bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collective Victimhood Resulting From Structural Violence
Authors: Mari Silvia , Bentrovato Denise , Durante Federica , Wassermann Johan 
Published: May 2020

Abstract
This chapter discusses collective victimization resulting from structural violence, and how the effects of inequality can have similar deleterious consequences for peoples’ ability to meet basic needs. Social class and structural violence have been underexamined so far in the literature on collective victimhood. However, considering collective victim beliefs due to structural violence—which are related to, but distinct from relative deprivation—enriches our understanding of relevant experiences and extends the collective victim beliefs that should be assessed. The authors show with empirical examples from Italy and South Africa that collective victim beliefs about structural violence are distinct from collective victim beliefs about direct violence. They also reveal that collective victim beliefs about structural violence may predict different outcomes, such as the preference for different forms of acknowledgment or the need for empowerment and acceptance.
Design and Application of Employee Engagement Surveys
Authors: Meyer John P. 
Published: May 2020

Abstract
This chapter provides an evidence-based guide to the design and application of employee engagement surveys. It begins with a discussion of the meaning of engagement and the importance of distinguishing it from related concepts such as job satisfaction and affective organizational commitment. Next, it addresses the question of “engagement with what?” Employees can be engaged (or disengaged) in specific tasks, the job itself, team projects, the organization’s mission, as well as other work and nonwork activities or entities. The behavioral implications of engagement in these various targets can be quite different, so it is important to consider carefully the intended target and the desired objectives of engagement when designing surveys. Finally, the chapter addresses the selection and measurement of key drivers of engagement. This information is useful in designing strategies for maintaining or building engagement. While the popular press is replete with “best practices” for fostering engagement, it is argued here that what works in one context will not necessarily work in another. However, research has identified a set of “best principles” that can be used to guide context-specific interventions. The chapter concludes with a set of recommendations for the design and administration of engagement surveys, interpretation and feedback of the results, and development of intervention strategies.
Judicial Review of the European Central Bank’s Actions
Authors: Markakis Menelaos 
Published: May 2020

Abstract
This chapter focuses on judicial review of the European Central Bank’s actions by the EU courts. It does not purport to examine all issues related to the legal accountability of the ECB across its main domains of action. Instead, it focuses on the role of the Court of Justice of the European Union (CJEU) in reviewing general reforms, in the light of cases such as Pringle, Gauweiler, and Weiss, and asks: what should be the scope, test (or standard) and intensity of review of the ECB’s actions? When should the CJEU defer to the ECB’s assessment? Though the emphasis in this chapter is on monetary policy measures, the final section of the chapter also looks briefly at the role of the CJEU in reviewing the ECB’s actions in its ‘new fields of competence’, namely banking supervision and in the context of financial assistance programmes, where the ECB is a member of the ‘Troika’ (or ‘Quadriga’).
Can Real Social Epistemic Networks Deliver the Wisdom of Crowds?
Authors: Sullivan Emily , Sondag Max , Rutter Ignaz , Meulemans Wouter , Cunningham Scott , Speckmann Bettina , Alfano Mark 
Published: May 2020

Abstract
Most experimental philosophy employs small-N studies with randomization. Additional light may be shed on philosophical questions by large-scale observational studies that employ Big Data methodologies. This chapter explains and showcases the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. The use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, the authors show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. Roughly half the accounts engaging with this topic are pro-vaccine, while the other half is con-vaccine. The results also indicate that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, the authors question whether testimonial networks as they are currently constituted on popular forums such as Twitter are living up to their promise of delivering the wisdom of crowds.
‘He is to freonde god’: Wealth and Avarice in Cynewulf’s Juliana
Authors: Michelet Fabienne L 
Published: May 2020

Abstract
This article explores the motif of wealth and avarice in the Old English Juliana. It examines the changes that Cynewulf brings to his Latin source, specifically to the portrayal of Juliana’s pagan antagonists. They are depicted as rich and greedy men, indicated by Heliseus’s hoarded wealth and by Affricanus’s mention of the financial obligations that a marriage between his daughter and Heliseus would create. Various relevant historical and theological contexts that the poem mobilizes are discussed, as they shed light on issues of wealth and its proper uses. This article argues that the pagans’ avarice is central to the poem’s design and it offers new readings of Juliana as a poem that speaks to the duties of those in power, to the contingent nature of earthly rule, and to the perils of wealth.
Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea
Authors: Korkalainen Henri , Aakko Juhani , Duce Brett , Kainulainen Samu , Leino Akseli , Nikkonen Sami , Afara Isaac O , Myllymaa Sami , Töyräs Juha , Leppänen Timo 
Published: May 2020

Abstract

Study Objectives
Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter.

Methods
PPG signals from the diagnostic polysomnographies of susptected OSA patients (n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep.

Results
The three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen’s κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes.

Conclusion
The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.
Sphingomonas montanisoli sp. nov., isolated from mountain soil
Authors: Xu Zixiao , Zhang Yuxiao , Muhammad Yasir , Wang Gejiao 
Published: May 2020

A soil bacterium, designated ZX9611T, was isolated from Taihang Mountain in Henan province, PR China. The strain was Gram-stain-negative and strictly aerobic. The cells were motile, rod-shaped and formed light pink-colored colonies. The 16S rRNA gene sequence of ZX9611T shared the highest similarities with those of

Sphingomonas crocodyli

CCP-7T (97.0%),

Sphingomonas jatrophae

S5-249T (96.6%) and

Sphingomonas starnbergensis

382T (95.9%). Phylogenetic analyses based on 16S rRNA gene sequences demonstrated that ZX9611T clustered with

S. crocodyli

CCP-7T,

S. jatrophae

S5-249T and

S. starnbergensis

382T. The average nucleotide identity (ANI) values between ZX9611T and two type strains (

S. crocodyli

BCRC 81096T and

S. jatrophae

DSM 27345T) were 88.3 and 68.6% respectively. ZX9611T exhibited genome-sequence-based digital DNA–DNA hybridization (dDDH) values of 53.3 % and 15.3 %, compared with

S. crocodyli

BCRC 81096T and

S

.

jatrophae

DSM 27345T, respectively. ZX9611T had a genome size of 4.12 Mb and an average DNA G+C content of 64.8 %. ZX9611T had major fatty acids (>5 %) including summed feature 8 (C18 : 1
ω7c and/or C18 : 1
ω6c), C14 : 0 2-OH, C16 : 0 and summed feature 3 (C16 : 1
ω7c and/or C16 : 1
ω6c), and the major polyamine was sym-homospermidine. The only respiratory quinone was ubiquinone-10. The polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine and sphingoglycolipid. On the basis of phenotypic, chemotaxonomic and phylogenetic characteristics, strain ZX9611T represents a novel species of genus Sphingomonas, for which the name Sphingomonas montanisoli sp. nov. is proposed. The type strain is ZX9611T (=KCTC 72622T=CCTCC AB 2019350T).
Maribacter algarum sp. nov., a new member of the family Flavobacteriaceae isolated from the red alga Gelidium amansii
Authors: Zhang Jin-Yu , Xia Yu , Feng Xi , Mu Da-Shuai , Du Zong-Jun 
Published: May 2020

A Gram-stain-negative, rod-shaped (0.2–0.3×1.0-2.4 µm), catalase-positive, oxidase-negative and non-motile bacterium, designated strain RZ26T, was isolated from the marine red algae collected from the coast of Weihai, PR China. Growth of strain RZ26T occurred at 15–33 °C (optimum, 25–28 °C), pH 6.0–9.5 (optimum, pH 7.0–7.5) and 0.5–5.0 % (w/v) NaCl (optimum, 2.0–3.0 %). Resuls of phylogenetic analysis based on 16S rRNA gene sequences showed that strain RZ26T was most closely related to

Maribacter spongiicola

DSM 25233T (96.2 % sequence similarity), followed by

Maribacter forsetii

DSM 18668T (96.1 %) and

Maribacter vaceletii

DSM 25230T (95.4 %). The average nucleotide identity and the average amino acid identity values between strain RZ26T and

M. sedimenticola

KCTC 12966T,

M. spongiicola

DSM 25233T,

M. vaceletii

DSM 25230T and

M. forsetii

DSM 18668T were 75.6, 76.2, 76.0, 76.7, 64.3, 63.9, 68.6 and 68.0 %, respectively. The digital DNA–DNAhybridization values based on the draft genomes between strain RZ26T and

M. sedimenticola

KCTC 12966T,

M. spongiicola

DSM 25233T and

M. vaceletii

DSM 25230T were 38.0, 35.1 and 37.1 %, respectively. The major fatty acids in strain RZ26T were iso-C17 : 0 3-OH, iso-C15 : 0 and C16 : 1
ω7c/C16 : 1
ω6c. The major respiratory quinone was MK-6. The dominant polar lipid was phosphatidylethanolamine. The DNA G+C content was 38.0 mol%. Phylogenetic analysis shows strain RZ26T fell within a clade comprising species of the genus

Maribacter

. Polyphasic taxonomy indicates that the isolate represents a novel species of the genus

Maribacter

, for which the name Maribacter algarum sp. nov. is proposed, with type strain RZ26T (=KCTC 62992T=MCCC 1H00362T).
Theoretical investigation of pre-symptomatic SARS-CoV-2 person-to-person transmission in households
Authors: Arav Yehuda , Klausner Ziv , Fattal Eyal 
Published: May 2020

ABSTRACTSince its emergence, the phenomenon of SARS-CoV-2 transmission by seemingly healthy individuals has become a major challenge in the effort to achieve control of the pandemic. Identifying the modes of transmission that drive this phenomenon is a perquisite in devising effective control measures, but to date it is still under debate. To address this problem, we have formulated a detailed mathematical model of discrete human actions (such as coughs, sneezes, and touching) and the decay of the virus in the environment. To take into account both discrete and continuous events we have extended the common modelling approach and employed a hybrid stochastic mathematical framework. This allows us to calculate higher order statistics which are crucial for the reconstruction of the observed distributions. We focused on transmission within a household, the venue with the highest risk of infection and validated the model results against the observed secondary attack rate and the serial interval distribution. Detailed analysis of the model results identified the dominant driver of pre-symptomatic transmission as the contact route via hand-face transfer and showed that wearing masks and avoiding physical contact are an effective prevention strategy. These results provide a sound scientific basis to the present recommendations of the WHO and the CDC.
FORECASTING COVID-19 PANDEMIC: A DATA-DRIVEN ANALYSIS
Authors: Nabi Khondoker Nazmoon 
Published: May 2020

AbstractIn this paper, a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEIDIUQHRD) deterministic compartmental model has been proposed and calibrated for interpreting the transmission dynamics of the novel coronavirus disease (COVID-19). The purpose of this study is to give a tentative prediction of the epidemic peak for Russia, Brazil, India and Bangladesh which could become the next COVID-19 hotspots in no time by using a Trust-region-reflective (TRR) algorithm which one of the well-known real data fitting techniques. Based on the publicly available epidemiological data from late January until 10 May, it has been estimated that the number of daily new symptomatic infectious cases for the above mentioned countries could reach the peak around the beginning of June with the peak size of ~ 15, 774 (95% CI, 13,814-17,734) symptomatic infectious cases in Russia, ~ 26, 449 (95% CI, 23,489-29,409) cases in Brazil, ~ 9, 504 (95% CI, 8,378-10,630) cases in India and ~ 2, 209 (95% CI, 1,878-2,540) cases in Bangladesh. As of May 11, 2020, incorporating the infectiousness capability of asymptomatic carriers, our analysis estimates the value of the basic reproduction number (R0) as of May 11, 2020 was found to be ~ 4.234 (95% CI, 3.764-4.7) in Russia,~ 5.347 (95% CI, 4.737-5.95) in Brazil, ~ 5.218 (95% CI, 4.56-5.81)in India, ~ 4.649 (95% CI, 4.17-5.12) in the United Kingdom and ~ 3.53 (95% CI, 3.12-3.94) in Bangladesh. Moreover, Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) which is a global sensitivity analysis (GSA) method is applied to quantify the uncertainty of our model mechanisms, which elucidates that for Russia, the recovery rate of undetected asymptomatic carriers, the rate of getting home-quarantined or self-quarantined and the transition rate from quarantined class to susceptible class are the most influential parameters, whereas the rate of getting home-quarantined or self-quarantined and the inverse of the COVID-19 incubation period are highly sensitive parameters in Brazil, India, Bangladesh and the United Kingdom which could significantly affect the trans-mission dynamics of the novel coronavirus. Our analysis also suggests that relaxing social distancing restrictions too quickly could exacerbate the epidemic outbreak in the above-mentioned countries.
The impact of containment measures and air temperature on mitigating COVID-19 transmission: non-classical SEIR modeling and analysis
Authors: Liu Di , Tai Qidong , Wang Yaping , Pu Miao , Ge Sikai , Ji Tingting , Zhang Lei , Su Bo 
Published: May 2020

AbstractEarly non-pharmaceutical interventions (NPIs) are crucial to prevent and control of COVID-19 pandemic. We established a stochastic non-classical SEIR NPIs model (ScEIQRsh) which can quantify the three kinds of NPIs measures simultaneously to mimic the clustered intra-family or intra-acquaintance spreading pattern of COVID-19 under the effective integrated NPIs in Mainland China. Model simulation demonstrated that measures to diminish contactable susceptible (Sc), such as home confinement, travel constraint, social distancing etc. and measures to avoid delay of diagnosis and hospitalized isolation (η) were more effective but consumptive than contact tracing (κ, ρ). From fitted model by MCMC method, the proportion of asymptomatic infectors was 14.88% (IQR 8.17%, 25.37%). The association between air temperature and the fitted transmission rate (β) of COVID-19 suggests that COVID-19 pandemic would be seasonal with the optimal temperature range of 5°C-14°C and peak of 10°C for spreading, and vaccine is indispensable to ultimate prevention COVID-19.
THE MUC5B PROMOTOR POLYMORPHISM ASSOCIATES WITH SEVERE COVID-19
Authors: van Moorsel Coline H.M. , van der Vis Joanne J. , Benschop Claudia , Ruven Henk J.T. , Quanjel Marian , Grutters Jan C. 
Published: May 2020

ABSTRACTBackgroundDiversity in response to exposition to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is common and may be related to the innate immune response. The mucin MUC5B is an important component of the innate immune response and expression levels are associated with the MUC5B promoter polymorphism, rs35705950. The high expressing T-allele of rs35705950 is an accepted risk allele for a non-infectious aging lung disease called idiopathic pulmonary fibrosis (IPF). However, given the theory of trade-offs in aging lung disease and the importance of high expression for an adequate immune response, we hypothesize that the T-allele is protective against severe coronavirus disease 2019 (COVID-19).MethodsWe collected demographics, radiology, survival data and MUC5B rs35705950 allele status for 108 patients requiring hospitalisation for COVID-19 at St Antonius Hospital in The Netherlands. For comparison of allele frequencies and allele carriership with a white control cohort, the patient cohort was divided in a white (n=83) and non-white cohort.ResultsThe patients had a median age of 66 years and consisted predominantly of males (74%) and 23 patients (21 %) died. The T-allele frequencies of rs35705950 in white patients was 0.04 which was significantly lower than the T-allele frequency of 0.10 in white controls (p= 0.02). Moreover, comparison of the number of carriers and non-carriers of the T allele showed that only 8.4% of patients carried the T-allele versus 18% of controls (p=0.029; OR= 0.41, CI=0.19-0.94).ConclusionsThe MUC5B rs35705950 promoter polymorphism associates with COVID-19. The risk allele (T) for IPF is protective against the development of severe COVID-19 disease. This is a further example of a trade-off between optimal expression levels in the respiratory system which associates with aging diseases. However, these results require further investigation.
Factors linked to changes in mental health outcomes among Brazilians in quarantine due to COVID-19
Authors: Stults-Kolehmainen Matthew , Filgueiras Alberto , Blacutt Miguel 
Published: May 2020

AbstractThis aim of this investigation was to track changes and risk factors for mental health outcomes during state-mandated quarantine in Brazil. Adults residing in Brazil (n = 360, 37.9 years old, 68.9% female) were surveyed at the start of quarantine and approximately three weeks later. Outcomes assessed included perceived stress, state anxiety and symptoms of depression. Aside from demographics, behaviours and attitudes assessed included exercise, diet, use of tele-psychotherapy and number of COVID-19 related risk factors, such as perceived risk of COVID-19, information overload, and feeling imprisoned. Overall, all mental health outcomes worsened from Time 1 to time 2, although there was a significant gender x time interaction for stress. 9.7% of the sample reported stress above the clinical cut-off (2 SD above mean), while 8.0% and 9.4% were above this cut-off for depression and anxiety, respectively. In repeated measures analysis, female gender, worsening diet and an excess of COVID-19 information was related to all mental health outcomes. Positive dietary changes were associated with decreases in depression and anxiety. Exercise frequency was positively related to state anxiety and perceived stress (0 days/week > 6 days/week). Those who did aerobic exercise did not have significantly increase in depression. Use of tele-psychotherapy predicted lower levels of depression and anxiety. In multiple regression, anxiety was predicted by the greatest number of COVID-19 specific factors. In conclusion, mental health outcomes worsened for Brazilians within the first month of quarantine and these changes are associated with a variety of risk factors.
A Risk Score to Predict Admission to Intensive Care Unit in Patients With COVID-19: The ABC-GOALS Score
Authors: Mejía-Vilet Juan M. , Córdova-Sánchez Bertha M. , Fernández-Camargo Dheni A. , Méndez-Pérez R. Angélica , Morales Buenrostro Luis E. , Hernández-Gilsoul Thierry 
Published: May 2020

ABSTRACTObjectivesCOVID-19 pandemic poses a burden on hospital resources and intensive care unit (ICU) occupation. This study aimed to provide a scoring system that, assessed upon first-contact evaluation at the emergency department, predicts the need for ICU admission.MethodsWe prospectively assessed patients admitted to a COVID-19 reference center in Mexico City between March 16th and May 21st, and split them into development and validation cohorts. Patients were segregated into a group that required admission to ICU, and a group that never required ICU admission and was discharged from hospitalization. By logistic regression, we constructed predictive models for ICU admission, including clinical, laboratory, and imaging findings from the emergency department evaluation. The ABC-GOALS score was created by assigning values to the weighted odd ratios. The score was compared to other COVID-19 and pneumonia scores through the area under the curve (AUC).ResultsWe included 569 patients divided into development (n=329) and validation (n=240) cohorts. One-hundred-fifteen patients from each cohort required admission to ICU. The clinical model (ABC-GOALSc) included sex, obesity, the Charlson comorbidity index, dyspnea, arterial pressure, and respiratory rate at triage evaluation. The clinical plus laboratory model (ABC-GOALScl) added serum albumin, glucose, lactate dehydrogenase, and S/F ratio to the clinical model. The model that included imaging (ABC-GOALSclx) added the CT scan finding of >50% lung involvement. The model AUC were 0.79 (95%CI 0.74-0.83) and 0.77 (95%CI 0.71-0.83), 0.86 (95%CI 0.82-0.90) and 0.87 (95%CI 0.83-0.92), 0.88 (95%CI 0.84-0.92) and 0.86 (95%CI 0.81-0.90) for the clinical, laboratory and imaging models in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclx scores outperformed other COVID-19 and pneumonia-specific scores.ConclusionThe ABC-GOALS score is a tool to evaluate patients with COVID-19 at admission to the emergency department, which allows to timely predict their risk of admission to an ICU.