bluuebunny/crossref_metadata_2025_split huggingface dataset:
Financial fair play and competitive balance in European football: a long term perspective
Authors: Serrano Raúl , Acero Isabel , Farquhar Stuart , Espitia Escuer Manuel Antonio 
Published: April 2022

PurposeThe paper analyzes the effects of financial fair play (FFP) in the competitive balance of European football industry throughout a long-term perspective.Design/methodology/approachThe authors analyze the evolution of the competitive balance in the European football industry through a time-series analysis from season 1992/93 to 2018/19.FindingsResults indicate an industry by nature dominated by a few clubs showing a general stationary behavior. FFP has had very little impact in local competitions. Just in some leagues, such as the Spanish, German, and French leagues, we can observe an increase in the imbalance in some indicators, but these results are not very robust. The improvement on the financial situation happens especially in a small group of firms that coincide with the big leagues with a strong European market orientation and strict local financial control standards.Research limitations/implicationsAlthough the study covered 17 European Leagues, there are several leagues not accounted for and thus results should be generalized with caution.Practical implicationsThe authors observe heterogeneity of the results of FFP in the competitive balance, associated to how the standard has been implemented in each market. This opens opportunities to study and deepen the local codes and their influence, especially in the recommendations of future financial control standards.Originality/valueThe authors’ main contribution to the literature is to examine the impact of the FFP rules in the competitive balance utilizing a very broad study of 17 European markets with a rich and unusual overview and long-term perspective.
Ratio‐Euler deconvolution and its applications
Authors: Huang Liang , Zhang Henglei , Li Chun‐Feng , Feng Jie 
Published: April 2022

ABSTRACTEuler deconvolution of potential field data is widely applied to obtain the location of concealed sources automatically. A lot of improvements have been proposed to eliminate the dependence of Euler deconvolution on the structural index that are based on the use of high‐order derivatives of the potential field and, therefore, sensitive to data noise. We describe the elimination of the dependence on the Ratio‐Euler method, which is based on the original Euler deconvolution function. The proposed method does not involve high‐order derivatives. Testing on simulated and field data indicates that the proposed method has better noise resistance than the existing Tilt‐Euler method, which is based on high‐order derivatives. The proposed method is first applied to the ground magnetic data from Weigang iron deposit, Eastern China. It reveals that the ore body could be approximated by a horizontal prism with a considerable vertical extent with a top depth of 55 m, which are very close to the information obtained from drill holes. In addition, the proposed method works well in estimating the depth to a cavity centre from the ground gravity anomaly.
Hedging Bets; British Business Lobbying in the European Union post‐Brexit
Authors: Coen David , Katsaitis Alexander 
Published: April 2022

AbstractIn Brexit's aftermath, appreciating how UK business will interact with Brussels is vital to understanding the mechanics of future economic and trade relations. Drawing from work on corporate political activity, the article outlines changes that UK business lobbying faces in Brussels post‐Brexit, focussing on three key aspects: (i) changes at the institutional‐systemic level; (ii) variance across policy fields; (iii) engagement with other organisations. The article discusses potential strategic responses for UK business and argues that lobbying for UK private interests will become a costlier and more complicated enterprise. While policy making access via political doors will become harder, demand for reliable technical expertise allows UK business to maintain its presence and influence in regulatory policy areas. Targeted lobbying and diversified mobilisation offer the safest way forward for UK business lobbying in Brussels.
The amount of skeletal muscle mass is associated with arterial stiffness in hemodialysis patients
Authors: Ulgen Cansu , Ozturk Ilyas , Sahin Murat , Guzel Fatma Betul , Oguz Ayten , Altunoren Orcun , Gungor Ozkan 
Published: April 2022

AbstractIntroductionSarcopenia was determined to be associated with increased arterial stiffness in the nondialysis patient population, but there is no available data on this subject in dialysis patients.MethodsA total of 79 patients were included in the study. Sarcopenia was diagnosed according to the EWSGOP‐2 criteria. Arterial stiffness was measured noninvasively with a mobile‐O‐Graph device.ResultsSkeletal muscle mass was observed to be positively correlated with weight, body mass index, creatinine, and uric acid, while negatively correlated with augmentation index. There was a correlation between augmentation index and sodium, phosphorus, systolic blood pressure, diastolic blood pressure, cardiac index, muscle percentage, fat percentage, and skeletal muscle mass. When the determinants of augmentation index in the linear regression analysis were viewed, just the systolic blood pressure and skeletal muscle mass were observed to be the determinant.ConclusionDecreased skeletal muscle mass contributes to increased arterial stiffness in hemodialysis patients.
Combating climate change: Dismantling the spatial and temporal assumptions of the core and periphery
Authors: Knox‐Hayes Janelle 
Published: April 2022

AbstractIn this commentary, I examine the implications of core periphery relationship with respect to challenges, implications, and responses to climate change. As authors in the special issue have demonstrated with respect to economic growth, the core‐periphery dynamic is challenged in climate change. Climate change is disrupting the fundamental conditions of human life. Its impacts are also profoundly unequal and the risks of climate change compound. Climate change exacerbates existing inequities by placing further burdens on communities that are already vulnerable. While some individuals and communities will have resources to adapt to or avoid the worst impacts of climate change, others will find their homes becoming uninhabitable, their livelihoods vanishing, and their health and security threatened. Sea level rise alone will create up to one billion climate refugees by the year 2100.
Age prediction of green turtles with an epigenetic clock
Authors: Mayne Benjamin , Mustin Walter , Baboolal Vandanaa , Casella Francesca , Ballorain Katia , Barret Mathieu , Vanderklift Mathew A. , Tucker Anton D. , Korbie Darren , Jarman Simon , Berry Oliver 
Published: April 2022

AbstractAge is a fundamental life history attribute that is used to understand the dynamics of wild animal populations. Unfortunately, most animals do not have a practical or nonlethal method to determine age. This makes it difficult for wildlife managers to carry out population assessments, particularly for elusive and long‐lived fauna such as marine turtles. In this study, we present an epigenetic clock that predicts the age of marine turtles from skin biopsies. The model was developed and validated using DNA from known‐age green turtles (Chelonia mydas) from two captive populations, and mark‐recapture wild turtles with known time intervals between captures. Our method, based on DNA methylation levels at 18 CpG sites, was highly accurate with a median absolute error of 2.1 years (4.7% of maximum age in data set). This is the first epigenetic clock developed for a reptile and illustrates their broad applicability across a broad variety of vertebrate species. It has the potential to transform marine turtle management through a nonlethal and inexpensive method to provide key life history information.
Evaluation of the NANDA International, Inc. diagnostic classification in Spain: Development and validation of the EVALUAN‐I tool
Authors: Rodríguez‐Suárez Claudio Alberto , Hernández‐De Luis María Naira , Mariscal‐Crespo María Isabel , Camacho‐Bejarano Rafaela 
Published: April 2022

AbstractPurposeTo develop and validate a tool to evaluate the NANDA International, Inc. diagnostic classification.MethodsThe tool, EVALUAN‐I, was validated in a non‐probabilistic sample (N = 460) on Spanish Registered Nurses (September–December 2019) in two phases. First, design and construct the instrument in three steps: (1) literature review to define the construct focusing on the orientation toward nursing concepts and theoretical foundations, the level of scientific evidence, the structural configuration, the applicability, the nurses’ clinical reasoning skills, and the attitudes toward nursing diagnosis, (2) substantiation of the questionnaire items and design according to the criteria for a diagnostic classification, (3) expert test to establish the face validity and content validity. The second phase revolved around (4) conducting a pilot test and measuring the temporal stability (test‐retest) and Cohen's kappa coefficient; assessing psychometric properties by measuring (5) reliability (internal consistency using Cronbach alpha and interfactor correlation) and (6) construct validity (exploratory and confirmatory factor analysis). The manuscript follows the STROBE checklist. The study was approved by the Research Ethics Committee with registration number 2019‐190‐1.FindingsEVALUAN‐I displayed moderate test‐retest stability, adequate construct validity, and excellent reliability. The confirmatory factor analysis provided evidence about the configuration of EVALUAN‐I in relation to nine analytical dimensions: clinical competence, nurses’ reasoning skills, attitudes towards nursing diagnosis, discipline's central concepts, classification's contents, physiopathological attributes, level of scientific evidence, diagnostic precision, and conceptual correspondence between terminologies.ConclusionsEVALUAN‐I is a valid and reliable instrument, which can be used to improve the epistemological, normative, and intuitive configuration of NANDA International, Inc. in a structured, systematic manner.Implications for Nursing PracticeComprehensive evaluation of NANDA International, Inc. in different clinical settings around the world using a validated instrument, like EVALUAN‐I, would allow strengths and weaknesses to be identified and contribute to the classification's development and practical application.
Using machine learning to predict heavy drinking during outpatient alcohol treatment
Authors: Roberts Walter , Zhao Yize , Verplaetse Terril , Moore Kelly E. , Peltier MacKenzie R. , Burke Catherine , Zakiniaeiz Yasmin , McKee Sherry 
Published: April 2022

AbstractBackgroundAccurate clinical prediction supports the effective treatment of alcohol use disorder (AUD) and other psychiatric disorders. Traditional statistical techniques have identified patient characteristics associated with treatment outcomes. However, less work has focused on systematically leveraging these associations to create optimal predictive models. The current study demonstrates how machine learning can be used to predict clinical outcomes in people completing outpatient AUD treatment.MethodWe used data from the COMBINE multisite clinical trial (n = 1383) to develop and test predictive models. We identified three priority prediction targets, including (1) heavy drinking during the first month of treatment, (2) heavy drinking during the last month of treatment, and (3) heavy drinking between weekly/bi‐weekly sessions. Models were generated using the random forest algorithm. We used “leave sites out” partitioning to externally validate the models in trial sites that were not included in the model training. Stratified model development was used to test for sex differences in the relative importance of predictive features.ResultsModels predicting heavy alcohol use during the first and last months of treatment showed internal cross‐validation area under the curve (AUC) scores ranging from 0.67 to 0.74. AUC was comparable in the external validation using data from held‐out sites (AUC range = 0.69 to 0.72). The model predicting between‐session heavy drinking showed strong classification accuracy in internal cross‐validation (AUC = 0.89) and external test samples (AUC range = 0.80 to 0.87). Stratified analyses showed substantial sex differences in optimal feature sets.ConclusionMachine learning techniques can predict alcohol treatment outcomes using routinely collected clinical data. This technique has the potential to greatly improve clinical prediction accuracy without requiring expensive or invasive assessment methods. More research is needed to understand how best to deploy these models.
Robust Approach to Combining Multiple Markers to Improve Surrogacy
Authors: Wang Xuan , Parast Layla , Han Larry , Tian Lu , Cai Tianxi 
Published: April 2022

Abstract
Identifying effective and valid surrogate markers to make inference about a treatment effect on long-term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long-term outcome with short-term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study.
Nonparametric and Semiparametric Estimation with Sequentially Truncated Survival Data
Authors: Betensky Rebecca A. , Qian Jing , Hou Jingyao 
Published: April 2022

AbstractIn observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or exceeds another random time, that is, the truncation time. In more complex settings, observation may require a particular ordering of event times; we refer to this as sequential truncation. Estimators of the event time distribution have been developed for simple left-truncated or right-truncated data. However, these estimators may be inconsistent under sequential truncation. We propose nonparametric and semiparametric maximum likelihood estimators for the distribution of the event time of interest in the presence of sequential truncation, under two truncation models. We show the equivalence of an inverse probability weighted estimator and a product limit estimator under one of these models. We study the large sample properties of the proposed estimators and derive their asymptotic variance estimators. We evaluate the proposed methods through simulation studies and apply the methods to an Alzheimer's disease study. We have developed an R package, seqTrun, for implementation of our method.
Machine learning approaches and their applications in drug discovery and design
Authors: Priya Sonal , Tripathi Garima , Singh Dev Bukhsh , Jain Priyanka , Kumar Abhijeet 
Published: April 2022

AbstractThis review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.
Intracardiac masses in a giant right atrium: Tumors or thrombus?
Authors: Wu Wenqian , Fan Qianqian , Chen Yan , Fang Lingyun , Xie Mingxing 
Published: April 2022

AbstractThe coexist of primary angiosarcoma and thrombosis is a rare clinical entity. A 46‐year‐old male was presented with multiple right atrial (RA) masses. Two different vascular patterns in these masses were identified by contrast echocardiography. The possibility of coexistence of malignant tumors and thrombus in RA was implicated, which was further confirmed by computed tomography, magnetic resonance imaging, positron emission tomography/computer tomography, and pathologic examination. This case highlights the advantages of contrast echocardiography in the differential diagnosis of cardiac masses.
Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis
Authors: Gromicho Marta , Leão Tiago , Oliveira Santos Miguel , Pinto Susana , Carvalho Alexandra M. , Madeira Sara C. , De Carvalho Mamede 
Published: April 2022

AbstractBackground and purposeProgression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate.MethodsIn all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS‐R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS‐R scores and sub‐scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables.ResultsDisease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub‐score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub‐score has little influence in all groups. ALSFRS‐R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively.ConclusionsThe sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.
Rapid auditory processing and medial geniculate nucleus anomalies in Kiaa0319 knockout mice
Authors: Perrino Peter A. , Chasse Renee Y. , Monaco Anthony P. , Molnár Zoltán , Velayos‐Baeza Antonio , Fitch R. Holly 
Published: April 2022

AbstractDevelopmental dyslexia is a common neurodevelopmental disorder characterized by difficulties in reading and writing. Although underlying biological and genetic mechanisms remain unclear, anomalies in phonological processing and auditory processing have been associated with dyslexia. Several candidate risk genes have also been identified, with KIAA0319 as a main candidate. Animal models targeting the rodent homolog (Kiaa0319) have been used to explore putative behavioral and anatomic anomalies, with mixed results. For example after downregulation of Kiaa0319 expression in rats via shRNA, significant adult rapid auditory processing impairments were reported, along with cortical anomalies reflecting atypical neuronal migration. Conversely, Kiaa0319 knockout (KO) mice were reported to have typical adult auditory processing, and no visible cortical anomalies. To address these inconsistencies, we tested Kiaa0319 KO mice on auditory processing tasks similar to those used previously in rat shRNA knockdown studies. Subsequent neuroanatomic analyses on these same mice targeted medial geniculate nucleus (MGN), a receptive communication‐related brain structure. Results confirm that Kiaa0319 KO mice exhibit significant auditory processing impairments specific to rapid/brief stimuli, and also show significant volumetric reductions and a shift toward fewer large and smaller neurons in the MGN. The latter finding is consistent with post mortem MGN data from human dyslexic brains. Combined evidence supports a role for KIAA0319 in the development of auditory CNS pathways subserving rapid auditory processing functions critical to the development of speech processing, language, and ultimately reading. Results affirm KIAA0319 variation as a possible risk factor for dyslexia specifically via anomalies in central acoustic processing pathways.