bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incorporation of regulatory DNA elements within a viral vector improves recombinant protein expression in plants
Authors: Coates Ryan J. , Scofield Simon , Young Mark T. 
Published: November 2024

AbstractPlants have significant potential as recombinant protein expression chassis, as they can produce complex post-translationally modified proteins that are unobtainable using prokaryotic production systems, with almost limitless scalability and substantially reduced costs relative to eukaryotic cell cultures. Transient protein expression reduces the time taken between transformation and recombinant protein extraction and purification, however low protein yields relative to conventional stable expression systems remain a major obstacle. Here, we have assessed the effectiveness of combining several established genetic components, including a promoter, 5’ UTR, 3’ UTR, double terminator, and matrix attachment region, to modify the TMV-based pJL-TRBO expression vector for improved recombinant protein expression in plants. Using enhanced green fluorescent protein (eGFP) as a reporter, we quantified expression using fluorescence imaging in planta together with SDS-PAGE and western blotting and showed that our optimum construct resulted in a significant increase relative to pJL-TRBO-eGFP. This increase was exclusively due to the presence of the additional 5’ UTR. We anticipate that our expression constructs will be a useful tool for high-yield plant recombinant protein production and may serve as a template for further improvements.
Application of component failure physics for the reliability assessment of an autonomous braking system
Authors: Banerjee Debraj , Tan Cher Ming , Baruah Nilim Akash 
Published: November 2024

AbstractThe growing demand for Cyber-Physical Systems (CPS) requires strong reliability. However, implementing Design for Reliability (DfR) in CPS requires a deep understanding of the components’ Physics of Failure. In this work, an autonomous braking system, a Cyber-Physical System is chosen to demonstrate the application of failure physics for DfR of CPS. The component under investigation is a crystal oscillator in the control circuit of the braking system. By subjecting it to a temperature cycling that mimics the realistic environment of the component, its degradation is found to significantly increase vehicle stopping distances, thereby posing potential safety hazards. Importantly, the relationship between the oscillator’s degradation and the stopping distance is non-linear, which is critical in avoiding simplistic extrapolations from initial degradation data to determine the time to replace the braking system. The outcomes of this study also provide essential design guidelines to enhance the reliability and safety of autonomous vehicle braking systems.
Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making
Authors: Hassan Nehal , Slight Robert , Bimpong Kweku , Bates David W. , Weiand Daniel , Vellinga Akke , Morgan Graham , Slight Sarah P. 
Published: November 2024

AbstractArtificial intelligence (AI)-enabled decision aids can contribute to the shared decision-making process between patients and clinicians through personalised recommendations. This systematic review aims to understand users’ perceptions on using AI-enabled decision aids to inform shared decision-making. Four databases were searched. The population, intervention, comparison, outcomes and study design tool was used to formulate eligibility criteria. Titles, abstracts and full texts were independently screened and PRISMA guidelines followed. A narrative synthesis was conducted. Twenty-six articles were included, with AI-enabled decision aids used for screening and prevention, prognosis, and treatment. Patients found the AI-enabled decision aids easy to understand and user-friendly, fostering a sense of ownership and promoting better adherence to recommended treatment. Clinicians expressed concerns about how up-to-date the information was and the potential for over- or under-treatment. Despite users’ positive perceptions, they also acknowledged certain challenges relating to the usage and risk of bias that would need to be addressed.Registration: PROSPERO database: (CRD42020220320)
Magnetic domain walls interacting with dislocations in micromagnetic simulations
Authors: Kaappa Sami , Santa-aho Suvi , Honkanen Mari , Vippola Minnamari , Laurson Lasse 
Published: November 2024

AbstractDefects, impurities, and embedded particles in ferromagnetic materials are long known to be responsible for the Barkhausen effect due to the jerky field-driven motion of domain walls and have more recently been shown to play a role also in domain wall dynamics in nanoscale ferromagnetic structures used in spintronics devices. Simulating the magnetic domain wall dynamics in the micromagnetic framework offers a straightforward route to study such systems and phenomena. However, the related work in the past suffers from material imperfections being introduced without proper physical foundation. Here, we implement dislocation stress fields in micromagnetic simulations through the induced anisotropy fields by inverse magnetostriction. The effects of individual dislocations on domain wall dynamics in thin films of different Fe surface lattice planes are characterized numerically. As a demonstration of the applicability of the implementation, we consider disorder fields due to randomly positioned dislocations with different densities, and study the avalanche-like transient approach towards the depinning transition of a domain wall driven by a slowly increasing external magnetic field.
Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls
Authors: Kinsey Spencer , Kazimierczak Katarzyna , Camazón Pablo Andrés , Chen Jiayu , Adali Tülay , Kochunov Peter , Adhikari Bhim M. , Ford Judith , van Erp Theo G. M. , Dhamala Mukesh , Calhoun Vince D. , Iraji Armin 
Published: November 2024

AbstractSchizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case–control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis. Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.
Reduced cross-protective potential of Omicron compared to ancestral SARS-CoV-2 spike vaccines against potentially zoonotic coronaviruses
Authors: Renner Tyler M. , Stuible Matthew , Cass Brian , Perret Sylvie , Guimond Julie , Lord-Dufour Simon , McCluskie Michael J. , Durocher Yves , Akache Bassel 
Published: November 2024

AbstractThe COVID-19 pandemic has emphasised the importance of vaccines and preparedness against viral threats crossing species barriers. In response, a worldwide vaccination campaign targeting SARS-CoV-2 was implemented, which provides some cross-protective immunological memory to other coronavirus species with zoonotic potential. Following a vaccination regimen against SARS-CoV-2 spike in a preclinical mouse model, we were able to demonstrate the induction of neutralizing antibodies towards multiple human ACE2 (hACE2)-binding Sarbecovirus spikes. Importantly, compared to vaccines based on the SARS-CoV-2 Reference strain, vaccines based on Omicron spike sequences induced drastically less broadly cross-protective neutralizing antibodies against other hACE2-binding sarbecoviruses. This observation remained true whether the vaccination regimens were based on protein subunit or mRNA / LNP vaccines. Overall, while it may be necessary to update vaccine antigens to combat the evolving SARS-CoV-2 virus for enhanced protection from COVID-19, Reference-based vaccines may be a more valuable tool to protect against novel coronavirus zoonoses.
DARWEN: Data-driven Algorithm for Reduction of Wide Exoplanetary Networks
Authors: Lira-Barria A. , Harvey J. N. , Konings T. , Baeyens R. , Henríquez C. , Decin L. , Venot O. , Veillet R. 
Published: November 2024

Context. Exoplanet atmospheric modeling is advancing toward complex coupled circulation-chemistry models, from chemically diverse 1D models to 3D global circulation models (GCMs). These models are crucial for interpreting observations from facilities like JWST and ELT and understanding exoplanet atmospheres. However, maintaining chemical diversity in 1D models and especially in GCMs is computationally expensive, limiting their complexity. Optimizing the number of reactions and species in the simulated atmosphere can address this tradeoff, but there is a lack of transparent and efficient methods for this optimization in the current exoplanet literature.
Aims. We aim to develop a systematic approach for reducing chemical networks in exoplanetary atmospheres, balancing accuracy and computational efficiency. Our method is data-driven, meaning we do not manually add reactions or species. Instead, we test possible reduced chemical networks and select the optimal one based on metrics for accuracy and computational efficiency. Our approach can optimize a network for similar planets simultaneously, can assign weights to prioritize either accuracy or efficiency, and is applicable in the presence of photochemistry.
Methods. We propose an approach based on a sensitivity analysis of a typical 1D chemical kinetics model. Principal component analysis was applied to the obtained sensitivities. To achieve a fast and reliable reduction of chemical networks, we utilized a genetic algorithm (GA), a machine-learning optimization method that mimics natural selection to find solutions by evolving a population of candidate solutions.
Results. We present three distinct schemes tailored for different priorities: accuracy, computational efficiency, and adaptability to photochemistry. These schemes demonstrate improved performance and reduced computational costs. Our work represents the first reduction of a chemical network with photochemistry in exoplanet research.
Conclusions. Our GA-based method offers a versatile and efficient approach to reduce chemical networks in exoplanetary atmospheres, enhancing both accuracy and computational efficiency.
Accurate sticking coefficient calculation for carbonaceous dust growth through accretion and desorption in astrophysical environments
Authors: Bossion D. , Sarangi A. , Aalto S. , Esmerian C. , Hashemi S. R. , Knudsen K. K. , Vlemmings W. , Nyman G. 
Published: November 2024

Context. Cosmic dust is ubiquitous in astrophysical environments, where it significantly influences the chemistry and the spectra. Dust grains are likely to grow through the accretion of atoms and molecules from the gas-phase onto them. Despite their importance, only a few studies have computed the sticking coefficients for relevant temperatures and species, along with their direct impact on grain growth. Overall, the formation of dust and its growth are not well understood.
Aims. This study is aimed at calculating the sticking coefficients, binding energies, and grain growth rates over a broad range of temperatures, for various gas species interacting with carbonaceous dust grains.
Methods. We performed molecular dynamics simulations with a reactive force field algorithm to compute accurate sticking coefficients and obtain the binding energies. These results were used to build an astrophysical model of nucleation regions to study dust growth.
Results. We present, for the first time, the sticking coefficients of H, H2 , C, O, and CO on amorphous carbon structures for temperatures ranging from 50 K to 2250 K. In addition, we estimated the binding energies of H, C, and O in carbonaceous dust to calculate the thermal desorption rates. Combining accretion and desorption allows us to determine an effective accretion rate and sublimation temperature for carbonaceous dust.
Conclusions. We find that sticking coefficients can differ substantially from what is commonly used in astrophysical models. This offers us new insights into carbonaceous dust grain growth via accretion in dust-forming regions.
Does robotic arm-assisted total knee arthroplasty have a role to play in large deformities?
Authors: Vermorel Pierre-Henri , Ciccullo Carlo , De Berardinis Luca , Gigante Antonio Pompilo , Neri Thomas , Philippot Rémi 
Published: November 2024

Background: Total knee arthroplasty (TKA) for patients with a large preoperative deformity (more than 10° varus or valgus) remains a challenge leading to a high rate of outliers, unsatisfactory functional results, or early prosthetic loosening. Robotic arm-assisted TKA (RATKA) has shown improvements in implant positioning accuracy. This study aimed to assess RATKA implant positioning accuracy and functional results at one year postoperative for patients with a large preoperative deformity. Methods: From November 2019 to July 2022, 500 RATKA were performed. About 74 patients with more than 10° of varus or valgus global deformity were included. Each patient received a semi-constrained implant. The difference between the valgus or varus value planned intra-operatively and the varus or valgus measured on one-year postoperative X-rays has been assessed. Functional outcomes (VAS, range of motion, KOOS) have also been evaluated. Results: For varus, the mean difference was 0.54 ± 1.21°, all patients (100%) had a difference of less than 3° at one-year post-operative. For valgus, the mean difference was 0.63 ± 1.29°, most patients (92%) had a difference of less than 3° at one year postoperative. Overall, 98.6% (n = 73) of cases had a difference of less than 3° at one-year postoperative. The mean VAS was 1.6 ± 1.4 [1;4]. Mean flexion was 132 ± 7.6° [100;145]. A total of 69 patients (93%) had a good or excellent KOOS score (KOOS total > 70) at one year post-operative. Conclusion: For large preoperative deformities, RATKA provides a high degree of accuracy in implant positioning, permitting it to fit the desired alignment without compromising knee stability, and giving the possibility of using semi-constrained implants. At one year postoperative, functional results are encouraging and most patients have recovered an optimal range of motions.
Recontextualizing knowledge in academic video publications
Authors: Velilla Sánchez María Ángeles 
Published: November 2024

Abstract
Online videos have gained popularity as a means for academics to communicate complex scientific ideas both to specialist and non-specialist audiences (Erviti & Stengler 2016; León & Bourk 2018; Luzón & Pérez-Llantada 2019). Nonetheless, concerns are raised about the potential journalistic or oversimplified nature of such science communication efforts. Consequently, this paper aims to shed light on how researchers can enhance transparency without reducing the significance of the content. The study is accomplished through an analysis of a corpus consisting of 10 videos compiled from the ‘Chemistry’ section of the website Latest Thinking (lt.org). This study adopts a discourse analysis approach, focusing on the discursive strategies employed in these videos to recontextualize knowledge for a wide audience. The findings reveal three types of recontextualization strategies performed through the orchestration of various semiotic modes: simplification strategies, strategies to construct an authorial persona and bonding strategies.
Credibility-driven rumor spreader and debunker co-evolutionary mechanisms for rumor propagation
Authors: Nian Fuzhong , Jia Yi , Wang Zhen 
Published: November 2024

Abstract
Rumor-propagation models have been an active research topic, while few methods consider the dynamic mutual transformation of the rumor spreaders and debunkers during the rumor propagation. To address the problem, we consider the possibility of co-evolution between spreaders and debunkers due to their suspicion of the message’s authenticity. Specifically, we define three dyadic rules that specify the transformation of ignorants to spreaders, ignorants to debunkers, and the mutual transformation between spreaders and debunkers in the competition of spreading-debunking during rumor propagation. Utilizing the proposed dyadic rule, we establish a new SI

r

I

d
rumor propagation model (susceptible ignorants (S), rumor spreaders (I

r
), rumor debunkers (I

d
)). Additionally, we introduce credibility as an indicating factor for the mutual transformation between spreaders and debunkers, and the credibility itself is updated according to the individual influence of each node, the local changes of neighboring spreader and debunker nodes, and the global changes of spreader and debunker nodes in the entire network. We first evaluate the proposed SI

r

I

d
model with simulation experiments in three typical networks, namely, Watts-Strogatz (WS), Erdős-Rényi (ER), and Barabasi-Albert (BA) networks. The results show that the proposed SI

r

I

d
model is strongly adaptable to these networks. We then conduct a series of parameter and ablation studies to analyze the proposed model theoretically and experimentally. Finally, we evaluate the proposed SI

r

I

d
model with multiple real retweet datasets collected from Weibo and Twitter to verify the generality and adaptability of the proposed model. The results show that our SI

r

I

d
can mimic rumor propagation in real-world scenarios.
An innovative method for solving the nonlinear fractional diffusion reaction equation with quadratic nonlinearity analysis
Authors: Khan Alamgir , Ullah Ikram , Iqbal Javed , Shah Kamal , Bilal Muhammad 
Published: November 2024

Abstract
This study generates and investigates spreading solitons in the fractional DR quadratic equation (FDE) with fractional derivatives using the Extended Direct Algebraic Method (EDAM). In population growth, mathematical biology, and reaction-diffusion mechanisms, the FDE is crucial. Applying the series form solution to the NODE from the FDE conversion into a recommended EDAM yields many traveling soliton solutions. To characterize and explore soliton structure propagation, we draw shock and kink soliton solutions. Through reaction-diffusion mechanics and mathematical biology, we may explain complex processes in many academic subjects.
Investigation of total ionizing dose effects on SOI FinFETs at elevated temperatures
Authors: Zhang Z J , Wu Y C , Liu F Y , Zhao S P , Liu H Y , Luo H Z , Zhang X , Li Y L 
Published: November 2024

Abstract
The synergistic effects of total ionization dose (TID) and high temperature (HT) are investigated for n-type Silicon-on-Insulator (n-SOI) FinFETs. Experimental results reveal that both HT and TID will lead to a negative shift in threshold voltage (ΔV
TH) in n-SOI FinFETs. While the ΔV
TH induced by the synergistic effects is smaller than the sum of the ΔV
TH shifts caused by TID and HT effects individually. Theoretical analysis suggests that the weaken degradation phenomenon can be attributed to the decrease of shallow-level electron traps in the HfO2 layer of stacked gate oxide. Based on this analysis, an analytical model is developed to simulate the synergistic effects of TID and HT, which has been validated by both experimental results and TCAD simulations. The proposed model enables accurate prediction of the potential distribution within the fin, allowing for precise calculation of V
TH variations under HT and TID irradiation.
Synthesis of Cu doped Ba(OH)2 thin film by SILAR method for supercapacitor electrode application
Authors: Sahu Abhishek Kumar , Pandey S N 
Published: November 2024

Abstract
An economically suitable successive ionic layer adsorption and reaction (SILAR) method has been used to deposit 0.25, 0.50 and 0.75 wt% copper doped barium hydroxide on stainless steel (SS) substrate. X-ray diffraction analysis shows the orthorhombic crystal structure of Cu doped Ba(OH)2 electrode. FTIR analysis reveals the chemical bonds present in the sample. The energy-dispersive x-ray spectroscopy (EDAX) and x-ray photo electron spectroscopy (XPS) measurements have been performed to confirm Cu doping into Ba(OH)2. The electrochemical properties of Cu doped Ba(OH)2 electrode have been analysed by cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) techniques. The 0.50 wt% Cu doped barium hydroxide electrode exhibits the highest specific capacitance (Csp) of 356.12 F g−1 (59.55 mF cm−2) at 1 mV s−1 scan rate in 1 M Na2SO4 electrolyte solution. This electrode also shows

~

85% capacitance retention up to 8000 cycles. A symmetric device has also been fabricated by using two electrodes. This device shows energy and power densities of 1.13 Wh Kg-1 and 279.7 W Kg-1 respectively. This cyclic retention and capacitive behaviour of Cu-doped Ba(OH)2 shows its utility as an energy storage material in the near future.
Self-powered methane (CH4) gas sensor based on vanadium oxide (VOx) nanostructures
Authors: Alipour Baladeh Saed , Haratizadeh Hamid 
Published: November 2024

Abstract
Gas detection technology has undergone significant changes over time. In coal mines, where methane gas (CH4) is typically found, swift detection of this gas is crucial for safety. This study developed the VOx pile rock nanostructures as a sensing material using DC magnetron sputtering and a quartz tube furnace to prepare vanadium nanostructures from a thin sputtered vanadium film. The sensor detected 1000, 2000, and 4000 ppm CH4 at room temperature without UV excitation, with ultrafast response/recovery times and 7%, 12%, and 27% responses, respectively. In addition, sensing analysis of this sample was performed for 125 days for 2000 ppm of CH4. The results exhibit that despite the decrease in the response value, the response and recovery times are almost the same as initial values. Using self-powered and rapid response/recovery time methane gas sensors is important for various applications, such as underground mining environments, where it can identify and alert methane gas levels that exceed the limit immediately, thus saving many lives in case of an explosion.
Tunneling dynamics of the relativistic Schrödinger/Salpeter equation
Authors: Daem F , Matzkin A 
Published: November 2024

Abstract
We investigate potential scattering and tunneling dynamics of a particle wavepacket evolving according to the relativistic Schrödinger equation (also known as the Salpeter equation). The tunneling properties of the Salpeter equation differ from those of the standard relativistic wave equations (such as the Klein–Gordon or Dirac equations). In particular, the tunneling solutions must be found by working in momentum space, given that the equation in configuration space contains a pseudo-differential operator. The resulting integral equations are derived and solved numerically for wavepackets scattering on model potential barriers. The solutions are characterized by the absence of Klein tunneling and an effect of the potential on the fraction of the transmitted wavepacket that propagates outside the light cone, a feature that has in the past been well-studied only for free propagation.