bluuebunny/crossref_metadata_2025 huggingface dataset:
Tissue characterisation and primary percutaneous coronary intervention guidance using intravascular ultrasound: rationale and design of the SPECTRUM study
Authors: Groenland Frederik T W , Mahmoud Karim D , Neleman Tara , Ziedses des Plantes Annemieke C , Scoccia Alessandra , Ligthart Jurgen , Witberg Karen T , Nuis Rutger-Jan , den Dekker Wijnand K , Wilschut Jeroen M , Diletti Roberto , Zijlstra Felix , Kardys Isabella , Cummins Paul , Van Mieghem Nicolas M , Daemen Joost
Published: April 2022

Introduction

Intravascular ultrasound (IVUS) improves clinical outcome in patients undergoing percutaneous coronary intervention (PCI) but dedicated prospective studies assessing the safety and efficacy of IVUS guidance during primary PCI are lacking.

Methods and analysis

The SPECTRUM study is a prospective investigator-initiated single-centre single-arm observational cohort study aiming to enrol 200 patients presenting with ST-segment elevation myocardial infarct undergoing IVUS-guided primary PCI. IVUS will be performed at baseline, postintervention and postoptimisation (if applicable), using a 40–60 MHz high-definition (HD) system. Baseline tissue characterisation includes the morphological description of culprit lesion plaque characteristics and thrombus as assessed with HD-IVUS. The primary endpoint is target vessel failure at 12 months (defined as a composite of cardiac death, target vessel myocardial infarction and clinically driven target vessel revascularisation). The secondary outcome of interest is IVUS-guided optimisation, defined as IVUS-guided additional balloon dilatation or stent placement. Other endpoints include clinical and procedural outcomes along with post-PCI IVUS findings.

Ethics and dissemination

The protocol of this study was approved by the Ethics Committee of the Erasmus University Medical Center, Rotterdam, the Netherlands. Written informed consent is obtained from all patients. Study findings will be submitted to international peer-reviewed journals in the field of cardiovascular imaging and interventions and will be presented at international scientific meetings.

Trial registration number

NCT05007535.
Validity of semiclassical limit to quantum gravity in two-mode oscillating quantized massive scalar field quantum cosmology
Authors: Rathore Meghna , Dhayal Renu , Venkataratnam K. K.
Published: April 2022

Semiclassical Einstein equations are used to describe the interaction of the back-reaction of the classical gravitational field with quantum matter fields in semiclassical gravity. We in our previous studies have made use of the semiclassical approximation to demonstrate the phenomenon of particle production, often called preheating/reheating of the universe, which occurs after the inflationary epoch during the oscillatory phase of two-mode quantized scalar field of chaotic inflationary model. During this oscillatory phase, back-reaction effects from the created particles, on account of the quantum nature of the states considered, could be significant and one might be concerned about the validity of the semiclassical approximation in these two-mode quantum optical states. The validity of the semiclassical approximation in these states is examined and it is presented how the magnitude of states parameter draws limit on the applicability and reliability of semiclassical theory of gravity. It is argued that semiclassical theory to gravity is a good approximation for states which are closer to coherent states i.e., with coherent parameters greater than unity and with squeezed parameter much smaller than unity.
Surface terms of quintic quasitopological gravity and thermodynamics of quasi-topological magnetic brane coupled to nonlinear electrodynamics
Authors: Bazrafshan A. , Olamaei A. R.
Published: April 2022

For the the quintic quasitopological action which has no well-defined variational principle, we introduced a surface term that for a spacetime with flat boundaries make the action well-defined. Moreover, we investigated the numerical solutions of the above-mentioned gravity coupled to the nonlinear logarithmic and exponential electrodynamics. It has no horizon and curvature except one conical singularity at $$r=0$$

r
=
0

with a deficit angle $$\delta \phi $$

δ
ϕ

. Also we found the counterterm which removes non-logarithmic divergences for the static quintic quasitopological gravity. Using this counterterm one can calculate a finite action and conserved quantities for the quintic quasitopological gravity.
Mapping state-sponsored information operations with multi-view modularity clustering
Authors: Uyheng Joshua , Cruickshank Iain J. , Carley Kathleen M.
Published: April 2022

This paper presents a new computational framework for mapping state-sponsored information operations into distinct strategic units. Utilizing a novel method called multi-view modularity clustering (MVMC), we identify groups of accounts engaged in distinct narrative and network information maneuvers. We then present an analytical pipeline to holistically determine their coordinated and complementary roles within the broader digital campaign. Applying our proposed methodology to disclosed Chinese state-sponsored accounts on Twitter, we discover an overarching operation to protect and manage Chinese international reputation by attacking individual adversaries (Guo Wengui) and collective threats (Hong Kong protestors), while also projecting national strength during global crisis (the COVID-19 pandemic). Psycholinguistic tools quantify variation in narrative maneuvers employing hateful and negative language against critics in contrast to communitarian and positive language to bolster national solidarity. Network analytics further distinguish how groups of accounts used network maneuvers to act as balanced operators, organized masqueraders, and egalitarian echo-chambers. Collectively, this work breaks methodological ground on the interdisciplinary application of unsupervised and multi-view methods for characterizing not just digital campaigns in particular, but also coordinated activity more generally. Moreover, our findings contribute substantive empirical insights around how state-sponsored information operations combine narrative and network maneuvers to achieve interlocking strategic objectives. This bears both theoretical and policy implications for platform regulation and understanding the evolving geopolitical significance of cyberspace.
Physical viability of anisotropic compact stars solutions under Karmarkar condition in f(ℛ,𝒯) theory of gravity
Authors: Zubair M. , Mustafa G. , Saleem Rabia , Javaid Hina
Published: April 2022

In this paper, we investigate the generalized symmetric, static compact objects under anisotropic fluid in the background of Karmarkar embedding class-1 condition. To find the exact solutions of the modified field equations, we consider the gravitational Lagrangian as a linear function of the Ricci scalar and the trace of the stress–energy tensor, i.e. [Formula: see text], where [Formula: see text] is a constant. By employing the concept of Karmarkar condition, we calculate a [Formula: see text] metric component, while for a specific form of [Formula: see text], the gravitational potential is chosen. To attain the closed-form solutions of the modified field equations, we match the interior spacetime metric with the exterior Schwarzschild metric at the boundary and using the numerical values of mass and radius of the compact stars like PSR J1903+327, 4U 1820-30, LMC X-4, and EXO 1785-248, we constraint some unknown parameters. We have discussed physical stability of the stellar configuration by adopting the energy bounds, Tolman–Oppenheimer–Volkov (TOV) equation, adiabatic index, central density and pressure as well. It is concluded that our resulting anisotropic solutions did well behave and a reasonable degree of precision with recent observations.
Intricate and multiple chirped waves geometric structures solutions of two-mode KdV equation, spectral and stability analysis
Authors: Abdel-Gawad H. I.
Published: April 2022

In a fluid, when two modes are propagating simultaneously in the same direction with different velocities ([Formula: see text] and [Formula: see text]), they are regarded as first and second modes. The two-mode Korteweg de Vries equation (TMKdVE) has been currently studied in numerous works in the literature. However, the distinction between the two modes was not studied. Here, exact solutions of the TMKdVE are obtained by using the unified method. It is found that each solution depends either on [Formula: see text] or on [Formula: see text]. These solutions are taken to assign to the first mode (FM) and the second mode (SM), respectively. These results are completely new. In this work, attention is focused on investigating the different chirped wave structures. It is shown that intricate, complex and multiple chirped waves in the two modes occur. Furthermore, it is shown that each mode can be seen in two visions: right wave (RW) and left wave (LW). The stability of steady-state solution is analyzed, where it is found that it is stable against varying the velocity [Formula: see text]. While when varying the velocity [Formula: see text], it is unstable when [Formula: see text] and stable when [Formula: see text]. The properties of the waves, the characteristic wavelength, frequency, traveling speed and spectrum content are shown graphically.
Experimental study of flat plate micro heat pipe with multi-layer structured wick
Authors: Zhao Zhengang , Zhang Yanhui , Ye Xiaoda , Zhang Fan
Published: April 2022

With the increased requirement of heat dissipation in the microelectronics field, the study of Flat plate Micro-Heat Pipe (FMHP) has become a hot topic. In this work, by analyzing the characteristics of the gas–liquid two-phase flow cycle within the FMHP, a Multi-Layer structured wick FMHP (MLHP) was prepared and used to improve the heat dissipation efficiency of microelectronic devices. The performance test system was set upto measure the heat transfer performance of the MLHP at different thermal powers and different liquid filling rates. The experimental results show that the MLHP of 150% filling rate can operate stably under 22 W thermal power, and it has an operating thermal resistance of 0.44[Formula: see text]C/W. The liquid filling rate has a significant effect on the thermal performance of the MLHP. Around the suitable filling rate, the MLHP has sufficient working fluid inside and low resistance to vapor flow. Under the high thermal power, the advantages are more significant.
Three-state quantum walks on cycles
Authors: Han Qi , Bai Ning , Kou Yaxin , Wang Huan
Published: April 2022

In this paper, on the basis of constructing a new shift operator [Formula: see text] and choosing the Grover coin [Formula: see text] as coin operator [Formula: see text], we get the standard evolution operator [Formula: see text] on cycles. Using [Formula: see text], we not only got the analytical expression of wavefunction [Formula: see text], but also obtained the conclusion that the limit distribution [Formula: see text] of [Formula: see text], which is not uniform distribution, regardless of [Formula: see text] is odd or even.
Newton Algorithm Based DELM for Enhancing Offline Tamil Handwritten Character Recognition
Authors: Shanmugam K. , Vanathi B.
Published: April 2022

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer’s complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system.
Lagrangian approach of structure formation in Newtonian cosmologies with two fluids
Authors: Li Yong-Zhuang
Published: April 2022

The Lagrangian approach of the structure formation is generalized to Newtonian cosmologies with two gravity-coupled fluids by applying the adhesion approximation to the multistreaming regime in the absence of thermodynamic pressures. The dynamic pressures arising from the velocity dispersion are considered as second-order terms. We obtain a semi-exact master evolution equation for the mixed fluid, and derive the evolution equation of each fluid by perturbation up to second order. We find that the effects of the relative motion of two fluids on the evolution of the fluid mixture are limited but will be comparatively significant at third and higher orders.
Approximation of random diffusion equations by nonlocal diffusion equations in free boundary problems of one space dimension
Authors: Du Yihong , Ni Wenjie
Published: April 2022

We show how the Stefan type free boundary problem with random diffusion in one space dimension can be approximated by the corresponding free boundary problem with nonlocal diffusion. The approximation problem is a slightly modified version of the nonlocal diffusion problem with free boundaries considered in [J. Cao, Y. Du, F. Li and W.-T. Li, The dynamics of a Fisher–KPP nonlocal diffusion model with free boundaries, J. Functional Anal. 277 (2019) 2772–2814; C. Cortazar, F. Quiros and N. Wolanski, A nonlocal diffusion problem with a sharp free boundary, Interfaces Free Bound. 21 (2019) 441–462]. The proof relies on the introduction of several auxiliary free boundary problems and constructions of delicate upper and lower solutions for these problems. As usual, the approximation is achieved by choosing the kernel function in the nonlocal diffusion term of the form [Formula: see text] for small [Formula: see text], where [Formula: see text] has compact support. We also give an estimate of the error term of the approximation by some positive power of [Formula: see text].
Credit Risk Early Warning of Small and Medium-Sized Enterprises Based on Blockchain Trusted Data
Authors: Tong Shekun , Zhang Ting , Zhang Zhigang
Published: April 2022

Small and medium-sized enterprises (SMEs) are now growing rapidly and playing an important role in the development of the national economy. As the economy grows, the contradiction between the credit risk of SMEs and the credit risk early warning mechanism of traditional supply chain financing has become increasingly important. In response to the issues of a single source of business information, the high investment cost of the existing early risk early warning mechanism, etc., from a commercial bank credit risk management perspective, this paper proposes to build an SMEs credit risk early warning system based on reliable blockchain data. The reliability of the data obtained is assessed utilising a hierarchical analysis and a vague overall judgement method. The results show that the use of blockchain technology can enhance the credibility and accuracy of the data, which provides a data guarantee for more rapid risk alert.
Construction of symmetric multiwavelets using standard pairs
Authors: Mithun A. T. , Lineesh M. C.
Published: April 2022

A multiscaling equation in the Fourier domain accommodates a trigonometric matrix polynomial. This trigonometric matrix polynomial is known as the symbol function. The existence and properties of a multiscaling function, which is the solution of a multiscaling equation, depend on the symbol function. It is possible to construct symbol functions corresponding to compactly supported and symmetric multiscaling functions from standard pairs. A standard pair carries the spectral information about the symbol function. In this paper, we briefly explain the construction of compactly supported and symmetric multiscaling functions and the corresponding mulitwavelets using standard pairs. We derive the necessary as well as sufficient condition, on the eigenspace of the square matrix in the standard pair, for the existence of a symbol function corresponding to a multiscaling equation with a compactly supported solution. We create a pseudo bi-orthogonal pair of symmetric and compactly supported multiscaling functions and the corresponding multiwavelets using standard pairs.
A Posteriori Error Estimates of Edge Residual-Type of Weak Galerkin Mixed FEM Solving Second-Order Elliptic Problems on Polytopal Mesh
Authors: Wang Huijuan , Xu Shipeng , He Xiaoxiao
Published: April 2022

In this paper, we present a posteriori error estimator of edge residual-type Weak Galerkin mixed finite element method (WG-MFEM) solving second-order elliptic problems, where two different ways of a posteriori error estimator are presented, both of which hold on polygonal mesh. The two analyses are based on the standard FEM method and continuous local mass conservation property, respectively. In order to prove the reliability holding on polygonal meshes, the a posteriori error estimator is disposed by the post-processed numerical scalar solution. Compared with some other methods, the proposed a posteriori error estimator does not contain the post-processed solution and the efficiency can be obtained automatically. Numerical experiments are conducted to confirm the efficiency of this estimator.
Non-minimally coupled transit cosmology in f(R, T) gravity
Authors: Tiwari R. K. , Sofuoglu D. , Isik R. , Shukla B. K. , Baysazan E.
Published: April 2022

A non-minimally coupled cosmological model is studied in [Formula: see text] gravity for a particular choice of the function in the background of flat Friedmann–Robertson–Walker universe. The modified field equations are solved with the help of a varying deceleration parameter. The time evolution of the model is analyzed for both the dynamic and kinematic quantities. For testing the viability of the results, energy conditions and the statefinder diagnostic are used. It has been shown that this model, which we discussed to examine the phase transition in the expansion of the universe, is compatible with current astrophysical observations, and that the DE model dominating the early universe is Chaplygin gas, while the model dominating the late universe is quintessence.
Restrained k-rainbow reinforcement number in graphs
Authors: Ebrahimi N. , Amjadi J. , Chellali M. , Sheikholeslami S. M.
Published: April 2022

Let [Formula: see text] be a positive integer. A restrained [Formula: see text]-rainbow dominating function (RkRD-function) of a graph [Formula: see text] is a function [Formula: see text] from the vertex set [Formula: see text] to the set of all subsets of the set [Formula: see text] satisfying: (i) for any vertex [Formula: see text] with [Formula: see text] the condition [Formula: see text] is fulfilled, where [Formula: see text] is the set of all vertices adjacent to [Formula: see text] (ii) the subgraph induced by the vertices assigned [Formula: see text] under [Formula: see text] has no isolated vertices. The weight of an RkRD-function [Formula: see text] is the value [Formula: see text], and the restrained [Formula: see text]-rainbow domination number of [Formula: see text], denoted by [Formula: see text], is the minimum weight of an RkRD-function of [Formula: see text]. In this paper, we continue the study of the restrained [Formula: see text]-rainbow reinforcement number [Formula: see text] of a graph [Formula: see text] defined as the cardinality of a smallest set of edges that we must add to [Formula: see text] to decrease [Formula: see text] We shall first show that the decision problem associated with [Formula: see text] is NP-hard for arbitrary graphs. Then several properties as well as some sharp bounds of the restrained [Formula: see text]-rainbow reinforcement number are investigated.
XDEVS: A hybrid system modeling framework
Authors: Xie Kunyu , Zhang Lin , Laili Yuanjun , Wang Xiaohan
Published: April 2022

When Discrete Event System Specification (DEVS) is used as a modeling tool, there is a semantic gap between a DEVS model and the mathematical representation, which may result in understanding difficulties. To provide a more intuitive form of modeling, XDEVS expands the concept of states in DEVS. The continuous state is introduced in XDEVS, which enhances the ability to model hybrid systems. Based on the DEVS simulation framework, a simulation engine is developed to drive the XDEVS model safely and efficiently and avoid the wrong location of state events during the simulation of the continuous model. A hybrid model is constructed and simulated using XDEVS. A comparison between the XDEVS model and models described by DEV&DESS and GDEVS shows that XDEVS can clearly express the structure of the model and reduce the burden on modelers.
Event-Triggered-Backstepping-Based Parallel Approaching Guidance Method for Maneuvering Target Interception
Authors: Shen Yaohua , Chen Mou , Zheng Zixuan , Guo Hongzhen
Published: April 2022

In order to achieve a straight ballistic trajectory of missile and reduce the update frequency of the missile normal acceleration for the interception of maneuvering target, a backstepping-based parallel approaching guidance method is designed with nonlinear disturbance observer (NDO) technique and event-triggered (ET) mechanism in this paper. In order to suppress the adverse effect of target maneuver, the NDO is designed to estimate the target maneuvering acceleration. Then, the NDO-based backstepping method is used to obtain the normal acceleration of missile and realize the parallel approaching guidance. In order to reduce the update frequency of missile normal acceleration, the ET mechanism is employed in the parallel approaching guidance method. If the missile trajectory is relatively straight, the normal acceleration of missile remains unchanged. On the contrary, if the missile trajectory is not straight, the normal acceleration of missile is updated to make the missile trajectory straight. In this way, the ET-based parallel approaching guidance can be obtained. Furthermore, a determined method for the initial missile flight-path angle is proposed to keep the normal acceleration of missile at zero in the initial stage of interception. Besides, Lyapunov stability analysis method is used to prove that all signals in the closed-loop guidance system are uniformly ultimately bounded. Finally, simulation results show the effectiveness of the proposed guidance method.
Evaluating Human–Robot Interaction Algorithms in Shared Autonomy via Quality Diversity Scenario Generation
Authors: Fontaine Matthew C. , Nikolaidis Stefanos
Published: April 2022

The growth of scale and complexity of interactions between humans and robots highlights the need for new computational methods to automatically evaluate novel algorithms and applications. Exploring diverse scenarios of humans and robots interacting in simulation can improve understanding of the robotic system and avoid potentially costly failures in real-world settings. We formulate this problem as a quality diversity (QD) problem, of which the goal is to discover diverse failure scenarios by simultaneously exploring both environments and human actions. We focus on the shared autonomy domain, in which the robot attempts to infer the goal of a human operator, and adopt the QD algorithms CMA-ME and MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending. Some of the generated scenarios confirm previous theoretical findings, while others are surprising and bring about a new understanding of state-of-the-art implementations. Our experiments show that the QD algorithms CMA-ME and MAP-Elites outperform Monte-Carlo simulation and optimization-based methods in effectively searching the scenario space, highlighting their promise for automatic evaluation of algorithms in human–robot interaction.
GrOD
: Deep Learning with Gradients Orthogonal Decomposition for Knowledge Transfer, Distillation, and Adversarial Training
Authors: Xiong Haoyi , Wan Ruosi , Zhao Jian , Chen Zeyu , Li Xingjian , Zhu Zhanxing , Huan Jun
Published: April 2022

Regularization that incorporates the linear combination of empirical loss and explicit regularization terms as the loss function has been frequently used for many machine learning tasks. The explicit regularization term is designed in different types, depending on its applications. While regularized learning often boost the performance with higher accuracy and faster convergence, the regularization would sometimes hurt the empirical loss minimization and lead to poor performance. To deal with such issues in this work, we propose a novel strategy, namely

Gr
adients
O
rthogonal
D
ecomposition

(

GrOD
), that improves the training procedure of regularized deep learning. Instead of linearly combining gradients of the two terms,

GrOD
re-estimates a new direction for iteration that does not hurt the empirical loss minimization while preserving the regularization affects, through orthogonal decomposition. We have performed extensive experiments to use

GrOD
improving the commonly used algorithms of transfer learning [
2
], knowledge distillation [
3
], and adversarial learning [
4
]. The experiment results based on large datasets, including Caltech 256 [
5
], MIT indoor 67 [
6
], CIFAR-10 [
7
], and ImageNet [
8
], show significant improvement made by

GrOD
for all three algorithms in all cases.
Evaluating the Utility of High-Resolution Proximity Metrics in Predicting the Spread of COVID-19
Authors: Mehrab Zakaria , Adiga Aniruddha , Marathe Madhav V. , Venkatramanan Srinivasan , Swarup Samarth
Published: April 2022

High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. However, there are open questions on how such mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ordinary differential equation based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We also evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and an 87% F1-score.
Yellow Dwarf Viruses of Cereals: Taxonomy and Molecular Mechanisms
Authors: Miller W. Allen , Lozier Zachary
Published: April 2022

Yellow dwarf viruses are the most economically important and widespread viruses of cereal crops. Although they share common biological properties such as phloem limitation and obligate aphid transmission, the replication machinery and associated cis-acting signals of these viruses fall into two unrelated taxa represented by Barley yellow dwarf virus and Cereal yellow dwarf virus. Here, we explain the reclassification of these viruses based on their very different genomes. We also provide an overview of viral protein functions and their interactions with the host and vector, replication mechanisms of viral and satellite RNAs, and the complex gene expression strategies. Throughout, we point out key unanswered questions in virus evolution, structural biology, and genome function and replication that, when answered, may ultimately provide new tools for virus management.
Protective effect of d-alanine against acute kidney injury
Authors: Iwata Yasunori , Nakade Yusuke , Kitajima Shinji , Yoneda-Nakagawa Shiori , Oshima Megumi , Sakai Norihiko , Ogura Hisayuki , Sato Koichi , Toyama Tadashi , Yamamura Yuta , Miyagawa Taro , Yamazaki Hiroka , Hara Akinori , Shimizu Miho , Furuichi Kengo , Mita Masashi , Hamase Kenji , Tanaka Tomohiro , Nishida Motohiro , Muramatsu Wataru , Yamamoto Hisashi , Shichino Shigeyuki , Ueha Satoshi , Matsushima Kouji , Wada Takashi
Published: April 2022

d-Alanine has protective effects on I/R-induced kidney injury. d-Ala inhibits ROS production and improves mitochondrial membrane potential, resulting in reduced TEC necrosis by hypoxic stimulation. The administration of d-Ala protects the tubules from I/R injury in mice. Moreover, the plasma level of d-Ala is conversely associated with eGFR in patients with AKI. Our data suggest that d-Ala is an appealing therapeutic target and a potential biomarker for AKI.
Physiological roles of hydrogen sulfide in mammalian cells, tissues, and organs
Authors: Cirino Giuseppe , Szabo Csaba , Papapetropoulos Andreas
Published: April 2022

Over the last two decades, hydrogen sulfide (H2S) has emerged as an endogenous regulator of a broad range of physiological functions. H2S belongs to the class of molecules known as gasotransmitters, which typically include nitric oxide (NO) and carbon monoxide (CO). Three enzymes are recognized as endogenous sources of H2S in various cells and tissues: cystathionine γ-lyase (CSE), cystathionine β-synthase (CBS), and 3-mercaptopyruvate sulfurtransferase (3-MST). The present article reviews the regulation of these enzymes as well as the pathways of their enzymatic and nonenzymatic degradation and elimination. The multiple interactions of H2S with other labile endogenous molecules (e.g., NO) and reactive oxygen species are also outlined. Next, the various biological targets and signaling pathways are outlined, with special reference to H2S or oxidative posttranscriptional modification (persulfidation or sulfhydration) of proteins and the effect of H2S on various channels and intracellular second messenger pathways, the regulation of gene transcription and translation, and the regulation of cellular bioenergetics and metabolism. The pharmacological and molecular tools currently available to study H2S physiology are also reviewed, including their utility and limitations. In subsequent sections, the role of H2S in the regulation of various physiological and cellular functions is reviewed, including the regulation of membrane potential, endo- and exocytosis, regulation of various cell organelles (endoplasmic reticulum, Golgi, mitochondria), regulation of cell movement, cell cycle, cell differentiation, and physiological aspects of regulated cell death. Next, the physiological roles of H2S in various cell types and organ systems are overviewed, including the role of H2S in red blood cells, immune cells, the central and peripheral nervous systems (with focus on neuronal transmission, learning, and memory formation), and regulation of vascular function (including angiogenesis as well as its specialized roles in the cerebrovascular, renal, and pulmonary vascular beds) and the role of H2S in the regulation of special senses, vision, hearing, taste and smell, and pain-sensing. Finally, the roles of H2S in the regulation of various organ functions (lung, heart, liver, kidney, urogenital organs, reproductive system, bone and cartilage, skeletal muscle, and endocrine organs) are presented, with a focus on physiology (including physiological aging) but also extending to some common pathophysiological conditions. From these data, a wide array of significant roles of H2S in the physiological regulation of all organ functions emerges and the characteristic bell-shaped biphasic effects of H2S are highlighted. In addition, key pathophysiological aspects, debated areas, and future research and translational areas are identified.
The Expression of PPAR Pathway-Related Genes Can Better Predict the Prognosis of Patients with Colon Adenocarcinoma
Authors: Zhou Xiao-Yu , Wang Jian-Qi , Chen Jin-Xu , Chen Jing-Song
Published: April 2022

The postoperative survival time and quality of life of patients with colon adenocarcinoma (COAD) varies widely. In order to make accurate decisions after surgery, clinicians need to distinguish patients with different prognostic trends. However, we still lack effective methods to predict the prognosis of COAD patients. Accumulated evidences indicated that the inhibition of peroxisome proliferator-activated receptors (PPARs) and a portion of their target genes were associated with the development of COAD. Our study found that the expression of several PPAR pathway-related genes were linked to the prognosis of COAD patients. Therefore, we developed a scoring system (named PPAR-Riskscore) that can predict patients’ outcomes. PPAR-Riskscore was constructed by univariate Cox regression based on the expression of 4 genes (NR1D1, ILK, TNFRSF1A, and REN) in tumor tissues. Compared to typical TNM grading systems, PPAR-Riskscore has better predictive accuracy and sensitivity. The reliability of the system was tested on six external validation datasets. Furthermore, PPAR-Riskscore was able to evaluate the immune cell infiltration and chemotherapy sensitivity of each tumor sample. We also combined PPAR-Riskscore and clinical features to create a nomogram with greater clinical utility. The nomogram can help clinicians make precise treatment decisions regarding the possible long-term survival of patients after surgery.
Clinical Efficacy of Ulinastatin Combined with Meglumine Adenosine Cyclophosphate in the Treatment of Acute Myocardial Infarction
Authors: Zhang Zhenying , Sun Xiaojing , Ma Jiagui , Ma Qiaoyan , Cao Qian , Zhang Liu , Wang Lizhong
Published: April 2022

Ulinastatin, a common adjuvant drug in the clinical treatment of acute circulatory failure, has a good effect on various inflammatory diseases. In this study, we aim to explore the clinical efficacy of ulinastatin combined with meglumine adenosine cyclophosphate in patients with acute myocardial infarction (AMI) and its effect on cardiac function and endothelial function of patients. 100 AMI patients treated in our hospital (February 2020-October 2020) were randomly chosen and divided into group J and group Q, with 50 cases in each group. Group Q was treated with meglumine adenosine cyclophosphate only, while group J was treated with ulinastatin combined with meglumine adenosine cyclophosphate to compare the treatment efficiency, cardiac structure indexes, cardiac systolic function, blood lipid levels, vascular endothelial function, QLI (quality of life) scores, BI indexes, and FMA (motor function) scores between the two groups. The treatment efficiency, QLI score, BI index, and FMA score in group J were notably higher compared with group Q (

P
<
0.05

). The cardiac structure indexes, cardiac systolic function, blood lipid level, and vascular endothelial function in group J were notably better compared with group Q (

P
<
0.05

). Ulinastatin combined with meglumine adenosine cyclophosphate can obviously enhance the therapeutic effect of AMI patients and improve the endothelial function and cardiac function of patients, which is very promising in this medical area.
A Personalized Recommendation Method for Short Drama Videos Based on External Index Features
Authors: Gong Xiaohui
Published: April 2022

Dramatic short videos have quickly gained a huge number of user views in the current short video boom. The information presentation dimension of short videos is higher, and it is easier to be accepted and spread by people. At present, there are a large number of drama short video messages on the Internet. These short video messages have brought serious information overload to users and also brought great challenges to short video operators and video editors. Therefore, how to process short videos quickly has become a research hotspot. The traditional episode recommendation process often adopts collaborative filtering recommendation or content-based recommendation to users, but these methods have certain limitations. Short videos have fast dissemination speed, strong timeliness, and fast hot search speed. These have become the characteristics of short video dissemination. Traditional recommendation methods cannot recommend short videos with high attention and high popularity. To this end, this paper adds external index features to extract short video features and proposes a short video recommendation method based on index features. Using external features to classify and recommend TV series videos, this method can quickly and accurately make recommendations to target customers. Through the experimental analysis, it can be seen that the method in this paper has a good effect.
[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma
Authors: Si Mengqing , Cao Xianwei
Published: April 2022

Malignant melanoma is one of the most threatening cancers to human health. Only 14% of patients with malignant melanoma have a remaining life span of 5 years. At present, there have been some studies looking for potential prognostic indicators of esophageal cancer from the level of genes and infiltrating immune cells, but there are still some problems that need to be resolved urgently. This paper proposes IGHG3 as the immune infiltration of malignant melanoma, which takes into account the computational mathematics. It aims to deduce the characteristics of immune cell infiltration in malignant melanoma and study the relationship between different immune cell infiltration characteristics and prognosis. The method in this article is to establish a computational mathematical model for the immunotherapy of melanoma, then study the method of identification of the affinity of the IGHG3 reagent, and finally obtain the gene expression of immune infiltration. The functions of these methods are, respectively, to predict the dynamic behavior of T cells with two different specificities through mathematical models and to test the matching degree of different concentrations of IGHG3 reagent with the human body. Then use the ssGSEA algorithm to obtain immune infiltration related data and calculate the difference between the weighted empirical cumulative distribution function of all genes in the effect of IGHG3 on melanoma that was carried out. The experimental results showed the computational mathematical method genome and all the remaining genes. In this study, a computational mathematical method to detect the IGHG3 gene expression had a significant inhibitory effect on A375 cells in the experimental group, and the knockdown efficiency reached 85.6%.
Aircraft Image Recognition Network Based on Hybrid Attention Mechanism
Authors: Wang Yanfeng , Chen Yinan , Liu Runmin
Published: April 2022

With the deepening of deep learning research, progress has been made in artificial intelligence. In the process of aircraft classification, the precision rate of aircraft picture recognition based on traditional methods is low due to various types of aircraft, large similarities between different models, and serious texture interference. In this article, the hybrid attention network model (BA-CNN) to implement an aircraft recognition algorithm is proposed to solve the above problems. Using two-channel ResNet-34 as a characteristic extraction function, the depth of network is increased to improve fine-grained characteristic extraction capability without increasing the output characteristic dimension. In the network to introduce a hybrid attention mechanism, respectively, between the residual units of two ResNet-34 channels, channel attention and spatial attention modules are added, more abundant mixed characteristics of attention are obtained, space and characteristics of the local characteristics of the channel response are focused, the characteristics of redundancy are reduced, and the fine-grained characteristics of learning ability are further enhanced. Trained and tested on FGVC-aircraft, a public fine-grained pictures dataset, the recognition precision rate of the BA-CNN networks model reached 89.2%. It can be seen from the experimental results, the recognition precision rate of the original model is improved effectively by using this method, and the recognition precision rate is higher than most of the existing mainstream aircraft recognition ways.
Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting
Authors: Yang Jian , Li Jinhong , Wei Lu , Gao Lei , Mao Fuqi
Published: April 2022

As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic speed forecasting. In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). DeepWalk is used to extract semantic information from road networks. Three publicly available datasets with different time granularities of 15, 30, and 60 min are used to validate the short- and long-time prediction effect of this model. The results show that the ST-DWGRU model significantly outperforms the state-of-the-art baselines.
Potential Effects of Nrf2 in Exercise Intervention of Neurotoxicity Caused by Methamphetamine Oxidative Stress
Authors: Chen Lin , Ru Qin , Xiong Qi , Yang Jun , Xu Guodong , Wu Yuxiang
Published: April 2022

Methamphetamine can cause oxidative stress-centered lipid peroxidation, endoplasmic reticulum stress, mitochondrial dysfunction, excitatory neurotoxicity, and neuroinflammation and ultimately lead to nerve cell apoptosis, abnormal glial cell activation, and dysfunction of blood-brain barrier. Protecting nerve cells from oxidative destroy is a hopeful strategy for treating METH use disorder. Nrf2 is a major transcriptional regulator that activates the antioxidant, anti-inflammatory, and cell-protective gene expression through endogenous pathways that maintains cell REDOX homeostasis and is conducive to the survival of neurons. The Nrf2-mediated endogenous antioxidant pathway can also prevent neurodegenerative effects and functional defects caused by METH oxidative stress. Moderate exercise activates this endogenous antioxidant system, which involves in many diseases, including neurodegenerative diseases. Based on evidence from existing literature, we argue that appropriate exercise can play an endogenous antioxidant regulatory role in the Nrf2 signaling pathway to reduce a number of issues caused by METH-induced oxidative stress. However, more experimental evidence is needed to support this idea. In addition, further exploration is necessary about the different effects of various parameters of exercise intervention (such as exercise mode, time, and intensity) on the Nrf2 signaling pathway intervention. Whether there are synergistic effects between exercise and plant-derived Nrf2 activators is worth further investigation.
Machine Learning for Healthcare Wearable Devices: The Big Picture
Authors: Sabry Farida , Eltaras Tamer , Labda Wadha , Alzoubi Khawla , Malluhi Qutaibah
Published: April 2022

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.
The Application and Efficacy Evaluation of Autologous Fat Transplantation in Antiaging of the Face: Systematic Review and Meta-Analysis
Authors: Li Jin , Zhang Kairui , Zhang Hongwei
Published: April 2022

Objective. To evaluate the therapy of autologous fat fine-grain transplantation in the application of antiaging facial by metaelaboration method. Methods. In Chinese and English databases, such as Wanfang, Weipu, CNKI, PubMed, Cochrane Library, and EMBASE, the literature on the use of autologous fat fines graft in antiaging facial was collected, covering randomized comparative trials, nonrandomized comparative studies, comparative case studies, cohort studies, case reports, and so forth. Meta-analyses were also conducted on complication rates, beauty seeker satisfaction, and one-shot success rates. Results. This study included 23 studies that held metaelaborations, including a total of 2852 beauty seekers. According to metaelaboration, the complication rate was 0.02 (95% CI: 0.01, 0.04), the satisfaction rate of beauty seekers was 0.95 (95% CI: 0.91, 0.97), and the success rate of one injection is 0.79 (95% CI: 0.73, 0.84). Conclusion. Autologous fat fine-grain transplantation is obvious and stable in the use of antiaging facial, and it is necessary to popularize the use in clinical practice.
The Role of Health Education in Vaccination Nursing
Authors: Yao Mengjiao , Gu Xiaoping , Mo Yujie , Xia Chunjing , Tang Liwei
Published: April 2022

The role of health education in vaccination is very important. Through various forms of activities, comprehensive and systematic education of health knowledge for people can promote students and others to be aware of vaccination and actively cooperate with vaccination work. Therefore, this article intends to conduct an in-depth study of the role of health education in prevention and treatment and to enhance people’s awareness of vaccination. This article mainly uses questionnaire survey method, interview method, and controlled experiment to explain and analyze the effect of health education. The subjects of this questionnaire are students, parents, and staff. They have mixed reviews for its role in vaccination, but the overall situation is positive. 239 out of 500 people believe that health education can reduce allergic reactions and improve the effectiveness of vaccines. In the control experiment, after health education and publicity training, the parents of the observation group were significantly better than the control group in terms of mastering the relevant knowledge of vaccination, successfully vaccinating unplanned vaccines, etc. This shows that the importance of health education in vaccination care is incomparable.