University of Swabi

Triterpenoids exhibit considerable potential and are extensively utilized in both food and pharmaceutical industries. However, Athelia termitophila (TMB) naturally harbors only trace amounts of these compounds. Consequently, this study sought to optimize the composition of the culture medium and its cultivation parameters to augment both triterpenoids biosynthesis and TMB biomass. To this end, a series of systematic experiments were carried out. At the outset, a One Factor at a Time (OFAT) approach was employed to identify key culture components and conditions. Based on the OFAT findings, six factors were selected for further investigation using the Plackett–Burman design (PBD) to assess their influence on triterpenoids production and biomass yield. The PBD outcomes pinpointed three critical factors—cultural duration, yeast extract powder, and KH2PO4—each of which was subjected to further optimization through the Box-Behnken design (BBD). The BBD analysis determined the optimal culture medium and conditions: 30 g/L corn starch, 13.44 g/L yeast extract powder, 4.74 g/L KH2PO4, a liquid-to-volume ratio of 130/250 mL, 6% inoculum volumes, and a cultivation period of 7.8 days. Upon optimization, both triterpenoids yield (1.9-fold increase) and mycelial biomass (1.66-fold increase) were significantly enhanced compared to the unoptimized medium. This study not only provides a robust methodology for enhancing triterpenoids content and mycelial biomass in TMB, but also contributes novel insights into the biosynthesis of triterpenoids.

In recent years, the urgent issue of how climate change affects food security has emerged as a significant concern. This paper highlights the complex interplay between food security and global climate change by examining the role of climate change in the food system, the interrelationship between food security and global climate change, and adaptation strategies to address these challenges. With a comprehensive analysis focused on China, this study systematically examines the complex dynamics linking food security and global climate change. The findings reveal important insights: (1) Global climate change is exacerbating insecurity in the food system and increasing its impact on Chinese food production; (2) Food demand emerges as the main driver of global climate change, while redistribution of food production factors exacerbates the climate crisis. (3) A synergistic and sustainable response can be achieved through a multi-pronged approach to addressing global climate change while ensuring food security and micro level, resilience. To effectively combat global climate change and ensure food security, this study highlights the critical importance of using micro-technologies for grain storage, prioritizing ecological building, pursuing a market-based approach at the macro level, and improving the food policy framework. In the context of global climate change, this study argues for a paradigm shift in food security research and a transition from a singular disciplinary, dimensional, and resource-centered approach to a multidisciplinary, multifactorial, and systematic integration of research. This transformative approach aims to promote a low-carbon and efficient food system that’s resilient to the challenges of global climate change.

The current investigation was designed to explore the fixed oil derived from C. digitatum (leaves) and to evaluate its in vitro antioxidant and antibacterial properties for the first time. The fixed oil of C. digitatum was analyzed both qualitatively and quantitatively using a GC-MS instrument. The analysis revealed the presence of 28 saturated and unsaturated fatty acids. Among these, methyl ester of linolenic acid was detected in the highest concentration at 3.594%, followed by methyl ester of linoleic acid at 2.422%, palmitic acid at 2.213%, and oleic acid at 1.933%. The antioxidant activity of the fixed oil was evaluated against the DPPH radical using various concentrations of the oil in comparison with vitamin C. At a concentration of 100 µg/ml, the fixed oil exhibited a radical scavenging capacity of 67.574%. Additionally, the oil demonstrated significant antibacterial activity against S. epidermidis, S. aureus, S. typhimurium, and B. subtilis. The findings from this study suggest that the fixed oil of C. digitatum (leaves) represents a promising source of antibacterial and antioxidant agents

Syzygium cumini, commonly known as Jamun or Indian Blackberry, is a fruit‐bearing tree native to the Indian subcontinent, revered for its medicinal uses, and characterized by a rich phytochemical profile abundant in polyphenols. This study aimed to characterize various fractions, including the roots, stem, and leaves of S. cumini (SC). Purposefully, conventional extraction was carried out by using ethanol (70%) with formic acid (1%) to extract the phytochemicals. The resultant extracts were subjected to phenolic contents (TPC, TFC, TTC) and antioxidant activity estimation (DPPH, FRAP, ABTS RPA, OH, FICA, and TAC). Further, LC‐ESI‐QTOF‐MS/MS identification of phenolics was also performed. The outcomes showed that S. cumini leaf exhibited the highest phenolic content and antioxidant activity among different fractions compared to stem and root. The recorded TPC, TFC, and TTC in the leaf were 52.17 ± 1.60 mg GAE/g, 2.76 ± 0.054 mg QE/g, and 17.22 ± 0.43 mg ce/g, respectively. Correlation analysis revealed a strong positive correlation (0.50 < r < 0.80, p < 0.01) between TPC, TFC, and TTC. The LC‐ESI‐QTOF‐MS/MS screening showed the presence of 12 compounds in the stem, leaf, and root of S. cumini, showing the diversity of phenolics between different parts. The majority of the compounds belonged to flavonoids (4), phenolic acids (3), other polyphenols (3) and lignans (2). Among the notable compounds, naringin 4'‐O‐glucoside, 3,4‐O‐dimethylgallic acid, scutellarein, and demethyloleuropein were identified, highlighting the therapeutic potential of different fractions of S. cumini. In conclusion, the results indicated that SC fractions contained a considerable amount of phenolics, thus showcasing higher antioxidant activity. Moreover, the concentration of different phenolics varied among fractions, as confirmed through a Venn diagram.

This paper addresses an efficient approach based on Haar wavelets to solve multi-dimensional nonlinear partial differential equations (PDEs). The proposed Haar wavelets method (PHWM) approximates the highest-order partial derivatives in the governing equation by utilizing the Haar wavelet series. These series are then integrated within the given limits of integration to derive lower-order partial derivatives and the Haar wavelet solutions. Then, we express the derivatives of the wavelet solution u in terms of u itself by eliminating the unknown coefficients through the wavelet solution expressions. This technique can be effectively applied to scenarios involving nonlinear problems and is more efficient and easier to implement compared to conventional Haar wavelet methods. Unlike the conventional Haar wavelets methods, the PHWM requires the inversion of a coefficient matrix of size $(2^{J+1})^d \times (2^{J+1})^d$, where d is the dimensional parameter and J is the maximum level of resolution of the Haar wavelet. The PHWM has the same order of convergence as the conventional Haar wavelet method; that is, the PHWM also has a second order of convergence. We demonstrate the effectiveness of our approach through multiple examples in one-, two-, and three-dimensional spaces. In the current work, we also use the new splitting technique in the wavelet method, introduced recently in Liu et al. (Eng. Anal. Bound. Elem. 125, 124–134, 2021), to linearize the nonlinear PDEs. Our experimental results validate the accuracy and efficiency of the proposed algorithms, rendering them highly suitable for solving high-dimensional elliptic PDEs among wavelet methods.

Good corporate governance is a structured framework of managing and controlling an organization to enhance stockholder wealth while ensuring accountability to all stakeholders. It has emerged as a cornerstone of sustainable economic development. Despite its significance, a comprehensive understanding of its historical evolution and intellectual trajectory remains unexplored. Hence, the current study employs bibliometric analysis to systematically examine the concept of good corporate governance. Drawing on 986 publications indexed in the Scopus database over the period from 1994 to 2022, this study aims to identify insights into publication trends, geographical distribution, key hotspot areas, influential and prolific authors, affiliations, and academic institutions along with citation dynamics as well as offering a comprehensive scholastic knowledge mapping. The current study contributes to the scientific knowledge by presenting a holistic view of the research theme evolution and recognizing emerging research areas. The study results reveal a significant increase in the research over the past two decades, with the highest volume of publications emerging from Southeast Asia and Europe. Moreover, the most prolific authors and productive institutions were identified, highlighting the global impact of research on good corporate governance. Additionally, by analyzing citation analysis and co-authorship networks, the study attempts to offer valuable guidance for future research avenues, emphasizing the importance of good governance in promoting corporate performance and sustainability. The results also illustrate that the dominant themes include board structure, transparency, Islamic banking, and corporate sustainability, while new areas such as blockchain technology, fintech, and digital finance are emerging hotspots gaining attention. Finally, the findings underscore the global relevance of good governance principles and their increasing integration with sustainable development goals for a sustainable future. Graphical Abstract

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) as a recognized cause of community-acquired and hospital infections has brought about a need for the efficient and accurate identification of peptides with anti-MRSA properties in drug discovery and development pipelines. However, current experimental methods often tend to be labor- and resource-intensive. Thus, there is an immediate requirement to develop practical computational solutions for identifying sequence-based anti-MRSA peptides. Lately, pre-trained protein language models (pLMs) have emerged as a remarkable advancement for encoding peptide sequences as discriminative feature embeddings, uncovering plentiful protein-level information and successfully repurposing it for in silico peptide property prediction. In this study, we present pLM4MRSA, a framework based on pLMs designed to enhance the accuracy of predicting anti-MRSA peptides. In this framework, we combine feature embeddings from various pLMs, such as ProtTrans, and evolutionary-scale modeling (ESM-2) which provide complementary information for prediction. These individual pLM strengths are integrated to form hybrid feature embeddings. Next, we apply principal component analysis (PCA) to process these hybrid embeddings. The resulting PCA-transformed feature vectors are then used as inputs for constructing the predictive model. Experimental results on the independent test dataset showed that the proposed pLM4MRSA approach achieved a balanced accuracy and Matthew correlation coefficient of 0.983 and 0.980, respectively, representing remarkable improvements over the state-of-the-art methods by 2.53%-4.83% and 7.73%-13.23%, respectively. This indicates that pLM4MRSA is a high-performance prediction model with excellent scope of applicability. Additionally, comparison with well-known hand-crafted features demonstrated that the proposed hybrid feature embeddings complement each other effectively, capturing discriminative patterns for more accurate anti-MRSA peptide prediction. We anticipate that pLM4MRSA will serve as an effective solution for accurate and high-capacity prediction of anti-MRSA peptides from peptide sequences.

Efficient nitrogen management in wheat cultivation is essential for achieving high yields while minimizing environmental impacts. This study aims to improve soil fertility and wheat productivity by optimizing nitrogen levels (NL) in combination with spent mushroom substrate (SMS). A two-year field experiment was conducted using a Randomized Complete Block (RCB) design with two factors: nitrogen rates (0, 60, 90, and 120 kg ha⁻1) and SMS levels (0, 2.5, 5.0, and 10 t ha⁻1), replicated three times. Data were analyzed to evaluate plant growth, soil fertility, and microbial activity, supported by Principal Component Analysis and economic analysis. Application of 120 kg ha⁻1 N, particularly when combined with SMS at 10 t ha−1, significantly enhanced plant height, grain yield, and biological yield which was statistically at par with 90 kg ha−1 when combined with 10 t ha−1 SMS. Moreover, soil organic matter, total nitrogen, and extractable potassium peaked with 120 kg ha⁻1 N and 10 t ha⁻1 SMS, while lime content decreased with increased NL and SMS. Higher NL and SMS levels increased soil respiration and microbial biomass carbon, with protease activity highest at 60–90 kg ha⁻1 N and urease activity at 120 kg ha⁻1 N, maximized at 10 t ha⁻1 SMS. Economic analysis demonstrated that 90 kg ha⁻1 N with 5 t ha⁻1 SMS provided the optimal economic benefit. The combined application of 90 kg ha⁻1 N and 5 t ha⁻1 SMS is recommended as the most sustainable and economical practice for wheat cultivation. Future research should explore this approach across different crops and soil types to validate its broader commercial and environmental impacts.

In this article, we discuss the qualitative analysis and develop an optimal control mechanism to study the dynamics of the novel coronavirus disease (2019-nCoV) transmission using an epidemiological model. With the help of a suitable mathematical model, health officials often can take positive measures to control the infection. To develop the model, we assume two disease transmission sources (humans and reservoirs) keeping in view the characteristics of novel coronavirus transmission. We formulate the model to study the temporal dynamics and determine an optimal control mechanism to minimize the infected population and control the spreading of the novel coronavirus disease propagation. In addition, to understand the significance of each model parameter, we compute the threshold quantity and perform the sensitivity analysis of the basic reproductive number. Based on the temporal dynamics of the model and sensitivity analysis of the threshold parameter, we develop a control mechanism to identify the best control policy for eradicating the disease. We then conduct numerical experiments using large-scale numerical simulations to validate the theoretical findings.

Acetylsalicylic acid (ASA), commonly known as aspirin, is an organic compound with the formula C9H8O4 obtained from the natural compound salicylic acid, recognized for its analgesic, anti-inflammatory, antipyretic, and anticancer properties. Its role in medicine and plant biology is well-established, but its emerging potential in cancer treatment has garnered increased attention. This review aims to provide a comprehensive overview of the therapeutic applications of ASA as an anticancer agent, focusing on its mechanisms, effectiveness, and role as an adjuvant therapy, preventive compound, and radioprotective agent. Recent research papers, including mechanistic studies, preclinical investigations, and clinical trials related to the effects of ASA on various cancer types, were reviewed. The review places particular emphasis on the enhancement of traditional chemotherapy drugs by ASA and considers toxicological aspects. The analysis of recent studies highlights the potential of ASA to improve the effectiveness of chemotherapy and its role in cancer inhibition through specific molecular pathways. Mechanistic insights suggest that ASA may influence cellular processes that contribute to cancer growth suppression and increased sensitivity to conventional treatments. ASA exhibits promising potential as an adjunct therapy in cancer treatment, with evidence supporting its benefits in improving therapeutic outcomes when used alongside conventional chemotherapy. Further studies are needed to clarify its mechanisms and ensure its safe and effective application in clinical settings.

Ergosterol is a component of fungal cell membranes that has physiological functions and applications in drugs, such as anti-inflammatory, anti-tumor, anti-fungal, and other immunosuppressive activities. The fungus Athelia termitophila, also known as the termite ball fungus, primarily contains secondary metabolites (like active ingredients) that are similar to ergosterol. To enhance the synthesis of ergosterol and mycelial biomass in termite ball fungus, methyl jasmonate and salicylic acid were used to stimulate the biosynthesis of ergosterol compounds during the growth of TMB mycelium and relative quantitative levels of gene transcripts. The conditions of the inducers were optimized. Under 80 µmol/L MJ incubation conditions, the content of ergosterol compounds in TMB was increased by 2.23-fold compared with the wild-type strain. RT-qPCR results at the transcriptional level of ergosterol synthesis pathway genes showed that MJ significantly induced the expression of HMGR (3-Hydroxy-3-Methylglutaryl-Coa Reductase), HMGS (3-Hydroxy-3-Methylglutaryl-Coa Synthase), SE (Squalene Epoxidase), and FPS (Farnesyl Diphosphate Synthase) genes in the ergosterol synthesis pathway. For expression levels at different induction days, we collected 7/10 d and 4/6/8 d samples with similar expression patterns, as well as SS (Squalene Synthase)/FPS (Farnesyl Diphosphate Synthase), SE (Squalene Epoxidase)/MVD (Mevalonate Diphosphate Decarboxylase), and HMGS (3-Hydroxy-3-Methylglutaryl-Coa Synthase)/HMGR (3-Hydroxy-3-Methylglutaryl-Coa Reductase) genes with similar expression patterns, which resulted in gene transcription data during ergosterol content synthesis. The current study provides an effective method to increase the ergosterol contents in termite ball fungus and a new idea for the mechanism of MJ-induced ergosterol compound biosynthesis.

Asexual queen succession (AQS) species produce queens via thelytokous parthenogenesis, which significantly impacts their social life history. For the first time, we discovered that Reticulitermes aculabialis exhibits the phenomenon of parthenogenesis under experimental conditions, and we also investigated the genetic structure of wild colonies of this species using polymorphic microsatellite loci. Our genetic analysis revealed that 93.2% of the secondary queens in the wild colonies were homozygous at all loci, indicating parthenogenesis in these secondary queens, while workers (2.5%), soldiers (0%), nymphs (0%), and alates (6.7%) had low rates. Genetic analysis revealed that the mean number of alleles per group (Na) ranged from 2.000 ± 0.000 to 2.500 ± 0.428, with 83.3% polymorphic loci (PPL). The observed heterozygosity (Ho) varied from 0.467 ± 0.141 to 0.583 ± 0.098, indicating significant genetic diversity among workers and soldiers. In contrast, soldiers and nymph develop predominantly through sexual reproduction than alates and workers. The occurrence of AQS in R. aculabialis suggests a different mechanism of ploidy restoration, highlighting the diversity of reproductive mechanisms across various lineages of the Termitidae and non-Termitidae termites.

Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of morbidity worldwide. In Algeria, it ranks second in mortality-related deaths. Poor lifestyle, characterized by a low-fiber diet, insufficient physical activity, tobacco use, and alcohol consumption, is strongly associated with an increased risk of developing this disease. Probiotics have demonstrated anti-inflammatory and antitumor effects in preclinical and clinical studies. The World Health Organization (WHO) and European Food Safety Authority (EFSA) have recognized their safety and effectiveness, classifying them as Generally Recognized as Safe (GRAS) and Qualified Presumption of Safety (QPS), respectively. Probiotics exhibit immunomodulatory effects and maintain the equilibrium of the gut microbiota. However, the evidence for their clinical efficacy is inadequate, and additional research is requisite to establish them as therapeutic agents rather than simply as dietary supplements. Although probiotics are, in most cases, safe, high-risk patients should exercise caution due to the potential risk of infection. This review examines the current knowledge on probiotic strains, their therapeutic potential for colorectal cancer, limitations, and areas where further research is imperative to improve their efficacy.

Background: The presented study aimed to evaluate the effect of aging on the quality attributes of ODTs. Experimental: ODTs prepared in one of our previous studies, packed in standard blister packing, were stored at ambient conditions in a laboratory (protected from direct sunlight) for 5 years and evaluated for their quality attributes, including physical parameters, mechanical strength (crushing strength, specific crushing strength, tensile strength, and friability), disintegration behavior (in vitro disintegration time, oral disintegration time, and wetting time), assay and dissolution rates. Drug content of all the samples was determined by HPLC. Results: Physically, the ODTs were oval (10 mm), shallow, and convex with a bisection line, and their compression weight was 200 mg/tablet. With the passage of time, the moisture content of the ODTs increased from 2.4 ± 0.39% to 3.48 ± 0.62%. After 5 years, the drug content decreased to 92.38 ± 0.93%. Discussion: Initially, there was an increase in the crushing strength of the ODTs (up to 3 years), and then it gradually decreased. At the time of preparation, disintegration time of the ODTs was 53 s, which gradually increased up to 89 s, at the 4th year. After completion of the study, it slightly decreased to 85 s. The dissolution rate of domperidone from the ODTs remained unaffected by aging. The FTIR spectra of the ODTs showed insignificant cahnges, indicating absence of degradation during the study period. Conclusion: ODTs remained stable for five years and insignificant changes were observed in their quality attributes.

This study explored the synthesis and characterization of the iron nanoparticles (FeNPs) using Micromeria biflora extract. The rapid reduction of iron ions, evidenced by a distinct color change, signifies an efficient interaction, leading to successful FeNPs formation. UV–visible spectroscopy confirmed the synthesis, revealing an absorption peak at 295 nm that intensified over time. Fourier transform infrared (FTIR) spectroscopy demonstrates phytochemical involvement. Field emission scanning electron microscopy (FESEM) images displayed cuboctahedron‐shaped NPs with various facet formations, which are crucial for diverse applications. DISCUS package was used to simulate the shape and decorate the surface with organic molecules obtained from the extract. Energy dispersive X‐ray spectroscopy (EDS) was used to confirm the elemental composition. Additionally, potential applications, including enzyme effects and sedative and anti‐inflammatory properties, were explored. The extract and FeNPs showed anticancer effects against MDR2780AD cell lines, with IC50 values of 1.99 and 0.91, respectively. The tested FeNPs showed 92.22%, 76.22%, and 88.23% inhibitory effects against urease, CA‐II, and XO, respectively. The maximum percentage analgesic effects of the extract (100 mg/kg) and FeNPs (10 mg/kg) were 65 and 82, respectively. The maximum anti‐inflammatory effect was observed at the third hour of treatment. The anti‐inflammatory effect of FeNPs (90%) was superior to that of the extract (60%).

The marble manufacturing industry is one of the major waste-generating industries that requires special mitigation and environmental assessment to minimize its harmful environmental impacts. The study's main objective is to investigate the pollutant load of marble industries, with particular emphasis on wastewater treatment techniques. To achieve the objective of the study, Hayatabad Industrial Estate, Peshawar was visited for marble processing plants to learn about the mitigation process for marble-waste management. During the fieldwork, 25% of the operational marble processing plants were investigated and kept under observation for production to waste management process. To determine the quality of wastewater, samples were also taken at industrial discharge points. To know about the energy and economic costs, the marble processing industries were investigated for consumption of environmental resources as products and byproducts. To reduce the pollution load, wastewater from the marble industry was treated in the laboratory by adopting physical and chemical treatment methods. The results obtained were compared with the required permissible limits recommended by the Pak-National Environmental Quality Standards and the World Health Organization. The findings of the study revealed that the majority of the chemical parameters of wastewater were observed above the required limits suggested by the Pak-NEQS/WHO. In terms of water, air, and land resources, contamination has been observed through unsafe waste disposal strategies adopted by the marble industries. Results of the chemical treatment (coagulation) with hydrated ferric chloride showed maximum removal of suspended load with 92% efficiency. It is concluded that marble processing units pose serious threats to the environmental quality by degrading water resources. Keeping in view the national standards, these industries need special attention for waste generation and its safe disposal. For this, eco-innovation techniques are recommended to achieve the sustainability of resources.

Background/Objectives: CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats)-associated protein 9 is now widely used in agriculture and medicine. Off-target effects can lead to unexpected results that may be harmful, and these effects are a common concern in both research and therapeutic applications. Methods: In this study, using pineapple as the gene-editing material, eighteen target sequences with varying numbers of PAM (Protospacer-Adjacent Motif) sites were used to construct gRNA vectors. Fifty mutant lines were generated for each target sequence, and the off-target rates were counted. Results: Selecting sequences with multiple flanking PAM sites as editing targets resulted in a lower off-target rate compared to those with a single PAM site. Target sequences with two 5′-NGG (“N” represents any nucleobase, followed by two guanine “G”) PAM sites at the 3′ end exhibited greater specificity and a higher probability of binding with the Cas9 protein than those only with one 5′-NGG PAM site at the 3′ end. Conversely, although the target sequence with a 5′-NAG PAM site (where “N” is any nucleobase, followed by adenine “A” and guanine “G”) adjacent and upstream of an NGG PAM site had a lower off-target rate compared to sequences with only an NGG PAM site, their off-target rates were still higher than those of sequences with two adjacent 5′-NAG PAM sites. Among the target sequences of pineapple mutant lines (AcACS1, AcOT5, AcCSPE6, AcPKG11A), more deletions than insertions were found. Conclusions: We found that target sequences with multiple flanking PAM sites are more likely to bind with the Cas9 protein and induce mutations. Selecting sequences with multiple flanking PAM sites as editing targets can reduce the off-target effects of the Cas9 enzyme in pineapple. These findings provide a foundation for improving off-target prediction and engineering CRISPR-Cas9 complexes for gene editing.

Introduction Alzheimer's disease (AD) is a progressive neurological disorder for which no effective cure currently exists. Research has identified β-Secretase (BACE1) as a promising therapeutic target for the management of AD. BACE1 is involved in the rate-limiting step and produces toxic amyloid-beta (Aβ) peptides that lead to deposits in the form of amyloid plaques extracellularly, resulting in AD. Method In this connection, 60 small peptides were evaluated for their in-silico studies to predict the bonding orientation with BACE1. Next, 5 peptides (12, 20, 21, 51, and 52) were selected based on high scoring of Vander Waal interactions with the catalytic site of the enzyme. Results The identified hit peptides were synthesized using Solid-Phase Peptide Synthesis (SPPS), and Electrospray Ionization Mass Spectrometry (ESI-MS) elucidated their structures and 1 1 HNMR spectroscopy. According to their In-vitro BACE1 inhibitory study, peptides 21 having high Vander Waal forces showed significant BACE1 inhibition with IC50 = 4.64 ± 0.1μM). Moreover, the kinetic study revealed that peptide 21 is a mixed-type inhibitor and can interact at the active site and the allosteric site of BACE1. Conclusion According to the cytotoxicity study, peptide 21 was found to be noncytotoxic at 4.64 μM, 10 μM and 20 μM. The forthcoming target of this study is to evaluate further the effect of peptide 21 in an in-vivo mice model.

Tyrosinase plays a crucial role as an enzyme in the production of melanin, which is the pigment accountable for determining the color of the hair, eyes, and skin. Tyrosinase inhibitory peptides (TIPs), mainly designed to regulate the activity of the enzyme tyrosinase, are of interest in various domains, including cosmetics, dermatology, and pharmaceuticals, due to their potential applications in controlling skin pigmentation. To date, a few machine learning-based models have been proposed for predicting TIPs, but their predictive performance remains unsatisfactory. In this study, we propose an innovative computational approach, named TIPred-MVFF, to accurately predict TIPs using only sequence information. Firstly, we established an up-to-date and high-quality dataset by collecting samples from various sources. Secondly, we applied a multi-view feature fusion (MVFF) strategy to extract and explore probability and category information embedded in TIPs, employing several machine learning (ML) algorithms coupled with different commonly used sequence-based feature encodings. Then, we employed resampling approaches to address the class imbalance issue. Finally, to maximize the utility of each feature, we fused probability-based and sequence-based features, generating more informative feature that were used to develop the final prediction model. Based on the independent test, experimental results showed that TIPred-MVFF outperformed several conventional ML classifiers and existing methods in terms of prediction accuracy and robustness, achieving an accuracy of 0.937 and a Matthew’s correlation coefficient of 0.847. This new computational approach is anticipated to aid community-wide efforts in rapidly and cost-effectively discovering novel peptides with strong tyrosinase inhibitory activities.

In last few decades, the agriculture sector is facing various type of crops diseases originated by crop pests. Among various crops the tomato plant is greatly affected by many pests such as aphids and whiteflies, which are badly decreasing tomato plant yield and effecting its growth. In last few years, various type of pesticides such as Neonicotinoids and Pyrethroids are employed which are badly effecting eco‐system and water bodies. In this research work, we prepared SnO2 nanosheets (SONS) by in‐situ and green synthesis approach. Remarkably, SONS exhibit a larger surface area, tailored pore size, and higher catalytic performance than SnO2 nanoparticles (SONP). To further improve the efficiency of SONS, we coupled it with covalent organic farmwork nanosheets (COFNS) via the hydrothermal approach. The SONS@COFNS hybrid nanocatalysts exhibit improved carrier migration, enhanced porosity, multiple active sites, and exceptional light absorption capabilities. The as prepared green nanomaterials delivered improved activities for Neonicotinoids and Pyrethroids degradation. Remarkably, the most active sample 6COFNS/SONS showed the highest degradation efficiency (94 %), which is approximately 1.92 times higher than the degradation efficiency of pristine SONS (49 %). This work will ultimately contribute to developing green, ecofriendly nanomaterials for pesticides degradation and promoting tomato plants growth.