bluuebunny/crossref_metadata_2025_split huggingface dataset:
Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach
Authors: Babasafari Amir Abbas , Rezaei Shiba , Salim Ahmed Mohamed Ahmed , Kazemeini Sayed Hesammoddin , Ghosh Deva Prasad 
Published: October 2020

AbstractFor estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance orVp/Vsratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.
Introduction: Studying Under-Floor Church Burials in Finland––Challenges in Stewarding the Past for the Future
Authors: Lipkin Sanna , Kallio-Seppä Titta 
Published: October 2020

AbstractTwo ongoing projects at the University of Oulu, Finland, are studying burials situated below the floorboards of Finnish churches. The projects examine mortality and materiality in Finland using an exceptionally well-preserved assemblage of mummified human remains. Multidisciplinary research methods, including CT scanning, μCT scanning, sampling for DNA and isotope analysis, and insect and plant samples, are providing multifaceted information concerning past societies. The samples provide rich insight into the material dimensions of mortality in northern Europe in the 18th and 19th centuries. Since the studied material is located under the floors of church buildings that are still in use by congregations, obtaining permits for research and sampling has required managing relationships with separate parishes. This article outlines the projects and examines the ethical and legal dimensions of research on human remains conducted in partnership with individual parishes.
Significant Increase of Sexual Dysfunction in Patients with Renal Failure Receiving Renal Replacement Therapy: A Systematic Review and Meta-Analysis
Authors: Luo Lianmin , Xiao Chenglin , Xiang Qian , Zhu Zhiguo , Liu Yangzhou , Wang Jiamin , Deng Yihan , Zhao Zhigang 
Published: October 2020

Abstract

Background
It has been shown that sexual dysfunction (SD) is highly prevalent among patients with chronic renal failure (CRF), and starting renal replacement therapy may even increase it. However, SD is an infrequently reported problem in these treated patients.

Aim
To investigate the prevalence of SD among patients with CRF undergoing renal replacement therapy, by a meta-analysis method.

Methods
PubMed, Embase, and the Cochrane Library were systematically searched for all studies assessing sexual function in patients with CRF receiving renal replacement therapy from January 2000 to April 2020. Relative risk (RR) with 95% CIs was used for analysis to assess the risk of SD in patients with CRF receiving renal replacement therapy. The cross-sectional study quality methodology checklist was used for the cross-sectional study. The methodologic quality of the case-control and cohort studies was assessed with the Newcastle-Ottawa Scale. Data were pooled for the random-effect model. Sensitivity analyses were conducted to assess potential bias. The Begg and Egger tests were used for publication bias analysis.

Outcomes
The prevalence of SD among patients with CRF receiving renal replacement therapy was summarized using pooled RR and 95% CI.

Results
This meta-analysis included 3,725 participants from 10 studies. Of these, 737 were patients with CRF receiving renal replacement therapy. The mean age of participants ranged from 32.75 to 56.1 years. Based on the random-effect model, synthesis of results demonstrated that the prevalence of SD was significantly increased among patients with CRF receiving renal replacement therapy in women (RR = 2.07, 95% CI: 1.47–2.91, P = .000; heterogeneity: I2 = 78.7%, P = .000) and in men (RR = 2.95, 95% CI: 2.16–4.02, P = .000; heterogeneity: I2 = 86.1%, P = .000). Estimates of the total effects were generally consistent in the sensitivity analysis. No evidence of publication bias was observed.

Clinical Implications
Patients with CRF receiving renal replacement therapy had a significantly increased risk of SD, which suggests that clinicians should evaluate sexual function, when managing patients with CRF receiving renal replacement therapy.

Strengths and Limitations
This is the first study to explore the prevalence of SD among patients with CRF undergoing renal replacement therapy based on all available epidemiologic studies. However, all included studies were an observational design, which may downgrade this evidence.

Conclusion
The prevalence of SD is significantly increased among patients with CRF receiving renal replacement therapy. More research studies are warranted to clarify the relationship.
Predicting the fuel flow rate of commercial aircraft via multilayer perceptron, radial basis function and ANFIS artificial neural networks
Authors: Baklacioglu T. 
Published: October 2020

ABSTRACTA first attempt is made to use recently developed, non-conventional Artificial Neural Network (ANN) models with Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Neuro-Fuzzy Interference System (ANFIS) architectures to predict the fuel flow rate of a commercial aircraft using real data obtained from Flight Data Records (FDRs) of the cruise, climb and descent phases. The training of the architectures with a single hidden layer is performed by utilising the Delta-Bar-Delta (DBD), Conjugate Gradient (CG) and Quickprop (QP) algorithms. The optimum network topologies are sought by varying the number of processing elements in the hidden layer of the networks using a trial-and-error method. An evaluation of the approximate fuel intake values against the ideal fuel intake data from the FDRs indicates a good fit for all three ANN models. Thus, more accurate fuel intake estimations can be obtained by applying the RBF-ANN model during the climb and descent flight stages, whereas the MLP-ANN model is more effective for the cruise phase. The best accuracy obtained in terms of the linear correlation coefficient is 0.99988, 0.91946 and 0.95252 for the climb, cruise and descent phase, respectively.
Evaluation of nutritional behaviour related to COVID-19
Authors: Elmacıoğlu Funda , Emiroğlu Elif , Ülker Mutlu Tuçe , Özyılmaz Kırcali Berkin , Oruç Sena 
Published: October 2020

AbstractObjective:It is known that social isolation process has an impact on individuals’ eating behaviours. Continuing nutritional behaviour resulting from emotional eating, uncontrolled eating and cognitive restriction may turn into eating disorders in the future. The purpose of this study is to evaluate the possible effects of Corona Virus Disease-2019 (COVID-19) pandemic and social isolation process on individuals’ nutritional behaviours and body weight changes.Design:Retrospective cohort study.Setting:Nutritional behaviours of the participants before the COVID-19 pandemic and in the social isolation process were evaluated with the Three Factor Nutrition Questionnaire. The changes in individuals’ body weight during this period were also evaluated.Participants:A total of 1036 volunteer individuals (827 women, 209 men) aged 18 years and over participated in the study.Results:During the COVID-19 pandemic and social isolation process, there was an increase in emotional eating and uncontrolled eating behaviours of individuals, but no significant change in cognitive restriction behaviour occurred (P = <0·00; P = <0·00 and P = 0·53, respectively). It was reported that the body weight of 35 % of the individuals who participated in the study increased during this period.Conclusion:Social isolation process practiced as a result of COVID-19 pandemic may lead to changes in some nutritional behaviours. Some precautions should be taken to prevent this situation that occurs in nutritional behaviours from causing negative health problems in the future.
The utilisation of VERT™ in the training of Image-Guided Radiotherapy for therapeutic radiographers
Authors: Stewart-Lord Adele , Swayne Thomas , Johnson Rebecca , Neshiri Innocent , Gnanamoney Julius , Van Blerk Bronwyn 
Published: October 2020

AbstractAim:The overall aim of this study was to evaluate the use of Virtual Environment for Radiotherapy Training Image-Guided Radiotherapy (VERT™IGRT) as a teaching and assessment tool for 3D image matching competency within the radiotherapy clinical setting and explore radiographer perceptions, experiences and integration of VERT™IGRT as an imaging tool.Materials and methods:A mixed-methods study was utilised to measure clinical image matching competencies in the first quantitative phase through means of a workbook and imaging assessment. Phase II used qualitative semi-structured interviews to explore radiographer perceptions.Results:Workbooks enabled participants to prepare for image assessments. Interview findings were highlighted in three distinctive themes: (1) The need for supervision, guidance and feedback; (2) Experience and practice leads to confidence and competence and (3) Technology supports process but not evaluation.Findings:VERT™IGRT provides a sound platform for training therapeutic radiographer image matching skills, but needs to be delivered with continuous feedback to develop individual decision-making skills. The technology provides opportunities for staff to increase confidence in utilsing image matching technology, but analytical and evaluation skills require supervision and continuous feedback which should be embedded in any educational training programme.
Quantifying Progress Across Different CMIP Phases With the ESMValTool
Authors: Bock L. , Lauer A. , Schlund M. , Barreiro M. , Bellouin N. , Jones C. , Meehl G. A. , Predoi V. , Roberts M. J. , Eyring V. 
Published: October 2020

AbstractMore than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high‐resolution versions of the models show significant reductions in many long‐standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root‐mean‐square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.
From Pleistocene to Pyrocene: Fire Replaces Ice
Authors: Pyne S. J. 
Published: October 2020

AbstractFire offers a special perspective by which to understand the Earth being remade by humans. Fire is integrative, so intrinsically interdisciplinary. Fire use is unique to humans, so a tracer of humanity's ecological impacts. Anthropogenic fire history shows the long influence of humans on Earth and even climate; in particular, it tracks the continuities between the burning of living landscapes and the transition to burning lithic (fossil) ones, an inflection so immense that climate history is now a subnarrative of fire history. Through our varied burnings, humans are driving out all the relics of the Pleistocene and replacing them with fire equivalents, or in short, creating a Pyrocene.
Coordinated Ground‐Based and Space‐Borne Observations of Ionospheric Response to the Annular Solar Eclipse on 26 December 2019
Authors: Aa Ercha , Zhang Shun‐Rong , Erickson Philip J. , Goncharenko Larisa P. , Coster Anthea J. , Jonah Olusegun F. , Lei Jiuhou , Huang Fuqing , Dang Tong , Liu Lei 
Published: October 2020

AbstractThis paper studies the ionosphere's response to the annular solar eclipse on 26 December 2019, utilizing the following ground‐based and space‐borne measurements: Global Navigation Satellite System (GNSS) total electron content (TEC) data, spectral radiance data from the Sentinel‐5P satellite, in situ electron density and/or temperature measurements from DMSP and Swarm satellites, and local magnetometer data. Analysis concentrated on ionospheric effects over low‐latitude regions with respect to obscuration, local time, latitude, and altitude. The main results are as follows: (1) a local TEC reduction of ∼4–6 TECU (30–50%) was identified along the annular eclipse path, with larger depletion and longer recovery periods in the morning eclipse compared to midday. (2) The equatorial electrojet current was significantly weakened when the eclipse trajectory crossed the magnetic equator in the morning (India) sector, which contributed to large and prolonged TEC depletion therein. (3) At midday, equatorial ionization anomaly exhibited enhancements of 20–40% as well as poleward shifting of 3–4°, likely triggered by modified neutral wind and electrodynamics patterns. (4) The behavior of equatorial ionospheric electron density showed considerable altitudinal differences in the topside, exhibiting ∼30% reduction around 500 km and ∼30% enhancement with 300–500 K Te reduction around 850 km, before the arrival of maximum eclipse. This may have been caused by the enhanced eastward electric field and equatorward neutral wind, and other possible factors are also discussed.
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application
Authors: Fritzner Sindre , Graversen Rune , Christensen Kai H. 
Published: October 2020

AbstractIn this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state‐of‐the‐art coupled ocean and sea ice ensemble‐prediction system with assimilation. The observations assimilated are high‐resolution sea ice concentration from synthetic aperture radar (SAR) and sea surface temperature from infrared instruments. The machine‐learning prediction models are a fully convolutional network and a k‐nearest neighbors method. These methods use several variables as input for the prediction: sea ice concentration, sea surface temperature, and 2‐m air temperature. Earlier studies have applied machine‐learning approaches primarily for seasonal ice forecast. Here we focus on short‐term predictions with a length of 1–4 weeks, which are of high interest for marine operations. The goal is to predict the future state of the sea ice using the same categories as traditional ice charts. The machine‐learning forecasts were compared to persistence, which is the assumption that the sea ice does not change over the forecasting period. The machine‐learning forecasts were found to improve upon persistence in periods of substantial change. In addition, compared to the dynamical model, the k‐nearest neighbor algorithm was found to improve upon the 7‐day forecast during a period of small sea ice variations. The fully convolutional network provided similar quality as the dynamical forecast. The study shows that there is a potential for sea ice predictions using machine‐learning methods.
Predictive Inverse Model for Advective Heat Transfer in a Short‐Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration
Authors: Hawkins Adam J. , Fox Don B. , Koch Donald L. , Becker Matthew W. , Tester Jefferson W. 
Published: October 2020

AbstractIdentifying fluid flow maldistribution in planar geometries is a well‐established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two‐dimensional Hele‐Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C‐Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir‐scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding‐plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.
A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
Authors: Mei Yiwen , Maggioni Viviana , Houser Paul , Xue Yuan , Rouf Tasnuva 
Published: October 2020

AbstractThe accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground‐ and satellite‐based observations. Results suggest improvements with respect to the original resolution MERRA‐2 precipitation product and comparable performance with gauge‐adjusted satellite precipitation products.
Asymmetric optical camouflage: tuneable reflective colour accompanied by the optical Janus effect
Authors: Kim Taehyun , Yu Eui-Sang , Bae Young-Gyu , Lee Jongsu , Kim In Soo , Chung Seok , Lee Seung-Yeol , Ryu Yong-Sang 
Published: October 2020

AbstractGoing beyond an improved colour gamut, an asymmetric colour contrast, which depends on the viewing direction, and its ability to readily deliver information could create opportunities for a wide range of applications, such as next-generation optical switches, colour displays, and security features in anti-counterfeiting devices. Here, we propose a simple Fabry–Perot etalon architecture capable of generating viewing-direction-sensitive colour contrasts and encrypting pre-inscribed information upon immersion in particular solvents (optical camouflage). Based on the experimental verification of the theoretical modelling, we have discovered a completely new and exotic optical phenomenon involving a tuneable colour switch for viewing-direction-dependent information delivery, which we define as asymmetric optical camouflage.
Development of synthetic lethality in cancer: molecular and cellular classification
Authors: Li Shijie , Topatana Win , Juengpanich Sarun , Cao Jiasheng , Hu Jiahao , Zhang Bin , Ma Diana , Cai Xiujun , Chen Mingyu 
Published: October 2020

AbstractRecently, genetically targeted cancer therapies have been a topic of great interest. Synthetic lethality provides a new approach for the treatment of mutated genes that were previously considered unable to be targeted in traditional genotype-targeted treatments. The increasing researches and applications in the clinical setting made synthetic lethality a promising anticancer treatment option. However, the current understandings on different conditions of synthetic lethality have not been systematically assessed and the application of synthetic lethality in clinical practice still faces many challenges. Here, we propose a novel and systematic classification of synthetic lethality divided into gene level, pathway level, organelle level, and conditional synthetic lethality, according to the degree of specificity into its biological mechanism. Multiple preclinical findings of synthetic lethality in recent years will be reviewed and classified under these different categories. Moreover, synthetic lethality targeted drugs in clinical practice will be briefly discussed. Finally, we will explore the essential implications of this classification as well as its prospects in eliminating existing challenges and the future directions of synthetic lethality.
Structural basis of heterotetrameric assembly and disease mutations in the human cis-prenyltransferase complex
Authors: Bar-El Michal Lisnyansky , Vaňková Pavla , Yeheskel Adva , Simhaev Luba , Engel Hamutal , Man Petr , Haitin Yoni , Giladi Moshe 
Published: October 2020

AbstractThe human cis-prenyltransferase (hcis-PT) is an enzymatic complex essential for protein N-glycosylation. Synthesizing the precursor of the glycosyl carrier dolichol-phosphate, mutations in hcis-PT cause severe human diseases. Here, we reveal that hcis-PT exhibits a heterotetrameric assembly in solution, consisting of two catalytic dehydrodolichyl diphosphate synthase (DHDDS) and inactive Nogo-B receptor (NgBR) heterodimers. Importantly, the 2.3 Å crystal structure reveals that the tetramer assembles via the DHDDS C-termini as a dimer-of-heterodimers. Moreover, the distal C-terminus of NgBR transverses across the interface with DHDDS, directly participating in active-site formation and the functional coupling between the subunits. Finally, we explored the functional consequences of disease mutations clustered around the active-site, and in combination with molecular dynamics simulations, we propose a mechanism for hcis-PT dysfunction in retinitis pigmentosa. Together, our structure of the hcis-PT complex unveils the dolichol synthesis mechanism and its perturbation in disease.