bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rapid and direct detection of artificially aged papers employing easy ambient sonic‐spray ionization mass spectrometry
Authors: Oliveira Luan Felipe Campos , de Morais Damila Rodrigues , Correa Deleon Nascimento , de Morais Campêlo Jacqueline , Sussulini Alessandra , Eberlin Marcos Nogueira , Santos Jandyson Machado 
Published: January 2021

RationaleThe effort to make fake documents look real leads to the use of crickets and beverages to produce artificially aged papers, as land titles, based on yellowing caused by the use of these methods. An old practice in Brazil, called “cricketing”, has led to the misappropriation of Brazilian land using these documents. We propose a rapid, simple, instantaneous and non‐destructive method to identify artificially aged papers by easy ambient sonic‐spray ionization mass spectrometry (EASI‐MS) analysis.MethodsThree typical aging procedures were used to obtain artificially aged papers using coffee, cola drink, and crickets, with the papers being analyzed by EASI‐MS. Multivariate statistical analyses were performed on the data to find the sample groups and to study the most relevant ions of each ageing procedure. High‐resolution MS (HRMS) was used to obtain the exact masses and attribute formulae to relevant ions present in the samples.ResultsThe combination of EASI‐MS and multivariate statistical analyses allowed us to identify the most relevant ions to classify the adulteration of documents and HRMS identified most of these relevant ions. TMS fingerprinting in combination with multivariate analysis also demonstrated that this approach can qualitatively differentiate all the examined paper samples.ConclusionsWe developed a cheap, fast and easy method that can help to elucidate counterfeit documents that have been artificially aged, helping to identify chemical additives and one that can be used in forensic laboratories.
Reagent and analyte ion hydrates in secondary electrospray ionization mass spectrometry (SESI‐MS), their equilibrium distributions and dehydration in an ion transfer capillary: Modelling and experiments
Authors: Dryahina Kseniya , Som Suman , Smith David , Španěl Patrik 
Published: January 2021

RationaleSecondary electrospray ionization (SESI) in a water spray environment at atmospheric pressure involves the reactions of hydrated hydronium reagent ions, H3O+(H2O)n, with trace analyte compounds in air samples. Understanding the formation and dehydration of reagent and analyte ions is the foundation for meaningful quantification of trace compounds by SESI‐mass spectrometry (MS).MethodsA numerical model based on gas‐phase ion thermochemistry is developed that describes equilibria in H3O+(H2O)nreagent cluster ion distributions and ligand switching reactions with polar NH3molecules leading to equilibrated hydrated ammonium ions NH4+(H2O)m. The model predictions are compared with experimental results obtained using a cylindrical SESI source coupled to an ion‐trap mass spectrometer via a heated ion transfer capillary. Non‐polar isoprene, C5H8, was used to further probe the nature of the reagent ions.ResultsEquilibrium distributions of H3O+(H2O)nions and their reactions with NH3molecules have been characterized by the model in the near‐atmospheric pressure SESI source. NH3analyte molecules displace H2O ligands from the H3O+(H2O)nions at the collisional rate forming NH4+(H2O)mions, which travel through the heated ion transfer capillary losing H2O molecules. The data for variable NH3concentrations match the model predictions and the C5H8test substantiates the notion of dehydration in the heated capillary.ConclusionsLarge cluster ions formed in the SESI region are dehydrated to H3O+(H2O)1,2,3and NH4+(H2O)1,2while passing through the heated capillary, and considerable diffusion losses also occur. This phenomenon is also predicted for other polar analyte molecules, A, that can undergo similar switching reactions, thus forming AH+and AH+(H2O)manalyte ions.
The impact of phthalate on reproductive function in women with endometriosis
Authors: Chou Ya‐Ching , Tzeng Chii‐Ruey 
Published: January 2021

AbstractBackgroundEndometriosis is a common gynecological condition in which stromal or glandular epithelium is implanted in extrauterine locations. Endometriosis causes detrimental effects on the granulosa cells, and phthalate interferes with the biological and reproductive function of endometrial cells at a molecular level.MethodsThis article retrospectively reviewed the studies on phthalate exposure and its relationship with endometriosis. A literature search was performed for scientific articles using the keywords “phthalate and endometriosis,” “endometriosis and granulosa cells,” “phthalate and granulosa cells,” and “phthalates and endometrial cells.”ResultsEndometriosis can affect cytokine production, steroidogenesis, cell cycle progression, expression of estrogen receptor‐α (ER‐α)/progesterone receptor (PR), and cause endoplasmic reticulum stress, senescence, apoptosis, autophagy, and oxidative stress in the granulosa cells. Mono‐n‐butyl phthalate (MnBP) alters the expression of cytokines, cell cycle‐associated genes, ovarian stimulation, steroidogenesis, and progesterone production. Several in vitro studies have demonstrated that phthalate caused inflammation, invasion, change in cytokines, increased oxidative stress, viability, resistance to hydrogen peroxide, and proliferation of endometrial cells.ConclusionThis might provide new insights about the impact of phthalate on the pathogenesis of endometriosis and its consequences on the ovarian function.
Potential detrimental role of soluble ACE2 in severe COVID‐19 comorbid patients
Authors: Rahman Mohammad Mahmudur , Hasan Maruf , Ahmed Asif 
Published: January 2021

SummarySevere acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) enters the host cell by binding to angiotensin‐converting enzyme 2 (ACE2) receptor. Other important proteins involved in this process include disintegrin and metalloproteinase domain‐containing protein 17 (ADAM17) also known as tumour necrosis factor‐α‐converting enzyme and transmembrane serine protease 2. ACE2 converts angiotensin II (Ang II) to angiotensin (1–7), to balance the renin angiotensin system. Membrane‐bound ACE2 ectodomain shedding is mediated by ADAM17 upon viral spike binding, Ang II overproduction and in several diseases. The shed soluble ACE2 (sACE2) retains its catalytic activity, but its precise role in viral entry is still unclear. Therapeutic sACE2 is claimed to exert dual effects; reduction of excess Ang II and blocking viral entry by masking the spike protein. Nevertheless, the paradox is why SARS‐CoV‐2 comorbid patients struggle to attain such benefit in viral infection despite having a high amount of sACE2. In this review, we discuss the possible detrimental role of sACE2 and speculate on a series of events where protease primed or non‐primed virus–sACE2 complex might enter the host cell. As extracellular virus can bind many sACE2 molecules, sACE2 level could be reduced drastically upon endocytosis by the host cell. A consequential rapid rise in Ang II level could potentially aggravate disease severity through Ang II‐angiotensin II receptor type 1 (AT1R) axis in comorbid patients. Hence, monitoring sACE2 and Ang II level in coronavirus disease 2019 comorbid patients are crucial to ensure safe and efficient intervention using therapeutic sACE2 and vaccines.
Online learning‐based model predictive control with Gaussian process models and stability guarantees
Authors: Maiworm Michael , Limon Daniel , Findeisen Rolf 
Published: January 2021

AbstractModel predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data‐driven and machine learning approaches, such as Gaussian processes, that allow to refine the model online during operation. We present a combination of an output feedback model predictive control scheme and a Gaussian process‐based prediction model that is capable of efficient online learning. To this end, the concept of evolving Gaussian processes is combined with recursive posterior prediction updates. The presented approach guarantees recursive constraint satisfaction and input‐to‐state stability with respect to the model–plant mismatch. Simulation studies underline that the Gaussian process prediction model can be successfully and efficiently learned online. The resulting computational load is significantly reduced via the combination of the recursive update procedure and by limiting the number of training data points while maintaining good performance.
Tracking control for lower limb rehabilitation robots based on polynomial nonlinear uncertain models
Authors: Li Ying , Ke Jin , Zeng Jianping 
Published: January 2021

AbstractThis paper aims to propose a novel idea for the modeling and trajectory tracking control of a lower limb rehabilitation robot. A polynomial nonlinear uncertain model is established to deal with the difficulty of accurate modeling for the lower limb rehabilitation robot with complex dynamic characteristics. A high‐order nonlinear disturbance observer (HONDO) is utilized to estimate the lumped disturbance with fast time‐varying and the corresponding observer gain matrix is obtained byH∞performance. Then an HONDO‐based composite tracking controller is designed to achieve good tracking performance of the lower limb rehabilitation robot. In addition, all solvability conditions can be directly handled by sum of squares programming, effectively avoiding the calculation problem. Finally, numerical simulations are conducted to verify the validity of the proposed method.
Fusion of neural networks, for LIDAR‐based evidential road mapping
Authors: Capellier Edouard , Davoine Franck , Cherfaoui Véronique , Li You 
Published: January 2021

AbstractLIDAR sensors are usually used to provide autonomous vehicles with three‐dimensional representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such point‐level classification results need to be converted into a dense representation, that can be used by an autonomous vehicle. We thus second present an evidential road mapping algorithm, that fuses consecutive road detection results. We benefitted from a reinterpretation of logistic classifiers, which can be seen as generating a collection of simple evidential mass functions. An evidential grid map that depicts the road can then be obtained, by projecting the classification results from RoadSeg into grid cells, and by handling moving objects via conflict analysis. The system was trained and evaluated on real‐life data. A python implementation maintains a 10 Hz framerate. Since road labels were needed for training, a soft labeling procedure, relying lane‐level HD maps, was used to generate coarse training and validation sets. An additional test set was manually labeled for evaluation purposes. So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.
The Merger of Isocyanide‐Based Multicomponent Reaction and Ring‐Closing Metathesis (IMCR/RCM)
Authors: Pharande Shrikant G. 
Published: January 2021

AbstractIsocyanide‐based multicomponent reaction (IMCR) is an excellent synthetic tool for the synthesis of versatile and complex organic molecules in a one‐pot manner. Their extraordinary ability in generating intermediate adducts bearing bi‐ or multi‐functional groups helps in the construction of even more complex products via their combination with various post‐transformation processes. Another efficient tool in modern organic synthesis is the ruthenium‐catalyzed ring‐closing olefin metathesis (RCM). Merging both expedient reactions would give us an extremely powerful approach towards the facile synthesis of complex cyclic olefins which are otherwise difficult to access. Despite their vast utility in various fields of organic chemistry, there is not a single exclusively devoted review on the potential synthetic utility of the IMCR/RCM merger. Thus, in this review, we have discussed the numerous useful IMCR/RCM combination strategies for the synthesis of cyclic olefins from the literature from 1999 until 2019. This review is organized according to the ring size formed during the process.
Direct hydroxylation of 1,4‐Dichlorobenzene to 2,5‐Dichlorophenol over Activated Carbon Catalysts
Authors: Guo Lixian , Liu Li , Pang Conglin , Li Guiying , Hu Changwei 
Published: January 2021

AbstractThe direct hydroxylation of 1,4‐dichlorobenzene (1,4‐DCB) to 2,5‐dichlorophenol (2,5‐DCP) with green oxidant, to replace the conventional multi‐step processes attracted much attention. In this work, activated carbons were prepared by modification firstly using nitric acid, and then the resultant samples were treated with hydrogen peroxide or thermally under nitrogen flow. The prepared samples were applied as heterogeneous catalysts in the one‐step hydroxylation of 1,4‐DCB, and were characterized by XRD, BET, XPS, and Boehm titration. Acetonitrile, isopropanol, glacial acetic acid, and the mixture of them were used as the reaction solvents and EPR was carried to characterize the reaction systems in these solvents. The reaction parameters, such as the feeding speed of H2O2, moles of 1,4‐DCB used, molar ratio of H2O2 to 1,4‐DCB, mass ratio of catalyst to 1,4‐DCB, reaction temperature, and reaction time were optimized. It was found that the direct hydroxylation of 1,4‐DCB could be efficiently catalyzed by activated carbon with acetonitrile as a suitable solvent. Under the optimized conditions, the highest 2,5‐DCP yield of 14.4 % with selectivity of 86.4 % was obtained. The amount of oxygen functional groups on the activated carbon samples resulted in differences in adsorption capacity of 1,4‐DCB and decomposition rate of H2O2, the integrated effect of which might be responsible for the activity.
Connecting the dots: A network approach to post‐traumatic stress symptoms in Chinese healthcare workers during the peak of the Coronavirus Disease 2019 outbreak
Authors: Hoorelbeke Kristof , Sun Xiaoxiao , Koster Ernst H. W. , Dai Qin 
Published: January 2021

AbstractHealthcare workers are at elevated risk to develop symptoms of post‐traumatic stress disorder (PTSD) in response to an outbreak of a highly infectious disease. The current study set‐out to model the complex interrelations between PTSD symptoms during the peak of the Coronavirus Disease 2019 outbreak in 291 Chinese healthcare workers and 291 matched control cases that were selected from the general population. For this purpose, we estimated regularized partial correlation networks. Within the network of healthcare workers, we observed a central role for avoidance of reminders of the traumatic event, physiological cue reactivity, anger/irritability, re‐experiencing, and startle. We identified three clusters of closely interconnected PTSD symptoms in healthcare workers, consisting of (a) symptoms of re‐experiencing and anxious arousal, (b) symptoms of avoidance and amnesia and (c) symptoms of emotional numbing and dysphoric arousal. Respectively, startle, avoidance of reminders and feeling detached emerged as bridging nodes in these communities. Although yielding highly similar network models, the PTSD symptom structure of healthcare workers showed several unique features compared to the matched control sample. This is informative for interventions aimed at targeting PTSD symptoms in healthcare workers in the context of a public health emergency.
Understanding Superatomic Ag Nanohydrides
Authors: He Xiang , Walter Michael , Jiang De‐en 
Published: January 2021

AbstractBulk Ag hydrides are extremely challenging to make even at very high pressures, but they may become stable as the particle size shrinks to the nanometer regime. Here, the formation and electronic structure of Ag nanohydrides are investigated from a superatomic perspective by density functional theory. It is found that as the coverage increases, adsorption energy of hydrogen atoms on Ag38 cluster to form Ag38H2n nanohydride (n is from 1 to 15) can be energetically favorable with respect to bare Ag38 and H2. Furthermore, the adsorbed hydrogen atoms contribute their 1s electrons to the superatom electron count and behave as a metal instead of a ligand. The electronic structure of the silver nanohydrides follows the superatomic complex model, leading to magic or relatively more stable compositions such as Ag38H2, Ag38H20, and Ag38H30, which correspond to 40‐electron, 58‐electron, and 68‐electron shell closings, respectively. Angular momentum analyses of the superatomic orbitals suggest a convoluted interaction of geometry, symmetry, and orbital splitting.
Carbon‐Supported Single Metal Site Catalysts for Electrochemical CO2 Reduction to CO and Beyond
Authors: Zhu Yuanzhi , Yang Xiaoxuan , Peng Cheng , Priest Cameron , Mei Yi , Wu Gang 
Published: January 2021

AbstractThe electrochemical CO2 reduction reaction (CO2RR) is a promising strategy to achieve electrical‐to‐chemical energy storage while closing the global carbon cycle. The carbon‐supported single‐atom catalysts (SACs) have great potential for electrochemical CO2RR due to their high efficiency and low cost. The metal centers’ performance is related to the local coordination environment and the long‐range electronic intercalation from the carbon substrates. This review summarizes the recent progress on the synthesis of carbon‐supported SACs and their application toward electrocatalytic CO2 reduction to CO and other C1 and C2 products. Several SACs are involved, including MNx catalysts, heterogeneous molecular catalysts, and the covalent organic framework (COF) based SACs. The controllable synthesis methods for anchoring single‐atom sites on different carbon supports are introduced, focusing on the influence that precursors and synthetic conditions have on the final structure of SACs. For the CO2RR performance, the intrinsic activity difference of various metal centers and the corresponding activity enhancement strategies via the modulation of the metal centers’ electronic structure are systematically summarized, which may help promote the rational design of active and selective SACs for CO2 reduction to CO and beyond.
Nanotechnology for Hepatocellular Carcinoma: From Surveillance, Diagnosis to Management
Authors: Wu Han , Wang Ming‐Da , Liang Lei , Xing Hao , Zhang Cheng‐Wu , Shen Feng , Huang Dong‐Sheng , Yang Tian 
Published: January 2021

AbstractHepatocellular carcinoma (HCC) remains the fourth leading cause of cancer‐related death worldwide. However, the clinical diagnosis and treatment modalities are still relatively limited, which urgently require the development of new effective technologies. Recently, nanotechnology has gained extensive attention in HCC surveillance, imaging and pathological diagnosis, and therapeutic strategies. Typically, nanomedicines have been focused on early HCC diagnosis and precise treatment of advanced HCC, which has developed and improved a variety of new technologies and agents for future clinical practice. Furthermore, strategies of facilitating drug release and delivery in current treatment processes such as ablation, systematic therapy, transcatheter arterial chemoembolization, molecular targeted therapy, and immune‐modulating therapy have also been studied widely. This review summarizes the recent advances in this area according to current clinical HCC guidelines: 1) Nanoparticle‑based HCC surveillance; 2) Nanotechnology for HCC diagnosis; 3) Therapeutic advances for HCC Management; 4) Limitations of applications in nanotechnology for HCC; 5) Conclusions and perspectives. Although there are still many limitations and difficulties to overcome, the investigations of nanomedicines are believed to show potential applications in clinical practice.
Visible‐Light Induced Sustainable Water Treatment Using Plasmo‐Semiconductor Nanogap Bridge Array, PNA
Authors: Murphy Emma , Liu Yunbo , Krueger Daniel , Prasad Meghna , Lee Somin Eunice , Park Younggeun 
Published: January 2021

AbstractThe development of sustainable methods for energy‐intensive water treatment processes continues to be a challenging issue. Plasmonic‐semiconductor nanoparticles, which absorb large amounts of sunlight in the visible range for conversion into chemical energy efficiently, can form the basis of a sustainable water treatment method. However, the potential uses of plasmonic semiconductor particles for water treatment have not been fully explored yet because of the limitations associated with the imbalance between light capture, charge transfer, and the required recycling steps for the particles themselves. Herein, a significantly improved visible‐light‐induced water treatment method that uses a plasmo‐semiconductor nanogap bridge array (PNA) is reported. As an arrangement of antenna‐reactors, the PNA enables the balancing of the largely enhanced electromagnetic field in the plasmonic nanogap coupling region and optimal separation of charge carriers in the semiconductor. The simultaneous effects of visible‐light absorption and charge transfer lead to the generation of a highly enhanced visible‐light‐induced OH radical (•OH). Consequently, visible‐light‐induced 5‐log N/N0 water disinfection and 100% chemical decomposition for sustainable water treatment were demonstrated. Owing to the large light absorption, charge carrier utilization, and array‐oriented scalability, the PNA will be valuable in various sustainable energy and environmental applications.
Exploring reliable edge‐cloud computing for service latency optimization in sustainable cyber‐physical systems
Authors: Cao Kun , Wei Tongquan , Chen Mingsong , Li Keqin , Weng Jian , Tan Wuzheng 
Published: January 2021

AbstractIn recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.