bluuebunny/crossref_metadata_2025_split huggingface dataset:
The use of clozapine in a rural and remote region of Australia
Authors: Tahnee Bridson , Hesitha Abeysundera , Arulmathy Arunachalam , Anthea Lam , Linda Mora , Patrick McGorry , Brian O’Donoghue 
Published: December 2020

Objective: Clozapine is the most effective antipsychotic available for treatment-resistant schizophrenia. Unfortunately, it is often underutilised or administered late in the treatment course, if at all. This issue is further augmented in rural and remote regions where access to treatment is sparse. This study is the first of its kind to investigate the use of clozapine in a remote and regional area of Australia with a high proportion of Aboriginal and/or Torres Strait Islander individuals. Methods: This study includes all individuals who were commenced on clozapine between June 2013 and June 2018. Relevant clinical information was sourced from electronic medical records and descriptive statistical analysis performed. Results: A total of 3850 patients with psychotic disorders were reviewed over 5 years, of whom 63 were commenced on clozapine. The majority were male ( n = 50, 79.4%) with a mean age of 33.9 years (standard deviation 11.0). A total of 34.9% ( n = 22) identified as Aboriginal and/or Torres Strait Islander. The mean length of time between diagnosis and commencement of clozapine was 7.2 years (standard deviation ±4.6 years), while the mean number of antipsychotic medications tried prior to clozapine was 4.2 (standard deviation ±1.6 antipsychotics). Clozapine was discontinued in 57.1% of the individuals in this study (41% within the first 12 months). The most common cause for cessation was poor compliance ( n = 19, 30.2%). Conclusion: While clozapine remains the most effective antipsychotic treatment, it is often commenced late in the treatment or ceased prematurely. Further work is required, particularly in remote and regional populations, to ensure treatment adherence for optimal patient outcomes.
Engagement and Outcomes Associated with Contextual Annotation Features of a Digital Health Solution
Authors: Dugas Michelle , Wang Weiguang , Crowley Kenyon , Iyer Anand K. , Peeples Malinda , Shomali Mansur , Gao Guodong (Gordon) 
Published: December 2020

Background: Digital health solutions targeting diabetes self-care are popular and promising, but important questions remain about how these tools can most effectively help patients. Consistent with evidence of the salutary effects of note-taking in education, features that enable annotation of structured data entry might enhance the meaningfulness of the interaction, thereby promoting persistent use and benefits of a digital health solution. Method: To examine the potential benefits of note-taking, we explored how patients with type 2 diabetes used annotation features of a digital health solution and assessed the relationship between annotation and persistence in engagement as well as improvements in glycated hemoglobin (A1C). Secondary data from 3142 users of the BlueStar digital health solution collected between December 2013 and June 2017 were analyzed, with a subgroup of 372 reporting A1C lab values. Results: About a third of patients recorded annotations while using the platform. Annotation themes largely reflected self-management behaviors (diet, physical activity, medication adherence) and well-being (mood, health status). Early use of contextual annotations was associated with greater engagement over time and with greater improvements in A1C. Conclusions: Our research provides preliminary evidence of the benefits of annotation features in a digital health solution. Future research is needed to assess the causal impact of note-taking and the moderating role of thematic content reflected in notes.
Gallium maltolate has in vitro antiviral activity against SARS-CoV-2 and is a potential treatment for COVID-19
Authors: Bernstein Lawrence R , Zhang Leike 
Published: December 2020

Background Gallium has demonstrated strong anti-inflammatory activity in numerous animal studies, and has also demonstrated direct antiviral activity against the influenza A H1N1 virus and the human immunodeficiency virus (HIV). Gallium maltolate (GaM), a small metal-organic coordination complex, has been tested in several Phase 1 clinical trials, in which no dose-limiting or other serious toxicity was reported, even at high daily oral doses for several months at a time. For these reasons, GaM may be considered a potential candidate to treat coronavirus disease 2019 (COVID-19), which is caused by the SARS-CoV-2 virus and can result in severe, sometimes lethal, inflammatory reactions. In this study, we assessed the ability of GaM to inhibit the replication of SARS-CoV-2 in a culture of Vero E6 cells. Methods The efficacy of GaM in inhibiting the replication of SARS-CoV-2 was determined in a screening assay using cultured Vero E6 cells. The cytotoxicity of GaM in uninfected cells was determined using the Cell Counting Kit-8 (CCK-8) colorimetric assay. Results The results showed that GaM inhibits viral replication in a dose-dependent manner, with the concentration that inhibits replication by 50% (EC50) being about 14 µM. No cytotoxicity was observed at concentrations up to at least 200 µM. Conclusion The in vitro activity of GaM against SARS-CoV-2, together with GaM’s known anti-inflammatory activity, provide justification for testing GaM in COVID-19 patients.
Cardiac Safety of Osimertinib: A Review of Data
Authors: Ewer Michael S. , Tekumalla Sri Harsha , Walding Andrew , Atuah Kwame N. 
Published: December 2020

PURPOSE Osimertinib is a third-generation, CNS-active, irreversible, oral epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) that potently and selectively inhibits both EGFR-TKI-sensitizing and T790M resistance mutations. We assess the cardiac failure risk in patients receiving osimertinib by evaluating the available data. METHODS Post hoc analyses of cardiac data from (1) studies in patients with advanced non–small-cell lung cancer, FLAURA (osimertinib, n = 279; comparator EGFR-TKI, n = 277) and AURA3 (osimertinib, n = 279; chemotherapy, n = 140), and (2) a pooled data set of patients treated with osimertinib 80 mg from across the clinical trial program (n = 1,142), including cardiac failure-related adverse events and left ventricular ejection fraction (LVEF) reductions. An LVEF pharmacokinetic or pharmacodynamic analysis of the pooled data set was performed. The sponsor's global safety database was analyzed for cardiac failure-related adverse events, and a literature search was conducted. RESULTS Decreases in LVEF from a baseline of ≥ 10 percentage points to an absolute value of < 50% following osimertinib treatment were observed in 8 (3.1%) and 14 (5.5%) patients in FLAURA and AURA3, respectively, and in 35 (3.9%) patients in the pooled population. Most events were asymptomatic and resolved without treatment of the event or osimertinib discontinuation. The pharmacokinetic or pharmacodynamic analysis did not indicate a relationship between exposure to osimertinib and decreases in LVEF from baseline. The database and literature search showed no specific trend or pattern that was suggestive of a safety issue in patients receiving osimertinib. CONCLUSION These data do not suggest a causal relationship between osimertinib and cardiac failure. However, because of LVEF decreases that were observed in patients with cardiac risk factors before osimertinib treatment, cardiac monitoring, including an assessment of LVEF at baseline and during osimertinib treatment, is advised.
Freezing-damped impact of a water drop
Authors: Thiévenaz Virgile , Séon Thomas , Josserand Christophe 
Published: December 2020

Abstract
We experimentally investigate the effect of freezing on the spreading of a water drop. Whenever a water drop impacts a cold surface, whose temperature is lower than 0 °C, a thin layer of ice grows during the spreading. This freezing has a notable effect on the impact: at given Reynolds and Weber numbers, we show that lowering the surface temperature reduces the drop maximal extent. Using an analogy between this ice layer and the viscous boundary layer, which also grows during the spreading, we are able to model the effect of freezing as an effective viscosity. The scaling laws designed for viscous drop impact can therefore be applied to such a solidification problem, avoiding the recourse to a full and complex modelling of the thermal dynamics.
A glucose-starvation response governs endocytic trafficking and eisosomal retention of surface cargoes in budding yeast
Authors: Laidlaw Kamilla M. E. , Bisinski Daniel D. , Shashkova Sviatlana , Paine Katherine M. , Veillon Malaury A. , Leake Mark C. , MacDonald Chris 
Published: December 2020

ABSTRACT
Eukaryotic cells adapt their metabolism to the extracellular environment. Downregulation of surface cargo proteins in response to nutrient stress reduces the burden of anabolic processes whilst elevating catabolic production in the lysosome. We show that glucose starvation in yeast triggers a transcriptional response that increases internalisation from the plasma membrane. Nuclear export of the Mig1 transcriptional repressor in response to glucose starvation increases levels of the Yap1801 and Yap1802 clathrin adaptors, which is sufficient to increase cargo internalisation. Beyond this, we show that glucose starvation results in Mig1-independent transcriptional upregulation of various eisosomal factors. These factors serve to sequester a portion of nutrient transporters at existing eisosomes, through the presence of Ygr130c and biochemical and biophysical changes in Pil1, allowing cells to persist throughout the starvation period and maximise nutrient uptake upon return to replete conditions. This provides a physiological benefit for cells to rapidly recover from glucose starvation. Collectively, this remodelling of the surface protein landscape during glucose starvation calibrates metabolism to available nutrients.
This article has an associated First Person interview with the first author of the paper.
Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey
Authors: Pauwels Ruben , Del Rey Yumi Chokyu 
Published: December 2020

Objectives: The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude. Methods: A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated. Results: Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased. Conclusions: A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.
Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering
Authors: Dong Zhenfen , Men Yuheng , Li Zhengming , Liu Zhenzhen , Ji Jianwei 
Published: December 2020

HighlightsChlorophyll fluorescence imaging can be used to evaluate chilling injury.Chilling injury area heterogeneity in the L*a*b* color space is significant.Improved k-means++ clustering has a good segmentation effect on chilling injury.Abstract. The application of fluorescence imaging in the detection of tomato chilling injury was investigated. With the segmentation of the chilling injury area serving as the experimental target, an algorithm based on chlorophyll fluorescence image analysis and improved k-means++ clustering was proposed. First, the extraction of lateral heterogeneity values algorithm was used to analyze the horizontal heterogeneity in five color spaces of the fluorescence images of tomato seedling leaves, and it was found that the chilling injury area was significant in the L*a*b* color space. Second, the fluorescence image was converted from the RGB color space to the L*a*b* color space, and the k-means++ algorithm was used to cluster the two-dimensional data of the a*b* space. Third, insertion sorting was used to reorder the different label regions obtained by the k-means++ clustering algorithm, and the region with the largest value was used as the target region. Finally, the binary image of the target region was filtered using a morphological noise filter, and the cold-damaged area was outputted by the mask operation. The results showed that the cold-damaged area was well segmented when the fluorescence imaging contained yellow cold traces. The mean match rate of the proposed algorithm was 37.08%, 13.52%, and 0.96% higher than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. Similarly, the mean error rate was 13.69%, 5.56%, and 0.16% lower than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. These findings provide a foundation for research on early warning of chilling injury by identifying the chilling injury status of tomato leaves using a computer vision method. Keywords: Chlorophyll fluorescence, Fluorescence image, Image segmentation, k-Means++.
Predicting Quality and Yield of Growing Alfalfa from a UAV
Authors: Dvorak Joseph S. , Pampolini L. Felipe , Jackson Josh J. , Seyyedhasani Hassan , Sama Michael P. , Goff Ben 
Published: December 2020

HighlightsThe canopy height distributions from photogrammetry were mostly Gaussian distributions.The mean canopy height and standard deviation from photogrammetry can be used to predict yield and nutritive values.Including average field maturity and pest pressures improves the models to R2 values of around 0.8.The best predictive model types were generally Gaussian random processes.Abstract. Alfalfa producers would be able to manage their crop production practices better if they knew the distribution of yield and nutritive values of the alfalfa growing throughout their fields. Unmanned aerial vehicles (UAVs) equipped with cameras and photogrammetry techniques provide methods to quickly capture the plant canopy structure at field scale. The goal of this study was to determine how to use the point clouds produced by the photogrammetry process to estimate the yield and nutritive value of alfalfa throughout its growth cycle. During the 2017 growing season, weekly measurements were taken of 1 m2 quadrats (~20 per week, 325 total) in a field of alfalfa managed for forage production. Measurements in each quadrat included manual measurements of maximum and average height, weed presence, disease damage, insect damage, maturity level, stand plant density, and many images of the quadrat from a UAV. After processing to remove outliers, the canopy heights from the photogrammetry point clouds were largely Gaussian distributions. Models were developed using supervised machine learning to estimate yield and nutritive values, including acid detergent fiber (ADF), neutral detergent fiber (NDF), and crude protein (CP), with different numbers of predictor (input) variables. Simple models with two predictor variables were only based on the mean and standard deviation of the heights of the photogrammetry point cloud. The models with three predictor variables added average field maturity level. Finally, the models with six predictor variables added weed presence, insect damage, and disease damage. A linear regression with all interaction terms was found to be the best type of model for predicting yield with six variables. For all other outputs and numbers of predictor variables, a Gaussian random process (GRP) model was best. The models improved with additional predictor variables, so the six-variable models were best able to predict yield and nutritive value. The R2 values for the six-variable models for predicting yield, ADF, NDF, and CP were 0.81, 0.81, 0.78, and 0.79, respectively. Keywords: Alfalfa, Machine learning, Nutritive value, Photogrammetry, Unmanned aerial vehicle, Yield.
“WHAT’S FOREIGN IS BETTER”: A FUZZY AHP ANALYSIS TO EVALUATE FACTORS THAT INFLUENCE FOREIGN PRODUCT CHOICE AMONG INDIAN CONSUMERS
Authors: Lohan Amanish , Ganguly Anirban , Kumar Chitresh 
Published: December 2020

Emerging economies across the globe have observed drastic changes in consumer purchasing behavior in the last three decades. This has been attributed to rapid globalization, coupled with technological advancement that has led to consumer affinity towards foreign brands. In this paper an attempt has been made to understand this affinity towards foreign brands across product categories. We used the Fuzzy Analytic Hierarchy Process to evaluate the factors that influence foreign product purchasing behavior among Indian consumers. A set of five important influencing factors were identified and responses from over 100 Indian consumers were collected across five product categories (automobiles, home appliances, apparel, cosmetics and watches). It was found that for apparel, watches and cosmetics, perceived quality of the brand/product was the most valued attribute. Whereas, for home appliances the country of origin mattered the most, and for watches perceived quality as well as country of manufacture were equally important. The findings of this study will help shape marketing strategies for foreign brands trying to make inroads or gain market share in emerging markets like India
Low-loss, centimeter-scale plasmonic metasurface for ultrafast optoelectronics
Authors: Traverso Andrew J. , Huang Jiani , Peyronel Thibault , Yang Guoce , Tiecke Tobias G. , Mikkelsen Maiken H. 
Published: December 2020

Plasmonics can dramatically improve the radiative properties of fluorescent materials by precisely tailoring the local density of states, but has largely been dismissed for practical optoelectronic applications due to losses and lack of scalability to macroscopic areas. Here, we demonstrate a low-loss plasmonic metasurface that can collect fast-modulated light with a 3 dB bandwidth exceeding 14 GHz and a 120º acceptance angle and convert it to a directional source with, to the best of our knowledge, a record-high overall efficiency of

∼

30

%

. This large-area metasurface composed of fluorescent dye coupled to nanopatch antennas, exhibits a 910-fold increase in the overall fluorescence and a 133-fold emission rate enhancement—values previously only observable for isolated, highly optimized single nanostructures. Critical for future applications ranging from optoelectronics to biosensing, this metasurface was created over macroscopic areas with scalable techniques and the performance was validated over centimeter-scale regions. In particular, we believe this approach shows promise for the burgeoning field of visible/near-infrared wireless communications, where radical new designs and materials are needed for ultrafast, efficient, omnidirectional detectors and incoherent sources.
Best-Practices für die Gestaltung von IT-Service-Katalogen und den Einsatz von Self-Service-Portalen
Authors: Floerecke Sebastian 
Published: December 2020

ZusammenfassungDie Gestaltung von IT-Service-Katalogen stellt Unternehmen unterschiedlichster Branchen vielfach vor eine große Herausforderung. Denn ihnen mangelt es an bewährten, hinsichtlich ihrer Eignung evaluierten Vorlagen. Selbst Empfehlungen allgemeiner Art sind in der wissenschaftlichen Literatur nur vereinzelt zu finden. Vorliegender Beitrag greift diese Problemstellung auf und liefert auf Basis einer Einzelfallstudie innerhalb eines Großunternehmens unter Einbeziehung der vorhandenen Literatur – Forschungsarbeiten, Erfahrungsberichte aus der Praxis und Webauftritte von Tool-Herstellern – eine Zusammenstellung von Best-Practices, welche die Neuerstellung und Überarbeitung von IT-Service-Katalogen leiten können. Besonderes Augenmerk liegt auf dem in der Forschung bislang weitgehend außer Acht gelassenen Zusammenspiel mit Self-Service-Portalen. Damit können sich interne und externe Kunden über die angebotenen IT-Services informieren und verschiedenste Arten von Service-Requests wie Neubestellungen, Änderungen und Kündigungen initiieren. Unternehmensentscheider und IT-Verantwortliche erhalten mit diesem Beitrag eine Aufstellung an Kriterien und Anforderungen, die bei der Auswahl, der prozessualen und systemischen Integration sowie dem Customizing eines Self-Service-Portals von besonderer Bedeutung sind.
Maternal lipid profile and risk of pre-eclampsia in African pregnant women: A systematic review and meta-analysis
Authors: Tesfa Endalamaw , Nibret Endalkachew , Munshea Abaineh 
Published: December 2020

IntroductionSome studies have reported the association between maternal serum lipid profile abnormalities and pre-eclampsia. However, many studies have reported controversial results. Hence, this systematic review and meta-analysis was planned to generate summarized evidence on the association between maternal serum lipid profiles and pre-eclampsia in African women.MethodsFour electronic databases such as; PubMed, Hinari, Google Scholar, and African Journals Online were searched for studies published in English. Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument and Newcastle-Ottawa Scale were used for data extraction and quality assessment of the included studies. The meta- regression analysis was performed by Stata 14 software. The standardized mean difference (SMD) values of lipid profiles were computed to assess their association with pre-eclampsia at 95% CI.ResultsIn this review a total of 15 observational studies were included. The mean values of triglyceride (TG), total cholesterol (TC), low density lipoprotein- cholesterol (LDL-c) and very low density lipoprotein- cholesterol (VLDL-c) were significantly higher in pre-eclamptic women as compared with normotensive pregnant women (TG = 229.61±88.27 and 147.00 ± 40.47, TC = 221.46 ± 45.90 and 189.67 ± 39.18, LDL = 133.92 ± 38.77 and 112.41 ± 36.08, VLDL = 41.44 ± 19.68 and 26.64 ± 7.87), respectively. The serum high density lipoprotein cholesterol (HDL-c) level was lower, but it is not statistically significant (HDL-c = 51.02 ± 16.01 and 61.80 ± 25.63) in pre-eclamptic women as compared with controls. The pooled standardized mean difference (SMD) of TG, TC, LDL-C and VLDL-C were significantly increased in pre-eclamptic women as compared with normotensive pregnant women with the SMD of (TG = 1.65 (1.10, 2.21), TC = 0.84 (0.40, 1.29), LDL-C = 0.95 (0.46, 1.45) and VLDL-C = 1.27 (0.72, 1.81)) at 95% CI, respectively, but the pooled SMD of HDL-cholesterol was decreased in pre-eclamptic women as compared with normotensive pregnant women (SMD = -0.91 (95% CI: -1.43, -0.39).ConclusionsIn this review, the maternal serum levels of TG, TC, LDL-c and VLDL-c were significantly associated with the risk of preeclampsia. However, HDL- cholesterol was not significantly associated but it was lower in pre-eclamptic women. Further, large scale prospective studies should verify these outcomes and it is recommended that lipid profiles should be included as a routine diagnostic test for pre-eclamptic women.