bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engineering bio‐brick protein scaffolds for organizing enzyme assemblies
Authors: Ledesma‐Fernandez Alba , Velasco‐Lozano Susana , Campos‐Muelas Pedro , Madrid Ricardo , López‐Gallego Fernando , Cortajarena Aitziber L. 
Published: April 2024

AbstractEnzyme scaffolding is an emerging approach for enhancing the catalytic efficiency of multi‐enzymatic cascades by controlling their spatial organization and stoichiometry. This study introduces a novel family of engineered SCAffolding Bricks, named SCABs, utilizing the consensus tetratricopeptide repeat (CTPR) domain for organized multi‐enzyme systems. Two SCAB systems are developed, one employing head‐to‐tail interactions with reversible covalent disulfide bonds, the other relying on non‐covalent metal‐driven assembly via engineered metal coordinating interfaces. Enzymes are directly fused to SCAB modules, triggering assembly in a non‐reducing environment or by metal presence. A proof‐of‐concept with formate dehydrogenase (FDH) and L‐alanine dehydrogenase (AlaDH) shows enhanced specific productivity by 3.6‐fold compared to free enzymes, with the covalent stapling outperforming the metal‐driven assembly. This enhancement likely stems from higher‐order supramolecular assembly and improved NADH cofactor regeneration, resulting in more efficient cascades. This study underscores the potential of protein engineering to tailor scaffolds, leveraging supramolecular spatial‐organizing tools, for more efficient enzymatic cascade reactions.
Lipid exchange of apolipoprotein A‐I amyloidogenic variants in reconstituted high‐density lipoprotein with artificial membranes
Authors: Correa Yubexi , Ravel Mathilde , Imbert Marie , Waldie Sarah , Clifton Luke , Terry Ann , Roosen‐Runge Felix , Lagerstedt Jens O. , Moir Michael , Darwish Tamim , Cárdenas Marité , Del Giudice Rita 
Published: April 2024

AbstractHigh‐density lipoproteins (HDLs) are responsible for removing cholesterol from arterial walls, through a process known as reverse cholesterol transport. The main protein in HDL, apolipoprotein A‐I (ApoA‐I), is essential to this process, and changes in its sequence significantly alter HDL structure and functions. ApoA‐I amyloidogenic variants, associated with a particular hereditary degenerative disease, are particularly effective at facilitating cholesterol removal, thus protecting carriers from cardiovascular disease. Thus, it is conceivable that reconstituted HDL (rHDL) formulations containing ApoA‐I proteins with functional/structural features similar to those of amyloidogenic variants hold potential as a promising therapeutic approach. Here we explored the effect of protein cargo and lipid composition on the function of rHDL containing one of the ApoA‐I amyloidogenic variants G26R or L174S by Fourier transformed infrared spectroscopy and neutron reflectometry. Moreover, small‐angle x‐ray scattering uncovered the structural and functional differences between rHDL particles, which could help to comprehend higher cholesterol efflux activity and apparent lower phospholipid (PL) affinity. Our findings indicate distinct trends in lipid exchange (removal vs. deposition) capacities of various rHDL particles, with the rHDL containing the ApoA‐I amyloidogenic variants showing a markedly lower ability to remove lipids from artificial membranes compared to the rHDL containing the native protein. This effect strongly depends on the level of PL unsaturation and on the particles' ultrastructure. The study highlights the importance of the protein cargo, along with lipid composition, in shaping rHDL structure, contributing to our understanding of lipid–protein interactions and their behavior.
An AK‐MCS‐based probabilistic fatigue life prediction framework for turbine disc with a mean stress correction model
Authors: Wang Yun , Li Yan‐Feng , Huang Hong‐Zhong , Bai Song 
Published: April 2024

AbstractThe turbine disc plays a crucial role in aerospace engine, significantly influencing both their fatigue life and overall reliability. Due to the substantial uncertainties present, devising a method for the probabilistic analysis of turbine disc fatigue life becomes crucial. This paper introduces a novel framework that combines adaptive Kriging Monte Carlo simulation (AK‐MCS) with a mean stress correction model to evaluate fatigue life distribution effectively. AK‐MCS, stands out as an algorithm that constructs a Kriging model, effectively reducing the need for an extensive finite element analysis. Furthermore, a mean stress correction model is proposed for the Masson‐Coffin equation tailored for fatigue life prediction of GH4133 disc material. This model has been empirically validated to be effective. The presented framework for probabilistic fatigue life analysis not only holds considerable engineering value but also offers innovative approaches for tackling analogous challenges in the field.
Theoretical designs of ORR/OER single‐atom catalysts TM@Ti2CT2 (T = O, S, Cl)
Authors: Shen Pengcheng , Li Jice , Zhang Zhizhao , Liu Hui , Liang LiMin , Chen Cong , Li Ying 
Published: April 2024

AbstractDriven by the goal of establishing a fossil‐fuel‐free and nuclear‐power‐free economy based on renewable energy, metal‐air batteries are regarded as promising energy conversion and storage devices. Developing efficient oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) bifunctional electrocatalysts for the air electrode of metal‐air batteries is becoming increasingly important. In this work, 36 transition metal (TM) single‐atom catalysts are designed based on MXenes Ti2CT2 with different surface terminal atoms (T = O, S, Cl), and their ORR/OER catalytic activity and stability are evaluated by the density functional theory. Ni@Ti2CO2, Pd@Ti2CS2, and Co@Ti2CCl2 are found to exhibit good catalytic activity with ORR/OER overpotentials of .54 V/.62 V, .59 V/.29 V, .44 V/.40 V. The aggregation behavior of three catalysts is estimated by comparing the average binding energy of one, two, three, and four TM atoms anchored on Ti2CT2. This work cannot only provide a theoretical guide to develop bifunctional single‐atom catalysts, but also help us understand the effect of terminal atoms on the electronic structures and catalytic activity of TM@Ti2CT2.
A clinical trial termination prediction model based on denoising autoencoder and deep survival regression
Authors: Qi Huamei , Yang Wenhui , Zou Wenqin , Hu Yuxuan 
Published: April 2024

AbstractEffective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of resources. Survival models can be used to predict survival probabilities in such trials. However, survival data from clinical trials are sparse, and DeepSurv cannot accurately capture their effective features, making the models weak in generalization and decreasing their prediction accuracy. In this paper, we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder (DAE) and DeepSurv models. The DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training, and then the robust features are provided to DeepSurv as input for training. The clinical trial dataset for training the model was obtained from the ClinicalTrials.gov dataset. A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant women. The experimental results showed that the denoising autoencoder and deep survival regression (DAE‐DSR) model was able to extract meaningful and robust features for survival analysis; the C‐index of the training and test datasets were 0.74 and 0.75 respectively. Compared with the Cox proportional hazards model and DeepSurv model, the survival analysis curves obtained by using DAE‐DSR model had more prominent features, and the model was more robust and performed better in actual prediction.
Improving a nitrogen mineralization model for predicting unfertilized corn yield
Authors: Arrington Kathleen E. , Ordóñez Raziel A. , Rivera‐Ocasio Zoelie , Luthard Madeline , Tierney Sarah , Spargo John , Finney Denise , Kaye Jason , White Charles 
Published: April 2024

AbstractCrop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (ε). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha−1; r2 = 0.69). In the new model, clay has a stronger influence than sand on ε, corresponding to lower predicted mineralization rates on fine‐textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha−1; r2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.
Understanding the nonlinear effect of digital technology development on CO2 reduction
Authors: Kwilinski Aleksy 
Published: April 2024

AbstractDigital technology, including advancements in artificial intelligence, the Internet of Things, and data analytics, has the potential to revolutionize tackling the problem of carbon dioxide emissions and achieving sustainable development. The paper aims at checking the nature of the impact of digital technology development on carbon dioxide reduction for the EU countries in 2013–2020. The study applies the following methods: entropy methods, panel‐corrected standard error, and feasible generalized least squares. The digital technology development index enabled the classification of all the EU countries into two distinct groups: High Digital Technology Development and Moderate/Lower Digital Technology Development. The findings confirm that digital technology development has a significant impact on decreasing carbon dioxide emissions. In the first stage, the growth of digital technology causes a faster decrease in carbon dioxide emissions. However, beyond a certain threshold, the further improvement of digital technology results in diminishing marginal benefits of CO2 emissions reduction. Thus, the EU government should catalyze the extension of digital technologies among all sectors and levels. In addition, it requires enhancing digital literacy of the society and local authorities.
Re‐Unveiling the energy efficiency impact: Paving the way for sustainable growth in ASEAN countries
Authors: Adha Rishan , Hong Cheng‐Yih , Yang Su‐Fen , Muzayyanah Syamsiyatul 
Published: April 2024

AbstractThe Association of Southeast Asian Nations (ASEAN) heavily relies on fossil fuels to drive economic growth, making it one of the world's largest greenhouse gas emitters. Promoting energy efficiency has been proposed as a solution to mitigate this issue. However, the potential negative impact of energy efficiency on economic growth remains unclear. Therefore, this study aims to re‐evaluate the dynamic impact of overall energy efficiency on economic growth in ASEAN countries, considering climate change factors. Unlike prior studies, this research measures overall efficiency based on both persistent and transient components of energy efficiency. This study employs two key techniques: stochastic frontier analysis (SFA) to estimate the overall energy efficiency and panel vector autoregression (PVAR) to analyze the dynamic causal effects of determinants' shocks. The findings suggest that enhancing ASEAN's overall energy efficiency could result in an 85.9% reduction in total energy consumption, indicating high levels of inefficiency in the region. While extensive energy efficiency measures might induce a temporary economic contraction in ASEAN countries, the rate of decline seems to abate over the long term. This implies that the impact of the energy efficiency shock will be most prominent during initial growth stages, progressively waning over time. Additionally, this study confirms the presence of the Environmental Kuznets Curve (EKC) for climate change and economic growth.
Unpacking the black box: Investigating the role of social protection programmes in promoting decent work and economic growth in low‐income countries
Authors: Mishra Naman , Grima Simon , Ozen Ercan 
Published: April 2024

AbstractThis study investigates the intricate nexus between low‐income countries, Sustainable Development Goals (SDGs), and the multifaceted phenomenon of unemployment. Amid the global endeavour to achieve SDGs, these marginalised nations assume a pivotal role, confronting distinctive challenges and opportunities. We employ meticulous analysis and structural equation modelling techniques to unveil a complex network of relationships among diverse socio‐economic indicators and unemployment across 27 low‐income countries, encompassing nations such as Afghanistan, Zambia, and others. Our empirical findings underscore the paramount importance of economic advancement, as epitomised by GDP per capita, in fostering employment prospects. Concurrently, environmental endeavours, epitomised by CO2 emissions, underscore the potential of green employment in mitigating unemployment. Investments in education and healthcare, as indicated by primary completion rates and life expectancy, emerge as pivotal determinants of employability. Moreover, access to clean energy sources and mobile cellular subscriptions indirectly influence unemployment rates, underscoring the multifaceted nature of the issue. Furthermore, our analysis reveals the intricate interplay between economic progress and social well‐being. These insights give policymakers invaluable direction, stressing the significance of tailored strategies that harness education, healthcare, technology, and environmental initiatives to alleviate unemployment, cultivate inclusive growth, and advance the overarching aspirations of sustainable development in low‐income countries.
Synthesis, Characterization and Optical Properties of Two Novel Metal Borates
Authors: Zhou Menghan , Yang Hui , Guo Zhifen , Xu Cuixia , Gu Xiaomin , Lin Yuan 
Published: April 2024

AbstractTwo novel metal borates, namely, Ag7B9O16(OH)2 (1) and AgCa2B7O11(OH)4 (2) were obtained under hydrothermal conditions. Ag7B9O16(OH)2 crystallizes into the centrosymmetric space group of C2/c in the monoclinic crystal system and displays a unique three‐dimensional (3D) framework built by 1D double borate chains further connected by Ag cations. AgCa2B7O11(OH)4 also belongs to monoclinic space group C2/c and it features borate chains with B8O8 8‐membered rings along the c axis which are further coordinated via AgO3 into 2D layers. These layers are bridged via CaO8 polyhedron into the final 3D framework. Two compounds were further characterized through thermogravimetric and optical analysis and density functional theory calculation have also been carried out, addressing their electronic structures.
A Highly Efficient and Selective Glycine Based Schiff Base Fluorescent Probe for Dual Sensing of Zn(II) and Cu(II) and its Antibacterial Activity and BSA Interaction Studies
Authors: Kharpan Bandashisha , Chetia Jagritima , Pyngrope Hunshisha , Kakati Udeshna Priya , Wangkheimayum Jayalaxmi , Paul Pradip C. , Singh T. Sanjoy , Paul Subrata , Bhattacharjee Amitabha 
Published: April 2024

AbstractA mesogenic glycine based Schiff base compound of the type, (E)‐methyl 2‐((2‐hydroxy‐4‐(tetradecyloxy)benzylidene)amino)acetate (HL) has been successfully synthesized and characterized using analytical and spectroscopic techniques. The Schiff base compound was evaluated for chemosensor activity towards different metal ions using fluorescence spectroscopy. The experiment reveals highly efficient and selective “turn‐on” and “turn‐off” responses of the fluorescence intensity towards zinc(II) and copper(II), respectively, in presence of other metal ions. The binding constant and the stoichiometry ratio were determined using Benesi‐Hildebrand equation giving 1 : 1 stoichiometry. The limits of detection for both the metal ions were estimated from the fluorescence titration experiments. The reversibility nature of the Schiff base probe was also studied using EDTA as chelating ligand. The compound, HL exhibits two INHIBIT logic gates with chemical inputs (i) Zn2+ (IN1) and Cu2+ (IN2) and (ii) Zn2+ (IN1) and EDTA (IN2). An IMPLICATION logic gate was also obtained when Cu2+ (IN1) and EDTA (IN2) serve as chemical inputs. The compound was found to exhibit potent antibacterial activity and efficient binding interaction with BSA. Molecular docking studies were carried out in order to ascertain the degree of interaction and the nature of binding of the compound with the proteins.
SiC@NiO Core–Shell Nanowire Networks‐Based Optoelectronic Synapses for Neuromorphic Computing and Visual Systems at High Temperature
Authors: Shen Weikang , Wang Pan , Wei Guodong , Yuan Shuai , Chen Mi , Su Ying , Xu Bingshe , Li Guoqiang 
Published: April 2024

Abstract1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain‐like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high‐efficient sensing and computing function with high information processing rates and low power consumption. Here, high‐temperature neuromorphic synaptic devices based on SiC@NiO core–shell nanowire networks optoelectronic memristors (NNOMs) are developed. Experimental results demonstrate that NNOMs attain synaptic short/long‐term plasticity and modulation plasticity under both electrical and optical stimulation, and exhibit advanced functions such as short/long‐term memory and “learning–forgetting–relearning” under optical stimulation at both room temperature and 200 °C. Based on the advanced functions under light stimulus, the constructed 5 × 3 optoelectronic synaptic array devices exhibit a stable visual memory function up to 200 °C, which can be utilized to develop artificial visual systems. Additionally, when exposed to multiple electronic or optical stimuli, the NNOMs effectively replicate the principles of Pavlovian classical conditioning, achieving visual heterologous synaptic functionality and refining neural networks. Overall, with abundant synaptic characteristics and high‐temperature thermal stability, these neuromorphic synaptic devices offer a promising route for advancing neuromorphic computing and visual systems.
Chiral Transfer and Evolution in Cysteine Induced Cobalt Superstructures
Authors: Wang Zimo , Yin Xiuxiu , Ba Junjie , Li Junpeng , Wei Yingjin , Wang Yizhan 
Published: April 2024

AbstractChiral organic additives have unveiled the extraordinary capacity to form chiral inorganic superstructures, however, complex hierarchical structures have hindered the understanding of chiral transfer and growth mechanisms. This study introduces a simple hydrothermal synthesis method for constructing chiral cobalt superstructures with cysteine, demonstrating specific recognition of chiral molecules and outstanding electrocatalytic activity. The mild preparation conditions allow in situ tracking of chirality evolution in the chiral cobalt superstructure, offering unprecedented insights into the chiral transfer and amplification mechanism. The resulting superstructures exhibit a universal formation process applicable to other metal oxides, extending the understanding of chiral superstructure evolution. This work contributes not only to the fundamental understanding of chirality in self‐assembled structures but also provides a versatile method for designing chiral inorganic nanomaterials with remarkable molecular recognition and electrocatalytic capabilities.
In‐Situ Construction of Electronically Insulating and Air‐Stable Ionic Conductor Layer on Electrolyte Surface and Grain Boundary to Enable High‐Performance Garnet‐Type Solid‐State Batteries
Authors: Zhou Xiaoming , Liu Jin , Ouyang Zejian , Liu Fangyang , Zhang Zongliang , Lai Yanqing , Li Jie , Jiang Liangxing 
Published: April 2024

AbstractLithophobic Li2CO3/LiOH contaminants and high‐resistance lithium‐deficient phases produced from the exposure of garnet electrolyte to air leads to a decrease in electrolyte ion transfer ability. Additionally, garnet electrolyte grain boundaries (GBs) with narrow bandgap and high electron conductivity are potential channels for current leakage, which accelerate Li dendrites generation, ultimately leading to short‐circuiting of all‐solid‐state batteries (ASSBs). Herein, a stably lithiophilic Li2ZO3 is in situ constructed at garnet electrolyte surface and GBs by interfacial modification with ZrO2 and Li2CO3 (Z+C) co‐sintering to eliminate the detrimental contaminants and lithium‐deficient phases. The Li2ZO3 formed on the modified electrolyte (LLZTO‐(Z+C)) surface effectively improves the interfacial compatibility and air stability of the electrolyte. Li2ZO3 formed at GBs broadens the energy bandgaps of LLZTO‐(Z+C) and significantly inhibits lithium dendrite generation. More Li+ transport paths found in LLZTO‐Z+C by first‐principles calculations increase Li+ conductivity from 1.04×10−4 to 7.45×10−4 S cm−1. Eventually, the Li|LLZTO‐(Z+C)|Li symmetric cell maintains stable cycling for over 2000 h at 0.8 mA cm−2. The capacity retention of LiFePO4|LLZTO‐(Z+C)|Li battery retains 70.5% after 5800 ultralong cycles at 4 C. This work provides a potential solution to simultaneously enhance the air stability and modulate chemical characteristics of the garnet electrolyte surface and GBs for ASSBs.