bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cauliflower‐like Platinum Particles Decorated Reduced Graphene Oxide for Sensitive Determination of Acetaminophen
Authors: Pham Tien Song Hiep , Mahon Peter J. , Lai Guosong , Fu Li , Lin Cheng‐Te , Yu Aimin 
Published: June 2019

AbstractOver the past years, the development of electrochemical sensing platforms for the sensitive detection of drug molecules have received great interests. In this research study, we introduced cauliflower‐like platinum particles decorated reduced graphene oxide modified glassy carbon electrode (Pt−RGO/GCE) as an electrochemical sensing platform for highly sensitive determination of acetaminophen (ACTM). The sensor was prepared via a simple and environmentally friendly two‐step electrodeposition method at room temperature. The combination of conductive RGO nanosheets and unique structured Pt particles (average 232 nm in diameter) provided an efficient interface with large effective surface area which greatly facilitated the electron transfer of ACTM. The experimental conditions that might affect the drug detection were studied in detail and optimized for best performance. The Pt−RGO/GCE was able to detect ACTM up to the limit of 2.2 nM with a linear concentration range from 0.01 to 350 μM. With its high reproducibility, excellent stability and selectivity, the as‐fabricated sensor was successfully applied to the ACTM content measurement in commercial tablets.
Trend assessment for daily snow depths with changepoint considerations
Authors: Lee J. , Lund R. , Woody J. , Xu Y. 
Published: June 2019

AbstractThis paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.
Altered cortical folding and reduced sulcal depth in adults with anorexia nervosa
Authors: Nickel Kathrin , Joos Andreas , Tebartz van Elst Ludger , Holovics Lukas , Endres Dominique , Zeeck Almut , Maier Simon 
Published: June 2019

AbstractObjectivePrevious studies on anorexia nervosa (AN) focused on cortical volume and mainly reported grey matter reduction. They also investigated cortical surface parameters, for example, absolute mean curvature (AMC) providing information on cortical folding or sulcal depth (SD). For the first time, we also analysed cortical complexity using fractal dimension (FD) in AN.MethodIn a cross‐sectional study, we performed surface analyses (AMC, SD, and FD) on 34 women with AN, 24 recovered from AN (REC), and 41 healthy controls (HC). Structural MR data was processed using the Computational Anatomy Toolbox and statistically compared across groups on a vertex‐ and region‐of‐interest‐wise level using statistical parametric mapping.ResultsWe found a lower AMC and SD in AN, especially in temporal areas. FD was increased in the left precentral gyrus in AN. No differences of the parameters AMC, SD, or FD were evident between REC and HC participants.ConclusionsAlterations in AMC, SD, and FD in AN patients, but not between the REC and HC groups, suggest that these alterations are state related. The findings concur with other structural AN studies that suggest restitution with clinical recovery. The changes may be due to malnutrition, dehydration, osmotic dysregulation, or hormonal aberrations during the acute stage.
Online, real‐time, and direct use of SIFT‐MS to measure garlic breath deodorization: a review
Authors: Castada Hardy Z. , Barringer Sheryl Ann 
Published: June 2019

AbstractThis review summarizes the application of selected‐ion flow‐tube mass spectrometry (SIFT‐MS) to recent deodorization studies of garlic‐derived volatile organosulphur compounds. Briefly, organosulphur chemistry, proposed theories on deodorization mechanisms, and technical aspects of SIFT‐MS were revisited. The most effective deodorization treatments from various food and food components were identified, which include whole milk, parsley, spinach, mint, and apple; however, chlorophyll and whey protein did not provide effective deodorization. The extent of deodorization by each of the treatments varies according to the active components that are either able to bind, capture, oxidize, or reduce specific sulphur compounds, leading to deodorization. It was determined that certain varieties (cultivars) of food produce more effective deodorization than other varieties. In addition, the effectiveness of enzymes for deodorization could depend upon the rate of enzymatic activity, which catalyses the subsequent oxidation of phenolic compounds to initiate deodorization. Moreover, the high phenolic content of a food did not automatically correlate with greater deodorization, although the type of phenolic compound could be an important factor for providing effective deodorization. Finally, other challenges and considerations were suggested for further advancement of the research on deodorization of malodorous garlic breath using food and food components
Childhood Socioeconomic Disadvantage and Risk of Fatty Liver in Adulthood: The Cardiovascular Risk in Young Finns Study
Authors: Laitinen Tomi T. , Vahtera Jussi , Pahkala Katja , Magnussen Costan G. , Nuotio Joel , Hutri‐Kähönen Nina , Kivimäki Mika , Lehtimäki Terho , Jokinen Eero , Laitinen Tomi , Tossavainen Päivi , Pentti Jaana , Viikari Jorma S.A. , Juonala Markus , Raitakari Olli T. 
Published: June 2019

Fatty liver is a preventable cause of liver failure, but early risk factors for adulthood fatty liver are poorly understood. We examined the association of childhood socioeconomic disadvantage with adulthood fatty liver and tested adulthood risk factors of fatty liver as possible mediators of this link. The study population comprised 2,042 participants aged 3‐18 years at baseline (1980) from the longitudinal Cardiovascular Risk in Young Finns Study. Follow‐up with repeated clinical examinations was 31 years. Childhood socioeconomic disadvantage was assessed using data from parents’ socioeconomic position and socioeconomic circumstances in participants’ residential neighborhoods, categorized as high versus low socioeconomic disadvantage. Fatty liver was determined by ultrasound during the last follow‐up (2011) at ages 34‐49 years. Childhood and adulthood risk factors, including metabolic biomarkers and lifestyle variables, were assessed in clinical examinations. A total of 18.9% of the participants had fatty liver in adulthood. High childhood socioeconomic disadvantage was associated with an increased risk of fatty liver (risk ratio [95% confidence interval], 1.42 [1.18‐1.70]; P = 0.0002). This association was robust to adjustment for age, sex, and childhood risk factors of fatty liver, including high body mass index, elevated insulin, and low birth weight (1.33 [1.09‐1.62]; P = 0.005). High childhood socioeconomic disadvantage was also associated with the development of risk factors of fatty liver in adulthood. Adulthood risk factors linking childhood socioeconomic disadvantage with fatty liver included waist circumference (proportion mediated of the total effect of childhood socioeconomic disadvantage, 45%), body mass index (40%), systolic blood pressure (29%), insulin (20%), physical activity (15%), triglycerides (14%), and red meat consumption (7%). Conclusion: Childhood socioeconomic disadvantage was associated with multiple risk factors of fatty liver and increased likelihood of fatty liver in adulthood.
Organoids are promising tools for species‐specific in vitro toxicological studies
Authors: Augustyniak Justyna , Bertero Alessia , Coccini Teresa , Baderna Diego , Buzanska Leonora , Caloni Francesca 
Published: June 2019

AbstractOrganoids are three‐dimensional self‐aggregating structures generated from stem cells (SCs) or progenitor cells in a process that recapitulates molecular and cellular stages of early organ development. The differentiation process leads to the appearance of specialized mature cells and is connected with changes in the organoid internal structure rearrangement and self‐organization. The formation of organ‐specific structures in vitro with highly ordered architecture is also strongly influenced by the extracellular matrix. These features make organoids as a powerful model for in vitro toxicology. Nowadays this technology is developing very quickly. In this review we present, from a toxicological and species‐specific point of view, the state of the art of organoid generation from adult SCs and pluripotent SCs: embryonic SCs or induced pluripotent SCs. The current culture organoid techniques are discussed for their main advantages, disadvantages and limitations. In the second part of the review, we concentrated on the characterization of species‐specific organoids generated from tissue‐specific SCs of different sources: mammary (bovine), epidermis (canine), intestinal (porcine, bovine, canine, chicken) and liver (feline, canine).
A deep learning based pipeline for optical coherence tomography angiography
Authors: Liu Xi , Huang Zhiyu , Wang Zhenzhou , Wen Chenyao , Jiang Zhe , Yu Zekuan , Liu Jingfeng , Liu Gangjun , Huang Xiaolin , Maier Andreas , Ren Qiushi , Lu Yanye 
Published: June 2019

AbstractOptical coherence tomography angiography (OCTA) is a relatively new imaging modality that generates microvasculature map. Meanwhile, deep learning has been recently attracting considerable attention in image‐to‐image translation, such as image denoising, super‐resolution and prediction. In this paper, we propose a deep learning based pipeline for OCTA. This pipeline consists of three parts: training data preparation, model learning and OCTA predicting using the trained model. To be mentioned, the datasets used in this work were automatically generated by a conventional system setup without any expert labeling. Promising results have been validated by in‐vivo animal experiments, which demonstrate that deep learning is able to outperform traditional OCTA methods. The image quality is improved in not only higher signal‐to‐noise ratio but also better vasculature connectivity by laser speckle eliminating, showing potential in clinical use. Schematic description of the deep learning based optical coherent tomography angiography pipeline.
Improving skin integration around long‐term percutaneous devices using fibrous scaffolds in a reconstructed human skin equivalent model
Authors: Bolle Eleonore C. L. , Bartnikowski Nicole , Haridas Parvathi , Parker Tony J. , Fraser John F. , Gregory Shaun D. , Dargaville Tim R. 
Published: June 2019

AbstractThe interface between synthetic percutaneous devices and skin is a common area for bacterial infection, which may ultimately result in failure of the device. Better integration of percutaneous devices with skin may help reduce infection rates due to the creation of a dermal seal. However, the mismatch in material and chemical properties of devices and skin presents a challenge for closing the dermal gap at the skin–device interface. Here, we have used a tissue engineering approach to tissue integration by creating a highly fibrous poly(ε‐caprolactone) scaffold using melt electrowriting and seeding this with dermal fibroblasts, followed by maturation and insertion into a full‐thickness defect made in an ex vivo skin model. The integration of seeded scaffolds was compared with controls including a non‐seeded scaffold and a polymer tube with a smooth surface. Dermal fibroblast inclusion in the scaffold and epidermal upgrowth versus downgrowth/marsupialization around the device were used as measures of integration. Based on these measures, almost all pre‐seeded scaffolds performed better than both the non‐seeded scaffolds and smooth tubes. The hypothesis is that the fibroblasts act as a barrier to epithelial downward migration, and provide healthy tissue for nascent epidermal development.
A 7‐lncRNA signature predict prognosis of Uterine corpus endometrial carcinoma
Authors: Ouyang Dong , Li Ruyi , Li Yaxian , Zhu Xueqiong 
Published: June 2019

AbstractCurrent research indicate that long noncoding RNAs (lncRNAs) are associated with the progression of various cancers and can be used as prognostic biomarkers. This study aims to construct a prognostic lncRNA signature for the risk assessment of Uterine corpus endometrial carcinoma (UCEC). The RNA‐Seq expression profile and corresponding clinical data of UCEC patients obtained from The Cancer Genome Atlas database. First, some prognosis‐related lncRNAs were obtained by univariate Cox analysis. The minimum absolute contraction and selection operator (LASSO) regression and the Cox proportional hazard regression method were used to further identify the lncRNA prognostic model. Finally, seven lncRNAs (AC110491.1, AL451137.1, AC005381.1, AC103563.2, AC007422.2, AC108025.2, and MIR7‐3HG) were identified as potential prognostic factors. According to the model constructed by the above analysis, the risk score of each UCEC patient was calculated, and the patients were classified into high and low‐risk groups. The low‐risk group had significant survival benefits. Moreover, we constructed a nomogram that incorporated independent prognostic factors (age, tumor stage, tumor grade, and risk score). The c‐index value for evaluating the predictive nomogram model was 0.801. The area under the curve was 0.797 (3‐year survival). The calibration curve also showed that there was a satisfactory agreement between the predicted and observed values in the probability of 1‐, 3‐, and 5‐year overall survival. On the basis of the coexpression relationship, we established a coexpression network of lncRNA‐messenger RNA (mRNA) of the 7‐lncRNA. The Kyoto Encyclopedia of Genes and Genomes analysis of the coexpressing mRNAs showed that the main pathways related to the 7‐lncRNA signature were neuroactive ligand‐receptor interaction, serotonergic synapse, and gastric cancer pathway. Therefore, our study revealed that the 7‐lncRNA could be used to predict the prognosis of UCEC and for postoperative treatment and follow‐up.
Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis
Authors: Liu Lu , He Chen , Zhou Qing , Wang Ganlu , Lv Zhiwu , Liu Jintao 
Published: June 2019

AbstractThyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer‐related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.
Mixed methods analysis strategies in program evaluation beyond “a little quant here, a little qual there”
Authors: Reeping David , Taylor Ashley R. , Knight David B. , Edwards Cherie 
Published: June 2019

AbstractBackgroundMixed methods research designs in engineering education often frame the “mixed” aspect of the design from the perspective of data collection. However, intentional mixing throughout the research design—particularly during data analysis—may enable richer meta‐inferences about a phenomenon.PurposeThis paper provides examples of strategies for mixing during data analysis in mixed methods program evaluation in engineering education. Although the context is program evaluation, the procedures are applicable in engineering education research more broadly.Scope/MethodThis review presents examples of mixed methods analysis strategies in the context of a data set with qualitative and quantitative data from a global engineering program at a large Mid‐Atlantic university. The structure of the examples presents mixed research questions, pragmatic purposes for such studies, and examples of different mixing strategies for the analysis stage. The mixing strategies highlighted include extreme case sampling, converting, creating a blended variable, blending variables and themes across strands, and cross‐case comparison.ConclusionsThis review of mixed methods in program evaluation prompted a reflection of processes in the design of studies, how the designs are described beyond the usual taxonomies in the mixed methods literature, and implications for the greater community of engineering education researchers. Five mixed methods designs were formulated around the mixing strategies.
Characterizing the interplay of cognitive and metacognitive knowledge in computational modeling and simulation practices
Authors: Magana Alejandra J. , Fennell Hayden W. , Vieira Camilo , Falk Michael L. 
Published: June 2019

AbstractBackgroundModeling and simulation practices have become commonplace in modern engineering, but in‐depth research on the development of modeling and simulation expertise is needed to identify the learning processes involved in successfully acquiring these skills.PurposeThis study investigated student dimensions of expertise when engaged in modeling and simulation practices. The guiding research question was “How do students use cognitive and metacognitive knowledge when engaged with computational modeling and simulation practices?”Design/MethodThis study included 11 undergraduate engineering students enrolled in a course on computational materials science. Data were collected from one of five computational challenges administered during the course. Students' cognitive and metacognitive knowledge use was first analyzed using thematic analysis and then through a phenomenographic approach. Findings were interpreted using the lens of adaptive expertise.ResultsResults describe how students used their cognitive and metacognitive knowledge to approach the computational challenge. Two categories in the cognitive dimension were identified: implementation‐oriented and knowledge‐oriented. Two categories identified in the metacognitive dimension were plan‐oriented and action‐oriented approaches. These categories are further presented as an outcome space by aligning them with the dimensions of adaptive expertise.ConclusionsResults indicated that students in the action/implementation‐oriented category exhibited many of the qualities expected of inexperienced novices, while students in the plan/knowledge‐oriented category demonstrated more of the qualities expected of adaptive experts. However, results were less conclusive for students in the two intermediate thematic categories, who often exhibited some but not all of the qualities of both novices and experts.
Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection
Authors: Afza Farhat , Khan Muhammad A. , Sharif Muhammad , Rehman Amjad 
Published: June 2019

AbstractAmong precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co‐occurrences matrices features are extracted and covariance‐based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.
Frontline Science: Endotoxin-induced immunotolerance is associated with loss of monocyte metabolic plasticity and reduction of oxidative burst
Authors: Grondman Inge , Arts Rob J W , Koch Rebecca M , Leijte Guus P , Gerretsen Jelle , Bruse Niklas , Kempkes Rosalie W M , ter Horst Rob , Kox Matthijs , Pickkers Peter , Netea Mihai G , Gresnigt Mark S 
Published: June 2019

AbstractSecondary infections are a major complication of sepsis and associated with a compromised immune state, called sepsis-induced immunoparalysis. Molecular mechanisms causing immunoparalysis remain unclear; however, changes in cellular metabolism of leukocytes have been linked to immunoparalysis. We investigated the relation of metabolic changes to antimicrobial monocyte functions in endotoxin-induced immunotolerance, as a model for sepsis-induced immunoparalysis. In this study, immunotolerance was induced in healthy males by intravenous endotoxin (2 ng/kg, derived from Escherichia coli O:113) administration. Before and after induction of immunotolerance, circulating CD14+ monocytes were isolated and assessed for antimicrobial functions, including cytokine production, oxidative burst, and microbial (Candida albicans) killing capacity, as well metabolic responses to ex vivo stimulation. Next, the effects of altered cellular metabolism on monocyte functions were validated in vitro. Ex vivo lipopolysaccharide stimulation induced an extensive rewiring of metabolism in naive monocytes. In contrast, endotoxin-induced immunotolerant monocytes showed no metabolic plasticity, as they were unable to adapt their metabolism or mount cytokine and oxidative responses. Validation experiments showed that modulation of metabolic pathways, affected by immunotolerance, influenced monocyte cytokine production, oxidative burst, and microbial (C. albicans) killing in naive monocytes. Collectively, these data demonstrate that immunotolerant monocytes are characterized by a loss of metabolic plasticity and these metabolic defects impact antimicrobial monocyte immune functions. Further, these findings support that the changed cellular metabolism of immunotolerant monocytes might reveal novel therapeutic targets to reverse sepsis-induced immunoparalysis.