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Smart films incorporated with anthocyanins as color indicators to monitor the food spoilage has attracted great attention. However, the effectiveness comparison among anthocyanin sources remains limited. Hence in this study, chitosan-based films incorporated with varying concentrations of anthocyanins sourced from either roselle or mulberry were developed. Detailed structural, morphological, physical, mechanical, and functional characteristics of these films were evaluated and compared. In general, the composite films, incorporating anthocyanins from roselle or mulberry, exhibited a deeper red–purple hue and a rougher cross-sectional appearance than the one without anthocyanins. Furthermore, while all composite films shared some representative absorption bands of FTIR spectra, there were slight variations in their area and height due to the interaction between anthocyanins and film components. Incorporating anthocyanins into chitosan-based films did not significantly affect film thickness, ranging from 74.90 to 88.35 µm. However, it notably reduced light transmittance and water vapor permeability (from 9.46 to 4.69–7.88 × 10–11 gm−1 s−1 Pa−1) while enhancing tensile strength (from 4.68 to 5.66–11.78 MPa), elongation (from 12.4 to 19.63–23.60%) and antioxidant activity (from 126.4 to 315.63–1044.54 µg Trolox equivalent/g film). Additionally, the color change patterns of chitosan-based films embedded with anthocyanins from roselle or mulberry aligned with the pH values, transitioning from red to reddish-pink (pH 3–4), pink (pH 5–6), purple (pH 7–10), and green-yellow (pH 12). During the exposure to ammonia vapor, a dark red–purple was initially observed in composite films, then faded over time, and finally shifted into light yellowish color after one hour. Both anthocyanin sources exhibited quick sensitivity to ammonia and fish spoilage. However, the color change with MARE might be more noticeable. As a result, the composite films containing anthocyanins from roselle or mulberry could be employed as intelligent films for real-time food freshness indication. [ABSTRACT FROM AUTHOR]

We study the boundary layer theory for slightly viscous stationary flows forced by an imposed slip velocity at the boundary. According to the theory of Prandtl (in: International mathematical congress, Heidelberg, 1904; see Gesammelte Abhandlungen II, 1961) and Batchelor (J Fluid Mech 1:177–190, 1956), any Euler solution arising in this limit and consisting of a single “eddy” must have constant vorticity. Feynman and Lagerstrom (in: Proceedings of IX international congress on applied mechanics, 1956) gave a procedure to select the value of this vorticity by demanding a necessary condition for the existence of a periodic Prandtl boundary layer description. In the case of the disc, the choice—known to Batchelor (1956) and Wood (J Fluid Mech 2:77–87, 1957)—is explicit in terms of the slip forcing. For domains with non-constant curvature, Feynman and Lagerstrom give an approximate formula for the choice which is in fact only implicitly defined and must be determined together with the boundary layer profile. We show that this condition is also sufficient for the existence of a periodic boundary layer described by the Prandtl equations. Due to the quasilinear coupling between the solution and the selected vorticity, we devise a delicate iteration scheme coupled with a high-order energy method that captures and controls the implicit selection mechanism. [ABSTRACT FROM AUTHOR]

In their classical work (Sammartino and Caflisch in Commun Math Phys 192(2):463–491, 1998), Caflisch and Sammartino established the inviscid limit and boundary layer expansions of vanishing viscosity solutions to the incompressible Navier–Stokes equations for analytic data on a half-space. The extension to an exterior domain faces a fundamental difficulty that the corresponding linear semigroup may not be contractive in analytic spaces as was the case on the half-space (Nguyen and Nguyen in Arch Ration Mech Anal 230(3):1103–1129, 2018). In this paper, we resolve this open problem for a much larger class of initial data. The resolution is due to the fact that it suffices to propagate solutions that are analytic only near the boundary, following the framework developed in the recent works that involve the boundary vorticity formulation, the analyticity estimates on the Green function, the adapted geodesic coordinates near a boundary, and the Sobolev-analytic iterative scheme. [ABSTRACT FROM AUTHOR]

Recently, two research directions have been noticed in data mining: frequent itemset mining (FIM) and high utility itemset mining (HUIM). The FIM process will output itemsets whose number of occurrences together exceeds or equals the required threshold, but this process ignores the beneficial attribute of each item. HUIM algorithms are proposed to overcome the disadvantage of FIM, but these algorithms only use a single threshold, which is unsuitable in the real world when applications often require different utility thresholds. HUIM algorithms with multi-threshold utilities are proposed, but these have high mining time and memory consumption. This paper thus presents an efficient method for Mining High Utility Itemsets with Multiple Utility Thresholds (MHUI-MUT). The article applies upper bounds and the strategy of pruning, thus reducing database scanning, and proposes a cut-off threshold to minimize the mining time.We also present a method to parallelize the algorithm to make the most of the performance of multi-core computers. The experimental results show the superior speed of the MHUI-MUT algorithm compared to the previous one, and the parallel version also outperforms the proposed sequential algorithm. [ABSTRACT FROM AUTHOR]

A conceptual framework is proposed, to identify flood affected locations that should be considered in order to lessen the consequences of naturally occurring disaster. Sentinel-1 data are used to evaluate the performance of automatic Otsu's method and machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), CART, Minimum Distance (MD), K-nearest neighbour (KNN) and KD Tree KNN (KD-KNN)) to characterise flooded region. The study provided a holistic spatial assessment of flood inundation in the region due to impact of the extreme precipitation. The most adequate performance based on compound value is achieved by KNN ( C v = 2 ) followed by SVM ( C v = 2.25 ) ML model and Otsu's thresholding method ( C v = 2.5 ). The validation site results reveal that Vertical transmit and Vertical received (VV) polarization performs significantly better than Vertical transmit and Horizontal received (VH) polarization. The most accurate flood extent produced by Otsu's thresholding method (overall accuracy of 94.98%) and MD (overall accuracy of 88.98%) are used to evaluate the indicative number of individuals and buildings at risk within the study areas using Gridded Population of the World Version 4 (GPWv4), Global ML Building Footprints by Microsoft and OpenStreetMap building data. [ABSTRACT FROM AUTHOR]

Land degradation encompasses ecological, biological, and physical deterioration of land resulting from natural or anthropogenic factors, including soil erosion. Piping erosion, specifically in arid and semi-arid regions, is recognized as a significant cause of land degradation. Therefore, it is crucial to identify the influential factors and spatially model this phenomenon to effectively control it. This investigation aims to study the spatial modeling and evaluation of piping erosion using machine learning models: random forest (RF) and two hybrid models, regularized random forest (RRF) and weighted subspace random forest (WSRF), in the Khoshkrood watershed of Markazi Province, Iran. In this study, 70% of the 179 recorded locations of piping erosion were randomly selected for model construction, while the remaining 30% were used for model validation. Fourteen geo-environmental variables affecting piping erosion were used as independent factors for modeling. The performance of the machine learning models was evaluated using five criteria, including sensitivity, specificity, and area under the curve (AUC). The results demonstrated that all three models performed well in predicting piping erosion. The AUC values for the WSRF, RRF, and RF models were 80.93%, 80.43%, and 79.11%, respectively. Moreover, the analysis of relative variable importance revealed that elevation was the most influential variable across all three models. The findings emphasize the significance of utilizing machine learning models for predicting and managing land degradation phenomena. By identifying influential geo-environmental and employing accurate spatial models, effective control measures can be implemented to mitigate the adverse impacts of piping erosion in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The direct conversion of glucose to 5-(hydroxymethyl)furfural (HMF) has been considered a challenging process. In this study, we developed a new method using gadolinium(III) trifluoromethanesulfonate (Gd(OTf)3) in combination with Brønsted acidic ionic liquids as a catalytic system to search possibly more environmentally benign process. The reaction was carried out under a continuous-flow system. The one-step isomerization/dehydration of glucose to give the highest HMF with Gd(OTf)3 together with DBU-based Brønsted acidic ionic liquid in a low-cost home-built continuous system. The plausible mechanism was proposed with the Lewis acidic sites of Gd(OTf)3 for the isomerization step and DBU-based Brønsted acidic ionic liquid sites for the dehydration step. The recycling of catalytic system Gd(OTf)3/DBU-based Brønsted acidic ionic liquid in DMSO was examined. Our findings provided a safe and environmental process for the continuous conversion of glucose into HMF. The use of continuous flow for HMF synthesis ensures that the current method can be applied to the large-scale process. [ABSTRACT FROM AUTHOR]

Cervical cancer is the second most encountered cancer in pregnant patients. The 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer updated the staging of primary cervical carcinoma and disease process, with formal incorporation of imaging as a vital source of information in the management process to improve accuracy. Diagnosis and treatment of the pregnant population is a complex interplay of achieving adequate diagnostic information and optimal treatment while minimizing toxicity and risks to the mother and fetus. While novel imaging techniques and anticancer therapies are rapidly developed, much information on the safety and feasibility of different therapies is not yet available in the pregnant population. Therefore, managing pregnant patients with cervical cancer is complex and requires a multidisciplinary approach. [ABSTRACT FROM AUTHOR]

Pollution of waste and natural waters by antibiotics is a major health issue that induces the development of resistant pathogens. Pollutant may be removed by photocatalysis, yet classical photocatalysis is limited by the short carrier lifetime. Here, we review multicomponent photocatalysts for the synergic degradation of antibiotics. Components include metallic nanoparticles, semiconductors and adsorbents. The synergy of these components provide photocatalysts with advantages such as extended visible-light range with bandgap energy lower than 2.8 eV, improved charge transfer properties, enhanced adsorption capacity and more active reaction sites. The removal of the antibiotics, based on the decrease of the initial concentration, reaches 80–100%, but mineralization is rarely measured in reports. Proposed mechanisms for the photocatalytic degradation of antibiotics over single and multicomponent photocatalysts are discussed. [ABSTRACT FROM AUTHOR]

In this study, the g-C3N4 with simultaneous isotype heterojunction and porous structure showed superior photocatalytic decomposition of tetracycline hydrochloride (TCH) under white LED light irradiation fabricated via a thermal polycondensation method using melamine and thiourea as precursors and NH4Cl as soft-template. X-ray diffraction and transmission electron microscopy demonstrated the successful fabrication of the heterojunction between two different phases of g-C3N4 metal-free semiconductor. Besides, the increase in surface area as well as the porosity of g-C3N4 isotype heterojunction compared with CN-M (the g-C3N4 was synthesized using melamine as precursor) or CN-T (the g-C3N4 was synthesized using thiourea as precursor) was confirmed through nitrogen adsorption–desorption isotherms. The improved photocatalytic degradation of TCH over CN-MT (the g-C3N4 was synthesized using melamine and thiourea as precursors and NH4Cl as soft-template) compared to CN-M and CN-T could be assigned to the visible light absorption expansion, the increase in surface area as well as the porosity and reduction of photo-generated electron–hole pair recombination. Specifically, the CN-MT-1:1 isotype heterojunction revealed the highest photocatalytic activity for TCH degradation (81.55%) within 180 min. Furthermore, this catalyst can be recycled four times, which demonstrates the potential of CN-MT as a visible light-sensitive catalyst for organic pollutant removal. [ABSTRACT FROM AUTHOR]

Graphitic carbon nitride (g-C3N4) has received much interest as a visible-light-driven photocatalyst for degrading pollutants such as organic dyes and antibiotics. However, g-C3N4 bulk activity could not meet expectations due to its rapid recombination of photogenerated electron–hole pairs and low specific surface area. In our study, melamine was thermally treated one-step in the presence of NH4Cl to produce g-C3N4 nanosheets. The characterizations of surface morphology and optical properties of all g-C3N4 samples were investigated by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectrum (XPS), transmission electron microscopy (TEM), and UV–visible diffuse reflectance spectroscopy. Compared to bulk g-C3N4, g-C3N4 nanosheets demonstrated excellent photocatalytic activities, with approximately 98% RhB removal after 210 min of visible light irradiation. Furthermore, the effect of catalyst dosage, pH, and RhB concentration on the removal percentage dye of g-C3N4 nanosheets was also investigated. h+ and •O2− species were demonstrated as the key reactive species for the RhB. Besides, ECN exposed a tetracycline degradation efficiency of 80.5% under visible-light irradiation for 210 min, which is higher than BCN (60.8%). The improved photocatalytic activity of g-C3N4 nanosheets is due to the restriction of the recombination of photogenerated electrons/hole pairs, as provided by photoluminescence spectra and Nyquist plot. As a result, our research may offer an effective approach to fabricating g-C3N4 nanosheets with high photocatalytic activity and high stability for environmental decontamination. [ABSTRACT FROM AUTHOR]

The effects of the substitution of V5+ with Ni2+ at the corresponding sites in BiVO4 on the crystal structures, optical properties, and photocatalytic efficiency of BiVO4 was investigated. Ni2+ cations doped at the V5+ sites in BiVO4 was confirmed by X-ray diffraction, Raman, X-ray photoelectron spectroscopy, ultraviolet–visible diffuse reflectance spectra, and photoluminescence spectra. Ni-doped BiVO4 exhibited excellent degradation of crystal violet (CV) compared with the bare BiVO4. For optimal Ni2+ doping of 5%, the degradation rate of CV, which reached about 95% within 180 min of LED light irradiation, was obtained. Ni doping can introduce advantageous defect states that significantly increase the separation and diffusion efficiency of the photo-induced charge carriers, thereby boosting the photocatalytic activity of crystal structures. [ABSTRACT FROM AUTHOR]

There is increasing evidence that sex hormones may impact the development of obstructive lung disease (OLD). Therefore, we studied the effect of bilateral oophorectomy (oophorectomy) on the development of OLD. Women were identified from the Mayo Clinic Cohort Study of Oophorectomy and Aging-2. Data were collected using the Rochester Epidemiology Project records-linkage system. A total of 1653 women who underwent oophorectomy and 1653 referent women of similar age were assessed for OLD using diagnostic codes and medical record abstraction. Women who underwent oophorectomy had an overall higher risk of all OLD, all chronic obstructive pulmonary disease (COPD), emphysema, and chronic bronchitis but not of all asthma, confirmed asthma, or confirmed COPD. The association with all OLD was stronger in women who were age ≤45 years at oophorectomy, never smokers, non-obese, and in women with benign indications; however, the interactions were not statistically significant. There was an increased risk of all asthma in women age ≤45 years at oophorectomy who took estrogen therapy. Never smokers of all ages had a stronger association of oophorectomy with all asthma and all COPD, whereas smokers had a stronger association of oophorectomy with emphysema and chronic bronchitis. Non-obese women of all ages had a stronger association of oophorectomy with all COPD, emphysema, and chronic bronchitis. The results of this study combined with the increased risk of several chronic diseases reported in previous studies suggest that oophorectomy in premenopausal women should be avoided unless there is clear evidence of a high genetic risk of ovarian cancer. [ABSTRACT FROM AUTHOR]

Lymphomatous involvement of the genitourinary system, particularly the kidneys, is commonly detected on autopsies; yet on conventional diagnostic imaging renal lymphoma is significantly underestimated and underreported, in part due to its variable imaging appearance and overlapping features with other conditions. We present a spectrum of typical and atypical appearances of renal lymphoma using multimodality imaging, while reviewing the roles of imaging in the detection, diagnosis, staging, and surveillance of patients with lymphoma. We also illustrate a breadth of benign and malignant entities with similar imaging features confounding the diagnosis of renal lymphoma, emphasizing the role of percutaneous image-guided biopsy. Understanding the spectrum of appearances of renal lymphoma and recognizing the overlapping entities will help radiologists improve diagnostic confidence and accuracy. [ABSTRACT FROM AUTHOR]

We establish the validity of the Euler + Prandtl approximation for solutions of the Navier-Stokes equations in the half plane with the Dirichlet boundary conditions, in the vanishing viscosity limit, for initial data which are analytic only near the boundary, and Sobolev smooth away from the boundary. Our proof does not require higher order correctors, and works directly by estimating an L 1 -type norm for the vorticity of the error term in the expansion Navier-Stokes - ( Euler + Prandtl). An important ingredient in the proof is the propagation of local analyticity for the Euler equation, a result of independent interest [ABSTRACT FROM AUTHOR]

High-utility itemset mining (HUIM) is a useful tool for analyzing customer behavior in the field of data mining. HUIM algorithms can discover the most beneficial itemsets in transaction databases, namely the high-utility itemsets (HUIs), in contrast to frequent itemset mining (FIM) algorithms that rely on detecting frequent patterns. Several algorithms have been proposed to effectively carry out this task, but most of them ignore the categorization of items. In many real-world transaction databases, this helpful information about the categories and subcategories of items, represented as a taxonomy, is useful. Therefore, traditional HUIM algorithms can only discover itemsets at the lowest level of abstraction and leave out several important patterns from higher levels. To address this limitation, this work suggests the use of items taxonomy. Besides, to further enhance the performance of the task several effective pruning techniques are also revised and utilized to tighten the search space when considering the taxonomy of items. To accurately find multi-level HUIs from transaction databases enhanced with taxonomy information, a new algorithm called MLHMiner (Multiple-Level HMiner) is proposed, which is an extended version of the HMiner algorithm. We also prove that the pruning techniques of HMiner can be applied in different abstraction levels to efficiently mine multi-level HUIs. It can be seen from the experimental evaluations on several databases (both real and synthetic) that the designed approach is capable of identifying useful patterns from different abstraction levels with high efficiency. [ABSTRACT FROM AUTHOR]

Semi-synthetic derivatives of camptothecin, a quinoline alkaloid found in the Camptotheca acuminata tree, are potent anticancer agents. Here we discovered two C. acuminata cytochrome P450 monooxygenases that catalyze regio-specific 10- and 11-oxidations of camptothecin, and demonstrated combinatorial chemoenzymatic C-H functionalizations of the camptothecin scaffold using the new enzymes to produce a suite of anticancer drugs, including topotecan (Hycamtin®) and irinotecan (Camptosar®). This work sheds new light into camptothecin metabolism, and represents greener approaches for accessing clinically relevant camptothecin derivatives. Camptothecin derivatives are precursors of potent anticancer agents, but their biosynthesis remains largely unknown. Here two cytochrome P450 monooxygenases are shown to regiospecifically oxidize camptothecin, yielding 10- and 11- hydroxylated derivatives, which are subsequently used to produce a suite of known anticancer drugs and derivatives. [ABSTRACT FROM AUTHOR]

Key message: This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-methyladenine sites has been a very active topic of computational biology due to the unavailability of suitable methods to identify them accurately, especially in plants. Substantial results were obtained with a great effort put in extracting, heuristic searching, or fusing a diverse types of features, not to mention a feature selection step. In this study, we regarded DNA sequences as textual information and employed natural language processing techniques to decipher hidden biological meanings from those sequences. In other words, we considered DNA, the human life book, as a book corpus for training DNA language models. K-mer embeddings then were generated from these language models to be used in machine learning prediction models. Skip-gram neural networks were the base of the language models and ensemble tree-based algorithms were the machine learning algorithms for prediction models. We trained the prediction model on Rosaceae genome dataset and performed a comprehensive test on 3 plant genome datasets. Our proposed method shows promising performance with AUC performance approaching an ideal value on Rosaceae dataset (0.99), a high score on Rice dataset (0.95) and improved performance on Rice dataset while enjoying an elegant, yet efficient feature extraction process. [ABSTRACT FROM AUTHOR]

The emergent occurrence of sulfonamide species involving sulfadiazine (SDZ) and sulfamethazine (SMZ) in aquatic systems can cause a wide range of potential risks; hence, remediation strategies need to be necessary. Here, we develop the novel metal-organic framework-derived nanocomposite, and apply for the adsorption of SDZ and SMZ antibiotics. To assess the best-fitting kinetic (pseudo first-order, pseudo second-order) and isotherm (Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Redlich-Peterson, Sips, Toth, and Khan) models, a series of numerous statistical analysis was performed. Numerous error functions including squares of the errors (SSE), hybrid fractional error function (HYBRID), Marquardt's percent standard deviation (MPSD), and mean relative error (MRE) were also analyzed to assess the linear and nonlinear models. The results indicated that both linear and nonlinear kinetic models were mostly fitted well with pseudo second-order models (Radj)2 > 0.97. Although linear kinetics gave better (Radj)2, error functions (MRE, SSE, HYBRID, and MPSD) were mostly higher than those of nonlinear kinetics. For adsorption isotherm, nonlinear Redlich-Peterson was the most compatible model with extremely high adjusted coefficients of determination (Radj)2 ~ 1.0000. While nonlinear Langmuir model gave relatively high (Radj)2 (0.9898–0.9960) and acceptable error functions, we found the considerable difference of error functions and parameters among four types of linear Langmuir (Types I, II, III, IV). The findings indicate potential errors as selecting one of linearized Langmuir types in equilibrium study. It is suggested that nonlinear models should be applied for better fitness. [ABSTRACT FROM AUTHOR]

The present work investigates the primary adsorption mechanisms of lead (II) and cadmium (II) cations onto pomelo fruit peel (PFP) from aqueous solution. pH, adsorption time, ion strength, and initial metal cation concentrations, which are factors affecting the uptake of these cations, are investigated. Results show that pH and ion strengths strongly affect the removal of these cations from aqueous solution. Different isotherm adsorption models, such as Langmuir, Freundlich, and Sips, are utilized to fit the experimental data in order to determine the adsorption in nature. The Langmuir monolayer adsorption capacities are found to be 47.18 mg/g for lead (II) and 13.35 mg/g for cadmium (II). Kinetic and thermodynamic studies based on a combination of FT-IR and TG-DSC spectroscopies demonstrate that electrostatic attraction plays a primary adsorption mechanism of lead (II) and cadmium (II) cations onto pomelo fruit peel. [ABSTRACT FROM AUTHOR]

Energy security and pollution are becoming critical issues in the context of climate change and the circular economy, calling for sustainable resources. Asteraceae species, commonly referred as the aster, daisy, composite, or sunflower family, are among the largest flowering plants with 23,000 species belonging to 1600 genera, and are a major source of biomass. Here we review botanical characteristics and applications of Asteraceae for bioenergy, remediation, nanotechnology, medicine, food and agriculture. Sunflower is the most used to produce bio-oils with a yearly production of 47 million tons. Sunflower is also used for phytoremediation of heavy metals such as cadmium, lead, uranium, and chromium. Artichoke, chicory, purple coneflower, and wormwood display medicinal potential with 27–103 bioactive compounds. Artemisia, calendula, and sunflower oil are precursors for cosmetic and perfume production. Asteraceae species exhibited high efficiencies of 90−96% for removal of remazol red, and reactive blue dyes. Asteraceae species are used for fabrication of nanoparticles, biochar, activated carbon and biocomposites. [ABSTRACT FROM AUTHOR]