bluuebunny/crossref_metadata_2025 huggingface dataset:
Tethered capsule en face optical coherence tomography for imaging Barrett’s oesophagus in unsedated patients
Authors: Liang Kaicheng , Ahsen Osman O , Murphy Annalee , Zhang Jason , Nguyen Tan H , Potsaid Benjamin , Figueiredo Marisa , Huang Qin , Mashimo Hiroshi , Fujimoto James G
Published: September 2020

Objective

Barrett’s oesophagus (BE) screening outside the endoscopy suite can identify patients for surveillance and reduce mortality. Tethered capsule optical coherence tomography (OCT) can volumetrically image oesophageal mucosa in unsedated patients and detect features of BE. We investigated ultrahigh-speed tethered capsule swept-source OCT (SS-OCT), improved device design, developed procedural techniques and measured capsule contact, longitudinal pullback non-uniformity and patient toleration.

Design

OCT was performed in 16 patients prior to endoscopic surveillance/treatment. Unsedated patients swallowed the capsule with sips of water and the tether was pulled back to image the oesophagus. SS-OCT at 1 000 000 A-scans/s enabled imaging 10 cm oesophageal lengths in 10 s with 30 µm transverse and 8 µm axial resolution. Capsule contact, longitudinal image coverage and patient toleration were assessed.

Results

Nine patients had non-dysplastic BE, three had ablative treatment-naïve neoplasia and four had prior ablation for dysplasia. Dry swallows facilitated capsule transit through the lower oesophageal sphincter (LES), and waiting 10 s before pullback reduced swallow induced LES relaxation. Slow nasal inhalation facilitated capsule retrieval and minimised gag reflex. The procedure was well tolerated. Ultrahigh-speed SS-OCT generated cross-sectional and subsurface en face images showing BE features, while subsurface en face images were required to assess the gastro-oesophageal junction. Candidate features of dysplasia were also identified which could inform follow-up endoscopy/biopsy. BE features were seen in all patients with histologically confirmed BE. Mean capsule contact over BE was 75%±27% for all patients and better in short segment BE. Mean longitudinal image coverage over BE was 59%±34% and better for long segment BE.

Conclusions

Ultrahigh-speed tethered capsule SS-OCT can image en face and cross-sectional mucosal features over wide areas. Device and procedure optimisation improved performance. BE features could be identified in all patients, but limited capsule contact and longitudinal coverage could cause sampling errors for focal pathologies.
Efficacy of second-line regimens forHelicobacter pylorieradication treatment: a systemic review and network meta-analysis
Authors: Chang Yen-Lin , Tung Yu-Chun , Tu Yu-Kang , Yeh Hong-Zen , Yang Jyh-Chin , Hsu Ping-I , Kim Sung-Eun , Wu Ming-Fen , Liou Wen-Shyong , Shiu Sz-Iuan
Published: September 2020

Background

Current guidelines recommend bismuth-containing quadruple therapy (BQT) and quinolone-containing therapy after failure of first-lineHelicobacter pylorieradication therapy. However, the optimum regimen of second-line eradication therapy remains elusive. We conducted a network meta-analysis to compare the relative efficacy of 16 second-lineH. pylorieradication regimens.

Methods

Three major bibliographic databases were reviewed to enrol relevant randomised controlled trials between January 2000 and September 2018. Network meta-analysis was conducted by STATA software and we performed subgroup analysis in countries with high clarithromycin resistance and high levofloxacin resistance, and in patients with documented failure of first-line triple therapy.

Results

Fifty-four studies totalling 8752 participants who received 16 regimens were eligible for analysis. Compared with a 7-day BQT, use of probiotic add-on therapy during, before, and after second-line antibiotic regimens, quinolone-based sequential therapy for 10–14 days, quinolone-based bismuth quadruple therapy for 10–14 days, bismuth quadruple therapy for 10–14 days, and quinolone-based triple therapy for 10–14 days were significantly superior to the other regimens. Subgroup analysis of countries with high clarithromycin resistance and high levofloxacin resistance revealed that the ranking of second-line eradication regimens was distributed similarly in each group, as well as in patients with failure of first-line triple therapy.

Conclusion

We conducted a detailed comparison of second-lineH. pyloriregimens according to different antibiotic resistance rates and the results suggest alternative treatment choices with potential benefits beyond those that could be achieved using salvage therapies recommended by guidelines.
Integrative clinical, genomics and metabolomics data analysis for mainstream precision medicine to investigate COVID-19
Authors: Ahmed Zeeshan , Zeeshan Saman , Foran David J , Kleinman Lawrence C , Wondisford Fredric E , Dong XinQi
Published: September 2020

Despite significant scientific and medical discoveries, the genetics of novel infectious diseases like COVID-19 remains far from understanding. SARS-CoV-2 is a single-stranded RNA respiratory virus that causes COVID-19 by binding to the ACE2 receptor in the lung and other organs. Understanding its clinical presentation and metabolomic and genetic profile will lead to the discovery of diagnostic, prognostic and predictive biomarkers, which may lead to more effective medical therapy. It is important to investigate correlations and overlap between reported diagnoses of a patient with COVID-19 in clinical data with identified germline and somatic mutations, and highly expressed genes from genomics data analysis. Timely model clinical, genomics and metabolomics data to find statistical patterns across millions of features to identify underlying biological pathways, modifiable risk factors and actionable information that supports early detection and prevention of COVID-19, and development of new therapies for better patient care. Next, ensuring security reconcile noise, need to build and train machine learning prognostic models to find actionable information that supports early detection and prevention of COVID-19. Based on the myriad data, applying appropriate machine learning algorithms to stratify patients, understand scenarios, optimise decision-making, identify high-risk rare variants (including ACE2, TMPRSS2) and making medically relevant predictions. Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments, when confronted with the challenges of pandemic situations.
Quit attempts among tobacco users identified in the Tamil Nadu Tobacco Survey of 2015/2016: a 3 year follow-up mixed methods study
Authors: Veeraiah Surendran , Elangovan Vidhubala , Tripathy Jaya Prasad , Krishnamurthy Arvind , Anand Tanu , Reddy Mahendra M , Sudhakar Revathy , K Niraimathi , Subramani Divyarajprabhakar , Rajaraman Swaminathan , Elluswami Hemanth Raj , Nirgude Abhay
Published: September 2020

Objectives

To determine current tobacco use in 2018/2019, quit attempts made and to explore the enablers and barriers in quitting tobacco among tobacco users identified in the Tamil Nadu Tobacco Survey (TNTS) in 2015/2016.

Setting

TNTS was conducted in 2015/2016 throughout the state of Tamil Nadu (TN) in India covering 111 363 individuals. Tobacco prevalence was found to be 5.2% (n=5208).

Participants

All tobacco users in 11 districts of TN identified by TNTS (n=2909) were tracked after 3 years by telephone. In-depth interviews (n=26) were conducted in a subsample to understand the enablers and barriers in quitting.

Primary and secondary outcomes

Current tobacco use status, any quit attempt and successful quit rate were the primary outcomes, while barriers and enablers in quitting were considered as secondary outcomes.

Results

Among the 2909 tobacco users identified in TNTS 2015/2016, only 724 (24.9%) could be contacted by telephone, of which 555 (76.7%) consented. Of those who consented, 210 (37.8%) were currently not using tobacco (ie, successfully quit) and 337 (60.7%) continued to use any form of tobacco. Of current tobacco users, 115 (34.1%) have never made any attempt to quit and 193 (57.3.8%) have made an attempt to quit. Those using smoking form of tobacco products (adjusted relative risk (aRR)=1.2, 95% CI: 1.1 to 1.4) and exposure to smoke at home (aRR=1.2, 95% CI: 1.1 to 1.3) were found to be positively associated with continued tobacco use (failed or no quit attempt). Support from family and perceived health benefits are key enablers, while peer influence, high dependence and lack of professional help are some of the barriers to quitting.

Conclusion

Two-thirds of the tobacco users continue to use tobacco in the last 3 years. While tobacco users are well aware of the ill-effects of tobacco, various intrinsic and extrinsic factors play a major role as a facilitator and lack of the same act as a barrier to quit.
Comparative CT with stress manoeuvres for diagnosing distal isolated tibiofibular syndesmotic injury in acute ankle sprain: a protocol for an accuracy- test prospective study
Authors: Rodrigues João Carlos , Santos Alexandre Leme Godoy , Prado Marcelo Pires , Alloza José Felipe Marion , Masagão Renato Amaral , Rosemberg Laercio Alberto , Barros Durval do Carmo Santos , Castro Adham do Amaral e , Demange Marco Kawamura , Lenza Mario , Ferretti Mario
Published: September 2020

Introduction

Although several imaging options are available for diagnosing syndesmotic injury, a fundamental question that guides treatment remains unanswered. Syndesmotic instability is still challenging to diagnose correctly, and syndesmotic disruption and true syndesmotic instability should be differentiated. Currently, imaging tests quickly diagnose severe syndesmotic instability but have difficulty in diagnosing mild and moderate cases. This study aims to investigate which strategy among an existing CT index test and two new add-on CT index tests with stress manoeuvres more accurately diagnoses syndesmotic instability. The secondary objective is to investigate the participants’ disability outcomes by applying the Foot and Ankle Ability Measure questionnaire.

Methods and analyses

This study of a diagnostic accuracy test will consecutively select individuals older than 18 years with a clinical diagnosis of a suspected acute syndesmotic injury. Three strategies of the CT index test (one in the neutral position and two with stress) will examine the accuracy using MRI as the reference standard. The external rotation and dorsiflexion of the ankle will guide the stress manoeuvres. A comparison of measurements between the injured syndesmosis and the uninjured contralateral side of the same individual will investigate the syndesmotic instability, by evaluating the rotational and translational relationships between the fibula and tibia. Sensitivity, specificity, area under the receiver operating characteristic curve and likelihood analyses will compare the diagnostic accuracies of the strategies.

Ethics and dissemination

The Internal Review Board and the Einstein Ethics Committee approved this study (registered number 62100016.5.0000.0071). All participants will receive an oral description of the study’s aim, and the choice to participate will be free and voluntary. Participants will be enrolled after they sign the written informed consent form, including the terms of confidentiality. The results will be presented at national and international conferences and published in peer-reviewed journals and social media.

Trial registration number

ClinicalTrials.gov Registry (NCT04095598; preresults).
How to thrive at work: tips for improving personal and team wellbeing
Authors: Crowe Carolyne
Published: September 2020

Wellbeing can be affected by many different factors, and it influences productivity at work. Following the Covid‐19 pandemic, the importance of understanding how to manage your personal and team wellbeing is even greater. This article highlights some key questions to ask yourself about your working life, and suggests some top wellbeing tips.
Associations of blood biomarkers with glomerular filtration rate in patients with TIA and stroke: population-based study
Authors: Kelly Dearbhla M. , Li Linxin , Burgess Annette I , Poole Deborah L , Duerden Julia M , Rothwell Peter M.
Published: September 2020

Background and purpose

Non-traditional risk factors such as chronic inflammation, oxidative stress and thrombogenic factors are believed to contribute to the excess stroke risk in chronic kidney disease (CKD) by triggering vascular injury and endothelial dysfunction. We aimed to determine how well a panel of biomarkers representative of these factors would correlate with estimated glomerular filtration rate (eGFR) in patients with recent transient ischaemic attack (TIA) or stroke. We also investigated whether eGFR would confound previously reported associations between biomarkers and mortality.

Methods

We studied a panel of 16 blood biomarkers related to inflammation, thrombosis, atherogenesis and cardiac or neuronal cell damage in TIA or ischaemic stroke in a population-based study (Oxford Vascular Study). Biomarker levels were log-transformed and correlated with eGFR, adjusted for age. Cox proportional hazard models were used for survival analysis.

Results

Among 1297 patients with TIA or stroke, 52.7% (n=684) of patients had CKD (eGFR <60 mL/min/1.73 m2). There was a moderate correlation between log-eGFR and the log-transformed soluble tumour necrosis factor receptor-1 (R2=0.21), attenuating with adjustment for age (R2=0.12). There were moderate-to-strong correlations with markers of cardiac injury, N-terminal pro-brain natriuretic peptide and heart-type fatty acid binding protein (hFABP, R2=0.14 and 0.34, respectively). The strongest correlation after adjustment for age was between hFABP and eGFR (R2=0.20). Adjusting for eGFR did not impact any biomarker associations with mortality.

Conclusions

Correlations between biomarkers related to inflammation and thrombosis with renal dysfunction in the setting of cerebrovascular events were generally modest after adjustment for age, suggesting that putative risk factors such as chronic inflammation or coagulopathy are unlikely to be important stroke mechanisms in patients with CKD.
Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional Network
Authors: Yu Yue , Xia Tong , Wang Huandong , Feng Jie , Li Yong
Published: September 2020

Recent years have witnessed a rapid proliferation of personalized mobile Apps, which poses a pressing need for user experience improvement. A promising solution is to model App usage by learning semantic-aware App usage representations which can capture the relation among time, locations and Apps. However, it is non-trivial due to the complexity, dynamics, and heterogeneity characteristics of App usage. To smooth over these obstacles and achieve the goal, we propose SA-GCN, a novel representation learning model to map Apps, location, and time units into dense embedding vectors considering spatio-temporal characteristics and unit properties simultaneously. To handle complexity and dynamics, we build an App usage graph by regarding App, time, and location units as nodes and their co-occurrence relations as edges. For heterogeneity, we develop a Graph Convolutional Network with meta path-based objective function to combine the structure of the graph and the attribute of units into the semantic-aware representations. We evaluate the performance of SA-GCN via a large-scale real-world dataset. In-depth analysis shows that SA-GCN characterizes the complex relationships among different units and recover meaningful spatio-temporal patterns. Moreover, we make use of the learned representations in App usage prediction task without post-training and achieve 8.3% of the performance gain compared with state-of-the-art baselines.
Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network
Authors: Liu Shengzhong , Yao Shuochao , Huang Yifei , Liu Dongxin , Shao Huajie , Zhao Yiran , Li Jinyang , Wang Tianshi , Wang Ruijie , Yang Chaoqi , Abdelzaher Tarek
Published: September 2020

Reliable data collection, transmission, and delivery on Internet of Things (IoT) systems is crucial in order to provide high-quality intelligent services. However, sensor data delivery can be interrupted for various reasons, such as sensor malfunction, network failures, and external attacks. Thus, only data from a partial set of sensors may be available. We call it the missing sensor problem. This problem can lead to severe performance degradation at inference time by neural-network-based recognition models trained on the complete sensor set. This paper enhances the robustness of neural network models to the missing sensor problem by introducing a novel feature reconstruction module, named the graph recovery module, that handles missing sensors directly inside the network. Specifically, we consider topology-aware IoT applications, where sensors are placed on a physically interconnected network. We design a novel neural message passing mechanism that logically mimics physical network topology, based on recent advances in graph neural networks (GNNs). We rely on a spatial locality assumption, where only correlations between physically connected sensors are explicitly explored. When encountering missing sensors, information is passed from available sensors to missing sensors to be used to reconstruct their features. Moreover, at each message passing step, we utilize a gating mechanism inspired by Gated Recurrent Units (GRUs) to automatically control information flow between available sensors and missing sensors. We empirically evaluate the reconstruction performance of the graph recovery module with two representative IoT applications; human activity recognition (HAR) and electroencephalogram (EEG)-based motor-imagery classification, on three public datasets. Two different backbone networks are utilized for the tasks. Our design is shown to effectively maintain model performance, suffering only 7% to 18% accuracy loss when as much as 90% of sensors are removed, compared to a drop of 15% to 47% in the accuracy of competing state-of-the-art algorithms under the same conditions. The accuracy gap is largest when more sensors are missing.
Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data
Authors: Rashid Haroon , Mendu Sanjana , Daniel Katharine E. , Beltzer Miranda L. , Teachman Bethany A. , Boukhechba Mehdi , Barnes Laura E.
Published: September 2020

Exploiting the capabilities of smartphones for monitoring social anxiety shows promise for advancing our ability to both identify indicators of and treat social anxiety in natural settings. Smart devices allow researchers to collect passive data unobtrusively through built-in sensors and active data using subjective, self-report measures with Ecological Momentary Assessment (EMA) studies. Prior work has established the potential to predict subjective measures from passive data. However, the majority of the past work on social anxiety has focused on a limited subset of self-reported measures. Furthermore, the data collected in real-world studies often results in numerous missing values in one or more data streams, which ultimately reduces the usable data for analysis and limits the potential of machine learning algorithms. We explore several approaches for addressing these problems in a smartphone based monitoring and intervention study of eighty socially anxious participants over a five week period. Our work complements and extends prior work in two directions: (i) we show the predictability of seven different self-reported dimensions of social anxiety, and (ii) we explore four imputation methods to handle missing data and evaluate their effectiveness in the prediction of subjective measures from the passive data. Our evaluation shows imputation of missing data reduces prediction error by as much as 22%. We discuss the implications of these results for future research.
WiBorder
Authors: Li Shengjie , Liu Zhaopeng , Zhang Yue , Lv Qin , Niu Xiaopeng , Wang Leye , Zhang Daqing
Published: September 2020

Recent research has shown great potential of exploiting Channel State Information (CSI) retrieved from commodity Wi-Fi devices for contactless human sensing in smart homes. Despite much work on Wi-Fi based indoor localization and motion/intrusion detection, no prior solution is capable of detecting a person entering a room with a precise sensing boundary, making room-based services infeasible in the real world. In this paper, we present WiBorder, an innovative technique for accurate determination of Wi-Fi sensing boundary. The key idea is to harness antenna diversity to effectively eliminate random phase shifts while amplifying through-wall amplitude attenuation. By designing a novel sensing metric and correlating it with human's through-wall discrimination, WiBorder is able to precisely determine Wi-Fi sensing boundaries by leveraging walls in our daily environments. To demonstrate the effectiveness of WiBorder, we have developed an intrusion detection system and an area detection system. Extensive results in real-life scenarios show that our intrusion detection system achieves a high detection rate of 99.4% and a low false alarm rate of 0.68%, and the area detection system's accuracy can be as high as 97.03%. To the best of our knowledge, WiBorder is the first work that enables precise sensing boundary determination via through-wall discrimination, which can immediately benefit other Wi-Fi based applications.
Trace2TAP
Authors: Zhang Lefan , He Weijia , Morkved Olivia , Zhao Valerie , Littman Michael L. , Lu Shan , Ur Blase
Published: September 2020

Two common approaches for automating IoT smart spaces are having users write rules using trigger-action programming (TAP) or training machine learning models based on observed actions. In this paper, we unite these approaches. We introduce and evaluate Trace2TAP, a novel method for automatically synthesizing TAP rules from traces (time-stamped logs of sensor readings and manual actuations of devices). We present a novel algorithm that uses symbolic reasoning and SAT-solving to synthesize TAP rules from traces. Compared to prior approaches, our algorithm synthesizes generalizable rules more comprehensively and fully handles nuances like out-of-order events. Trace2TAP also iteratively proposes modified TAP rules when users manually revert automations. We implemented our approach on Samsung SmartThings. Through formative deployments in ten offices, we developed a clustering/ranking system and visualization interface to intelligibly present the synthesized rules to users. We evaluated Trace2TAP through a field study in seven additional offices. Participants frequently selected rules ranked highly by our clustering/ranking system. Participants varied in their automation priorities, and they sometimes chose rules that would seem less desirable by traditional metrics like precision and recall. Trace2TAP supports these differing priorities by comprehensively synthesizing TAP rules and bringing humans into the loop during automation.
Battery-Free Game Boy
Authors: de Winkel Jasper , Kortbeek Vito , Hester Josiah , Pawełczak Przemysław
Published: September 2020

We present ENGAGE, the first battery-free, personal mobile gaming device powered by energy harvested from the gamer actions and sunlight. Our design implements a power failure resilient Nintendo Game Boy emulator that can run off-the-shelf classic Game Boy games like Tetris or Super Mario Land. This emulator is capable of intermittent operation by tracking memory usage, avoiding the need for always checkpointing all volatile memory, and decouples the game loop from user interface mechanics allowing for restoration after power failure. We build custom hardware that harvests energy from gamer button presses and sunlight, and leverages a mixed volatility memory architecture for efficient intermittent emulation of game binaries. Beyond a fun toy, our design represents the first battery-free system design for continuous user attention despite frequent power failures caused by intermittent energy harvesting. We tackle key challenges in intermittent computing for interaction including seamless displays and dynamic incentive-based gameplay for energy harvesting. This work provides a reference implementation and framework for a future of battery-free mobile gaming in a more sustainable Internet of Things.
Analysis of COVID-19 Tracking Tool in India
Authors: Gupta Rajan , Bedi Manan , Goyal Prashi , Wadhera Srishti , Verma Vaishnavi
Published: September 2020

COVID-19 tracking tools or contact-tracing apps are getting developed at a rapid pace by different governments in their respective countries. This study explores one such tool called
Aarogya Setu
, developed by the Government of India. It is a mobile application developed under the Health Ministry, as a part of the E-Governance initiative, to track and sensitize the citizens of India in a joint battle against COVID-19 spread. The study aims to understand various useful features of this tool and to present different concepts of data science applied within the application along with its importance in managing the ongoing pandemic. The App uses Bluetooth and GPS technologies to alert a user when they are nearby a COVID-19 infected person. The application uses various Data Science concepts such as Classification, Association Rule Mining, and Clustering to analyze COVID-19 spread in India. The study also shows potential upgradations in the application, which includes usage of Artificial Intelligence and Computer Vision to detect COVID-19 patients. The study would be useful for mobile technology professionals, data science professionals, medical practitioners, health-related frontline workers, public administrators, and government officials.
Technical Perspective of Concurrent Prefix Recovery
Authors: Bernstein Philip A.
Published: September 2020

Where do novel database system research results come from? In the 1970's, most systems research papers proposed mechanisms to support abstractions that were being explored for the first time, such as data translation, indexing, query optimization, high performance transactions, distributed databases, heterogeneous databases, and replicated databases. Novelty was easy to come by. These abstractions now form the core of the database systems field.
Concurrent Prefix Recovery
Authors: Prasaad Guna , Chandramouli Badrish , Kossmann Donald
Published: September 2020

This paper proposes a new recovery model based on group commit, called concurrent prefix recovery (CPR). CPR differs from traditional group commit implementations in two ways: (1) it provides a semantic description of committed operations, of the form "all operations until time ti from session i"; and (2) it uses asynchronous incremental checkpointing instead of a WAL to implement group commit in a scalable bottleneck-free manner. CPR provides the same consistency as a point-in-time commit, but allows a scalable concurrent implementation. We used CPR to make two systems durable: (1) a custom in-memory transactional database; and (2) FASTER, our state-of-theart, scalable, larger-than-memory key-value store. Our detailed evaluation of these modified systems shows that CPR is highly scalable and supports concurrent performance reaching hundreds of millions of operations per second on a multi-core machine.
Technical Perspective
Authors: Kimelfeld Benny
Published: September 2020

The challenge of extracting structured information from text, or sequential data in general, is prevalent across a multitude of data-science domains. This challenge, known as Information Extraction (IE), instantiates to core components in text analytics, and a plethora of IE paradigms have been developed over the past decades. Rules and rule systems have consistently been key components in such paradigms, yet their roles have varied and evolved over time. Analytics engines such as IBM's SystemT use IE rules for materializing relations inside relational query languages. Machinelearning classifiers and probabilistic graphical models (e.g., Conditional Random Fields) use rules for feature generation. They also serve as weak constraints in Markov Logic Networks (and extensions such as DeepDive), and generators of noisy training data in the state-of-the-art Snorkel system.
Technical Perspective
Authors: Getoor Lise
Published: September 2020

There has been an explosion of interest in fairness in machine learning. In large part, this has been motivated by societal issues highlighted in a string of well publicized cases such as gender biased job recommendation and racially biased criminal risk prediction algorithms. Both the recognition of the potential disparate impacts of machine learning due to historical bias in the data and the realization of how algorithmic decision making can exaggerate existing structural inequities has become increasingly well known.
Database Repair Meets Algorithmic Fairness
Authors: Salimi Babak , Howe Bill , Suciu Dan
Published: September 2020

Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they rely on background knowledge of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We demonstrate the effectiveness of our proposed techniques with experimental results.
Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation
Authors: Arenas Marcelo , Croquevielle Luis Alberto , Jayaram Rajesh , Riveros Cristian
Published: September 2020

We study two simple yet general complexity classes, which provide a unifying framework for efficient query evaluation in areas like graph databases and information extraction, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this respect.

Both complexity classes are defined in terms of non deterministic logarithmic-space transducers (NL transducers). For the first class, we consider the case of unambiguous NL transducers, and we prove constant delay enumeration, and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomialtime randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm (with preprocessing) for uniform generation. Remarkably, the key idea to prove these results is to show that the fundamental problem #NFA admits an FPRAS, where #NFA is the problem of counting the number of strings of length n (given in unary) accepted by a non-deterministic finite automaton (NFA). While this problem is known to be #P-complete and, more precisely, SpanL-complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem, and obtain as a welcome corollary that every function in SpanL admits an FPRAS.
Virtual conferences
Authors: Krauss Oliver
Published: September 2020

Due to the health and safety hazard introduced by Covid-19 and the measures of governments around the world restricting travel and large gatherings, most conferences in 2020 have decided to go virtual. This includes the recently held EvoStar conferences, as well as the upcoming Genetic and Evolutionary Computation Conference (GECCO), which many SIGEVO Members are attending regularly.

The following article will attempt to summarize the advantages, disadvantages and challenges that come with such virtual conferences from an attendee's perspective. As they are collected from discussions and personal opinions, take them with a grain of salt, as every experience on a conference is different.
Stress and Health: A Review of Psychobiological Processes
Authors: O'Connor Daryl B. , Thayer Julian F. , Vedhara Kavita
Published: September 2020

The cumulative science linking stress to negative health outcomes is vast. Stress can affect health directly, through autonomic and neuroendocrine responses, but also indirectly, through changes in health behaviors. In this review, we present a brief overview of ( a) why we should be interested in stress in the context of health; ( b) the stress response and allostatic load; ( c) some of the key biological mechanisms through which stress impacts health, such as by influencing hypothalamic-pituitary-adrenal axis regulation and cortisol dynamics, the autonomic nervous system, and gene expression; and ( d) evidence of the clinical relevance of stress, exemplified through the risk of infectious diseases. The studies reviewed in this article confirm that stress has an impact on multiple biological systems. Future work ought to consider further the importance of early-life adversity and continue to explore how different biological systems interact in the context of stress and health processes.
Energy Metabolism Mechanism of Anticardiogenic Shock Effect Component Ginsenoside Rc of Shenfu Injection on H9c2 Myocardial Injury Cells Induced by Hypoxia/Reoxygenation
Authors: Chen Yu , Li Yan , Xu Guoliang , Shang Guangbin , Liu Hongning , Jiang Li
Published: September 2020

Shenfu Injection (SFI) is a common drug used to treat cardiovascular diseases and has a significant effect on cardiogenic shock. Ginsenoside Rc (G‐Rc) was an anticardiogenic shock effect component of SFI screened by UHPLC‐Q‐TOF/MS and multivariate statistical analysis and further selected by molecular docking experiment in our previous study. However, most studies on SFI in the treatment of cardiogenic shock focus on the overall efficacy, and little is known about its effective component on energy metabolism in hypoxia/reoxygenation‐ (H/R‐) induced myocardial injury cells. Therefore, the present study was performed to investigate the dose‐effect and time‐effect relationship of G‐Rc in protecting hypoxic injury of H9c2 cardiomyocytes, and its mechanism on the energy metabolism‐related indicators, i.e., adenosine triphosphate (ATP) content, lactate dehydrogenase (LDH) release, and creatine kinase (CK) activity of the myocardial cells, was explored. In this paper, a stable and reliable H/R model of H9c2 cardiomyocytes was established. Compared with the control group, the activity of cardiomyocytes in the H/R group was significantly reduced (P < 0.01). The dose‐effect and time‐effect studies showed that G‐Rc could significantly increase cell viability at certain point compared with the H/R group (P < 0.01), and the optimum intervention dose and time was 3.33 μmol/L for 12 h. The results concerning energy metabolism mechanism demonstrated that G‐Rc pretreatment could improve ATP content, attenuate the LDH leakage, and decrease CK activity and apoptosis rate of H/R cardiomyocytes. Taken together, our findings suggest that G‐Rc pretreatment can significantly protect myocardial cells from H/R injury. In addition, G‐Rc is able to improve the energy metabolism ability of the injury cardiomyocytes by direct synthesis of ATP and reducing the activity of LDH, CK, and apoptosis rate. These results indicate that G‐Rc may be a promising therapeutic candidate for the treatment of cardiovascular disease caused by myocardial H/R injury.
Dynamic Analysis of Duopoly Price Game Based on Low-Carbon Technology Sharing
Authors: Si Fengshan , Yan Zhengkun , Wang Jing , Dai Daoming
Published: September 2020

Taking the carbon emissions per unit product as the standard to measure the low-carbon technology level of the enterprise, this article analyzed how the technology supplier enterprises realize low-carbon production and achieve a win-win situation for both supply and demand through technology sharing through technology research and development. Based on the positive effect of low-carbon technology level on product demand, we studied the optimal pricing strategy and the optimal low-carbon technology level in the technology supply enterprises under the Stackelberg game in 3 conditions (i.e., without technology research and development or technology sharing, with technology research and development but no technology sharing, and with both technology research and development and technology sharing). We also drew a comparative analysis of the optimal product price, the optimal low-carbon technology level, and the optimal profit in the three scenarios. Besides, by constructing a delayed differential price game model, we studied the equilibrium strategy of price competition between technology supply and demand companies and the local asymptotic stability of the game system at the equilibrium point. In addition, the effects of delay strategy on game equilibrium strategy, the influence of the degree of adjustment of decision variables on the stability of the game system, and the stability of the game system on the evolution trend of the game are also explored. By comparing and analyzing the game results of the oligopoly enterprises in the stable system and the unstable system, it confirmed that the system instability usually causes serious harm to the enterprise.
A Modified Three-Term Type CD Conjugate Gradient Algorithm for Unconstrained Optimization Problems
Authors: Wang Zhan , Li Pengyuan , Li Xiangrong , Pham Hongtruong
Published: September 2020

Conjugate gradient methods are well-known methods which are widely applied in many practical fields. CD conjugate gradient method is one of the classical types. In this paper, a modified three-term type CD conjugate gradient algorithm is proposed. Some good features are presented as follows: (i) A modified three-term type CD conjugate gradient formula is presented. (ii) The given algorithm possesses sufficient descent property and trust region property. (iii) The algorithm has global convergence with the modified weak Wolfe–Powell (MWWP) line search technique and projection technique for general function. The new algorithm has made great progress in numerical experiments. It shows that the modified three-term type CD conjugate gradient method is more competitive than the classical CD conjugate gradient method.
Inclusion Modeling of Bucket Elevator Conveyor Chain Links
Authors: Yin Edward , Muvengei Onesmus , Kihiu John M.
Published: September 2020

Inclusions, pores, and cracks which are defects in materials are important in determining the structural integrity and durability of components. Modeling accurately, these defects are hence of interest to both theorist and the practitioner. CAD models were designed using Autodesk Inventor software, and simulation was performed using ANSYS academic software. Material saving in design of conveyor chain links has gained popularity, and this has led to the introduction of necks in its geometry. However, the effect of necking on various parameters was not considered. Also, inclusions which are defects in material are known to have adverse effect on the conveyor chain link. It is, therefore, imperative to predict numerically, the effects that inclusions and necking have on conveyor chain links This paper focusses on modeling nonmetallic inclusions and also tests various models of different neck radii using the finite element method so as to predict its effect.
Game Theoretical Method for Anomaly-Based Intrusion Detection
Authors: Wang Zhiyong , Xu Shengwei , Xu Guoai , Yin Yongfeng , Zhang Miao , Sun Dawei
Published: September 2020

In this paper, the game theoretical analysis method is presented to provide optimal strategies for anomaly-based intrusion detection systems (A-IDS). A two-stage game model is established to represent the interactions between the attackers and defenders. In the first stage, the players decide to do actions or keep silence, and in the second stage, attack intensity and detection threshold are considered as two important strategic variables for the attackers and defenders, respectively. The existence, uniqueness, and explicit computation of the Nash equilibrium are analyzed and obtained by considering six different scenarios, from which the optimal detection and attack actions are provided. Numerical examples are provided to validate our theoretical results.
Acute Splenic Artery Thrombosis and Infarction Associated with COVID-19 Disease
Authors: Qasim Agha Osama , Berryman Ryan
Published: September 2020

Coronavirus 2019 disease (COVID-19) is a viral illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It emerged in Wuhan, China, in December 2019 and has caused a widespread global pandemic. The symptoms of COVID-19 can vary from mild upper respiratory symptoms to severe pneumonia with hypoxemic respiratory failure. Multiple studies and reports have reported a hypercoagulable state associated with this disease, and various recommendations have emerged to guide the use of anticoagulants for prophylaxis. We are reporting a case of symptomatic acute splenic thrombosis causing splenic infarction in a patient suffering from a severe case of COVID-19 and despite the use of an intermediate dose of low-molecular-weight heparin (LMWH). The patient was treated with full-dose anticoagulation and was eventually discharged home on a direct oral anticoagulant.
Study on the Static and Dynamic Fracturing Properties of Marble after Being Damaged Dynamically
Authors: Liu Yunqiu , Fu Anqi , Jiang Binsong , Yu Liyuan , Wang Xiaobing
Published: September 2020

A split Hopkinson pressure bar (SHPB) system was first used to perform the cyclic impact loading tests on notched semicircular bend (NSCB) marble specimens. Then, static and dynamic three‐point bending tests were conducted on these dynamically damaged specimens, respectively. In the cyclic impact loading tests, the dynamic elastic modulus decreases gradually as the impact number increases, but dynamic cumulative damage exhibits a growing trend. In the static and dynamic three‐point bending tests, when dynamic cumulative damage is less than 0.345, the dynamic fracture toughness values are larger than the static fracture toughness values, but the experimental data exhibit the opposite results when dynamic cumulative damage ranges from 0.345 to 0.369. Through the quantitative analysis of fracture surface morphologies, the roughness and area of the fracture surfaces increase with an increasing dynamic cumulative damage. Under the same dynamic cumulative damage of the specimens, both the roughness and area of the surfaces fractured by static three‐point bending are larger than those fractured by dynamic three‐point bending.
Stem Cell Therapy for Neurodegenerative Diseases: How Do Stem Cells Bypass the Blood-Brain Barrier and Home to the Brain?
Authors: Chia Yvonne Cashinn , Anjum Clarice Evey , Yee Hui Rong , Kenisi Yenny , Chan Mike K. S. , Wong Michelle B. F. , Pan Shing Yi
Published: September 2020

Blood-brain barrier (BBB) is a term describing the highly selective barrier formed by the endothelial cells (ECs) of the central nervous system (CNS) homeostasis by restricting movement across the BBB. An intact BBB is critical for normal brain functions as it maintains brain homeostasis, modulates immune cell transport, and provides protection against pathogens and other foreign substances. However, it also prevents drugs from entering the CNS to treat neurodegenerative diseases. Stem cells, on the other hand, have been reported to bypass the BBB and successfully home to their target in the brain and initiate repair, making them a promising approach in cellular therapy, especially those related to neurodegenerative disease. This review article discusses the mechanism behind the successful homing of stem cells to the brain, their potential role as a drug delivery vehicle, and their applications in neurodegenerative diseases.
Comparison of Anterior Ocular Biometric Measurements Using Swept-Source and Time-Domain Optical Coherence Tomography
Authors: Chen Sisi , Gao Rongrong , McAlinden Colm , Ye Junming , Wang Yiran , Chen Min , Huang Jinhai , Sun Yong , Yu A-Yong
Published: September 2020

Purpose. To compare central corneal thickness (CCT), aqueous depth (AQD), and anterior chamber depth (ACD) measurements using the swept-source (CASIA SS-1000, Tomey, Japan) and time-domain (Visante, Carl Zeiss Meditec, USA) anterior segment optical coherence tomographers (OCT) in normal eyes. Methods. Sixty-eight eyes of 68 subjects were included. Three consecutive scans of each subject were obtained using both devices in a random order by one experienced operator. Standard deviation (Sw), coefficient of repeatability (CoR), coefficients of variation (CoV), and intraclass correlation coefficients (ICC) were used to evaluate the intraoperator repeatability. Agreement was assessed using the Bland–Altman plots and 95% limits of agreement (LoA). Results. All measurements of the swept-source OCT (SS-OCT) and time-domain OCT (TD-OCT) showed high repeatability with low CoR (CCT: 2.34 μm and 6.16 μm; AQD: 0.05 mm and 0.09 mm; ACD: 0.06 mm and 0.09 mm), low CoV (CCT: 0.16% and 0.42%; AQD: 0.61% and 0.97%; ACD: 0.53% and 0.83%), and high ICC (>0.98). The mean CCT with SS-OCT was slightly thicker than the results with TD-OCT (difference = 4.55 ± 2.62 μm, P<0.001). There was no statistically significant difference in AQD or ACD measurements between the two devices (0.01 ± 0.05 mm, P=0.111; 0.02 ± 0.05 mm, P=0.022, respectively). The 95% LoA between the SS-OCT and TD-OCT were −0.59 to 9.69 μm for CCT, −0.10 to 0.12 mm for AQD, and −0.09 to 0.12 mm for ACD. Conclusions. High levels of repeatability and agreement were found between the two devices for all three parameters, suggesting interchangeability. SS-OCT demonstrated superior repeatability compared with TD-OCT.