bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biogeochemical responses over 37 years to manipulation of phosphorus concentrations in an Arctic river: The Upper Kuparuk River Experiment
Authors: Iannucci Frances M. , Beneš Joshua , Medvedeff Alexander , Bowden William B. 
Published: February 2021

AbstractThe climate of the Arctic region is changing rapidly, with important implications for permafrost, vegetation communities, and transport of solutes by streams and rivers to the Arctic Ocean. While research on Arctic streams and rivers has accelerated in recent years, long‐term records are relatively rare compared to temperate and tropical regions. We began monitoring the upper Kuparuk River in 1983 as part of a long‐term, low‐level, whole‐season phosphorus enrichment of a 4–6 km experimental reach, which was subsequently incorporated into the Arctic Long‐Term Ecological Research (Arctic LTER) programme. The phosphorus enrichment phase of the Upper Kuparuk River Experiment (UKRE) ran continuously for 34 seasons, fundamentally altering the community structure and function of the Fertilized reach. The objectives of this paper are to (a) update observations of the environmental conditions in the Kuparuk River region as revealed by long‐term, catchment‐level monitoring, (b) compare long‐term trends in biogeochemical characteristics of phosphorus‐enriched and reference reaches of the Kuparuk River, and (c) report results from a new ‘ReFertilization’ experiment. During the UKRE, temperature and discharge did not change significantly, though precipitation increased slightly. However, the UKRE revealed unexpected community state changes attributable to phosphorus enrichment (e.g., appearance of colonizing bryophytes) and long‐term legacy effects of these state changes after cessation of the phosphorus enrichment. The UKRE also revealed important biogeochemical trends (e.g., increased nitrate flux and benthic C:N, decreased DOP flux). The decrease in DOP is particularly notable in that this may be a pan‐Arctic trend related to permafrost thaw and exposure to new sources of iron that reduce phosphorus mobility to streams and rivers. The trends revealed by the UKRE would have been difficult or impossible to identify without long‐term, catchment level research and may have important influences on connections between Arctic headwater catchments and downstream receiving waters, including the Arctic Ocean.
Genome‐wide scan of long noncoding RNA single nucleotide polymorphisms and pancreatic cancer susceptibility
Authors: Corradi Chiara , Gentiluomo Manuel , Gajdán László , Cavestro Giulia Martina , Kreivenaite Edita , Di Franco Gregorio , Sperti Cosimo , Giaccherini Matteo , Petrone Maria Chiara , Tavano Francesca , Gioffreda Domenica , Morelli Luca , Soucek Pavel , Andriulli Angelo , Izbicki Jakob R. , Napoli Niccolò , Małecka‐Panas Ewa , Hegyi Péter , Neoptolemos John P. , Landi Stefano , Vashist Yogesh , Pasquali Claudio , Lu Ye , Cervena Klara , Theodoropoulos George E. , Moz Stefania , Capurso Gabriele , Strobel Oliver , Carrara Silvia , Hackert Thilo , Hlavac Viktor , Archibugi Livia , Oliverius Martin , Vanella Giuseppe , Vodicka Pavel , Arcidiacono Paolo Giorgio , Pezzilli Raffaele , Milanetto Anna Caterina , Lawlor Rita T. , Ivanauskas Audrius , Szentesi Andrea , Kupcinskas Juozas , Testoni Sabrina G. G. , Lovecek Martin , Nentwich Michael , Gazouli Maria , Luchini Claudio , Zuppardo Raffaella Alessia , Darvasi Erika , Brenner Hermann , Gheorghe Cristian , Jamroziak Krzysztof , Canzian Federico , Campa Daniele 
Published: February 2021

AbstractPancreatic ductal adenocarcinoma (PDAC) is projected to become the second cancer‐related cause of death by 2030. Identifying novel risk factors, including genetic risk loci, could be instrumental in risk stratification and implementation of prevention strategies. Long noncoding RNAs (lncRNAs) are involved in regulation of key biological processes, and the possible role of their genetic variability has been unexplored so far. Combining genome wide association studies and functional data, we investigated the genetic variability in all lncRNAs. We analyzed 9893 PDAC cases and 9969 controls and identified a genome‐wide significant association between the rs7046076 SNP and risk of developing PDAC (P = 9.73 × 10−9). This SNP is located in the NONHSAG053086.2 (lnc‐SMC2‐1) gene and the risk allele is predicted to disrupt the binding of the lncRNA with the micro‐RNA (miRNA) hsa‐mir‐1256 that regulates several genes involved in cell cycle, such as CDKN2B. The CDKN2B region is pleiotropic and its genetic variants have been associated with several human diseases, possibly though an imperfect interaction between lncRNA and miRNA. We present a novel PDAC risk locus, supported by a genome‐wide statistical significance and a plausible biological mechanism.
Race, ethnicity and risk of second primary contralateral breast cancer in the United States
Authors: Watt Gordon P. , John Esther M. , Bandera Elisa V. , Malone Kathleen E. , Lynch Charles F. , Palmer Julie R. , Knight Julia A. , Troester Melissa A. , Bernstein Jonine L. 
Published: February 2021

AbstractBreast cancer survivors have a high risk of a second primary contralateral breast cancer (CBC), but there are few studies of CBC risk in racial/ethnic minority populations. We examined whether the incidence and risk factors for CBC differed by race/ethnicity in the United States. Women with a first invasive Stage I‐IIB breast cancer diagnosis at ages 20‐74 years between 2000 and 2015 in the Surveillance, Epidemiology, and End Results Program (SEER) 18 registries were followed through 2016 for a diagnosis of invasive CBC ≥1 year after the first breast cancer diagnosis. We used cause‐specific Cox proportional hazards models to test the association between race/ethnicity and CBC, adjusting for age, hormone receptor status, radiation therapy, chemotherapy and stage at first diagnosis, and evaluated the impact of contralateral prophylactic mastectomy, socioeconomic status, and insurance status on the association. After a median follow‐up of 5.9 years, 9247 women (2.0%) were diagnosed with CBC. Relative to non‐Hispanic (NH) White women, CBC risk was increased in NH Black women (hazard ratio = 1.44, 95% CI 1.35‐1.54) and Hispanic women (1.11, 95% CI 1.02‐1.20), with the largest differences among women diagnosed at younger ages. Adjustment for contralateral prophylactic mastectomy, socioeconomic status and health insurance did not explain the associations. Therefore, non‐Hispanic Black and Hispanic women have an increased risk of CBC that is not explained by clinical or socioeconomic factors collected in SEER. Large studies of diverse breast cancer survivors with detailed data on treatment delivery and adherence are needed to inform interventions to reduce this disparity.
The impact of corruption on commercial banks' credit risk: Evidence from a panel quantile regression
Authors: Jenkins Hatice , Alshareef Ezuldeen , Mohamad Amer 
Published: February 2021

AbstractThis study explores the impact of countrywide corruption on the credit risk of commercial banks with different levels of credit risk. It applies the quantile regression (QR) estimation method for a panel data of 191 commercial banks, from 18 MENAP countries, between the years 2011–2018. The research finding indicates that corruption significantly exacerbates the problem of bad loans of banks. Furthermore, the QR results reveal that corruption does not affect all banks at the same level. Banks in higher quantiles (i.e., higher credit risk banks) appear to be affected more than the ones in lower quantiles (i.e., lower credit risk banks).Banks with high credit risk appear to be more vulnerable to corruption than banks with low credit risk.
Analyzing airlines stock price volatility during COVID‐19 pandemic through internet search data
Authors: Deb Soudeep 
Published: February 2021

AbstractRecent Coronavirus pandemic has prompted many regulations which are affecting the stock market. Especially because of lockdown policies across the world, the airlines industry is suffering. We analyse the stock price movements of three major airlines companies using a new approach which leverages a measure of internet concern on different topics. In this approach, Twitter data and Google Trends are used to create a set of predictors which then leads to an appropriately modified GARCH model. In the analysis, first we show that the ongoing pandemic has an unprecedented severe effect. Then, the proposed model is used to analyse and forecast stock price volatility of the airlines companies. The findings establish that our approach can successfully use the effects of internet concern for different topics on the movement of stock price index and provide good forecasting accuracy. Model confidence set (MCS) procedure further shows that the short‐term volatility forecasts are more accurate for this method than other candidate models. Thus, it can be used to understand the stock market during a pandemic in a better way. Further, the proposed approach is attractive and flexible, and can be extended to other related problems as well.
Dynamic spillover analysis of international and Turkish food prices
Authors: Ertuğrul Hasan Murat , Seven Ünal 
Published: February 2021

AbstractThis study analyses the dynamic spillover relationship between international and Turkish food prices by employing Markov Switching Regression (MSR) and Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity (DCC‐GARCH) models to monthly data from 2003 to 2019. We also investigate the factors that cause a price difference between Turkish and international food markets by employing alternative econometric models, including Autoregressive Distributed Lag (ARDL), Fully Modified OLS (FMOLS), Dynamic OLS (DOLS) and MSR. We find structural shifts in the dynamics of the international/domestic food price relationship, and the relationship varies across the low and high volatility regimes. We also find that the exchange rate significantly affects the growing difference between Turkish and international food prices, while oil prices reduce that difference. Our results point out essential policy recommendations for developing food policies and ensuring sustainability in prices.
Is causality between globalization and energy consumption bidirectional or unidirectional in top and bottom globalized economies?
Authors: Shahbaz Muhammad , Balcilar Mehmet , Mahalik Mantu Kumar , Akadiri Seyi Saint 
Published: February 2021

AbstractBy using quarterly data over the period 1970Q1‐2017Q4, this paper examines the dynamic causal relationship between globalization and energy consumption by using rolling and recursive rolling Granger causality methods. This study is pioneering effort to examine the dynamic causal relationship between globalization and energy consumption using time‐varying Granger causality tests for 20 top and bottom globalized economies. The empirical results reveal that the dynamic causality relationship between globalization and energy consumption is time‐varying. Although, the causal relationship could not be observed for some of the study periods, bidirectional causality is found in many sub‐samples. From the empirical findings, we observe that unidirectional causality running from globalization to energy consumption has grievous impact on trade and environmental quality. In general, our empirical results resonate with the previous findings of globalization energy‐driven hypothesis, with significant policy implications for top and bottom globalized countries.
A deep learning model for mass screening of COVID‐19
Authors: Dhaka Vijaypal Singh , Rani Geeta , Oza Meet Ganpatlal , Sharma Tarushi , Misra Ankit 
Published: February 2021

AbstractThe objective of this research is to develop a convolutional neural network model ‘COVID‐Screen‐Net’ for multi‐class classification of chest X‐ray images into three classes viz. COVID‐19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X‐ray images and accurately identifies the features responsible for distinguishing the X‐ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine‐tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X‐ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the ‘COVID‐Screen‐Net’ outperforms the existing systems for screening of COVID‐19. The effectiveness of the model is validated by the radiology experts on the real‐time dataset. Therefore, it may prove a useful tool for quick and low‐cost mass screening of patients of COVID‐19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number ‘SW‐13625/2020’.
Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique
Authors: Arumugam Selvapandian , Paulraj Sivakumar , Selvaraj Nagendra Prabhu 
Published: February 2021

AbstractBrain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre‐processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.
A New Data Landscape: IR's Role in Academic Analytics
Authors: Hawkins Christie , Bailey Lucy E. 
Published: February 2021

AbstractThe increasing volume of information and the intense pace of its circulation are changing the ways universities access, use, analyze, and provide data. Many have championed the use of large‐scale databases to track student admissions and retention, faculty productivity, student wellness, and other phenomena that shape our understandings of higher education. These changes have had major effects on analytics as well. This chapter outlines how analytics can assist the academic side of colleges and universities to best serve all academic stakeholders, including administrators, faculty, and staff. It also addresses the evolving roles of Institutional Research Offices in helping set institutional strategies, collaborating across university offices to develop analytics, and cultivating an institutional culture of informed decision making.IR Offices should consider various facets of the process of collecting, analyzing, and communicating information to serve academic departments. One priority is determining which software, tools, and approaches best align with institutional priorities and needs. Another is creating specific analytics that serve the academic missions of and strategic planning for the institution and representing data in accessible ways to serve academic administration's diverse purposes and roles. Finally, cultivating buy‐in and support from administration and other stakeholders is essential. IR offices can be instrumental in fostering academic colleagues' use, interpretation, and valuing of data and in developing collaborative relationships with academic leadership, colleges and departments. The new data analytics landscape requires expanding the roles and institutional reach of IR offices, thus creating opportunities for IR involvement in strategic planning and vision at high levels of their institutions.
The Evidence Imperative: Reflections on How Volatility and Data Are Reshaping the Relationship Between IR and College and University Presidents
Authors: Gagliardi Jonathan S. , Johnson Gina 
Published: February 2021

AbstractColleges have access to more data than ever before, and to unlock its full potential, the right culture, processes, and technologies are needed. Given the pressures now facing campuses, the demand for evidence‐based approaches to strategy have intensified. As a result, more attention has been placed on the importance of the relationship between Senior Leaders and IR, which, if fully harnessed, can help to ensure the viability of institutions nationwide. Yet, recent surveys and literature suggest that for this relationship to thrive, more must be done to ensure that IR is considered as an important functional area at a time when data‐informed decision‐making has become increasingly vital. To that end, the purpose of this chapter is to offer ideas for how executives can champion data‐informed decision‐making and the ways in which IR functions must take on greater leadership through an integrated and collaborative approach focused on analytics.
A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition
Authors: Song Dongliang , Chen Yishen , Li Jie , Wang Haifeng , Ning Tian , Wang Shuang 
Published: February 2021

AbstractIt is a practical necessity for non‐professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user‐friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky–Golay smoothing, area‐under‐curve normalization, mean‐centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares‐discriminant analysis, support vector machine (SVM), and PCA‐SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open‐source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.
In vivo optoretinography of phototransduction activation and energy metabolism in retinal photoreceptors
Authors: Ma Guangying , Son Taeyoon , Kim Tae‐Hoon , Yao Xincheng 
Published: February 2021

AbstractThe objective of this study is to verify the anatomic correlate of the second (2nd) outer retina band in optical coherence tomography (OCT), and to demonstrate the potential of using intrinsic optical signal (IOS) imaging for concurrent optoretinography (ORG) of phototransduction activation and energy metabolism in stimulus activated retinal photoreceptors. A custom‐designed OCT was employed for depth‐resolved IOS imaging in mouse retina activated by a visible light flicker stimulation. The spatiotemporal properties of the IOS changes at the photoreceptor outer segment (OS) and inner segment (IS) were quantitatively evaluated. Rapid IOS change was observed at the OS almost right away, and the IOS at the IS was relatively slow. Comparative analysis indicates that the OS‐IOS reflects transient OS deformation caused by the phototransduction activation, and IS‐IOS might reflect the energy metabolism caused by mitochondria activation in retinal photoreceptors. The consistency of the distribution of the IS‐IOS and the 2nd OCT band supports the IS ellipsoid (ISe), which has abundant mitochondria, as the signal source of the 2nd OCT band of the outer retina.
Inhibition of oral biofilm formation by zwitterionic nonfouling coating
Authors: Sun Fang , Hung Hsiang‐Chieh , Yan Weishi , Wu Kan , Shimchuk Andy A. , Gray Sterling D. , He Wei , Huang Xuelian , Zhang Hai 
Published: February 2021

AbstractInhibition of oral biofilm formation is critical to prevent and treat dental caries and periodontal diseases. In this study, we synthesized zwitterionic poly(carboxybetaine) (pCB) based polymer as a nonfouling coating to provide anti‐bacterial properties to tooth surfaces. Four catechol derived l‐3,4‐dihydroxyphenylalanine (DOPA) groups were conjugated to pCB to serve as a surface anchoring group. The pCB‐(DOPA)4 polymer was coated on the hydroxyapatite (HA) and enamel samples by simple immersion and characterized by Raman spectroscopy. The nonfouling effectiveness of the pCB based coating was determined by protein adsorption and bacterial adhesion assays. The coating was transparent on sample surfaces. The protein adsorption was significantly reduced to 8.2% and 6.9%, respectively, on pCB‐(DOPA)4 coated HA and enamel samples. The pCB‐(DOPA)4‐coated samples also demonstrated significantly fewer adhered Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus mutants compared to the control. This novel coating material provides an innovative approach to resist biofilm formation on tooth surfaces and has great potential in future dental clinical applications.
A Cross‐Sectional Cohort Study of the Effects of FGF23 Deficiency and Hyperphosphatemia on Dental Structures in Hyperphosphatemic Familial Tumoral Calcinosis
Authors: Lee Alisa E , Chu Emily Y , Gardner Pamela J , Duverger Olivier , Saikali Amanda , Wang Sean K , Gafni Rachel I , Hartley Iris R , Ten Hagen Kelly G , Somerman Martha J , Collins Michael T 
Published: February 2021

ABSTRACTHyperphosphatemic familial tumoral calcinosis (HFTC) is a rare autosomal recessive disorder caused by mutations in FGF23, GALNT3, KLOTHO, or FGF23 autoantibodies. Prominent features include high blood phosphate and calcific masses, usually adjacent to large joints. Dental defects have been reported, but not systematically described. Seventeen patients with HFTC followed at the National Institutes of Health underwent detailed clinical, biochemical, molecular, and dental analyses. Studies of teeth included intraoral photos and radiographs, high‐resolution μCT, histology, and scanning electron microscopy (SEM). A scoring system was developed to assess the severity of tooth phenotype. Pulp calcification was found in 13 of 14 evaluable patients. Short roots and midroot bulges with apical thinning were present in 12 of 13 patients. Premolars were most severely affected. μCT analyses of five HFTC teeth revealed that pulp density increased sevenfold, whereas the pulp volume decreased sevenfold in permanent HFTC teeth compared with age‐ and tooth‐matched control teeth. Histology revealed loss of the polarized odontoblast cell layer and an obliterated pulp cavity that was filled with calcified material. The SEM showed altered pulp and cementum structures, without differences in enamel or dentin structures, when compared with control teeth. This study defines the spectrum and confirms the high penetrance of dental features in HFTC. The phenotypes appear to be independent of genetic/molecular etiology, suggesting hyperphosphatemia or FGF23 deficiency may be the pathomechanistic driver, with prominent effects on root and pulp structures, consistent with a role of phosphate and/or FGF23 in tooth development. Given the early appearance and high penetrance, cognizance of HFTC‐related features may allow for earlier diagnosis and treatment. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC. on behalf of American Society for Bone and Mineral Research.