bluuebunny/crossref_metadata_2025_split huggingface dataset:
Poor sleep quality among newly diagnosed head and neck cancer patients: prevalence and associated factors
Authors: Santoso Angelina M. M. , Jansen Femke , Lissenberg-Witte Birgit I. , Baatenburg de Jong Robert J. , Langendijk Johannes A. , Leemans C. René , Smit Johannes H. , Takes Robert P. , Terhaard Chris H. J. , van Straten Annemieke , Verdonck-de Leeuw Irma M. ,   the NET-QUBIC consortium
Published: June 2020

AbstractBackgroundHead and neck cancer (HNC) patients often suffer from distress attributed to their cancer diagnosis which may disturb their sleep. However, there is lack of research about poor sleep quality among newly diagnosed HNC patients. Therefore, our aim was to investigate the prevalence and the associated factors of poor sleep quality among HNC patients before starting treatment.Materials and methodsA cross-sectional study was conducted using the baseline data from NET-QUBIC study, an ongoing multi-center cohort of HNC patients in the Netherlands. Poor sleep quality was defined as a Pittsburgh Sleep Quality Index (PSQI) total score of > 5. Risk factors examined were sociodemographic factors (age, sex, education level, living situation), clinical characteristics (HNC subsite, tumor stage, comorbidity, performance status), lifestyle factors, coping styles, and HNC symptoms.ResultsAmong 560 HNC patients, 246 (44%) had poor sleep quality before start of treatment. Several factors were found to be significantly associated with poor sleep: younger age (odds ratio [OR] for each additional year 0.98, 95% CI 0.96–1.00), being female (OR 2.6, 95% CI 1.7–4.1), higher passive coping style (OR 1.18, 95% CI 1.09–1.28), more oral pain (OR 1.10, 95% CI 1.01–1.19), and less sexual interest and enjoyment (OR 1.13, 95% CI 1.06–1.20).ConclusionPoor sleep quality is highly prevalent among HNC patients before start of treatment. Early evaluation and tailored intervention to improve sleep quality are necessary to prepare these patients for HNC treatment and its consequences.
Bugscope: A Practical Approach to Providing Remote Microscopy for Science Education Outreach
Authors: Potter Clinton S. , Carragher Bridget , Carroll Liana , Conway Charles , Grosser Benjamin , Hanlon Janet , Kisseberth Nick , Robinson Scott , Thakkar Umesh , Weber Daniel 
Published: June 2020

AbstractThe Bugscope project is an educational outreach program for kindergarten to grade 12 (K–12) classrooms. The project provides a resource to classrooms so that they may remotely operate a scanning electron microscope to image insects at high magnification. The microscope is remotely controlled in real time from a classroom computer over the Internet using a Web browser. Bugscope provides a state-of-the-art microscope resource for teachers that can be readily integrated into classroom activities. The Bugscope project provides a low-cost, sustainable model for research groups to support K–12 education outreach projects.
Atomic-focuser Imaging by Graphite Crystals in Carbon Nanoshells
Authors: Cowley J.M. , Hudis J.B. 
Published: June 2020

AbstractOne of the atomic-focuser modes for ultra-high resolution electron microscopy is described theoretically and illustrated by the observation of images formed within the diffraction spots of nanodiffraction patterns of carbon nanoshells. In this mode, the specimen is illuminated by the focused probe of a scanning transmission electron microscope and is followed by a thin crystal at a Fourier-image distance. The theory shows that each diffraction spot of the crystal contains a magnified image of the illuminated area of the specimen, having a resolution depending on the width of the intensity peak of electrons channeled along atomic rows in the crystal. A thin graphite crystal, contained within one wall of a carbon nanoshell, can act as an atomic focuser to image part of the other wall of the nanoshell, or tungsten atoms deposited on this wall. Simulations of the transmission of electrons through graphite crystals show that the images formed should have a resolution of about 0.06 nm. Experimental images suggest that this resolution has been attained in the imaging of tungsten atoms or clusters of tungsten atoms.
Nonlinear Dynamics in the Progression of Atherosclerotic Fatty Streaks: Morphometric Analysis
Authors: Bugelski Peter J. , Maleeff Beverly E. , Klinkner Anne M. , Louden Calvert S. , Hart Timothy K. 
Published: June 2020

AbstractAtherosclerotic lesions are heterogeneous in terms of their cellular and lipid composition. While heterogeneity can be the result of stochastic noise, an alternate hypothesis is that the differences observed among individual lesions arise from deterministic chaos. Five New Zealand white rabbits were fed a diet containing 0.15% cholesterol for 6 months. Segments of the aorta were fixed in formalin, stained en bloc with Nile red (NR) and filipin (F), and en face fluorescence microscopy was used to map the distribution of lipids in fatty streaks (FS). The smallest lesions detected stained only with filipin. Larger lesions stained with both Nile red and filipin and two distinct regions of Nile red staining, NR-orange (rich in polar lipids) and NR-yellow (rich in neutral lipids) were observed. Digital overlays revealed a “nested” arrangement of F, NR-orange, and NR-yellow. The lesions also showed marked heterogeneity in their lipid composition. Thus, although initially similar, as FS increased in size, their composition became divergent, suggesting that the ultimate composition of a FS was highly sensitive to its initial composition. Sensitivity to initial condition is one of the hallmarks of deterministic systems. To determine if FS were self-similar, another hallmark of deterministic chaos, the borders of the different regions defined by NR and F staining were subjected to fractal analysis. For each lesion, the borders of the F, NR-orange, and NR-yellow regions were found to be fractal. Return maps were constructed for the differently stained regions. Analysis of the entire 104-lesion data set showed that although the data could be described by a four-parameter logistic model, the population was not chaotic. However, return maps drawn for the maxima of the NR-orange stained regions demonstrated chaos. Taken together, the data suggest that deterministic chaos plays a role in the evolution of atheromatous disease but, in common with most biologic systems, as the lesions progress, chaotic behavior is dampened.
Development of Electron Probe Instrumentation during Those Early Days
When Professor Castaing Visited Japan
Authors: Shimizu Ryuichi 
Published: June 2020

Abstract
The development of the electron probe microanalyzer (EPMA) in Japan in
the early 1960s, when Prof. R. Castaing visited Japan, is briefly outlined.
In 1955, a review article was published by Prof. G. Shinoda in
Oyobutsuri, the most popular journal in
Japan, in which the EPMA was introduced. In 1957, a research group at the
University of Tokyo started to develop an EPMA with a Grant-in-Aid for
Developmental Research. Their research results led to the funding of a
2-year Grant-in-Aid for Cooperative Research Project (April 1960 to March
1962), which was chaired by Prof. Y. Sakaki. Prof. G. Shinoda who became the
chairman of that project in April of 1962 led that group for another year
until March of 1963. It was just after the start of the project that Prof.
R. Castaing visited Japan in September of 1960 as a representative of the
French Mission Culturelle. This visit gave a great push forward for the
commercial development of EPMA instruments in Japan. The first three
commercial EPMA instruments from Hitachi, JEOL, and Akashi Ltds. were
installed in Tohoku, Osaka, and Waseda Universities in 1962, 1963, and 1964,
respectively. Photographs of those first commercial EPMA systems, together
with a brief description of the activities of the cooperative research
projects, are presented.
Biological X-ray Microanalysis: The Past, Present Practices, and Future
Prospects
Authors: Echlin Patrick 
Published: June 2020

Abstract
A brief description is given of the events surrounding the development
of biological X-ray microanalysis during the last 30 years, with particular
emphasis on the contribution made by research workers in Cambridge, UK.
There then follows a broad review of some applications of biological X-ray
microanalysis. A more detailed consideration is given to the main thrust of
current procedures and applications that are, for convenience, considered as
four different kinds of samples. Thin frozen dried sections which are
analyzed at ambient temperatures in a transmission electron microscope
(TEM); semithin frozen dried sections which are analyzed at low temperature
in a scanning transmission electron microscope (STEM); thick frozen hydrated
sections which are analyzed at low temperature in a scanning electron
microscope (SEM), and bulk samples which are analyzed at low temperature in
the same type of instrument. A brief outline is given of the advantages and
disadvantages of performing low-voltage, low-temperature X-ray microanalysis
on frozen hydrated bulk biological material. The article concludes with a
consideration of alternative approaches to in situ analysis using either
high-energy beams or visible and near-visible photons.
Fundamental Constants for Quantitative X-ray Microanalysis
Authors: Joy David C. 
Published: June 2020

Abstract
Quantitative X-ray microanalysis requires the use of many fundamental
constants related to the interaction of the electron beam with the sample.
The current state of our knowledge of such constants in the particular areas
of electron stopping power, X-ray ionization cross-sections, X-ray
fluorescence yield, and the electron backscattering yield, is examined. It
is found that, in every case, the quality and quantity of data available is
poor, and that there are major gaps remaining to be filled.
Wavelength Dispersive Spectrometer and Energy Dispersive Spectrometer
Automation: Past and Future Development
Authors: McCarthy Jon J. , Friel John J. 
Published: June 2020

Abstract
As part of the Microbeam Analysis Society (MAS) symposium marking 50
years of electron microprobe analysis, this article reviews the important
advances made over the decades to the automation of data collection and
computerized analysis of data from the electron microprobe. Out of many
innovations that contributed to the advance of microprobe automation, we
have chosen to focus on a few developments that the authors feel represent
the major trends in advancement of the “state of the art” of this
instrumentation. After providing brief summaries of the three generations of
advances in the hardware and software of automation systems, several key
applications developments are described, followed by our prediction of which
current developments may impact the future automation of the
microprobe.
Minimizing Errors in Electron Microprobe Analysis
Authors: Lifshin Eric , Gauvin Raynald 
Published: June 2020

Abstract
Errors in quantitative electron microprobe analysis arise from many
sources including those associated with sampling, specimen preparation,
instrument operation, data collection, and analysis. The relative magnitudes
of some of these factors are assessed for a sample of NiAl used to
demonstrate important concerns in the analysis of even a relatively simple
system measured under standard operating conditions. The results presented
are intended to serve more as a guideline for developing an analytical
strategy than as a detailed error propagation model that includes all
possible sources of variability and inaccuracy. The use of a variety of
tools to assess errors is demonstrated. It is also shown that, as sample
characteristics depart from those under which many of the quantitative
methods were developed, errors can increase significantly.
Similar risk of cardiovascular events in idiopathic inflammatory myopathy and rheumatoid arthritis in the first 5 years after diagnosis
Authors: Párraga Prieto Cristina , Ibrahim Fowzia , Campbell Richard , Chinoy Hector , Galloway James , Gordon Patrick 
Published: June 2020

Abstract
Objectives
To estimate the incidence of cardiovascular (CV) events in idiopathic inflammatory myopathy (IIM) compared to patients with rheumatoid arthritis (RA) and the general population. To explore the contribution of traditional CV risk factors to any difference observed.

Methods
A retrospective matched population-based cohort study was conducted using UK Clinical Practice Research Datalink (CPRD) from 1987 to 2013. The incidence of CV events was calculated for each cohort over time and compared using Cox proportional hazards models. Multivariable analyses were used to adjust for traditional CV risk factors.

Results
A total of 603 patients with IIM 4047 RA and 4061 healthy controls were included. The rate of CV events in IIM was significantly greater than healthy controls [hazard ratio (HR) 1.47 (95% confidence interval (CI) 1.18–1.83)] and remained significant after adjustment for CV risk factors [HR 1.38 (95% CI 1.11–1.72)]. Risk was similar between IIM and RA [HR 1.01 (95% CI 0.78–1.31)]. The rate of myocardial infarction [HR 1.61 (95% CI 1.27–2.04)] but not stroke [HR 0.92 (95% CI 0.59–1.44)] was significantly greater in IIM compared to healthy controls. After the first 5 years, the rate of CV events for RA remained significantly greater compared to the control group, but appeared to return to that of the healthy controls in the IIM group.

Conclusion
IIM is associated with an increased risk of CV events in the first 5 years after diagnosis similar to that of RA. Beyond 5 years, the risk appears to return to that of the general population in IIM but not RA.

Key Points• The excess risk of cardiovascular events in IIM is similar to that found in RA.• The excess risk of cardiovascular events is greatest in the first 5 years after diagnosis.
Hormone replacement therapy and mammographic density: a systematic literature review
Authors: Azam Shadi , Jacobsen Katja Kemp , Aro Arja R. , Lynge Elsebeth , Andersen Zorana Jovanovic 
Published: June 2020

AbstractPurposeHormone replacement therapy (HRT) is used to reduce climacteric symptoms of menopause and prevent osteoporosis; however, it increases risk of breast cancer. Mammographic density (MD) is also a strong risk factor for breast cancer. We conducted this review to investigate the association between HRT use and MD and to assess the effect of different HRT regimens on MD.MethodsTwo of authors examined articles published between 2002 and 2019 from PubMed, Embase, and OVID using Covidence systematic review platform. Any disagreements were discussed until consensus was reached. The protocol used in this review was created in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality of each eligible study was assessed using the Oxford Center for Evidence-Based Medicine (OCEBM) hierarchy.ResultsTwenty-two studies met the inclusion criteria. Six studies showed that using estrogen plus progestin (E + P) HRT was associated with higher MD than estrogen alone. Four studies reported that continuous estrogen plus progestin (CEP) users had higher MD than sequential estrogen plus progestin (SEP) and estrogen alone users. However, two studies showed that SEP users had slightly higher MD than CEP users and estrogen alone users.ConclusionsEpidemiological evidence is rather consistent suggesting that there is a positive association between HRT use and MD with the highest increase in MD among current users, and CEP users. Our results suggest that due to increase in MD and masking effect, current E + P users may require additional screening procedures, shorter screening intervals, or using advanced imaging techniques.
Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice
Authors: Birkenbihl Colin , Emon Mohammad Asif , Vrooman Henri , Westwood Sarah , Lovestone Simon ,   On behalf of the AddNeuroMed Consortium, Hofmann-Apitius Martin , Fröhlich Holger ,   Alzheimer’s Disease Neuroimaging Initiative
Published: June 2020

AbstractArtificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.
Analysis of solid sorbents for control and removal processes for elemental mercury from gas streams: a review
Authors: Kunecki Piotr , Czarna-Juszkiewicz Dorota , Wdowin Magdalena 
Published: June 2020

Abstract
Due to the restriction such as the Minamata Convention as well as the IED of the European Commission, mercury removal from flue gases of coal-fired power plants (CPP) is an increasingly important environmental issue. This makes this topic very crucial for both the energy industry and scientists. This paper shows how mercury arises from natural resources, i.e., coals, through their combustion processes in CPP and considers the issue of mercury content in flue gases and solid-state coal combustion by-products. The main part of this paper presents a review of the solid sorbents available for elemental mercury control and removal processes, tested on a laboratory scale. The described solutions have a potential for wider usage in exhaust gas treatment processes in the energy production sector. These solutions represent the latest developments in the field of elemental mercury removal from gases. The authors present an overview of the wide range of solid sorbents and their modifications intended to increase affinity for Hg0. Among the presented sorbents are the well-known activated carbon solutions but also novel modifications to these and other innovative sorbent proposals based on, e.g., zeolites, biochars, other carbon-based materials, metal-organic frameworks. The paper presents a wide range of characteristics of the described sorbents, as well as the conditions for the Hg0 removal experiments summarizing the compendium of novel solid sorbent solutions dedicated to the removal of elemental mercury from gases.

Graphic abstract
Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem
Authors: Aklilu Tesfaye Andualem , Gessesse Awoke Berhan 
Published: June 2020

AbstractThe saturation property of vegetation indices posed a known limitation and this study was motivated to understand the saturation property of three widely used vegetation indices in mixed crop-forest ecosystem where limited knowledge existed. Normalized Difference Vegetation Index (NDVI), Simple Ratio Index (SRI) and Transformed Vegetation Index (TVI) were computed from sentinel-2 bands and; variations among bands and among vegetation indices were evaluated. The study employed green Leaf Area Index (gLAI) Version 1 product, derived from PROBA-V daily data for discriminating the saturation property of the indices. Although the study applied various methods of image preprocessing and processing, best curve fitting and correlation analysis were the key ones. The three vegetation indices: NDVI, SRI, and TVI computed from sentinel-2 bands: four (red) and five (red edge) coupled with bands 8 and 8a showed some levels of saturation. Nonetheless, TVI computed from bands 8a and 4 is the best outperforming combination, i.e., the least saturated one and it is an interesting output in a sense that a single index with significantly lower values of noise equivalent green Leaf Area Index as well as having strong association with gLAI is obtained that could be very useful for quantification of gLAI in similar ecosystems. For the rest of the bands and vegetation indices combination of the indices via setting thresholds could be one possible solution.
Valuing carbon capture in agricultural production: examples from Sweden
Authors: Linderholm K. , Katterer T. , Mattsson J. E. 
Published: June 2020

AbstractAgriculture is regarded as a net emitter of greenhouse gases (GHG), but sequesters huge amounts of carbon in soils, bioenergy substrates, and food products. The global accounting system for climate impact based on life cycle assessment (LCA) methodology only takes account of costs (emissions), and not income (carbon and energy binding), leading to the conclusion that agricultural activities should decrease to mitigate climate change. This study considered an alternative accounting system, carbon capture LCA (CC-LCA), that allocates value to carbon sequestration in agricultural products. For two case farms in Sweden (arable, dairy), CC-LCA was applied to (1) calculate the carbon footprint of agricultural production by accounting for net GHG emissions from farm production, rather than gross emissions only, and (2) assess the net impact of mineral nitrogen fertilizer. For the arable farm, CC-LCA revealed net carbon binding of 4 Mg CO2-eq per hectare (net sink), compared with emissions of 1.6 Mg CO2-eq per hectare in LCA. For the dairy farm, both approaches showed emissions of about 10 Mg CO2-eq per dairy cow, mainly due to ruminant digestion. The results also showed that mineral nitrogen fertilizer effectively contributed to carbon sequestration. Compared with an unfertilized wheat crop, a fertilizer dose of 200 kg N ha−1 was estimated to bind about eight-fold more GHG and energy in grain than was released or used during fertilizer production and crop cultivation. Thus, we argue that future strategies aiming for climate-friendly products and practices must acknowledge that agriculture sequesters carbon in products.