bluuebunny/crossref_metadata_2025 huggingface dataset:
Accounting Standards and Earnings Quality — Evidence from Registered ADRs
Authors: Alhaj-Yaseen Yaseen S. , Wu Kean , Fletcher Leslie B.
Published: January 2019

This paper examines the changes in earnings quality of registered American Depositary Receipts (ADRs) as a result of switching accounting standards. We aim to shed light on the potential impact of International Financial Reporting Standard (IFRS) adoption on US firms. A suboptimal approach to achieve this goal is through examination of US firms’ surrogates such as ADRs. Unlike previous studies, we made a distinction between registered and unregistered ADRs and affirmed that registered ADRs are the closest surrogates with which to conduct our analysis because they are exclusively required to adhere to the Securities and Exchange Commission (SEC)’s stringent disclosure requirements. When cross-listing their equity on the US exchanges, foreign issuers can file their financial reports with the SEC using IFRS, US GAAP (generally accepted accounting principles), or their domestic GAAP with reconciliation to US GAAP. An improvement in earnings quality is documented when ADRs adopt US GAAP or IFRS versus domestic GAAP. However, when the comparison is made between US GAAP and IFRS, no difference in earnings quality is documented. These results indicate that switching to high-quality accounting standards is likely to improve earnings quality. This improvement is maximized when the difference between reporting standards is high and minimized if otherwise. Our conclusion is that the adoption of IFRS in the US is unlikely to change earnings quality of local issuers. Moreover, we drew a distinction between reconciliation with and adoption of high-quality accountings standards and find that while the former can enhance earnings quality, the latter can further improve it.
Regularity of weak solutions for the relativistic Vlasov–Maxwell system
Authors: Besse Nicolas , Bechouche Philippe
Published: January 2019

We investigate the regularity of weak solutions of the relativistic Vlasov–Maxwell system by using Fourier analysis and the smoothing effect of low velocity particles. This smoothing effect has been used by several authors (see Glassey and Strauss 1986; Klainerman and Staffilani, 2002) for proving existence and uniqueness of [Formula: see text]-regular solutions of the Vlasov–Maxwell system. This smoothing mechanism has also been used to study the regularity of solutions for a kinetic transport equation coupled with a wave equation (see Bouchut, Golse and Pallard 2004). Under the same assumptions as in the paper “Nonresonant smoothing for coupled wave[Formula: see text]+[Formula: see text]transport equations and the Vlasov–Maxwell system”, Rev. Mat. Iberoamericana 20 (2004) 865–892, by Bouchut, Golse and Pallard, we prove a slightly better regularity for the electromagnetic field than the one showed in the latter paper. Namely, we prove that the electromagnetic field belongs to [Formula: see text], with [Formula: see text].
Three-phase fluid displacements in a porous medium
Authors: Andrade Patrício , de Souza Aparecido , Furtado Frederico , Marchesin Dan
Published: January 2019

Oil in a reservoir is usually found together with water and gas. Often a mixture of water and gas is used to displace such oil. In this work, we present the Riemann solution for such three-phase flow problem. This solution encodes the dependence of recovery on the injected proportion, the proportion initially present, and the viscosity of the several fluids. We use the wave curve method to determine the Riemann solution for initial and injection data in the above-mentioned class. We verify the [Formula: see text]-stability of the Riemann solution with variation of data. We do not establish uniqueness of the Riemann solution, but we believe that it is valid.
New Metrics and Models for a Post-ISA Era
Authors: Martonosi Margaret
Published: January 2019

Pushed by both application and technology trends, today's computer systems employ unprecedented levels of heterogeneity, parallelism, and complexity as they seek to extend performance scaling and support new application domains. From datacenters to Internet-of-Things (IoT) devices, these scaling gains come at the expense of degraded hardware-software abstraction layers, increased complexity at the hardware-software interface, and increased challenges for software reliability, interoperability, and performance portability This talk will explore how new metrics, models, and analysis techniques can be effective in this "Post-ISA" era of shifting abstractions. The talk will cover hardware and software design opportunities, methods for formal verification, and a look into the implications on technologies like IoT.
On Resource Pooling and Separation for LRU Caching
Authors: Tan Jian , Quan Guocong , Ji Kaiyi , Shroff Ness
Published: January 2019

Caching systems using the Least Recently Used (LRU) principle have now become ubiquitous. A fundamental question for these systems is whether the cache space should be pooled together or divided to serve multiple flows of data item requests in order to minimize the miss probabilities. In this paper, we show that there is no straight yes or no answer to this question, and depends on complex combinations of critical factors, including, e.g., request rates, overlapped data items across different request flows, data item popularities and their sizes. To this end, we characterize the performance of multiple flows of data item requests under resource pooling and separation when the cache size is large. Analytically we show that it is asymptotically optimal to jointly serve multiple flows if their data item sizes and popularity distributions are similar, and their arrival rates do not differ significantly; the self-organizing property of LRU caching automatically optimizes the resource allocation among them asymptotically. Otherwise, separating these flows could be better, e.g., when data sizes vary significantly. We also quantify critical points beyond which resource pooling is better than separation for each of the flows when the overlapped data items exceed certain levels. These results provide new insights on the performance of caching systems.
Dynamic Proportional Sharing
Authors: Freeman Rupert , Zahedi Seyed Majid , Conitzer Vincent , Lee Benjamin C.
Published: January 2019

Sharing computational resources amortizes cost and improves utilization and efficiency. When agents pool their resources, each becomes entitled to a portion of the shared pool. Static allocations in each round can guarantee entitlements and are strategy-proof, but efficiency suffers because allocations do not reflect variations in agents' demands for resources across rounds. Dynamic allocation mechanisms assign resources to agents across multiple rounds while guaranteeing agents their entitlements. Designing dynamic mechanisms is challenging, however, when agents are strategic and can benefit by misreporting their demands for resources. In this paper, we show that dynamic allocation mechanisms based on max-min fail to guarantee entitlements, strategy-proofness or both. We propose the flexible lending (FL) mechanism and show that it satisfies strategy-proofness and guarantees at least half of the utility from static allocations while providing an asymptotic efficiency guarantee. Our simulations with real and synthetic data show that the performance of the flexible lending mechanism is comparable to that of state-of-the-art mechanisms, providing agents with at least 0.98x, and on average 15x, of their utility from static allocations. Finally, we propose the T -period mechanism and prove that it satisfies strategy-proofness and guarantees entitlements for T łe 2.
Inherent Trade-Offs in Algorithmic Fairness
Authors: Kleinberg Jon
Published: January 2019

Recent discussion in both the academic literature and the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs between these conditions. We also consider a variety of methods for promoting fairness and related notions for classification and selection problems that involve sets rather than just individuals.
Working Set Size Estimation Techniques in Virtualized Environments
Authors: Nitu Vlad , Kocharyan Aram , Yaya Hannas , Tchana Alain , Hagimont Daniel , Astsatryan Hrachya
Published: January 2019

Energy consumption is a primary concern for datacenters' management. Numerous datacenters are relying on virtualization, as it provides flexible resource management means such as virtual machine (VM) checkpoint/restart, migration and consolidation. However, one of the main hindrances to server consolidation is physical memory. In nowadays cloud, memory is generally statically allocated to VMs and wasted if not used. Techniques (such as ballooning) were introduced for dynamically reclaiming memory from VMs, such that only the needed memory is provisioned to each VM. However, the challenge is to precisely monitor the needed memory, i.e., the working set of each VM. In this paper, we thoroughly review the main techniques that were proposed for monitoring the working set of VMs. Additionally, we have implemented the main techniques in the Xen hypervisor and we have defined different metrics in order to evaluate their efficiency. Based on the evaluation results, we propose Badis, a system which combines several of the existing solutions, using the right solution at the right time. We also propose a consolidation extension which leverages Badis in order to pack the VMs based on the working set size and not the booked memory.
PreFix
Authors: Zhang Shenglin , Liu Ying , Meng Weibin , Luo Zhiling , Bu Jiahao , Yang Sen , Liang Peixian , Pei Dan , Xu Jun , Zhang Yuzhi , Chen Yu , Dong Hui , Qu Xianping , Song Lei
Published: January 2019

In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84x10 -5 false positive ratio, outperforming the other failure prediction methods for computers and ISP devices.
What Your DRAM Power Models Are Not Telling You
Authors: Ghose Saugata , Yaglikçi Abdullah Giray , Gupta Raghav , Lee Donghyuk , Kudrolli Kais , Liu William X. , Hassan Hasan , Chang Kevin K. , Chatterjee Niladrish , Agrawal Aditya , O'Connor Mike , Mutlu Onur
Published: January 2019

Main memory (DRAM) consumes as much as half of the total system power in a computer today, due to the increasing demand for memory capacity and bandwidth. There is a growing need to understand and analyze DRAM power consumption, which can be used to research new DRAM architectures and systems that consume less power. A major obstacle against such research is the lack of detailed and accurate information on the power consumption behavior of modern DRAM devices. Researchers have long relied on DRAM power models that are predominantly based off of a set of standardized current measurements provided by DRAM vendors, called IDD values. Unfortunately, we find that state-of-the-art DRAM power models are often highly inaccurate when compared with the real power consumed by DRAM. This is because existing DRAM power models (1) are based off of the worst-case power consumption of devices, as vendor specifications list the current consumed by the most power-hungry device sold; (2) do not capture variations in DRAM power consumption due to different data value patterns; and (3) do not account for any variation across different devices or within a device.
Intel MPX Explained
Authors: Oleksenko Oleksii , Kuvaiskii Dmitrii , Bhatotia Pramod , Felber Pascal , Fetzer Christof
Published: January 2019

Memory-safety violations are the primary cause of security and reliability issues in software systems written in unsafe languages. Given the limited adoption of decades-long research in software-based memory safety approaches, as an alternative, Intel released Memory Protection Extensions (MPX)---a hardware-assisted technique to achieve memory safety. In this work, we perform an exhaustive study of Intel MPX architecture along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues.

We present the first detailed root cause analysis of problems in the Intel MPX architecture through a cross-layer dissection of the entire system stack, involving the hardware, operating system, compilers, and applications. To put our findings into perspective, we also present an in-depth comparison of Intel MPX with three prominent types of software-based memory safety approaches. Lastly, based on our investigation, we propose directions for potential changes to the Intel MPX architecture to aid the design space exploration of future hardware extensions for memory safety.

A complete version of this work appears in the 2018 proceedings of the ACM on Measurement and Analysis of Computing Systems.
On the Power-of-d-choices with Least Loaded Server Selection
Authors: Hellemans Tim , Van Houdt Benny
Published: January 2019

Motivated by distributed schedulers that combine the power-of-d-choices with late binding and systems that use replication with cancellation-on-start, we study the performance of the LL(d) policy which assigns a job to a server that currently has the least workload among d randomly selected servers in large-scale homogeneous clusters.

We consider general job size distributions and propose a partial integro-differential equation to describe the evolution of the system. This equation relies on the earlier proven ansatz for LL(d) which asserts that the workload distribution of any finite set of queues becomes independent of one another as the number of servers tends to infinity. Based on this equation we propose a fixed point iteration for the limiting workload distribution and study its convergence.
On redundancy-d with cancel-on-start a.k.a Join-shortest-work (d)
Authors: Ayesta Urtzi
Published: January 2019

Using redundancy to minimize latency in parallel server systems has become very popular in recent years [1-6]. While there are several variants of a redundancy-based system, the general notion of redundancy is to create multiple copies of the same job that will be sent to a subset of servers. By allowing for redundant copies, the aim is to minimize the system latency by exploiting the variability in the queue lengths of the different queues. Several recent works, both empirically [1,2] and theoretically [3-6], have provided indications that redundancy can help in reducing the response time of a system.
OCCAM
Authors: Anirudh Sabnis Anirudh , Sitaraman Ramesh K. , Towsley Donald
Published: January 2019

We study the problem of inferring the structure of a communication network based only on network measurements made from a set of hosts situated at the network periphery. Our novel approach called "OCCAM" is based on the principle of occam's razor and finds the "simplest" network that explains the observed network measurements. OCCAM infers the internal topology of a communication network, including the internal nodes and links of the network that are not amenable to direct measurement. In addition to network topology, OCCAM infers the routing paths that packets take between the hosts. OCCAM uses path metrics measurable from the hosts and expresses the observed measurements as constraints of a mixed-integer bilinear optimization problem that can then be feasibly solved to yield the network topology and the routing paths. We empirically validate OCCAM on a wide variety of real-world ISP networks and show that its inferences agree closely with the ground truth. Specifically, OCCAM infers the topology with an average network similarity score of 93% and infers routing paths with a path edit distance of 0.20. Further, OCCAM is robust to error in its measured path metric inputs, producing high quality inferences even when 20-30% of its inputs are erroneous. Our work is a significant advance in network tomography as it proposes and empirically evaluates the first method that infers the complete network topology, rather than just logical routing trees from sources.
ACM SIGMETRICS International Workshop on Critical Infrastructure Network Security
Authors: Thai My T. , Sen Arun , Das Arun
Published: January 2019

The second ACM SIGMETRICS International Workshop on Critical Infrastructure Network Security took place in Irvine, California, USA on June 18th 2018 in conjunction with ACM SIGMETRICS 2018. As in the previous year, the workshop received widespread community support and as a consequence we were able to conduct a successful workshop. The workshop provided a forum for scientists and engineers from research institutions and universities to present their findings on critical infrastructure security. It also provided a forum for an exchange of ideas and in-depth discussions on the future research directions in this area. The workshop featured several invited and contributed talks from researchers from institutions such as the University of Illinois at Urbana-Champaign, Princeton University, Sandia National Laboratories, Delft University (The Netherlands), ENEA (Italy), Northeastern University, Hamburg University (Germany), National University of Singapore, California Institute of Technology, New York University and University of Kentucky. To disseminate the outcome of this workshop, we are pleased to include extended abstracts of selected workshop presentations in Performance Evaluation Review (PER), the quarterly publication of ACM SIGMETRICS. A brief overview of the included papers is outlined below.
Vulnerability of Interdependent Infrastructures Under Random Attacks
Authors: Khamfroush Hana , Iloo Samuel Lofumbwa , Rahnamay-Naeini Mahshid
Published: January 2019

Most of today's critical infrastructure are in the form of interdependent networks with new vulnerabilities attributed to their interdependencies. Security and reliability attacks, which can trigger failures within and across these networks, will have different forms and impacts on interdependent networks. In this paper, we focus on random attacks in a twolayer interdependent network and quantify its vulnerability under two different types of such attacks: 1) single layer attack, 2) concurrent two-layer attack. We compare the vulnerability of the network given the two attack scenarios, to answer the question of whether one single attack in one layer of an interdependent network can be as severe as concurrent multi-layer attack? We define two quantitative metrics to evaluate the vulnerability of the interdependent network under a given attack, namely, long-term effect and critical times. The long-term effect represents the total number of affected nodes during a finite time window, while critical times capture the evolution of failure propagation over time. The impact of different types of network topologies, including Erdos-Renyi, Scale-Free, and Small-World, and different coupling scenarios between the layers, namely high and low intensity and random and designed coupling on the vulnerability of the network is studied. Our results show that two-layer attacks are more severe in most cases while a single attack in one layer can be more severe for certain scenarios of coupling and network topologies. This suggests that in interdependent networks severe attacks can be triggered using access to only one layer of the network if the network structure is vulnerable.
AlloX
Authors: Le Tan N. , Sun Xiao , Chowdhury Mosharaf , Liu Zhenhua
Published: January 2019

GPUs are considered as the accelerators for CPUs. We call these applications GPU applications. Some machine learning frameworks like Tensorflow support their machine learning (ML) jobs running either on CPUs or GPUs. Nvidia claims that Titan GPU K80 12GB can speed up 5-10x on average. Although GPUs offer the advantages on performance, they are very expensive. For example, a GPU K80 roughly costs $4000 while an Intel Xeon E5 Quad cores costs $350.

The coexist of traditional CPU and GPU applications urges cloud computing operators to build hybrid CPU/GPU clusters. While the traditional applications are executed on CPUs, the GPU applications can run on either CPUs or GPUs. In the CPU/GPU clusters, how to provision the hybrid CPU/GPU clusters for CPU and GPU applications and how to allocate the resources across CPUs and GPUs?

Interchangeable resources like CPUs and GPUs are not rare in large clusters. Some network I/O cards like wireless, ethernet, infinityband with different bandwidths can also be interchangeable.

In this paper, we focus on CPU/GPU systems. We develop a tool that estimates the performance and resource for an ML job in an online manner (§2). We implement AlloX system that supports resource allocation and places applications on right resources (CPU or GPU) to maximize the use of computational resource (§3). The proposed AlloX policy achieves up to 35% progress improvement compared to default DRF [2]. We build a model that minimizes the total cost of ownership for CPU/GPU data centers (§4).
An Efficient Network Monitor for SDN Networks
Authors: Shen Shan-Hsiang
Published: January 2019

With growing services running in clouds, it is critical to defence the services from Distributed Denial of Service (DDoS) attacks. To this end, network traffic should be monitored to detect malicious traffic. Software-defined Networking (SDN) provides a flexible platform for the network monitoring and relies on a central controller to ask switches for traffic statistic to get a global traffic view for security. However, the control plane resources are limited in SDN in terms of controller capacity, network bandwidth, and switch performance. Thus, too much network monitoring will affect data plane traffic performance. To address this issue, we propose SDN-Monitor, which carefully selects switches to monitor to reduce the resource consumption. Moreover, SDN-Monitorre-routes network traffic to further reduce the number of monitored switches.With growing services running in clouds, it is critical to defence the services from Distributed Denial of Service (DDoS) attacks. To this end, network traffic should be monitored to detect malicious traffic. Software-defined Networking (SDN) provides a flexible platform for the network monitoring and relies on a central controller to ask switches for traffic statistic to get a global traffic view for security. However, the control plane resources are limited in SDN in terms of controller capacity, network bandwidth, and switch performance. Thus, too much network monitoring will affect data plane traffic performance. To address this issue, we propose SDN-Monitor, which carefully selects switches to monitor to reduce the resource consumption. Moreover, SDN-Monitorre-routes network traffic to further reduce the number of monitored switches.
The Neural Hype and Comparisons Against Weak Baselines
Authors: Lin Jimmy
Published: January 2019

Recently, the machine learning community paused in a moment of self-reflection. In a widelydiscussed paper at ICLR 2018, Sculley et al. [13] wrote: "We observe that the rate of empirical advancement may not have been matched by consistent increase in the level of empirical rigor across the field as a whole." Their primary complaint is the development of a "research and publication culture that emphasizes wins" (emphasis in original), which typically means "demonstrating that a new method beats previous methods on a given task or benchmark". An apt description might be "leaderboard chasing"-and for many vision and NLP tasks, this isn't a metaphor. There are literally centralized leaderboards1 that track incremental progress, down to the fifth decimal point, some persisting over years, accumulating dozens of entries.

Sculley et al. remind us that "the goal of science is not wins, but knowledge". The structure of the scientific enterprise today (pressure to publish, pace of progress, etc.) means that "winning" and "doing good science" are often not fully aligned. To wit, they cite a number of papers showing that recent advances in neural networks could very well be attributed to mundane issues like better hyperparameter optimization. Many results can't be reproduced, and some observed improvements might just be noise.
Autumn School for Information Retrieval and Foraging 2018
Authors: Cole Amelia W. , Achilles Linda
Published: January 2019

The Autumn School for Information Retrieval and Information Foraging (ASIRF) is a five-day intensive seminar held in mid-September at Schloss Dagstuhl in Saarland, Germany. From September 16 - 21 in 2018, 20 people from five different countries engaged in a series of lectures, tutorials, and project-based work with internationally recognized senior scholars who specialize in information retrieval and behaviour.
Report on the 12th Russian Summer School in Information Retrieval (RuSSIR 2018)
Authors: Mejova Yelena , Chernyak Ekaterina , Tutubalina Elena , Braslavski Pavel
Published: January 2019

The 12th Russian Summer School in Information Retrieval (RuSSIR 2018) was held on August 27- 31 in Kazan, Russia.1 The school was co-organized by Kazan Federal University2 and the Russian Information Retrieval Evaluation Seminar (ROMIP).

Starting from 2007, the RuSSIR school series has become an internationally recognized venue [1]-[5]. Previously, RuSSIRs have taken place in Yekaterinburg, Taganrog, Petrozavodsk, Voronezh, Saint Petersburg, Yaroslavl, Kazan, Nizhny Novgorod, and Saratov. Over these years, RuSSIR courses have been taught by world-renowned researchers and industry leaders in Information Retrieval and related areas.

This year, we had a record number of around 120 participants in total (counting in students, teachers, organizers, and sponsors' representatives); there were 87 students attending RuSSIR 2018. While the majority of participants were from various Russian universities, there were also representatives from the USA, Spain, Ireland, Switzerland, the United Kingdom, Australia, the Netherlands, Estonia, China, Germany, Italy, Denmark, Austria, and Finland. School participation was free of charge due to support from the school's sponsors. In addition, 10 international students and two teachers received travel support from the BCS IRSG and ACM SIGIR.
Report on the 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2018)
Authors: Mayr Philipp , Chandrasekaran Muthu Kumar , Jaidka Kokil
Published: January 2019

The 3rd joint BIRNDL workshop was held at the 41st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) in Ann Arbor, USA. BIRNDL 2018 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the 4th edition of the CL-SciSumm Shared Task.
Report on GLARE 2018
Authors: Soboroff Ian , Ferro Nicola , Fuhr Norbert
Published: January 2019

This is a report on the first edition of the International Workshop on Generalization in Information Retrieval (GLARE 2018), co-located with the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018) held in Turin, Italy, on October 22, 2018.
Nontargeted Detection Methods for Food Safety and Integrity
Authors: Shao Bing , Li Hui , Shen Jianzhong , Wu Yongning
Published: January 2019

Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry–based technologies, including gas chromatography–mass spectrometry (GC-MS), liquid chromatography–mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis–mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry–based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.
Improving the Efficacy of Essential Oils as Antimicrobials in Foods: Mechanisms of Action
Authors: Rao Jiajia , Chen Bingcan , McClements David Julian
Published: January 2019

The consumer preference for clean-label products is requiring the food industry to reformulate their products by replacing artificial additives with natural alternatives. Essential oils are natural antimicrobials isolated from plant sources that have the potential to combat many foodborne pathogens and spoilage organisms. This review begins by discussing the antimicrobial properties of essential oils, the relationships between their chemical structure and antimicrobial efficacy, and their potential limitations for commercial applications (such as strong flavor, volatility, and chemical instability). We then review the commonly used methods for screening the antimicrobial efficacy of essential oils and elucidating their mechanisms of action. Finally, potential applications of essential oils as antimicrobials in foods are reviewed and the major types of food-grade delivery systems available for improving their efficacy are discussed.
Inhibition of GGPPS1 attenuated LPS-induced acute lung injury and was associated with NLRP3 inflammasome suppression
Authors: Xu Wu-jian , Wang Xiao-xia , Jin Jia-jia , Zou Qian , Wu Lin , Lv Tang-feng , Wan Bing , Zhan Ping , Zhu Su-hua , Liu Hong-bing , Zhao Ning-wei , Li Chao-jun , Song Yong
Published: January 2019

Inhibition of the mevalonate pathway using statins has been shown to be beneficial in the treatment of acute lung injury (ALI). Here, we investigated whether partial inhibition of this pathway by targeting geranylgeranyl pyrophosphate synthase large subunit 1 (GGPPS1), a catalase downstream of the mevalonate pathway, was effective at treating lung inflammation in ALI. Lipopolysaccharide (LPS) was intratracheally instilled to induce ALI in lung-specific GGPPS1-knockout and wild-type mice. Expression of GGPPS1 in lung tissues and alveolar epithelial cells was examined. The severity of lung injury and inflammation was determined in lung-specific GGPPS1 knockout and wild-type mice by measuring alveolar exudate, neutrophil infiltration, lung injury, and cell death. Change in global gene expression in response to GGPPS1 depletion was measured using mRNA microarray and verified in vivo and in vitro. We found that GGPPS1 levels increased significantly in lung tissues and alveolar epithelial cells in LPS-induced ALI mice. Compared with wild-type and simvastatin treated mice, the specific deletion of pulmonary GGPPS1 attenuated the severity of lung injury by inhibiting apoptosis of AECs. Furthermore, deletion of GGPPS1 inhibited LPS-induced inflammasome activation, in terms of IL-1β release and pyroptosis, by downregulating NLRP3 expression. Finally, downregulation of GGPPS1 reduced the membrane expression of Ras-related protein Rab10 and Toll-like receptor 4 (TLR4) and inhibited the phosphonation of IκB. This effect might be attributed to the downregulation of GGPP levels. Our results suggested that inhibition of pulmonary GGPPS1 attenuated LPS-induced ALI predominantly by suppressing the NLRP3 inflammasome through Rab10-mediated TLR4 replenishment.
Functional and biochemical responses of skeletal muscle following a moderate degree of systemic iron loading in mice
Authors: Liang Chen , Mickey Marisa C. , Receno Candace N. , Atalay Mustafa , DeRuisseau Keith C.
Published: January 2019

Excessive iron loading may cause skeletal muscle atrophy and weakness because of its free radical generating properties. To determine whether a clinically relevant degree of iron loading impairs skeletal muscle function, young male mice received injections of iron dextran (4 mg iron/200 µl) or 2 mM d-glucose (control) 5 days/week for 2 weeks ( n = 10/group). Systemic iron loading induced an approximate fourfold increase in the skeletal muscle nonheme iron concentration. Soleus specific tension (1, 30–250 Hz) was lower among iron-loaded animals compared with controls despite similar body mass and muscle mass. Soleus lipid peroxidation (4-hydroxynonenal adducts) and protein oxidation (protein carbonyls) levels were similar between groups. In gastrocnemius muscle, reduced glutathione (GSH) and glutathione peroxidase activity were similar but glutathione disulfide (GSSG) and the GSSG/GSH ratio were greater in iron-loaded muscle. A greater protein expression level of endogenous thiol antioxidant thioredoxin (TRX) was observed among iron-loaded muscle whereas its endogenous inhibitor thioredoxin-interacting protein (TXNip) and the TRX/TXNip ratio were similar. Glutaredoxin2, a thiol-disulfide oxidoreductase activated by GSSG-induced destabilization of its iron-sulfur [2Fe-2S] cluster, was lower following iron loading. Additionally, protein levels of α-actinin and αII-spectrin at 240 kDa were lower in the iron-loaded group. Ryanodine receptor stabilizing subunit calstabin1 was also lower following iron loading. In summary, the contractile dysfunction that resulted from moderate iron loading may be mediated by a disturbance in the muscle redox balance and from changes arising from an increased proteolytic response and aberrant sarcoplasmic reticulum Ca2+ release.

NEW & NOTEWORTHY Although severe iron loading is known to cause muscle oxidative stress and dysfunction, the effects of a moderate degree of systemic iron loading on muscle contractile function and biochemical responses remain unclear. This study demonstrates that a pathophysiological elevation in the skeletal muscle iron load leads to force deficits that coincide with impaired redox status, structural integrity, and lower ryanodine receptor-associated calstabin1 in the absence of muscle mass changes or oxidative damage.
Remote ischemic preconditioning increases accumulated oxygen deficit in middle-distance runners
Authors: Paull Emily J. , Van Guilder Gary P.
Published: January 2019

The mediators underlying the putative benefits of remote ischemic preconditioning (IPC) on dynamic whole body exercise performance have not been widely investigated. Our objective was to test the hypothesis that remote IPC improves supramaximal exercise performance in National Collegiate Athletic Association (NCAA) Division I middle-distance runners by increasing accumulated oxygen deficit (AOD), an indicator of glycolytic capacity. A randomized sham-controlled crossover study was employed. Ten NCAA Division I middle-distance athletes [age: 21 ± 1 yr; maximal oxygen uptake (V̇o2max): 65 ± 7 ml·kg−1·min−1] completed three supramaximal running trials (baseline, after mock IPC, and with remote IPC) at 110% V̇o2max to exhaustion. Remote IPC was induced in the right arm with 4 × 5 min cycles of brachial artery ischemia with 5 min of reperfusion. Supramaximal AOD (ml/kg) was calculated as the difference between the theoretical oxygen demand required for the supramaximal running bout (linear regression extrapolated from ~12 × 5 min submaximal running stages) and the actual oxygen demand for these bouts. Remote IPC [122 ± 38 s, 95% confidence interval (CI): 94–150] increased ( P < 0.001) time to exhaustion 22% compared with baseline (99 ± 23 s, 95% CI: 82–116, P = 0.014) and sham (101 ± 30 s, 95% CI: 80–123, P = 0.001). In the presence of IPC, AOD was 47 ± 36 ml/kg (95% CI: 20.8–73.9), a 29% increase compared with baseline (36 ± 28 ml/kg, 95% CI: 16.3–56.9, P = 0.008) and sham (38 ± 32 ml/kg, 95% CI: 16.2–63.0, P = 0.024). Remote IPC considerably improved supramaximal exercise performance in NCAA Division I middle-distance athletes. Greater glycolytic capacity, as estimated by increased AOD, is a potential mediator for these performance improvements.

NEW & NOTEWORTHY Our novel findings indicate that ischemic preconditioning enhanced glycolytic exercise capacity, enabling National Collegiate Athletic Association (NCAA) middle-distance track athletes to run ~22 s longer before exhaustion compared with baseline and mock ischemic preconditioning. The increase in “all-out” performance appears to be due to increased accumulated oxygen deficit, an index of better supramaximal capacity. Of note, enhanced exercise performance was demonstrated in a specific group of in-competition NCAA elite athletes that has already undergone substantial training of the glycolytic energy systems.
Two weeks of ischemic conditioning improves walking speed and reduces neuromuscular fatigability in chronic stroke survivors
Authors: Durand Matthew J. , Boerger Timothy F. , Nguyen Jennifer N. , Alqahtani Saad Z. , Wright Michael T. , Schmit Brian D. , Gutterman David D. , Hyngstrom Allison S.
Published: January 2019

This pilot study examined whether ischemic conditioning (IC), a noninvasive, cost-effective, and easy-to-administer intervention, could improve gait speed and paretic leg muscle function in stroke survivors. We hypothesized that 2 wk of IC training would increase self-selected walking speed, increase paretic muscle strength, and reduce neuromuscular fatigability in chronic stroke survivors. Twenty-two chronic stroke survivors received either IC or IC Sham on their paretic leg every other day for 2 wk (7 total sessions). IC involved 5-min bouts of ischemia, repeated five times, using a cuff inflated to 225 mmHg on the paretic thigh. For IC Sham, the cuff inflation pressure was 10 mmHg. Self-selected walking speed was assessed using the 10-m walk test, and paretic leg knee extensor strength and fatigability were assessed using a Biodex dynamometer. Self-selected walking speed increased in the IC group (0.86 ± 0.21 m/s pretest vs. 1.04 ± 0.22 m/s posttest, means ± SD; P < 0.001) but not in the IC Sham group (0.92 ± 0.47 m/s pretest vs. 0.96 ± 0.46 m/s posttest; P = 0.25). Paretic leg maximum voluntary contractions were unchanged in both groups (103 ± 57 N·m pre-IC vs. 109 ± 65 N·m post-IC; 103 ± 59 N·m pre-IC Sham vs. 108 ± 67 N·m post-IC Sham; P = 0.81); however, participants in the IC group maintained a submaximal isometric contraction longer than participants in the IC Sham group (278 ± 163 s pre-IC vs. 496 ± 313 s post-IC, P = 0.004; 397 ± 203 s pre-IC Sham vs. 355 ± 195 s post-IC Sham; P = 0.46). The results from this pilot study thus indicate that IC training has the potential to improve walking speed and paretic muscle fatigue resistance poststroke.

NEW & NOTEWORTHY This pilot study is the first to demonstrate that ischemic conditioning can improve self-selected walking speed and reduce paretic muscle fatigue in stroke survivors. Ischemic conditioning has been shown to be safe in numerous patient populations, can be accomplished at home or at the bedside in only 45 min, and requires no specialized training. Future larger studies are warranted to determine the efficacy of ischemic conditioning as a neurorehabilitation therapy poststroke.
Shortening-induced residual force depression in humans
Authors: Chen Jackey , Hahn Daniel , Power Geoffrey A.
Published: January 2019

When an isometric muscle contraction is immediately preceded by an active shortening contraction, a reduction in steady-state isometric force is observed relative to an isometric reference contraction at the same muscle length and level of activation. This shortening-induced reduction in isometric force, termed “residual force depression” (rFD), has been under investigation for over a half century. Various experimental models have revealed the positive relationship between rFD and the force and displacement performed during shortening, with rFD values ranging from 5 to 39% across various muscle groups, which appears to be due to a stress-induced inhibition of cross-bridge attachments. The current review will discuss the findings of rFD in humans during maximal and submaximal contractions.
Exercise intensity independently modulates thermal behavior during exercise recovery but not during exercise
Authors: Vargas Nicole T. , Chapman Christopher L. , Johnson Blair D. , Gathercole Rob , Schlader Zachary J.
Published: January 2019

We tested the hypothesis that thermal behavior is greater during and after high- compared with moderate-intensity exercise. In a 27°C, 20% relative humidity environment, 20 participants (10 women, 10 men) cycled for 30 min at moderate [53% (SD 6) peak oxygen uptake (V̇o2peak) or high [78% (SD 6) V̇o2peak] intensity, followed by 120 min of recovery. Mean skin and core temperatures and mean skin wettedness were recorded continuously. Participants maintained thermally comfortable neck temperatures with a custom-made neck device. Neck device temperature provided an index of thermal behavior. The weighted average of mean skin and core temperatures and mean skin wettedness provided an indication of the afferent stimulus to thermally behave. Mean skin and core temperatures were greater at end-exercise in high intensity ( P < 0.01). Core temperature remained elevated in high intensity until 70 min of recovery ( P = 0.03). Mean skin wettedness and the afferent stimulus were greater at 10–20 min of exercise in high intensity ( P ≤ 0.03) and remained elevated until 60 min of recovery ( P < 0.01). Neck device temperature was lower during exercise in high versus moderate intensity ( P ≤ 0.02). There was a strong relation between the afferent stimulus and neck device temperature during exercise (high: R2 = 0.82, P < 0.01; moderate: R2 = 0.95, P < 0.01) and recovery (high: R2 = 0.97, P < 0.01; moderate: R2 = 0.93, P < 0.01). During exercise, slope ( P = 0.49) and y-intercept ( P = 0.91) did not differ between intensities. In contrast, slope was steeper ( P < 0.01) and y-intercept was higher ( P < 0.01) during recovery from high-intensity exercise. Thermal behavior is greater during high-intensity exercise because of the greater stimulus to behave. The withdrawal of thermal behavior is augmented after high-intensity exercise.

NEW & NOTEWORTHY This is the first study to determine the effects of exercise intensity on thermal behavior. We show that exercise intensity does not independently modulate thermal behavior during exercise but is dependent on the magnitude of afferent stimuli. In contrast, the withdrawal of thermal behavior after high-intensity exercise is augmented. This may be a consequence of an attenuated perceptual response to afferent stimuli, which may be due to processes underlying postexercise hypoalgesia.
Effects of an Outpatient Diabetes Self-Management Education on Patients with Type 2 Diabetes in China: A Randomized Controlled Trial
Authors: Zheng Fan , Liu Suixin , Liu Yuan , Deng Lihua
Published: January 2019

Objective. This study is aimed at assessing the effectiveness of a simple outpatient diabetes self-management education programme. Methods. In the study, 60 patients with type 2 diabetes mellitus were randomly allocated into the control group (n=30) and intervention group (n=30). Regular and 2-session health education programmes were provided. The summary of diabetes self-care activity measure, problem areas in the diabetes scale, fasting blood glucose, postprandial 2 h blood glucose, and HbA1c were measured before and after the intervention to assess the effects of this 2-session diabetes education programme. Results. The total mean score of the summary of diabetes self-care activities measure was 17.60±6.63 points. The problem areas in the diabetes scale revealed that the total mean score was 29.82±15.22 points; 27% of the patients had diabetes-related distress, while 9% suffered from severe emotional distress. Compared with the control group, scores of the summary of diabetes self-care activities measure and problem areas in the diabetes scale, fasting blood glucose, postprandial 2 h blood glucose, and HbA1c were significantly improved in the intervention group after the intervention (P<0.01). Conclusion. This study showed that the 2-session diabetes education programme could effectively improve the level of self-reported self-management, psychological distress, and glycemic control in patients with type 2 diabetes mellitus.
Quetiapine Attenuates the Neuroinflammation and Executive Function Deficit in Streptozotocin-Induced Diabetic Mice
Authors: Wang Kexin , Song Feng , Wang Hongxing , Wang Jun-hui , Sun Yu
Published: January 2019

Diabetic patients are at increased risk for developing memory and cognitive deficit. Prior studies indicate that neuroinflammation might be one important underlying mechanism responsible for this deficit. Quetiapine (QTP) reportedly exerts a significant neuroprotective effect in animal and human studies. Here, we investigated whether QTP could prevent memory deterioration and cognitive impairment in a streptozotocin- (STZ-) induced diabetic mouse model. In this study, we found that STZ significantly compromised the behavioral performance of mice in a puzzle box test, but administering QTP effectively attenuated this behavioral deficit. Moreover, our results showed that QTP could significantly inhibit the activation of astrocytes and microglia in these diabetic mice and reduce the generation and release of two cytokines, tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCP-1). Meanwhile, QTP also prevented the protein loss of the synaptic protein synaptophysin (SYP) and myelin basic protein (MBP). Here, our results indicate that QTP could inhibit neuroinflammatory response from glial cells and block the injury of released cytokines to neurons and oligodendrocytes in diabetic mice (DM). These beneficial effects could protect diabetic mice from the memory and cognitive deficit. QTP may be a potential treatment compound to handle the memory and cognitive dysfunction in diabetic patients.