bluuebunny/crossref_metadata_2025_split huggingface dataset:
Immigration/migration and healthy publics: the threat of food insecurity
Authors: Carney Megan A. , Krause Keegan C. 
Published: May 2020

AbstractGlobal climate change and the continued neoliberalization of food systems have exacerbated levels of food insecurity and hunger, producing an ever-expanding population of displaced persons who are also nutritionally vulnerable. Restrictive immigration policies in post-arrival and resettlement contexts compound with other cultural, social, political, and economic conditions to negatively affect the food security and health of displaced persons. This article engages a comparative ethnographic perspective for examining the migration-food security nexus. Drawing on ethnographic research with Mexican and Central American im/migrants in the Western United States, Haitian im/migrants in the Dominican Republic, and African im/migrant populations in Italy, this article analyzes local experiences of food insecurity in restrictive immigration policy contexts through an intersectional lens. Finally, this article examines the possibilities for engaged research oriented toward generating “healthy publics” and addressing food insecurity across disparate geographical and political settings and amid structural and social constraints.
CLIL teachers’ views on cognitive development in primary education
Authors: Campillo-Ferrer José-María , Miralles-Martínez Pedro , Sánchez-Ibáñez Raquel 
Published: May 2020

AbstractContent and language integrated learning (CLIL) has been implemented in Spain for more than two decades, and the number of schools that have joined CLIL initiatives has progressively grown in an attempt to increase students’ proficiency in English; all of these developments are indicative of the importance of foreign language learning (FLL) in this country. The aim of this paper is to analyse the promotion of cognitive skills by teachers in the subjects of science and social science in primary education in the Spanish Region of Murcia. A mixed methodology combining techniques for collecting both quantitative and qualitative data was implemented. The results show a tendency to lapse into low-order thinking skills, which can hinder the development of demanding cognitive skills and creative learning processes in CLIL lessons. Some factors might affect these teaching practices, such as the type of teaching staff position or the experience that teachers have gained over time. The article concludes with some suggestions for further research.
Zimbabwe's 2018 elections: funding, public resources and vote buying
Authors: Ndakaripa Musiwaro 
Published: May 2020

SUMMARY
Using the concept of ‘competitive authoritarianism’, this briefing examines how the governing Zimbabwe African National Union – Patriotic Front (ZANU–PF) retained power in the July 2018 presidential, parliamentary and local government elections. It advances that, having come to power through military assistance in November 2017, the new ZANU–PF government instituted cosmetic political reforms to gain domestic and international legitimacy while maintaining financial networks and tentacles on public institutions. This briefing posits that, with a huge funding base, abuse of public resources and massive vote buying, materially, Zimbabwe's 2018 elections were heavily slanted in favour of ZANU–PF.
Change of impervious surface area and its impacts on urban landscape: an example of Shenyang between 2010 and 2017
Authors: Wu Wen , Li Chunlin , Liu Miao , Hu Yuanman , Xiu Chunliang 
Published: May 2020

ABSTRACT

Introduction
One of the most striking features of urbanization is the replacement of the original natural land cover type by artificial impervious surface area (ISA). However, the extent of the contribution of various environmental factors, especially the growth of 3D space to ISA expansion, and the scope and mechanism of their influences in dramatically expanding cities, are yet to be determined. The boosted regression tree (BRT) model was adopted to analyze the main influencing factors and driving mechanisms of ISA change in Shenyang, China between 2010 and 2017.

Outcomes
The nearly complete-coverage ISA (≥0.7) increased from 42% in 2010 to 47% in 2017. The percentage of landscape with a high ISA fraction increased, while the landscape evenness and diversity of ISA decreased. The BRT analysis revealed that elevation, regional population density, and landscape class had the largest influences on the change of urban ISA, contributing 22.55%, 18.16%, and 11.18% to the model, respectively.

Conclusion
Overall, topographic and socioeconomic factors had the greatest influence on urban ISA change in Shenyang, followed by land use type and building pattern indices. The trend of high aggregation was strong in large commercial and residential areas. The 3D expansion of the city had an influence on its areal expansion.
Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations
Authors: A A Akinsanola , G J Kooperman , A G Pendergrass , W M Hannah , K A Reed 
Published: May 2020

Abstract
Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the ‘standard’ horizontal-resolution can vary significantly across CMIP6 models, from ∼0.7° to ∼2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.
Effect of femtosecond laser power and overlap ratio on surface roughness parameters, contact angle, and tribological properties of the textured SKD 61 tool steel with oil lubrication
Authors: Chang Chang-Shuo , Hou Kung-Hsu , Chung Chen-Kuei , Tsai Tsung-Wen , Lin Jen-Fin 
Published: May 2020

Abstract
This study prepared samples with femtosecond(fs)-laser-induced periodic surface structures (LIPSSs) controlled by laser power (or peak fluence, F0) and overlap ratio (OR) to improve the friction and wear behavior of the SKD 61 tool steel sliding against a hard Si3N4 ceramic counterface in oil lubrications. Pin-on-plate (pin: Si3N4; plate: SKD 61 steel) tribological tests were conducted with the reciprocating motion perpendicular to the laser scanning direction and having an incline angle (40°∼50°) with respect to the LIPSSs. The contact angles (θ) formed on the textured surfaces were measured for an oil lubricant. The (θ)oil results were found to have their correlation with the areal surface roughness (Sa), skewness (Sk), and kurtosis (Ku) of the textured surfaces produced at various F0 and OR values. The mean friction coefficient (μ) and wear loss (W) of the steel specimens in oil lubrications are reduced by decreasing contact angle, (θ)oil. The combined effect of F0 and OR on the (θ)oil and the (θ)oil effect on μ and W are thus linked together, and the correlation provides an efficient and convenient way in the choices of laser operating conditions with the minimum μ and W. The minimum values of μ (=0.0089) and W (=2.124 × 104
μm3) were obtained with OR = 50% and a laser power of 17 mW.
Comparison of material measures for areal surface topography measuring instrument calibration
Authors: Eifler M , Hering J , Seewig J , Leach R K , von Freymann G , Hu X , Dai G 
Published: May 2020

Abstract
The calibration of areal surface topography measuring instruments is a topic that is currently under discussion in international standard committees and a specification standard that defines the so-called ‘metrological characteristics’ has been published. For the broad industrial adoption of the metrological characteristics for calibration, however, clear and easy-to-apply calibration guidelines are required. Thus, a single-sample calibration artefact has been developed which allows the determination of the standardised metrological characteristics. In order to promote the adoption of the metrological characteristics calibration framework, measurements of the material measures with different instruments have been conducted and the results are presented. We report early work on the uncertainty of the comparison results and discuss systematic deviations between the various surface topography measuring instruments.
Scatterometry and diffractometry techniques to monitor surfaces textured by rapid ultra-short pulse laser
Authors: Alleaume C , Alamri S , Kunze T , Ziegler J , Wilson A , Bola R 
Published: May 2020

Abstract
The overall aim of the PROMETHEUS project is to develop high power ultra-short pulse lasers and the associated optics to enable the precise periodic texturing of surfaces to impart a range of surface functionalities at unprecedented processing speeds. The project focusses upon the industrial application of ultra-short pulse lasers for manufacturing and on the development of high power ultra-short pulse laser technologies in the range of 700 ps – 10 ns. In summary, new textures will be manufactured to achieve functional surfaces utilising fewer raw materials, less energy and less waste; improve accuracy, power and control over existing technologies; increase achievable precision and minimize heat impact on sensitive materials. This article will provide the outcomes of the first results achieved from the monitoring solutions being used to assess surface textures with micrometer resolution.
Photoelectron spectroscopy of large water clusters ionized by an XUV comb
Authors: Trabattoni Andrea , Colaizzi Lorenzo , Ban Loren , Wanie Vincent , Saraswathula Krishna , Månsson Erik P , Rupp Philipp , Liu Qingcao , Seiffert Lennart , Herzig Elisabeth A , Cartella Andrea , Yoder Bruce L , Légaré François , Kling Matthias F , Fennel Thomas , Signorell Ruth , Calegari Francesca 
Published: May 2020

Abstract
Detailed knowledge about photo-induced electron dynamics in water is key to the understanding of several biological and chemical mechanisms, in particular for those resulting from ionizing radiation. Here we report a method to obtain photoelectron spectra from neutral water clusters following ionization by an extreme-ultraviolet (XUV) attosecond pulse train, representing a first step towards a time-resolved analysis. Typically, a large background signal in the experiment arises from water monomers and carrier gas used in the cluster source. We report a protocol to quantify this background in order to eliminate it from the experimental spectra. We disentangle the accumulated XUV photoionization signal into contributions from the background species and the photoelectron spectra from the clusters. This proof-of-principle study demonstrates feasibility of background free photoelectron spectra of neutral water clusters ionized by XUV combs and paves the way for the detailed time-resolved analysis of the underlying dynamics.
Kubrick, A.I., and the Problem of Pinocchio: Reassessing the Evidence of A.I.: Artificial Intelligence
Authors: Allen Graham 
Published: May 2020

Abstract
Utilizing the extensive materials available at the Stanley Kubrick Archive, this paper presents a re-evaluation of Kubrick’s part in the creation of Spielberg’s 2001 film, A.I. Artificial Intelligence. I begin by going over the manner in which the film’s contemporary reviewers produced the sense of a film full of tension and unresolvable contradiction, something largely attributed to the radically distinct styles and world-views of Spielberg and his mentor, Kubrick. The paper then goes on to read Kubrick’s work (mainly on treatments of the developing story) with the last two writers he worked with on the project. This return to the narrative work Kubrick and his writers expended on the story, demonstrates that far from being the project of irreconcilable differences between the two writers, Kubrick always faced and never successfully resolved a tension within his story between science and myth, between a narrative of scientific orientation (humanity evolving into machines) and a narrative of fantasy and myth (a machine-boy who becomes human).
Make Mission Impossible Feasible: The Experience of a Multidisciplinary Team Providing Treatment for Alcohol Use Disorder to Homeless Individuals
Authors: Dionisi Tommaso , Mosoni Carolina , Di Sario Giovanna , Tarli Claudia , Antonelli Mariangela , Sestito Luisa , D’Addio Stefano , Tosoni Alberto , Ferrarese Daniele , Iasilli Giovanna , Vassallo Gabriele A , Mirijello Antonio , Gialloreti Leonardo Emberti , Di Giuda Daniela , Gasbarrini Antonio , Addolorato Giovanni 
Published: May 2020

Abstract

Aim
People experiencing homelessness are often excluded from treatment programs for alcohol use disorder (AUD). The goal of this study was to describe the impact of a multidisciplinary treatment program on alcohol consumption and social reintegration in individuals with AUD experiencing homelessness.

Methods
Thirty-one individuals with AUD experiencing homelessness were admitted to an inpatient unit for 5–6 days for clinical evaluation and to treat potential alcohol withdrawal syndrome. A group of volunteers, in collaboration with the Community of Sant’Egidio, provided social support aimed to reintegrate patients. After inpatient discharge, all patients were followed as outpatients. Alcohol intake (number drinks/day), craving and clinical evaluation were assessed at each outpatient visit. Biological markers of alcohol use were evaluated at enrollment (T0), at 6 months (T1) and 12 months (T2).

Results
Compared with T0, patients at T1 showed a significant reduction in alcohol consumption [10 (3–24) vs 2 (0–10); P = 0.015] and in γ-glutamyl-transpeptidase [187 (78–365) vs 98 (74–254); P = 0.0021]. The reduction in alcohol intake was more pronounced in patients with any housing condition [10 (3–20) vs 1 (0–8); P = 0.008]. Similarly, compared with T0, patients at T2 showed significant reduction in alcohol consumption [10 (3–24) vs 0 (0–15); P = 0.001], more pronounced in patients with any housing condition [10 (3–20) vs 0 (0–2); P = 0.006]. Moreover, at T2 patients showed a significant reduction in γ-glutamyl-transpeptidase [187 (78–365) vs 97 (74–189); P = 0.002] and in mean cell volume [100.2 (95–103.6) vs 98.3 (95–102); P = 0.042].

Conclusion
Patients experiencing homelessness may benefit from a multidisciplinary treatment program for AUD. Strategies able to facilitate and support their social reintegration and housing can improve treatment outcomes.
Machine learning-based approaches for disease gene prediction
Authors: Le Duc-Hau 
Published: May 2020

AbstractDisease gene prediction is an essential issue in biomedical research. In the early days, annotation-based approaches were proposed for this problem. With the development of high-throughput technologies, interaction data between genes/proteins have grown quickly and covered almost genome and proteome; thus, network-based methods for the problem become prominent. In parallel, machine learning techniques, which formulate the problem as a classification, have also been proposed. Here, we firstly show a roadmap of the machine learning-based methods for the disease gene prediction. In the beginning, the problem was usually approached using a binary classification, where positive and negative training sample sets are comprised of disease genes and non-disease genes, respectively. The disease genes are ones known to be associated with diseases; meanwhile, non-disease genes were randomly selected from those not yet known to be associated with diseases. However, the later may contain unknown disease genes. To overcome this uncertainty of defining the non-disease genes, more realistic approaches have been proposed for the problem, such as unary and semi-supervised classification. Recently, more advanced methods, including ensemble learning, matrix factorization and deep learning, have been proposed for the problem. Secondly, 12 representative machine learning-based methods for the disease gene prediction were examined and compared in terms of prediction performance and running time. Finally, their advantages, disadvantages, interpretability and trust were also analyzed and discussed.
Discovery of G-quadruplex-forming sequences in SARS-CoV-2
Authors: Ji Danyang , Juhas Mario , Tsang Chi Man , Kwok Chun Kit , Li Yongshu , Zhang Yang 
Published: May 2020

Abstract
The outbreak caused by the novel coronavirus SARS-CoV-2 has been declared a global health emergency. G-quadruplex structures in genomes have long been considered essential for regulating a number of biological processes in a plethora of organisms. We have analyzed and identified 25 four contiguous GG runs (G2NxG2NyG2NzG2) in the SARS-CoV-2 RNA genome, suggesting putative G-quadruplex-forming sequences (PQSs). Detailed analysis of SARS-CoV-2 PQSs revealed their locations in the open reading frames of ORF1 ab, spike (S), ORF3a, membrane (M) and nucleocapsid (N) genes. Identical PQSs were also found in the other members of the Coronaviridae family. The top-ranked PQSs at positions 13385 and 24268 were confirmed to form RNA G-quadruplex structures in vitro by multiple spectroscopic assays. Furthermore, their direct interactions with viral helicase (nsp13) were determined by microscale thermophoresis. Molecular docking model suggests that nsp13 distorts the G-quadruplex structure by allowing the guanine bases to be flipped away from the guanine quartet planes. Targeting viral helicase and G-quadruplex structure represents an attractive approach for potentially inhibiting the SARS-CoV-2 virus.
PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs
Authors: Jia Cangzhi , Bi Yue , Chen Jinxiang , Leier André , Li Fuyi , Song Jiangning 
Published: May 2020

Abstract

Motivation
Different from traditional linear RNAs (containing 5′ and 3′ ends), circular RNAs (circRNAs) are a special type of RNAs that have a closed ring structure. Accumulating evidence has indicated that circRNAs can directly bind proteins and participate in a myriad of different biological processes.

Results
For identifying the interaction of circRNAs with 37 different types of circRNA-binding proteins (RBPs), we develop an ensemble neural network, termed PASSION, which is based on the concatenated artificial neural network (ANN) and hybrid deep neural network frameworks. Specifically, the input of the ANN is the optimal feature subset for each RBP, which has been selected from six types of feature encoding schemes through incremental feature selection and application of the XGBoost algorithm. In turn, the input of the hybrid deep neural network is a stacked codon-based scheme. Benchmarking experiments indicate that the ensemble neural network reaches the average best area under the curve (AUC) of 0.883 across the 37 circRNA datasets when compared with XGBoost, k-nearest neighbor, support vector machine, random forest, logistic regression and Naive Bayes. Moreover, each of the 37 RBP models is extensively tested by performing independent tests, with the varying sequence similarity thresholds of 0.8, 0.7, 0.6 and 0.5, respectively. The corresponding average AUC obtained are 0.883, 0.876, 0.868 and 0.883, respectively, highlighting the effectiveness and robustness of PASSION. Extensive benchmarking experiments demonstrate that PASSION achieves a competitive performance for identifying binding sites between circRNA and RBPs, when compared with several state-of-the-art methods.

Availability and implementation
A user-friendly web server of PASSION is publicly accessible at http://flagship.erc.monash.edu/PASSION/.

Supplementary information
Supplementary data are available at Bioinformatics online.
A morphometric assessment of species boundaries in a widespread anole lizard (Squamata: Dactyloidae)
Authors: Myers Tanner C , de Mello Pietro L H , Glor Richard E 
Published: May 2020

AbstractCryptic species – genetically distinct species that are morphologically difficult to distinguish – present challenges to systematists. Operationally, cryptic species are very difficult to identify and sole use of genetic data or morphological data can fail to recognize evolutionarily isolated lineages. We use morphometric data to test species boundaries hypothesized with genetic data in the North Caribbean bark anole (Anolis distichus), a suspected species complex. We use univariate and multivariate analyses to test if candidate species based on genetic data can be accurately diagnosed. We also test alternative species delimitation scenarios with a model fitting approach that evaluates normal mixture models capable of identifying morphological clusters. Our analyses reject the hypothesis that the candidate species are diagnosable. Neither uni- nor multivariate morphometric data distinguish candidate species. The best-supported model included two morphological clusters; however, these clusters were uneven and did not align with a plausible species divergence scenario. After removing two related traits driving this result, only one cluster was supported. Despite substantial differentiation revealed by genetic data, we recover no new evidence to delimit species and refrain from taxonomic revision. This study highlights the importance of considering other types of data along with molecular data when delimiting species.
Digit identity matters: origin and evolution of sexual dimorphism in the digit lengths of tropidurid lizards
Authors: Lofeu Leandro , Brandt Renata , Kohlsdorf Tiana 
Published: May 2020

AbstractSexual dimorphism in digit lengths reflects phenotypic evolution mediated by developmental steroids. Differences in the identity of the sexually-dimorphic digit may evolve if the concentrations of sex-steroid receptors in the digit are easily modified and the initial changes have low impact on fitness. Accordingly, sexual dimorphism in digit lengths might initially originate under neutral selective regimes, being subsequently co-opted by embryonic hormonal effects on sensitive traits that are more likely to be targeted by selection. Correlated variation among sexually-dimorphic traits might therefore reflect pleiotropic hormonal modulation during development. Moreover, the identity and trend of the sexually-dimorphic digit might be evolutionarily labile even among closely-related species. We evaluated this model by assessing the identity of sexually-dimorphic digits among 11 Tropiduridae lizard species and inferring evolutionary patterns of sexual dimorphism. Assuming that digit lengths can be used as a proxy for hormonal modulation of steroid-sensitive traits during development, we tested for evolutionary associations among sexual dimorphism of digit lengths, body size and other traits that may be direct targets of sexual selection in the Tropiduridae. Sexual dimorphism in digit lengths is evolutionarily labile in the Tropiduridae, and diversity, instead of conservatism, seems to rule developmental mechanisms underlying the evolution of sexual dimorphism in digit ratios.