bluuebunny/crossref_metadata_2025 huggingface dataset:
A Taxonomy of Supervised Learning for IDSs in SCADA Environments
Authors: Suaboot Jakapan , Fahad Adil , Tari Zahir , Grundy John , Mahmood Abdun Naser , Almalawi Abdulmohsen , Zomaya Albert Y. , Drira Khalil
Published: May 2020

Supervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems.
Content Delivery Networks
Authors: Zolfaghari Behrouz , Srivastava Gautam , Roy Swapnoneel , Nemati Hamid R. , Afghah Fatemeh , Koshiba Takeshi , Razi Abolfazl , Bibak Khodakhast , Mitra Pinaki , Rai Brijesh Kumar
Published: May 2020

Recently, Content Delivery Networks (CDN) have become more and more popular. The technology itself is ahead of academic research in this area. Several dimensions of the technology have not been adequately investigated by academia. These dimensions include outline management, security, and standardization. Discovering and highlighting aspects of this technology that may have or have not been covered by academic research is the first step toward helping academia bridge the gap with industry or even go one step further to lead industry in the right direction. This suggests a comprehensive survey on research works in this regard. The literature in this area has already come up with some surveys and taxonomies, but some of them are outdated or do not cover every aspect of CDN while others fail to detect existing trends or to develop a holistic roadmap for research on the technology. Furthermore, none of the existing surveys aim at enlightening the dark aspects of the technology that have not been subject to academic research. In this survey, we first extract the lifecycle of a CDN as suggested by the existing research. Then, we investigate previous relevant works on each phase of the lifecycle to clarify where the research is currently located and headed. We show how CDN technology tends to converge with emerging paradigms such as cloud computing, edge computing, and machine learning, which are more mature in terms of academic research. This helps us determine the right direction for further research by revealing the deficiencies in existing works.
Generating Realistic Ride-Hailing Datasets Using GANs
Authors: Jauhri Abhinav , Stocks Brad , Li Jian Hui , Yamada Koichi , Shen John Paul
Published: May 2020

This article focuses on the synthetic generation of human mobility data in urban areas. We present a novel application of generative adversarial networks (GANs) for modeling and generating human mobility data. We leverage actual ride requests from ride-sharing/hailing services from four major cities to train our GANs model. Our model captures the spatial and temporal variability of the ride request patterns observed for all four cities over a typical week. Previous works have characterized the spatial and temporal properties of human mobility datasets using the
fractal dimensionality
and the
densification power law
, respectively, which we utilize to validate our GANs-generated synthetic datasets. We also validate the synthetic datasets using a dynamic vehicle placement application. Such synthetic datasets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility.
Practical Constraint Solving for Generating System Test Data
Authors: Soltana Ghanem , Sabetzadeh Mehrdad , Briand Lionel C.
Published: May 2020

The ability to generate test data is often a necessary prerequisite for automated software testing. For the generated data to be fit for their intended purpose, the data usually have to satisfy various logical constraints. When testing is performed at a system level, these constraints tend to be complex and are typically captured in expressive formalisms based on first-order logic. Motivated by improving the feasibility and scalability of data generation for system testing, we present a novel approach, whereby we employ a combination of metaheuristic search and Satisfiability Modulo Theories (SMT) for constraint solving. Our approach delegates constraint solving tasks to metaheuristic search and SMT in such a way as to take advantage of the complementary strengths of the two techniques. We ground our work on test data models specified in UML, with OCL used as the constraint language. We present tool support and an evaluation of our approach over three industrial case studies. The results indicate that, for complex system test data generation problems, our approach presents substantial benefits over the state-of-the-art in terms of applicability and scalability.
Understanding Ethereum via Graph Analysis
Authors: Chen Ting , Li Zihao , Zhu Yuxiao , Chen Jiachi , Luo Xiapu , Lui John Chi-Shing , Lin Xiaodong , Zhang Xiaosong
Published: May 2020

Ethereum, a blockchain, supports its own cryptocurrency named Ether and smart contracts. Although more than 8M smart contracts have been deployed on Ethereum, little is known about the characteristics of its users, smart contracts, and the relationships among them. We conduct the
first
systematic study on Ethereum by leveraging graph analysis to characterize three major activities on Ethereum, namely money transfer, smart contract creation, and smart contract invocation. We collect all transaction data, construct three graphs from the data to characterize major activities via graph analysis, and discover new insights. Moreover, we address three security issues based on graphs.
A Survey of IIoT Protocols
Authors: Figueroa-Lorenzo Santiago , Añorga Javier , Arrizabalaga Saioa
Published: May 2020

Industrial Internet of Things (IIoT) is present in many participants from the energy, health, manufacturing, transport, and public sectors. Many factors catalyze IIoT, such as robotics, artificial intelligence, and intelligent decentralized manufacturing. However, the convergence between IT, OT, and IoT environments involves the integration of heterogeneous technologies through protocols, standards, and buses. However, this integration brings with it security risks. To avoid the security risks, especially when systems in different environments interact, it is important and urgent to create an early consensus among the stakeholders on the IIoT security. The default Common Vulnerability Scoring System (CVSS) offers a mechanism to measure the severity of an asset's vulnerability and therefore a way to characterize the risk. However, CVSS by default has two drawbacks. On the one hand, to carry out a risk analysis, it is necessary to have additional metrics to the one established by CVSSv3.1. On the other hand, this index has been used mostly in IT environments and although there are numerous efforts to develop a model that suits industrial environments, there is no established proposal. Therefore, we first propose a survey of the main 33 protocols, standards, and buses used in an IIoT environment. This survey will focus on the security of each one. The second part of our study consists of the creation of a framework to characterize risk in industrial environments, i.e., to solve both problems of the CVSS index. To this end, we created the Vulnerability Analysis Framework (VAF), which is a methodology that allows the analysis of 1,363 vulnerabilities to establish a measure to describe the risk in IIoT environments.
Tools for Reduced Precision Computation
Authors: Cherubin Stefano , Agosta Giovanni
Published: May 2020

The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, and manual tuning and code transformation is the approach usually adopted in most domains. In recent years, research have been studying ways to improve the automation of this process. This article surveys this body of work, identifying the critical steps of this process, the most advanced tools available, and the open challenges in this research area. We conclude that, while several mature tools exist, there is still a gap to close, especially for tools based on static analysis rather than profiling, as well as for integration within mainstream, industry-strength compiler frameworks.
Cloud Deployment Tradeoffs for the Analysis of Spatially Distributed Internet of Things Systems
Authors: Tsigkanos Christos , Garriga Martin , Baresi Luciano , Ghezzi Carlo
Published: May 2020

Internet-enabled devices operating in the physical world are increasingly integrated in modern distributed systems. We focus on systems where the dynamics of spatial distribution is crucial; in such cases, devices may need to carry out complex computations (e.g., analyses) to check satisfaction of spatial requirements. The requirements are partly global—as the overall system should achieve certain goals—and partly individual, as each entity may have different goals. Assurance may be achieved by keeping a model of the system at runtime, monitoring events that lead to changes in the spatial environment, and performing requirements analysis. However, computationally intensive runtime spatial analysis cannot be supported by resource-constrained devices and may be offloaded to the cloud. In such a scenario, multiple challenges arise regarding resource allocation, cost, performance, among other dimensions. In particular, when the workload is unknown at the system’s design time, it may be difficult to guarantee application-service-level agreements, e.g., on response times. To address and reason on these challenges, we first instantiate complex computations as microservices and integrate them to an IoT-cloud architecture. Then, we propose alternative cloud deployments for such an architecture—based on virtual machines, containers, and the recent Functions-as-a-Service paradigm. Finally, we assess the feasibility and tradeoffs of the different deployments in terms of scalability, performance, cost, resource utilization, and more. We adopt a workload scenario from a known dataset of taxis roaming in Beijing, and we derive other workloads to represent unexpected request peaks and troughs. The approach may be replicated in the design process of similar classes of spatially distributed IoT systems.
Context-Dependent Sequence-to-Sequence Turkish Spelling Correction
Authors: Büyük Osman
Published: May 2020

In this article, we make use of sequence-to-sequence (seq2seq) models for spelling correction in the agglutinative Turkish language. In the baseline system, misspelled and target words are split into their letters and the letter sequences are fed into the seq2seq model. We prefer letters as the unit of the model due to the agglutinative nature of Turkish, which results in an impractical dictionary size when words are used as a dictionary unit. In order to improve the baseline performance, we incorporate right and left context of the misspelled words. All context words are represented with their first three consonants in the context-dependent model. We train the seq2seq models using a large text corpus collected automatically from the Internet. The corpus contains approximately 4 million sentences. We randomly introduce substitution, deletion, and insertion spelling errors to the words in the corpus. We test the performance of the proposed context-dependent seq2seq model using synthetic and realistic test sets. The synthetic test set is constructed similar to the training set. The realistic test set contains human-made misspellings from Twitter messages. In the experiments, we observed that the proposed context-dependent model performs significantly better than the baseline system. Its correction accuracy reaches 94% on the synthetic dataset. Additionally, the proposed method provides 2.1% absolute improvement over a state-of-the-art Turkish spelling correction system on the Twitter test set.
Build It, Break It, Fix It
Authors: Parker James , Hicks Michael , Ruef Andrew , Mazurek Michelle L. , Levin Dave , Votipka Daniel , Mardziel Piotr , Fulton Kelsey R.
Published: May 2020

Typical security contests focus on breaking or mitigating the impact of buggy systems. We present the Build-it, Break-it, Fix-it (BIBIFI) contest, which aims to assess the ability to securely build software, not just break it. In BIBIFI, teams build specified software with the goal of maximizing correctness, performance, and security. The latter is tested when teams attempt to break other teams’ submissions. Winners are chosen from among the best builders and the best breakers. BIBIFI was designed to be open-ended—teams can use any language, tool, process, and so on, that they like. As such, contest outcomes shed light on factors that correlate with successfully building secure software and breaking insecure software. We ran three contests involving a total of 156 teams and three different programming problems. Quantitative analysis from these contests found that the most efficient build-it submissions used C/C++, but submissions coded in a statically type safe language were 11× less likely to have a security flaw than C/C++ submissions. Break-it teams that were also successful build-it teams were significantly better at finding security bugs.
In Praise of Software Practice
Authors: Glass Robert L.
Published: May 2020

It has been almost a third of a century since the world was caught up in a complex computing and software political clash.

The clash was over something originally called the Strategic Defense Intiative (SDI), later somewhat-facetiously called "Star Wars," the notion of an anti-missile system that would shoot down an incoming enemy missile. The time was around the mid- 1980s. The two most vehement sides on the issue were - academic and theoretic computer science types, who thought it couldn't be done. - practitioner engineers and software people, who thought it could.
Reports from SIGSOFT CAPS Travel Awardees of ASE 2019
Authors: Niu Nan
Published: May 2020

ACM SIGSOFT, via its CAPS program, supports students and professionals to attend conferences in software engineering and related fields. This report summarizes the conference experiences from CAPS awardees of ASE 2019, and introduces a recent initiative to broaden the CAPS travel support for professionals (e.g., junior researchers and faculty members).
Property Suffix Array with Applications in Indexing Weighted Sequences
Authors: Charalampopoulos Panagiotis , Iliopoulos Costas S. , Liu Chang , Pissis Solon P.
Published: May 2020

The suffix array is one of the most prevalent data structures for string indexing; it stores the lexicographically sorted list of suffixes of a given string. Its practical advantage compared to the suffix tree is space efficiency. In
Property Indexing
, we are given a string
x
of length
n
and a property Π, i.e., a set of Π-
valid
intervals over
x
so that a pattern
p
occurs in
x
if and only if
x
has an occurrence of
p
that lies entirely within an interval of Π. A suffix-tree-like index over the valid prefixes of suffixes of
x
can be built in time and space
O
(
n
). We show here how to directly build a suffix-array-like index, the
Property Suffix Array
(PSA), in time and space
O
(
n
). We mainly draw our motivation from weighted (probabilistic) sequences: sequences of probability distributions over a given alphabet. Given a probability threshold 1/
z
, we say that a string
p
of length
m
matches a weighted sequence
x
of length
n
at starting position
i
if the product of probabilities of the letters of
p
at positions
i
, …,
i
+
m
− 1 in
x
is at least 1/
z
. Our algorithm for building the PSA can be directly applied to build an
O
(
nz
)-sized suffix-array-like index over
x
in time and space
O
(
nz
). Finally, we present extensive experimental results showing that our new indexing data structure is well suited for real-world applications.
ACM's 2020 general election
Authors: Segal Gerald
Published: May 2020

Meet the candidates who introduce their plans---and stands---for the Association.

Please take this opportunity to vote.
A Linear-Time Algorithm for Seeds Computation
Authors: Kociumaka Tomasz , Kubica Marcin , Radoszewski Jakub , Rytter Wojciech , Waleń Tomasz
Published: May 2020

A seed in a word is a relaxed version of a period in which the occurrences of the repeating subword may overlap. Our first contribution is a linear-time algorithm computing a linear-size representation of all seeds of a word (the number of seeds might be quadratic). In particular, one can easily derive the shortest seed and the number of seeds from our representation. Thus, we solve an open problem stated in a survey by Smyth from 2000 and improve upon a previous
O
(
n
log
n
)-time algorithm by Iliopoulos et al. from 1996. Our approach is based on combinatorial relations between seeds and subword complexity (used here for the first time in the context of seeds).

In previous papers, compact representations of seeds consisted of two independent parts operating on the suffix tree of the input word and the suffix tree of its reverse, respectively. Our second contribution is a novel and significantly simpler representation of all seeds that avoids dealing with the suffix tree of the reversed word. This result is also of independent interest from a combinatorial point of view.

A preliminary version of this work, with a much more complex algorithm constructing a representation of seeds on two suffix trees, was presented at the 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’12).
Hierarchical Optimization Time Integration for CFL-Rate MPM Stepping
Authors: Wang Xinlei , Li Minchen , Fang Yu , Zhang Xinxin , Gao Ming , Tang Min , Kaufman Danny M. , Jiang Chenfanfu
Published: May 2020

We propose Hierarchical Optimization Time Integration (HOT) for efficient implicit timestepping of the material point method (MPM) irrespective of simulated materials and conditions. HOT is an MPM-specialized hierarchical optimization algorithm that solves nonlinear timestep problems for large-scale MPM systems near the CFL limit. HOT provides convergent simulations out of the box across widely varying materials and computational resolutions without parameter tuning. As an implicit MPM timestepper accelerated by a custom-designed Galerkin multigrid wrapped in a quasi-Newton solver, HOT is both highly parallelizable and robustly convergent. As we show in our analysis, HOT maintains consistent and efficient performance even as we grow stiffness, increase deformation, and vary materials over a wide range of finite strain, elastodynamic, and plastic examples. Through careful benchmark ablation studies, we compare the effectiveness of HOT against seemingly plausible alternative combinations of MPM with standard multigrid and other Newton-Krylov models. We show how these alternative designs result in severe issues and poor performance. In contrast, HOT outperforms existing state-of-the-art, heavily optimized implicit MPM codes with an up to 10× performance speedup across a wide range of challenging benchmark test simulations.
Editorial
Authors: Rula Anisa , Zaveri Amrapali , Simperl Elena , Demidova Elena
Published: May 2020

This editorial summarizes the content of the Special Issue on Quality Assessment of Knowledge Graphs of the Journal of Data and Information Quality (JDIQ). We dedicate this special issue to the memory of our colleague and friend Amrapali Zaveri.
When technology goes awry
Authors: Newport Cal
Published: May 2020

On engineers' obligation to tame their creations.
SIGCSE special projects grants awards
Authors: Murphy Laurie
Published: May 2020

Special Project Grants of up to $5,000 USD are available to SIGCSE members to support projects that will bring some clear benefit to the computing education community in the form of new knowledge, a resource, or good practice in learning, teaching, or assessment.
Computational Methods for Single-Particle Electron Cryomicroscopy
Authors: Singer Amit , Sigworth Fred J.
Published: May 2020

Single-particle electron cryomicroscopy (cryo-EM) is an increasingly popular technique for elucidating the three-dimensional (3D) structure of proteins and other biologically significant complexes at near-atomic resolution. It is an imaging method that does not require crystallization and can capture molecules in their native states. In single-particle cryo-EM, the 3D molecular structure needs to be determined from many noisy 2D tomographic projections of individual molecules, whose orientations and positions are unknown. The high level of noise and the unknown pose parameters are two key elements that make reconstruction a challenging computational problem. Even more challenging is the inference of structural variability and flexible motions when the individual molecules being imaged are in different conformational states. This review discusses computational methods for structure determination by single-particle cryo-EM and their guiding principles from statistical inference, machine learning, and signal processing, which also play a significant role in many other data science applications.
Modeling Costs and Benefits of Energy Storage Systems
Authors: Hittinger Eric , Ciez Rebecca E.
Published: May 2020

In recent years, analytical tools and approaches to model the costs and benefits of energy storage have proliferated in parallel with the rapid growth in the energy storage market. Some analytical tools focus on the technologies themselves, with methods for projecting future energy storage technology costs and different cost metrics used to compare storage system designs. Other tools focus on the integration of storage into larger energy systems, including how to economically operate energy storage, estimate the air pollution and greenhouse gas emissions effects of storage, or understand how policy and market rules influence storage deployment and operation. Given the confluence of evolving technologies, policies, and systems, we highlight some key challenges for future energy storage models, including the use of imperfect information to make dispatch decisions for energy-limited storage technologies and estimating how different market structures will impact the deployment of additional energy storage.
Na2Ti6O13 Coated with Carbon Produced by Citric Acid as an Anode Material in Sodium Ion Batteries
Authors: Wang Jingyu , Bi Jianqiang , Wang Weili , Xing Zheng , Bai Yunjun , Leng Mingzhe , Gao Xicheng
Published: May 2020

In this article, carbon coated sodium titanate (Na2Ti6O13) is prepared with citric acid as the carbon source, in which the graphitized carbon layer is produced after pyrolysis through a post heat-treatment process. Graphitized carbon layer can meliorate electrical conductivity of the sodium titanate, improve the capacity of anode. The electrochemical characterizations have demonstrated that carbon coated sodium titanate possesses enhanced sodium storage capability compared with the carbon free counterpart. With the increase of the number of the cycles, the capacity of the electrode is 150 mAh g−1 after 100 cycles at a current density of 50 mA g−1. At the current density of 800 mA g−1 the capacity is 106 mAh g−1 and keeps stable after 1000 cycles. We see this improvement as a result of two reasons, the graphitization of carbon, and the chemical reaction between the synthesized sodium titanate and the oxides of carbon produced by citric acid. The material is synthesized by a simple method but the capacity of which can is impressive. Thus, the synthesized sodium titanate may be a promising anode for sodium ion battery (SIB).
Review—Materials Science Predictions of Thermal Runaway in Layered Metal-Oxide Cathodes: A Review of Thermodynamics
Authors: Shurtz Randy C. , Hewson John C.
Published: May 2020

Accurate models of thermal runaway in lithium-ion batteries require quantitative knowledge of heat release during thermochemical processes. A capability to predict at least some aspects of heat release for a wide variety of candidate materials a priori is desirable. This work establishes a framework for predicting staged heat release from basic thermodynamic properties for layered metal-oxide cathodes. Available enthalpies relevant to thermal decomposition of layered metal-oxide cathodes are reviewed and assembled in this work to predict potential heat release in the presence of alkyl-carbonate electrolytes with varying state of charge. Cathode delithiation leads to a less stable metal oxide subject to phase transformations including oxygen release when heated. We recommend reaction enthalpies and show the thermal consequences of metal-oxide phase changes and solvent oxidation within the battery are of comparable magnitudes. Heats of reaction are related in this work to typical observations reported in the literature for species characterization and calorimetry. The methods and assembled databases of formation and reaction enthalpies in this work lay groundwork a new generation of thermal runaway models based on fundamental material thermodynamics, capable of predicting accurate maximum cell temperatures and hence cascading cell-to-cell propagation rates.
Sensitivity of urethral flow-evoked voiding reflexes decline with age in the rat: insights into age-related underactive bladder
Authors: Geramipour Arezoo , Danziger Zachary C.
Published: May 2020

The prevalence of underactive bladder (UAB) increases with age, suggesting a link between age-related processes and lower urinary tract (LUT) symptoms; however, the underlying mechanisms of age-related UAB are poorly understood. Understanding how aging affects LUT reflexes may help in the development of new treatments by identifying mechanistic targets. In this work, we studied the relationship between age and systems-level function of the LUT and tested the hypothesis that aging is related to weakening of reflexes that control voiding. Three groups of anesthetized female rats, young (4–7 mo old), mature (11–14 mo old), and old (18–24 mo old), were used to quantify the effect of aging on LUT reflexes. A double-lumen catheter enabled us to control the bladder volume and urethral flow rate independently, under quasi-isovolumetric bladder conditions. We systematically investigated the reflex bladder contractions evoked by combinations of rates of urethral infusion and bladder fill volumes as a function of age. Urethral infusion with the same flow rate evoked bladder contractions (via the augmenting reflex) in old animals less often than in younger animals. Furthermore, old animals needed more fluid in their bladders (relative to their bladder capacity) before urethra flow-evoked bladder contractions could be triggered at all, suggesting a delay in the switch of the LUT to “voiding mode.” Old rats also showed longer and weaker bladder contractions than young or mature rats. Taken together, this suggests there is an age-related functional weakening and loss of sensitivity in LUT reflexes, which may contribute to age-related UAB symptoms.
Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network
Authors: Du Yan , Wang Aiming , Wang Shuai , He Baomei , Meng Guoying
Published: May 2020

Fault diagnosis plays a very important role in ensuring the safe and reliable operations of machines. Currently, the deep learning-based fault diagnosis is attracting increasing attention. However, fault diagnosis under variable working conditions has been a significant challenge due to the domain discrepancy problem. This problem is also unavoidable in deep learning-based fault diagnosis methods. This paper contributes to the ongoing investigation by proposing a new approach for the fault diagnosis under variable working conditions based on STFT and transfer deep residual network (TDRN). The STFT was employed to convert vibration signal to time-frequency image as the input of the TDRN. To address the domain discrepancy problem, the TDRN was developed in this paper. Unlike traditional deep convolutional neural network (DCNN) methods, by combining with transfer learning, the TDRN can make a bridge between two different working conditions, thereby using the knowledge learned from a working condition to achieve a high classification accuracy in another working condition. Moreover, since the residual learning is introducing, the TDRN can overcome the problems of training difficulty and performance degradation existing in traditional DCNN methods, thus further improving the classification accuracy. Experiments are conducted on the popular CWRU bearing dataset to validate the effectiveness and superiority of the proposed approach. The results show that the developed TDRN outperforms those methods without transfer learning and/or residual learning in terms of the accuracy and feature learning ability for the fault diagnosis under variable working conditions.
Activation of the Nrf2/HO‐1 Pathway by Amomum villosum Extract Suppresses LPS‐Induced Oxidative Stress In Vitro and Ex Vivo
Authors: Lim Dong-Woo , Choi Hee-Jin , Park Sun-Dong , Kim Hyuck , Yu Ga-Ram , Kim Jai-Eun , Park Won-Hwan
Published: May 2020

Despite its deleterious effects on living cells, oxidative stress plays essential roles in normal physiological processes and provides signaling molecules for cell growth, differentiation, and inflammation. Macrophages are equipped with antioxidant mechanisms to cope with intracellular ROS produced during immune response, and Nrf2 (NF‐E2‐related factor 2)/HO‐1 (heme oxygenase‐1) pathway is an attractive target due to its protective effect against ROS‐induced cell damage in inflamed macrophages. We investigated the effects of ethanol extract of A. villosum (AVEE) on lipopolysaccharide‐ (LPS‐) stimulated inflammatory responses generated via the Nrf2/HO‐1 signaling pathway in murine peritoneal macrophages and RAW 264.7 cells. AVEE was found to suppress the NF‐κB signaling pathway, thus, to reduce proinflammatory cytokine, nitric oxide, and prostaglandin levels in peritoneal macrophages and Raw 264.7 cells treated with LPS, and to enhance HO‐1 expression by activating Nrf2 signaling. Furthermore, these anti‐inflammatory effects of AVEE were diminished when cells were pretreated with SnPP (a HO‐1 inhibitor). HPLC analysis revealed AVEE contained quercetin, a possible activator of the Nrf2/HO‐1 pathway. These results show A. villosum ethanol extract exerts anti‐inflammatory effects by activating the Nrf2/HO‐1 pathway in LPS‐stimulated macrophages.
Predictive Study of Tuberculosis Incidence by ARMA Model Combined with Air Pollution Variables
Authors: Zheng Yanling
Published: May 2020

China has the second largest number of tuberculosis (TB) cases in the world, and the Xinjiang province has the highest TB incidence in China. Urumqi is the capital city of Xinjiang; good TB prevention and control in Urumqi can provide an example for other parts of Xinjiang, considering that predicting the TB incidence is the prerequisite of prevention and control; therefore, it is necessary to do a prediction study on TB incidence in Urumqi. In this paper, based on the data of TB incidence and air pollution variables (PM2.5, PM10, SO2, CO, NO2, O3) in Urumqi, the ARMA (1, (1, 3)) + model was established by time series ARMA model method, cross-correlation analysis, and principal component regression method, and its predictive performance was superior to that of the ARMA (1, (1, 3)) model based on TB historical data. The research idea of this paper was good, which can provide a reference for other researchers. The prediction of the ARMA (1, (1, 3)) + model can provide scientific help for TB prevention and control in Urumqi, China. During the analysis, it was found that the higher the concentration of O3, the higher the incidence of TB. This study suggests that people in Urumqi should pay more attention to the hazards of O3 and do a good job of personal protection.
Exact Combined Solutions for the (2+1)-Dimensional Dispersive Long Water-Wave Equations
Authors: Wei Yi , Zhang Xing-Qiu , Shao Zhu-Yan , Gu Lu-Feng , Yang Xiao-Feng
Published: May 2020

The homogeneous balance of undetermined coefficient (HBUC) method is presented to obtain not only the linear, bilinear, or homogeneous forms but also the exact traveling wave solutions of nonlinear partial differential equations. Linear equation is obtained by applying the proposed method to the (2+1)-dimensional dispersive long water-wave equations. Accordingly, the multiple soliton solutions, periodic solutions, singular solutions, rational solutions, and combined solutions of the (2+1)-dimensional dispersive long water-wave equations are obtained directly. The HBUC method, which can be used to handle some nonlinear partial differential equations, is a standard, computable, and powerful method.
Towards Analysis and Optimization of Electrospun PVP (Polyvinylpyrrolidone) Nanofibers
Authors: Utkarsh  , Hegab Hussien , Tariq Muhammad , Syed Nabeel Ahmed , Rizvi Ghaus , Pop-Iliev Remon
Published: May 2020

In this study, the polymeric nanofibers of polyvinylpyrrolidone (PVP) were manufactured using the electrospinning technique. The electrospinning process parameters such as voltage, polymer concentration, rotational speed of the collecting drum, collecting distance, and flow rate were optimized to obtain the minimum fiber diameter for sound absorption applications. The effects of these parameters on the fiber diameter as output responses were investigated by analysis of variance (ANOVA) and Taguchi’s array design. Furthermore, a mathematical model was generated using response surface methodology (RSM) to model the electrospinning process. The high voltage and polymer concentration were observed to be the most significant parameters at 95% and 99% confidence level. The average model accuracy of 83.4% was observed for the predictive model of electrospinning which is considered acceptable as it is composed of complete experimental trials of 27 out of 243 runs. The experimental study offers a promising attempt in the open literature to carefully understand the effect of various electrospinning parameters when producing PVP nanofibers.
Evaluation of an Epoxy-Based Nanosilicacomposite Lining in H2SO4 Solution for Anticorrosion of Sewerage Concrete Structures
Authors: Dinh Chau Van
Published: May 2020

This work presents the performance of a SiO2-epoxy nanocomposite lining for the anticorrosion of the concrete structures in sewer networks. Nanocomposite lining on the concrete substrate surface of the 15 phr (part per hundred epoxy resin) amine-cured epoxy polymer containing 3.0 phr of nano-SiO2 was prepared by intercalating epoxy into the SiO2 via direct mixing process. The silica exfoliation was monitored by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The barrier feature of epoxy the base nanocomposite against water and sulfuric acid solution was evaluated. The results showed that the nanocomposite lining was of a great barrier against these corrosive environments due to the intercalation and exfoliation caused by the dispersion of the SiO2 nanoparticles into the epoxy matrix. The rate of the H2SO4 into the nanocomposite lining was also investigated by measurement of the depth of the sulfur element penetrated layer using the coupled Scanning Electron Microscopy (SEM)/Energy dispersive X-ray spectrometer (EDS). Deterioration behavior of the adhesion strength of the nanocomposite lining was studied. The nanocomposite lining shows a significant improvement of bond strength in comparison to the neat epoxy lining. The durability of the nanocomposite lining adhesion when the lining systems exposed to water and the sulfuric acid solution is also improved significantly.
Paternal Resistance Training Induced Modifications in the Left Ventricle Proteome Independent of Offspring Diet
Authors: de Sousa Neto Ivo Vieira , Tibana Ramires Alsamir , Prestes Jonato , de Oliveira da Silva Leonardo Gomes , Almeida Jeeser Alves , Franco Octavio Luiz , de Oliveira Edilamar Menezes , Voltarelli Fabricio Azevedo , Durigan João Luiz Quaglioti , de Sousa Marcelo Valle , Ricart Carlos André O. , Botelho Katyelle , Castro Mariana S. , Fontes Wagner , de Cassia Marqueti Rita
Published: May 2020

Ancestral obesogenic exposure is able to trigger harmful effects in the offspring left ventricle (LV) which could lead to cardiovascular diseases. However, the impact of the father’s lifestyle on the offspring LV is largely unexplored. The aim of this study was to investigate the effects of 8 weeks of paternal resistance training (RT) on the offspring left ventricle (LV) proteome exposed to control or high-fat (HF) diet. Wistar rats were randomly divided into two groups: sedentary fathers and trained fathers (8 weeks, 3 times per week with weights secured to the animals’ tails). The offspring were obtained by mating with sedentary females. Upon weaning, male offspring were divided into 4 groups (5 animals per group): offspring from sedentary fathers, exposed to control diet (SFO-C); offspring from trained fathers, exposed to control diet (TFO-C); offspring from sedentary fathers, exposed to high-fat diet (SFO-HF); and offspring from trained fathers, exposed to high-fat diet (TFO-HF). The LC-MS/MS analysis revealed 537 regulated proteins among groups. Offspring exposure to HF diet caused reduction in the abundance levels of proteins related to cell component organization, metabolic processes, and transport. Proteins related to antioxidant activity, transport, and transcription regulation were increased in TFO-C and TFO-HF as compared with the SFO-C and SFO-HF groups. Paternal RT demonstrated to be an important intervention capable of inducing significant effects on the LV proteome regardless of offspring diet due to the increase of proteins involved into LV homeostasis maintenance. This study contributes to a better understanding of the molecular aspects involved in transgenerational inheritance.
Structure Design and Bearing Capacity Analysis for Crawler Chassis of Rice Combine Harvester
Authors: Tang Zhong , Ren Hui , Li Xiyao , Liu Xin , Zhang Biao
Published: May 2020

Due to the current unstable travel performance and poor driving maneuverability of rice combine harvester crawler chassis with high load in the rice field, the driver’s standard sitting posture model was developed by analyzing the handling of the crawler chassis driving control panel. Based on this model, the joystick length and cab manipulation space layout were designed. The Finite Element Software was used to develop the loading and restraining model of the chassis frame, and then the structural characteristics and bearing capacity of the crawler chassis were analyzed. The high-bearing running stability and the rationality of operating force of the joystick of rice combine harvester crawler chassis designed in this paper through experiments were verified by experiments. The results showed that when the crawler chassis of rice combine harvester bears a load of 3.5 t, the driving speed is relatively stable in the three speed ranges of 1 m/s, 1.5 m/s, and 2 m/s, and the maximum variance of driving speed variation is 5.022 × 10−4. The actual average operating force of each operating lever on the crawler chassis ranges from 30.36 to 42.71 N, and the operating force of each operating lever is suitable for 95% of Chinese adult male operators. The research results provide a good method and reference for the future development of the crawler chassis structure of rice combine harvester.
ERK-Mediated Loss of miR-199a-3p and Induction of EGR1 Act as a “Toggle Switch” of GBM Cell Dedifferentiation into NANOG- and OCT4-Positive Cells
Authors: Almairac Fabien , Turchi Laurent , Sakakini Nathalie , Debruyne David Nicolas , Elkeurti Sarah , Gjernes Elisabet , Polo Beatrice , Bianchini Laurence , Fontaine Denys , Paquis Philippe , Chneiweiss Herve , Junier Marie-Pierre , Verrando Patrick , Burel-Vandenbos Fanny , Virolle Thierry
Published: May 2020

There is great interest in understanding how the cancer stem cell population may be maintained in solid tumors. Here, we show that tumor cells exhibiting stem-like properties and expression of pluripotency markers NANOG and OCT4 can arise from original differentiated tumor cells freshly isolated from human glioblastomas (GBM) and that have never known any serum culture conditions. Induction of EGR1 by EGFR/ERK signaling promoted cell conversion from a less aggressive, more differentiated cellular state to a self-renewing and strongly tumorigenic state, expressing NANOG and OCT4. Expression of these pluripotency markers occurred before the cells re-entered the cell cycle, demonstrating their capacity to change and dedifferentiate without any cell divisions. In differentiated GBM cells, ERK-mediated repression of miR-199a-3p induced EGR1 protein expression and triggered dedifferentiation. Overall, this signaling pathway constitutes an ERK-mediated "toggle switch" that promotes pluripotency marker expression and stem-like features in GBM cells.

Significance:

This study defines an ERK-mediated molecular mechanism of dedifferentiation of GBM cells into a stem-like state, expressing markers of pluripotency.

See related commentary by Koncar and Agnihotri, p. 3195
Systemic Immune Response and Cancer Risk: Filling the Missing Piece of Immuno-Oncology
Authors: Song Mingyang , Tworoger Shelley S.
Published: May 2020

While immuno-oncology has made significant advances in activating local tumor immune responses, leading to improved outcomes, the role of systemic immunity in cancer incidence remains poorly understood. Le Cornet and colleagues prospectively studied circulating immune cells quantified by DNA methylation markers in relation to incidence of breast, colorectal, lung, and prostate cancer among initially healthy individuals. A positive association with cancer risk was observed for higher FOXP3+ T-cell–mediated immune tolerance and lower CD8+ T-cell–mediated cytotoxicity. Further studies of systemic immunity in cancer development are crucial to identify novel prediction markers and interventional targets for cancer immunoprevention.

See related article by Le Cornet et al., p. 1885
First-in-Human Trial of Epichaperome-Targeted PET in Patients with Cancer
Authors: Dunphy Mark P.S. , Pressl Christina , Pillarsetty Nagavarakishore , Grkovski Milan , Modi Shanu , Jhaveri Komal , Norton Larry , Beattie Bradley J. , Zanzonico Pat B. , Zatorska Danuta , Taldone Tony , Ochiana Stefan O. , Uddin Mohammad M. , Burnazi Eva M. , Lyashchenko Serge K. , Hudis Clifford A. , Bromberg Jacqueline , Schöder Heiko M. , Fox Josef J. , Zhang Hanwen , Chiosis Gabriela , Lewis Jason S. , Larson Steven M.
Published: May 2020

Purpose:

124I-PU-H71 is an investigational first-in-class radiologic agent specific for imaging tumor epichaperome formations. The intracellular epichaperome forms under cellular stress and is a clinically validated oncotherapeutic target. We conducted a first-in-human study of microdose 124I-PU-H71 for PET to study in vivo biodistribution, pharmacokinetics, metabolism, and safety; and the feasibility of epichaperome-targeted tumor imaging.

Experimental Design:

Adult patients with cancer (n = 30) received 124I-PU-H71 tracer (201±12 MBq, <25 μg) intravenous bolus followed by PET/CT scans and blood radioassays.

Results:

124I-PU-H71 PET detected tumors of different cancer types (breast, lymphoma, neuroblastoma, genitourinary, gynecologic, sarcoma, and pancreas). 124I-PU-H71 was retained by tumors for several days while it cleared rapidly from bones, healthy soft tissues, and blood. Radiation dosimetry is favorable and patients suffered no adverse effects.

Conclusions:

Our first-in-human results demonstrate the safety and feasibility of noninvasive in vivo detection of tumor epichaperomes using 124I-PU-H71 PET, supporting clinical development of PU-H71 and other epichaperome-targeted therapeutics.
Dual Regulation of Histone Methylation by mTOR Complexes Controls Glioblastoma Tumor Cell Growth via EZH2 and SAM
Authors: Harachi Mio , Masui Kenta , Honda Hiroaki , Muragaki Yoshihiro , Kawamata Takakazu , Cavenee Webster K. , Mischel Paul S. , Shibata Noriyuki
Published: May 2020

Epigenetic regulation known for DNA methylation and histone modification is critical for securing proper gene expression and chromosomal function, and its aberration induces various pathologic conditions including cancer. Trimethylation of histone H3 on lysine 27 (H3K27me3) is known to suppress various genes related to cancer cell survival and the level of H3K27me3 may have an influence on tumor progression and malignancy. However, it remains unclear how histone methylation is regulated in response to genetic mutation and microenvironmental cues to facilitate the cancer cell survival. Here, we report a novel mechanism of the specific regulation of H3K27me3 by cooperatively two mTOR complexes, mTORC1 and mTORC2 in human glioblastoma (GBM). Integrated analyses revealed that mTORC1 upregulates the protein expression of enhancer of zeste homolog 2, a main component of polycomb repressive complex 2 which is known as H3K27-specific methyltransferase. The other mTOR complex, mTORC2, regulates production of S-adenosylmethionine, an essential substrate for histone methylation. This cooperative regulation causes H3K27 hypermethylation which subsequently promotes tumor cell survival both in vitro and in vivo xenografted mouse tumor model. These results indicate that activated mTORC1 and mTORC2 complexes cooperatively contribute to tumor progression through specific epigenetic regulation, nominating them as an exploitable therapeutic target against cancer.

Implications:

A dynamic regulation of histone methylation by mTOR complexes promotes tumor growth in human GBM, but at the same time could be exploitable as a novel therapeutic target against this deadly tumor.
Juckreiz als typische Missempfindung der Haut - nicht nur bei den klassischen allergischen Erkrankungen
Authors: Sticherling Michael
Published: May 2020

Pruritus in autoimmune and inflammatory dermatoses is a common symptom that can be severe and affect the quality of life of patients. In some diseases, pruritus is related to disorders activity and severity or may occur independent of the disease. Despite the high prevalence, the symptom is still underrated and there are only a few trials investigating the efficacy of drugs for disease-specific pruritus. In this review, the characteristics and possible pathomechanisms of pruritus in various dermatoses like autoimmune bullous diseases, connective tissue diseases as well as autoimmune-associated dermatoses (atopic dermatitis, psoriasis vulgaris) is illustrated. Additionally, studies analyzing the antipruritic treatment are discussed. Summarizing, the prevalence of pruritus in these diseases demonstrates the importance for symptom recognition and the need for an efficient antipruritic therapy.