bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perilaku Konsumen Terhadap E-Commerce Disaat Pandemi Covid 19 di Shop And Travel
Authors: Mardiani Eri , Rahmansyah Nur , Al Rizky Farid 
Published: May 2021

Saat ini kondisi covid-19 sangat berpengaruh terhadap kehidupan bermasyarakat khususnya kondisi ekonomi, dikondisi pandemi covid-19 seperti ini masyarakat dihadapkan faktor resiko yang banyak untuk menghindari melakukan transaksi secara langsung guna menghindari penyebaran virus corona ini. Dan Masyarakat diharuskan menghindari kontak fisik (physical distancing) sehingga dalam melakukan transaksi masyarakat lebih banyak menggunakan transaksi secara digital. Dengan kondisi seperti ini akhirnya membuat masyarakat mulai melakukan transaksi secara online dimulai dengan berbanja online, karena untuk menghindari kontak fisik. Dan dampak positif bagi perusahaan atau usaha mengalami peningkatan yang signifikan terhadap penjualan dan pembelian produk perusahaan dampai dari kondisi pandemi covid ini, dan menyebabkan perusahaan untuk bisa beralih fungsi sistem penjualan dan pembeli dengan berbasis digital dan ini bisa dilihat dari pergerakan transaksi ecommerce yang sangat meningkat,transaksi pembelian meningkat secara drastis dampak pandemi covid 19 ini, serta penggunaan aplikasi belanja online mengalami peningkatan yang signifikan.
Managing Conflict at Institution/s of Higher Learning: A Post-Positivist Perspective
Authors: Lukman Yusuf 
Published: May 2021

Institutions of Higher Learning in South Africa annually face challenges that often lead to student protests and demonstrations, mostly at the beginning of every academic year, which adversely impact the smooth running of academic programs. Stakeholders’ expectations were at the apex of causes that destabilise the academic environment, academic almanac and the overall academic professional reputation. The volatility of this kind retards productivity and negatively affects many tertiary institutions across the Country. This empirically grounded paper focuses on conflicting variables amongst universities, but with reference to an Eastern Cape University in South Africa spread across its Campuses. Adopting the post-positivist approach, this study obtained data from over 180 respondents and the data was analysed by using descriptive and inferential statistics, including analyses of variance and Pearson Product Moment correlations. In addition, content analysis techniques were used to analyse the data collected from the unstructured questionnaire. In this empirical study the findings highlighted two major variables that gave rise to conflicts, escalation of strikes and demonstrations at Higher Institutions of learning and recommend a conflict management style apposite for handling the conundrum. The factors dealt with in this study are not peculiar to the institution studied, but are analogous to other institutions. The findings also underscored Integrating conflict management as the most commendable style for managing conflicts at institutions of higher learning.
Implementácia autentického materiálu do výučby odborného cudzieho jazyka
Authors: Rizeková Iveta 
Published: May 2021

Príspevok sa zaoberá problematikou didaktického využitia autentického materiálu vo výučbe cudzieho jazyka v kontexte aplikovanej lingvistiky. Autorka príspevku vychádza z presvedčenia, že autenticita má v cudzojazyčnej výučbe dôležité miesto, pretože prejavy a dokumenty určené rodeným hovoriacim v danej krajine sprostredkúvajú kontakt so situáciami z reálneho života aj so živým jazykom. Článok prináša možnosti využitia jednotlivých typov autentických dokumentov na hodinách odborného cudzieho jazyka a prezentuje výsledky dotazníkového prieskumu uskutočneného v radoch študentov aplikovaných jazykov. Cieľom prieskumu bolo zistiť, aké je zastúpenie jednotlivých typov autentického materiálu a ako efektívne sa využíva na hodinách odborného francúzskeho jazyka, ako aj to, aký je postoj študentov k otázke implementácie autentických materiálov do jazykovej výučby. Autorka v závere konštatuje, že autentické zdroje významne prispievajú k rozvoju komunikačných zručností a vnímaniu interkultúrnych rozdielov a pozitívne vplývajú na motiváciu študentov. Hodiny jazyka sa vďaka nim stávajú dynamickými, interaktívnymi a viac orientovanými na potreby učiacich sa.
Application of a novel haplotype-based scan for local adaptation to study high-altitude adaptation in rhesus macaques
Authors: Szpiech Zachary A. , Novak Taylor E. , Bailey Nick P. , Stevison Laurie S. 
Published: May 2021

AbstractWhen natural populations split and migrate to different environments, they may experience different selection pressures that can lead to local adaptation. To capture the genomic patterns of a local selective sweep, we develop XP-nSL, a genomic scan for local adaptation that compares haplotype patterns between two populations. We show that XP-nSL has power to detect ongoing and recently completed hard and soft sweeps, and we then apply this statistic to search for evidence of adaptation to high altitude in rhesus macaques. We analyze the whole genomes of 23 wild rhesus macaques captured at high altitude (mean altitude &gt; 4000 m above sea level) to 22 wild rhesus macaques captured at low altitude (mean altitude &lt; 500 m above sea level) and find evidence of local adaptation in the high-altitude population at or near 303 known genes and several unannotated regions. We find the strongest signal for adaptation at EGLN1, a classic target for convergent evolution in several species living in low oxygen environments. Furthermore, many of the 303 genes are involved in processes related to hypoxia, regulation of ROS, DNA damage repair, synaptic signaling, and metabolism. These results suggest that, beyond adapting via a beneficial mutation in one single gene, adaptation to high altitude in rhesus macaques is polygenic and spread across numerous important biological systems.
Control with uncertain data of socially structured compartmental epidemic models
Authors: Albi Giacomo , Pareschi Lorenzo , Zanella Mattia 
Published: May 2021

AbstractThe adoption of containment measures to reduce the amplitude of the epidemic peak is a key aspect in tackling the rapid spread of an epidemic. Classical compartmental models must be modified and studied to correctly describe the effects of forced external actions to reduce the impact of the disease. The importance of social structure, such as the age dependence that proved essential in the recent COVID-19 pandemic, must be considered, and in addition, the available data are often incomplete and heterogeneous, so a high degree of uncertainty must be incorporated into the model from the beginning. In this work we address these aspects, through an optimal control formulation of a socially structured epidemic model in presence of uncertain data. After the introduction of the optimal control problem, we formulate an instantaneous approximation of the control that allows us to derive new feedback controlled compartmental models capable of describing the epidemic peak reduction. The need for long-term interventions shows that alternative actions based on the social structure of the system can be as effective as the more expensive global strategy. The timing and intensity of interventions, however, is particularly relevant in the case of uncertain parameters on the actual number of infected people. Simulations related to data from the first wave of the recent COVID-19 outbreak in Italy are presented and discussed.
Effects of daily consumption of wild blueberry on cognition and urinary metabolites in school-aged children: a pilot study
Authors: Barfoot Katie Louise , Istas Geoffrey , Feliciano Rodrigo Pedro , Lamport Daniel Joseph , Riddell Patricia , Rodriguez-Mateos Ana , Williams Claire Michelle 
Published: May 2021

Abstract
Purpose
Acute intervention with wild blueberry (WBB) has previously revealed positive cognitive and mood effects in typically developing children; however, it is unclear whether effects persist after daily supplementation. In addition, no data have been published exploring the metabolite profiles of children following berry consumption, to our knowledge. A study of this kind could provide insight into a mechanism of action for the cognitive and mood improvements observed previously in children. The aim of this pilot study was to assess cognitive performance and urinary metabolite concentrations in healthy 7–10-year-old children across a 4 week daily WBB drink intervention.

Methods
This pilot study examined the effects of daily WBB consumption for 4 weeks (766 mg total polyphenols; 253 mg anthocyanins; equivalent to 240 g fresh blueberries per day) on cognition and mood in 15 healthy 7–10-year-old children. Polyphenol metabolites were measured in 24 h urine before and after the 4 week intervention.

Results
Chronic WBB-related benefits were seen on cognitively demanding trials on the modified attention network task, a task measuring executive functioning. Specifically, the WBB group maintained significantly higher accuracy on incongruent trials (96%; SE 0.03) compared with placebo participants (85%; SE 0.03; p = 0.038) after the 4 week intervention, suggesting WBB was of most benefit on the more difficult aspects of the task. No significant WBB-related effects were observed on the auditory verbal learning task or the child’s version of the positive and negative affect schedule. Urinary metabolite analyses indicated significant increases in different metabolites in WBB and placebo groups after 4 week consumption.

Conclusion
The research demonstrates 24 h WBB bioavailability in a child cohort for the first time with increases in urinary hippuric acid excretion during 2 week daily WBB consumption. This study highlights the importance of conducting a larger study in children investigating the mechanism of action behind cognitive effects using bioavailability data.
Perspectives in systems nephrology
Authors: Lindenmeyer Maja T. , Alakwaa Fadhl , Rose Michael , Kretzler Matthias 
Published: May 2021

AbstractChronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.
Interaction of periodontitis and orthodontic tooth movement—an in vitro and in vivo study
Authors: Rath-Deschner Birgit , Nogueira Andressa V. B. , Beisel-Memmert Svenja , Nokhbehsaim Marjan , Eick Sigrun , Cirelli Joni A. , Deschner James , Jäger Andreas , Damanaki Anna 
Published: May 2021

Abstract
Objectives
The aim of this in vitro and in vivo study was to investigate the interaction of periodontitis and orthodontic tooth movement on interleukin (IL)-6 and C-X-C motif chemokine 2 (CXCL2).

Materials and methods
The effect of periodontitis and/or orthodontic tooth movement (OTM) on alveolar bone and gingival IL-6 and CXCL2 expressions was studied in rats by histology and RT-PCR, respectively. The animals were assigned to four groups (control, periodontitis, OTM, and combination of periodontitis and OTM). The IL-6 and CXCL2 levels were also studied in human gingival biopsies from periodontally healthy and periodontitis subjects by RT-PCR and immunohistochemistry. Additionally, the synthesis of IL-6 and CXCL2 in response to the periodontopathogen Fusobacterium nucleatum and/or mechanical strain was studied in periodontal fibroblasts by RT-PCR and ELISA.

Results
Periodontitis caused an increase in gingival levels of IL-6 and CXCL2 in the animal model. Moreover, orthodontic tooth movement further enhanced the bacteria-induced periodontal destruction and gingival IL-6 gene expression. Elevated IL-6 and CXCL2 gingival levels were also found in human periodontitis. Furthermore, mechanical strain increased the stimulatory effect of F. nucleatum on IL-6 protein in vitro.

Conclusions
Our study suggests that orthodontic tooth movement can enhance bacteria-induced periodontal inflammation and thus destruction and that IL-6 may play a pivotal role in this process.

Clinical relevance
Orthodontic tooth movement should only be performed after periodontal therapy. In case of periodontitis relapse, orthodontic therapy should be suspended until the periodontal inflammation has been successfully treated and thus the periodontal disease is controlled again.
Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow
Authors: Kiser Kendall J. , Barman Arko , Stieb Sonja , Fuller Clifton D. , Giancardo Luca 
Published: May 2021

AbstractAutomated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman’s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman’s rank correlation coefficients or Mann–Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ =  − 0.48 versus ρ =  − 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool’s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.
The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
Authors: Caton Michael Travis , Wiggins Walter F. , Pomerantz Stuart R. , Andriole Katherine P. 
Published: May 2021

AbstractConventional measures of radiologist efficiency, such as the relative value unit, fail to account for variations in the complexity and difficulty of a given study. For lumbar spine MRI (LMRI), an ideal performance metric should account for the global severity of lumbar degenerative disease (LSDD) which may influence reporting time (RT), thereby affecting clinical productivity. This study aims to derive a global LSDD metric and estimate its effect on RT. A 10-year archive of LMRI reports comprising 13,388 exams was reviewed. Objective reporting timestamps were used to calculate RT. A natural language processing (NLP) tool was used to extract radiologist-assigned stenosis severity using a 6-point scale (0 = “normal” to 5 = “severe”) at each lumbar level. The composite severity score (CSS) was calculated as the sum of each of 18 stenosis grades. The predictive values of CSS, sex, age, radiologist identity, and referring service on RT were examined with multiple regression models. The NLP tool accurately classified LSDD in 94.8% of cases in a validation set. The CSS increased with patient age and differed between men and women. In a univariable model, CSS was a significant predictor of mean RT (R2 = 0.38, p < 0.001) and independent predictor of mean RT (p < 0.001) controlling for patient sex, patient age, service location, and interpreting radiologist. The predictive strength of CSS was stronger for the low CSS range (CSS = 0–25, R2 = 0.83, p < 0.001) compared to higher CSS values (CSS > 25, R2 = 0.15, p = 0.05). Individual radiologist study volume was negatively correlated with mean RT (Pearson’s R =  − 0.35, p < 0.001). The composite severity score predicts radiologist reporting efficiency in LMRI, providing a quantitative measure of case complexity which may be useful for workflow planning and performance evaluation.
Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach
Authors: Kara Su , Akers Jake Y. , Chang Peter D. 
Published: May 2021

AbstractRapid and accurate assessment of endotracheal tube (ETT) location is essential in the intensive care unit (ICU) setting, where timely identification of a mispositioned support device may prevent significant patient morbidity and mortality. This study proposes a series of deep learning-based algorithms which together iteratively identify and localize the position of an ETT relative to the carina on chest radiographs. Using the open-source MIMIC Chest X-Ray (MIMIC-CXR) dataset, a total of 16,000 patients were identified (8000 patients with an ETT and 8000 patients without an ETT). Three different convolutional neural network (CNN) algorithms were created. First, a regression loss function CNN was trained to estimate the coordinate location of the carina, which was then used to crop the original radiograph to the distal trachea and proximal bronchi. Second, a classifier CNN was trained using the cropped inputs to determine the presence or absence of an ETT. Finally, for radiographs containing an ETT, a third regression CNN was trained to both refine the coordinate location of the carina and identify the location of the distal ETT tip. Model accuracy was assessed by comparing the absolute distance of prediction and ground-truth coordinates as well as CNN predictions relative to measurements documented in original radiologic reports. Upon five-fold cross validation, binary classification for the presence or absence of ETT demonstrated an accuracy, sensitivity, specificity, PPV, NPV, and AUC of 97.14%, 97.37%, 96.89%, 97.12%, 97.15%, and 99.58% respectively. CNN predicted coordinate location of the carina, and distal ETT tip was estimated within a median error of 0.46 cm and 0.60 cm from ground-truth annotations respectively. Overall final CNN assessment of distance between the carina and distal ETT tip was predicted within a median error of 0.60 cm from manual ground-truth annotations, and a median error of 0.66 cm from measurements documented in the original radiology reports. A serial cascaded CNN approach demonstrates high accuracy for both identification and localization of ETT tip and carina on chest radiographs. High performance of the proposed multi-step strategy is in part related to iterative refinement of coordinate localization as well as explicit image cropping which focuses algorithm attention to key anatomic regions of interest.
Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention
Authors: Kao Szu-Yu Zoe , Enns Eva A. , Tomamichel Megan , Doll Adam , Escobar Luis E. , Qiao Huijie , Craft Meggan E. , Phelps Nicholas B. D. 
Published: May 2021

AbstractConnectivity between waterbodies influences the risk of aquatic invasive species (AIS) invasion. Understanding and characterizing the connectivity between waterbodies through high-risk pathways, such as recreational boats, is essential to develop economical and effective prevention intervention to control the spread of AIS. Fortunately, state and local watercraft inspection programs are collecting significant data that can be used to quantify boater connectivity. We created a series of predictive models to capture the patterns of boater movements across all lakes in Minnesota, USA. Informed by more than 1.3 million watercraft inspection surveys from 2014–2017, we simulated boater movements connecting 9182 lakes with a high degree of accuracy. Our predictive model accurately predicted 97.36% of the lake pairs known to be connected and predicted 91.01% of the lake pairs known not to be connected. Lakes with high degree and betweenness centrality were more likely to be infested with an AIS than lakes with low degree (p < 0.001) and centrality (p < 0.001). On average, infested lakes were connected to 1200 more lakes than uninfested lakes. In addition, boaters that visited infested lakes were more likely to visit other lakes, increasing the risk of AIS spread to uninfested lakes. The use of the simulated boater networks can be helpful for determining the risk of AIS invasion for each lake and for developing management tools to assist decision makers to develop intervention strategies.
Asset bubbles in explaining top income shares
Authors: Sarkar Saikat , Tuomala Matti 
Published: May 2021

AbstractThis paper considers the role of asset price bubbles (crashes) as an important determinant in seeking a further explanation for top income shares. The asset price bubbles caused at least in part by monetary policies, along with other determinants such as top tax rates and innovativeness are the important drivers to explain the surge in top income shares. The empirical results show that correlation between asset bubbles and top inequality is positive and significant. The regression coefficient of stock and housing market bubbles have a positive effect on top income shares, while the stock and housing market crashes fail to reduce the surge in top income shares. In sum, as the asset markets grow, the share of income going to those at the very top increases and the accumulation of income accelerates if the duration of bubbles expands. Concentration of income at the very top is much more important when capital gains are counted as income.