bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synthesis, application and molecular docking of modified cellulose with diaminoguanidine as complexing agent for selective separation of Cu (II), Cd (II) and Hg (II) ions from alum sample
Authors: Saad Heba E. , El-Sayed Yusif S. , El-Reash Gaber M. Abu , gaber Mohamed , Hashem Mohamed A. 
Published: July 2024

AbstractA new modified cellulose with diaminoguanidine (Cel-Gua) synthesized for specific recovery of Cu (II), Cd (II), and Hg (II) from the alum sample. Cellulose was silanized by 3-chloropropyltrimethoxysilane and then was modified with diaminoguanidine to obtain N-donor chelating fibers. Fourier transform-infrared spectroscopy, scanning electron microscopy, X-ray diffraction, zeta potential, electrons disperse X-ray analysis, elemental analyses (C, H and N), and thermogravimetric analysis were used for characterization. Factors influencing the adsorption were thoroughly examined. Under the optimal conditions, the Cel-Gua sorbent displayed maximum adsorption capacities of 94.33, 112.10 and 95.78 mg/g for Cu (II), Cd (II), and Hg (II), respectively. The sorption process of metal ions is equipped by kinetic model PSO and Langmuir adsorption isotherm. The calculated thermodynamic variables confirmed that the adsorption of Cu (II), Cd (II) and Hg (II) by Cel-Gua sorbent is a spontaneous and exothermic process. In our study, we used the molecular operating environment software to conduct molecular docking simulations on the Cel-Gua compound. The results of the docking simulations showed that the Cel-Gua compound displayed greater potency and a stronger affinity for the Avr2 effector protein derived from Fusarium oxysporum, a fungal plant pathogen (code 5OD4). The adsorbent was stable for 7 cycles, thus allowing its safe reutilization.
Identifying strategies to improve adverse drug reporting through key informant interviews among community pharmacists in a developing country
Authors: Nduka Sunday Odunke , Ibe Chiamaka Omelebere , Nwaodu Mercy Adamma , Robert Chana Chapchet 
Published: July 2024

AbstractReporting adverse drug reactions (ADRs) is fundamental in improving medication safety. Community pharmacists (CPs) being the first point of contact for individuals seeking healthcare in a community, play a significant role in ADR reporting. However, this has been poorly implemented in many countries including Nigeria. This paper aims to explore stakeholders’ perspectives on current reporting practices and suggest ways to enhance ADR reporting among CPs in Nigeria. This qualitative study employed a purposive sampling approach to identify key informants. Key informant interviews (KIIs) were conducted with 25 carefully selected pharmacists, using a semi-structured interview guide between July 2023 and August 2023. The interview transcripts were analyzed using a thematic content approach. While a low ADR reporting trend was observed among all participating pharmacists, it was notably higher among those with less than five years of experience. The main barriers to ADR reporting, as identified by the interviewed community pharmacists were lack of awareness and knowledge, absence of motivation, and insufficient feedback from National Agency for Food Drug Administration and Control (NAFDAC). Training and awareness campaigns were the most frequently suggested methods for improving ADR reporting. Other proposed strategies included providing motivation, regular feedback, establishing mandatory reporting, and simplifying the reporting process. The study has highlighted the suboptimal ADR reporting practices among CPs in Anambra state. It underscores the significance of training, sensitization, advocacy, and other related interventions as pivotal means to enhance ADR reporting in this group. Furthermore, there is a pressing need for intervention-based studies to delve into and implement these approaches effectively.
Assessing the accuracy and efficacy of multiscale computational methods in predicting reaction mechanisms and kinetics of SN2 reactions and Claisen rearrangement
Authors: Haji Dehabadi Maryam , Saidi Hamid , Zafari Faezeh , Irani Mehdi 
Published: July 2024

AbstractThis study investigates the application of quantum mechanical (QM) and multiscale computational methods in understanding the reaction mechanisms and kinetics of SN2 reactions involving methyl iodide with NH2OH and NH2O−, as well as the Claisen rearrangement of 8-(vinyloxy)dec-9-enoate. Our aim is to evaluate the accuracy and effectiveness of these methods in predicting experimental outcomes for these organic reactions. We achieve this by employing QM-only calculations and several hybrids of QM and molecular mechanics (MM) methods, namely QM/MM, QM1/QM2, and QM1/QM2/MM methodologies. For the SN2 reactions, our results demonstrate the importance of explicitly including solvent effects in the calculations to accurately reproduce the transition state geometry and energetics. The multiscale methods, particularly QM/MM and QM1/QM2, show promising performance in predicting activation energies. Moreover, we observe that the size of the MM active region significantly affects the accuracy of calculated activation energies, highlighting the need for careful consideration during the setup of multiscale calculations. In the case of the Claisen rearrangement, both QM-only and multiscale methods successfully reproduce the proposed reaction mechanism. However, the activation free energies calculated using a continuum solvation model, based on single-point calculations of QM-only structures, fail to account for solvent effects. On the other hand, multiscale methods more accurately capture the impact of solvents on activation free energies, with systematic error correction enhancing the accuracy of the results. Furthermore, we introduce a Python code for setting up multiscale calculations with ORCA, which is available on GitHub at https://github.com/iranimehdi/pdbtoORCA.
Drivers and impact of the 2021 extreme warm event in the tropical Angolan upwelling system
Authors: Imbol Koungue Rodrigue Anicet , Brandt Peter , Prigent Arthur , Aroucha Léo Costa , Lübbecke Joke , Imbol Nkwinkwa Arielle Stela N. , Dengler Marcus , Keenlyside Noel 
Published: July 2024

AbstractBenguela Niños are extreme warm events that typically occur during the main downwelling season (austral fall) in the tropical Angolan upwelling system when the biological productivity is low. However, the extreme warm event that occurred between April and August 2021 stands out due to its late timing. It occurred and peaked during the main upwelling season in austral winter with sea surface temperature anomalies exceeding 2 °C in the Angola-Benguela area in June 2021. This led to an unprecedented reduction of the net primary production off southern Angola. Both local atmospheric processes and remote influences (via downwelling coastal trapped wave propagations) have contributed to the onset of the extreme warm event in April and its intensification towards the peak phase in June. Moreover, the poleward advection of warm equatorial waters toward the Angola-Benguela area in May 2021 might have contributed to the warming, since the transport of the Angola Current, as estimated from observations, was notably elevated, amounting to 2.1 Sv.
Legionella colonization and 3D spatial location within a Pseudomonas biofilm
Authors: Silva Ana Rosa , Melo Luis F. , Keevil C. William , Pereira Ana 
Published: July 2024

AbstractBiofilms are known to be critical for Legionella settlement in engineered water systems and are often associated with Legionnaire’s Disease events. One of the key features of biofilms is their heterogeneous three-dimensional structure which supports the establishment of microbial interactions and confers protection to microorganisms. This work addresses the impact of Legionella pneumophila colonization of a Pseudomonas fluorescens biofilm, as information about the interactions between Legionella and biofilm structures is scarce. It combines a set of meso- and microscale biofilm analyses (Optical Coherence Tomography, Episcopic Differential Interference Contrast coupled with Epifluorescence Microscopy and Confocal Laser Scanning Microscopy) with PNA-FISH labelled L. pneumophila to tackle the following questions: (a) does the biofilm structure change upon L. pneumophila biofilm colonization?; (b) what happens to L. pneumophila within the biofilm over time and (c) where is L. pneumophila preferentially located within the biofilm? Results showed that P. fluorescens structure did not significantly change upon L. pneumophila colonization, indicating the competitive advantage of the first colonizer. Imaging of PNA-labelled L. pneumophila showed that compared to standard culture recovery it colonized to a greater extent the 3-day-old P. fluorescens biofilms, presumably entering in VBNC state by the end of the experiment. L. pneumophila was mostly located in the bottom regions of the biofilm, which is consistent with the physiological requirements of both bacteria and confers enhanced Legionella protection against external aggressions. The present study provides an expedited methodological approach to address specific systematic laboratory studies concerning the interactions between L. pneumophila and biofilm structure that can provide, in the future, insights for public health Legionella management of water systems.
Molecular insights into the interactions between PEG carriers and drug molecules from Celastrus hindsii: a multi-scale simulation study
Authors: Ho Thi H. , Tong Hien Duy , Trinh Thuat T. 
Published: July 2024

AbstractEfficient drug delivery is crucial for the creation of effective pharmaceutical treatments, and polyethylene glycol (PEG) carriers have been emerged as promising candidates for this purpose due to their bio-compatibility, enhancement of drug solubility, and stability. In this study, we utilized molecular simulations to examine the interactions between PEG carriers and selected drug molecules extracted from Celastrus hindsii: Hindsiilactone A, Hindsiiquinoflavan B, Maytenfolone A, and Celasdin B. The simulations provided detailed insights into the binding affinity, stability, and structural properties of these drug molecules when complexed with PEG carriers. A multi-scale approach combining density functional theory (DFT), extended tight-binding (xTB), and molecular dynamics (MD) simulations was conducted to investigate both unbound and bound states of PEG/drug systems. The results from DFT and xTB calculations revealed that the unbound complex has an unfavorable binding free energy, primarily due to negative contributions of delta solvation free energy and entropy. The MD simulations provided more detailed insights into the interactions between PEG and drug molecules in water solutions. By integrating the findings from the multi-scale simulations, a comprehensive picture of the unbound and bound states of PEG and drug systems were obtained. This information is valuable for understanding the molecular mechanisms governing the binding of drugs in PEG-based delivery platforms, and it contributes to the rational design and optimization of these systems.
Corneal nerve fiber morphology following COVID-19 infection in vaccinated and non-vaccinated population
Authors: Szalai Eszter , Nagy Katalin , Kolkedi Zsofia , Csutak Adrienne 
Published: July 2024

AbstractTo examine corneal subbasal nerve changes in patients who received vaccination against SARS-CoV-2 virus and underwent COVID-19 infection compared to infected non-vaccinated patients and healthy controls. Twenty-nine eyes of 29 vaccinated patients (mean age: 36.66 ± 12.25 years) within six months after PCR or Ag test proven COVID-19 infection and twenty-eight eyes of 28 age-matched infected, non-vaccinated patients (mean age: 42.14 ± 14.17 years) were enrolled. Twenty-five age-matched healthy individuals (mean age: 47.52 ± 18.45 years) served as controls. In vivo confocal microscopy (Heidelberg Retina Tomograph II Rostock Cornea Module, Germany) was performed in each group. Corneal subbasal nerve plexus morphology and corneal dendritic cells (DC) were evaluated. Significantly higher corneal nerve fiber density (P < 0.001), nerve branch density (P < 0.001), nerve fiber length (P < 0.001), total branch density (P = 0.007), nerve fiber area (P = 0.001) and fractal dimension (P < 0.001) values were observed in vaccinated patients after COVID-19 infection compared to the non-vaccinated group. Significantly higher DC density was observed in the non-vaccinated group compared to the control group (P = 0.05). There was a statistically significant difference in the size of mature DCs (P < 0.0001) but the size of immature DCs did not differ significantly among the 3 groups (P = 0.132). Our results suggest that SARS-CoV-2 vaccination may have a protective effect against the complications of COVID-19 disease on the corneal subbasal nerve fibers.
Targeting monocytic Occludin impairs transendothelial migration and HIV neuroinvasion
Authors: Brychka Diana , Ayala-Nunez Nilda Vanesa , Dupas Amandine , Bare Yonis , Partiot Emma , Mittelheisser Vincent , Lucansky Vincent , Goetz Jacky G , Osmani Naël , Gaudin Raphael 
Published: July 2024

AbstractTransmigration of circulating monocytes from the bloodstream to tissues represents an early hallmark of inflammation. This process plays a pivotal role during viral neuroinvasion, encephalitis, and HIV-associated neurocognitive disorders. How monocytes locally unzip endothelial tight junction-associated proteins (TJAPs), without perturbing impermeability, to reach the central nervous system remains poorly understood. Here, we show that human circulating monocytes express the TJAP Occludin (OCLN) to promote transmigration through endothelial cells. We found that human monocytic OCLN (hmOCLN) clusters at monocyte-endothelium interface, while modulation of hmOCLN expression significantly impacts monocyte transmigration. Furthermore, we designed OCLN-derived peptides targeting its extracellular loops (EL) and show that transmigration of treated monocytes is inhibited in vitro and in zebrafish embryos, while preserving vascular integrity. Monocyte transmigration toward the brain is an important process for HIV neuroinvasion and we found that the OCLN-derived peptides significantly inhibit HIV dissemination to cerebral organoids. In conclusion, our study identifies an important role for monocytic OCLN during transmigration and provides a proof-of-concept for the development of mitigation strategies to prevent monocyte infiltration and viral neuroinvasion.
3-chymotrypsin-like protease in SARS-CoV-2
Authors: Al Adem Kenana , Ferreira Juliana C. , Villanueva Adrian J. , Fadl Samar , El-Sadaany Farah , Masmoudi Imen , Gidiya Yugmee , Gurudza Tariro , Cardoso Thyago H.S. , Saksena Nitin K. , Rabeh Wael M. 
Published: July 2024

Abstract
Coronaviruses constitute a significant threat to the human population. Severe acute respiratory syndrome coronavirus-2, SARS-CoV-2, is a highly pathogenic human coronavirus that has caused the coronavirus disease 2019 (COVID-19) pandemic. It has led to a global viral outbreak with an exceptional spread and a high death toll, highlighting the need for effective antiviral strategies. 3-Chymotrypsin-like protease (3CLpro), the main protease in SARS-CoV-2, plays an indispensable role in the SARS-CoV-2 viral life cycle by cleaving the viral polyprotein to produce 11 individual non-structural proteins necessary for viral replication. 3CLpro is one of two proteases that function to produce new viral particles. It is a highly conserved cysteine protease with identical structural folds in all known human coronaviruses. Inhibitors binding with high affinity to 3CLpro will prevent the cleavage of viral polyproteins, thus impeding viral replication. Multiple strategies have been implemented to screen for inhibitors against 3CLpro, including peptide-like and small molecule inhibitors that covalently and non-covalently bind the active site, respectively. In addition, allosteric sites of 3CLpro have been identified to screen for small molecules that could make non-competitive inhibitors of 3CLpro. In essence, this review serves as a comprehensive guide to understanding the structural intricacies and functional dynamics of 3CLpro, emphasizing key findings that elucidate its role as the main protease of SARS-CoV-2. Notably, the review is a critical resource in recognizing the advancements in identifying and developing 3CLpro inhibitors as effective antiviral strategies against COVID-19, some of which are already approved for clinical use in COVID-19 patients.
Low‐carbon economic operation of multi‐energy microgrid based on multi‐level robust optimisation
Authors: He Wenwen , Wu Xiong , Li Xiaofei , He Mingkang , Cao Binrui , Wang Xiuli 
Published: July 2024

AbstractThe economic and low‐carbon operation strategy of multi‐energy microgrids (MEM) has become an important research topic in smart grids. The operation of MEM is affected by uncertain factors from renewable energy and internal load. To handle uncertainties from both source and load sides, the authors propose a novel worst‐case‐and‐probability uncertainty sets and a novel worst‐expectation min‐max‐max‐min two‐stage four‐level robust optimisation (RO) model considering stepped carbon trading for MEM. This four‐level RO model is a more generalised model compared with the traditional two‐stage RO and distributionally robust optimisation based models. First, the unsolvable original four‐level model is decoupled into a master problem and a sub‐problem (SP). Second, Karush–Kuhn–Tucker condition is applied to convert SP into a solvable problem. Third, column and constraint generation (C&CG) algorithm is employed to solve the reformulated four‐level RO. Finally, the effectiveness of the proposed model and solution method is verified by case studies. The results indicate that the convergence behaviour of this solution method is excellent. The MEM operator can select appropriate robustness factors and comprehensive norms to control its conservatism level. Besides, MEM could reduce carbon emissions by participating in carbon trading and installing carbon capture devices.
A new fault location method for high‐voltage transmission lines based on ICEEMDAN‐MSA‐ConvGRU model
Authors: Jia Taorong , Yao Lixiao , Yang Guoqing 
Published: July 2024

AbstractGiven the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.
A simulation‐based impact assessment of autonomous vehicles in urban networks
Authors: Sadid Hashmatullah , Antoniou Constantinos 
Published: July 2024

AbstractThe behavioural differences between autonomous vehicles (AVs) and human‐driven vehicles (HDVs) can significantly impact traffic efficiency, safety, and emissions. Simulation‐based impact assessments using microscopic traffic models often modify car‐following (CF) and lane‐changing (LC) configurations to differentiate AVs from HDVs. Typically, researchers adjust CF model parameters to replicate AV driving behaviour, but these assumptions can lead to varying conclusions on AV impacts. The scope of each study (e.g., freeways, highways, urban links, intersections) also influences the outcomes. This research conducts an impact assessment utilizing optimized AV driving behavior rather than assumptions on a city network level (Munich) using a simulation‐based platform. The particle swarm optimization (PSO) algorithm is used to calibrate the base model and run simulation experiments under various penetration rates (PRs) and demand scenarios. Results show significant safety improvements throughout the network under higher PRs, while lower PRs might lead to deteriorating safety. At 100% AV PR, the total number of conflicts decreased by around 25% compared to a fully HDV environment. Considering AVs' sensing capabilities, additional safety improvements are found in almost any AV PR. However, AVs might not improve traffic efficiency; in some cases, they may slightly increase average network travel time, though this change is minimal.
Continuous wave operation of broad area and ridge waveguide laser diodes at 626 nm
Authors: Mauerhoff Felix , Senel Oktay , Wenzel Hans , Maaßdorf André , Boschker Jos , Glaab Johannes , Paschke Katrin , Tränkle Günther 
Published: July 2024

AbstractThe authors present continuous wave (CW) high‐power broad area and ridge waveguide lasers with laser emission at 626 nm at room temperature. For this, the authors employ GaAs‐based diode lasers in the AlGaInP system for laser emission at 626 nm at room temperature. The laser structure is grown on three‐inch wafers by metal organic vapour phase epitaxy. Broad area and ridge waveguide lasers are fabricated. Pulsed broad area laser characterisation on bar level shows laser operation at 20 C heat sink temperature. The authors measured peak lasing wavelengths as short as 625 nm and total maximum output power of both facets up to 1.4 W at an injection current of 2 A. Both, broad area and ridge waveguide lasers show laser operation and CW excitation at room temperature. The ridge waveguide lasers emit output powers of over 90 mW at 626 nm at a maximum injection current of 200 mA with a nearly diffraction‐limited beam profile.
Concurrent PV production and consumption load forecasting using CT‐Transformer deep learning to estimate energy system flexibility
Authors: Zarghami Mohammad , Niknam Taher , Aghaei Jamshid , Nezhad Azita Hatami 
Published: July 2024

AbstractThe integration of renewable energy sources (RESs) into power systems has increased significantly due to technical, economic, and environmental factors, necessitating greater flexibility to manage variable consumption loads and renewable energy generation. Accurate forecasting of solar energy production and consumption load is critical for enhancing power system flexibility. This study introduces a novel deep learning model, a spatial‐temporal hybrid convolutional‐transformer (CT‐Transformer) network with unique features and extended memory capacity. Additionally, a flexibility index (FI) is introduced to evaluate power system flexibility (PSF) based on the forecasting results. The CT‐Transformer forecasts PSF for the next 24 and 168 hours, using the FI to evaluate PSF based on forecasting results. The input data includes meteorological, solar energy production, load demand, and pricing data from France, comprising hourly data from 2015 and 2016 (about 17,520 entries). Data preprocessing involves correcting incomplete and irrelevant segments. The CT‐Transformer's performance is compared to other deep learning techniques, showing superior results with the lowest prediction error (2.5%) and a maximum error of 10.1% (MAE). It also achieved a prediction error of 0.08% for system flexibility, compared to the highest error of 0.96%. This research highlights the CT‐Transformer's potential for accurate RES and load forecasting and PSF evaluation.