bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved prediction of Canada lynx distribution through regional model transferability and data efficiency
Authors: Olson Lucretia E. , Bjornlie Nichole , Hanvey Gary , Holbrook Joseph D. , Ivan Jacob S. , Jackson Scott , Kertson Brian , King Travis , Lucid Michael , Murray Dennis , Naney Robert , Rohrer John , Scully Arthur , Thornton Daniel , Walker Zachary , Squires John R. 
Published: January 2021

AbstractThe application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal‐environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx‐environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well‐validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.
Using the 2018 AEA Evaluator Competencies for Effective Program Evaluation Practice
Authors: Stevahn Laurie , Berger Dale E. , Tucker Susan A. , Rodell Anna 
Published: January 2021

AbstractThe ultimate goal of using the 2018 AEA Evaluator Competencies is effective program evaluation practice. This chapter suggests how the competencies can be used to enhance professional practice by individual evaluators, organizations/institutions, and evaluation associations. Individual use includes self‐assessing expertise, pursuing professional development, and using common evaluation language. Organizational use includes guiding internal evaluations or university evaluator education programs and curricula, seeking external assistance, and cultivating institutional learning. Association use includes sponsoring professional development, credentialing or certifying evaluators, and assessing competence through measurement or some other form of documentation such as a practice‐based portfolio. All of these uses hold potential to further professionalize the field of program evaluation.
Adaptive management of deep-seabed mining projects: A systems approach
Authors: Hyman Jayden , Stewart Rodney A , Sahin Oz 
Published: January 2021

Abstract
Increasing demand for metals used in clean energy technologies, including electric vehicles, has led to an increased demand in certain metals such as Ni, Co, and Mn. This demand has in turn led to an expanding interest in deep-seabed mining (DSM) of polymetallic nodule deposits that contain these exact metals. The main concerns about DSM relate to the incomplete information available about the environmental risks associated with seabed mineral extraction. Key uncertainties need to be systematically addressed to refine environmental impact predictions and establish effective mitigation measures. Adaptive management is an iterative process for reducing the uncertainty that can be applied by both mining companies and regulatory bodies. This Special Series paper reviews the key opportunities and challenges to operationalizing adaptive management in DSM projects and highlights the need for a framework to move from theory to practice. The discussion proposes a systems approach to adaptive management, which could help to guide the environmental management of deep-sea mineral extraction. Integr Environ Assess Manag 2022;18:674–681. © 2021 SETAC
KEY POINTS
Adaptive management is a suitable approach to addressing residual uncertainty for the effective environmental management of deep-seabed mining projects. Active adaptive management can be implemented in environmental management and monitoring plans to prioritize “learning by doing” and avoid “trial and error.” The paper introduces a conceptual framework for Systemic Adaptive Management (SAM), a systems approach to the implementation of adaptive management in deep-seabed mining projects. Participatory modeling is highlighted as a method to operationalize SAM whereby stakeholders are engaged to build complex system models to support decision making under uncertainty.
Estimating pesticide environmental concentrations in Latin America: The importance of developing local scenarios
Authors: Casallanovo Fábio , Simone Daniela Mejias , Santos Gustavo Souza , Oliveira Kaminski Thamires Sá , Cione Ana Paola , Peranginangin Natalia 
Published: January 2021

Abstract
Data to assess pesticide exposure in soil and water are scarce and unevenly distributed in Latin America, especially due to the size of the region and the vast agricultural landscape. This makes it difficult to assess associated environmental risks. We suggest that the lack of pesticide exposure or monitoring data can be addressed by using validated models to provide estimated pesticide exposure concentrations in soil and water bodies. This exposure modeling approach has been used by regulatory agencies in other countries and regions such as the United States, Canada, and the European Union. In order to properly estimate pesticide exposure concentrations, we advocate for the development of local scenarios containing local weather, soil, and crop data to be used in the existing models. A sensitivity analysis of the models can be performed to determine parameters that are sensitive and therefore inputs to these parameters are derived locally. We believe the development of local scenarios in the region is attainable and can be a pragmatic approach for developing a more comprehensive picture of potential pesticide exposure in the region. Integr Environ Assess Manag 2021;17:901–904. © 2021 Syngenta Proteção de Cultivos Ltda
KEY POINTS
Pesticide exposure modeling can be an alternative to fulfill the gap on pesticide monitoring data. Developing local scenarios for use in modeling tools is important to support local risk assessment. Modeling tools should be well tested and validated prior to adoption. A sensitivity analysis is important to determine the parameters of importance.
Depth enhancement through correlation‐based guided filtering in shape from focus
Authors: Ali Usman , Mahmood Muhammad Tariq 
Published: January 2021

AbstractMostly, shape‐from‐focus (SFF) techniques do not consider any guidance or prior information from the input image sequence while improving the depth information. Consequently, the resultant depth maps may have inaccuracies due to missing the fine structural details. In this paper, we propose a depth enhancement method based on a novel guidance map and guided filtering. In the proposed method, first, a focus measure is applied on image sequence (volume) to compute the focus volume and an initial depth map is obtained by maximizing the focus measure in the optical direction. The guidance map is computed based on the correlation among the image volume and the focus volume along the optical axis. Finally, the improved depth map is obtained by applying guided filtering of initial depth by incorporating the weights from the suggested guidance map. Experiments were carried out using synthetic and real and microscopic image sequences of various objects. Experimental results have demonstrated the effectiveness of the proposed method and a reasonable improvement has been achieved in the quality of reconstructed depth maps.
A positive effect of egg consumption on macular pigment and healthy vision: a systematic review and meta‐analysis of clinical trials
Authors: Khalighi Sikaroudi Masoumeh , Saraf‐Bank Sahar , Clayton Zachary S , Soltani Sepideh 
Published: January 2021

AbstractIncreasing macular pigment optical density (MPOD) as a result of increased macular concentration of lutein and zeaxanthin may reduce the risk of age‐related macular degeneration (AMD). The aim of the present study was to determine whether the consumption of eggs, a rich source of dietary lutein and zeaxanthin, influences MPOD and serum lutein. In this systematic review and meta‐analysis we searched PubMed, Scopus, and ISI Web of Science up to July 2020, for relevant randomized clinical trials. Using a random‐effects model, pooled weighted mean differences, and standard deviations (SDs) for each outcome were obtained. The quality of the eligible studies was assessed by the Cochrane Collaboration's tool. A meta‐analysis of five trials (296 participants) revealed that egg consumption significantly increased MPOD (weighted mean differences (WMD): +0.037; 95% CI: 0.004, 0.069; P = 0.027) and serum lutein (WMD: +0.150 μmol L̂‐1; 95% CI: 0.037, 0.263; P = 0.009). Subgroup analyses showed that egg consumption: (a) had a larger effect on MPOD in studies with a parallel design; and (b) increased serum lutein to a greater extent in a healthy population. We did not detect any heterogeneity between studies. Daily egg consumption has beneficial effects on MPOD and serum lutein is inversely associated with reduced AMD progression. Further clinical trials are required to confirm the results of this study. © 2021 Society of Chemical Industry
Characterization of free, conjugated, and bound phenolics in early and late ripening kiwifruit cultivars
Authors: Gao Yuan , Ping Hua , Li Bingru , Li Yang , Zhao Fang , Ma Zhihong 
Published: January 2021

AbstractBACKGROUNDKiwifruit (Actinidia) has long been called the ‘king of fruits’ because of its unique flavor and the wide range of bioactive compounds which contains ascorbic acid, phenolics and minerals. These bioactivities are influenced by species and cultivar. However, to date few studies are concerned with the effect of ripening time on fruit quality. Here, early and late ripening kiwifruits were investigated to determine their content of ascorbic acid, organic acid, and phenolic compounds.RESULTSEarly ripening cultivars contained higher quinic acid and malic acid, while citric acid were found in large amounts in late ripening kiwifruits. Most of the early ripening cultivars contained higher free phenolic fractions than the late ripening fruits, mainly due to the high levels of epicatechin. However, conjugated phenolics, mainly including caffeic and 2,3,4‐trihydroxybenzoic acid, achieved higher levels in the late ripening cultivars. Free phenolics were higher than conjugated phenolics in the early ripening cultivars. Principal component analysis revealed some key compounds that differentiated the kiwifruits, and all the kiwifruits were divided into two subgroups as early and late ripening cultivars.CONCLUSIONRipening time had a great impact on the accumulation of bioactive compounds. The early ripening cultivars, compared to the late ripening ones, were characterized by higher levels of free neochlorogenic acid and epicatechin, while the late ripening kiwifruits contained higher amounts of conjugated phenolics. Results from this study provide further insights into the health‐promoting phenolic compounds in kiwifruit, and also provide good evidence to aid consumer selection. © 2021 Society of Chemical Industry
Dispersive liquid‐liquid microextraction based on a new hydrophobic deep eutectic solvent for the determination of phenolic compounds in environmental water samples
Authors: Hu Xizhou , Zhang Luyun , Xia Hong , Peng Maoming , Zhou Youxiang , Xu Zhimin , Peng Xitian 
Published: January 2021

AbstractDispersive liquid‐liquid microextraction has garnered increasing attention in sample preparation due to its rapid and efficient extraction process. In this study, a new terpineol‐based hydrophobic deep eutectic solvent was firstly synthesized by mixing α‐terpineol with 1‐octanoic acid, and then applied to analysis of phenols from water samples by dispersive liquid‐liquid microextraction combined with high‐performance liquid chromatography and diode array detection. Infrared spectroscopy indicated that hydrogen bonding was responsible for the formation of deep eutectic solvent between α‐terpineol and 1‐octanoic acid. After optimization of several parameters, such as the type and volume of deep eutectic solvent and the disperser, pH and ionic strength of sample solution, the developed method exhibited excellent extraction performance to the phenols with the enrichment factors from 27 to 32. Good linearity was acquired ranging from 5 to 5000 μg/L, and detection of limits of the proposed method for the phenols ranged from 0.15 to 0.38 μg/L. The recoveries measured by spiked samples at three concentration levels ranged from 81.6 to 99.3%, and precision was found with intra‐ and inter‐day relative standard deviations less than 8.7 and 9.2%, respectively. Finally, the proposed method was successfully applied to the determination of the phenols in environmental water samples.
Predictive Value of Delphian Lymph Node Metastasis in the Thyroid Cancer
Authors: Kim Do Hyun , Kim Sung Won , Hwang Se Hwan 
Published: January 2021

Objective/HypothesisTo evaluate the diagnostic accuracy of Delphian lymph node (DLN) metastasis for the prediction of central lymph node (CLN) metastasis and lateral lymph node (LLN) metastasis.MethodsTwo authors independently reviewed the six databases (Cochrane database, Embase, Google Scholar, PubMed, SCOPUS, and Web of Science). Four parameters were extracted from each study: true positive, true negative, false positive, and false negative. The quality of the methodology was evaluated using the Quality Assessment of Diagnostic Accuracy Studies ver. 2 tool.ResultsThe diagnostic odds ratio of DLN in CLN metastasis was 8.859 (95% confidence interval [CI], 4.419; 16.578). The area under the summary receiver operating characteristic curve was 0.748. The diagnostic odds ratio of DLN in LLN metastasis was 7.61 (95% CI, 4.48; 12.94). The area under the summary receiver operating characteristic curve was 0.837. DLN metastasis was moderately predictive of CLN metastasis (sensitivity = 32%, specificity = 95%), LLN metastasis (sensitivity = 52%, specificity = 89%), and contralateral CLN metastasis (sensitivity = 46%, specificity = 85%). DLN metastasis had statistically significant correlation with specific clinicopathological characteristics, including younger age (< 45 years old), bilaterality, capsule invasion, extrathyroidal extension, lymphovascular invasion, male sex, multifocality, and tumor size (> 1 cm).ConclusionsThe higher specificities of DLN pathology may help predict central and lateral compartment involvement in patients with thyroid cancer.Level of EvidenceNA Laryngoscope, 131:1990–1996, 2021
Effects of conservation agriculture on physicochemical soil health in 20 maize‐based trials in different agro‐ecological regions across Mexico
Authors: Fonteyne Simon , Burgueño Juan , Albarrán Contreras Blanca Aide , Andrio Enríquez Enrique , Castillo Villaseñor Luis , Enyanche Velázquez Fabian , Escobedo Cruz Helios , Espidio Balbuena Juan , Espinosa Solorio Avelino , Garcia Meza Paul , González Galindo Fidelia , González Regalado Jessica , Govaerts Bram , López Durante Juan Diego , López Ramírez Antonio , Martínez Gamiño Miguel Angel , Martínez Hernández Fermín , Mora Gutiérrez Manuel , Nieves Navarro Arturo Ismael , Noriega González Luis Alberto , Núñez Peñaloza Omar , Osorio Alcalá Leodegario , de la Piedra Constantino Rubén , Ponce Lira Brenda , Rivas Jacobo Isac Carlos , Saldivia Tejeda Abel , Tapia Moo Carlos Augusto , Tapia Naranjo Alfredo , Uribe Guerrero Miguel Angel , Vilchis Ramos Rodolfo , Villa Alcántara Jonatán , Verhulst Nele 
Published: January 2021

AbstractMaize (Zea mays L.) is Mexico's primary staple food, but the Country's degrading soils and climate variability limit its productivity. Conservation agriculture (CA), a management technique that combines minimal tillage, permanent soil cover, and crop diversification, could reduce soil degradation and help improve soil health. There is however a lack of information about the effects of CA on soil health in the diverse agroecological conditions in Mexico. This study reports results of a field trial network established to adapt CA to Mexico's diverse cropping systems and local conditions. Physicochemical soil health, also referred to as soil quality, was studied in 20 trials in agro‐ecologies ranging from handplanted traditional systems to intensive irrigated systems, initiated between 1991 and 2016. Soil in CA was compared to the local conventional practice (CP), which commonly involves tillage, residue removal, and continuous maize production. Across the sites, organic matter and nitrates were higher in the top (0–5 cm) layer of soil and soil aggregate stability was greater under CA than under CPs. For other soil health parameters, such as nutrient content, pH or penetration resistance, the effects of management varied widely across sites and soil types and most were determined more by local soil type than by management. CA increased maize yields at most sites and on average by 0.85 ± 1.80 t ha−1. Given the significant variation across agro‐ecologies, local adaptive trials are important to assess the effects of CA on soil health and fit the practice to local conditions.
A roadmap to operationalize and evaluate impact in a learning health system
Authors: Allen Claire , Coleman Katie , Mettert Kayne , Lewis Cara , Westbrook Emily , Lozano Paula 
Published: January 2021

AbstractBackgroundMany health systems invest in initiatives to accelerate translation of knowledge into practice. However, organizations lack guidance on how to develop and operationalize such Learning Health System (LHS) programs and evaluate their impact. Kaiser Permanente Washington (KPWA) launched our LHS program in June 2017 and developed a logic model as a foundation to evaluate the program's impact.ObjectiveTo develop a roadmap for organizations that want to establish an LHS program, understand how LHS core components relate to one another when operationalized in practice, and evaluate and improve their progress.MethodsWe conducted a narrative review on LHS models, key model components, and measurement approaches.ResultsThe KPWA LHS Logic Model provides a broad set of constructs relevant to LHS programs, depicts their relationship to LHS operations, harmonizes terms across models, and offers measurable operationalizations of each construct to guide other health systems. The model identifies essential LHS inputs, provides transparency into LHS activities, and defines key outcomes to evaluate LHS processes and impact. We provide reflections on the most helpful components of the model and identify areas that need further improvement using illustrative examples from deployment of the LHS model during the COVID‐19 pandemic.ConclusionThe KPWA LHS Logic Model is a starting point for future LHS implementation research and a practical guide for healthcare organizations that are building, operationalizing, and evaluating LHS initiatives.
Evolutionary psychology in marketing: Deep, debated, but fancier with fieldwork
Authors: Otterbring Tobias 
Published: January 2021

AbstractThis guest editorial starts with an introduction of evolutionary psychology (EP) in the marketing domain and delineates some of the building blocks of EP, both generally and when applied to consumer research. While EP is a debated discipline among marketing scholars, with some praising its presence and others perceiving it as patriarchal, politically incorrect, and problematic, a central tenet of this metaframework is a focus on deep‐rooted, ultimate explanations for human behavior. Marketing scholars have traditionally focused on proximate “how” and “what” questions, which are indeed important to address. However, unlike such proximate questions, EP strives to capture the ultimate “why” reasons behind our purchases and product preferences in terms of which adaptive functions they may have in giving us an evolutionary advantage. Having highlighted and exemplified this proximate‐ultimate distinction, I then present each state‐of‐the‐art paper included in this special issue. All special issue articles use a variety of EP arguments and theories to elucidate several consumption‐relevant phenomena with implications for marketing theory and practice. In closing, I provide a set of suggestions for future EP‐based consumer research, meant to make this field flourish further.
Analysis of environmental organic matters by Ultrahigh‐Resolution mass spectrometry—A review on the development of analytical methods
Authors: Kim Sungjune , Kim Donghwi , Jung Maeng‐Joon , Kim Sunghwan 
Published: January 2021

AbstractOwing to the increasing environmental and climate changes globally, there is an increasing interest in the molecular‐level understanding of environmental organic compound mixtures, that is, the pursuit of complete and detailed knowledge of the chemical compositions and related chemical reactions. Environmental organic molecule mixtures, including those in air, soil, rivers, and oceans, have extremely complex and heterogeneous chemical compositions. For their analyses, ultrahigh‐resolution and sub‐ppb level mass accuracy, achievable using Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR MS), are important. FT‐ICR MS has been successfully used to analyze complex environmental organic molecule mixtures such as natural, soil, particulate, and dissolved organic matter. Despite its success, many limitations still need to be overcome. Sample preparation, ionization, structural identification, chromatographic separation, and data interpretation are some key areas that have been the focus of numerous studies. This review describes key developments in analytical techniques in these areas to aid researchers seeking to start or continue investigations for the molecular‐level understanding of environmental organic compound mixtures.