bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparation of Tunable Cu−Ag Nanostructures by Electrodeposition in a Deep Eutectic Solvent
Authors: Plaza‐Mayoral Elena , Dalby Kim N. , Falsig Hanne , Chorkendorff Ib , Sebastián‐Pascual Paula , Escudero‐Escribano María 
Published: March 2024

AbstractThe green transition requires new, clean, inexpensive, and sustainable strategies to prepare controllable bimetallic and multimetallic nanostructures. Cu−Ag nanostructures, for example, are promising bimetallic catalysts for different electrocatalytic reactions such as carbon monoxide and carbon dioxide reduction. In this work, we present the one‐step preparation method of electrodeposited Cu−Ag with tunable composition and morphology from choline chloride plus urea deep eutectic solvent (DES), a non‐toxic and green DES. We have assessed how different electrodeposition parameters affect the morphology and composition of our nanostructures. We combine electrochemical methods with ex‐situ scanning electron microscopy (SEM), energy dispersive X‐ray spectroscopy (EDS), and X‐ray photoelectron spectroscopy (XPS) to characterize the nanostructures. We have estimated the electrochemically active surface area (ECSA) and roughness factor (R) by lead underpotential deposition (UPD). The copper/silver ratio in the electrodeposited nanostructures is highly sensitive to the applied potential, bath composition, and loading. We observed that silver‐rich nanostructures were less adherent whereas the increase in copper content led to more stable and homogenous films with disperse rounded nanostructures with tiny spikes. These spikes were more stable when the deposition rate was fast enough and the molar ratio of Cu and Ag was no greater than approximately two to one.
Formation of Peroxynitrite, [O−N−O−O]−, via a Cascade of Reactions between Ozonide and Ammonia
Authors: Schmid Jonas R. , Rennefeld Oliver R. H. , Wiesner Anja , Jansen Martin , Riedel Sebastian 
Published: March 2024

AbstractWe report on an unexpected reaction between ammonia and potassium ozonide dissolved in liq. NH3 resulting in the formation of peroxynitrite, [ONOO]−, which exclusively happens in the presence of a specific partially fluorinated aniline‐based ammonium cation. High‐resolution structural data of the peroxynitrite anion in cis‐conformation have been obtained. We further studied this molecule anion by single crystal Raman spectroscopy. The cis and trans isomers of peroxynitrite were analysed computationally with respect to their relative energies, the cis‐trans transition barrier and their decomposition pathways to the nitrate anion. By experimentally examining cations decorated with fluorine ligands to different degrees, we demonstrate that fluorine specific interactions play a crucial role in the unexpected formation of peroxynitrite and as a conspicuously structure directing factor for the underlying crystalline solid phases, exhibiting distinct micro‐separations of fluorine and hydrogen enriched regions.
Quantifying the efficacy of voltage protocols in characterising ion channel kinetics: A novel information‐theoretic approach
Authors: Jennings Matthew W. , Nithiarasu Perumal , Pant Sanjay 
Published: March 2024

AbstractVoltage‐clamp experiments are commonly utilised to characterise cellular ion channel kinetics. In these experiments, cells are stimulated using a known time‐varying voltage, referred to as the voltage protocol, and the resulting cellular response, typically in the form of current, is measured. Parameters of models that describe ion channel kinetics are then estimated by solving an inverse problem which aims to minimise the discrepancy between the predicted response of the model and the actual measured cell response. In this paper, a novel framework to evaluate the information content of voltage‐clamp protocols in relation to ion channel model parameters is presented. Additional quantitative information metrics that allow for comparisons among various voltage protocols are proposed. These metrics offer a foundation for future optimal design frameworks to devise novel, information‐rich protocols. The efficacy of the proposed framework is evidenced through the analysis of seven voltage protocols from the literature. By comparing known numerical results for inverse problems using these protocols with the information‐theoretic metrics, the proposed approach is validated. The essential steps of the framework are: (i) generate random samples of the parameters from chosen prior distributions; (ii) run the model to generate model output (current) for all samples; (iii) construct reduced‐dimensional representations of the time‐varying current output using proper orthogonal decomposition (POD); (iv) estimate information‐theoretic metrics such as mutual information, entropy equivalent variance, and conditional mutual information using non‐parametric methods; (v) interpret the metrics; for example, a higher mutual information between a parameter and the current output suggests the protocol yields greater information about that parameter, resulting in improved identifiability; and (vi) integrate the information‐theoretic metrics into a single quantitative criterion, encapsulating the protocol's efficacy in estimating model parameters.
Efficient Atomically Dispersed Co/N‐C Catalysts for Formic Acid Dehydrogenation and Transfer Hydrodeoxygenation of Vanillin
Authors: Li Xiaoyu , Lu Guilong , Wang Tianyu , Yang Jia‐Yue , Herrendorf Tim , Schwiderowski Philipp , Schulwitz Jonas , Chen Peirong , Kleist Wolfgang , Zhao Guixia , Muhler Martin , Peng Baoxiang 
Published: March 2024

AbstractAtomically dispersed catalysts have gained considerable attention due to their unique properties and high efficiency in various catalytic reactions. Herein, a series of Co/N‐doped carbon (N‐C) catalysts was prepared using a metal‐lignin coordination strategy and employed in formic acid dehydrogenation (FAD) and hydrodeoxygenation (HDO) of vanillin. The atomically dispersed Co/N‐C catalysts showed outstanding activity, acid resistance, and long‐term stability in FAD. The improved activity and stability may be attributed to the high dispersion of Co species, increased surface area, and strong Co−N interactions. XPS and XAS characterization revealed the formation of Co‐N3 centers, which are assumed to be the active sites. In addition, DFT calculations demonstrated that the adsorption of formic acid on single‐atom Co was stronger than that on Co13 clusters, which may explain the high catalytic activity. The Co/N‐C catalyst also showed promising performance in the transfer HDO of vanillin with formic acid, without any external additional molecular H2.
Potential enhancements in commercial glucose biosensors utilizing electrochemical faradaic spectroscopy: Analyzing the sum component in the EC’ mechanism
Authors: Mirčeski Valentin , Peacock Martin , Stojanov Leon 
Published: March 2024

AbstractHerein we report the use of electrochemical faradaic spectroscopy in biosensors, with application on a commercially used first‐generation glucose biosensor manufactured by Zimmer and Peacock. The mechanism for the glucose sensor was explained and approximated as an EC’ mechanism, where E is for electron transfer step, whereas C’ signifies catalytical chemical step. Experimental chronoamperograms in electrochemical faradaic spectroscopy are compared to chronoamperometry, where it was found that the sum component in electrochemical faradaic spectroscopy gives higher response, resulting in better sensitivity of the sensor. Theoretical simulations give an insight of the response in electrochemical faradaic spectroscopy for an EC’ mechanism and its dependence on different parameters (dimensionless electrode kinetic parameter, mid potential, dimensionless chemical kinetic parameter). For some specific set of parameters (large electrode and catalytic reaction kinetics), theoretical chronoamperograms in electrochemical faradaic spectroscopy can become similar to the experimental. The property of the sum component to have higher response in EC’ mechanism for specific parameters is not limited only for electrochemical faradaic spectroscopy. Exemplified with square‐wave voltammetry, it is shown that other pulse techniques for an EC’ mechanism can also result with higher sum component. Hence, for better sensitivity in quantitative analysis in EC’ mechanism, one should quantify the sum component.
Selective sensing of hexavalent chromium in food grains using an electrodeposited bimetallic Au‐Pd nanoparticles modified electrode
Authors: Man Suning , Zheng Junying , Zhang Yi , Huang Lei , Wu Xiaoping 
Published: March 2024

AbstractGiven the high solubility and mobility of hexavalent chromium in the environment and edible plants, the development of a suitable method for the screening and quantification of Cr(VI) is highly desirable. Herein, a selective and sensitive sensing method for detecting Cr(VI) in food grains was established, using an electrodeposited gold‐palladium bimetallic nanoparticles (BNPs) based electrochemical sensor and a microwave digestion procedure for chromium dissolution. The sensor interface was characterized by scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDX) and cyclic voltammetry. The results indicate that the fabricated sensor has a larger active surface area and an enhanced electrocatalytic effect compared to the monometallic modification, leading to a significant increase in the electrochemical reduction current of Cr(VI) as a consequence of synergistic effects. Using square wave anodic stripping voltammetry, the sensor demonstrates a wide linear range (5–3000 μg L−1) and a low detection limit of 2.7 μg L−1 for Cr(VI), as well as considerable repeatability and stability. Satisfactory selectivity towards Cr(VI) was also demonstrated, even in the presence of 500‐fold Cr(III) and 100‐fold common heavy metal ions like Cd(II), Pb(II), and Hg(II). The proposed sensing method offers a promising alternative for rapidly identifying toxic Cr(VI) in water and food grains.
Idiopathic generalized epilepsy in a family with SCN4A‐related myotonia
Authors: Talarico Mariagrazia , Fortunato Francesco , Labalme Audrey , Januel Louis , Chatron Nicolas , Sanlaville Damien , Sammarra Ilaria , Gagliardi Monica , Procopio Radha , Valentino Paola , Annesi Grazia , Lesca Gaetan , Gambardella Antonio 
Published: March 2024

AbstractObjectivesMyotonia is a clinical sign typical of a group of skeletal muscle channelopathies, the non‐dystrophic myotonias. These disorders are electrophysiologically characterized by altered membrane excitability, due to specific genetic variants in known causative genes (CLCN1 and SCN4A). Juvenile Myoclonic Epilepsy (JME) is an epileptic syndrome identified as idiopathic generalized epilepsy, its genetics is complex and still unclarified. The co‐occurrence of these two phenotypes is rare and the causes likely have a genetic background. In this study, we have genetically investigated an Italian family in which co‐segregates myotonia, JME, or abnormal EEG without seizures was observed.MethodsAll six individuals of the family, 4 affected and 2 unaffected, were clinically evaluated; EMG and EEG examinations were performed. For genetic testing, Exome Sequencing was performed for the six family members and Sanger sequencing was used to confirm the candidate variant.ResultsFour family members, the mother and three siblings, were affected by myotonia. Moreover, EEG recordings revealed interictal generalized sharp‐wave discharges in all affected individuals, and two siblings were affected by JME. All four affected members share the same identified variant, c.644 T > C, p.Ile215Thr, in SCN4A gene. Variants that could account for the epileptic phenotype alone, separately from the myotonic one, were not identified.SignificanceThese results provide supporting evidence that both myotonic and epileptic phenotypes could share a common genetic background, due to variants in SCN4A gene. SCN4A pathogenic variants, already known to be causative of myotonia, likely increase the susceptibility to epilepsy in our family.Plain Language SummaryThis study analyzed all members of an Italian family, in which the mother and three siblings had myotonia and epilepsy. Genetic analysis allowed to identify a variant in the SCN4A gene, which appears to be the cause of both clinical signs in this family.
A Quantitative Analysis of Microplastics in the Gastrointestinal Tracts of Odontocetes in the Southeast Region of the United States
Authors: Courville Julia M. , Borkowski Rose , Sonnenberg Lucy , Bielmyer-Fraser Gretchen K. 
Published: March 2024

Abstract
Microplastics (&lt;5 mm in diameter) are ubiquitous in the oceanic environment, yet microplastic accumulation in marine mammals is vastly understudied. In recent years, efforts have been made to document microplastic profiles in odontocetes. The objective of the present study was to describe and quantify microplastics in the gastrointestinal (GI) tracts of deceased odontocetes that stranded in the southeastern United States. Our study included 24 bottlenose dolphins (Tursiops truncatus), two pygmy sperm whales (Kogia breviceps), one pantropical spotted dolphin (Stenella attenuata), one short-snouted spinner dolphin (Stenella clymene), one Risso's dolphin (Grampus griseus), and one dwarf sperm whale (Kogia sima) obtained from stranding networks in Texas, Alabama, Florida, and Puerto Rico. Contents found in the GI tracts, namely, the stomach and portions of the intestinal tract, were subjected to a laboratory procedure to isolate microplastics. The physical characteristics of microparticles were analyzed with a stereomicroscope, and microplastics were classified by polymer type via Fourier-transform infrared spectroscopy. There was an average of 47.6 ± 41.4 microparticles, ranging from 1 to 193 items per stomach. More specifically, there was an average of 5.6 ± 4.7 microplastics per stomach. The predominant morphologies, colors, and polymer types were fibers, white-colored items, and polyester, respectively. This research contributes to the current knowledge of microplastic exposure in top marine mammal predators and sets the stage for further exploration into the associated risks of microplastics in odontocetes within the United States and worldwide. Environ Toxicol Chem 2024;43:1260–1273. © 2024 SETAC
Predicting Hydrocarbon Primary Biodegradation in Soil and Sediment Systems Using System Parameterization and Machine Learning
Authors: Davis Craig W. , Brown David M. , Swansborough Chesney , Hughes Christopher B. , Camenzuli Louise , Saunders Leslie J. , Lyon Delina Y. 
Published: March 2024

Abstract
Technical complexity associated with biodegradation testing, particularly for substances of unknown or variable composition, complex reaction products, or biological materials (UVCB), necessitates the advancement of non-testing methods such as quantitative structure–property relationships (QSPRs). Models for describing the biodegradation of petroleum hydrocarbons (HCs) have been previously developed. A critical limitation of available models is their inability to capture the variability in biodegradation rates associated with variable test systems and environmental conditions. Recently, the Hydrocarbon Biodegradation System Integrated Model (HC-BioSIM) was developed to characterize the biodegradation of HCs in aquatic systems with the inclusion of key test system variables. The present study further expands the HC-BioSIM methodology to soil and sediment systems using a database of 2195 half-life (i.e., degradation time [DT]50) entries for HCs in soil and sediment. Relevance and reliability criteria were defined based on similarity to standard testing guidelines for biodegradation testing and applied to all entries in the database. The HC-BioSIM soil and sediment models significantly outperformed the existing biodegradation HC half-life (BioHCWin) and virtual evaluation of chemical properties and toxicities (VEGA) quantitative Mario Negri Institute for Pharmacological Research (IRFMN) models in soil and sediment. Average errors in predicted DT50s were reduced by up to 6.3- and 8.7-fold for soil and sediment, respectively. No significant bias as a function of HC class, carbon number, or test system parameters was observed. Model diagnostics demonstrated low variability in performance and high consistency of parameter usage/importance and rule structure, supporting the generalizability and stability of the models for application to external data sets. The HC-BioSIM provides improved accuracy of Persistence categorization, with correct classification rates of 83.9%, and 90.6% for soil and sediment, respectively, demonstrating a significant improvement over the existing BioHCWin (70.7% and 58.6%) and VEGA (59.5% and 18.5%) models. Environ Toxicol Chem 2024;43:1352–1363. © 2024 Concawe. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Tumor cell membrane‐based vaccines: A potential boost for cancer immunotherapy
Authors: Yang Muyang , Zhou Jie , Lu Liseng , Deng Deqiang , Huang Jing , Tang Zijian , Shi Xiujuan , Lo Pui‐Chi , Lovell Jonathan F. , Zheng Yongfa , Jin Honglin 
Published: March 2024

AbstractBecause therapeutic cancer vaccines can, in theory, eliminate tumor cells specifically with relatively low toxicity, they have long been considered for application in repressing cancer progression. Traditional cancer vaccines containing a single or a few discrete tumor epitopes have failed in the clinic, possibly due to challenges in epitope selection, target downregulation, cancer cell heterogeneity, tumor microenvironment immunosuppression, or a lack of vaccine immunogenicity. Whole cancer cell or cancer membrane vaccines, which provide a rich source of antigens, are emerging as viable alternatives. Autologous and allogenic cellular cancer vaccines have been evaluated as clinical treatments. Tumor cell membranes (TCMs) are an intriguing antigen source, as they provide membrane‐accessible targets and, at the same time, serve as integrated carriers of vaccine adjuvants and other therapeutic agents. This review provides a summary of the properties and technologies for TCM cancer vaccines. Characteristics, categories, mechanisms, and preparation methods are discussed, as are the demonstrable additional benefits derived from combining TCM vaccines with chemotherapy, sonodynamic therapy, phototherapy, and oncolytic viruses. Further research in chemistry, biomedicine, cancer immunology, and bioinformatics to address current drawbacks could facilitate the clinical adoption of TCM vaccines.
Prevalence of autism spectrum disorder among children in Southeast Asia from 2002 to 2022: An updated systematic review and meta‐analysis
Authors: Shrestha Manju , Basukala Sunil , Thapa Niranjan , Shrestha Oshan , Basnet Mahima , Shrestha Kala , Regmi Shiva , Chhetri Suchit Thapa , Kunwor Bishal 
Published: March 2024

AbstractBackground and AimsAutism spectrum disorder (ASD) is a neurodevelopmental condition that impacts the brain, characterized by challenges in social communication and interaction, often accompanied by repetitive behaviors or focused interests. This study sheds light on the prevalence of ASD within the Southeast Asian region.MethodsThe study protocol was registered in PROSPERO (Registration No: CRD42023413915). Appropriate search terms and Boolean operators were employed to explore electronic databases for relevant articles. Data thus extracted were prepared in Excel and analyzed in Comprehensive Meta‐Analysis Software. The effect measure utilized in the study was represented by the proportion, and the choice between a fixed or random‐effect model depended on the observed heterogeneity. Visual feedback was provided through the use of forest plots and funnel plots.ResultsA total of 14 studies were included in the qualitative and quantitative synthesis after screening the imported studies. The prevalence of ASD was six per 1000 population (proportion: 0.006; CI: 0.002–0.017; I2: 99.263%). Among the ASD cases, 64.4% (proportion: 0.644; CI: 0.590–0.693; I2: 9.937%) were males and 35.6% (proportion: 0.356; CI: 0.307–0.410; I2: 9.937%) were females.ConclusionThe prevalence of ASD in Southeast Asia was estimated to be six cases per 1000 individuals, with a higher prevalence among males. This study contributes to our understanding of ASD prevalence in the region, although it is essential to note certain limitations in estimating prevalence.
Unveiling financial inclusion dynamics: Fintech's resonance in Association of Southeast Asian Nations (ASEAN)
Authors: Ha Dao , Nguyen Mai , Nguyen Kim , Şensoy Ahmet 
Published: March 2024

AbstractThis article examines the determinants of financial inclusion in the Association of Southeast Asian Nations (ASEANs), with a particular focus on the role of financial technology (fintech). We constructed an extensive and up‐to‐date Global Financial Inclusion database (2011, 2014, 2017, and 2021) to generate 26,185 observations for seven ASEAN countries over a decade, and conducted a separate case study for Singapore, the region's most financially developed member. The results reveal that financial inclusion and financial technology have experienced robust growth in ASEAN but to varying degrees amongst the member countries. Fintech has a significant impact on financial inclusion over the specified period. The relationship between age and financial inclusion follows an inverted U‐shaped pattern, with the turning point occurring between the ages of 29 and 45. Surprisingly, gender does not appear to be a determining factor. These results align with the aspirations of ASEAN policymakers to promote financial inclusion in line with the sustainable development goals.
A semantic big data analysis method based on enhanced neural networks in IoT
Authors: Wang Chongke 
Published: March 2024

AbstractDue to the growth of neural networks, the semantic big data analysis method can classify images at the pixel level, which is very suitable for the needs of IoT. In semantic big data analysis methods, the DeepLab algorithm is an improved and highly accurate algorithm based on enhanced neural networks. However, the DeepLab algorithm does not fully utilize global information, resulting in poor performance for complex scenes. Therefore, this article makes improvements by introducing a global context information module and providing prior information of complex scenes in images. It extracts global information and merges with original features. It improves the expression ability of features. This global context can enhance the accuracy of semantic big data analysis method, and an attention mechanism is designed. The experimental results display that the improved DeepLab semantic big data analysis method based on self‐attention and global context module has good average pixel accuracy and average intersection to union ratio performance on the Pascal VOC 2012 dataset. And the improvement effect is significant.