bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in the Main Physicochemical Properties and Electrochemical Fingerprints in the Production of Sea Buckthorn Juice by Pectinase Treatment
Authors: Guo Kaihua
Published: February 2024

Enzymatic hydrolysis using pectinase is critical for producing high-yield and quality sea buckthorn juice. This study determined the optimal temperature, time, and enzyme dosage combinations to guide manufacturers. A temperature of 60 °C, hydrolysis time of 3 h, and 0.3% enzyme dosage gave 64.1% juice yield—25% higher than without enzymes. Furthermore, monitoring physicochemical properties reveals enzyme impacts on composition. Higher dosages increase soluble solids up to 15% and soluble fiber content by 35% through cell wall breakdown. However, excessive amounts over 0.3% decrease yields. Pectin concentration also declines dose-dependently, falling by 91% at 0.4%, improving juice stability but needing modulation to retain viscosity. Electrochemical fingerprinting successfully differentiates process conditions, offering a rapid quality control tool. Its potential for commercial inline use during enzymatic treatment requires exploration. Overall, connecting optimized parameters to measured effects provides actionable insights for manufacturers to boost yields, determine enzyme impacts on nutrition/functionality, and introduce novel process analytical technology. Further investigations of health properties using these conditions could expand sea buckthorn juice functionality.
In Vitro and In Silico of Cholinesterases Inhibition and In Vitro and In Vivo Anti-Melanoma Activity Investigations of Extracts Obtained from Selected Berberis Species
Authors: Tuzimski Tomasz , Petruczynik Anna , Kaproń Barbara , Plech Tomasz , Makuch-Kocka Anna , Janiszewska Daria , Sugajski Mateusz , Buszewski Bogusław , Szultka-Młyńska Małgorzata
Published: February 2024

Berberis species have a long history of use in traditional Chinese medicine, Ayurvedic medicine, and Western herbal medicine. The aim of this study was the quantification of the main isoquinoline alkaloids in extracts obtained from various Berberis species by HPLC, in vitro and in silico determination of anti-cholinesterase activity, and in vitro and in vivo investigations of the cytotoxic activity of the investigated plant extracts and alkaloid standards. In particular, Berberis species whose activity had not been previously investigated were selected for the study. In the most investigated Berberis extracts, a high content of berberine and palmatine was determined. Alkaloid standards and most of the investigated plant extracts exhibit significant anti-cholinesterase activity. Molecular docking results confirmed that both alkaloids are more favourable for forming complexes with acetylcholinesterase compared to butyrylcholinesterase. The kinetic results obtained by HPLC-DAD indicated that berberine noncompetitively inhibited acetylcholinesterase, while butyrylcholinesterase was inhibited in a mixed mode. In turn, palmatine exhibited a mixed inhibition of acetylcholinesterase. The cytotoxic activity of berberine and palmatine standards and plant extracts were investigated against the human melanoma cell line (A375). The highest cytotoxicity was determined for extract obtained from Berberis pruinosa cortex. The cytotoxic properties of the extract were also determined in the in vivo investigations using the Danio rerio larvae xenograft model. The obtained results confirmed a significant effect of the Berberis pruinosa cortex extract on the number of cancer cells in a living organism. Our results showed that extracts obtained from Berberis species, especially the Berberis pruinosa cortex extract, can be recommended for further in vivo experiments in order to confirm the possibility of their application in the treatment of neurodegenerative diseases and human melanoma.
Glycemic Changes Related to Arsenic Exposure: An Overview of Animal and Human Studies
Authors: Rosendo Geovanna Beatriz Oliveira , Ferreira Rannapaula Lawrynhuk Urbano , Aquino Séphora Louyse Silva , Barbosa Fernando , Pedrosa Lucia Fatima Campos
Published: February 2024

Background: Arsenic (As) is a risk factor associated with glycemic alterations. However, the mechanisms of action and metabolic aspects associated with changes in glycemic profiles have not yet been completely elucidated. Therefore, in this review, we aimed to investigate the metabolic aspects of As and its mechanism of action associated with glycemic changes. Methods: We searched the PubMed (MEDLINE) and Google Scholar databases for relevant articles published in English. A combination of free text and medical subject heading keywords and search terms was used to construct search equations. The search yielded 466 articles; however, only 50 were included in the review. Results: We observed that the relationship between As exposure and glycemic alterations in humans may be associated with sex, smoking status, body mass index, age, occupation, and genetic factors. The main mechanisms of action associated with changes induced by exposure to As in the glycemic profile identified in animals are increased oxidative stress, reduced expression of glucose transporter type 4, induction of inflammatory factor expression and dysfunction of pancreatic β cells. Conclusions: Therefore, As exposure may be associated with glycemic alterations according to inter-individual differences.
Intake Modalities of Amino Acid Supplements: A Real-World Data Collection from Phenylketonuria Patients
Authors: Tummolo Albina , Carella Rosa , Carone Pasquale , Paterno Giulia , De Giovanni Donatella
Published: February 2024

Background: To achieve a normal nutritional status, patients suffering from phenylketonuria (PKU) are typically prescribed amino acid (AA) supplements with low or no phenylalanine (Phe) content. Studies evaluating patient preferences regarding the intake modalities of AA supplements are limited. This study aimed to collect real-world data regarding prescription adherence and intake modalities of AA supplements reported by PKU patients while monitoring metabolic control. Methods: This cross-sectional study included 33 PKU patients (16 female and 17 male) with a mean age of 27.2 years. Questionnaires were provided to assess information on AA supplement intake, such as prescription adherence rate, frequency and timing of administration, supplement formulation, and combination with food or drinks. Plasma phenylalanine levels were monitored during the study period. Results: 51.5% (n = 17) of patients reported to lay within an adherence range of 75–100%. The majority of patients consumed AA supplements twice daily, with breakfast (87.9%) and afternoon snacks (51.5%). Powder supplements were most commonly used (72.7%) and often combined with milk and/or fruit juices (45.4%). Conclusions: Despite the known concerns related to treatment compliance among PKU adolescents and adults, most of the study participants reported a high level of adherence to AA supplement prescription. The personalized dietary regimens followed by the patients included in the current study represent a treatment approach that might be worth trying in non-compliant patients.
Reply to Curtis, L. Comment on “Magner et al. Sulforaphane Treatment in Children with Autism: A Prospective Randomized Double-Blind Study. Nutrients 2023, 15, 718”
Authors: Magner Martin , Švandová Ivana , Houška Milan
Published: February 2024

We thank Dr. Curtis for his interest and feedback [...]
The Risk of Orthorexia and the Prevalence of Emotional Eating Behaviours among Polish Military Flying Personnel in Relation to Body Mass Index (BMI) and Sociodemographic Factors
Authors: Gaździńska Agata , Jagielski Paweł , Baran Paulina
Published: February 2024

Background: Proper nutrition has a positive impact on health. Paradoxically, excessive preoccupation with healthy eating may lead to the emergence of abnormal eating behaviours and increase the risk of developing disorders. The aim of this study was to assess the risk of orthorexia (ON) and the prevalence of emotional eating (EE) in military flying personnel of the Polish Air Force in relation to BMI and sociodemographic factors. Methods: This study included 760 soldiers (including 60 females) taking part in the National Health Programme 2021–2025. The ORTO-15 questionnaire and EEQ were used to assess the risk of ON and EE. Results: The risk of ON occurred in 28.9% of military flying personnel and was significantly more frequent in soldiers with a normal weight (46.4%), under 40 years of age (42.7%), and with higher education (42.7%). The prevalence of EE was found in approximately 12.3% of the respondents and was significantly more common in soldiers with diagnosed obesity (17.5%), women (21.7%), and soldiers with higher education (13.6). Other sociodemographic variables did not differentiate the results of the ORTO-15 questionnaire and EEQ. Conclusions: The obtained results indicate that the problem of eating disorders also occurs in military populations. The necessity of continuing research in this area is discussed.
Nutritional Approaches Targeting Gut Microbiota in Oxidative-Stress-Associated Metabolic Syndrome: Focus on Early Life Programming
Authors: Tain You-Lin , Hsu Chien-Ning
Published: February 2024

Metabolic syndrome (MetS) denotes a constellation of risk factors associated with the development of cardiovascular disease, with its roots potentially traced back to early life. Given the pivotal role of oxidative stress and dysbiotic gut microbiota in MetS pathogenesis, comprehending their influence on MetS programming is crucial. Targeting these mechanisms during the early stages of life presents a promising avenue for preventing MetS later in life. This article begins by examining detrimental insults during early life that impact fetal programming, ultimately contributing to MetS in adulthood. Following that, we explore the role of oxidative stress and the dysregulation of gut microbiota in the initiation of MetS programming. The review also consolidates existing evidence on how gut-microbiota-targeted interventions can thwart oxidative-stress-associated MetS programming, encompassing approaches such as probiotics, prebiotics, postbiotics, and the modulation of bacterial metabolites. While animal studies demonstrate the favorable effects of gut-microbiota-targeted therapy in mitigating MetS programming, further clinical investigations are imperative to enhance our understanding of manipulating gut microbiota and oxidative stress for the prevention of MetS.
The Maternal Omega-3 Long-Chain Polyunsaturated Fatty Acid Concentration in Early Pregnancy and Infant Neurodevelopment: The ECLIPSES Study
Authors: Shahabi Behnaz , Hernández-Martínez Carmen , Voltas Núria , Canals Josefa , Arija Victoria
Published: February 2024

Omega-3 Long-Chain Polyunsaturated Fatty Acids (n-3 LCPUFAs) play a key role in early neurodevelopment, but evidence from observational and clinical studies remains inconsistent. This study investigates the association between maternal n-3 LCPUFA, Docosahexaenoic Acid (DHA), and eicosapentaenoic acid (EPA) concentrations during pregnancy and infant development functioning at 40 days. This study includes 348 mother–infant pairs. Maternal serum concentrations were assessed in the first and third trimesters alongside sociodemographic, clinical, nutritional, psychological, and obstetrical data. At 40 days, the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) was administered. An adjusted analysis revealed that lower first-trimester n-3 LCPUFA and DHA concentrations are associated with better infant motor development. These results underscore the potential significance of the maternal n-3 LCPUFA status in early pregnancy for influencing fetal neurodevelopment. However, the complexity of these associations necessitates further investigation, emphasizing the urgent need for additional studies to comprehensively elucidate the nuanced interplay between the maternal n-3 LCPUFA status and infant neurodevelopment.
Surface Topological Plexcitons: Strong Coupling in a Bi2Se3 Topological Insulator Nanoparticle-Quantum Dot Molecule
Authors: Kountouris George , Yannopapas Vassilios
Published: February 2024

Strong coupling of quantum states with electromagnetic modes of topological matter offer an interesting platform for the exploration of new physics and applications. In this work, we report a novel hybrid mode, a surface topological plexciton, arising from strong coupling between the surface topological plasmon mode of a Bi2Se3 topological insulator nanoparticle and the exciton of a two-level quantum emitter. We study the power absorption spectrum of the system by working within the dipole and rotating-wave approximations, using a density matrix approach for the emitter, and a classical dielectric-function approach for the topological-insulator nanoparticle. We show that a Rabi-type splitting can appear in the spectrum suggesting the presence of strong coupling. Furthermore, we study the dependence of the splitting on the separation of the two nanoparticles as well as the dipole moment of the quantum emitter. These results can be useful for exploring exotic phases of matter, furthering research in topological insulator plasmonics, as well as for applications in the far-infrared and quantum computing.
Identification of Novel Flavonoids and Ansa-Macrolides with Activities against Leishmania donovani through Natural Product Library Screening
Authors: Phan Trong-Nhat , Lee Hyeryon , Baek Kyung-Hwa , No Joo Hwan
Published: February 2024

The protozoan parasite Leishmania donovani is the causative agent of visceral leishmaniasis (VL), a potentially fatal disease if left untreated. Given the limitations of current therapies, there is an urgent need for new, safe, and effective drugs. To discover novel antileishmanial compounds from previously unexplored chemical spaces, we conducted a high-throughput screening (HTS) of 2562 natural compounds, assessing their activity against L. donovani promastigotes and intracellular amastigotes. Utilizing the criteria of ≥70% parasite growth inhibition and ≥70% host cell (THP-1) viability, we selected 100 inhibitors for half-maximal inhibitory concentration (IC50) value determination. Twenty-six compounds showed activities in both forms of Leishmania with a selectivity index of over 3. Clustering analysis resulted in four chemical clusters with scaffolds of lycorine (cluster 1), 5-hydroxy-9,10-dihydro-4H,8H-pyrano[2,3-f]chromene-4,8-dione (cluster 2), and semi-synthetic derivatives of ansamycin macrolide (cluster 4). The enantiomer of lycorine, BMD-NP-00820, showed the highest anti-amastigote activity with an IC50 value of 1.74 ± 0.27 μM and a selectivity index (SI) > 29. In cluster 3, the most potent compound had an IC50 value of 2.20 ± 0.29 μM with an SI > 23, whereas in cluster 4, with compounds structurally similar to the tuberculosis drug rifapentine, BMD-NP-02085 had an IC50 value of 1.76 ± 0.28 μM, but the SI value was 7.5. Taken together, the natural products identified from this study are a potential source for the discovery of antileishmanial chemotypes for further development.
Modeling the Role of Weather and Pilgrimage Variables on Dengue Fever Incidence in Saudi Arabia
Authors: Altassan Kholood K. , Morin Cory W. , Hess Jeremy J.
Published: February 2024

The first case of dengue fever (DF) in Saudi Arabia appeared in 1993 but by 2022, DF incidence was 11 per 100,000 people. Climatologic and population factors, such as the annual Hajj, likely contribute to DF’s epidemiology in Saudi Arabia. In this study, we assess the impact of these variables on the DF burden of disease in Saudi Arabia and we attempt to create robust DF predictive models. Using 10 years of DF, weather, and pilgrimage data, we conducted a bivariate analysis investigating the role of weather and pilgrimage variables on DF incidence. We also compared the abilities of three different predictive models. Amongst weather variables, temperature and humidity had the strongest associations with DF incidence, while rainfall showed little to no significant relationship. Pilgrimage variables did not have strong associations with DF incidence. The random forest model had the highest predictive ability (R2 = 0.62) when previous DF data were withheld, and the ARIMA model was the best (R2 = 0.78) when previous DF data were incorporated. We found that a nonlinear machine-learning model incorporating temperature and humidity variables had the best prediction accuracy for DF, regardless of the availability of previous DF data. This finding can inform DF early warning systems and preparedness in Saudi Arabia.
HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS
Authors: Guo Zhongjing , Duan Yu , Zhao Zhimin , Yang Depo , Xu Xinjun
Published: February 2024

To establish the fingerprint of Cibotii rhizoma using high-performance liquid chromatography (HPLC) and evaluate the quality of Cibotii rhizoma from different regions using chemometrics to identify the potential quality markers, thirteen batches of Cibotii rhizoma samples were analyzed. the similarity evaluation system of TCM chromatographic fingerprint similarity evaluation was used to confirm common peaks. The SPSS 27 software was used for hierarchical cluster analysis (HCA), and SIMCA 14.1 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, a batch of Cibotii rhizoma was selected for LC-MS analysis and speculated on 15 common components. HPLC fingerprint were established, 15 common peaks were matched, two chromatographic peaks were identified using standard substances (protocatechuic acid and protocatechuic aldehyde), and 13 common components were inferred through liquid chromatograph-mass spectrometer (LC-MS). The 13 batches of the samples showed good similarities (>0.910). The results of HCA, PCA and OPLS-DA showed that 13 batches of samples were divided into three groups, and different markers were selected. The method is simple, rapid and reproducible, and can provide a reference for the overall quality evaluation of Cibotii rhizoma.
Reduced Production of Pro-Inflammatory and Pro-Catabolic Factors by Human Serum Metabolites Derived from a Patented Saffron Extract Intake
Authors: Pourtau Line , Wauquier Fabien , Boutin-Wittrant Line , Gaudout David , Moras Benjamin , Vignault Adeline , Vaysse Carole , Richard Tristan , Courtois Arnaud , Krisa Stéphanie , Roux Véronique , Macian Nicolas , Pickering Gisèle , Wittrant Yohann
Published: February 2024

Safe and anti-inflammatory plant-based natural products present an increasing focus in the treatment of chronic inflammatory diseases such as osteoarthritis or inflammatory bowel diseases. Among them, saffron, a spice derived from the stigma of Crocus sativus, could have anti-inflammatory properties and would be therefore a promising therapeutic agent for the treatment of such conditions. However, the anti-inflammatory molecular mechanisms of saffron in humans are still understudied and unclear. In this study, combining human serum metabolites and cell cultures, we evaluated the effect of circulating metabolites from the consumption of a patented saffron extract (Safr’InsideTM) on the chondrocytes and colon epithelial cell responses to inflammatory stress. Parametric or non-parametric Analysis of Variance with post hoc tests was performed. We demonstrated that human serum containing metabolites from saffron intake attenuated IL-1β-stimulated production of PGE2 and MMP-13 in chondrocyte cells and limited the increase in ICAM-1, MCP-1, iNOS, and MMP-3 in human epithelial cells following combined IL-1β and TNF-α inflammatory stimulation. Altogether, these data provide new findings into the mechanisms underlying the beneficial effects of saffron on chondrocytes and enterocyte cells at the cellular level and in the context of chronic inflammatory disorders.
Functional Chitosan and Its Derivative-Related Drug Delivery Systems for Nano-Therapy: Recent Advances
Authors: Wang Zixu , Yu Fangying , Hu Fuqiang
Published: February 2024

In the struggle against diseases, the development of nano-therapy has certainly been a tremendous progression owing to the various superiority, and chitosan is no doubt a kind of prominent biopolymer material with versatility for applications in disease treatments. For the rational construction of chitosan-related nano-biodevices, it is necessary to pay full attention to the material itself, where it is the material properties that guide the design criteria. Additionally, the well-matched preparation methods between material carriers and therapeutic agents draw much attention to the final construction since they seem to be more realistic. In detail, we present a comprehensive overview of recent advances in rational construction of chitosan-related nano-therapies with respect to material-property-oriented design criteria and preparation methods in the current review article, based on the foundation of continuous investigations. Based on this review, a portion of the various uses of chitosan-related nano-biodevices for biomedical applications are specifically discussed. Here, the strategies demonstrate the versatility of chitosan well, and the concept of being simple yet effective is well illustrated and vividly communicated. Altogether, a fresh concept concerning multi-functional chitosan and its derivative-related drug delivery systems for nano-therapy is proposed in this review, and this could be applied to other materials, which seems to be a novel angle.
Studying the API Distribution of Controlled Release Formulations Produced via Continuous Twin-Screw Wet Granulation: Influence of Matrix Former, Filler and Process Parameters
Authors: Denduyver Phaedra , Vervaet Chris , Vanhoorne Valérie
Published: February 2024

Hydroxypropyl methylcellulose (HPMC) is a preferred hydrophilic matrix former for controlled release formulations produced through continuous twin-screw wet granulation. However, a non-homogeneous API distribution over sieve fractions with underdosing in the fines fraction (<150 µm) was previously reported. This could result in content uniformity issues during downstream processing. Therefore, the current study investigated the root cause of the non-homogeneous theophylline distribution. The effect of process parameters (L/S-ratio and screw configuration) and formulation parameters (matrix former and filler type) on content uniformity was studied. Next, the influence of the formulation parameters on tableting and dissolution behavior was investigated. Altering the L/S-ratio or using a more aggressive screw configuration did not result in a homogeneous API distribution over the granule sieve fractions. Using microcrystalline cellulose (MCC) as filler improved the API distribution due to its similar behavior as HPMC. As excluding HPMC or including a hydrophobic matrix former (Kollidon SR) yielded granules with a homogeneous API distribution, HPMC was identified as the root cause of the non-homogeneous API distribution. This was linked to its fast hydration and swelling (irrespective of the HPMC grade) upon addition of the granulation liquid.
Casimir Physics beyond the Proximity Force Approximation: The Derivative Expansion
Authors: Fosco César D. , Lombardo Fernando C. , Mazzitelli Francisco D.
Published: February 2024

We review the derivative expansion (DE) method in Casimir physics, an approach which extends the proximity force approximation (PFA). After introducing and motivating the DE in contexts other than the Casimir effect, we present different examples which correspond to that realm. We focus on different particular geometries, boundary conditions, types of fields, and quantum and thermal fluctuations. Besides providing various examples where the method can be applied, we discuss a concrete example for which the DE cannot be applied; namely, the case of perfect Neumann conditions in 2+1 dimensions. By the same example, we show how a more realistic type of boundary condition circumvents the problem. We also comment on the application of the DE to the Casimir–Polder interaction which provides a broader perspective on particle–surface interactions.
Influence of Polymer Fibers on the Structure and Properties of Modified Variatropic Vibrocentrifuged Concrete
Authors: Shcherban’ Evgenii M. , Stel’makh Sergey A. , Mailyan Levon R. , Beskopylny Alexey N. , Smolyanichenko Alla S. , Chernil’nik Andrei , Elshaeva Diana , Beskopylny Nikita
Published: February 2024

The application of polymer materials in concrete structures is widespread and effectively used. However, there is a lack of a systematic knowledge base about the structure formation and properties of variatropic vibrocentrifuged modified fiber-reinforced concrete. The purpose of this work is the investigation of the influence of polypropylene (PF) and basalt fiber (BF) and modification with microsilica (MS) on the properties of variatropic concretes obtained using the synthesized vibration centrifugation technology. Test samples were made using vibration centrifugation technology, followed by sawing. Various types of fiber reinforcement were studied, both individually and in combination. To determine the degree of effectiveness of each recipe solution, the following main characteristics were monitored: the density and workability of concrete mixtures; the density of hardened composites; compressive strength (CS); bending strength (BS); water absorption (WA). In variatropic vibrocentrifuged concrete, the greatest efficiency is achieved with dispersed BF reinforcement in an amount of 1.5%. Compared to the control composition, the increase in CS was 8.50%, the increase in BS was 79.17%, and WA decreased by 27.54%. With PF reinforcement, the greatest effect was recorded at a dosage of 1.0%. The increase in CS was 3.16%, the increase in BS was 10.42%, and WA decreased by 17.39%. The MS modification showed the best effect with 8% replacement of part of the Portland cement. The increase in CS was 17.43%, the increase in BS was 14.58%, and WA decreased by 33.30%. The most effective and economically rational formulation solution for vibrocentrifuged concrete is combined fiber reinforcement in combination with the MS modification in the following quantities: BF—1.0%; PF—0.5%; MS—8%. The increase in CS was 22.82%, the increase in BS was 85.42%, and WA decreased by 37.68%.
Preparation and Characterization of Theophylline Controlled Release Matrix System Incorporating Poloxamer 407, Stearyl Alcohol, and Hydroxypropyl Methylcellulose: A Novel Formulation and Development Study
Authors: Sakkal Molham , Arafat Mosab , Yuvaraju Priya , Beiram Rami , AbuRuz Salahdein
Published: February 2024

Background: Theophylline (THN), a bronchodilator with potential applications in emerging conditions like COVID-19, requires a controlled-release delivery system due to its narrow therapeutic range and short half-life. This need is particularly crucial as some existing formulations demonstrate impaired functionality. This study aims to develop a new 12-h controlled-release matrix system (CRMS) in the form of a capsule to optimize dosing intervals. Methods: CRMSs were developed using varying proportions of poloxamer 407 (P-407), stearyl alcohol (STA), and hydroxypropyl methylcellulose (HPMC) through the fusion technique. Their in vitro dissolution profiles were then compared with an FDA-approved THN drug across different pH media. The candidate formulation underwent characterization using X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. Additionally, a comprehensive stability study was conducted. Results: In vitro studies showed that adjusting the concentrations of excipients effectively controlled drug release. Notably, the CRMS formulation 15 (CRMS-F15), which was composed of 30% P-407, 30% STA, and 10% HPMC, closely matched the 12 h controlled-release profile of an FDA-approved drug across various pH media. Characterization techniques verified the successful dispersion of the drug within the matrix. Furthermore, CRMS-F15 maintained a consistent controlled drug release and demonstrated stability under a range of storage conditions. Conclusions: The newly developed CRMS-F15 achieved a 12 h controlled release, comparable to its FDA-approved counterpart.
Proteinaceous Microsphere-Based Water-in-Oil Pickering Emulsions for Preservation of Chlorella Cells
Authors: Qi Lin , Hang Teng , Jiang Weijie , Li Sinong , Zhang Hui , Liang Xiang , Lei Le , Bi Qiangqiang , Jiang Hang , Li Yunxing
Published: February 2024

Microalgae are highly regarded as ideal materials for the creation of liquid biofuels and have substantial potential for growth and utilization. However, traditional storage and culture methods for microalgae are plagued by challenges such as uncontrolled growth, bacterial contamination, and self-shading among algae. These issues severely impede the photosynthetic process and the efficient extraction of biomass energy. This study tackles these problems by utilizing magnetic hydrophobic protein particles to stabilize water-in-oil Pickering emulsions. This allows for the micro-compartment storage and magnetic transfer of algae. Additionally, the successful encapsulation of Chlorella cells in high-internal-phase water-in-oil Pickering emulsions effectively mitigates the settling problem of Chlorella cells in the liquid phase, thereby enabling the potential use of Pickering emulsions for the confined cultivation of microalgae.
Electrospinning Silk-Fibroin-Based Fibrous Membranes with AgNPs for Antimicrobial Application
Authors: Li Qing , Gong Hongyu , Jia Xiang , Wang Ran , Liu Zhiwei , Zhang Lexin , Li Jisheng , Jiao Tifeng
Published: February 2024

Silk fibroin (SF) has excellent biocompatibility and is one of the most commonly used polymer materials. However, SF fibers have serious drawbacks as antibacterial materials due to their lack of stability and bacterial resistance. Therefore, it is of paramount significance to enhance the stability and bolster the bacterial resistance of SF fibers. In this study, SF fibers were fabricated and loaded with Ag nanoparticles (AgNPs) to improve the antimicrobial properties of the fibers. The impact of reduction conditions on the size of AgNPs was also investigated. In an antibacterial test, the fibers that were prepared exhibited over 98% bacterial resistance against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Therefore, as an efficient antibacterial material, these fibers are expected to become a candidate material in medical and textile fields. This study offers a novel approach for the utilization of SF fibers in the realm of antibacterial applications.
Ion-Imprinted Polymer-Based Sensor for the Detection of Mercury Ions
Authors: Low Kit Meng , Lin Xuanhao , Wu Huanan , Li Sam Fong Yau
Published: February 2024

In this work, the development of a novel method for the detection of mercury (II) ions in wastewater using a mercury ion-imprinted polymer (IIP) combined with a quartz crystal microbalance (QCM) is described. The IIP was successfully synthesized via the polymerization of a of a novel fluorescein- and 2-aminophenol-functionalized methacrylic acid monomer, which was noted to have high binding affinity to mercury (II) ions. This polymer was subsequently coated on a QCM chip to create an IIP-QCM sensor. This sensor was established to have high selectivity and good sensitivity to mercury (II) ions, and had a limit of detection (LOD) of 14.17 ppb, a limit of quantification (LOQ) of 42.94 ppb, a signal-to-noise ratio (S/N) of 4.29, good repeatability, and a working range of 42.94 ppb to 2 ppm. The sensor was also able to analyze tap water and wastewater samples. The IIP-QCM is, therefore, promising as a highly selective, cost-effective, and rapid mercury ion sensor for applications involving the detection of mercury in wastewater.
Poly(ester imide)s with Low Linear Coefficients of Thermal Expansion and Low Water Uptake (VII): A Strategy to Achieve Ultra-Low Dissipation Factors at 10 GHz
Authors: Hasegawa Masatoshi , Fukuda Taro , Ishii Junichi
Published: February 2024

In this study, a series of ester-linked tetracarboxylic dianhydrides (TCDAs) with 2,6-naphthalene-containing longitudinally extended structures consisting of different numbers of aromatic rings (NAr = 6–8) was synthesized to obtain novel modified polyimides, poly(ester imide)s (PEsIs). These TCDAs were fully compatible with the conventional manufacturing processes of conventional polyimide (PI) systems. As an example, the PEsI film obtained from the ester-linked TCDA (NAr = 8) and an ester-linked diamine achieved unprecedented outstanding dielectric properties without the support of fluorinated monomers, specifically an ultra-low dissipation factor (tan δ) of 0.00128 at a frequency of 10 GHz (50% RH and 23 °C), in addition to an extremely high glass transition temperature (Tg) of 365 °C, extremely low linear coefficient of thermal expansion (CTE) of 6.8 ppm K−1, suppressed water uptake (0.24%), requisite film ductility, and a low haze. Consequently, certain PEsI films developed in this study are promising candidates for heat-resistant dielectric substrates for use in 5G-compatible high-speed flexible printed circuit boards (FPCs). The chemical and physical factors denominating tan δ are also discussed.
Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Authors: Safakish Aryan , Sannachi Lakshmanan , Moslemi Amir , Pejović-Milić Ana , Czarnota Gregory J.
Published: February 2024

(1) Background: Some cancer patients do not experience tumour shrinkage but are still at risk of experiencing unwanted treatment side effects. Radiomics refers to mining biomedical images to quantify textural characterization. When radiomics features are labelled with treatment response, retrospectively, they can train predictive machine learning (ML) models. (2) Methods: Radiomics features were determined from lymph node (LN) segmentations from treatment-planning CT scans of head and neck (H&N) cancer patients. Binary treatment outcomes (complete response versus partial or no response) and radiomics features for n = 71 patients were used to train support vector machine (SVM) and k-nearest neighbour (k-NN) classifier models with 1–7 features. A deep texture analysis (DTA) methodology was proposed and evaluated for second- and third-layer radiomics features, and models were evaluated based on common metrics (sensitivity (%Sn), specificity (%Sp), accuracy (%Acc), precision (%Prec), and balanced accuracy (%Bal Acc)). (3) Results: Models created with both classifiers were found to be able to predict treatment response, and the results suggest that the inclusion of deeper layer features enhanced model performance. The best model was a seven-feature multivariable k-NN model trained using features from three layers deep of texture features with %Sn = 74%, %Sp = 68%, %Acc = 72%, %Prec = 81%, %Bal Acc = 71% and with an area under the curve (AUC) the receiver operating characteristic (ROC) of 0.700. (4) Conclusions: H&N Cancer patient treatment-planning CT scans and LN segmentations contain phenotypic information regarding treatment response, and the proposed DTA methodology can improve model performance by enhancing feature sets and is worth consideration in future radiomics studies.
Making Knowledge in the Local Settings: Vernacular Education and Cantonese Elementary Textbooks
Authors: Chen Sixing
Published: February 2024

A growing number of Protestant missionaries engaged in vernacular education in the late nineteenth century. To meet the demands of the new era, Christian church education faced challenges not only in its curriculum design but also in the way it presented new knowledge. Previous studies have focused on church education at the tertiary level while overlooking the elementary level. This article discusses vernacular church education and vernacular textbooks at the elementary level in the late Qing, with specific reference to Youxue baoshen yaoyan 幼學保身要言 (The Human Body for Children). It argues that the demand for spreading new knowledge urged Protestant missionaries to compile vernacular textbooks and present Western knowledge in the local settings. Vernacular church education should be regarded as the precursor of indigenous education proposed by the late Qing Court. The local dialect, Cantonese in this case, bridged the linguistic gap between new terms and children’s cognition and became an effective means of presenting new knowledge. Vernacular textbooks had an unparalleled significance in the cultural sphere of dialect writing, since the language of textbooks could drastically influence the writing and reading habits of the young generation and further influence people’s attitudes towards dialects and dialect literature.
A Review of MDA-5 Dermatomyositis and Associated Interstitial Lung Disease
Authors: Bhandari Sambhawana , Zickuhr Lisa , Baral Maun Ranjan , Bhalla Sanjeev , Jones Heather , Bucelli Robert , Sen Deepali
Published: February 2024

Anti-melanoma differentiation-associated gene 5 (MDA-5) dermatomyositis (DM) is noteworthy for its association with rapidly progressive interstitial lung disease (RP-ILD), vasculopathy, and distinctive cutaneous features. First identified in a Japanese cohort in 2005, MDA-5 DM carries a significant mortality risk, emphasizing the crucial need for early diagnosis. This review explores the pathogenesis, clinical presentation, diagnosis, management, and prognosis of MDA-5 DM and ILD and includes new research and recommendations regarding disease management.
Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations
Authors: Minh Nguyen Quang , Huong Nguyen Thi Thu , Khanh Pham Quoc , Hien La Phu , Bui Dieu Tien
Published: February 2024

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.
Individual Tree Segmentation Based on Seed Points Detected by an Adaptive Crown Shaped Algorithm Using UAV-LiDAR Data
Authors: Yu Jiao , Lei Lei , Li Zhenhong
Published: February 2024

Unmanned aerial vehicle–light detection and ranging (UAV-LiDAR) provides a convenient and economical means of forest data acquisition that can penetrate canopy gaps to obtain abundant ground information, offering huge potential in forest inventory. Individual tree segmentation is a prerequisite to obtain individual tree details but is highly dependent on the accuracy of seed point detection. However, most of the existing methods, such as the local maximum (LM) and CHM-based methods, are strongly dependent on the window size, and, for individual tree segmentation, they can result in over-segmentation and under-segmentation, especially in natural forests. In this paper, we propose an adaptive crown shaped algorithm for individual tree segmentation without consideration of the window size. It was implemented in four plots with different forest types and topographies (i.e., planted coniferous forest with flat terrain, coniferous forest with sloping terrain, mixed forest with flat terrain and broadleaf forest with flat terrain). First, the normalized point clouds were rotated and blocked at multiple angles to extract the surface points of the forest. Then, the crown boundaries were delineated by analyzing the crown profiles to extract the treetops as seed points. Finally, a region growing method based on seed points was applied for individual tree segmentation. Our results showed that the recall, precision and F1-score of seed point detection reached 91.6%, 95.9% and 0.94, respectively, and that the accuracy rates for individual tree segmentation for the four plots were 87.7%, 80.6%, 73.2% and 70.5%, respectively. Our proposed method can effectively detect seed points via the adaptive crown shaped algorithm and reduce the impacts of elongated branches by applying distance thresholds between trees, enhancing the accuracy of seed point detection and subsequently improving the precision of individual tree segmentation. In addition, the proposed algorithm demonstrated superior performance in comparison to LM and CHM-based methods for the calculation of seed points, as well as outperforming PCS in individual tree segmentation. The proposed method demonstrates effectiveness and feasibility in dense forests and natural forests, providing an important reference for future research on seed point detection and individual tree segmentation.
A Novel Fusion-Based Methodology for Drought Forecasting
Authors: Zhang Huihui , Loaiciga Hugo A. , Sauter Tobias
Published: February 2024

Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran’s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.
Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China
Authors: Li Ying , Liu Sijin , Bashiri Khuzestani Reza , Huang Kai , Bao Fangwen
Published: February 2024

Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R2 = 0.78, RMSE = 1.37 μgm−3) and test case databases (R2 = 0.77, RMSE = 1.35 μgm−3) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R2 ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm−3), followed by autumn and winter (R2 ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm−3). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R2 = 0.73, RMSE = 1.88 μgm−3). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future.