bluuebunny/crossref_metadata_2025_split huggingface dataset:
Designing gene manipulation schedules for high throughput parallel construction of objective strains
Authors: Cai Jingyi , Liao Xiaoping , Mao Yufeng , Wang Ruoyu , Li Haoran , Ma Hongwu 
Published: June 2023

AbstractRecent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design‐build‐test‐learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time‐consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent‐children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree‐like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas‐mtm.biodesign.ac.cn/
Generation of IL‐2‐Fc‐antibody conjugates by click chemistry
Authors: Williams Lindsay , Li Lin , Yazaki Paul J. , Wong Patty , Miller Aaron , Hong Teresa , Poku Erasmus K. , Bhattacharya Supriyo , Shively John E. , Kujawski Maciej 
Published: June 2023

AbstractBackgroundImmunocytokines (ICKs) are antibody directed cytokines produced by genetic fusion of an antibody to a cytokine.MethodsWe now show that antibodies conjugated by click chemistry to interleukin‐2 (IL‐2)‐Fc form fully active conjugates, and in one example, equivalent activity to a genetically produced ICK.ResultsAn IL‐2‐Fc fusion protein was optimized for click chemistry at hinge cysteines using protein stabilizing IL‐2 mutations at Lys35 and Cys125 and Fc hinge mutations at Cys142 and Cys148. The IL‐2‐Fc fusion protein with K35E and C125S mutations with 3 intact hinge cysteines, designated as IL‐2‐Fc Par, was selected based on its minimal tendency to aggregate. IL‐2‐Fc‐antibody clicked conjugates retained high IL‐2 activity and bound target antigens comparable to parent antibodies. An IL‐2‐Fc‐anti‐CEA click conjugate showed comparable anti‐tumor activity to an anti‐CEA‐IL‐2 ICK in immunocompetent CEA transgenic mice bearing CEA positive orthotopic breast tumors. Significant increases in IFNγ+/CD8+ and decreases in FoxP3+/CD4+ T‐cells were found for the clicked conjugate and ICK therapies, suggesting a common mechanism of tumor reduction.ConclusionThe production of antibody targeted IL‐2 therapy via a click chemistry approach is feasible with comparable activity to genetically produced ICKs with the added advantage of multiplexing with other monoclonal antibodies.
The evaluation of biotechnological potential of Gp144, the key molecule of natural predator bacteriophage K in Staphylococcus aureus hunting mechanism
Authors: Tasdurmazli Semra , Dokuz Senanur , Erdogdu Berna , Var Isil , Chen John Yu‐Shen , Ozbek Tulin 
Published: June 2023

AbstractBacteriophages, which selectively infect bacteria, and phage‐derived structures are considered promising agents for the diagnosis and treatment of bacterial infections due to the increasing antibiotic resistance. The binding of phages to their specific receptors on host bacteria is highly specific and irreversible, and therefore, the characterization of receptor‐binding proteins(RBPs), which are key determinants of phage specificity, is crucial for the development of new diagnostic and therapeutic products. This study highlights the biotechnological potential of Gp144, an RBP located in the tail baseplate of bacteriophage K and responsible for adsorption of phageK to S. aureus. Once it was established that recombinant Gp144 (rGp144)is biocompatible and does not exhibit lytic effects on bacteria, its interaction with the host, the binding efficiency and performance were assessed in vitro using microscopic and serological methods. Results showed that rGp144 has a capture efficiency (CE) of over 87% and the best CE score is %96 which captured 9 CFU mL−1 out of 10 CFU mL−1 bacteria, indicating that very low number of bacteria could be detected. Additionally, it was shown for the first time in the literature that rGp144 binds to both S. aureus and methicillin‐resistant S. aureus (MRSA) cells in vitro, while its affinity to different Gram‐positive bacteria (E. faecalis and B. cereus) was not observed. The findings suggest that rGp144 can be effectively used for the diagnosis of S. aureus and MRSA, and that the use of RBPs in host‐phage interaction can be a novel and effective strategy for imaging and diagnosing the site of infection.
Balloon assisted hematoma fenestration in spontaneous coronary artery dissection. Case presentation and literature review
Authors: Besis George , De Biase Chiara , Subkovas Eduardas , Rakhit Roby 
Published: June 2023

AbstractThe management of spontaneous coronary artery dissection (SCAD) is lacking randomized data. This also holds for SCAD with ST‐segment elevation myocardial infarction where stenting has been used to restore coronary flow. This approach can be associated with many drawbacks. Therefore, we present an alternative approach to stenting when coronary flow cannot be restored using cutting balloons alone.
Theory‐guided Design of Atomically Dispersed Dual‐Metal Catalysts for Superior Oxygen Reduction Reaction Activity
Authors: Yang Yuqi , Wang Qing , Mei Bingbao , Han Zengyu , Sun Fanfei , Shang Lu , Yang Shuai , Wei Yao , Wu Dongshuang , Jiang Zheng 
Published: June 2023

AbstractThe widespread application of electrochemical energy conversion devices, such as proton exchange membrane fuel cells, is hindered by the kinetically sluggish oxygen reduction reaction (ORR) at the cathode. Transition‐metal and nitrogen codoped carbon materials (TM−N−C) are among the most promising catalysts to solve this problem. Particularly, dual‐metal TM−N−C have already displayed excellent performance. However, further knowledge on the reaction mechanism and the structure−activity relationship is still required. In this study, we established three dual‐metal TM−N−C models (FeMn−N−C, FeCo−N−C, and FeNi−N−C) to investigate the electronic interaction between the metallic sites and their corresponding adsorption strength for oxygenated intermediates in ORR electrocatalysis. Then, using density functional theory calculations, we determined that the ORR activity of the dual‐metal TM−N−C models followed the order of FeCo−N−C > FeNi−N−C > FeMn−N−C. We confirmed the theoretically predicted activity by synthesizing atomically dispersed FeMn−N−C, FeCo−N−C, and FeNi−N−C catalysts using metal‐organic framework precursors, among which FeCo−N−C showed the best results in terms of ORR onset potential and half‐wave potential (0.92 and 0.81 V vs. the reference hydrogen electrode in 0.1 M HClO4, respectively.). The results demonstrate the feasibility of the theory‐guided rational design of efficient dual‐metal catalysts for ORR electrocatalysis.
Surface Engineering in Covalent Organic Polymers for High‐Performance Li−S Batteries
Authors: Lu Bing‐Yi , Chen Zhi‐Peng , Wang Hong‐Rui , Li Jiang‐Yu , Qi Qiao‐Yan , Cui Fu‐Zhi , Jiang Guo‐Fang , Zhao Xin 
Published: June 2023

AbstractLithium−sulfur (Li−S) batteries are a promising energy storage technology due to their tempting high theoretical capacity and energy density. Nevertheless, the wastage of active materials that originates from the shuttling effect of polysulfides still hinders advancement of Li−S batteries. The effective design of cathode materials is extremely pivotal to solve this thorny problem. Herein, surface engineering in covalent organic polymers (COPs) has been performed to investigate the influence of pore wall polarity on the performance of COP‐based cathodes used for Li−S batteries. With the assistance of experimental investigation and theoretical calculations, performance improvement by increasing pore surface polarity and a synergy effect of the polarized functionalities, along with nano‐confinement effect of the COPs, are disclosed, to which the improved performance of Li−S batteries including outstanding Coulombic efficiency (99.0 %) and extremely low capacity decay (0.08 % over 425 cycles at 1.0 C) is attributed. This work not only enlightens the designable synthesis and applications of covalent polymers as polar sulfur hosts with high utilization of active materials, but also provides a feasible guide for the design of effective cathode materials for future advanced Li−S batteries.
Corrosion Engineering towards NiFe‐Layered Double Hydroxide Macroporous Arrays with Enhanced Activity and Stability for Oxygen Evolution Reaction
Authors: Song Yi‐Fu , Zhang Zi‐Yang , Tian Hao , Bian Lei , Bai Yu , Wang Zhong‐Li 
Published: June 2023

AbstractNiFe‐layered double hydroxide (NiFe‐LDH) is the benchmark catalyst for the oxygen evolution reaction (OER) in alkaline medium, however, it is still challenging to improve its activity and stability. Herein, NiFe‐LDH macroporous array electrodes are demonstrated to significantly enhance the activity and stability for oxygen evolution reaction. The electrodes are fabricated by the chemical and electrochemical corrosion process of Ni foam induced by ferric nitrate, hydrochloric acid and oxygen. By optimizing the amount of iron salt and acid and selecting the appropriate reaction temperature and time, the NiFe‐LDH electrodes only need the overpotential of 180 mV and 248 mV to reach the current density of 10 mA cm−2 and 500 mA cm−2, respectively, and remain highly stable for 1000 h at 500 mA cm−2. The unique macroporous array not only significantly increases the active area of NiFe‐LDH catalyst, but also creates a stable nanostructure that avoids severe reconstruction.
Mechanochemical Ammonia Synthesis: Old is New Again
Authors: Lee Jae Seong , Han Gao‐Feng , Baek Jong‐Beom 
Published: June 2023

AbstractHydrogen is a promising clean energy source, an alternative to fossil fuels, and can potentially play a crucial role in reducing carbon emissions. The transportation and storage of hydrogen are the biggest hurdles to realizing a hydrogen economy. Ammonia is considered to be one of the most promising hydrogen carriers, because of its high hydrogen content and easy liquefaction in mild conditions. To date, ammonia is mostly produced by the ‘thermocatalytic’ Haber‐Bosch process, which requires high temperature and pressure. As a result, it can only produce ammonia in ‘centralized’ manufacturing systems. Mechanochemistry, a newly emerging method for efficient ammonia synthesis, offers potential advantages over the Haber‐Bosch process. Mechanochemical ammonia synthesis under near ambient conditions can be connected with ‘localized’ sustainable energy systems. In this perspective, the state‐of‐the‐art mechanochemical ammonia synthesis processes will be introduced. Challenges and opportunities are also discussed in relation to its role in a hydrogen economy.
Sampling methodology influences habitat suitability modeling for chiropteran species
Authors: Gaulke Sarah M. , Hohoff Tara , Rogness Brittany A. , Davis Mark A. 
Published: June 2023

AbstractTechnological advances increase opportunities for novel wildlife survey methods. With increased detection methods, many organizations and agencies are creating habitat suitability models (HSMs) to identify critical habitats and prioritize conservation measures. However, multiple occurrence data types are used independently to create these HSMs with little understanding of how biases inherent to those data might impact HSM efficacy. We sought to understand how different data types can influence HSMs using three bat species (Lasiurus borealis, Lasiurus cinereus, and Perimyotis subflavus). We compared the overlap of models created from passive‐only (acoustics), active‐only (mist‐netting and wind turbine mortalities), and combined occurrences to identify the effect of multiple data types and detection bias. For each species, the active‐only models had the highest discriminatory ability to tell occurrence from background points and for two of the three species, active‐only models preformed best at maximizing the discrimination between presence and absence values. By comparing the niche overlaps of HSMs between data types, we found a high amount of variation with no species having over 45% overlap between the models. Passive models showed more suitable habitat in agricultural lands, while active models showed higher suitability in forested land, reflecting sampling bias. Overall, our results emphasize the need to carefully consider the influences of detection and survey biases on modeling, especially when combining multiple data types or using single data types to inform management interventions. Biases from sampling, behavior at the time of detection, false positive rates, and species life history intertwine to create striking differences among models. The final model output should consider biases of each detection type, particularly when the goal is to inform management decisions, as one data type may support very different management strategies than another.
Generalized gamma ARMA process for synthetic aperture radar amplitude and intensity data
Authors: da Silva Willams B. F. , Almeida‐Junior Pedro M. , Nascimento Abraão D. C. 
Published: June 2023

AbstractWe propose a new autoregressive moving average (ARMA) process with generalized gamma (G) marginal law, called G‐ARMA. We derive some of its mathematical properties: moment‐based closed‐form expressions, score function, and Fisher information matrix. We provide a procedure for obtaining maximum likelihood estimates for the G‐ARMA parameters. Its performance is quantified and discussed using Monte Carlo experiments, considering (among others) various link functions. Finally, our proposal is applied to solve remote sensing problems using synthetic aperture radar (SAR) imagery. In particular, the G‐ARMA process is applied to real data from images taken in the Munich and San Francisco regions. The results show that G‐ARMA describes the neighborhoods of SAR features better than the gamma‐ARMA process (a reference for asymmetric positive data). For pixel ray modeling, our proposal outperforms and gamma‐ARMA.
Vibration characteristics and comfort evaluation of a large shake table during operation
Authors: Guo Tong , Zong Yueran , Zhu Ruizhao , Zhang Minte , Soh Chee Kiong 
Published: June 2023

AbstractShake tables are useful instruments for investigating structural seismic responses, while they may introduce excessive vibrations on surrounding environments during operation. This study investigates the impact of vibration generated by a 6 m×9 m large shake table on the shake table laboratory and the surrounding buildings. To begin, vibration sensors were strategically installed throughout the shake table laboratory and the surrounding buildings to investigate their vibration characteristics during operations of the shake table. Subsequently, the vibration comfort of the shake table laboratory and the surrounding buildings was thoroughly evaluated. Finally, the overall finite element model of the ground‐shake table laboratory‐surrounding buildings was established by simulating the infinite soil with a viscoelastic boundary, and the parametric study of the vibration isolation trench was conducted. This study is the first to accurately, objectively, and quantitatively investigate the impact of large shake table operation on the surrounding environment. The results presented will be useful for the laboratory managers to develop reasonable loading schemes, as well as to optimize the design of other similar laboratories and vibration isolation measures.
Hybrid simulation of a steel dual system with buckling‐induced first‐story column shortening: A mixed control mode approach
Authors: Wang Kung‐Juin , Chou Chung‐Che , Huang Cheng‐Wei , Shen Hou‐Kuan , Sepulveda Claudio , Mosqueda Gilberto , Uang Chia‐Ming 
Published: June 2023

AbstractThis paper presents a hybrid simulation (HS) with a proposed substructuring technique that captures the effect of first‐story column local buckling and axial shortening on the frame behavior. The HS is applied to examine the seismic response of a two‐dimensional seven‐story, two‐bay steel dual system. The experimental substructure consists of a full‐scale interior column and beam cruciform subassemblage, including a moderately ductile first‐story built‐up box column and two I‐shaped beams. Under combined axial and lateral loads, local buckling can occur near the column base, resulting in column shortening. The specimen is loaded through a four‐degree‐of‐freedom (DOF) mixed‐mode control (three displacement‐ and one force‐control) actuation system that simplifies the actual complex boundary conditions of the frame structure. To account for column shortening in the HS, a new approach consisting of a set of fictitious equivalent forces applied to columns in the numerical model to achieve compatible displacements with the experiment is implemented. Shortening of the two exterior columns in the model is simulated through finite element analysis using the computer program ABAQUS in an attempt to capture the redistribution of axial loads with column shortening. The test results confirm that the proposed modeling and control methods successfully integrate the experimental substructure and ABAQUS simulation into the HS, resulting in a more realistic frame response that captures the effect of column shortening in the analysis. The moderately ductile built‐up box column is verified to perform well in near‐fault earthquake loadings.
Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines
Authors: Zhang Wenyang , Ayello Francois , Honegger Doug , Bozorgnia Yousef , Taciroglu Ertugrul 
Published: June 2023

AbstractHerein, we utilized machine‐learning (ML) and data‐driven (regression) techniques to tackle a critical infrastructure engineering problem—namely, predicting the seismic response of natural gas pipelines crossing earthquake faults. Such a 3D nonlinear problem can take up to 10 h to solve by performing finite element analysis (FEA), considering the length of the pipeline and a large number of pipe and soil elements. However, the ML and data‐driven techniques can learn the projection rule of input‐output and predict the pipeline response instantaneously given a set of input features. In addition, the well‐trained ML model can be implemented for regional‐scale risk and rapid post‐event damage assessments. In this study, the input for ML comprised approximately 217K nonlinear FEAs, which covered a wide range of combinations of soil, structural and fault properties and yielded critical pipe strain responses under fault‐rupture displacements. We adopted various regression models and physics‐constrained neural networks, which can accurately and rapidly predict the tensile and compressive strains for a broad range of probable fault‐rupture displacements. Performances of various ML and conventional statistical models were systematically examined. Not surprisingly, neural networks exhibited the best performance for this multi‐output regression problem, in whichR2 > 0.95 was achieved for a wide range of fault displacement (FD) levels. Further, we used the trained neural network with 14.5 million Monte‐Carlo‐generated input samples to predict the maximum tensile and compressive strain curves of pipelines. This new dataset aimed at filling the missing input‐output points from the 217K FEAs, and improved the accuracy of the prediction of probability of failure for natural gas pipelines under FD hazards.
Fast recognition model of Fusarium in agaric based on hyperspectral imaging
Authors: Sun Yuhan , Wang Huanzi , Gan Yuan , Qing Yudie , Yue Tianli , Yuan Yahong , Shi Yiheng 
Published: June 2023

AbstractAgaric is a dual‐purpose fungus for medicine and food, which is considered a healthcare product. However, in cultivation and production, agaric is contaminated by a variety of molds, resulting in a decline in quality and yield. Fusarium, as one of the poisonous filamentous fungi, seriously threatens the healthy development of the agaric industry and even affects human health. Therefore, it is necessary to establish timely and accurate identification and rapid detection of pathogens and crops. In this study, the visible near infrared hyperspectral imaging was used to obtain the image and spectral information, and the best classification model was selected and established to quickly distinguish Fusarium oxysporum from Fusarium verticillioides, which was also successfully applied for the preliminary identification of agaric infection. This approach could provide a nondestructive manner for rapid identification and detection of pathogenic fungi contamination in agaric.
Applications of finite difference‐based physics‐informed neural networks to steady incompressible isothermal and thermal flows
Authors: Jiang Qinghua , Shu Chang , Zhu Lailai , Yang Liming , Liu Yangyang , Zhang Zhilang 
Published: June 2023

AbstractPhysics‐informed neural networks (PINNs) have aroused an upsurge in scientific machine/deep learning community, especially in research areas involving solving partial differential equations. In this work, based on the multilayer perceptron neural network and finite difference (FD) method, we apply the finite difference‐based PINNs (FD‐PINNs) to simulate the steady incompressible flows. The spatial derivatives in nonlinear governing equations are discretized by the central FD scheme, and the physical boundary conditions can be exactly satisfied in a similar way to conventional fluid flow solvers. Compared to original PINNs using automatic differentiation (AD) to discretize the governing equations, FD‐PINNs hold three‐fold potential advantages in decoupling the predictive accuracy of derivatives from the network's approximating accuracy, fulfilling the physical boundary conditions, and saving computational costs when large‐size networks or meshes are used. We first attempt to make relatively fair comparisons between AD‐PINNs, FD‐PINNs and conventional flow solvers using a benchmark flow problem—steady incompressible laminar isothermal lid‐driven cavity flow across a wide range of Reynolds numbers (Re). Our results indicate that FD‐PINNs are superior to AD‐PINNs in both predictive precision, computational efficiency and training convergence. Moreover, FD‐PINNs exhibit higher accuracy than flow solvers when insufficient mesh points are used to solve high‐Re cavity flow. Even at Re = 10,000, with only 10 internal samples, FD‐PINNs can still precisely recover the flow details on a uniform mesh of size 51 × 51. Besides, the time consumption of FD‐PINNs grows much slower than that of conventional flow solvers. We also investigate the robustness of FD‐PINNs w.r.t. the noise level of samples and the effects of pressure boundary condition imposed on solid cavity walls. Furthermore, FD‐PINNs are extensively applied to predict the incompressible thermal flows: natural convective flows in a square cavity and in a concentric annulus, where the effects of governing equations on the predictive accuracy are discovered and discussed for the first time.