage to find the similarity index among all members. The three viruses (i.e., SPPV, GTPV, and LSDV) joined the clade of Capripoxvirus of the Poxviridae family in the phylogenetic tree and exhibited close evolutionary relationships. The multiple sequence alignment using ClustalOmega revealed significant variations in the protein sequences of the DNA-dependent RNA polymerase of SPPV, GTPV, and LSDV. The three-dimensional structures of five selected bee peptides and DNA-directed RNA polymerase of SPPV, GTPV, and LSDV were predicted using trRosetta and I-TASSER and used for molecular docking and simulation studies. The protein–protein docking was carried out using HADDOCK server to explore the antiviral activity of peptides as honey bee proteins against SPPV, GTPV, and LSDV. In total, five peptides were docked to DNA-directed RNA polymerase of these viruses. The peptides mellitin and secapin-1 displayed the lowest binding scores (−106.9 +/− 7.2 kcal/mol and −101.4 +/− 11.3 kcal/mol, respectively) and the best patterns with stable complexes. The molecular dynamics simulation indicated that the complex of protein DNA-dependent RNA polymerase and the peptide melittin stayed firmly connected and the peptide binding to the receptor protein was stable. The findings of this study provide the evidence of bee peptides as potent antimicrobial agents against sheeppox, goatpox, and lumpy skin disease viruses with no complexity.

The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques.

Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

Subdivision schemes are equipped with some rules that take a polygon as an input and produce smooth curves or surfaces as an output. This presents the issue of how accurately the polygon approximates the limit curve and surface. What number of iterations/levels would be necessary to achieve the required shape at a user-specified error tolerance? In fact, several methods have been introduced in the case of stationary schemes to address the issue in terms of the error bounds (distance between polygon/polyhedron and limiting shape) and subdivision depth (the number of iterations required to obtain the result at a user-specified error tolerance). However, in the case of non-stationary schemes, this topic needs to be further studied to meet the requirements of new practical applications. This paper highlights a new approach based on a convolution technique to estimate error bounds and subdivision depth for non-stationary schemes. The given technique is independent of any condition on the coefficient of the non-stationary subdivision schemes, and it also produces the best results with the least amount of computational effort. In this paper, we first associated constants with the vectors generated by the given non-stationary schemes, then formulated an expression for the convolution product. This expression gives real values, which monotonically decrease with the increase in the order of the convolution in both the curve and surface cases. This convolution feature plays an important role in obtaining the user-defined error tolerance with fewer iterations. It achieves a trade-off between the number of iterations and user-specified errors. In practice, more iterations are needed to achieve a lower error rate, but we achieved this goal by using fewer iterations.

Among primary liver cancers, hepatocellular carcinoma (HCC) is one of the most common forms and it has been categorized as the joint-fourth largest reason of cancer-related deaths globally. Different factors such as alcohol abuse, hepatitis B and C, viral infections, and fatty liver diseases are mainly related to the pathogenesis of HCC. In the current study, 1000 total various plant phytochemicals were docked to proteins involved in HCC. The compounds were docked to the active site amino acids of epidermal growth factor receptor and caspase-9 as receptor proteins in order to explore their inhibiting potential. The top five compounds against each receptor protein were explored as potential drug candidates on the basis of their binding affinity and root-mean square deviation values. The top two compounds against each protein were found to be liquoric acid (S-score −9.8 kcal/mol) and madecassic acid (S-score −9.3 kcal/mol) against EGFR, and limonin (S-score −10.5 kcal/mol) and obamegine (S-score −9.3 kcal/mol) against the caspase-9 protein. The selected phytochemicals were further assessed through drug scanning using Lipinski’s rule of five to explore their molecular properties and druggability. According to the ADMET analysis, the selected phytochemicals were found to be non-toxic and non-carcinogenic. Finally, the molecular dynamics simulation study revealed that liquoric acid and limonin were stabilized within the binding pockets of EGFR and capase-9, respectively, and stayed firmly bound throughout the simulation. In light of the current findings, the phytochemicals reported in this study, especially liquoric acid and limonin, could be used as potential drugs for the treatment of HCC in the future.

Organic solar cells (OSCs) made of electron-acceptor and electron-donor materials have significantly developed in the last decade, demonstrating their enormous potential in cutting-edge optoelectronic applications. Consequently, we designed seven novel non-fused ring electron acceptors (NFREAs) (BTIC-U1 to BTIC-U7) using synthesized electron-deficient diketone units and reported end-capped acceptors, a viable route for augmented optoelectronic properties. The DFT and TDDFT approaches were used to measure the power conversion efficiency (PCE), open circuit voltage (Voc), reorganization energies (λh, λe), fill factor (FF), light harvesting efficiency (LHE) and to evaluate the potential usage of proposed compounds in solar cell applications. The findings confirmed that the photovoltaic, photophysical, and electronic properties of the designed molecules BTIC-U1 to BTIC-U7 are superior to those of reference BTIC-R. The TDM analysis demonstrates a smooth flow of charge from the core to the acceptor groups. Charge transfer analysis of the BTIC-U1:PTB7-Th blend revealed orbital superposition and successful charge transfer from HOMO (PTB7-Th) to LUMO (BTIC-U1). The BTIC-U5 and BTIC-U7 outperformed the reference BTIC-R and other developed molecules in terms of PCE (23.29% and 21.18%), FF (0.901 and 0.894), normalized Voc (48.674 and 44.597), and Voc (1.261 eV and 1.155 eV). The proposed compounds enclose high electron and hole transfer mobilities, making them the ideal candidate for use with PTB7-Th film. As a result, future SM-OSC design should prioritize using these constructed molecules, which exhibit excellent optoelectronic properties, as superior scaffolds.

The effect of spacetime curvature on optical properties may provide an opportunity to suggest new tests for gravity theories. In this paper, we investigated gravitational weak lensing around a Bardeen black hole with the string clouds parameter. First, we examined the horizon structure in the presence of string clouds around the gravitational compact object defined by Bardeen spacetime. The effect of gravitational weak lensing in a plasma medium is also discussed. According to the findings, the influence of the string cloud parameter on the circular orbits of a light ray around the black hole is greater than that in the Schwarzschild case, while the influence of the charge is reversed. The deflection angle of light rays in weak lensing is also used to study how much the image is magnified.

The synthesis and design of low-cost visible-light-active catalysts for the photodegradation of organic dyes have been regarded as an efficient way to use solar energy in addressing environmental issues. We report the fabrication of MoS2/CdS nanoparticles functionalized with Bi2S3 nanoflakes. The ternary composites of “MoS2/CdS/Bi2S3” were synthesized in situ by a hydrothermal method at different temperatures. The changes in structural, optical, and morphological properties of the synthesized CdS/MoS2/Bi2S3 were explored. The effects of Bi2S3 on CdS/MoS2 were thoroughly studied by performing an X-ray diffractometer (XRD), a scanning electron microscope (SEM), an ultra-violet–visible spectrometer (Uv–vis), and Fourier transform infrared spectroscopic (FT-IR) studies of the nanoparticles. XRD confirms the cubical crystal structure of the nanoparticles. SEM studies possess the modulation in the surface morphology with the tenability in volume ratios of “MoS2/CdS/Bi2S3” composites. It was observed that the bandgaps calculated using absorption measurements could be manipulated from 2.40 eV to 0.97 eV with varying Bi2S3 in the MoS2/CdS nanostructures. FT-IR confirmed the synthesis of “MoS2/CdS/Bi2S3” nanoparticles. On allowing the visible light to fall for 120 min, it was observed that “MoS2/CdS/Bi2S3” degrades the methylene blue up to 90%. The calculated results of “MoS2/CdS/Bi2S3” suggest that the synthesized material could be a strong candidate for photodegradation applications. This research work explains the synthesis of MoS2/CdS/Bi2S3-based nanocomposites for the degradation of dye using a photocatalytic process. The final results show that this catalyst effectively degrades the dye.

Hepatitis E virus (HEV) is the notable causative agent of acute and chronic hepatic, renal, pancreatic, neurological, and hematopoietic blood cell infections with high risk in immunocompromised patients. Hepatic failure is mostly documented among adults, pregnant women, and patients with preexisting liver disease. HEV is a positive sense RNA virus of 7.2 kb genome size with typically three open reading frames (ORFs) which play essential roles in viral replication, genome assembly, and transcription. The mutational substitution in the viral RNA genome makes more it difficult to understand the actual relationship in the host–virus association. ORFs of HEV encode different structural and non-structural proteins and one of them is the capsid protein which is coded by ORF2. The capsid protein mediates the encapsulation of the viral genome as well as being involved in virion assembly. In the current study, the ligand-based docking approach was employed to inhibit the active amino acids of the viral capsid protein. Depending upon S-score, ADMET profiling, and drug scanning, the top ten tetrapeptides were selected as potential drug candidates with no toxicity counter to HEV receptor protein. The S-score or docking score is a mathematical function which predicts the binding affinities of docked complexes. The binding affinity of the predicted drug–target complexes helps in the selectivity of the desired compound as a potential drug. The best two selected peptides (i.e., TDGH with S-score of −8.5 and EGDE with S-score of −8.0) interacted with the active site amino acids of the capsid protein (i.e., Arg399, Gln420, and Asp444). The molecular dynamics simulations of RMSD trajectories of TDGH–capsid protein and EDGE–capsid protein have revealed that both docked complexes were structurally stable. The study revealed that these tetrapeptides would serve as strong potential inhibitors and a starting point for the development of new drug molecules against the HEV capsid protein. In future, in vivo studies are needed to explore selected peptides as potential drug candidates.

Ginger (Zingiber officinale Rosc; Zingiberaceae family) is an herb commonly used as a spice and remedy for a broad spectrum of diseases. The essential oil extracted from ginger is an effective antioxidant, anti-inflammatory, and antifungal agent. The present study has investigated the variations in yield and chemical composition of essential oils of two cultivars (Chinese and Thailand) of ginger locally available in Pakistan. Two different drying pretreatments were employed to observe the changes in compositional variations of the essential oils of ginger. The essential oil extracted from fresh, oven-dried, and sun-dried samples of two different cultivars of ginger was analyzed using gas chromatography-mass spectrometry (GC-MS). The essential oil yield was found to be highest for the sun-dried sample of each variety. The major compounds (>4%) overall in the essential oil of fresh, oven-dried, and sun-dried ginger samples from Thailand origin were camphene, 3-carene, o-cymene, caryophyllene, α-curcumene, sabinol trans, citral, and santalol. Major compounds overall in the essential oil of fresh, oven-dried, and sun-dried ginger samples of Chinese origin were α-pinene, Camphene, limonene, longicyclene, copaene, longifolene, β-sesquiphellandrene, alloaromadendrene, γ-muurolene, α-curcumene, α-farnesene, and citral. The inter-varietal variations and pretreatment methods considerably affected yield and chemical composition. Cluster analysis was performed to validate the results further. Significantly varying compounds responsible for the significant variation among varieties and treatments of the ginger were identifies by using the heat map. There was clear differentiation among Chinese and Thailand varieties due to the variation in the concentrations of the volatile compounds. The results obtained can be helpful for the ginger growers and end users to choose the ginger variety and the way of use that is more beneficial.

Several studies have reported farmers’ perceptions of climate change, but there is inadequate knowledge available on the farm households’ climate change awareness (CCA) in Pakistan. This study was undertaken to assess farmers’ CCA. For this purpose, the study collected the data from southern and central Punjab, Pakistan, through a purposively multistage random sampling technique. Binary logit and odds ratio were used to analyse the data. The analysis of the study shows that the majority of respondents were aware of climate change but had differing perceptions of climate change. This research showed that 70.8% of farmers are aware of climate change and reported their awareness level on winter and summer rainfall and temperature, the growing season length (GSL) of crops, the sea level rise, and the causes of climate changes and conceptual understanding of it while persistently denying climate change. However, many farmers did not perceive decreasing winter (48%) and summer (31.2%) precipitation, the majority of the farmers could not perceive in the GSL of summer (63.2%) and winter (64.4%) crops, while few did not notice increased winter (36.4%) and summer (33.6%) temperature, respectively. Financial and non-financial factors such as education (1.16), experience (1.07), distance to markets (1.07), non-agricultural income (2.83), access to agricultural credit (0.29) and marketing of produce (6.10), access to extension services (3.87) and the number of adaptation strategies (1.30) were pointedly related to farm households’ CCA. These odds values in the parenthesis show that the likelihood of CCA increases/decreases as these determinants increase. Moreover, the results of the study show that CCA is a significant predictor of adaptation to climate change. Main adaptation strategies opted for by farmers include changing crop variety and type, changing planting dates, tree plantation, increasing/changing fertilizer, soil and water conservation, off-farm income and diversification. Further, the study finds that some farmers did not perceive climate change as it takes time to be visible, but they are aware of climate change. Therefore, there is a need to reshape the households’ perception of climate change and enhance farmers’ CCA through existing extension services.

Molecularly imprinted polymers have been used for the creation of an electrochemical sensor for the detection of sucrose, which are modified by using functionalized graphene (fG). Using AIBN as the free radical initiator and sucrose as the template, imprinted polymers are synthesized. The monomer, 4,4′-diisocyanatodiphenylmethane (DPDI), has both proton donor groups (N-H or O-H) and lone-pair donor groups (C=O). By creating H-bonds with electron donor groups (C=O), the proton donor group in this polymer may interact with the sugar molecule serving as its template. The sensor signals have improved as a result of the interaction between the monomer and the template. Thermogravimetric and differential thermal analysis (TGA/DTA) curves, scanning electron microscopy (SEM), and FT-IR spectroscopy have been employed to characterize the fabricated receptors. The fabricated sensor has exhibited a limit of detection of 16 ppb for the target analyte that is highly sensitive, linear, reversible, regenerative, and selective. Moreover, the sensor’s stability, reproducibility, and reusability have been evaluated for six months, following the device’s manufacturing, and the results revealed similar responses with the percentage error of less than 1%. Most importantly, this sensor has demonstrated a quick response time, which is very sensitive, stable, and selective.

Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields.

Star anise (Illicium verum Hook. fil.) is commonly utilized as a culinary and medicinal fruit and is most famous in indigenous systems of medicine. The present research work aims to appraise and validate the potential of polyphenol-rich star anise tea (SAT) on oxidative stress, obesity and related biochemical parameters in high-fat-sugar-diet (HFSD)-induced obesity model in rats. SAT was prepared using the traditional method in warm water. The Reverse Phase High Pressure Liquid Chromatography (RP-HPLC) analysis was performed for the simultaneous determination of phenolic acids and flavonoids in SAT. Two doses (250 and 500 mg/kg body weight) were selected to investigate the anti-obesity potential of SAT using HFSD-induced obese rat model. Major (>5 mg/100 mL) phenolic acids in SAT were p-coumeric acid, gallic aid, cinamic acid, chlorogenic acid and ferulic acid while catechin and rutin were the major flavonoids detected in the SAT. SAT exhibited 51.3% DPPH radical scavenging activity. In vivo study showed that higher doses of SAT (500 mg/kg body weight) significantly reduced the body weight increase (74.82%) and BMI (0.64 g/cm2). Moreover, significant reductions in the levels of serum total cholesterol, triglyceride, LDL and VLDL were recorded in all the treatment groups in comparison to the HFSDC group. Furthermore, SAT reduced the alterations in MDA, SOD and GSH levels of experimental groups thus showing the potential against oxidative stress. The SAT-500 group showed a significant decrease in the elevated kidney and liver weights and atherogenic index in comparison to the HFSDC group. The present study proved that SAT exhibited strong protective effects against obesity and oxidative stress, especially at higher doses.

Oat bran has gained significant attention among cereal brans owing to its comparatively higher presence of dietary fibers and phytochemicals. The objective of the current research is to personify the nutritional and functional aspects of oat bran after yeast-induced (Saccharomyces cerevisiae) fermentation. For this purpose, a comparative study of raw and fermented oat bran was conducted to investigate the nutritional profile, antioxidant activity and functional characteristics of oat bran. Furthermore, pre- and post-structural variations on fermented bran were determined through scanning electron microscopy (SEM). The results show that crude fat, protein and total dietary fiber (soluble and insoluble) contents were significantly improved after fermentation. Moreover, the post-fermentation value of soluble dietary fiber was increased from 5.01 ± 0.21 to 7.2 ± 0.1%. Antioxidant activity, DPPH-RSA and ferric reducing antioxidant power values of bran samples were also enhanced through fermentation and the anti-nutritional factor, i.e., phytate, was significantly reduced from 1113.3 ± 8.5 to 283.4 ± 3.5 mg/100 g in bran samples after fermentation. Furthermore, the surface morphology of fermented oat bran shows scattering and pores, while raw oat bran shows visible aggregation in SEM micrographs. Water-holding capacity was also enhanced up to 2.11 (5.68%) after fermentation. In conclusion, the post-fermentation results revealed that yeast-induced fermentation enhanced the physicochemical, structural and antioxidant characteristics of oat bran.

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

Increasing rice production has become one of the ultimate goals for South Asian countries. The yield and area under rice production are also facing threats due to the consequences of climate change such as erratic rainfall and seasonal variation. Thus, the main aim of this work was to find out the supply response of rice in Malaysia in relation to both price and non-price factors. To achieve this target, time series analysis was conducted on data from 1970 to 2014 using cointegration, unit root test, and the vector error correction model. The results showed that the planted area and rainfall have a significant effect on rice production; however, the magnitude of the impact of rainfall is less conspicuous for off-season (season 2) rice as compared to main-season rice (season 1). The speed of adjustment from short-run to long-run for season-1 rice production is almost two-and-a-half years (five production seasons), while for season-2 production, it is only about one-and-a-half year (three production seasons). Consequently, the study findings imply the supply of water to be enhanced through better water infrastructure for both seasons. Moreover, the area under season 2 is continuously declining to the point where the government has to make sure that farmers are able to cultivate the same area for rice production by providing uninterrupted supply of critical inputs, particularly water, seed and fertilizers.

This article describes the study of wormhole solutions in f(Q) gravity with noncommutative geometry. Here, we considered two different f(Q) models—a linear model f(Q)=αQ and an exponential model f(Q)=Q−α1−e−Q, where Q is the non-metricity and α is the model parameter. In addition, we discussed the existence of wormhole solutions with the help of the Gaussian and Lorentzian distributions of these linear and exponential models. We investigated the feasible solutions and graphically analyzed the different properties of these models by taking appropriate values for the parameter. Moreover, we used the Tolman–Oppenheimer–Volkov (TOV) equation to check the stability of the wormhole solutions that we obtained. Hence, we found that the wormhole solutions obtained with our models are physically capable and stable.

Sugarcane being the major contributor of sugar and potential source of biofuel around the globe, occupies significant commercial importance. Red rot is the most devastating disease of sugarcane, severely affecting its quality as well as yield. Here we report the overexpression of SUGARWIN1 and SUGARWIN2 genes in any field crop for the first time. For this purpose, SUGAWIN1 and SUGARWIN2 were cloned downstream of maize ubiquitin (Ubi-1) promoter to construct two independent expression cassettes. The bar gene conferring resistance against phosphinothricin was used as selectable marker. Embryogenic calli of sugarcane were bombarded with both expression cassettes and selected on regeneration medium supplemented with phosphinothricin. The phosphinothricin-resistant shoots were rooted and then, analyzed using molecular tools at the genomic as well as transcriptomic levels. The transcriptomic analysis, using real time qPCR, showed that expression of SUGARWIN1 (SWO) and SUGARWIN2 (SWT) was higher in transgenic plants as compared to untransformed plants. Our results further demonstrated that over expression of these genes under maize ubiquitin (Ubi-1) promoter causes significant restriction in proliferation of red rot causal agent, Colletotrichum falcatum in sugarcane transgenic plants, under in vitro conditions. This report may open up exciting possibilities to extend this technology to other monocots for the development of crops with better ability to withstand fungal pathogens.

Heart attack and other heart-related diseases are among the main causes of fatalities in the world. These diseases and some other severe problems like kidney failure and paralysis are mainly caused by hypertension. Since bioactive peptides extracted from naturally existing food substances possess antihypertensive activity, these antihypertensive peptides (AHTP) can function as prospective replacements for existing pharmacological drugs with no or fewer side effects. Such naturally existing peptides can be identified using in-silico approaches. The in-silico methods have been proven to save huge amounts of time and money in the identification of effective peptides. The proposed methodology is a deep learning-based in-silico approach for the identification of antihypertensive peptides (AHTPs). An ensemble method is proposed that combines convolutional neural network (CNN) and support vector machine (SVM) classifiers. Amino acid composition (AAC) and g-gap dipeptide composition (DPC) techniques are used for feature extraction. The proposed methodology has been evaluated on two standard antihypertensive peptide sequence datasets. The model yields 95% accuracy on the benchmarking dataset and 88.9% accuracy on the independent dataset. Comparative analysis is provided to demonstrate that the proposed method outperforms existing state-of-the-art methods on both of the benchmarking and independent datasets.

We report here plastid transformation in sugarcane using biolistic transformation and embryogenesis-based regeneration approaches. Somatic embryos were developed from unfurled leaf sections, containing preprogrammed progenitor cells, to recover transformation events on antibiotic-containing regeneration medium. After developing a proficient regeneration system, the FLARE-S (fluorescent antibiotic resistance enzyme, spectinomycin and streptomycin) expression cassette that carries species-specific homologous sequence tails was used to transform plastids and track gene transmission and expression in sugarcane. Plants regenerated from streptomycin-resistant and genetically confirmed shoots were subjected to visual detection of the fluorescent enzyme using a fluorescent stereomicroscope, after genetic confirmation. The resultant heteroplasmic shoots remained to segregate on streptomycin-containing MS medium, referring to the unique pattern of division and sorting of cells in C4 monocotyledonous compared to C3 monocotyledonous and dicotyledonous plants since in sugarcane bundle sheath and mesophyll cells are distinct and sort independently after division. Hence, the transformation of either mesophyll or bundle sheath cells will develop heteroplasmic transgenic plants, suggesting the transformation of both types of cells. Whilst developed transgenic sugarcane plants are heteroplasmic, and selection-based regeneration protocol envisaging the role of division and sorting of cells in the purification of transplastomic demands further improvement, the study has established many parameters that may open up exciting possibilities to express genes of agricultural or pharmaceutical importance in sugarcane.

Free fatty acids serve as important bioactive molecules in the brain. They are involved in message transfer in the brain. There are many reports available in the literature regarding the role of cerebral fatty acids in message transfer; however, most of the studies are mainly focused on limited fatty acid species or only a few specific brain regions. To understand the relationship between cerebral functions and free fatty acids, it is necessary to investigate the distribution of the free fatty acids among different regions in the whole brain. In this study, free fatty acids were extracted from different brain regions and analyzed qualitatively and quantitatively using the stable isotopic labeling liquid chromatography–mass spectrometry approach. In total, 1008 potential free fatty acids were detected in the whole brain out of which 38 were found to be commonly present in all brain regions. Among different brain regions, the highest and the smallest amounts of potential free fatty acids were detected in the olfactory bulb and cerebellum, respectively. From a statistical point of view, 4-methyl-2-oxovaleric acid, cis-11, 14-eicosadienoic acid, tridecanoic acid, myristic acid, nonadecanoic acid, and arachidic acid were found to significantly vary among the four different brain regions (olfactory bulb, occipital lobe, hippocampus, and cerebellum). The variation in the composition of free fatty acids among different brain regions may be very important for investigating the relationship between free fatty acids and functions of cerebral regions.

Seed quality is accessed by appropriate seed germination, seed moisture contents, insect/pathogen infestations and seed vigor. Seed storage conditions are essential to protect from deterioration. In this study, knowledge and practices of wheat and cotton seeds storage were accessed among commercial seed growers (CSGs) and non-commercial seeds growers (NCSGs) in the Vehari District of Pakistan, while samples of stored wheat and cotton seeds were also collected to assess the quality of stored seeds. Stored seeds in the study area were contaminated by a variety of fungi, with infestation percentages reaching 13% for wheat and 20.7% for cotton in seeds from NCSGs, compared to 9% (wheat) and 9.5% (cotton) in seeds from CSGs. The majority of seed growers (75.0%) did not have any training on seed storage. The growers (60.9%) were unaware that seed should not be stored in closed polythene bags and most (62.2%) were not well aware about seed-borne crop diseases. Most growers did not maintain the temperature and humidity of storage rooms (82.7%) and did not calculate the seed rate before sowing after seed germination tests (87.2%). However, seed dressing with fungicides was implemented by most farmers (69.9%). Controlling the temperature of the seed storage was significantly influenced by growers’ age, while controlling the humidity of the seed storage was significantly influenced by growers’ farming experience. Seed dressing with fungicides was positively associated with the graduation level of growers, while checking seed maturity was positively associated with the higher education (Master’s level) of growers. There was a lack of active information centers in the study areas and a lack of agricultural information provision to farmers. Awareness regarding bad-quality seeds should be initiated to increase growers’ knowledge. Training programs for providing adequate knowledge to growers and skills in seed storage should be organized. Legislation regarding the seed business and seed storage protective measures should be enforced to minimize problems by a seed-borne inoculum and deterioration of seed quality.

The present study is devoted to explore the existence of a new family of compact star solutions by adopting the Karmarkar as well as Pandey–Sharma condition in the background of f ( R , T ) modified gravitational framework. For this purpose, we consider static spherically symmetric spacetime with anisotropic fluid distribution in absence of electric charge. In respect of Karmarkar condition, we assume a specific model of g r r metric potential representing a new family of solutions which is also compatible with the Pandey–Sharma condition. This assumed model permits us to calculate the g t t component of metric tensor by making the use of Karmarkar condition. Further, we investigate the interior solutions for V e l a X − 1 model of compact star by utilizing this new family of solutions for different values of parameter λ . We have tuned the solution for V e l a X − 1 so that the solutions matches the observed mass and radius. For the same star we have extensively discussed the behavior of the solutions. It is found that these solutions fulfill all the necessary conditions under the observational radii and mass attribute data for small values of parameter λ and hence physically well-behaved and promising. Through graphical analysis, it is observed that our obtained analytical solutions are physically acceptable with a best degree of accuracy for n ∈ [ 1.8 , 7 ) − { 2 , 4 , 6 } , where parameter n is involved in the discussed model. It is also noticed the causality condition is violated for all n ≥ 7 and the tangential sound velocity v t is observed as complex valued for all 0 < n < 1.8 . Likewise, we explore these properties by considering large parameter λ values. It is seen that the presented model violates all the physical conditions for n ∈ { 2 , 4 , 6 } , while some of these for large values of λ . Consequently, it can be concluded that the parameters n and λ have a strong impact on the obtained solutions.

Subdivision schemes are extensively used in scientific and practical applications to produce continuous shapes in an iterative way. This paper introduces a framework to compute subdivision depths of ternary schemes. We first use subdivision algorithm in terms of convolution to compute the error bounds between two successive polygons produced by refinement procedure of subdivision schemes. Then, a formula for computing bound between the polygon at k-th stage and the limiting polygon is derived. After that, we predict numerically the number of subdivision steps (depths) required for smooth limiting shape based on the demand of user specified error (distance) tolerance. In addition, extensive numerical experiments were carried out to check the numerical outcomes of this new framework. The proposed methods are more efficient than the method proposed by Song et al.

Human infertility is considered as a serious disease of the reproductive system that affects more than 10% of couples across the globe and over 30% of the reported cases are related to men. The crucial step in the assessment of male infertility and subfertility is semen analysis that strongly depends on the sperm head morphology, i.e., the shape and size of the head of a spermatozoon. However, in medical diagnosis, the morphology of the sperm head is determined manually, and heavily depends on the expertise of the clinician. Moreover, this assessment as well as the morphological classification of human sperm heads are laborious and non-repeatable, and there is also a high degree of inter and intra-laboratory variability in the results. In order to overcome these problems, we propose a specialized convolutional neural network (CNN) architecture to accurately classify human sperm heads based on sperm images. It is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficiency and effectiveness. It is demonstrated that our proposed architecture outperforms state-of-the-art methods, exhibiting 88% recall on the SCIAN dataset in the total agreement setting and 95% recall on the HuSHeM dataset for the classification of human sperm heads. Our proposed method shows the potential of deep learning to surpass embryologists in terms of reliability, throughput, and accuracy.

The current paper is devoted to investigating wormhole solutions with an exponential gravity model in the background of f ( R ) theory. Spherically symmetric static spacetime geometry is chosen to explore wormhole solutions with anisotropic fluid source. The behavior of the traceless matter is studied by employing a particular equation of state to describe the important properties of the shape-function of the wormhole geometry. Furthermore, the energy conditions and stability analysis are done for two specific shape-functions. It is seen that the energy condition are to be violated for both of the shape-functions chosen here. It is concluded that our results are stable and realistic.

Subdivision schemes are extensively used in scientific and practical applications to produce continuous geometrical shapes in an iterative manner. We construct a numerical algorithm to estimate subdivision depth between the limit curves/surfaces and their control polygons after k-fold subdivisions. In this paper, the proposed numerical algorithm for subdivision depths of binary subdivision curves and surfaces are obtained after some modification of the results given by Mustafa et al in 2006. This algorithm is very useful for implementation of the parametrization.

Modern diagnostic tools and immunoassay protocols urges direct analyte recognition based on its intrinsic behavior without using any labeling indicator. This not only improves the detection reliability, but also reduces sample preparation time and complexity involved during labeling step. Label-free biosensor devices are capable of monitoring analyte physiochemical properties such as binding sensitivity and selectivity, affinity constants and other dynamics of molecular recognition. The interface of a typical biosensor could range from natural antibodies to synthetic receptors for example molecular imprinted polymers (MIPs). The foremost advantages of using MIPs are their high binding selectivity comparable to natural antibodies, straightforward synthesis in short time, high thermal/chemical stability and compatibility with different transducers. Quartz crystal microbalance (QCM) resonators are leading acoustic devices that are extensively used for mass-sensitive measurements. Highlight features of QCM devices include low cost fabrication, room temperature operation, and most importantly ability to monitor extremely low mass shifts, thus potentially a universal transducer. The combination of MIPs with quartz QCM has turned out as a prominent sensing system for label-free recognition of diverse bioanalytes. In this article, we shall encompass the potential applications of MIP-QCM sensors exclusively label-free recognition of bacteria and virus species as representative micro and nanosized bioanalytes.

A piezoelectric 10 MHz multichannel quartz crystal microbalance (MQCM), coated with six molecularly imprinted polystyrene artificial recognition membranes have been developed for selective quantification of terpenes emanated from fresh and dried Lamiaceae family species, i.e., rosemary (Rosmarinus Officinalis L.), basil (Ocimum Basilicum) and sage (Salvia Officinalis). Optimal e-nose parameters, such as layer heights (1–6 KHz), sensitivity

It has been a strong consumer interest to choose high quality food products with clear information about their origin and composition. In the present study, a total of 22 Asian soy sauce samples have been analyzed in terms of 13C-NMR spectroscopy. Spectral data were analyzed by multivariate statistical methods in order to find out the important metabolites causing the discrimination among typical soy sauces from different Asian regions. It was found that significantly higher concentrations of glutamate in Chinese red cooking (CR) soy sauce may be the result of the manual addition of monosodium glutamate (MSG) in the final soy sauce product. Whereas lower concentrations of amino acids, like leucine, isoleucine and valine, observed in CR indicate the different fermentation period used in production of CR soy sauce, on the other hand, the concentration of some fermentation cycle metabolites, such as acetate and sucrose, can be divided into two groups. The concentrations of these fermentation cycle metabolites were lower in CR and Singapore Kikkoman (SK), whereas much higher in Japanese shoyu (JS) and Taiwan (China) light (TL), which depict the influence of climatic conditions. Therefore, the results of our study directly indicate the influences of traditional ways of fermentation, climatic conditions and the selection of raw materials and can be helpful for consumers to choose their desired soy sauce products, as well as for researchers in further authentication studies about soy sauce.