bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging and legacy PFAS and cytokine homeostasis in women of childbearing age
Authors: Nian Min , Zhou Wei , Feng Yan , Wang Yan , Chen Qian , Zhang Jun 
Published: April 2022

AbstractPer- and polyfluoroalkyl substances (PFAS) are widespread chemicals. Legacy PFAS have been phased out of production in most developed countries and emerging PFAS (short-chain PFAS and polyfluorinated compounds) are used as legacy PFAS alternatives. The effect of legacy and emerging PFAS on cytokine homeostasis in human remains poorly understood. This study aimed to evaluate the associations between legacy and emerging PFAS and cytokine profiles, and identify the main contributors to the disturbance of cytokine homeostasis. We quantified 21 PFAS in 198 Chinese women of childbearing age from 2015 to 2016. 13 cytokines were measured using the Meso Scale Discovery U-PLEX and V-PLEX platforms. The associations between PFAS exposure and cytokine levels were assessed using multiple linear regression (single-exposure), and Bayesian kernel machine regression (BKMR) models (PFAS mixture exposure). In single PFAS models, legacy and alternative PFAS were positively associated with Th1 and Treg cytokines, and negatively associated with Th2 and Th17 cytokines. For instance, each ln-unit increase in 6:2 chlorinated perfluoroalkyl ether sulfonic acid (6:2 Cl-PFESA), perfluorooctanoic acid (PFOA), and perfluorooctane sulfonate (PFOS) was associated with a decrease in IL-10 by − 0.228 (95% CI: − 0.336, − 0.120), − 0.153 (95% CI: − 0.277, − 0.030), and − 0.174 (95% CI: − 0.339, − 0.010), respectively. The BKMR model showed a significantly positive association of PFAS mixture with TGF-β and a negative association with IL-10. Overall, these results indicate that both legacy and emerging PFAS may affect the homeostasis of cytokines.
Pan-cancer prognostic genetic mutations and clinicopathological factors associated with survival outcomes: a systematic review
Authors: Gammall Jurgita , Lai Alvina G. 
Published: April 2022

AbstractCancer is a leading cause of death, accounting for almost 10 million deaths annually worldwide. Personalised therapies harnessing genetic and clinical information may improve survival outcomes and reduce the side effects of treatments. The aim of this study is to appraise published evidence on clinicopathological factors and genetic mutations (single nucleotide polymorphisms [SNPs]) associated with prognosis across 11 cancer types: lung, colorectal, breast, prostate, melanoma, renal, glioma, bladder, leukaemia, endometrial, ovarian. A systematic literature search of PubMed/MEDLINE and Europe PMC was conducted from database inception to July 1, 2021. 2497 publications from PubMed/MEDLINE and 288 preprints from Europe PMC were included. Subsequent reference and citation search was conducted and a further 39 articles added. 2824 articles were reviewed by title/abstract and 247 articles were selected for systematic review. Majority of the articles were retrospective cohort studies focusing on one cancer type, 8 articles were on pan-cancer level and 6 articles were reviews. Studies analysing clinicopathological factors included 908,567 patients and identified 238 factors, including age, gender, stage, grade, size, site, subtype, invasion, lymph nodes. Genetic studies included 210,802 patients and identified 440 gene mutations associated with cancer survival, including genes TP53, BRCA1, BRCA2, BRAF, KRAS, BIRC5. We generated a comprehensive knowledge base of biomarkers that can be used to tailor treatment according to patients’ unique genetic and clinical characteristics. Our pan-cancer investigation uncovers the biomarker landscape and their combined influence that may help guide health practitioners and researchers across the continuum of cancer care from drug development to long-term survivorship.
Machine learning approaches to predicting no-shows in pediatric medical appointment
Authors: Liu Dianbo , Shin Won-Yong , Sprecher Eli , Conroy Kathleen , Santiago Omar , Wachtel Gal , Santillana Mauricio 
Published: April 2022

AbstractPatients’ no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients’ health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients’ care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83% of no-shows at the time of scheduling and led to a false alert rate less than 17%. Our method is capable of producing meaningful predictions even when some information in a patient’s records is missing. We find that patients’ past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.
Association between residential greenspace structures and frailty in a cohort of older Chinese adults
Authors: He Qile , Chang Hao-Ting , Wu Chih-da , Ji John S. 
Published: April 2022

AbstractBackgroundFrailty is a late-life clinical syndrome resulting from the accumulation of aging-induced decline. Greenspaces measured with normalized difference vegetation index (NDVI) are protective of frailty. However, NDVI is not as informative as structure indices in describing greenspaces’ constitution, shape, and connectivity measured by the largest patch index (LPI), shape index, and cohesion index representing larger, more complex, and more dense greenspaces through higher values. We aim to study the association between greenness structures and frailty in a cohort of Chinese older adults.MethodsWe included older adults from 2008–2014 China Longitudinal Healthy Longevity Survey (CLHLS). We used greenspace indices from satellite to quantify structures (area-edge, shape, proximity) at county-level, and calculated frailty index (FI) as an outcome. We did cross-sectional analyses using linear and logistical regression, and longitudinal analyses using the generalized estimating equations (GEE).ResultsAmong 8776 baseline participants, mean LPI, shape, cohesion, and FI are 7.93, 8.11, 97.6, and 0.17. In cross-sectional analyses, we find negative dose-response relationships for greenspace structures and frailty, especially in females, centenarians, illiterate people, city residents, unmarried people, and individuals with increased frailty. Participants living in the highest quartile of LPI, shape, and cohesion have 32% (95%CI: 21–42%), 35% (95%CI: 24–44%), and 37% (95%CI: 26%–46%) lower odds of frailty than the lowest quartile. However, we do not find a significant association in longitudinal analyses.ConclusionsHigher levels of greenness structures (area-edge, shape, and proximity) might be related to lower frailty, while a clear longitudinal benefit cannot be identified in this analysis.
MicroRNA-21 as a diagnostic and prognostic biomarker of lung cancer: a systematic review and meta-analysis
Authors: Wang Wei , Li Xinyao , Liu Chengfei , Zhang Xin , Wu Ying , Diao Mingxin , Tan Siyu , Huang Shubin , Cheng Yin , You Tao 
Published: April 2022

Abstract
Background: The relationship between microRNA-21 (miRNA-21) and pathogenesis of lung cancer is a considerable focus of research interest. However, to our knowledge, no in-depth meta-analyses based on existing evidence to ascertain the value of miRNA-21 in diagnosis and clinical prognosis of lung cancer have been documented.
Methods: We comprehensively searched all the literature pertaining to ‘miRNA-21’ and ‘lung cancer’ from four databases from the period of inception of each database until May 2020. Using specific inclusion and exclusion criteria, the literature for inclusion was identified and the necessary data extracted.
Results: In total, 46 articles were included in the meta-analysis, among which 31 focused on diagnostic value and 15 on prognostic value. Combined sensitivity (SEN) of miRNA-21 in diagnosis of lung cancer was 0.77 (95% confidence interval (CI): 0.72–0.81), specificity (SPE) was 0.86 (95% CI: 0.80–0.90), diagnostic odds ratio (DOR) was (95% CI: 12–33), and area under the SROC curve (AUC) was 0.87 (95% CI: 0.84–0.90). No significant correlations were observed between abnormal expression of miRNA-21 and gender, smoking habits, pathological type and clinical stage of lung cancer (P&gt;0.05). In terms of overall survival (OS), univariate analysis (hazards ratio (HR) = 1.49, 95% CI: 1.22–1.82) revealed high expression of miRNA-21 as an influencing factor for lung cancer. MiRNA-21 was confirmed as an independent risk factor for poor prognosis in multivariate analysis (HR = 1.65, 95% CI: 1.24–2.19).
Conclusion: MiRNA-21 has potential clinical value in the diagnosis and prognosis of lung cancer and may serve as an effective diagnostic marker and therapeutic target in the future.
A novel visual classification framework on panoramic attention mechanism network
Authors: Li Wenshu , Li Shenhao , Yin Lingzhi , Guo Xiaoying , Yang Xu 
Published: April 2022

AbstractFine‐grained classification is a challenging task due to the difficulty of finding discriminative features and the localization of feature regions. To handle these challenges, a novel visual classification framework on panoramic attention mechanism that combines multiple attention networks to locate and identify features with more semantic interest is proposed. Firstly, based on the classical convolutional neural network, the global information of the image feature is expressed by linear fusion. Secondly, the foreground attention branch is used to further extract the distinguishing details of the salient features. Then, more features are mined from the complementary object area through the background attention branch to learn more perfect fine‐grained feature expression. Finally, three network branches are trained together to enhance the network's ability to express representative features of fine‐grained images. Our model can be viewed as a multi‐branch network, which benefits each other and optimizes the network together. Experiments were conducted on CUB‐200‐2011, Stanford Dogs and FGVC‐Aircraft datasets, and the accuracy was used as the quantitative measurement. Experimental results show that the proposed method has the highest accuracy; the average accuracy is 89.8%. It is effective and superior to the current advanced methods.
A new fast estimating floor region based on image segmentation for smart rovers
Authors: Ahamad Afaroj , Sun Chi‐Chia , Yang Nian‐Jyun , Kuo Wen‐Kai 
Published: April 2022

AbstractIn this paper, a new algorithm that hardware acceleration has enabled to estimate floor regions in a single image for unmanned ground vehicle (UGV) robots is presented. In general, the cluttered indoor surroundings patterned floors, shadows, and reflections make it extremely difficult to identify floor regions. The proposed algorithm combines extracting surface texture characteristics with specific geometric areas to determine object boundaries and then uses SVM classification to distinguish between the floor and non‐floor regions. To achieve real‐time performance, the implementation of the proposed algorithm on an SoC FPGA embedded platform gives a heterogeneous hardware acceleration methodology. In experimental results, a public MIT scene dataset and indoor database were used to verify detection accuracy. Compared to other research, the proposed algorithm accuracy can reach up to 94.72% on average without the assistance of any other physical sensors, such as RGB‐D or laser ranger sensors.
Gewichtszunahme bei Kindern und Jugendlichen während der Covid-19-Pandemie
Authors: Vogel Mandy , Geserick Mandy , Gausche Ruth , Beger Christoph , Poulain Tanja , Meigen Christoph , Körner Antje , Sobek Carolin , Keller Eberhard , Pfäffle Roland , Kiess Wieland 
Published: April 2022

ZUSAMMENFASSUNGEs wurde viel darüber spekuliert, ob durch Schulschließungen, eingeschränkte soziale Kontakte und den Wegfall sportlicher Aktivitäten während der Covid-19-Pandemie die Adipositasepidemie im Kindesalter verstärkt werden würde. Deshalb haben wir die BMI-Verläufe während der 15 Jahre vor und während der Covid-19-Pandemie untersucht. Der Trend der BMI-Veränderungen (als delta-BMI-SDS) und die Anteile der Kinder, die Gewicht zunahmen bzw. verloren, zwischen 2005 und 2019 sowie entsprechend Daten von 2019 vor der Pandemie und von 2020 nach dem Einsetzen der ersten Pandemiemaßnahmen wurden bei mehr als 150 000 Kindern (ca. 10 000 in der pandemischen Phase) verglichen. Während der Covid-19-Pandemie fand sich eine substanzielle Gewichtszunahme über alle Gewichtsklassen und Altersstufen hinweg. Die Änderung des mittleren BMI-SDS war wesentlich höher als in den Jahren zuvor. Ebenso stieg der Anteil der Kinder, die Gewicht zunahmen. Der Anteil der Kinder, die Gewicht über den Zeitraum verloren, nahm hingegen ab. Außerdem fanden wir ähnliche Trends, der zwar auf eine wesentlich schwächere, aber doch stetige Gewichtszunahme hinweisen, bereits seit 2005. Es ist alarmierend, dass sowohl der langfristige Trend als auch die kurzfristigen, pandemie-bezogenen Effekte bei Kindern, die bereits übergewichtig oder adipös waren, am größten waren.Die Trends, die wir in mehreren Parametern der Gewichtsveränderung über einen Zeitraum von mehr als 15 Jahren beobachten konnten, weisen auf eine Zunahme des BMI-SDS. Besonders bei adipösen Kindern ist diese Dynamik ausgeprägt. Covid-19-bezogene Maßnahmen verstärken die Effekte und könnten damit die Adipositasepidemie im Kindesalter weiter eskalieren.
The evolution of fixed-supply and variable-supply currencies
Authors: Wang Guizhou , Hausken Kjell 
Published: April 2022

AbstractCompetition is analyzed between a fixed-supply currency (e.g. Bitcoin) and a variable-supply currency (e.g. a fiat currency). Two kinds of players support the currencies differently and choose their volume fractions of transactions in each currency. The variable-supply currency enables money printing/withdrawal and inflation/deflation, which counteract each other in each player’s utility. The exponentially increasing 1959–2021 US M2 money supply and the positive inflation cause this utility to increase over time with high weight assigned to money printing/withdrawal, and decrease otherwise. Three replicator equations determine each player’s volume fraction of transactions in each currency, and which kind of player each player prefers to be. High weight assigned to money supply relative to inflation induces players to prefer the variable-supply currency. A player’s utility of transacting in each currency is proportional to the player’s support of that currency, the volume fraction of all players’ transactions in that currency, and the fraction of players of the same kind as the given player. A player’s utility of transacting in the variable-supply currency is additionally proportional to two ratios. The first is the initial money supply plus the accumulative money printing/withdrawal divided by the initial money supply. The second is the inverse of the accumulative inflation/deflation. The players’ fractions of transactions in each currency may be inverse U shaped or U shaped before typically converging towards preferring one or the other currency. If each player can choose which kind of player to be, it may choose to be the kind with the highest support of a given currency. If a player’s utility of transacting in a given currency depends more on the fraction of players being of one kind than the other kind, the player prefers to be of the first kind, thus assigning less weight to its support of that currency and the volume fractions of transactions in that currency.
Exploring local meaning-making resources
Authors: Jiang Yaqian , Vásquez Camilla 
Published: April 2022

Abstract
This study examines various combinations of visual and textual meaningmaking resources in a popular Chinese meme. The meme features an exogenous image – the grinning facial expression of a U.S. wrestler, D’Angelo Dinero – that has been recontextualized into numerous other visual texts, to create semiotic ensembles with local meanings, which are then distributed across Chinese social media platforms. We analyzed 60 of these image macros, and our findings show that local meanings are created when Dinero’s facial expression is blended with visual references to Chinese digital culture, Chinese popular culture, Chinese social class issues, Chinese politics, and Chinese institutions. The majority of textual elements in the image macros are Chinese; however, the handful of examples that also include other languages typically involve multilingual wordplay and carnivalesque themes. We argue that although the multivalency of the wrestlers’ facial expression invites interpretations of a wide range of affective meanings, an overarching rebellious or transgressive stance is consistent across individual texts.
Internet memes as multilayered re‑contextualization vehicles in lay-political online
discourse
Authors: Kirner-Ludwig Monika 
Published: April 2022

Abstract
It is well established that the internet meme has come to represent a highly creative discursive device used to “facilitate the […] communication of one’s own political beliefs, attitudes and orientations” (Ross and Rivers 2017: 1). Although internet memes and political internet memes in particular have been addressed to many communicative situations such as participatory culture (e.g., Jenkins 2006; Shifman 2014; Theocharis 2015), one aspect that has not been paid enough attention to concerns the forms in which users refer to individual political figures and events in political memes. This being said, the present paper focuses on referring strategies (see Kirner-Ludwig and Zimmermann 2015; Kirner-Ludwig 2020) as employed in political internet memes on Reddit, including direct and indirect quotes, citations and allusions. A specific focus is going to be on such political internet memes that employ pop-cultural and telecinematic reference points and recontextualize them from their original into new target contexts (see Bublitz 2015; Gruber 2019). As shall be shown, practices such as combining constructed speech elements into recontextualized elements in political internet memes create multiple intertextual references that may enhance visibility, saliency and, thus, the ‘lifetime’ of a political meme.
Weaving gender into sustainable intensification interventions.
Authors: Fischer Gundula 
Published: April 2022

Abstract

Sustainable intensification (SI) is understood as increasing productivity without causing harm to the environment. SI can be achieved by introducing more or different inputs (e.g., new knowledge and skills, labor, chemicals, and machinery); a change to higher-yielding crops or varieties, and more productive livestock breeds; a conversion to more productive farming systems (e.g., through irrigation); or a combination of these. This chapter introduces gender concepts in agricultural development and discusses the ways in which gender concerns can be woven into SI interventions to produce more equitable outcomes. Referring to Africa RISING experiences, the chapter describes activities that can enhance women's participation in SI, measure the benefits, and transform gender relations.
Taking technologies to a greater scale.
Authors: Sseguya Haroon , Chikowo Regis , Chimonyo Vimbayi , Chipungu Felistus , Groot Jeroen , Muthoni Francis , Ngulu Festo , Thierfelder Christian 
Published: April 2022

Abstract

This chapter presents a definition of 'scaling' and outlines the key elements for success based on the experiences gained from Africa RISING research and dissemination in East and Southern Africa (ESA). Three examples are presented: (a) research and development partnerships; (b) community based scaling through seed systems; and (c) outdoor advertising for orange-fleshed sweet potato. It presents experiences and lessons learned from using these approaches to transfer and scale the technologies.
Galaxy Spectra Neural Networks (GaSNets). I. Searching for Strong Lens Candidates in eBOSS Spectra Using Deep Learning
Authors: Zhong Fucheng , Li Rui , Napolitano Nicola R. 
Published: April 2022

Abstract
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized in identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra. We first discuss the training and testing of these networks and define a threshold probability, P

L
, of 95% for the high-quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with the Hubble Space Telescope (HST), we estimate a completeness of ∼80% as the fraction of lenses recovered above the adopted P

L
. We finally apply the GaSNets to ∼1.3M eBOSS spectra to collect the first list of ∼430 new high-quality candidates identified with deep learning from spectroscopy and visually graded as highly probable real events. A preliminary check against ground-based observations tentatively shows that this sample has a confirmation rate of 38%, in line with previous samples selected with standard (no deep learning) classification tools and confirmed by the HST. This first test shows that machine learning can be efficiently extended to feature recognition in the wavelength space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and the China Space Station Telescope.