1,354,240 research outputs found
Green pigment hybrid of natural melanin and cellulose nanofibers for sustainable UV-shielding and antioxidant activity
No full textThe present work reports on a sustainable hybrid pigment obtained from a combination of melanin nanoparticles (MNPs) from the cuttlefish Sepia pharaonis and cellulose nanofibers (CNFs) from the sea squirt Pyura chilensis. The characterization of the cellulose nanofiber–melanin nanoparticle (CNF-MNP) hybrid was carried out by FTIR, XRD, DLS, FESEM and EDS analyses confirming that the two sources had been successfully combined, with some degree of crystalline disruption of cellulose. DLS characterisation of the hybrid showed its uniform hydrodynamic size around ~300 nm. UV–Vis spectroscopy illustrated its good and stable absorbance in the 200–400 nm region, indicating an efficient capacity for blocking ultraviolet radiation. When added to an emulsion formulation at 0.5 % (w/v) dosage, the CNF-MNP hybrid produced a high Sun Protection Factor (SPF) of 26.6 ± 2.6, compared to formulations containing only CNFs (SPF 12.9 ± 1.2) or MNP (SPF 13.7 ± 1.3), or to the blank base (SPF 5.7 ± 0.6). Moreover, the CNF-MNP hybrid demonstrated a concentration-dependent antioxidant activity by DPPH assay (31.2 % inhibition at 1 mg mL 1), suggesting a free radical scavenging potential and a synergistic effect of CNFs and MNPs. Finally, cytotoxicity tests, using MTT assay on Hu02 and Vero cells, indicated that the CNF-MNP hybrid is highly biocompatible at low concentrations (>90 % cell viability at 1 μg mL 1) and >50 % cell viability at 1 mg mL 1. In summary, the proposed CNF-MNP hybrid represents a novel sustainable and multifunctional material thet can be effectively applied as sunscreen product, providing UV protection ability and antioxidant properties, and can be considered a promising future candidate for bio-based cosmetic products
Immobilized papain on gold nanorods as heterogeneous biocatalysts
No full textPapain, a thiol protease present in the latex of Carica papaya, is an enzyme which exhibits broad proteolytic activity, and, for this reason, it is utilized in a variety of industrial applications. Immobilization of papain on gold nanoparticles highly preserves its activity and enhances the stability, allowing the reuse of the linked enzyme many times without any significant loss of its catalytic performance. In particular, k cat and K M values remain substantially unchanged, while immobilized form shows a higher activity on a wider pH range retains 80 % residual activity also at 90 °C and shows higher functionality than the free form when incubated for long time (1 h) at 90°C and at extreme pH values (3 and 12). A higher activity of immobilized papain with respect to the free form in the presence of various bivalent metal ions, known as strong inhibitors of papain, was also found. The reasons of this enhanced stability of gold nanorods immobilized papain are discussed. © 2014 Springer-Verlag
Estimation of carbon pools in the biomass and soil of mangrove forests in Sirik Azini creek, Hormozgan province (Iran)
No full textDespite the increasing interest in mangroves as one of the most carbon-rich ecosystems, arid mangroves are still poorly investigated. We aimed to improve the knowledge of biomass and soil carbon sequestration for an arid mangrove forest located at the Azini creek, Sirik, Hormozgan Province (Iran). We investigated the biomass and organic carbon stored in the above and belowground biomass for three different regions selected based on the composition of the principal species: (1) Avicennia marina, (2) mixed forest of A. marina and Rhizophora mucronata, and (3) R. mucronata. Topsoil organic carbon storage to 30 cm depth was also estimated for each analyzed area. Biomass carbon storage, considering both aboveground (AGB) and belowground biomass (BGB), was significantly different between the cover areas. Overall, the mean forest biomass (MFB) was 283.1 ± 89 Mg C ha−1 with a mean C stored in the biomass of 128.9 ± 59 Mg C ha−1. Although pure Rhizophora stand showed the lowest value of above and below tree carbon (AGC + BGC); 17.6 ± 1.9 Mg C ha−1), soil organic carbon stock in sites under Rhizophora spp. was significantly higher than in the site with pure stand of Avicennia spp. Overall, forest soil stored the highest proportion of Sirik mangrove ecosystem organic carbon (59%), with a mean value of 188.3 ± 27 Mg C ha−1. These results will contribute to broaden the knowledge and the dataset available, reducing the uncertainties related to estimates and modeling of carbon pools in arid mangrove ecosystem, which also represent an important climatic threshold of mangrove worldwide distribution
Enzyme immobilization: an update
No full textCompared to free enzymes in solution, immobilized enzymes are more robust and more resistant to environmental changes. More importantly, the heterogeneity of the immo-bilized enzyme systems allows an easy recovery of both enzymes and products, multiple re-use of enzymes, continuous operation of enzymatic processes, rapid termination of reactions, and greater variety of bioreactor designs. This paper is a review of the recent literatures on enzyme immobilization by various techniques, the need for immobilization and different applications in industry, covering the last two decades. The most recent papers, patents, and reviews on immobilization strategies and application are reviewed
Identification of a novel tailor-made chitinase from white shrimp Fenneropenaeus merguiensis
No full textFenneropenaeus merguiensis (commonly named banana shrimp) is one of the most important farmed crustacean worldwide species for the fisheries and aquaculture industry. Besides its nutritional value, it is a good source of chitinase, an enzyme with excellent biological and catalytic properties for many industrial applications. In the present study, a putative chitinase-encoding cDNA was synthesized from mRNA from F. merguiensis hepatopancreas tissue. Subsequently, the corresponding cDNA was cloned, sequenced and functionally expressed in Escherichia coli, and the recombinant F. merguiensis chitinase (rFmCHI) was purified by His-tag affinity chromatography. The bioinformatics analysis of aminoacid sequence of rFmCHI displayed a cannonical multidomain architecture in chitinases which belongs to glycoside hydrolase family 18 (GH18 chitinase). Biochemical characterization revealed rFmCHI as a monomeric enzyme of molecular weight 52 kDa with maximum activity at 40 °C and pH 6.0 Moreover, the recombinant enzyme is also stable up to 60 °C, and in the pH range 5.0-8.0. Steady-state kinetic studies for colloidal chitin revealed KM, Vmax and kcat values of 78.18 μM, 0.07261 μM. min−1 and 43.37 s−1, respectively. Overall, our results aim to demonstrate the potential of rFmCHI as suitable catalyst for bioconversion of chitin waste.Sin financiación5.999 Q1 JCR 20210.882 Q1 SJR 2021No data IDR 2021UE
Ensemble-Based Fraud Detection: A Robust Approach Evaluated on IEEE-CIS
No full textCredit card fraud has increased with the fast expansion of online financial transactions, requiring the implementation of advanced detection systems. According to the IEEE-CIS dataset, this paper presents an extensive empirical assessment of ensemble learning methods for class-imbalanced fraud detection. By evaluating ensemble techniques such as Random Forest, XGBoost, LightGBM, and stacking approaches systematically, we address the critical issues of extreme class imbalance, concept drift, and real-time detection requirements. Our solution involves comprehensive feature engineering strategies tuned to the IEEE-CIS dataset, which consists of 590,540 transactions with a fraud rate of 3.5%, as well as advanced data balancing techniques (SMOTE, ADASYN, and Borderline-SMOTE). From experimental results, our ensemble stacking approach maintains low false positive rates while fraud is detected at high rates (0.918 AUC-ROC, 0.891 AUC-PR) and outperforms. The study offers useful implications for real-world practical implementation and empirical proof of the proficiency of ensemble approaches in dealing with highly imbalanced financial fraud datasets
Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering
No full textDetecting fraud in modern supply chains is difficult due to global complexity and limited labeled data. Traditional methods often fail with class imbalance and weak supervision. This paper proposes a two-phase framework to address these issues. First, Isolation Forest performs unsupervised anomaly detection to flag possible fraud and cut data volume. Second, a self-training SVM refines predictions with labeled and high-confidence pseudo-labeled samples for semi-supervised learning. We test the method on the DataCo Smart Supply Chain Dataset with fraud indicators. It achieves an F1-score of 0.817 and a false positive rate below 3.0%. These results show the value of combining unsupervised pre-filtering with semi-supervised refinement for fraud detection, though concept drift and lack of deep learning comparison remain as limits
Uncertainty-Aware Deep Ensembles for Confident Customer Churn Prediction with Rejection Option
No full textCustomer churn prediction is an important problem in business intelligence, especially in industries where keeping customers is both difficult and expensive. Many existing models can predict churn accurately but do not show how confident they are in their results, which can cause costly or incorrect decisions. To solve this issue, this paper introduces an Uncertainty-Aware Ensemble (UA-Ensemble) framework that predicts churn while also estimating confidence levels. The model combines five neural network types, including attention-based LSTMs, Bayesian networks, and Monte Carlo Dropout, to measure both aleatoric and epistemic uncertainty. A cost-aware rejection rule is used to avoid unreliable predictions. Experiments on large banking, e-commerce, and telecom datasets reached 94.2% accuracy and reduced intervention costs by 18.3% compared to baseline models. The proposed approach outperforms traditional machine learning and deep learning methods, proving effective and trustworthy for business decision-making across various industries
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Purification and characterization of a novel thermostable luciferase from Benthosema pterotum
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