bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Scale X-Ray Computed Tomography Analysis to Aid Automated Mineralogy in Ore Geology Research
Authors: Warlo Mathis , Bark Glenn , Wanhainen Christina , Butcher Alan R. , Forsberg Fredrik , Lycksam Henrik , Kuva Jukka 
Published: December 2021

Ore characterization is crucial for efficient and profitable production of mineral products from an ore deposit. Analysis is typically performed at various scales (meter to microns) in a sequential fashion, where sample volume is reduced with increasing spatial resolution due to the increasing costs and run times of analysis. Thus, at higher resolution, sampling and data quality become increasingly important to represent the entire ore deposit. In particular, trace metal mineral characterization requires high-resolution analysis, due to the typical very fine grain sizes (sub-millimeter) of trace metal minerals. Automated Mineralogy (AM) is a key technique in the mining industry to quantify process-relevant mineral parameters in ore samples. Yet the limitation to two-dimensional analysis of flat sample surfaces constrains the sampling volume, introduces an undesired stereological error, and makes spatial interpretation of textures and structures difficult. X-ray computed tomography (XCT) allows three-dimensional imaging of rock samples based on the x-ray linear attenuation of the constituting minerals. Minerals are visually differentiated though not chemically classified. In this study, decimeter to millimeter large ore samples were analyzed at resolutions from 45 to 1 μm by AM and XCT to investigate the potential of multi-scale correlative analysis between the two techniques. Mineralization styles of Au, Bi-minerals, scheelite, and molybdenite were studied. Results show that AM can aid segmentation (mineralogical classification) of the XCT data, and vice versa, that XCT can guide (sub-)sampling (e.g., for heavy trace minerals) for AM analysis and provide three-dimensional context to the two-dimensional quantitative AM data. XCT is particularly strong for multi-scale analysis, increasingly higher resolution scans of progressively smaller volumes (e.g., by mini-coring), while preserving spatial reference between (sub-)samples. However, results also reveal challenges and limitations with the segmentation of the XCT data and the data integration of AM and XCT, particularly for quantitative analysis, due to their different functionalities. In this study, no stereological error could be quantified as no proper grain separation of the segmented XCT data was performed. Yet, some well-separated grains exhibit a potential stereological effect. Overall, the integration of AM with XCT improves the output of both techniques and thereby ore characterization in general.
The Performance of Three Exponential Decay Models in Estimating Tropical Cyclone Intensity Change After Landfall Over China
Authors: Liu Lu , Wang Yuqing , Wang Hui 
Published: December 2021

In this study, the performance of three exponential decay models in estimating intensity change of tropical cyclones (TCs) after landfall over China is evaluated based on the best-track TC data during 1980–2018. Results indicate that the three models evaluated can reproduce the weakening trend of TCs after landfall, but two of them (M1 and M2) tend to overestimate TC intensity and one (M3) tends to overestimate TC intensity in the first 12 h and underestimate TC intensity afterwards. M2 has the best performance with the smallest errors among the three models within 24 h after landfall. M3 has better performance than M1 in the first 20 h after landfall, but its errors increase largely afterwards. M1 and M2 show systematic positive biases in the southeastern China likely due to the fact that they have not explicitly included any topographic effect. M3 has better performance in the southeastern China, where it was originally attempted, but shows negative biases in the eastern China. The relative contributions of different factors, including landfall intensity, translational speed, 850-hPa moist static energy, and topography, to model errors are examined based on classification analyses. Results indicate that the landfall intensity contributes about 18%, translational speed, moist static energy and topography contribute equally about 15% to the model errors. It is strongly suggested that the TC characteristics and the time-dependent decay constant determined by environmental conditions, topography and land cover properties, should be considered in a good exponential decay model of TC weakening after landfall.
Nature Experiences in Science Education in School: Review Featuring Learning Gains, Investments, and Costs in View of Embodied Cognition
Authors: Schilhab Theresa 
Published: December 2021

This review examines the didactic use of nature experiences in science education, in primary and secondary school (7–16 years) globally. From the perspective of embodied cognition the review explores the types of nature experiences used in science teaching. Focus is on returns when we invest in nature-based science learning, such as specific academic achievements in the form of long-term effects on learning and memory and how we maximize those returns. The review also addresses challenges and barriers, such as costs and labour involved when using nature experiences in science teaching. Initially, 3,659 articles were selected, with the initial screening leading to the inclusion of 159 studies. Of these articles, 34 studies forming the corpus in this review investigated the effect of using nature experiences as an intervention. These studies are divided into four themes: content understanding, environmental education, teaching scientific methods, and costs and challenges to teaching science outdoors. Informed by the perspective of embodied cognition, the review addresses the returns in terms of learning and academic achievements, the mode of action of the intervention, the investment, costs in the form of labour, challenges, and gaps in the theoretical underpinning of the field. Based on the review, using nature experiences in science education seems promising regarding increasing content knowledge, insight into science methodologies and pro-environmental behaviours. Interventions exploiting the schoolyard, school gardens, or nearby park areas are particularly promising due to the simultaneous strengthening of local engagement at low costs. However, using nature experiences as an alternative to traditional in-class teaching depends on profound didactic deliberations and preparations, which are difficult for the individual teacher to address single-handedly. The review also reveals an urgent need for research that thoroughly explores the connections between teaching practices and theoretical foundations to consolidate the field. To that end, it is noteworthy that a few studies also reported on prior pilot studies demonstrating the need for testing the entire design before conducting the actual research. Teachers seldom experience the opportunity to preview their teaching strategies before performing in front of their students.
Using Passive Samplers to Track per and Polyfluoroalkyl Substances (PFAS) Emissions From the Paper Industry: Laboratory Calibration and Field Verification
Authors: Hale Sarah E. , Canivet Baptiste , Rundberget Thomas , Langberg Håkon A. , Allan Ian J. 
Published: December 2021

Per and polyfluoroalkyl substances (PFAS) are becoming more stringently regulated and as such, a more diverse suite of environmental monitoring methods is needed. In this work a polar organic chemical integrative sampler (POCIS) with a nylon membrane and a combination of Oasis WAX and Fluoroflash® sorbents was calibrated in the laboratory and deployed in the field. A static renewal system was used to determine sampling rates for 12 PFAS which ranged between 0.69 ± 0.27 to 5.68 ± 1.80 L/day. POCIS devices were deployed for 10 days in lake Tyrifjorden, Norway which is known to be contaminated by a closed down factory producing paper products, in order to track the evolution of the PFAS contamination in a river system draining into the lake. Th sampling campaign enabled the stretch of the river which was responsible for the emissions of PFAS to lake Tyrifjorden to be identified. Freely dissolved concentrations determined with the POCIS were lowest at the site considered to reflect a diffuse PFAS contamination and highest at the site located downstream the factory. Perfluorooctanesulfonic acid (PFOS), perfluorohexanoic acid (PFHxA) and perfluorooctane sulfonamidoacetic acid (EtFOSAA) dominated the concentration profile at this site. Emissions of PFAS to lake Tyrifjorden were estimated to be 3.96 g/day for the sum of the 12 investigated PFAS.
Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury
Authors: He Jiawei , Lin Jin , Duan Meili 
Published: December 2021

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.
Determination of Histidine Protonation States in Proteins by Fast Magic Angle Spinning NMR
Authors: Zadorozhnyi Roman , Sarkar Sucharita , Quinn Caitlin M. , Zadrozny Kaneil K. , Ganser-Pornillos Barbie K. , Pornillos Owen , Gronenborn Angela M. , Polenova Tatyana 
Published: December 2021

Histidine residues play important structural and functional roles in proteins, such as serving as metal-binding ligands, mediating enzyme catalysis, and modulating proton channel activity. Many of these activities are modulated by the ionization state of the imidazole ring. Here we present a fast MAS NMR approach for the determination of protonation and tautomeric states of His at frequencies of 40–62 kHz. The experiments combine 1H detection with selective magnetization inversion techniques and transferred echo double resonance (TEDOR)–based filters, in 2D heteronuclear correlation experiments. We illustrate this approach using microcrystalline assemblies of HIV-1 CACTD-SP1 protein.
Risk Estimation of Infectious and Inflammatory Disorders in Hospitalized Patients With Acute Ischemic Stroke Using Clinical-Lab Nomogram
Authors: Li Junhong , Huang Jingjing , Pang Tingting , Chen Zikun , Li Jing , Wu Lin , Hu Yuqiang , Chen Wei 
Published: December 2021

Background: Infections after acute ischemic stroke are common and likely to complicate the clinical course and negatively affect patient outcomes. Despite the development of various risk factors and predictive models for infectious and inflammatory disorders (IAID) after stroke, more objective and easily obtainable predictors remain necessary. This study involves the development and validation of an accessible, accurate nomogram for predicting in-hospital IAID in patients with acute ischemic stroke (AIS).Methods: A retrospective cohort of 2,257 patients with AIS confirmed by neurological examination and radiography was assessed. The International Statistical Classification of Diseases and Health related Problem's definition was used for IAID. Data was obtained from two hospitals between January 2016 and March 2020.Results: The incidence of IAID was 19.8 and 20.8% in the derivation and validation cohorts, respectively. Using an absolute shrinkage and selection operator (LASSO) algorithm, four biochemical blood predictors and four clinical indicators were optimized from fifty-five features. Using a multivariable analysis, four predictors, namely age (adjusted odds ratio, 1.05; 95% confidence interval [CI], 1.038–1.062; p &lt; 0.001), comatose state (28.033[4.706–536.403], p = 0.002), diabetes (0.417[0.27–0.649], p &lt; 0.001), and congestive heart failure (CHF) (5.488[2.451–12.912], p &lt; 0.001) were found to be risk factors for IAID. Furthermore, neutrophil, monocyte, hemoglobin, and high-sensitivity C-reactive protein were also found to be independently associated with IAID. Consequently, a reliable clinical-lab nomogram was constructed to predict IAID in our study (C-index value = 0.83). The results of the ROC analysis were consistent with the calibration curve analysis. The decision curve demonstrated that the clinical-lab model added more net benefit than either the lab-score or clinical models in differentiating IAID from AIS patients.Conclusions: The clinical-lab nomogram predicted IAID in patients with acute ischemic stroke. As a result, this nomogram can be used for identification of high-risk patients and to further guide clinical decisions.
Prediction Efficacy of Prognostic Nutritional Index and Albumin–Bilirubin Grade in Patients With Intrahepatic Cholangiocarcinoma After Radical Resection: A Multi-Institutional Analysis of 535 Patients
Authors: Li Qi , Chen Chen , Zhang Jian , Wu Hong , Qiu Yinghe , Song Tianqiang , Mao Xianhai , He Yu , Cheng Zhangjun , Zhai Wenlong , Li Jingdong , Zhang Dong , Geng Zhimin , Tang Zhaohui 
Published: December 2021

BackgroundThe preoperative nutritional status and the immunological status have been reported to be independent prognostic factors of patients with intrahepatic cholangiocarcinoma (ICC). This study aimed to investigate whether prognostic nutritional index (PNI) + albumin–bilirubin (ALBI) could be a better predictor than PNI and ALBI alone in patients with ICC after radical resection.MethodsThe prognostic prediction evaluation of the PNI, ALBI, and the PNI+ALBI grade was performed in 373 patients with ICC who underwent radical resection between 2010 and 2018 at six Chinese tertiary hospitals, and external validation was conducted in 162 patients at four other Chinese tertiary hospitals. Overall survival (OS) and relapse-free survival (RFS) were estimated using the Kaplan–Meier method. Multivariate analysis was conducted to identify independent prognostic factors. A time-dependent receiver operating characteristic (ROC) curve and a nomogram prediction model were further constructed to assess the predictive ability of PNI, ALBI, and the PNI+ALBI grade. The C-index and a calibration plot were used to assess the performance of the nomogram models.ResultsUnivariate analysis showed that PNI, ALBI, and the PNI+ALBI grade were prognostic factors for the OS and RFS of patients with ICC after radical resection in the training and testing sets (p &lt; 0.001). Multivariate analysis showed that the PNI+ALBI grade was an independent risk factor for OS and RFS in the training and testing sets (p &lt; 0.001). Analysis of the relationship between the PNI+ALBI grade and clinicopathological characteristics showed that the PNI+ALBI grade correlated with obstructive jaundice, alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), PNI, ALBI, Child–Pugh grade, type of resection, tumor size, major vascular invasion, microvascular invasion, T stage, and N stage (p &lt; 0.05). The time-dependent ROC curves showed that the PNI+ALBI grade had better prognostic predictive ability than the PNI, ALBI, and the Child–Pugh grade in the training and testing sets.ConclusionPreoperative PNI+ALBI grade is an effective and practical predictor for the OS and RFS of patients with ICC after radical resection.
Prognostic Analysis of Primary Breast Signet Ring Cell Carcinoma and Mucinous Breast Adenocarcinoma: A SEER Population-Based Study
Authors: Wang Song , Zhang Yiyuan , Yin Fangxu , Zhang Xiangsheng , Yang Zhenlin , Wang Xiaohong 
Published: December 2021

BackgroundPrimary breast signet ring cell carcinoma (SRCC) is a rare type of breast cancer with typical morphological characteristics, high aggressiveness, and poor prognosis. SRCC is different from mucinous breast adenocarcinoma (MBC). However, only a few studies have explored the clinicopathological features and prognosis of SRCC and MBC.MethodsData retrieved from the Surveillance, Epidemiology, and End-Results (SEER) database (2004–2015) were used to explore the prognostic effect of clinicopathological features and treatment modalities on survival outcomes of SRCC and MBC patients. Kaplan–Meier plot analysis, multivariate Cox proportional risk model, propensity score matching (PSM), and subgroup analysis were performed.ResultsA total of 167 patients with SRCC and 11,648 patients with MBC were included in the study. SRCC patients exhibited higher histological grade (p &lt; 0.001), larger tumor volume (p &lt; 0.001), higher rate of lymph node metastasis (p &lt; 0.001), and higher frequency of distal metastasis (p &lt; 0.001) compared with MBC patients. Cox proportional hazards regression analysis showed that SRCC patients had lower overall survival (OS) and breast cancer-specific survival (BCSS) compared with MBC patients. Subgroup survival analysis showed that the SRCC patients had lower OS and BCSS in subgroups including younger than 60 years old, white race, married, without chemotherapy, and received radiotherapy compared with the MBC patients in these subgroups. In addition, the SRCC patients had lower BCSS in subgroups including other races (including Asian or Pacific Islander and American Indian/Alaska Native), without surgery, and lymph node metastasis.ConclusionThe findings showed that primary breast SRCC patients have unique clinical characteristics and worse prognosis compared with MBC patients. Notably, different treatment methods resulted in different prognosis for SRCC and MBC types; therefore, SRCC patients should be distinguished from MBC patients to improve efficacy of treatment.
Impact of the French National Lockdown on Admissions to 14 Pediatric Intensive Care Units During the 2020 COVID-19 Pandemic–A Retrospective Multicenter Study
Authors: Breinig Sophie , Mortamet Guillaume , Brossier David , Amadieu Romain , Claudet Isabelle , Javouhey Etienne , Angoulvant François , Arnaud Catherine 
Published: December 2021

Background: After the COVID-19 pandemic reached France in January 2020, a national lockdown including school closures was officially imposed from March 17, 2020, to May 10, 2020. Pediatric intensive care units (PICUs) admit critically ill infants, children and teenagers with severe acute conditions, in particular infectious and traumatic diseases. We hypothesized that PICU admissions would be considerably modified by the lockdown.Aims: The objectives of the study were to describe the type of admissions to French PICUs and to compare the occupation of PICU beds according to local epidemic conditions during the French national lockdown period, compared with the same period the previous year.Methods: We conducted a retrospective multicenter study in 14 French PICUs. All children aged from 7 days to 18 years admitted to one of the 14 participating PICUs over two 3-month period (March 1, 2020, to May 31, 2020 and March 1, 2019, to May 31, 2019) were included. Analysis was based on data extracted from the medicalized information systems program (a national database used in all French hospitals, into which all admissions and their diagnoses are coded for the purpose of calculating hospital funding). Each main diagnosis was reclassified in 13 categories, corresponding to normal PICU admissions.Results: We analyzed a total of 3,040 admissions, 1,323 during the 2020 study period and 1,717 during the same period in 2019. Total admissions decreased by 23% [incidence rate ratio (IRR) 0.77, 95% CI 0.71–0.83, p &lt; 0.001], in particular for viral respiratory infections (−36%, IRR 0.64, 95% CI 0.44–0.94, p = 0.001). Admissions for almost all other diagnostic categories decreased, except intoxications and diabetes which increased, while admissions for cardiac and hemodynamic disorders were stable. Patient age and the sex ratio did not differ between the two periods. Median length of stay in the PICU was longer in 2020 [4 (IQR 2–9) vs. 3 (IQR 1–8) days, p = 0.002] in 2019. Mortality remained stable.Conclusions: In this large national study, we showed a decrease in the number of PICU admissions. The most severe patients were still admitted to intensive care and overall mortality remained stable.
The Pharmacokinetics of Beta-Lactam Antibiotics Using Scavenged Samples in Pediatric Intensive Care Patients: The EXPAT Kids Study Protocol
Authors: Schouwenburg Stef , Wildschut Enno D. , de Hoog M. , Koch Birgit C.P. , Abdulla Alan 
Published: December 2021

Background: Emerging evidence supports the importance of optimized antibiotic exposure in pediatric intensive care unit (PICU) patients. Traditional antibiotic dosing is not designed for PICU patients, as the extreme pharmacokinetic (PK) behavior of drugs threatens the achievement of optimal antibiotic treatment outcomes. Scavenged sampling is a sampling strategy which may have positive implications for routine TDM and PK research, as well as monitoring other biomarkers. EXPAT Kids study was designed to analyze whether current empiric dosing regimens of frequently used beta-lactam antibiotics achieve defined therapeutic target concentrations in PICU patients.Methods: A mono-centre, exploratory pharmacokinetic and pharmacodynamic study was designed to assess target attainment of beta-lactam antibiotics. One hundred forty patients will be included within 24 months after start of inclusion. At various time points serum concentration of the study antibiotic (cefotaxime, ceftazidime, ceftriaxone, cefuroxime, flucloxacillin, and meropenem) are determined. In parallel with these sampling moments, residual material is collected to validate the use of blood of scavenged heparinized astrup syringes for the quantification of antibiotic exposure. The primary outcome is the time that the free (unbound) concentration of the study antibiotic remains above one to four the minimal inhibitory concentration during a dosing interval (100%ƒT &gt; MIC and 100%ƒT&gt;4xMIC). Other included outcomes are disease severity, safety, length of stay, and inflammatory biomarkers.Discussion: Potentially, scavenged sampling may enrich the EXPAT Kids dataset, and reduce additional blood sampling and workload for clinical personnel. The findings from the EXPAT Kids study will lead to new insights in the PK parameters of beta-lactams and consecutive effects on target attainment and clinical outcomes. Is there a need for more precision in dosing? Netherlands Trial Register Number: Trial NL9326.