bluuebunny/crossref_metadata_2025 huggingface dataset:
Anxiety and depressive symptoms are associated with poor sleep health
during a period of COVID-19-induced nationwide lockdown: a cross-sectional
analysis of adults in Jordan
Authors: Al-Ajlouni Yazan A , Park Su Hyun , Alawa Jude , Shamaileh Ghaith , Bawab Aziz , El-Sadr Wafaa M , Duncan Dustin T
Published: May 2021

Background

Jordan, a Middle Eastern country, declared a state of national
emergency due to COVID-19 and a strict nationwide lockdown on 17 March
2020, banning all travel and movement around the country, potentially
impacting mental health. This study sought to investigate the
association between mental health (eg, anxiety and depressive symptoms)
and sleep health among a sample of Jordanians living through a state of
COVID-19-induced nationwide lockdown.

Methods

Using Facebook, participants (n=1240) in Jordan in March 2020 were
recruited and direct to a web-based survey measuring anxiety (items from
General Anxiety Disorder 7-item (GAD-7) scale instrument), depressive
symptoms (items from Center for Epidemiologic Studies Depression Scale),
sleep health (items from the Pittsburgh Sleep Quality Index) and
sociodemographic. A modified Poisson regression model with robust error
variance. Adjusted prevalence ratios (aPRs) and 95% CIs were estimated
to examine how anxiety and depressive symptoms may affect different
dimensions of sleep health: (1) poor sleep quality, (2) short sleep
duration, (3) encountering sleep problems.

Results

The majority of participants reported having experienced mild
(33.8%), moderate (12.9%) or severe (6.3%) levels of anxiety during
lockdown, and nearly half of respondents reported depressive symptoms
during lockdown. Similarly, over 60% of participants reported having
experienced at least one sleep problem in the last week, and nearly half
reported having had short sleep duration. Importantly, anxiety was
associated with poor sleep health outcomes. For example, corresponding
to the dose–response relationship between anxiety and sleep health
outcomes, those reporting severe anxiety were the most likely to
experience poor sleep quality (aPR =8.95; 95% CI=6.12 to 13.08), short
sleep duration (aPR =2.23; 95% CI=1.91 to 2.61) and at least one problem
sleep problem (aPR=1.73; 95% CI=1.54 to 1.95). Moreover, depressive
symptoms were also associated with poor sleep health outcomes. As
compared with scoring in the first quartile, scoring fourth quartile was
associated with poor sleep quality (aPR=11.82; 95% CI=6.64 to 21.04),
short sleep duration (aPR=1.87; 95% CI=1.58 to 2.22), and experiencing
at least one sleep problem (aPR=1.90; 95% CI=1.66 to 2.18).

Conclusions

Increased levels of anxiety and depressive symptoms can negatively
influence sleep health among a sample of Jordanian adults living in a
state of COVID-19-induced nationwide lockdown.
Pulmonary hypertension and home-based (PHAHB) exercise intervention: protocol for a feasibility study
Authors: McCormack Ciara , Kehoe Brona , Hardcastle Sarah J , McCaffrey Noel , McCarren Andrew , Gaine Sean , McCullagh Brian , Moyna Niall
Published: May 2021

Introduction

Novel therapies for pulmonary hypertension (PH) have improved survival and slowed disease progression. However, patients still present with symptoms of exertional dyspnoea and fatigue, which impacts their ability to perform activities of daily living, reduces exercise tolerance and impairs their quality of life (QoL). Exercise training has shown to be safe and effective at enhancing QoL and physical function in PH patients, yet it remains an underused adjunct therapy. Most exercise training for PH patients has been offered through hospital-based programmes. Home-based exercise programmes provide an alternative model that has the potential to increase the availability and accessibility of exercise training as an adjunct therapy in PH. The purpose of this study is to investigate the feasibility, acceptability, utility and safety of a novel remotely supervised home-based PH exercise programme.

Methods

Single arm intervention with a pre/post comparisons design and a follow-up maintenance phase will be employed. Eligible participants (n=25) will be recruited from the Mater Misericordiae University Hospital PH Unit. Participants will undergo a 10-week home-based exercise programme, with induction training, support materials, telecommunication support and health coaching sessions followed by a 10-week maintenance phase. The primary outcomes are feasibility, acceptability, utility and safety of the intervention. Secondary outcomes will include the impact of the intervention on exercise capacity, physical activity, strength, health-related QoL and exercise self-efficacy.

Ethics and dissemination

Ethics approval has been obtained from the Mater Misericordiae Institutional Review Board REF:1/378/2032 and Dublin City University Research Ethics DCUREC/2018/246. A manuscript of the results will be submitted to a peer-reviewed journal and results will be presented at conferences, community and consumer forums and hospital research conferences.

Trial registration number

ISRCTN83783446; Pre-results.
Preconception alcohol consumption and risk of miscarriage in over 4.5 million Chinese women aged 20–49 years
Authors: Chai Jian , Guo Tonglei , Deng Yuzhi , Jiang Lifang , Zhang Junxi , Xu Qin , Peng Zuoqi , He Yuan , Wang Yuanyuan , Zhang Ya , Zhang Hongguang , Wang Qiaomei , Shen Haiping , Zhang Yiping , Yan Donghai , Yang Ying , Ma Xu
Published: May 2021

Objective

To assess the impact of preconception alcohol consumption on risk of miscarriage incidence, and further evaluate the association between maternal periconception drinking abstinence and miscarriage.

Methods

We performed a population-based, retrospective cohort study in China between 1 January 2013 and 31 December 2016. Alcohol intake and potential confounding factors were reported in standard questionnaires. Participants who became pregnant were recontacted for pregnancy outcome information within 1 year. A total 4 531 680 women with available data on preconception alcohol intake and miscarriage were included in the final analyses. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs).

Results

The prevalence of miscarriage was 2.70% among 4 531 680 women. Compared with non-drinkers, the adjusted OR of miscarriage was 1.06 (95% CI 1.02 to 1.10) and 1.59 (95% CI 1.15 to 2.20) in maternal occasional drinkers and regular drinkers, respectively. Compared with couples in which neither the male nor the female consumed alcohol, the adjusted OR for miscarriage among women was 1.09 (95% CI 1.07 to 1.10), 1.13 (95% CI 1.06 to 1.21) and 1.12 (95% CI 1.07 to 1.17) in the couples in which only the female drank alcohol, only the male drank alcohol, and both drank alcohol, respectively. The adjusted OR was 0.58 (95% CI 0.51 to 0.65) in women with alcohol abstinence compared with alcohol drinkers.

Conclusions

Preconception alcohol consumption was associated with higher odds of miscarriage, and an increasing risk was found with paternal and maternal alcohol drinking. Periconception alcohol abstinence was inversely associated with miscarriage.
Bowel transit studies in children: evidence base, role and practicalities
Authors: Popescu Mara , Mutalib Mohamed
Published: May 2021

Constipation is common in children and adults with varied worldwide prevalence. The majority of children have functional constipation as defined by Rome clinical criteria and respond favourably to standard medical therapy; up to one-third can develop difficult-to-treat constipation requiring investigation and specialist treatment. Colon function tests aim to assess the neuromuscular integrity, the movement of faeces across the colon and evaluate/predict response to the therapy. The ‘ideal’ test should be practical, non-invasive, widely available and cost-effective. None of the available diagnostic tools is designed to provide a comprehensive assessment of colon function and clinicians often have to combine more than one test to answer different questions. In this review, we aim to assess the strengths and limitations of the commonly available diagnostic investigations (radiopaque marker studies, scintigraphy, wireless motility capsule and colonic manometry) used to assess colon transit in children and to provide guidance on the most appropriate test for particular clinical settings.
First-line thrombectomy strategy for anterior large vessel occlusions: results of the prospective ETIS egistry
Authors: Maïer Benjamin , Finitsis Stephanos , Bourcier Romain , Papanagiotou Panagiotis , Richard Sébastien , Marnat Gaultier , Sibon Igor , Dargazanli Cyril , Arquizan Caroline , Blanc Raphael , Piotin Michel , Lapergue Bertrand , Consoli Arturo , Eugene Francois , Vannier Stephane , Saleme Suzana , Macian Francisco , Clarençon Frédéric , Rosso Charlotte , Naggara Olivier , Turc Guillaume , Viguier Alain , Cognard Christophe , Wolff Valerie , Pop Raoul , Mazighi Mikael , Gory Benjamin
Published: May 2021

Background

The best recanalization strategy for mechanical thrombectomy (MT) remains unknown as no randomized controlled trial has simultaneously evaluated first-line stent retriever (SR) versus contact aspiration (CA) versus the combined approach (SR+CA).

Objective

To compare the efficacy and safety profiles of SR, CA, and SR+CA in patients with acute ischemic stroke (AIS) treated by MT.

Methods

We analyzed data of the Endovascular Treatment in Ischemic Stroke (ETIS) Registry, a prospective, multicenter, observational study of patients with AIS treated by MT. Patients with M1 and intracranial internal carotid artery (ICA) occlusions between January 2015 and March 2020 in 15 comprehensive stroke centers were included. We assessed the association of first-line strategy with favorable outcomes at 3 months (modified Rankin Scale score 0–2), successful recanalization rates (modified Thrombolysis In Cerebral Infarction (mTICI) 2b/3), and safety outcomes.

Results

We included 2643 patients, 406 treated with SR, 1126 with CA, and 1111 with SR+CA. CA or SR+CA achieved more successful recanalization than SR for M1 occlusions (aOR=2.09, (95% CI 1.39 to 3.13) and aOR=1.69 (95% CI 1.12 to 2.53), respectively). For intracranial ICA, SR+CA achieved more recanalization than SR (aOR=2.52 (95% CI 1.32 to 4.81)), no differences were observed between CA and SR+CA. SR+CA was associated with lower odds of favorable outcomes compared with SR (aOR=0.63 (95% CI 0.44 to 0.90)) and CA (aOR=0.71 (95% CI 0.55 to 0.92)), higher odds of mortality at 3 months (aOR=1.56 (95% CI 1.22 to 2.0)) compared with CA, and higher odds of symptomatic intracranial hemorrhage (aOR=1.59 (95% CI 1.1 to 2.3)) compared with CA.

Conclusions

Despite high recanalization rates, our results question the safety of the combined approach, which was associated with disability and mortality. Randomized controlled trials are needed to evaluate the efficacy and safety of these techniques.
Exploring direct detection suppressed regions in a simple 2-scalar mediator model of scalar dark matter
Authors: Claude Jérôme , Godfrey Stephen
Published: May 2021

We explore regions of parameter space that give rise to suppressed direct detection cross sections in a simple model of scalar dark matter with a scalar portal that mixes with the standard model Higgs. We found that even this simple model allows considerable room in the parameter space that has not been excluded by direct detection limits. A number of effects leading to this result have been previously noted. Our main new result explores interference effects between different contributions to DM annihilation when the DM mass is larger than the scalar portal mass. New annihilation channels open up and the parameters of the model need to compensate to give the correct DM relic abundance, resulting in smaller direct detection cross sections. We find that even in a very simple model of DM there are still sizeable regions of parameter space that are not ruled out by experiment.
3D graphene aerogel-supported copper azide nanoparticles as primary explosive with excellent initiation performance
Authors: Zhang Lei , Chen Jianhua , Fu Dongxiao , Lu Feipeng , Wang Yanlan , Zhang Rui
Published: May 2021

Copper azide (CA), a high energy primary explosive, had been investigated with various supporter materials to reduce its sensitivity characteristics. However, effective supporter had not been designed to fully reduce CAs sensitivity characteristics. Hence, we presented a three-dimensional (3D) graphene aerogel-supported CA (CA-GAs) strategy to meet those needs. CA-GAs was prepared using 3D GA as the framework to uniformly grip CA. The SEM and XRD measurements revealed that CA nanoparticles were fully dispersed on GAs, the in-situ azidation reaction of Cu precursor and CAs crystalline structure were not affected by GAs. DSC further verified the thermal stability of CA-GAs. Moreover, electrostatic ignition experiment was employed to investigate CA-GAs’ ignition time, input energy, and electrical ignition performance. Those results showed that CA-GAs could be reliably detonated by a 3.5 [Formula: see text] SCB, the average electric ignition voltage, ignition time, and input energy of CA-GAs were 19.125 V, 21 [Formula: see text]s, and 0.56 mJ, respectively. All these results illustrated that GAs was an effective supporter for the stabilization of CA.
A Novel Method for Nonlinear Boundary Value Problems Based on Multiscale Orthogonal Base
Authors: Zhang Yingchao , Mei Liangcai , Lin Yingzhen
Published: May 2021

In this paper, a new algorithm is presented to solve the nonlinear second-order differential equations. The approach combines the Quasi-Newton’s method and the multiscale orthogonal bases. It is worth mentioning that the convergence of Newton’s method is verified for solving the nonlinear differential equations. The uniform convergence of the numerical solution as well as its derivatives are also proved. Numerical examples are given to demonstrate the efficiency and feasibility of the proposed method.
Cell Resistance and Antimicrobial Resistance with Waning Vaccination
Authors: Ahmed E. , Sohaly M. A.
Published: May 2021

Viruses are obligatory minute intra-cellular infectious agents with very simple composition. They are nonliving (not active) macromolecules outside the host cell while turning into living active organisms inside host cells. The genetic material (DNA or RNA) carrying the information is crucial for virus replication and enforces the cell to approve virus replication. Consequently, it is cellular resistance against the virus that determines whether a cell at any site is infected or not. In this study, we are interested in the resistance of cells which may be infected by some disturbance such as a function of [Formula: see text] or as a random variable. Antimicrobial resistance (AMR) is the wider word for resistance in various kinds of microorganisms and includes resistance to antibacterial, antiviral, anti-parasitic, and anti-fungal medicines. Here we study the AMR problem and also, the waning vaccination in the Percolation area. Percolation is a purely geometric problem in which clusters of connected sites or bonds are clearly defined static objects. We are studying cellular automata from Domany–Kinzel on the population of AMRs as on the spreading network. Each connection is rewired on a one-dimensional chain and combined with any probability p node. Additionally, the Domany–Kinzel model will be applied for AMR and waning vaccination in two dimensions.
The study of mixed assets allocation based on Black–Litterman model
Authors: Lin Jianwu , Tang Mengwei , Wang Jiachang , He Ping
Published: May 2021

With Private Funds having a new type of license for asset allocation practice in China, comprehensive asset allocation cross private equity and stock market has received more attention. However, most of the studies focus more on the stock market, and asset allocation models for private equity market that are mainly made based on experience. Thus, the joint allocation of assets crosses both markets making it a challenging research topic. This paper introduces the Black–Litterman model into the private equity market, realizing the transition from qualitative models to quantitative models. It lays a solid quantitative ground for the mixed asset allocation model in both the markets.
Linked Data and Cultural Heritage
Authors: Davis Edie , Heravi Bahareh
Published: May 2021

The cultural heritage sector has traditionally been concerned with sharing resources and furthering human knowledge, with particular interest to the issues associated with metadata and interoperability, especially when it comes to the use of technology. These goals and interests in the cultural heritage sector are natural alignments with those of linked data; hence. there has been an increasing interest in the application of linked data in this sector. This article studies the implementation of linked data in the cultural heritage sector, through a systematic literature review of case studies of linked data implementation projects in this sector. The results reflect on the parties involved, the level of collaboration, and the motivation behind these projects. The study suggests that universities and national institutions were the main players in implementing such technologies in the cultural heritage sector, suggesting that there may be some barriers preventing smaller GLAM institutions from implementing linked data projects. The results further suggest that many linked data projects in this sector were primarily exploratory projects, and often performed in a collaborative manner. They further indicate that the most common motivating factors behind these projects were research needs, a desire to contribute to linked data as a movement, and other specific user needs. Reflecting on this systematic literature review, this article makes a set of recommendations for future work to increase the use of linked data in the cultural heritage sector and to remove barriers to adoption.
Hidden Town in 3D
Authors: Lewis Richard , Taylor-Poleskey Molly
Published: May 2021

This article presents a case of collaborative pedagogy of digital humanities involving a virtual version of historic Salem, North Carolina. “Hidden Town in 3D” is a partnership between Middle Tennessee State University’s Public History, Animation, and Aerospace programs, and Old Salem Museums and Gardens. The object of Hidden Town in 3D is to use digital technologies to recover and represent the stories of the African Americans of Salem. The anticipated outputs for this project are an augmented reality application that can be used on-site at Old Salem, allowing visitors to see slave dwellings where they once stood and a virtual tour using gaming technology of the entire town in the year 1860 with African-American stories and homes reintegrated. Along with enhancing the museum’s visitor experience, these outputs enhance the educational experiences of undergraduate and graduate students through interdisciplinary, project-based learning. Thus, the technical work of modeling, animating, and augmented reality benefits teaching, historical scholarship, and museum offerings.
Parental Involvement in Computer Science Education and Computing Attitudes and Behaviours in the Home: Model and Scale Development
Authors: Bresnihan Nina , Bray Aibhín , Fisher Lorraine , Strong Glenn , Millwood Richard , Tangney Brendan
Published: May 2021

This work is situated in research on Parental Involvement (PI) in Computer Science (CS) Education. While the importance of PI in children's education is well established, most parents have little experience in CS and struggle to facilitate the learning of a child in the area. If PI in CS Education is to happen, then we argue that parents need support and that understanding the current behaviours and attitudes toward CS in the family context is important to discerning the form that support should take. This article therefore describes the development of an instrument to identify factors relating to parental attitudes toward and motivation for PI in CS education. Relevant variables situated in the context of parental computing behaviours and attitudes in the home were identified using a literature review and expert focus group. These include computing usage, availability, confidence, and experience. To measure these variables, a survey instrument was developed and administered to a large sample of parents (
n
= 1228). Results of exploratory and confirmatory factor analysis confirm that the instrument measures five constructs, namely “Confidence,” measuring parental confidence levels with computing; “Attitude to PI”; “Motivation for PI”; and two types of “Usage”: Creation and Consumption. Results of Pearson correlation revealed significant positive relationships between confidence and both positive attitudes toward, and motivation for, PI, with linear regressions confirming that confidence was a significant predictor of both. Regression analysis also identified that creative usage was a predictor of positive attitudes to PI, and that programming experience was a predictor of attitude to, and motivation for, PI. These findings were further validated through triangulation with qualitative data from focus groups with the target population. We conclude that this understanding of the predictors of PI attitudes and motivation should inform the design of initiatives to address parental engagement in CS Education.
Improved Customer Lifetime Value Prediction With Sequence-To-Sequence Learning and Feature-Based Models
Authors: Bauer Josef , Jannach Dietmar
Published: May 2021

The prediction of the Customer Lifetime Value (CLV) is an important asset for tool-supported marketing by customer relationship managers. Since standard methods based on purchase recency, frequency, and past profit and revenue statistics often have limited predictive power, advanced machine learning (ML) techniques were applied to this task in recent years. However, existing approaches are often not fully capable of modeling certain temporal patterns that can be commonly found in practice, such as periodic purchasing behavior of customers. To address these shortcomings, we propose a novel method for CLV prediction based on a combination of several ML techniques. At its core, our method consists of a tailored deep learning approach based on encoder–decoder sequence-to-sequence recurrent neural networks with augmented temporal convolutions. This model is then combined with gradient boosting machines (GBMs) and a set of novel features in a hybrid framework. Empirical evaluations based on real-world data from a larger e-commerce company and a public dataset from the domain of online retail show that already the sequence-based model leads to competitive performance results. Stacking it with the GBM model is synergistic and further improves accuracy, indicating that the two models capture different patterns in the data.
A Real-world Approach to Motivate Students on the First Class of a Computer Science Course
Authors: Bellino Alessio , Herskovic Valeria , Hund Michael , Munoz-Gama Jorge
Published: May 2021

A common belief among students is that computing is a boring subject that lacks a connection to the real world. The first class (one 80-minute session) in an introductory computer science course may be an appropriate instance to combat such a belief. Previous studies have used coursewide interventions, e.g., games and physical/tangible devices to improve students’ motivation. However, although other approaches help motivate students, they may lack real-world context or have a high cost of deployment. This article proposes a novel real-world based approach to introduce programming concepts in the first class of the introductory computer science course. This approach, called Protobject based, is applicable to courses with over 100 students, has a low deployment entry barrier, requires low investment, and may be used creatively to implement different experiences. Furthermore, the Protobject-based approach has an equivalent motivational effect—at least in the short-term—to a Game-based approach even if it is entirely focused on the real world. The low requirements of the approach make it especially suitable for an 80-minute first class in an introductory computer science course. The Protobject-based approach has been preliminarily validated and compared to a pure game-based approach with a study with 376 participants, and we present the analysis of motivation questionnaires, a pre-test and post-test, and a homework assignment given to the students. We posit that more research into initiatives such as this one—that can show students how computer science can impact the real world around them—is warranted.
CSF: Formative Feedback in Autograding
Authors: Haldeman Georgiana , Babeş-Vroman Monica , Tjang Andrew , Nguyen Thu D.
Published: May 2021

Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a knowledge map, which is the set of concepts and skills that are necessary to complete an assignment, followed by the design of the assignment and that of a comprehensive test suite for identifying logical errors in the submitted code. Test cases are used to test the student submissions and learn classes of common errors. For each assignment, we train a classifier that automatically categorizes errors in a submission based on the outcome of the test suite. The instructor maps the errors to corresponding concepts and skills and writes hints to help students find their misconceptions and mistakes. We apply this methodology to two assignments in our Introduction to Computer Science course and find that the automatic error categorization has a 90% average accuracy. We report and compare data from two semesters, one semester when hints are given for the two assignments and one when hints are not given. Results show that the percentage of students who successfully complete the assignments after an initial erroneous submission is three times greater when hints are given compared to when hints are not given. However, on average, even when hints are provided, almost half of the students fail to correct their code so that it passes all the test cases. The initial implementation of the framework focuses on the functional correctness of the programs as reflected by the outcome of the test cases. In our future work, we will explore other kinds of feedback and approaches to automatically generate feedback to better serve the educational needs of the students.
Self-Adaptive Skeleton Approaches to Detect Self-Organized Coalitions From Brain Functional Networks Through Probabilistic Mixture Models
Authors: Liu Kai , Liu Hongbo , Ward Tomas E. , Wang Hua , Yang Yu , Zhang Bo , Wu Xindong
Published: May 2021

Detecting self-organized coalitions from functional networks is one of the most important ways to uncover functional mechanisms in the brain. Determining these raises well-known technical challenges in terms of scale imbalance, outliers and hard-examples. In this article, we propose a novel self-adaptive skeleton approach to detect coalitions through an approximation method based on probabilistic mixture models. The nodes in the networks are characterized in terms of robust
k
-order complete subgraphs (

k
-clique

) as essential substructures. The
k
-clique enumeration algorithm quickly enumerates all
k
-cliques in a parallel manner for a given network. Then, the cliques, from
max
-clique down to
min
-clique, of each order
k
, are hierarchically embedded into a probabilistic mixture model. They are self-adapted to the corresponding structure density of coalitions in the brain functional networks through different order
k
. All the cliques are merged and evolved into robust skeletons to sustain each unbalanced coalition by eliminating outliers and separating overlaps. We call this the
k
-CLIque Merging Evolution (CLIME) algorithm. The experimental results illustrate that the proposed approaches are robust to density variation and coalition mixture and can enable the effective detection of coalitions from real brain functional networks. There exist potential cognitive functional relations between the regions of interest in the coalitions revealed by our methods, which suggests the approach can be usefully applied in neuroscientific studies.
Automatic Sublining for Efficient Sparse Memory Accesses
Authors: Heirman Wim , Eyerman Stijn , Bois Kristof Du , Hur Ibrahim
Published: May 2021

Sparse memory accesses, which are scattered accesses to single elements of a large data structure, are a challenge for current processor architectures. Their lack of spatial and temporal locality and their irregularity makes caches and traditional stream prefetchers useless. Furthermore, performing standard caching and prefetching on sparse accesses wastes precious memory bandwidth and thrashes caches, deteriorating performance for regular accesses. Bypassing prefetchers and caches for sparse accesses, and fetching only a single element (e.g., 8 B) from main memory (subline access), can solve these issues.

Deciding which accesses to handle as sparse accesses and which as regular cached accesses, is a challenging task, with a large potential impact on performance. Not only is performance reduced by treating sparse accesses as regular accesses, not caching accesses that do have locality also negatively impacts performance by significantly increasing their latency and bandwidth consumption. Furthermore, this decision depends on the dynamic environment, such as input set characteristics and system load, making a static decision by the programmer or compiler suboptimal.

We propose the
Instruction Spatial Locality Estimator
(
ISLE
), a hardware detector that finds instructions that access isolated words in a sea of unused data. These sparse accesses are dynamically converted into uncached subline accesses, while keeping regular accesses cached. ISLE does not require modifying source code or binaries, and adapts automatically to a changing environment (input data, available bandwidth, etc.). We apply ISLE to a graph analytics processor running sparse graph workloads, and show that ISLE outperforms the performance of no subline accesses, manual sublining, and prior work on detecting sparse accesses.
The April 2021 issue
Authors: Uhlig Steve
Published: May 2021

This April 2021 issue contains one technical paper as well as five editorial notes.
Surviving switch failures in cloud datacenters
Authors: Singh Rachee , Mukhtar Muqeet , Krishna Ashay , Parkhi Aniruddha , Padhye Jitendra , Maltz David
Published: May 2021

Switch failures can hamper access to client services, cause link congestion and blackhole network traffic. In this study, we examine the nature of switch failures in the datacenters of a large commercial cloud provider through the lens of survival theory. We study a cohort of over 180,000 switches with a variety of hardware and software configurations and find that datacenter switches have a 98% likelihood of functioning uninterrupted for over 3 months since deployment in production. However, there is significant heterogeneity in switch survival rates with respect to their hardware and software: the switches of one vendor are twice as likely to fail compared to the others. We attribute the majority of switch failures to hardware impairments and unplanned power losses. We find that the in-house switch operating system, SONiC, boosts the survival likelihood of switches in datacenters by 1% by eliminating switch failures caused by software bugs in vendor switch OSes.
Workshop on Internet Economics (WIE 2020) final report
Authors: claffy kc , Clark David
Published: May 2021

On 16-17 December 2020, CAIDA hosted the 11th interdisciplinary Workshop on Internet Economics (WIE) in a virtual Zoom conference. This year our goal was to gather feedback from researchers on their experiences using CAIDA’s data for economics or policy research. We invited all researchers who reported use of CAIDA data in these disciplines. We discussed their successes and challenges of using the data, and how CAIDA could help these fields via Internet measurement and data curation. To avoid Zoom fatigue, we had a conversation-focused rather than presentation-focused workshop. Research topics we discussed included: Internet data for macroeconomics; connectivity and its effect on economic interdependence; effects of the EU’s new GDPR on internet interconnection; measuring corporate cyber risk; measuring work-from-home trends; measuring the economic value of open source software; and more generally how to best support evidence-based policymaking.
Great educators in computer networking
Authors: Caesar Matthew , Davie Bruce
Published: May 2021

This interview is part of a series on Great Educators in Computer Networking, where we interview some of the most impactful and skilled educators in our field. Here, we interviewed Australian Bruce Davie, the self-described computer scientist/engineer/runner/cyclist, who agreed to talk to us about his thoughts on computer networking education, his role in it, his thoughts about the big ideas in our field, and how the pandemic is changing our work. Bruce has over 30 years of industry experience and is well known for a broad spectrum of educational initiatives such as co-authoring several textbooks, as well as his contributions to many networking standards and technologies, including IP quality of service, network virtualization, software defined networking, and more
Metabolic physiology and skeletal muscle phenotypes in male and female myoglobin knockout mice
Authors: Ono-Moore Kikumi D. , Olfert I. Mark , Rutkowsky Jennifer M. , Chintapalli Sree V. , Willis Brandon J. , Blackburn Michael L. , Williams D. Keith , O’Reilly Juliana , Tolentino Todd , Lloyd K. C. Kent , Adams Sean H.
Published: May 2021

The roles of myoglobin remain to be elaborated. We address sexual dimorphism in terms of outcomes in response to the loss of myoglobin in knockout mice and perform, for the first time, a series of comprehensive metabolic studies under conditions in which fat is mobilized (high-fat diet, cold). The results highlight that myoglobin is not necessary and sufficient for maintaining oxidative metabolism and point to alternative roles for this protein in muscle and heart.
On the potential role of globins in brown adipose tissue: a novel conceptual model and studies in myoglobin knockout mice
Authors: Blackburn Michael L. , Wankhade Umesh D. , Ono-Moore Kikumi D. , Chintapalli Sree V. , Fox Renee , Rutkowsky Jennifer M. , Willis Brandon J. , Tolentino Todd , Lloyd K. C. Kent , Adams Sean H.
Published: May 2021

Myoglobin confers the distinct red color to muscle and heart, serving as an oxygen-binding protein in oxidative fibers. Less attention has been paid to brown fat, a thermogenic tissue that also expresses myoglobin. In a mouse knockout model lacking myoglobin, brown fat had larger fat droplets and lower markers of mitochondrial oxidative metabolism, especially in females. Gene expression patterns suggest a role for myoglobin as an oxygen/nitric oxide-sensor that regulates cellular metabolic and signaling pathways.
Inward Ca2+ current through the polycystin-2-dependent channels of renal primary cilia
Authors: Kleene Steven J. , Kleene Nancy K.
Published: May 2021

In autosomal dominant polycystic kidney disease, it is proposed that Ca2+ entering the primary cilium through polycystin-2 (PC2) channels may limit the formation of cysts. Recent studies predict that any macroscopic Ca2+ influx through these channels should be small. We report that the native PC2 channels in primary cilia of cultured renal epithelial cells can allow a small macroscopic calcium influx. This may allow a significant accumulation of Ca2+ in the cilium in vivo.
Vascular control of kidney epithelial transporters
Authors: Sparks Matthew A. , Dilmen Emre , Ralph Donna L. , Rianto Fitra , Hoang Thien A. , Hollis Alison , Diaz Edward J. , Adhikari Rishav , Chew Gabriel , Petretto Enrico G. , Gurley Susan B. , McDonough Alicia A. , Coffman Thomas M.
Published: May 2021

The use of drugs to block the renin-angiotensin system to reduce blood pressure is common. However, the precise mechanism for how these medications control blood pressure is incompletely understood. Here, we show that mice lacking angiotensin receptors specifically in smooth muscle cells lead to alternation in tubular transporter amount and function. Thus, demonstrating the importance of vascular-tubular cross talk in the control of blood pressure.
Time-dependent progression of neurogenic lower urinary tract dysfunction after spinal cord injury in the mouse model
Authors: Saito Tetsuichi , Gotoh Daisuke , Wada Naoki , Tyagi Pradeep , Minagawa Tomonori , Ogawa Teruyuki , Ishizuka Osamu , Yoshimura Naoki
Published: May 2021

This is the first paper to evaluate the time-course changes of bladder dysfunction associated with mechanosensitive channels in a mouse model.
Preexisting hypertension and pregnancy-induced hypertension reveal molecular differences in placental proteome in rodents
Authors: Mary Sheon , Small Heather , Herse Florian , Carrick Emma , Flynn Arun , Mullen William , Dechend Ralf , Delles Christian
Published: May 2021

Preexisting or new onset of hypertension affects pregnancy and is one of the leading causes of maternal and fetal morbidity and mortality. In certain cases, it also leads to long-term maternal cardiovascular complications. The placenta is a key player in the pathogenesis of complicated hypertensive pregnancies, however the pathomechanisms leading to an abnormal placenta are poorly understood. In this study, we compared the placental proteome of two pregnant hypertensive models with their corresponding normotensive controls: a preexisting hypertension pregnancy model (stroke-prone spontaneously hypertensive rats; SHRSP) versus Wistar–Kyoto and the transgenic RAS activated gestational hypertension model (transgenic for human angiotensinogen Sprague-Dawley rats; SD-PE) versus Sprague-Dawley rats, respectively. Label-free proteomics using nano LC-MS/MS was performed for identification and quantification of proteins. Between the two models, we found widespread differences in the expression of placental proteins including those related to hypertension, inflammation, and trophoblast invasion, whereas pathways such as regulation of serine endopeptidase activity, tissue injury response, coagulation, and complement activation were enriched in both models. We present for the first time the placental proteome of SHRSP and SD-PE and provide insight into the molecular make-up of models of hypertensive pregnancy. Our study informs future research into specific preeclampsia and chronic hypertension pregnancy mechanisms and translation of rodent data to the clinic.
Key Markers and Epigenetic Modifications of Dental-Derived Mesenchymal Stromal Cells
Authors: Chen Jingqiu , Zheng Xiaodan , Rao Nanquan , Huang Yao , Liu Juan , Li Yanhong , Zhang Jun
Published: May 2021

As a novel research hotspot in tissue regeneration, dental-derived mesenchymal stromal cells (MSCs) are famous for their accessibility, multipotent differentiation ability, and high proliferation. However, cellular heterogeneity is a major obstacle to the clinical application of dental-derived MSCs. Here, we reviewed the heterogeneity of dental-derived MSCs firstly and then discussed the key markers and epigenetic modifications related to the proliferation, differentiation, immunomodulation, and aging of dental-derived MSCs. These messages help to control the composition and function of dental-derived MSCs and thus accelerate the translation of cell therapy into clinical practice.
Name Disambiguation Based on Graph Convolutional Network
Authors: Chen Ya , Yuan Hongliang , Liu Tingting , Ding Nan
Published: May 2021

Recently, massive online academic resources have provided convenience for scientific study and research. However, the author name ambiguity degrades the user experience in retrieving the literature bases. Extracting the features of papers and calculating the similarity for clustering constitute the mainstream of present name disambiguation approaches, which can be divided into two branches: clustering based on attribute features and clustering based on linkage information. They cannot however get high performance. In order to improve the efficiency of literature retrieval and provide technical support for the accurate construction of literature bases, a name disambiguation method based on Graph Convolutional Network (GCN) is proposed. The disambiguation model based on GCN designed in this paper combines both attribute features and linkage information. We first build paper-to-paper graphs, coauthor graphs, and paper-to-author graphs for each reference item of a name. The nodes in the graphs contain attribute features and the edges contain linkage features. The graphs are then fed to a specialized GCN and output a hybrid representation. Finally, we use the hierarchical clustering algorithm to divide the papers into disjoint clusters. Finally, we cluster the papers using a hierarchical algorithm. The experimental results show that the proposed model achieves average F1 value of 77.10% on three name disambiguation datasets. In order to let the model automatically select the appropriate number of convolution layers and adapt to the structure of different local graphs, we improve upon the prior GCN model by utilizing attention mechanism. Compared with the original GCN model, it increases the average precision and F1 value by 2.05% and 0.63%, respectively. What is more, we build a bilingual dataset, BAT, which contains various forms of academic achievements and will be an alternative in future research of name disambiguation.
Relation between Borweins’ Cubic Theta Functions and Ramanujan’s Eisenstein Series
Authors: Kumar B. R. Srivatsa , Shruthi  , Radha D. Anu
Published: May 2021

Two-dimensional theta functions were found by the Borwein brothers to work on Gauss and Legendre’s arithmetic-geometric mean iteration. In this paper, some new Eisenstein series identities are obtained by using (

p

,

k

)-parametrization in terms of Borweins’ theta functions.
Neferine Protects against Hypoxic‐Ischemic Brain Damage in Neonatal Rats by Suppressing NLRP3‐Mediated Inflammasome Activation
Authors: Zhu Jin-jin , Yu Bin-yuan , Huang Xiao-kai , He Min-zhi , Chen Bin-wen , Chen Ting-ting , Fang Huang-yi , Chen Shang-qin , Fu Xiao-qin , Li Pei-jun , Lin Zhen-lang , Zhu Jiang-hu
Published: May 2021

Hypoxic‐ischemic encephalopathy (HIE) is recognized as the main cause of neonatal death, and efficient treatment strategies remain limited. Given the prevalence of HIE and the associated fatality, further studies on its pathogenesis are warranted. Oxidative stress and neuroinflammatory injury are two important factors leading to brain tissue injury and nerve cell loss in HIE. Neferine, an alkaloid extracted from lotus seed embryo, exerts considerable effects against several diseases such as cancers and myocardial injury. In this study, we demonstrated the neuroprotective effect of neferine on HIE and hypothesized that it involves the inhibition of neuronal pyroptosis, thereby ameliorating neurological inflammation and oxidative stress. We demonstrated that the mRNA levels of proteins associated with pyroptosis including caspase‐1, the caspase adaptor ASC, gasdermin D, interleukin‐ (IL‐) 18, IL‐1β, and some inflammatory factors were significantly increased in neonatal HIBD model rats compared to those in the control group. The increase in these factors was significantly suppressed by treatment with neferine. We stimulated PC12 cells with CoCl2 to induce neuronal HIBD in vitro and investigated the relationship between neferine and pyroptosis by altering the expression of the NLRP3 inflammasome. The overexpression of NLRP3 partially reversed the neuroprotective effect of neferine on HIBD, whereas NLRP3 knockdown further inhibited caspase‐1 activation and IL‐1β and IL18 expression. In addition, simultaneous alteration of NLRP3 expression induced changes in intracellular oxidative stress levels after HIBD. These findings indicate that neferine ameliorates neuroinflammation and oxidative stress injury by inhibiting pyroptosis after HIBD. Our study provides valuable information for future studies on neferine with respect to neuroinflammation and pyroptosis.
Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model
Authors: Mai Hai-Van Thi , Nguyen Thuy-Anh , Ly Hai-Bang , Tran Van Quan
Published: May 2021

Improvement of compressive strength prediction accuracy for concrete is crucial and is considered a challenging task to reduce costly experiments and time. Particularly, the determination of compressive strength of concrete using ground granulated blast furnace slag (GGBFS) is more difficult due to the complexity of the composition mix design. In this paper, an approach using random forest (RF), which is one of the powerful machine learning algorithms, is proposed for predicting the compressive strength of concrete using GGBFS. The RF model is first evaluated to determine the best architecture, which constitutes 500 growth trees and leaf size of 1. In the next step, the evaluation of the model is conducted over 500 simulations considering the effect of random sampling. Finally, the best prediction results are given in function of statistical measures such as the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), respectively, which are 0.9729, 4.9585, and 3.9423 for the testing dataset. The results show that the RF algorithm is an excellent predictor and practically used for engineers to reduce experimental cost.
[Retracted] Economic Structure Analysis Based on Neural Network and Bionic Algorithm
Authors: Dai Yanjun , Su Lin
Published: May 2021

In this article, an in‐depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA‐based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.