bluuebunny/crossref_metadata_2025 huggingface dataset:
The Possible Impact of COVID-19 on Glycated Hemoglobin and Systolic Blood Pressure in Type 2 Diabetes and Obesity
Authors: Minari Tatiana Palotta , Manzano Carolina Freitas , Tácito Yugar Louise Buonalumi , Sedenho-Prado Luis Gustavo , Rubio Tatiane de Azevedo , Tácito Lúcia Helena Bonalumi , Pires Antônio Carlos , Vilela-Martin José Fernando , Cosenso-Martin Luciana Neves , Ludovico Nelson Dinamarco , Fattori André , Yugar-Toledo Juan Carlos , Moreno Heitor , Pisani Luciana Pellegrini
Published: October 2024

Background: There are still discrepancies in the literature as to whether COVID-19 infection could impact biochemical, anthropometric, and cardiovascular markers. The purpose of this study was firstly to observe the effects of COVID-19 infection over 12 months on Type 2 diabetes (T2D) and obesity. Secondarily, we analyzed the individual influence of COVID-19 infection on changes in biochemical, anthropometric, and cardiovascular markers. Methods: This study is part of a secondary analysis of a recently published article. The research involved 84 participants with T2D, divided into two groups: the control group (40 participants) received only medical care, while the intervention group (44 participants) received both medical care and nutritional assessment. Consultations were held quarterly over 12 months, with a follow-up after 3 months. Data Analysis: For influence analysis, non-normal variables were compared using the Mann–Whitney test, and normal variables were compared using unpaired t-tests. For all cases, α = 0.05 and p < 0.05 were considered significant. Results: The analysis revealed a high percentage of patients in both groups who had a COVID-19 infection (70% control and 72.7% intervention) over 12 months. Regarding the influence analysis, participants in the intervention group who were infected with COVID-19 showed smaller reductions in glycated hemoglobin (HbA1c) (p = 0.0120) and systolic blood pressure (SBP) (p = 0.0460). For the other biochemical, anthropometric, and cardiovascular markers, in both groups, no significant differences were found (p > 0.05). Conclusion: COVID-19 possibly influenced SBP and HbA1c levels over 12 months in people with T2D and obesity. However, caution should be exercised in generalizing these results due to the limitations of this study. Additionally, influence analysis does not establish a causal relationship, and more clinical trials in different populations are needed to fully analyze this topic.
Impact of Extent of Resection on Overall Survival in Glioblastomas: An Umbrella Review of Meta-Analyses
Authors: Jagtiani Pemla , Karabacak Mert , Carrasquilla Alejandro , Yong Raymund , Margetis Konstantinos
Published: October 2024

(1) Background: Glioblastoma (GBM) is the most common malignant brain tumor in adults. Due to a lack of level 1 evidence, there is no clear consensus on the optimal extent of resection to improve overall survival. This umbrella review aggregates existing meta-analyses (MAs) to assess overall survival in patients undergoing subtotal resection (STR) versus gross total resection (GTR). (2) Methods: A systematic search of PubMed, Scopus, and Web of Science identified 441 studies, with four MAs meeting inclusion criteria. Data were analyzed using the metaumbrella R package, focusing on overall survival. Quality was assessed using AMSTAR2, with scores ranging from 0 to 11. The Ioannidis criteria were applied to evaluate the credibility of the evidence. (3) Results: The quality assessment rated all four studies highly, with a mean AMSTAR2 score of 10.25. The pooled analysis revealed a significant survival advantage for GTR over STR. However, the Ioannidis classification graded the evidence as Class III, indicating weak credibility. (4) Conclusions: GTR offers a slight survival benefit over STR in GBM patients, but the credibility of the evidence is weak, highlighting the need for further research.
A Novel Immunochromatographic Test Strip Using Lanthanide-Labeled Fluorescent Nanoparticles for the Serological Detection of Toxoplasma gondii in Dogs and Cats
Authors: Zhang Manyu , Liu Qi , Li Ruifang , Jiang Wei , Zhao Hongjin , Sheng Wenwei , Xia Luming , Li Zengqiang , Sun Qing , Du Jingying , Lei Lei , Wang Quan
Published: October 2024

Toxoplasma gondii (T. gondii) is an important zoonotic pathogen which induces both acute and chronic toxoplasmosis. Timely diagnosis of T. gondii is crucial for effective disease management. Here, we present a pioneering approach using europium (III)-chelated nanoparticles (EuNPs) in a rapid lateral flow immunochromatographic test strip (ICTS) for detecting T. gondii antibodies in serum samples. By conjugating EuNPs with Staphylococcus aureus protein A, we efficiently captured T. gondii-specific antibodies, which bound to T. gondii antigens on the test line (T-line), generating a distinct fluorescent signal. Employing this novel method, we conducted an extensive epidemiological investigation of T. gondii infections among dogs and cats in Shanghai, China. This innovative ICTS allows for rapid results within 25 min, which include a qualitative result through naked-eye observation under an ultraviolet lamp and a quantitative one derived using a strip reader. With a detection limit of 1:6400 for dog positive serum and no cross-reactivity with other canine and feline pathogens, the EuNPs-ICTS demonstrated excellent consistency with standard enzyme-linked immunosorbent assay results for dogs (κ = 0.91) and cats (κ = 0.92). In addition, 20.38% of 996 dog serum samples and 14.18% of 416 cat serum samples revealed T. gondii antibodies, highlighting the efficacy of this approach. Our study presents a rapid, sensitive, specific, and reproducible EuNPs-ICTS, serving as a promising tool for on-the-spot diagnosis of T. gondii infections in dogs and cats.
Synthesis, Characterizations, Anti-Diabetic and Molecular Modeling Approaches of Hybrid Indole-Oxadiazole Linked Thiazolidinone Derivatives
Authors: Khan Shoaib , Iqbal Tayyiaba , Hussain Rafaqat , Khan Yousaf , Fiaz Zanib , Rahim Fazal , Darwish Hany W.
Published: October 2024

Objective: To synthesize hybrid compounds of indole and oxadiazole in search of highly effective anti-diabetic therapeutic agent. Methods: With the goal of advancing diabetes research, our group designed and synthesized a library of 15 compounds based on indole-derived oxadiazole bearing varied substituted thiazolidinone via a multistep synthetic route. 13C-NMR, 1H-NMR and HREI-MS were applied for the characterization of all the synthesized compounds. Their biological inhibitory activity against diabetic enzymes, i.e., α-amylase and α-glucosidase was also determined. Results: Compound 7, 9 and 15 exhibited excellent inhibition against α-amylase and α-glucosidase than the standard acarbose (IC50 = 8.50 ± 0.10 µM for α-amylase and 9.30 ± 0.30 µM for α-glucosidase. To ensure the inhibitory actions of these potent analogs in molecular docking, an in silico approach was used. To determine the drug likeness of the reported analogs, an ADMET investigation was also carried out to explore the nature of the designed compounds if used as a drug. Conclusion: Fluoro-substituted analog 15 has stronger inhibition profile against both enzymes. All the potent compounds can be used as effective anti-diabetic therapeutic agents in future.
Emerging Cationic Nanovaccines
Authors: Carmona-Ribeiro Ana Maria , Pérez-Betancourt Yunys
Published: October 2024

Cationic vaccines of nanometric sizes can directly perform the delivery of antigen(s) and immunomodulator(s) to dendritic cells in the lymph nodes. The positively charged nanovaccines are taken up by antigen-presenting cells (APCs) of the lymphatic system often originating the cellular immunological defense required to fight intracellular microbial infections and the proliferation of cancers. Cationic molecules imparting the positive charges to nanovaccines exhibit a dose-dependent toxicity which needs to be systematically addressed. Against the coronavirus, mRNA cationic nanovaccines evolved rapidly. Nowadays cationic nanovaccines have been formulated against several infections with the advantage of cationic compounds granting protection of nucleic acids in vivo against biodegradation by nucleases. Up to the threshold concentration of cationic molecules for nanovaccine delivery, cationic nanovaccines perform well eliciting the desired Th 1 improved immune response in the absence of cytotoxicity. A second strategy in the literature involves dilution of cationic components in biocompatible polymeric matrixes. Polymeric nanoparticles incorporating cationic molecules at reduced concentrations for the cationic component often result in an absence of toxic effects. The progress in vaccinology against cancer involves in situ designs for cationic nanovaccines. The lysis of transformed cancer cells releases several tumoral antigens, which in the presence of cationic nanoadjuvants can be systemically presented for the prevention of metastatic cancer. In addition, these local cationic nanovaccines allow immunotherapeutic tumor treatment.
Modulation of Multispecific Transporters by Uncaria tomentosa Extract and Its Major Phytoconstituents
Authors: Szilvásy Nóra , Lajer Panna , Horváth Attila , Veres Katalin , Hohmann Judit , Schelz Zsuzsanna , Minorics Renáta , Zupkó István , Gáborik Zsuzsanna , Kis Emese , Temesszentandrási-Ambrus Csilla
Published: October 2024

Background/Objectives: One of the major risks associated with the concomitant use of herbal products and therapeutic drugs is herb–drug interactions (HDIs). The most common mechanism leading to HDIs is the inhibition and/or induction of transport proteins and drug-metabolizing enzymes by herbal ingredients, causing changes in the pharmacokinetic disposition of the victim drug. The present study aimed to determine the potential interactions of Uncaria tomentosa (UT) (cat’s claw), a popular herb due to its supposed health benefits. Methods: The effect of UT extract and its major oxindole alkaloids was investigated on multispecific solute carrier (SLC) and ATP-binding cassette (ABC) drug transporters, using SLC transporter-overexpressing cell lines and vesicles prepared from ABC transporter-overexpressing cells. Results: UT extract significantly inhibited all ABC transporters and the majority of the SLC transporters tested. Of the investigated oxindole alkaloids, isopteropodine significantly inhibited OATP, OCT1 and OCT2, OAT3, ENT4, MDR1, and BCRP transporters. OCTs, OCTN1-, ENT1-, and MDR1-mediated substrate accumulation was below 50% in the presence of mitraphylline. Conclusions: Based on the calculated intestinal concentration of UT extract, interactions with intestinal transporters, especially OATP2B1, ENTs, MRP1, MRP2, MDR1, and BCRP could be relevant in vivo. Our data can help to predict the clinical consequences of UT co-administration with drugs, such as increased toxicity or altered efficacy. In conclusion, the use of these in vitro models is applicable for the analysis of transporter-mediated HDIs similar to drug–drug interaction (DDI) prediction.
The Impact of COVID-19 on RNA Therapeutics: A Surge in Lipid Nanoparticles and Alternative Delivery Systems
Authors: Parvin Nargish , Mandal Tapas K. , Joo Sang-Woo
Published: October 2024

The COVID-19 pandemic has significantly accelerated progress in RNA-based therapeutics, particularly through the successful development and global rollout of mRNA vaccines. This review delves into the transformative impact of the pandemic on RNA therapeutics, with a strong focus on lipid nanoparticles (LNPs) as a pivotal delivery platform. LNPs have proven to be critical in enhancing the stability, bioavailability, and targeted delivery of mRNA, facilitating the unprecedented success of vaccines like those developed by Pfizer-BioNTech and Moderna. Beyond vaccines, LNP technology is being explored for broader therapeutic applications, including treatments for cancer, rare genetic disorders, and infectious diseases. This review also discusses emerging RNA delivery systems, such as polymeric nanoparticles and viral vectors, which offer alternative strategies to overcome existing challenges related to stability, immune responses, and tissue-specific targeting. Additionally, we examine the pandemic’s influence on regulatory processes, including the fast-tracked approvals for RNA therapies, and the surge in research funding that has spurred further innovation in the field. Public acceptance of RNA-based treatments has also grown, laying the groundwork for future developments in personalized medicine. By providing an in-depth analysis of these advancements, this review highlights the long-term impact of COVID-19 on the evolution of RNA therapeutics and the future of precision drug delivery technologies.
Categorisation of Patients’ Anticholinergic Burden at Admission and Discharge from the Geriatric Ward of Sønderjylland Hospital
Authors: Kehman Cecilie Marie Bæk , Schlünsen Maja , Kjeldsen Lene Juel
Published: October 2024

Background: High anticholinergic burden is associated with an increased risk of hospitalisation, readmission, and mortality in geriatric patients. The objectives were to develop an updated anticholinergic burden scale for drugs registered in Denmark and to estimate the burden at admission and discharge for hospitalised patients at the Geriatric Ward of Sønderjylland Hospital. Methods: The updated scale was developed through a systematic evaluation of the anticholinergic effect for all active pharmaceutical ingredients (APIs) listed on validated burden scales. APIs registered in 2020 and 2021 were evaluated separately for possible anticholinergic effect. The anticholinergic effect of each API was scored from 1 (low) to 3 (high). The scale was applied to medical records for patients hospitalised between October 2021 and March 2022. Results: The scale comprised 87 APIs with anticholinergic effect. We applied the scale on 196 patients aged (median [IQR]) 84 (78–89) years. Of these patients, 75 (38.3%) had a high burden (≥3) on admission. These patients had significantly higher drug use and higher risk of 30-day readmission but no relationship with length of stay. Overall, the anticholinergic burden was unchanged at discharge for 109 (55.1%) patients. Conclusion: An updated scale for estimation of the anticholinergic burden in geriatric patients was successfully developed, and a high burden among the admitted geriatric patients was found.
A Linear, Direct Far-Field Subwavelength Imaging Method: Microparticle-Assisted Nanoscopy
Authors: Simovski Constantin
Published: October 2024

Microparticle-assisted nanoscopy (MAN) is a novel emerging technique of direct far-field deeply subwavelength imaging, which has been developed since 2011 as a set of experimental techniques. For a decade, the capability of a simple glass microsphere without fluorescent labels or plasmonic elements to grant a direct, broadband, deeply subwavelength image of a nanostructured object was unexplained. Four years ago, the explanation of MAN via the suppression of diffraction was suggested by the author of the present overview. This explanation was confirmed by extensive full-wave simulations, which agreed with available experimental data and revealed new opportunities for MAN. Although the main goal of the present paper is to review recent works, state-of-the-art concepts in MAN are also reviewed. Moreover, so that the peculiarities of MAN are better outlined, its uniqueness compared to other practically important methods of far-field subwavelength imaging is also discussed.
Genome-Wide Identification and Analysis of Phospholipase C Gene Family Reveals Orthologs, Co-Expression Networks, and Expression Profiling Under Abiotic Stress in Sorghum bicolor
Authors: Wang Hongcheng , Yu Junxing , Zhang Xingyu , Zeng Qian , Zeng Tuo , Gu Lei , Zhu Bin , Yu Feng , Du Xuye
Published: October 2024

Phospholipase C (PLC) is an essential enzyme involved in lipid signaling pathways crucial for regulating plant growth and responding to environmental stress. In sorghum, 11 PLC genes have been identified, comprising 6 PI-PLCs and 5 NPCs. Through phylogenetic and interspecies collinearity analyses, structural similarities between SbPLCs and ZmPLCs proteins have been observed, with a particularly strong collinearity between SbPLCs and OsPLCs. Promoter function analysis has shown that SbPLCs are significantly enriched under abiotic stress and hormonal stimuli, like ABA, jasmonic acid, drought, high temperature, and salt. Gene co-expression networks, constructed using a weighted gene co-expression network analysis (WGCNA), highlight distinct expression patterns of SbPLC1, SbPLC3a, and SbPLC4 in response to abiotic stress, providing further insights into the expression patterns and interactions of SbPLCs under various environmental stimuli. qRT-PCR results reveal variations in expression levels among most SbPLCs members under different stress conditions (drought, NaCl, NaHCO3), hormone treatments (ABA), and developmental stages, indicating both specific and overlapping expression patterns. This comprehensive analysis offers valuable insights into the roles of SbPLCs in sorghum, shedding light on their specific expression patterns, regulatory elements, and protein interactions across different environmental stimuli and developmental stages.
Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning
Authors: Liu Xia , Du Ruiqi , Xiang Youzhen , Chen Junying , Zhang Fucang , Shi Hongzhao , Tang Zijun , Wang Xin
Published: October 2024

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model’s R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.
Utilizing Short Interspersed Nuclear Element as a Genetic Marker for Pre-Harvest Sprouting in Wheat
Authors: Kandpal Purnima , Kaur Karminderbir , Dhariwal Raman , Kaur Simranjeet , Brar Gagandeep Kaur , Randhawa Harpinder , Singh Jaswinder
Published: October 2024

Pre-harvest sprouting (PHS) is a complex abiotic stress caused by multiple exogenous and endogenous variables that results in random but significant quality and yield loss at the terminal crop stage in more than half of the wheat-producing areas of the world. Systematic research over more than five decades suggests that addressing this challenge requires tools beyond the traditional genetic manipulation approach. Previous molecular studies indicate a possible role of epigenetics in the regulation of seed dormancy and PHS in crops, especially through RNA-directed DNA methylation (RdDM) pathways mediated by Argonaute (AGO) proteins. In this study, we explore the role of the AGO802B gene associated with PHS resistance in wheat, through the presence of a SINE retrotransposon insertion. The current study found the SINE insertion at 3′UTR of the TaAGO802B present in 73.2% of 41 cultivars analyzed and in 92.6% of the resistant cultivar subset. The average expression of TaAGO802B in cultivars with the SINE insertion was 73.3% lower than in cultivars without insertion. This study also indicated a significant positive correlation between the PHS score and methylation levels in the cultivars. The resistant cultivars with the SINE insertion recorded 54.7% lower methylation levels than susceptible cultivars. Further analysis of a DH population (Sadash × P2711) reveals that SINE insertion co-segregates with PHS resistance. This sets forth the SINE insertion in TaAGO802B as a genetic marker for screening wheat germplasm and as an efficient tool for breeding PHS-resistant wheat cultivars.
Leveraging Spruce Bark Particle Morphology for Enhanced Internal Bonding in Particleboard Production
Authors: Gößwald Jakob , Barbu Marius Cătălin , Tudor Eugenia Mariana , Král Pavel
Published: October 2024

The continuous rise in global demand for wood products has led to an increase in prices and a surge in research into alternative resources. As a byproduct of the timber industry, bark has emerged as a promising supplement in particleboard (PB) production. However, its anatomical structure, the presence of extractives, and its inferior mechanical properties complicate the production process, which have not yet been fully overcome at a commercial scale. This study proposes a paradigm shift, advocating for separate and specialized bark constituent processing in a wet state. Three bark-based raw materials—namely, outer bark particles, bark fiber clumps, and bark fibers—were investigated under varying wood content scenarios. PBs with a target density of 0.7 g/cm3 and a thickness of 16 mm were produced using mixtures of these bark-based materials and wood particles in different ratios bonded with a urea–formaldehyde adhesive. The results demonstrated that these bark constituents exhibit distinct properties that can be optimized through tailored processing techniques. Compared to bark fibers, outer bark particles displayed about 40% lower water absorption and thickness swelling. However, bark fibers improved the internal bond by about 50% due to their favorable morphology compared to outer bark. These findings highlight the potential of bark as a valuable resource for particleboard production and pave the way for its efficient utilization through specialized processing strategies.
New Eco-Friendly Thermal Insulation and Sound Absorption Composite Materials Derived from Waste Black Tea Bags and Date Palm Tree Surface Fibers
Authors: Ali Mohamed , Almuzaiqer Redhwan , Al-Salem Khaled , Alshehri Hassan , Nuhait Abdullah , Alabdullatif Abdullah , Almubayrik Abdulrahman
Published: October 2024

A tremendous amount of waste black tea bags (BTBs) and date palm surface fibers (DPSFs), at the end of their life cycle, end up in landfills, leading to increased pollution and an increase in the negative impact on the environment. Therefore, this study aims to utilize these normally wasted materials efficiently by developing new composite materials for thermal insulation and sound absorption. Five insulation composite boards were developed, two were bound (BTB or DPSF with polyvinyl Acetate resin (PVA)) and three were hybrids (BTB, DPSF, and resin). In addition, the loose raw waste materials (BTB and DPSF) were tested separately with no binder. Thermal conductivity and sound absorption coefficients were determined for all boards. Thermal stability analysis was reported for the components of the tea bag (string, label, and bag) and one of the composite hybrid boards. Mechanical properties of the boards such as flexural strain, flexural stress, and flexural elastic modulus were determined for the bound and hybrid composites. The results showed that the thermal conductivity coefficients for all the hybrid composite sample boards are less than 0.07 at the ambient temperature of 24 °C and they were enhanced as the BTB ratio was reduced in the hybrid composite boards. The noise reduction coefficient for bound and all hybrid composite samples is greater than 0.37. The composite samples are thermally stable up to 291 °C. Most composite samples have a high flexure modulus between 4.3 MPa and 10.5 MPa. The tea bag raw materials and the composite samples have a low moisture content below 2.25%. These output results seem promising and encouraging using such developed sample boards as eco-friendly thermal insulation and sound absorption and competing with the synthetic ones developed from petrochemicals in building insulation. Moreover, returning these waste materials to circulation and producing new eco-friendly composites can reduce the number of landfills, the level of environmental pollution, and the use of synthetic materials made from fossil resources.
Impact of Various Smoking Techniques on Polycyclic Aromatic Hydrocarbon (PAH) Formation in Dry-Cured Pork Neck (Buđola)
Authors: Puljić Leona , Kartalović Brankica , Habschied Kristina , Kajić Nikolina , Kovačević Dragan , Kovač Mario , Banožić Marija , Mastanjević Krešimir
Published: October 2024

Traditional meat products that are smoked may pose health risks due to polycyclic aromatic hydrocarbons (PAHs). Recently, concerns have grown about the health implications of meat products smoked under traditional, uncontrolled conditions. This study compares the levels of polycyclic aromatic hydrocarbons in specimens of the dry-cured meat product “Buđola” made in traditional smokehouses versus industrial chambers. PAHs were measured upon completion of smoking and when the production was complete. The findings indicate that traditional smoking methods lead to higher PAH contamination compared to industrial methods. Among the 16 PAHs analyzed, 10 (NA, AL, FL, ANT, PHE, FLT, BA, PR, BBF, BKF) were detected in traditionally smoked “Buđola” samples, whereas only 2 (NA, AL) were found in samples smoked by industrial methods. The BP levels in all samples were undetectable. PAH4 levels in industrial smoked “Buđola” were below the quantification limit, while those in traditional products were 28.77 μg·kg−1 for the surface layers and 21.14 μg·kg−1 for inner layers. The total PAH16 content ranged from 4.32 μg·kg−1 to 3587.83 μg·kg−1. The inner layers had lower concentrations of overall and specific PAHs in relation to the product surface. The results suggest that, from a health perspective, industrially produced “Buđola” is safer for consumption than the product smoked in uncontrolled conditions.
Research on Wellbore Integrity Evaluation Model of CO2 Enhanced Composite Fracturing
Authors: Cao Jing , Ma Gedi , Zhao Gang , Yang Shangyu , Han Lihong , Wang Jianjun , Mou Yisheng , Cai Meng
Published: October 2024

CO2 injection composite fracturing is an effective method for shale oil and gas well development. The downhole casing is prone to uniform corrosion, pitting, perforation, and even corrosion fracture in the CO2 environment. Therefore, it is particularly important to reveal the physical characteristics of CO2 under actual geological conditions and the impact of CO2 corrosion on the performance of casing. A mathematical model for the temperature and pressure field of CO2 in the wellbore under fracturing conditions is established in this paper, and the temperature and pressure distribution along the depth of the well is calculated. By optimizing the CO2 state equation and using the S-W equation, Lee model, and RK model to calculate the CO2 density, viscosity and compression factor, respectively, the phase distribution pattern of CO2 along the actual wellbore is obtained. Through CO2 corrosion tests on the casing, the influence of temperature and CO2 concentration on the corrosion rate of the casing is clarified. The peak corrosion rate of Q125 steel corresponds to 80 °C, and the corrosion rate increases with the increase in CO2 concentration. Finally, a prediction model for the uniform corrosion rate of casing under different temperatures and CO2 concentration conditions is obtained, which can provide technical support for the design of CO2-enhanced fracturing technology.
Application ICP-OES to Multielement Analysis on Plastic Waste and Blends with Vacuum Gas Oil: Developing a Sample Preparation Protocol
Authors: Poirier Laura , Timken Hye-Kyung , Lopez-Linares Francisco
Published: October 2024

This paper introduces a new methodology for a routine metal analysis of plastic waste (PW) and PW blended with petroleum feedstock such as vacuum gas oil and VGO (PW/VGO). For such purposes, recycled polyethylene and polypropylene plastic were selected to mimic the potential feeds to be integrated at the Fluid Catalytic Cracking unit (FCC) to produce valuable products. Elements such as P, Ca, Al, Mg, Na, Zn, B, Fe, Ti, and Si were included in the method development. Different sample preparation methods were evaluated, such as microwave-assisted acid digestion (MWAD) and dry/wet ashing, followed by a fusion of the ash with lithium borate flux. Some PW homogenization pretreatments, such as cryogenic grinding and hot press molding, were also covered. The finding of this work suggests that MWAD with HNO3 and H2O2 is adequate for both types of samples and is the quickest sample preparation; however, the sample needed to be homogenized, and recoveries for Si and Ti may be biased for PW due to the limited solubilities of these elements in the nitric acid media. Carbon removal is required before fusion sample preparation and analysis due to the amount of carbon in PW samples. The sample needed to be homogenized for wet ash fusion but not for the pre-ash (dry) method. A benefit to the damp ash pretreatment is that the ash for the sample was created in the same crucible used for fusion digestion, avoiding material loss during sample management. Fusion from wet ash or carbon removal allowed for better acid solubility for Si and Ti in PW. The results of the PW samples evaluated matched well with those of both sample preparation methodologies. For most elements, precision was <10% regardless of the sample preparation; however, Fe and P had some variation using wet ash fusion, possibly due to contamination in an open digestion system or variation due to being close to the method limit of quantification (LOQ). The methodology reported here is robust enough to be implemented as routine analysis in any laboratory facility.
Optimization of Steaming Conditions for Bellflower Root (Platycodon grandiflorus) Using K-Means Clustering-Based Morphological Grading System
Authors: Oyinloye Timilehin Martins , An Seohee , Cho Chang-Won , Yoon Won Byong
Published: October 2024

Bellflower roots were categorized into three clusters (class 0, class 1, and class 2) using K-means clustering based on their morphological factors: length (282.8 ± 29.53, 138.75 ± 26.8, and 209.89 ± 20.49 mm), thickness (16.25 ± 2.82, 16.77 ± 3.35, and 16.52 ± 3.05 mm), and body shape coefficient (5.80 ± 1.15, 12.73 ± 4.82, and 7.95 ± 1.71). Internal void formation, a key quality factor for bellflower root, was analyzed under pre-steaming conditions, identifying temperatures between 20 and 25 °C as optimal for storage. Within the clustered class, steaming for a prolonged duration increased the formation of internal voids and caused a decrease in normal stress values, total dissolved solids (TDS), and pectin content. Class 0, with larger and thicker roots, exhibited higher internal voids (57% void rate) due to uneven heat distribution and incomplete starch gelatinization. Class 2 roots demonstrated better structural integrity, with a void rate of 26% and a stress value of 48 kN/m2. These findings highlight the importance of morphological classification and optimal storage temperatures to improve the quality of steamed bellflower roots.
Combining Vascular Targeting Agents with Radiation: An Effective Anti-Tumor Treatment but Associated with Radiation-Induced Systemic Toxicity
Authors: Nomura Miwako , Murata Rumi , Brøndum Line , Ehrnrooth Eva , Sørensen Brita S. , Horsman Michael R.
Published: October 2024

This study investigated the effect of combining radiation with an angiogenesis inhibitor and vascular disrupting agent on tumor response and systemic toxicity. CDF1 mice with 200 mm3 foot implanted C3H mammary carcinomas were treated with TNP-470 (100 mg/kg every second day for 2 weeks; s.c.) and combretastatin A-4 phosphate (CA4P; 1 × 250 mg/kg, i.p.). Radiation (230-kV X-rays) was locally administered to tumors of restrained non-anesthetized mice. Response was tumor growth delay and change in mouse body weight. Radiation induced changes in serum levels of 10 cytokines up to 72-h after irradiation were measured using a Luminex assay. The results showed that TNP-470 (100 mg/kg × 7) or CA4P (250 mg/kg × 1) significantly (Student’s t-test; p < 0.05) inhibited tumor growth; the greatest effect when these two drugs were combined. TNP-470 and CA4P, alone or together, also significantly enhanced tumor response to radiation. No systemic toxicity occurred with drugs administered alone or in combination, but toxicity was observed when TNP-470 was combined with radiation. Serum cytokine levels only showed a significant transient increase in IL-6 1-h after irradiating. In conclusion, combining different acting vascular targeting agents with radiation increased anti-tumor activity. However, this benefit may sometimes be associated with a radiation-induced inflammatory response increasing systemic toxicity.
Towards a Unified Framework for Software-Hardware Integration in Evolutionary Robotics
Authors: Buchanan Edgar , Le Goff Léni , Hale Matthew , Hart Emma , Eiben Agoston , De Carlo Matteo , Angus Mike , Woolley Robert , Timmis Jon , Winfield Alan , Tyrrell Andy
Published: October 2024

The discrepancy between simulated and hardware experiments, the reality gap, is a challenge in evolutionary robotics. While strategies have been proposed to address this gap in fixed-body robots, they are not viable when dealing with populations and generations where the body is in constant change. The continual evolution of body designs necessitates the manufacturing of new robotic structures, a process that can be time-consuming if carried out manually. Moreover, the increased manufacturing time not only prolongs hardware experimental durations but also disrupts the synergy between hardware and simulated experiments. Failure to effectively manage these challenges could impede the implementation of evolutionary robotics in real-life environments. The Autonomous Robot Evolution project presents a framework to tackle these challenges through a case study. This paper describes the main three contributions of this work: Firstly, it analyses the different reality gap experienced by each different robot or the heterogenous reality gap. Secondly, it emphasizes the importance of automation in robot manufacturing. And thirdly, it highlights the necessity of a framework to orchestrate the synergy between simulated and hardware experiments. In the long term, integrating these contributions into evolutionary robotics is envisioned to enable the continuous production of robots in real-world environments.
Learn from Simulations, Adapt to Observations: Super-Resolution of Isoprene Emissions via Unpaired Domain Adaptation
Authors: Giganti Antonio , Mandelli Sara , Bestagini Paolo , Tubaro Stefano
Published: October 2024

Plants emit biogenic volatile organic compounds (BVOCs), such as isoprene, significantly influencing atmospheric chemistry and climate. BVOC emissions estimated from bottom-up (BU) approaches (derived from numerical simulations) usually exhibit denser and more detailed spatial information compared to those estimated through top-down (TD) approaches (derived from satellite observations). Moreover, numerically simulated emissions are typically easier to obtain, even if they are less reliable than satellite acquisitions, which, being derived from actual measurements, are considered a more trustworthy instrument for performing chemistry and climate investigations. Given the coarseness and relative lack of satellite-derived measurements, fine-grained numerically simulated emissions could be exploited to enhance them. However, simulated (BU) and observed (TD) emissions usually differ regarding value range and spatiotemporal resolution. In this work, we present a novel deep learning (DL)-based approach to increase the spatial resolution of satellite-derived isoprene emissions, investigating the adoption of efficient domain adaptation (DA) techniques to bridge the gap between numerically simulated emissions and satellite-derived emissions, avoiding the need for retraining a specific super-resolution (SR) algorithm on them. For this, we propose a methodology based on the cycle generative adversarial network (CycleGAN) architecture, which has been extensively used for adapting natural images (like digital photographs) of different domains. In our work, we depart from the standard CycleGAN framework, proposing additional loss terms that allow for better DA and emissions’ SR. We extensively demonstrate the proposed method’s effectiveness and robustness in restoring fine-grained patterns of observed isoprene emissions. Moreover, we compare different setups and validate our approach using different emission inventories from both domains. Eventually, we show that the proposed DA strategy paves the way towards robust SR solutions even in the case of spatial resolution mismatch between the training and testing domains and in the case of unknown testing data.
Mapping the Normalized Difference Vegetation Index for the Contiguous U.S. Since 1850 Using 391 Tree-Ring Plots
Authors: Li Hang , Thapa Ichchha , Xu Shuang , Yang Peisi
Published: October 2024

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions—Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)—we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.
Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning
Authors: Nemmaoui Abderrahim , Aguilar Fernando J. , Aguilar Manuel A.
Published: October 2024

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).
Development of High-Performance Ethanol Gas Sensors Based on La2O3 Nanoparticles-Embedded Porous SnO2 Nanofibers
Authors: Li Gen , Hou Jian , Hilal Muhammad , Kim Hyojung , Chen Zhiyong , Cui Yunhao , Kim Jun-Hyun , Cai Zhicheng
Published: October 2024

Porous pure SnO2 nanofibers (NFs) and La2O3 nanoparticles (NPs)-embedded porous SnO2 NFs were successfully synthesized via electrospinning followed by calcination. These materials were systematically evaluated as gas-sensing elements in metal-oxide-semiconductor (MOS) sensors. The La2O3 NPs embedded in porous SnO2 NFs demonstrated superior gas-sensing performance compared to pure SnO2 NFs. Specifically, the incorporation of La2O3 resulted in a 12-fold enhancement in gas-sensing response towards ethanol, significantly improving both sensitivity and selectivity by tuning the carrier concentration and modifying oxygen deficiencies and chemisorbed oxygen levels. Thus, La2O3 NPs embedded in SnO2 NFs present a promising strategy for the development of high-performance ethanol gas sensors.
Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds
Authors: Chin Chiun-Li , Chen Jun-Ren , Lin Wan-Xuan , Hung Hsuan-Chiao , Chiang Shang-En , Wang Chih-Hui , Lee Liang-Ching , Liu Shing-Hong
Published: October 2024

People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.
Enhancing Accuracy of Flame Equivalence Ratio Measurements: An Attention-Based Convolutional Neural Network Approach for Overcoming Limitations in Traditional Color Modeling
Authors: Zheng Lukai , Yang Tiantian , Liu Wenjia , Lai Yufeng , Yang Jiansheng
Published: October 2024

This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (Φ), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D color distribution. To overcome these issues, this study introduces an attention-based convolutional neural network (ACN) model, a novel approach that transcends the conventional reliance on B/G color features (Tf). The ACN model leverages adaptive feature extraction, augmented by a spatial attention mechanism, to more effectively analyze flame images. By amplifying key features, autonomously minimizing background noise, and standardizing variations in color distribution, the ACN model in this experiment achieved a prediction accuracy of 99%, with a 76% reduction in error rate compared to the original model, significantly improving the accuracy and objectivity of flame Φ measurement. This method marks a substantial development in the precision and reliability of flame analysis.
Airborne LiDAR Point Cloud Classification Using Ensemble Learning for DEM Generation
Authors: Ciou Ting-Shu , Lin Chao-Hung , Wang Chi-Kuei
Published: October 2024

Airborne laser scanning (ALS) point clouds have emerged as a predominant data source for the generation of digital elevation models (DEM) in recent years. Traditionally, the generation of DEM using ALS point clouds involves the steps of point cloud classification or ground point filtering to extract ground points and labor-intensive post-processing to correct the misclassified ground points. The current deep learning techniques leverage the ability of geometric recognition for ground point classification. However, the deep learning classifiers are generally trained using 3D point clouds with simple geometric terrains, which decrease the performance of model inferencing. In this study, a point-based deep learning model with boosting ensemble learning and a set of geometric features as the model inputs is proposed. With the ensemble learning strategy, this study integrates specialized ground point classifiers designed for different terrains to boost classification robustness and accuracy. In experiments, ALS point clouds containing various terrains were used to evaluate the feasibility of the proposed method. The results demonstrated that the proposed method can improve the point cloud classification and the quality of generated DEMs. The classification accuracy and F1 score are improved from 80.9% to 92.2%, and 82.2% to 94.2%, respectively, by using the proposed methods. In addition, the DEM generation error, in terms of mean squared error (RMSE), is reduced from 0.318–1.362 m to 0.273–1.032 m by using the proposed ensemble learning.