bluuebunny/crossref_metadata_2025 huggingface dataset:
Trade or Trick?
Authors: Xia Pengcheng , Wang Haoyu , Gao Bingyu , Su Weihang , Yu Zhou , Luo Xiapu , Zhang Chao , Xiao Xusheng , Xu Guoai
Published: July 2022

The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Uniswap, as the most prominent cryptocurrency decentralized exchange (DEX), is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first investigate the landscape of cryptocurrency trading on Uniswap from different perspectives based on its transactions. Then, we propose an accurate approach for flagging scam tokens on Uniswap. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams. The scammers have gained a profit of at least $16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements
Authors: Jin Lin , Hao Shuai , Wang Haining , Cotton Chase
Published: July 2022

It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deployment without manual efforts. The core of Disguiser is a control server that replies with a static payload to provide the ground truth of server responses. As such, we send requests from various types of vantage points across the world to our control server, and the censorship activities can be recognized if a vantage point receives a different response. In particular, we design and conduct a cache test to pre-exclude the vantage points that could be interfered by cache proxies along the network path. Then we perform application traceroute towards our control server to explore censors' behaviors and their deployment. With Disguiser, we conduct 58 million measurements from vantage points in 177 countries. We observe 292 thousand censorship activities that block DNS, HTTP, or HTTPS requests inside 122 countries, achieving a 106 false positive rate and zero false negative rate. Furthermore, Disguiser reveals the censor deployment in 13 countries.
Xatu
Authors: Nam Yun Seong , Gao Jianfei , Bothra Chandan , Ghabashneh Ehab , Rao Sanjay , Ribeiro Bruno , Zhan Jibin , Zhang Hui
Published: July 2022

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
A Comprehensive Empirical Study of Query Performance Across GPU DBMSes
Authors: Suh Young-Kyoon , An Junyoung , Tak Byungchul , Na Gap-Joo
Published: July 2022

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. To fill this gap, we have conducted a rigorous empirical study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems still need to resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization.
Data-Driven Network Path Simulation with iBox
Authors: Ashok Sachin , Tiwari Shubham , Natarajan Nagarajan , Padmanabhan Venkata N. , Sellamanickam Sundararajan
Published: July 2022

While network simulation is widely used for evaluating network protocols and applications, ensuring realism remains a key challenge. There has been much work on simulating network mechanisms faithfully (e.g., links, buffers, etc.), but less attention on the critical task of configuring the simulator to reflect reality.

We present iBox ("Internet in a Box"), which enables data-driven network path simulation, using input/output packet traces gathered at the sender/receiver in the target network to create a model of the end-to-end behaviour of a network path. Our work builds on recent work in this direction [2, 6] and makes three contributions: (1) estimation of a lightweight non-reactive cross-traffic model, (2) estimation of a more powerful reactive cross-traffic model based on Bayesian optimization, and (3) evaluation of iBox in the context of congestion control variants in an Internet research testbed and also controlled experiments with known ground truth.

This paper represents an abridged version of [3].
Differentially Private Reinforcement Learning with Linear Function Approximation
Authors: Zhou Xingyu
Published: July 2022

Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP). Compared to existing private RL algorithms that work only on tabular finite-state, finite-actions MDPs, we take the first step towards privacy-preserving learning in MDPs with large state and action spaces. Specifically, we consider MDPs with linear function approximation (in particular linear mixture MDPs) under the notion of joint differential privacy (JDP), where the RL agent is responsible for protecting users' sensitive data. We design two private RL algorithms that are based on value iteration and policy optimization, respectively, and show that they enjoy sub-linear regret performance while guaranteeing privacy protection. Moreover, the regret bounds are independent of the number of states, and scale at most logarithmically with the number of actions, making the algorithms suitable for privacy protection in nowadays large-scale personalized services. Our results are achieved via a general procedure for learning in linear mixture MDPs under changing regularizers, which not only generalizes previous results for non-private learning, but also serves as a building block for general private reinforcement learning.
NG-Scope: Fine-Grained Telemetry for NextG Cellular Networks
Authors: Xie Yaxiong , Jamieson Kyle
Published: July 2022

Accurate and highly-granular channel capacity telemetry of the cellular last hop is crucial for the effective operation of transport layer protocols and cutting edge applications, such as video on demand and videotelephony. This paper presents the design, implementation, and experimental performance evaluation of NG-Scope, the first such telemetry tool able to fuse physical-layer channel occupancy readings from the cellular control channel with higher-layer packet arrival statistics and make accurate capacity estimates. NG-Scope handles the latest cellular innovations, such as when multiple base stations aggregate their signals together to serve mobile users. End-to-end experiments in a commercial cellular network demonstrate that wireless capacity varies significantly with channel quality, mobility, competing traffic within each cell, and the number of aggregated cells. Our experiments demonstrate significantly improved cell load estimation accuracy, missing the detection of less than 1% of data capacity overall, a reduction of 82% compared to OWL [2], the state-of-the-art in cellular monitoring. Further experiments show that MobileInsight-based [4] CLAW [6] has a root-mean-squared capacity error of 30.5 Mbit/s, which is 3.3× larger than NG-Scope (9.2 Mbit/s).
Mean Field and Refined Mean Field Approximations for Heterogeneous Systems
Authors: Allmeier Sebastian , Gast Nicolas
Published: July 2022

Mean field approximation is a powerful technique to study the performance of large stochastic systems represented as n interacting objects. Applications include load balancing models, epidemic spreading, cache replacement policies, or large-scale data centers. Mean field approximation is asymptotically exact for systems composed of n homogeneous objects under mild conditions. In this paper, we study what happens when objects are heterogeneous. This can represent servers with different speeds or contents with different popularities. We define an interaction model that allows obtaining asymptotic convergence results for stochastic systems with heterogeneous object behavior and show that the error of the mean field approximation is of order O(1/n). More importantly, we show how to adapt the refined mean field approximation, developed by the authors of Gast et al. 2019, and show that the error of this approximation is reduced to O(1/n2). To illustrate the applicability of our result, we present two examples. The first addresses a list-based cache replacement model, RANDOM(m), which is an extension of the RANDOM policy. The second is a heterogeneous supermarket model. These examples show that the proposed approximations are computationally tractable and very accurate. For moderate system sizes (n ≈ 30) the refined mean field approximation tends to be more accurate than simulations for any reasonable simulation time.
NURA
Authors: Darabi Sina , Mahani Negin , Baxishi Hazhir , Yousefzadeh Ehsan , Sadrosadati Mohammad , Sarbazi-Azad Hamid
Published: July 2022

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize GPU resources, is still challenging. Some pieces of prior work (e.g. spatial multitasking) have limited opportunity to improve resource utilization, while others, e.g. simultaneous multi-kernel, provide fine-grained resource sharing at the price of unfair execution. This paper proposes a new multi-application paradigm for GPUs, called NURA, that provides high potential to improve resource utilization and ensure fairness and Quality-of-Service(QoS). The key idea is that each streaming multiprocessor (SM) executes the Cooperative Thread Arrays (CTAs) that belong to only one application (similar to spatial multi-tasking) and shares its unused resources with the SMs running other applications demanding more resources. NURA handles resource sharing process mainly using a software approach to provide simplicity, low hardware overhead, and flexibility.We also perform some hardware modifications as an architectural support for our software-based proposal. Our conservative analysis reveals that the hardware area overhead of our proposal is less than 1.07% with respect to the whole GPU die. Our experimental results over various mixes of GPU workloads show that NURA improves throughput by 26% compared to the state-of-the-art spatial multi-tasking, on average, while meeting QoS targets. In terms of fairness, NURA has almost similar results to spatial multitasking, while it outperforms simultaneous multi-kernel by 76%, on average.
Online Optimization with Feedback Delay and Nonlinear Switching Cost
Authors: Pan Weici , Shi Guanya , Lin Yiheng , Wierman Adam
Published: July 2022

We study a variant of online optimization in which the learner receives k-round delayed feedback about hitting cost and there is a multi-step nonlinear switching cost, i.e., costs depend on multiple previous actions in a nonlinear manner. Our main result shows that a novel Iterative Regularized Online Balanced Descent (iROBD) algorithm has a constant, dimension-free competitive ratio that is O(L2k), where L is the Lipschitz constant of the nonlinear switching cost. Additionally, we provide lower bounds that illustrate the Lipschitz condition is required and the dependencies on k and L are tight. Finally, via reductions, we show that this setting is closely related to online control problems with delay, nonlinear dynamics, and adversarial disturbances, where iROBD directly offers constant-competitive online policies. This extended abstract is an abridged version of [2].
Age-Dependent Differential Privacy
Authors: Zhang Meng , Wei Ermin , Berry Randall , Huang Jianwei
Published: July 2022

The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, and hence may lead to unnecessary accuracy loss when trying to achieve meaningful privacy guarantees in time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we establish a connection between classical DP and age-dependent DP, based on which we characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization demonstrates that aging, i.e., using stale data inputs and/or postponing the release of outputs, can be a new strategy to protect data privacy in addition to noise injection in the traditional DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP. We then characterize and achieve the optimal tradeoffs between privacy risk and utility. Finally, case studies show that, when achieving a target of an arbitrarily small privacy risk in a single-query case, the approach of combining aging and noise injection can achieve a bounded accuracy loss, whereas using noise injection only (as in the DP benchmark) will lead to an unbounded accuracy loss.
Unleashing the Power of Paying Multiplexing Only Once in Stochastic Network Calculus
Authors: Bouillard Anne , Nikolaus Paul , Schmitt Jens
Published: July 2022

The stochastic network calculus (SNC) holds promise as a versatile and uniform framework to calculate probabilistic performance bounds in networks of queues. A great challenge to accurate bounds and efficient calculations are stochastic dependencies between flows due to resource sharing inside the network. However, by carefully utilizing the basic SNC concepts in the network analysis the necessity of taking these dependencies into account can be minimized. To that end, we unleash the power of the pay multiplexing only once principle (PMOO, known from the deterministic network calculus) in the SNC analysis. We choose an analytic combinatorics presentation of the results in order to ease complex calculations. In tree-reducible networks, a subclass of general feedforward networks, we obtain an effective analysis in terms of avoiding the need to take internal flow dependencies into account. In a comprehensive numerical evaluation, we demonstrate how this unleashed PMOO analysis can reduce the known gap between simulations and SNC calculations significantly, and how it favourably compares to state-of-the art SNC calculations in terms of accuracy and computational effort. Motivated by these promising results, we also consider general feedforward networks, when some flow dependencies have to be taken into account. To that end, the unleashed PMOO analysis is extended to the partially dependent case and a case study of a canonical topology, known as the diamond network, is provided, again displaying favourable results over the state of the art.
Competitive Online Optimization with Multiple Inventories
Authors: Lin Qiulin , Mo Yanfang , Su Junyan , Chen Minghua
Published: July 2022

We study an online inventory trading problem where a user seeks to maximize the aggregate revenue of trading multiple inventories over a time horizon. The trading constraints and concave revenue functions are revealed sequentially in time, and the user needs to make irrevocable decisions. The problem has wide applications in various engineering domains. Existing works employ the primal-dual framework to design online algorithms with sub-optimal, albeit near-optimal, competitive ratios (CR). We exploit the problem structure to develop a new divide-and-conquer approach to solve the online multi-inventory problem by solving multiple calibrated single-inventory ones separately and combining their solutions. The approach achieves the optimal CR of $łn θ + 1$ if $Nłeq łn θ + 1$, where N is the number of inventories and θ represents the revenue function uncertainty; it attains a CR of $1/[1-e^-1/(łnθ+1) ] \in [łn θ +1, łn θ +2)$ otherwise. The divide-and-conquer approach reveals novel structural insights for the problem, (partially) closes a gap in existing studies, and generalizes to broader settings. For example, it gives an algorithm with a CR within a constant factor to the lower bound for a generalized one-way trading problem with price elasticity with no previous results. When developing the above results, we also extend a recent CR-Pursuit algorithmic framework and introduce an online allocation problem with allowance augmentation, both of which can be of independent interest.
Signaling Pathways in Neurovascular Development
Authors: Rattner Amir , Wang Yanshu , Nathans Jeremy
Published: July 2022

During development, the central nervous system (CNS) vasculature grows to precisely meet the metabolic demands of neurons and glia. In addition, the vast majority of the CNS vasculature acquires a unique set of molecular and cellular properties—collectively referred to as the blood–brain barrier—that minimize passive diffusion of molecules between the blood and the CNS parenchyma. Both of these processes are controlled by signals emanating from neurons and glia. In this review, we describe the nature and mechanisms-of-action of these signals, with an emphasis on vascular endothelial growth factor (VEGF) and beta-catenin (canonical Wnt) signaling, the two best-understood systems that regulate CNS vascular development. We highlight foundational discoveries, interactions between different signaling systems, the integration of genetic and cell biological studies, advances that are of clinical relevance, and questions for future research.
Riding the wave: a quantitative report of electrocardiogram utilization for myocardial infarction confirmation
Authors: Broughton Philip , Troncoso Miguel , Corker Alexa , Williams Alexus , Bolus Dawson , Munoz Gualberto , McWhorter Caroline , Roerden Hallie , Huebsch Penny , DeLeon-Pennell Kristine Y.
Published: July 2022

Our study describes a quantitative method for using an electrocardiogram (ECG) to determine which animals have infarcts that reflect coronary artery ligation. Using a quantitative ECG, investigators will have the benefit of having real-time feedback during the procedure, which will ultimately decrease the amount of time investigators spend performing experiments. This overall increase in efficiency will help investigators decrease animal numbers used due to better surgical outcomes.
Leisure Sports Behavior Recognition Algorithm Based on Deep Residual Network
Authors: Chou Zhen
Published: July 2022

Aiming at the related problems existing in the field of leisure sports computing, in order to study the behavior recognition of leisure sports by deep residual network, based on the deep residual neural network theory, the behavior recognition algorithm and the corresponding robust model are used to analyze the leisure sports related samples, and the correlation model is used to predict and analyze the leisure sports related content. The results show that the change curves of Sig and Tanh functions can be divided into slow increasing stage, linear increasing stage, and stable stage. The y value corresponding to ReLU curve shows a linear change trend with the increase of x value. The Leaky function’s corresponding curve can be divided into two stages. The function coincides with the ReLU function in the first quadrant and remains linear in the third quadrant. The activation function curves corresponding to layers 56 and 20 have a relatively large variation range, and both of them show an overall trend of gradual decline. On the whole, the curve value corresponding to layer 56 is higher than that corresponding to layer 20, indicating that the method of layer 20 is relatively good and the corresponding training error is relatively low. It can be seen from the robustness recognition rate of various methods under different training samples that Fl has the highest overall data recognition rate while Sc has relatively poor stability. However, the recognition rate of IDCC and DCC shows a relatively flat trend, indicating that these two methods have certain advantages in describing the robust recognition rate. The research results can provide theoretical support for the application of deep residual neural networks in other fields.
Study on the Correlation between Pain and Cytokine Expression in the Peripheral Blood of Patients with Bone Metastasis of Malignant Cancer Treated Using External Radiation Therapy
Authors: Lou Yaling , Chen Yu , Yuan Yumei , Wang Ronghua , Shan Hanmin
Published: July 2022

The incidence of cancer is increasing worldwide on a yearly basis, with the number of patients with bone metastases also increasing annually. Events associated with bone metastases can seriously affect patient quality of life, through pain, hypercalcemia, bone marrow regeneration disorders, and spinal cord compression. In this nonrandomized controlled clinical trial study, we focused on the relationship between bone metastasis, pain, and cytokines before and after radiotherapy. We hypothesized that radiotherapy alters the cytokine profile of the local bone environment. Combined with the analgesic effects of radiotherapy, certain cytokines may be very sensitive to radiation. External radiation therapy is commonly used to treat cancer patients with bone metastases and can effectively relieve metastasis-related pain, although its underlying mechanisms have not been fully elucidated. For this case-control study, we recruited 30 cancer patients with bone metastasis and 30 healthy individuals. Peripheral venous blood from healthy individuals was collected. The clinical characteristics and peripheral venous blood were collected from patients one week before and one week after radiotherapy. The preradiotherapy and postradiotherapy pain scores, quality of life (QOL), and blood cytokine profiles of the patients to that of the controls were collected to identify pain-related cytokines. Finally, the pain score and the quality of life score improved significantly after radiotherapy. Moreover, the preradiotherapy and postradiotherapy blood cytokine profiles of the patients showed significant differences, indicating that the analgesic effect of radiotherapy against bone metastases is mediated via altered cytokine production. Furthermore, some cytokines were more sensitive to radiotherapy. The levels of MIP-1δ, MCP-2, TIMP-1, RANTES, IGFBP3, and TNF-α showed significant differences in the pairwise comparative analysis and may therefore mediate pain associated with bone metastasis.
HIF1α: A Novel Biomarker with Potential Prognostic and Immunotherapy in Pan-cancer
Authors: Tian Yonggang , Bai Feihu , Zhang Dekui
Published: July 2022

Cancer is a catastrophic disease that seriously affects human health. HIF1α plays an important role in cancer initiation, progression, and prognosis. However, little is known about the specific role of HIF1α in pan-cancer. Therefore, we systematically and comprehensively analyzed HIF1α using GEPIA, HPA, GeneMANIA, STRING, SMPDB, cBioPortal, UALCAN, and TISDB databases and also 33 cancer and normal tissues in TCGA downloaded from the Genome Data Commons (GDC) data portal. Data and statistical analysis were performed using R software v4.0.3. Our results found that there were differences in the mRNA expression levels of HIF1α in human pan-cancer and its corresponding normal tissues. The expression level of HIF1α correlated with tumor stage in LIHC and also significantly correlated with prognosis in LIHC, LUSC, STAD, OV, PAAD, PRAD, THCA, LUAD, MESO, and READ. The small molecule pathways involved in HIF1α include succinate signaling, fumarate, and succinate carcinogenesis-related pathways. The highest mutation frequency of the HIF1α gene in pan-cancer was head and neck cancer, and the HIF1α methylation level in most tumors is significantly reduced. HIF1α was not only associated with immune cell infiltration but also with immune checkpoint genes and immune regulators TMB and MSI. There were currently 5 small molecule drugs targeting HIF1α.
Tail Bounds forℓ1Norm of Gaussian Random Matrices with Applications
Authors: Gao Xianjie , Zhang Mingliang , Luo Jinming
Published: July 2022

As major components of the random matrix theory, Gaussian random matrices have been playing an important role in many fields, because they are both unitary invariant and have independent entries and can be used as models for multivariate data or multivariate phenomena. Tail bounds for eigenvalues of Gaussian random matrices are one of the hot study problems. In this paper, we present tail and expectation bounds for theℓ1norm of Gaussian random matrices, respectively. Moreover, the tail and expectation bounds for theℓ1norm of the Gaussian Wigner matrix are calculated based on the resulting bounds. Compared with existing results, our results are more suitable for the high‐dimensional matrix case. Finally, we study the tail bounds for the parameter vector of some existing regularization algorithms.
Decision Support System Based on CoCoSo Method with the Picture Fuzzy Information
Authors: Qiyas Muhammad , Naeem Muhammad , Khan Saifullah , Abdullah Saleem , Botmart Thongchi , Shah Tajammul
Published: July 2022

Coronavirus disease 2019 (COVID‐19) is currently threatening the entire world, and a novel coronavirus is a virus from the corona family that has spread a new infection. The number of instances of this disease is increasing at an exponential, but there are now commercially accessible COVID‐19 vaccines. The weak symptoms of COVID‐19 disease, on the other hand, are treated with a variety of antiviral treatments. It is still choosing the optimal antiviral medicine to manage COVID‐19s. It is a challenging and difficult alternative to reduce the risk of infection. In this study, an improved combined compromise solution (CoCoSo) method is proposed to identify the ranking of alternatives. The introduction of a logarithmic picture fuzzy set is a more effective technique for representing variance, represented by three memberships (positive, neutral, and negative membership) degrees. This work introduces a fresh logarithmic picture fuzzy score function, to deal with the problem of comparison. The CoCoSo method‐based logarithmic picture fuzzy decision‐making algorithm is given. To achieve so, a new divergence measure for the logarithmic picture fuzzy number is introduced. To demonstrate the viability and efficacy of the established approach in real‐world applications, a case study of COVID‐19 disease drug selection is discussed.
Maxillary Fungal Osteomyelitis Mimicking and Treated as Endodontic Pathosis
Authors: Hotchandani Sarang Suresh , Kalhoro Feroze Ali , Harjani Priya Rani , Memon Mahwish
Published: July 2022

Fungal osteomyelitis is a lethal opportunistic infection that affects only a small percentage of patients present to endodontists. It is a highly contagious infection that requires urgent treatment. We discuss three cases in which endodontic pathology was initially recognized but subsequent follow-up revealed severe osteolytic fungal infection of the maxilla manifesting as acute apical abscesses in the maxillary canine region. CBCT demonstrated osteolytic abnormalities, and further histology confirmed the illness was caused by mucormycosis in patients with uncontrolled diabetes mellitus and exposure to COVID-19.
Mechanical Study of Jian-Gan-Xiao-Zhi Decoction on Nonalcoholic Fatty Liver Disease Based on Integrated Network Pharmacology and Untargeted Metabolomics
Authors: Cao Yong-Jun , Li Han-Zhou , Zhao Jie , Sun Yu-Meng , Jin Xiao-Wen , Lv Shu-Quan , Luo Jun-Yu , Fang Xi-Xing , Wen Wei-Bo , Liao Jia-Bao
Published: July 2022

Jian-Gan-Xiao-Zhi decoction (JGXZ) has demonstrated beneﬁcial eﬀects on nonalcoholic fatty liver disease (NAFLD). However, the mechanisms by which JGXZ improve NAFLD are still unclear. Methods. In this study, we first used a high-fat diet (HFD) to establish a NAFLD rat model to clarify the therapeutic effect of JGXZ on NAFLD. Secondly, we used network pharmacology to predict the potential targets of JGXZ on NAFLD, and then the key targets obtained from network pharmacology were verified. Finally, we used untargeted metabolomics to study the metabolic regulatory mechanism of JGXZ. Results. JGXZ treatment could decrease body weight and ameliorate dyslipidemia in NAFLD model rats. H&E and oil red O staining indicated that JGXZ reduced steatosis and inﬁltration of inﬂammatory cells in the liver. In addition, network pharmacology research found that the potential targets of JGXZ on NAFLD pathway were mainly associated with improving oxidative stress, apoptosis, inflammation, lipid metabolism disorders, and insulin resistance. Further experimental verification confirmed that JGXZ could inhibit inflammation and improve oxidative stress, insulin resistance, and lipid metabolism disorders. Serum untargeted metabolomics analyses indicated that the JGXZ in the treatment of NAFLD may work through the linoleic acid metabolism, alpha-linolenic acid metabolism, tryptophan metabolism, and glycerophospholipid metabolism pathways. Conclusions. In conclusion, this study found that JGXZ has an ameliorative effect on NAFLD, and JGXZ alleviates the inflammatory response and oxidative stress and lipid metabolism disorders in NAFLD rats. The mechanism of action of JGXZ in the treatment of NAFLD may be related to the regulation of linoleic acid metabolism, tryptophan metabolism, alpha-linolenic acid metabolism, and glycerophospholipid metabolism.
A Cognitive Failure Model of Construction Workers’ Unsafe Behavior
Authors: Deng Shuwen , Peng Rui , Pan Yonggang
Published: July 2022

The construction industry is one of the most dangerous industries globally, and construction workers are the leading cause of safety accidents. Unsafe behavior research focuses on behavioral safety of observable behaviors and the safety culture of organizational behaviors. However, these studies lack a systematic and comprehensive explanation of unsafe behavior. Therefore, this study constructs a cognitive failure model based on cognitive safety theory to explain the mechanisms of construction workers’ unsafe behaviors. Five main causes of cognitive link failure were derived from the cognitive process of construction workers’ unsafe behaviors: safety vigilance, hazard identification, safety knowledge, safety behavior attitude, and professional skills. Questionnaires were developed based on five cognitive failure factors and validated by structural equation modeling. The results show that the cognitive failure model fits well, and all five cognitive failure factors may lead to construction workers’ unsafe behaviors. Among the five cognitive failure factors, safety vigilance is the main cognitive failure factor in obtaining information link; hazard identification is the major cognitive failure factor in understanding information link; and safety behavior attitude is the chief cognitive failure cause for selecting response link. These findings deepen the understanding of construction workers’ unsafe behavior mechanisms.
Can Autonomous Vehicles Save Fuel? Findings from Field Experiments
Authors: Zhang Licheng , Zhang Ting , Peng Kun , Zhao Xiangmo , Xu Zhigang
Published: July 2022

The majority of the more recent studies have mainly focused on how to achieve energy-efficient goals by optimizing the driving behavior for human-driven vehicles or designing trajectory planning and tacking algorithms for autonomous vehicles. However, the energy-saving advantages of autonomous vehicles have not been quantitatively and theoretically explored. Therefore, this study aims to specifically clarify whether autonomous vehicles use less fuel than human-driven ones. First, the differences in driving behavior, regarding speed control, between autonomous vehicles and human-driven vehicles were compared. The most notable difference between them is that an autonomous vehicle can control the vehicle speed more effectively, with less speed fluctuations than a human driver. Subsequently, the influence of speed fluctuation on vehicle fuel consumption (L/100 km) was formulated based on the vehicle specific power (VSP) model. The mathematical deduction showed that the fuel consumption is proportional to the speed fluctuation under the same mean speed. Finally, simulation experiments were conducted under real scenarios. The simulation data showed that the fuel consumption increases almost linearly with the increase in speed fluctuation. Field experiments were also conducted on the fuel consumption of an autonomous vehicle under different driving modes. The experimental data showed that the fuel consumption also increases almost linearly with the increase in speed fluctuation. In the human-driven mode, the fuel consumption increased by 5.6% and 14.7%, respectively, compared with that in the autonomous mode at average speeds of 20 km/h and 40 km/h. Furthermore, the maximum fuel consumption was up to 60% more when the autonomous vehicle was driven by a driver, as the driving behavior displayed greater speed fluctuations.
MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method
Authors: Yin Xiao-Xia , Gao Mingyong , Wang Wei , Zhang Yanchun
Published: July 2022

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
Analysis of Influencing Factors of Social Public Management Score Based on Augmented Regression Model
Authors: Li Zhuo
Published: July 2022

To improve the analysis effect of the factors influencing the score of social public management, this paper combines the enhanced regression model to analyze the factors affecting the score of social public management and constructs a factor analysis model through an intelligent algorithm. Moreover, this paper uses numerical simulation to verify that when the parameters estimated by SEM are linear functions and the relationship between the research variables and auxiliary variables is nonlinear, the estimation effect of SEM estimators is better than that of traditional generalized regression estimators. In addition, this paper proposes a public service channel management model through the measurement and analysis of channel mode public service feature matching, channel type public service feature matching, and channel type and channel mode public service feature matching. Finally, this paper combines the simulation research to verify the effectiveness of the method in this paper.
Metaphorical Description and Model Description of Complex Systems
Authors: Huang Huaxin , Li Yuhang
Published: July 2022

From metaphorical description to model description is the symbol of the maturity of complexity science. This paper focuses on two model description methods of complex systems: formal system model and relevance theory model, and compares their advantages and disadvantages. These two models are descriptions of complex systems from different perspectives and have important and irreplaceable significance in the theoretical research and practical application of complex systems. The future research direction of complex systems is not the “integration” of the two models but the alternation of the two models, which scientifically reveals the essence of complex systems from multiple perspectives. In view of the complexity of education and the scientificity of educational evaluation, a fuzzy evaluation method is proposed. Fuzzy evaluation is a mathematical thinking mode based on fuzzy theory and mathematical methods, which studies fuzzy and complex phenomena, and provides quantitative relationship and description for educational evaluation.
The Diagnostic Value of Combined Detection of Serum Lp-PLA2 and Hcy and Color Doppler in Elderly Patients with Acute Coronary Syndrome and Effect on Endothelial Function
Authors: Zhao Li , Qi Jingrui , Luo Fan , Zhao Na
Published: July 2022

Objective. To evaluate the application value of serum lipoprotein-associated phospholipase A2 (Lp-PLA2) and homocysteine (Hcy) combined with color Doppler detection in elderly patients with acute coronary syndrome (ACS) and the influence on their endothelial function. Methods. From February 2019 to October 2020, 100 elderly patients with suspected ACS treated in our hospital were recruited and assigned (1 : 1) to a control group or an experimental group. The control group received color Doppler detection, and the experimental group received the combined detection of serum Lp-PLA2 and Hcy and color Doppler. Positive results were determined by the combined detection of the levels of LP-PLA2 and Hcy and color Doppler. The two groups were compared in terms of specificity, susceptibility, positive detection rate, negative detection rate, test accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), the expression levels of interleukin-2 (IL-2), interleukin-12 (IL-12), interleukin-13 (IL-13), and the vascular endothelial function. The patients with ACS were divided into three subgroups according to the Gensini score three-quartile interval, and their Lp-PLA2 levels were calculated for analysis. Results. The test specificity, susceptibility, positive detection rate, negative detection rate, test accuracy, and AUC in the experimental group were much better than the those in the control group (all

P
<
0.05

), and there were no significant differences in the expression levels of IL-2, IL-12, IL-13, and vascular endothelial function between the two groups (

P
>
0.05

). The three subgroups showed similar levels of fasting blood glucose, total cholesterol, triglyceride, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, apolipoprotein A-I, glutamic-pyruvic transaminase, lutamic-oxaloacetic transaminase, homocysteine, and amino-terminal pro-brain natriuretic peptide (all

P
>
0.05

). The severe condition group had the highest Lp-PLA2 level, followed by the moderate group, and then the mild group (

P
<
0.05

). Moreover, the levels of LP-PLA2, aspartate transaminase (AST), and N-terminal pro hormone B-type natriuretic peptide (NT-proBNP) were all influencing factors for the coronary Gensini score (all

P
<
0.05

). Conclusion. The combined detection of serum Lp-PLA2, Hcy, and color Doppler significantly improves the diagnostic accuracy, specificity, and susceptibility in the elderly with ACS, with little impact on their vascular endothelial function.
Information-Based Sensor Control for Multitask Planning via Multi-Bernoulli Filter
Authors: Wang Li , Deng Dongming , He Zhongliang
Published: July 2022

In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking.
[Retracted] Human Umbilical Cord Mesenchymal Stem Cells Improve Premature Ovarian Failure through Cell Apoptosis of miR‐100‐5p/NOX4/NLRP3
Authors: Niu Jing , Yu Fan , Luo Xiping , Chen Shiling
Published: July 2022

Premature ovarian failure refers to a series of symptoms of perimenopausal hot flashes, night sweats, decreased libido, vaginal dryness, insomnia, reduced menstruation, sparse hair, even amenorrhea, and even infertility before the age of 40 due to the decline of ovarian function. Premature ovarian failure is a common and difficult disease in gynecology. Its prevalence is increasing gradually, and the trend is younger. The aim of this experiment was to elucidate the role of human umbilical cord mesenchymal stem cells (HUCMSCs) in premature ovarian failure and its mechanism. HUCMSCs, KGN cells, and HEK293T cells were used in this experiment. Quantitative PCR and microarray analysis, ELISA inflammation and oxidative stress kits, RNA pull‐down assay, luciferase reporter assay, proliferation assay, EDU staining, and Western blot analysis were used. In an in vitro model of premature ovarian failure, HUCMSCs attenuated inflammatory response, oxidative stress, and apoptosis. HUCMSCs ameliorated the premature ovarian failure model. The miR‐100‐5p expression was induced by HUCMSCs through methylation. miR‐100‐5p regulation influenced the role of HUCMSCs in an in vitro model of premature ovarian failure. HUCMSCs inhibited the in vitro expression of NOX4, NLRP3, and GSDMD proteins in the model. NOX4/NLRP3 signaling pathway affects the role of HUCMSCs in an in vitro model of premature ovarian failure through miR‐100‐5p. This experiment elucidated the role of HUCMSCs in premature ovarian failure and its mechanism, with a view to providing a clinical reference.
lncRNA ZFAS1 Promotes HMGCR mRNA Stabilization via Binding U2AF2 to Modulate Pancreatic Carcinoma Lipometabolism
Authors: Wang Luoluo , Ruan Yi , Wu Xiang , Zhou Xinhua
Published: July 2022

Being one of the most lethal malignant tumors worldwide, pancreatic carcinoma (PC) shows strong invasiveness and high mortality. In tumorigenesis and progression, the role played by long-chain noncoding RNAs (lncRNAs) cannot be ignored. This article mainly probes into the function of lncRNA ZFAS1 in PC. ZFAS1 expression in PC and normal counterparts retrieved from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) database was analysed by GEPIA2. Its expression profile in clinical specimens and human PC cell strains was quantified using qRT-PCR. Measurements of BxPC-3 cell multiplication and invasiveness employed CCK-8, plate clone formation test, and Transwell chamber assay. ZFAS1’s impact on lipid content in BxPC-3 cells was detected. RNA pulldown and RIP assays analyzed the interaction of ZFAS1 with U2AF2 and HMGCR in BxPC-3 cells. Finally, the impacts of U2AF2 and HMGCR on the biological behavior of BxPC-3 were observed. ZFAS1 was kept at a high level in PC tissues versus the normal counterparts. ZFAS1 gene knockout remarkably suppressed PC cell multiplication and invasiveness and decreased the contents of free fatty acids, total cholesterol, triglycerides, and phospholipids. Mechanistically, ZFAS1 stabilized HMGCR mRNA through U2AF2, thus increasing HMGCR expression and promoting PC lipid accumulation. Meanwhile, reduced PC cell viability and invasiveness were observed after downregulating U2AF2 and HMGCR. As an oncogene of PC, ZFAS1 can modulate lipometabolism and stabilize HMGCR mRNA expression by binding with U2AF2 in PC, which is a candidate target for PC diagnosis and treatment.