bluuebunny/crossref_metadata_2025 huggingface dataset:
Cooperation between Different CRISPR-Cas Types Enables Adaptation in an RNA-Targeting System
Authors: Hoikkala Ville , Ravantti Janne , Díez-Villaseñor César , Tiirola Marja , Conrad Rachel A. , McBride Mark J. , Moineau Sylvain , Sundberg Lotta-Riina
Published: March 2021

CRISPR-Cas systems are immune systems that protect bacteria and archaea against their viruses, bacteriophages. Immunity is achieved through the acquisition of short DNA fragments from the viral invader’s genome.
Mycobacterium abscessus Strain Morphotype Determines Phage Susceptibility, the Repertoire of Therapeutically Useful Phages, and Phage Resistance
Authors: Dedrick Rebekah M. , Smith Bailey E. , Garlena Rebecca A. , Russell Daniel A. , Aull Haley G. , Mahalingam Vaishnavi , Divens Ashley M. , Guerrero-Bustamante Carlos A. , Zack Kira M. , Abad Lawrence , Gauthier Christian H. , Jacobs-Sera Deborah , Hatfull Graham F.
Published: March 2021

Mycobacterium abscessus
infections in cystic fibrosis patients are challenging to treat due to widespread antibiotic resistance. The therapeutic use of lytic bacteriophages presents a new potential strategy, but the great variation among clinical
M. abscessus
isolates demands determination of phage susceptibility prior to therapy.
The Prophage and Plasmid Mobilome as a Likely Driver of Mycobacterium abscessus Diversity
Authors: Dedrick Rebekah M. , Aull Haley G. , Jacobs-Sera Deborah , Garlena Rebecca A. , Russell Daniel A. , Smith Bailey E. , Mahalingam Vaishnavi , Abad Lawrence , Gauthier Christian H. , Hatfull Graham F.
Published: March 2021

Mycobacterium abscessus
is an important emerging pathogen that is challenging to treat with current antibiotic regimens. There is substantial genomic variation in
M. abscessus
clinical isolates, but little is known about how this influences pathogenicity and
in vivo
growth.
Systems Biology behind Immunoprotection of Both Sheep and Goats after Sungri/96 PPRV Vaccination
Authors: Wani Sajad Ahmad , Praharaj Manas Ranjan , Sahu Amit R. , Khan Raja Ishaq Nabi , Saxena Shikha , Rajak Kaushal Kishor , Muthuchelvan Dhanavelu , Sahoo Aditya , Mishra Bina , Singh Raj Kumar , Mishra Bishnu Prasad , Gandham Ravi Kumar
Published: March 2021

Peste des petits ruminants (PPR) by PPR virus (PPRV) is an World Organisation for Animal Health (OIE)-listed acute, contagious transboundary viral disease of small ruminants. The attenuated Sungri/96 PPRV vaccine used all over India against this PPR provides long-lasting robust innate and adaptive immune response.
Collective behavior of thermophoretic dimeric active colloids in three-dimensional bulk
Authors: Wagner Martin , Roca-Bonet Sergi , Ripoll Marisol
Published: March 2021

Colloids driven by phoresis constitute one of the main avenues for the design of synthetic microswimmers. For these swimmers, the specific form of the phoretic and hydrodynamic interactions dramatically influences their dynamics. Explicit solvent simulations allow the investigation of the different behaviors of dimeric Janus active colloids. The phoretic character is modified from thermophilic to thermophobic, and this, together with the relative size of the beads, strongly influences the resulting solvent velocity fields. Hydrodynamic flows can change from puller-type to pusher-type, although the actual flows significantly differ from these standard flows. Such hydrodynamic interactions combined with phoretic interactions between dimers result in several interesting phenomena in three-dimensional bulk conditions. Thermophilic dimeric swimmers are attracted to each other and form large and stable aggregates. Repulsive phoretic interactions among thermophobic dimeric swimmers hinder such clustering and lead, together with long- and short-ranged attractive hydrodynamic interactions, to short-lived, aligned swarming structures.

Graphic Abstract
Reducibility index and sum-reducibility index
Authors: Nguyen An Tran , Dung Tran Duc , Kumashiro Shinya , Nhan Le Thanh
Published: March 2021

Let [Formula: see text] be a commutative Noetherian ring. For a finitely generated [Formula: see text]-module [Formula: see text], Northcott introduced the reducibility index of [Formula: see text], which is the number of submodules appearing in an irredundant irreducible decomposition of the submodule [Formula: see text] in [Formula: see text]. On the other hand, for an Artinian [Formula: see text]-module [Formula: see text], Macdonald proved that the number of sum-irreducible submodules appearing in an irredundant sum-irreducible representation of [Formula: see text] does not depend on the choice of the representation. This number is called the sum-reducibility index of [Formula: see text]. In the former part of this paper, we compute the reducibility index of [Formula: see text], where [Formula: see text] is a flat homomorphism of Noetherian rings. Especially, the localization, the polynomial extension, and the completion of [Formula: see text] are studied. For the latter part of this paper, we clarify the relation among the reducibility index of [Formula: see text], that of the completion of [Formula: see text], and the sum-reducibility index of the Matlis dual of [Formula: see text].
Left non-degenerate set-theoretic solutions of the Yang–Baxter equation and dynamical extensions of q-cycle sets
Authors: Castelli Marco , Catino Francesco , Stefanelli Paola
Published: March 2021

A first aim of this paper is to give sufficient conditions on left non-degenerate bijective set-theoretic solutions of the Yang–Baxter equation so that they are non-degenerate. In particular, we extend the results on involutive solutions obtained by Rump in A decomposition theorem for square-free unitary solutions of the quantum Yang–Baxter equation, Adv. Math. 193 (2005) 40–55, https://doi.org/10.1016/j.aim.2004.03.019 and answer positively a question posed by Cedó et al. in Question 4.2 in Structure monoids of set-theoretic solutions of the Yang–Baxter equation, preprint (2019), https://arxiv.org/abs/1912.09710 . Moreover, we develop a theory of extensions for left non-degenerate set-theoretic solutions of the Yang–Baxter equation that allows one to construct new families of set-theoretic solutions.
On Π-property of subgroups of a finite group
Authors: Wu Xinwei , Li Xianhua
Published: March 2021

Let [Formula: see text] be a finite group, [Formula: see text] a prime, [Formula: see text] a Sylow [Formula: see text]-subgroup of [Formula: see text] and [Formula: see text] a power of [Formula: see text] such that [Formula: see text]. Let [Formula: see text] denote the unique smallest normal subgroup of [Formula: see text] for which the corresponding factor group is abelian of exponent dividing [Formula: see text]. Let [Formula: see text], [Formula: see text], [Formula: see text] be classes of all [Formula: see text]-groups, [Formula: see text]-nilpotent groups and [Formula: see text]-supersolvable groups, respectively, [Formula: see text] be the [Formula: see text]-residual of [Formula: see text]. Let [Formula: see text]. A subgroup [Formula: see text] of a finite group [Formula: see text] is said to have [Formula: see text]-property in [Formula: see text], if for any [Formula: see text]-chief factor [Formula: see text], [Formula: see text] is a [Formula: see text]-number. A normal subgroup [Formula: see text] of [Formula: see text] is said to be [Formula: see text]-hypercyclically embedded in [Formula: see text] if every [Formula: see text]-[Formula: see text]-chief factor of [Formula: see text] is cyclic, where [Formula: see text] is a fixed prime. In this paper, we prove that [Formula: see text] is [Formula: see text]-hypercyclically embedded in [Formula: see text] if and only if for some [Formula: see text]-subgroups [Formula: see text] of [Formula: see text], [Formula: see text] have [Formula: see text]-property in [Formula: see text].
Real-time, High-resolution Depth Upsampling on Embedded Accelerators
Authors: Langerman David , George Alan
Published: March 2021

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps.
1
SIKE in 32-bit ARM Processors Based on Redundant Number System for NIST Level-II
Authors: Seo Hwajeong , Sanal Pakize , Azarderakhsh Reza
Published: March 2021

We present an optimized implementation of the post-quantum Supersingular Isogeny Key Encapsulation (SIKE) for 32-bit ARMv7-A processors supporting NEON engine (i.e., SIMD instruction). Unlike previous SIKE implementations, finite field arithmetic is efficiently implemented in a redundant representation, which avoids carry propagation and pipeline stall. Furthermore, we adopted several state-of-the-art engineering techniques as well as hand-crafted assembly implementation for high performance. Optimized implementations are ported to Microsoft SIKE library written in “a non-redundant representation” and evaluated in high-end 32-bit ARMv7-A processors, such as ARM Cortex-A5, A7, and A15. A full key-exchange execution of SIKEp503 is performed in about 109 million cycles on ARM Cortex-A15 processors (i.e., 54.5 ms @2.0 GHz), which is about 1.58× faster than previous state-of-the-art work presented in CHES’18.
Continual Activity Recognition with Generative Adversarial Networks
Authors: Ye Juan , Nakwijit Pakawat , Schiemer Martin , Jha Saurav , Zambonelli Franco
Published: March 2021

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called
HAR-GAN
, to grow the activity model over time.
HAR-GAN
does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated
HAR-GAN
on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of
HAR-GAN
in continual activity recognition and shed insight on the future challenges.
Cooperative Coevolution-based Design Space Exploration for Multi-mode Dataflow Mapping
Authors: Yuan Bo , Lu Xiaofen , Tang Ke , Yao Xin
Published: March 2021

Some signal processing and multimedia applications can be specified by synchronous dataflow (SDF) models. The problem of SDF mapping to a given set of heterogeneous processors has been known to be NP-hard and widely studied in the design automation field. However, modern embedded applications are becoming increasingly complex with dynamic behaviors changes over time. As a significant extension to the SDF, the multi-mode dataflow (MMDF) model has been proposed to specify such an application with a finite number of behaviors (or modes) and each behavior (mode) is represented by an SDF graph. The multiprocessor mapping of an MMDF is far more challenging as the design space increases with the number of modes. Instead of using traditional genetic algorithm (GA)-based design space exploration (DSE) method that encodes the design space as a whole, this article proposes a novel cooperative co-evolutionary genetic algorithm (CCGA)-based framework to efficiently explore the design space by a new problem-specific decomposition strategy in which the solutions of node mapping for each individual mode are assigned to an individual population. Besides, a problem-specific local search operator is introduced as a supplement to the global search of CCGA for further improving the search efficiency of the whole framework. Furthermore, a fitness approximation method and a hybrid fitness evaluation strategy are applied for reducing the time consumption of fitness evaluation significantly. The experimental studies demonstrate the advantage of the proposed DSE method over the previous GA-based method. The proposed method can obtain an optimization result with 2×−3× better quality using less (1/2−1/3) optimization time.
Improving Performance-Power-Programmability in Space Avionics with Edge Devices: VBN on Myriad2 SoC
Authors: Leon Vasileios , Lentaris George , Petrongonas Evangelos , Soudris Dimitrios , Furano Gianluca , Tavoularis Antonis , Moloney David
Published: March 2021

The advent of powerful edge devices and AI algorithms has already revolutionized many terrestrial applications; however, for both technical and historical reasons, the space industry is still striving to adopt these key enabling technologies in new mission concepts. In this context, the current work evaluates an heterogeneous multi-core system-on-chip processor for use on-board future spacecraft to support novel, computationally demanding digital signal processors and AI functionalities. Given the importance of low power consumption in satellites, we consider the
Intel Movidius Myriad2
system-on-chip and focus on SW development and performance aspects. We design a methodology and framework to accommodate efficient partitioning, mapping, parallelization, code optimization, and tuning of complex algorithms. Furthermore, we propose an avionics architecture combining this commercial off-the-shelf chip with a field programmable gate array device to facilitate, among others, interfacing with traditional space instruments via SpaceWire transcoding. We prototype our architecture in the lab targeting vision-based navigation tasks. We implement a representative computer vision pipeline to track the 6D pose of ENVISAT using megapixel images during hypothetical spacecraft proximity operations. Overall, we achieve 2.6 to 4.9 FPS with only 0.8 to 1.1 W on
Myriad2
, i.e., 10-fold acceleration versus modern rad-hard processors. Based on the results, we assess various benefits of utilizing
Myriad2
instead of conventional field programmable gate arrays and CPUs.
UBAR
Authors: Shamsa Elham , Pröbstl Alma , TaheriNejad Nima , Kanduri Anil , Chakraborty Samarjit , Rahmani Amir M. , Liljeberg Pasi
Published: March 2021

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.
Precise Cache Profiling for Studying Radiation Effects
Authors: Marshall James , Gifford Robert , Bloom Gedare , Parmer Gabriel , Simha Rahul
Published: March 2021

Increased access to space has led to an increase in the usage of commodity processors in radiation environments. These processors are vulnerable to transient faults such as single event upsets that may cause bit-flips in processor components. Caches in particular are vulnerable due to their relatively large area, yet are often omitted from fault injection testing because many processors do not provide direct access to cache contents and they are often not fully modeled by simulators. The performance benefits of caches make disabling them undesirable, and the presence of error correcting codes is insufficient to correct for increasingly common multiple bit upsets.

This work explores building a program’s cache profile by collecting cache usage information at an instruction granularity via commonly available on-chip debugging interfaces. The profile provides a tighter bound than cache utilization for cache vulnerability estimates (50% for several benchmarks). This can be applied to reduce the number of fault injections required to characterize behavior by at least two-thirds for the benchmarks we examine. The profile enables future work in hardware fault injection for caches that avoids the biases of existing techniques.
Human-Collective Collaborative Target Selection
Authors: Cody Jason R. , Roundtree Karina A. , Adams Julie A.
Published: March 2021

Robotic collectives are composed of hundreds or thousands of distributed robots using local sensing and communication that encompass characteristics of biological spatial swarms, colonies, or a combination of both. Interactions between the individual entities can result in emergent collective behaviors. Human operators in future disaster response or military engagement scenarios are likely to deploy semi-autonomous collectives to gather information and execute tasks within a wide area, while reducing the exposure of personnel to danger. This article presents and evaluates two action selection models in an experiment consisting of a single human operator supervising four simulated collectives. The action selection models have two parts: (1) a best-of-
n
decision-making model that attempts to choose the highest-quality target from a set of
n
targets and (2) a quorum sensing task sequencing model that enables autonomous target site occupation. An original biologically inspired insect colony decision model is compared to a bias-reducing model that attempts to reduce environmental bias, which can negatively influence collective best-of-
n
decisions when poorer-quality targets are easier to evaluate than higher-quality targets. The collective decision-making models are compared in both supervised and unsupervised trials. The bias-reducing model without human supervision is slower than the original model but is 57% more accurate for decisions where evaluating the optimal target is more difficult. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions compared to the teams using the original model.
An Investigation of the Portrayal of Social Media Challenges on YouTube and Twitter
Authors: Khasawneh Amro , Madathil Kapil Chalil , Zinzow Heidi , Wisniewski Pamela , Ponathil Amal , Rogers Hunter , Agnisarman Sruthy , Roth Rebecca , Narasimhan Meera
Published: March 2021

A social media phenomenon that has received limited research attention is the advent and propagation of viral online challenges. Several of these challenges entail self-harming behavior, which, combined with their viral nature, poses physical and psychological risks for both participants and viewers. The objective of this study is to identify the nature of what people post about the social media challenges that vary in their level of risk. To do so, we conducted a qualitative analysis of three viral social media challenges, the Blue Whale, Tide Pod, and Ice Bucket challenges, based on 180 YouTube videos, 3,607 comments on those YouTube videos, and 450 Twitter posts. We identified common themes across the YouTube videos, comments, and Twitter posts: (1) promoting education and awareness, (2) criticizing the participants, (3) providing detailed information about the participants, (4) giving viewers a tutorial on how to participate, and (5) attempting to understand this seemingly senseless online behavior. We used social norm theory to discuss what leads people to post about the challenges and how posts intended to raise awareness about harmful challenges could potentially create a contagion effect by spreading knowledge about them, thereby increasing participation. Finally, we proposed design implications that could potentially minimize the risks and propagation of harmful social media challenges.
Elk Audio OS
Authors: Turchet Luca , Fischione Carlo
Published: March 2021

As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.
The Role of Oxidative Stress in Hyperuricemia and Xanthine Oxidoreductase (XOR) Inhibitors
Authors: Liu Ning , Xu Hu , Sun Qianqian , Yu Xiaojuan , Chen Wentong , Wei Hongquan , Jiang Jie , Xu Youzhi , Lu Wenjie
Published: March 2021

Uric acid is the end product of purine metabolism in humans. Hyperuricemia is a metabolic disease caused by the increased formation or reduced excretion of serum uric acid (SUA). Alterations in SUA homeostasis have been linked to a number of diseases, and hyperuricemia is the major etiologic factor of gout and has been correlated with metabolic syndrome, cardiovascular disease, diabetes, hypertension, and renal disease. Oxidative stress is usually defined as an imbalance between free radicals and antioxidants in our body and is considered to be one of the main causes of cell damage and the development of disease. Studies have demonstrated that hyperuricemia is closely related to the generation of reactive oxygen species (ROS). In the human body, xanthine oxidoreductase (XOR) catalyzes the oxidative hydroxylation of hypoxanthine to xanthine to uric acid, with the accompanying production of ROS. Therefore, XOR is considered a drug target for the treatment of hyperuricemia and gout. In this review, we discuss the mechanisms of uric acid transport and the development of hyperuricemia, emphasizing the role of oxidative stress in the occurrence and development of hyperuricemia. We also summarize recent advances and new discoveries in XOR inhibitors.
Intellectual Capital and Firm Performance in the Context of Venture‐Capital Syndication Background in China
Authors: Lu Yuzhong , Tian Zengrui , Buitrago Guillermo Andres , Gao Shuiwen , Zhao Yuanjun , Zhang Shuai
Published: March 2021

This paper is intended to investigate the role of Venture‐Capital Syndication (VCS) background in the relationship between intellectual capital (IC) and portfolio firm performance (PFP); specifically, this article examines the moderating effect of VCS’s leading firm background and member heterogeneity on the effect of IC on PFP. This study used a modified VAIC model to measure IC to compose a 4‐component variable including human capital, structural capital, relational capital, and innovation capital. The data were collected from VCS‐backed and listed firms in China during 2014 to 2018 applying the pooled OLS model for hypotheses test, Generalized Method of Moments (GMMs) to reduce endogeneity and unobserved factor control, and also return on equity (ROE) instead of ROA for the robustness test. Empirical results showed that IC and its components can improve PFP for VCS‐backed firms in China; in detail, IC showed greater impact on performance of firms invested by foreign lead investors than in private or government VCS, specially reflected in the impact of innovation capital on PFP. Furthermore, IC showed weaker impact on PFP of mixed VCS‐backed firms compared to pure VCS‐backed firms and showed diminished effect on higher VCS member heterogeneity mainly reflected in the impact of relational capital on firm performance. These findings propose a new way of combining IC and VC to improve firm performance and are beneficial to theoretical development of IC and VC as well as a perspective for VC firm managers to choose suitable partners prior to join a VCS.
Research on the Cutting Principle and Tool Design of Gear Skiving Based on the Theory of Conjugate Surface
Authors: Wang Peng , Li Jingcai , Han Lin
Published: March 2021

Tool design is one of the key factors that restrict the development of gear skiving technology since the design principle does not correspond to the cutting principle. The existing skiving tool cannot achieve ideal machining accuracy and reasonable cutting angles. In view of this, some research has been done in this paper. Firstly, the skiving principle is investigated essentially according to the skiving motions. Then, the principle of tool design is analyzed based on the theory of conjugate surface, and a new tool design method is proposed to match the skiving principle. For this, all the skiving patterns for various kinds of workpieces are enumerated and summarized to abstract a normalized skiving model. Based on this, the mathematical model of the conjugate surface is then derived to lay the foundation for tool design. Then, the design methods of cutting edge, rake face, and flank face are proposed. An example is presented at last, and the cutting simulation is conducted. The result proves that the proposed methods are correct and valid. The theoretical research in this paper could promote the improvement of skiving tools.
Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks
Authors: Liu Yang , Xiao Jing , Li Chaoqun , Qin Hu , Zhou Jie
Published: March 2021

The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.
[Retracted] Experimental Study of lncRNA RP11‐815M8.1 Promoting Osteogenic Differentiation of Human Bone Marrow Mesenchymal Stem Cells
Authors: Sun Xiang , Cao Junchuan , Han Jiusong , Jia Bo , Wang Jing , Lian Junxiang , Gao Jianwei , Liu Shuguang , Xiao Hui
Published: March 2021

Objective. This study is aimed at investigating the role of long noncoding RNA (lncRNA) RP11‐815M8.1 in the osteogenic differentiation of human bone marrow mesenchymal stem cells (hBMSCs). Methods. RT‐PCR was used to detect the expression of lncRNA RP11‐815M8.1 before and after osteogenic differentiation of hBMSCs. The lncRNA RP11‐815M8.1 in hBMSCs was overexpressed or silenced via lentiviral transfection. The transfection efficiency was detected by RT‐PCR, and the proliferation of hBMSCs was determined by CCK‐8. After 14 days of osteogenic differentiation of transfected hBMSCs, the expression of osteogenic transcription factors (ALP, OCN, OPN, Runx2, and Osterix) was detected by alizarin red staining and RT‐PCR. The mRNAs directly regulated by lncRNA RP11‐815M8.1 and targeted miRNAs were analyzed according to the positional relationship between lncRNA and mRNA in the genome and miRanda software. Results. The expression of lncRNA RP11‐815M8.1 enhanced with increasing osteogenic differentiation time of hBMSCs. Two days after the transfection of hBMSCs, lncRNA RP11‐815M8.1 expression was significantly increased in the overexpression group and significantly decreased in the knockdown group, compared to control cells. The CCK‐8 assay showed that overexpression and knockdown of lncRNA RP11‐815M8.1 did not affect the proliferation of hBMSCs. After 14 days of differentiation of hBMSCs, stronger alizarin red staining was observed in the overexpression groups, and the expression of osteogenic transcription factors was increased in the overexpression group compared to the control. In the knockdown group, alizarin red staining and the expression of osteogenic transcription factors were decreased. Bioinformatics analysis showed that lncRNA RP11‐815M8.1 was directly associated with one mRNA, 27 interacting miRNAs, and 20 miRNA‐targeted mRNAs. Conclusion. The osteogenic differentiation of hBMSCs can be promoted by lncRNA RP11‐815M8.1 in vitro.
LSD1 as a Biomarker and the Outcome of Its Inhibitors in the Clinical Trial: The Therapy Opportunity in Tumor
Authors: Agboyibor Clement , Dong Jianshu , Effah Clement Yaw , Drokow Emmanuel Kwateng , Pervaiz Waqar , Liu Hong-Min
Published: March 2021

Tumors are the foremost cause of death worldwide. As a result of that, there has been a significant enhancement in the investigation, treatment methods, and good maintenance practices on cancer. However, the sensitivity and specificity of a lot of tumor biomarkers are not adequate. Hence, it is of inordinate significance to ascertain novel biomarkers to forecast the prognosis and therapy targets for tumors. This review characterized LSD1 as a biomarker in different tumors. LSD1 inhibitors in clinical trials were also discussed. The recent pattern advocates that LSD1 is engaged at sauce chromatin zones linking with complexes of multi-protein having an exact DNA-binding transcription factor, establishing LSD1 as a favorable epigenetic target, and also gives a large selection of therapeutic targets to treat different tumors. This review sturdily backing the oncogenic probable of LSD1 in different tumors indicated that LSD1 levels can be used to monitor and identify different tumors and can be a useful biomarker of progression and fair diagnosis in tumor patients. The clinical trials showed that inhibitors of LSD1 have growing evidence of clinical efficacy which is very encouraging and promising. However, for some of the inhibitors such as GSK2879552, though selective, potent, and effective, its disease control was poor as the rate of adverse events (AEs) was high in tumor patients causing clinical trial termination, and continuation could not be supported by the risk-benefit profile. Therefore, we propose that, to attain excellent clinical results of inhibitors of LSD1, much attention is required in designing appropriate dosing regimens, developing in-depth in vitro/in vivo mechanistic works of LSD1 inhibitors, and developing inhibitors of LSD1 that are reversible, safe, potent, and selective which may offer safer profiles.
Research on Music Multiterminal Audio Authentication Based on Wireless Network
Authors: Li Dongmei
Published: March 2021

The current music multiterminal audio authentication algorithm does not consider the mutation of music signal, which leads to poor tamper detection ability and long time of audio authentication. By analyzing the characteristics and key technologies of wireless network, a wireless multiterminal audio system is established. The short‐term energy calculation method is used to consider the sudden change of music signal. The music signal is divided into note segments, and chroma features of half order notes are extracted. The robust hash value is calculated by nonuniform quantization method. The dynamic time warping algorithm is used to align the notes, and the Hamming distance between the hash values of each two corresponding notes is calculated to obtain the measurement values of error series, statistical characteristics, and time distribution characteristics. According to the measurement value, the fuzzy classification method is applied to calculate the membership degree of the signals belonging to two different types of operation, and the authentication confidence degree is obtained. The tampered area of the music signal that has not passed the authentication is detected, and the music multiterminal audio authentication is realized. Experimental results show that the proposed algorithm has good tamper detection ability and can effectively shorten the audio authentication time.
Impact of an Acute Care Surgery Model on the Management of Acute Appendicitis in South Korea: A Retrospective Cohort Study
Authors: Yi Gun-Hee , Lee Hak-Jae , Lee Seul , Yoon Jong-Hee , Hong Suk-Kyung
Published: March 2021

Background. The acute care surgery (ACS) system is a new model for the prompt management of diseases that require rapid treatment in patients with acute abdomen. This study compared the outcomes and characteristics of the ACS system and traditional on-call system (TROS) for acute appendicitis in South Korea. Methods. This single-center, retrospective study included all patients (aged ≥18 years) who underwent surgery for acute appendicitis in 2016 and 2018. The TROS and ACS system were used for the 2016 and 2018 groups, respectively. We retrospectively obtained data on each patient from the electrical medical records. The independent samples t-test and Mann–Whitney U-test were used for continuous and nonnormally distributed data, respectively. Results. In total, 126 patients were included. The time taken to get from the emergency room admission to the operating room, operation times, and postoperative complication rates were similar between both groups. However, the length of the hospital stay was shorter in the ACS group than in the TROS group (4.3 ± 3.2 days vs. 7.2 ± 9.6 days,

p
=
0.039

). Conclusions. Since the introduction of the ACS system, the length of hospital stay for surgical patients has decreased. This may be due to the application of an integrated medical procedure, such as a new clinical pathway, rather than differences in the surgical techniques.
IF-MABAC Method for Evaluating the Intelligent Transportation System with Intuitionistic Fuzzy Information
Authors: Li Yanping
Published: March 2021

Intelligent transportation system (ITS) is the development direction of the future traffic system. ITS can effectively employ the existing traffic facilities and ensure the safety of traffic, urban traffic, and public security management for effective control in order to satisfy people’s travel demand. Therefore, the results of the system in-depth understanding and objective evaluation are very necessary. And it is frequently regarded as a multiattribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is required to tackle it. Depending on the conventional multiattributive border approximation area comparison (MABAC) method and intuitionistic fuzzy sets (IFSs), this article designs a novel intuitive distance-based IF-MABAC method to assess the performance of financial management. First of all, a related literature review is conducted. Furthermore, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing the maximizing deviation method. Afterwards, relying on novel distance measures between intuitionistic fuzzy numbers (IFNs), the conventional MABAC method is extended to the IFSs to calculate the final value of each enterprise. Therefore, all enterprises can be ranked, and the one with the best environmental behaviors and awareness can be identified. Eventually, an application for evaluating the intelligent transportation system and some comparative analyses have been given. The results illustrate that the designed algorithm is useful for assessing the performance of financial management.
Circuit Modeling and Signal Transmission Analysis of Inductive Coupler in Wired Drill Pipe
Authors: Jia Jia , Su Yinao , Shen Yue , Wang Long , Zhang Lingtan , Ling Yan
Published: March 2021

The telemetry drill string transmits the measured drilling and formation parameters near the bit to the ground at high speed through the transmission line in the wired drill pipe and the inductive coupler formed by the coil embedded in the drill pipe joint. The voltage transfer function of the inductive coupler seriously affects the signal transmission characteristics of the telemetry drill string. Present research methods cannot accurately and quantitatively describe the influence of the electromagnetic parameters of the inductive coupler on its voltage transfer function. This paper quantitatively analyzes the effects of the electromagnetic parameters of the inductive coupler on signal transmission through reasonable high-frequency circuit modeling. The generation mechanism of eddy current in the magnetic core and the interaction mechanism between eddy current and coupling coils are studied using the electromagnetic detection analysis method of nondestructive testing (NDT) technology. Then, the high-frequency circuit model of the coupler containing eddy current loss is established. The mathematical model of input impedance and signal transmission characteristics of the inductive coupler is established through circuit analysis. The influence of the electromagnetic parameters of the magnetic core and the load resistance of the coupler on the voltage transmission characteristics and power transmission characteristics of the coupler is studied. The research results show that the relative permeability of the magnetic core, load resistance of the coupler, and signal frequency seriously affect the signal voltage transmission. When the relative permeability of the magnetic core material reaches 50, the voltage transmission of the inductive coupler is effectively improved. The electromagnetic simulation results are consistent with the theoretical analysis of the circuit model, which proves the correctness of the circuit model. This paper provides theoretical guidance for the electromagnetic design of inductive couplers and the improvement of signal transmission characteristics of telemetry drill string.
[Retracted] In Silico Characterization of Growth Differentiation Factors as Inhibitors of TNF‐Alpha and IL‐6 in Immune‐Mediated Inflammatory Disease Rheumatoid Arthritis
Authors: Mustafa Ghulam , Mahrosh Hafiza Salaha , Arif Rawaba
Published: March 2021

Tumor necrosis factor alpha (TNF‐α) plays a critical role in the progression of inflammation and affects the cells of the synovial membrane. Another key factor in the progression of rheumatoid inflammation is interleukin‐6 (IL‐6). Both TNF‐α and IL‐6 promote the proliferation of synovial membrane cells thus stimulating the production of matrix metalloproteinases and other cytotoxins and leading towards bone erosion and destruction of the cartilage. Growth differentiation factor‐11 (GDF11) and growth differentiation factor‐8 (GDF8) which is also known as myostatin are members of the transforming growth factor‐β family and could be used as antagonists to inflammatory responses which are associated with rheumatoid arthritis. In the current study, to elucidate the evolutionary relationships of GDF11 with its homologs from other closely related organisms, a comprehensive phylogenetic analysis was performed. From the phylogram, it was revealed that the clade of Primates that belong to superorder Euarchontoglires showed close evolutionary relationships with order Cetartiodactyla of the Laurasiatheria superorder. Fifty tetrapeptides were devised from conserved regions of GDF11 which served as ligands in protein‐ligand docking against TNF‐α and IL‐6 followed by drug scanning and ADMET profiling of best selected ligands. The peptides SAGP showed strong interactions with IL‐6, and peptides AFDP and AGPC showed strong interactions with TNF‐α, and all three peptides fulfilled all the pharmacokinetic parameters which are important for bioavailability. The potential of GDF8 as an antagonist to TNF‐α and IL‐6 was also explored using a protein‐protein docking approach. The binding patterns of GDF8 with TNF‐α and IL‐6 showed that GDF8 could be used as a potential inhibitor of TNF‐α and IL‐6 to treat rheumatoid arthritis.
Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet
Authors: Noh Sun-Kuk
Published: March 2021

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short‐term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer‐learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know‐how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.
Electromagnetic Design and Flux Regulation Analysis of New Hybrid Excitation Generator for Electric Vehicle Range Extender
Authors: Hu Wenjing , Zhang Xueyi , Geng Huihui , Gao Ting , Shi Liwei , You Di
Published: March 2021

Aiming at the problem of uncontrollable magnetic field of permanent magnet generators, a new hybrid excitation generator (HEG) with parallel magnetic circuit is proposed. The HEG consists of combined permanent magnet rotor (PMR) and brushless electric excitation rotor (EER). The PMR has surface-mounted and embedded magnets. The PMR provides the main air gap field, and the brushless EER is used to adjust the air gap field. The operating principle and electromagnetic design scheme of the proposed generator are given in detail. Besides, the matching with two different types of rotors and the flux regulation characteristics is analyzed by using the finite element method. Finally, the output performance of the proposed generator including no-load and load characteristics and output voltage are tested. The results show that the two different types of rotors can be matched efficiently and operated reliably. The internal magnetic flux is easy to adjust in both directions, and the proposed HEG can output stable voltage in the range of wide speed and load.
Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost
Authors: Zhou Hongzhi , Yu Gan
Published: March 2021

In order to solve the problem of low accuracy of pedestrian detection of real traffic cameras and high missed detection rate of small target pedestrians, this paper combines autoencoding neural network and AdaBoost to construct a fast pedestrian detection algorithm. Aiming at the problem that a single high‐level output feature map has insufficient ability to express pedestrian features and existing methods cannot effectively select appropriate multilevel features, this paper improves the traditional AdaBoost algorithm structure, that is, the sample weight update formula and the strong classifier output formula are reset, and the two‐input AdaBoost‐DBN classification algorithm is proposed. Moreover, in view of the problem that the fusion video is not smoothly played, this paper considers the motion information of the video object, performs pixel interpolation by motion compensation, and restores the frame rate of the original video by reconstructing the dropped interframe image. Through experimental research, we can see that the algorithm constructed in this paper has a certain effect.
Evacuation Model of Emotional Contagion Crowd Based on Cellular Automata
Authors: Xiao Qian , Li Jiayang
Published: March 2021

Crowd evacuation under emergency is an important task of world public security research and practice. In order to describe the microemotional contagion of evacuation individuals, a cellular automata-based evacuation model of emotional contagion crowd based on the classical SIS model of infectious diseases is proposed in this paper. Firstly, the state of evacuation individual is defined as “emotional susceptible” and “emotional infective.” Then, a dynamic model considering emotional contagion is established with cellular automata. Based on the models of static floor field and dynamic floor field, the emotion updating rules and state updating rules are constructed. The influence of perception domain radius on pedestrian evacuation process is analyzed through experiments. The conclusion can provide evacuation guidance for evacuation individuals. The comparative experiment results show that the improved model can reflect the movement characteristics of evacuation individuals effectively. The evacuation efficiency of the whole system is also effectively improved due to the consideration of emotional contagion and evacuation strategy.