bluuebunny/crossref_metadata_2025_split huggingface dataset:
Christian Conversion and Colonial “Native Policy”: The Role of Missionaries in Formulating Reservation Policy in German Southwest Africa
Authors: Morgan Stephen 
Published: February 2021

AbstractThis article examines how German Protestant missionaries to the Herero people influenced colonial “native policy” in German Southwest Africa in the years leading up to the Colonial War of 1904 to 1907. By the late 1890s, burgeoning European settlement increasingly displaced the Herero from their traditional territory. While colonial officials promoted more settlement, missionaries had developed a concept of conversion that linked Christianization with living in self-sufficient agricultural communities, and hoped to place limits on Herero displacement. Thus, missionaries and colonial officials engaged in protracted political negotiations over the creation of inalienable “native reservations” for the Herero. I show that missionaries’ model of Herero conversion prompted them to promote an alternative mode of settler colonialism that would make room in Southwest Africa for self-sufficient Herero settlements. Prior to the Colonial War, missionaries succeeded in convincing the colonial government to begin creating reservations, thus shaping colonial policy according to missionary priorities.
Excess mortality in patients with schizophrenia spectrum disorders in Malaga (Spain): A cohort study
Authors: Moreno-Küstner Berta , Guzman-Parra Jose , Pardo Yolanda , Sanchidrián Yolanda , Díaz-Ruiz Sebastián , Mayoral-Cleries Fermin 
Published: February 2021

Abstract

Aims
There is evidence that patients with schizophrenia spectrum disorders present higher mortality in comparison with the general population. The aim of this study was to analyse the causes of mortality and sociodemographic factors associated with mortality, standardised mortality ratios (SMRs), life expectancy and potential years of life lost (YLL) in patients with schizophrenia spectrum disorders in Spain.

Methods
The study included a cohort of patients from the Malaga Schizophrenia Case Register (1418 patients; 907 males; average age 42.31 years) who were followed up for a minimum of 10 years (median = 13.43). The factors associated with mortality were analysed with a survival analysis using Cox's proportional hazards regression model.

Results
The main causes of mortality in the cohort were circulatory disease (21.45%), cancer (17.09%) and suicide (13.09%). The SMR of the cohort was more than threefold that of the population of Malaga (3.19). The life expectancy at birth was 67.11 years old, which is more than 13 years shorter than that of the population of Malaga. The YLL was 20.74. The variables associated with a higher risk of mortality were age [adjusted hazard ratio (AHR) = 1.069, p < 0.001], male gender (AHR = 1.751, p < 0.001) and type of area of residence (p = 0.028; deprived urban zone v. non-deprived urban area, AHR = 1.460, p = 0.028). In addition, receiving welfare benefit status in comparison with employed status (AHR = 1.940, p = 0.008) was associated with increased mortality.

Conclusions
There is excess mortality in patients with schizophrenia spectrum disorders and also an association with age, gender, socioeconomic inequalities and receiving welfare benefits. Efforts directed towards improved living conditions could have a positive effect on reducing mortality.
Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother
Authors: Kang Xueyuan , Kokkinaki Amalia , Kitanidis Peter K. , Shi Xiaoqing , Lee Jonghyun , Mo Shaoxing , Wu Jichun 
Published: February 2021

AbstractDetailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited information. With limited data, the estimation accuracy of traditional geostatistical methods is strongly affected by the parameterization of the prior description of the SZA. To improve characterization performance, we parameterized the DNAPL saturation field using a physics‐based approach. We trained a convolutional variational autoencoder (CVAE) using data from multiphase modeling that captures the physics of DNAPL infiltration. The trained CVAE network was used in SZA inversion to obtain an improved prior DNAPL saturation field, instead of the typical stationary prior covariances. We then integrated the CVAE network into an iterative ensemble smoother (ES), to formulate a joint inversion framework. To overcome difficulties from limited/sparse data, we incorporated hydrogeological and geophysical datasets in the proposed inversion framework. To evaluate the performance of our method, we conducted numerical experiments in a hypothetical heterogeneous aquifer with an intricate SZA. The results show that the CVAE was an effective and efficient parameterization method which can capture the DNAPL infiltration patterns better than a Gaussian prior. The improved prior, combined with multisource datasets, can result in better resolution, and overall improved SZA characterization. In contrast to the standard ES method, the proposed framework reconstructed the SZA more accurately. We also demonstrated that DNAPL depletion behavior and dissolved concentration profiles can be predicted accurately using the estimated SZA.
Variable-order fracture mechanics and its application to dynamic fracture
Authors: Patnaik Sansit , Semperlotti Fabio 
Published: February 2021

AbstractThis study presents the formulation, the numerical solution, and the validation of a theoretical framework based on the concept of variable-order mechanics and capable of modeling dynamic fracture in brittle and quasi-brittle solids. More specifically, the reformulation of the elastodynamic problem via variable and fractional-order operators enables a unique and extremely powerful approach to model nucleation and propagation of cracks in solids under dynamic loading. The resulting dynamic fracture formulation is fully evolutionary, hence enabling the analysis of complex crack patterns without requiring any a priori assumption on the damage location and the growth path, and without using any algorithm to numerically track the evolving crack surface. The evolutionary nature of the variable-order formalism also prevents the need for additional partial differential equations to predict the evolution of the damage field, hence suggesting a conspicuous reduction in complexity and computational cost. Remarkably, the variable-order formulation is naturally capable of capturing extremely detailed features characteristic of dynamic crack propagation such as crack surface roughening as well as single and multiple branching. The accuracy and robustness of the proposed variable-order formulation are validated by comparing the results of direct numerical simulations with experimental data of typical benchmark problems available in the literature.
Quantum sensors for microscopic tunneling systems
Authors: Bilmes Alexander , Volosheniuk Serhii , Brehm Jan David , Ustinov Alexey V. , Lisenfeld Jürgen 
Published: February 2021

AbstractThe anomalous low-temperature properties of glasses arise from intrinsic excitable entities, so-called tunneling Two-Level-Systems (TLS), whose microscopic nature has been baffling solid-state physicists for decades. TLS have become particularly important for micro-fabricated quantum devices such as superconducting qubits, where they are a major source of decoherence. Here, we present a method to characterize individual TLS in virtually arbitrary materials deposited as thin films. The material is used as the dielectric in a capacitor that shunts the Josephson junction of a superconducting qubit. In such a hybrid quantum system the qubit serves as an interface to detect and control individual TLS. We demonstrate spectroscopic measurements of TLS resonances, evaluate their coupling to applied strain and DC-electric fields, and find evidence of strong interaction between coherent TLS in the sample material. Our approach opens avenues for quantum material spectroscopy to investigate the structure of tunneling defects and to develop low-loss dielectrics that are urgently required for the advancement of superconducting quantum computers.
An aggregate method for thorax diseases classification
Authors: Nugroho Bayu Adhi 
Published: February 2021

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.
Nasopharyngeal SARS-CoV-2 viral loads in young children do not differ significantly from those in older children and adults
Authors: Madera Sharline , Crawford Emily , Langelier Charles , Tran Nam K. , Thornborrow Ed , Miller Steve , DeRisi Joseph L. 
Published: February 2021

AbstractThe role of children in the spread of the SARS-CoV-2 coronavirus has become a matter of urgent debate as societies in the US and abroad consider how to safely reopen schools. Small studies have suggested higher viral loads in young children. Here we present a multicenter investigation on over five thousand SARS-CoV-2 cases confirmed by real-time reverse transcription (RT) PCR assay. Notably, we found no discernable difference in amount of viral nucleic acid among young children and adults.
16S rRNA gene-based microbiome analysis identifies candidate bacterial strains that increase the storage time of potato tubers
Authors: Buchholz Franziska , Junker Robert , Samad Abdul , Antonielli Livio , Sarić Nataša , Kostić Tanja , Sessitsch Angela , Mitter Birgit 
Published: February 2021

AbstractIn the past, the potato plant microbiota and rhizosphere have been studied in detail to improve plant growth and fitness. However, less is known about the postharvest potato tuber microbiome and its role in storage stability. The storage stability of potatoes depends on genotype and storage conditions, but the soil in which tubers were grown could also play a role. To understand the ecology and functional role of the postharvest potato microbiota, we planted four potato varieties in five soil types and monitored them until the tubers started sprouting. During storage, the bacterial community of tubers was analysed by next-generation sequencing of the 16S rRNA gene amplicons. The potato tubers exhibited soil-dependent differences in sprouting behaviour. The statistical analysis revealed a strong shift of the tuber-associated bacterial community from harvest to dormancy break. By combining indicator species analysis and a correlation matrix, we predicted associations between members of the bacterial community and tuber sprouting behaviour. Based on this, we identifiedFlavobacteriumsp. isolates, which were able to influence sprouting behaviour by inhibiting potato bud outgrowth.
Unravelling the nature of magneto-electric coupling in room temperature multiferroic particulate (PbFe0.5Nb0.5O3)–(Co0.6Zn0.4Fe1.7Mn0.3O4) composites
Authors: Bhoi Krishnamayee , Mohanty H. S. , Ravikant  , Abdullah Md. F. , Pradhan Dhiren K. , Babu S. Narendra , Singh A. K. , Vishwakarma P. N. , Kumar A. , Thomas R. , Pradhan Dillip K. 
Published: February 2021

AbstractMultiferroic composites are promising candidates for magnetic field sensors, next-generation low power memory and spintronic devices, as they exhibit much higher magnetoelectric (ME) coupling and coupled ordering parameters compared to the single-phase multiferroics. Hence, the 3-0 type particulate multiferroic composites having general formula (1 − Φ)[PbFe0.5Nb0.5O3]-Φ[Co0.6Zn0.4Fe1.7Mn0.3O4] (Φ = 0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1.0, (1 − Φ) PFN-ΦCZFMO) were prepared using a hybrid synthesis technique. Preliminary structural and microstructural analysis were carried out using XRD and FESEM techniques, which suggest the formation of 3-0 type particulate composite without the presence of any impurity phases. The multiferroic behaviour of the composites is studied with polarization versus electric field (P-E) and magnetization versus magnetic field (M-H) characteristics at room temperature. The nature of ME coupling was investigated elaborately by employing the Landau free energy equation along with the magneto-capacitance measurement. This investigation suggests the existence of biquadratic nature of ME coupling (P2M2). The magneto-electric coupling measurement also suggests that strain mediated domain coupling between the ferroelectric and magnetic ordering is responsible for the magneto-electric behaviour. The obtained value of direct ME coefficient 26.78 mV/cm-Oe for Φ = 0.3, found to be higher than the well-known single-phase materials and polycrystalline composites.
Assessing spatio-temporal patterns and driving force of ecosystem service value in the main urban area of Guangzhou
Authors: He Yi , Wang Wenhui , Chen Youdong , Yan Haowen 
Published: February 2021

AbstractIncreasing human activity around the world has greatly changed the natural ecosystem and the services it provides. In the past few decades, a series of significant changes have taken place in land use/land cover (LULC) in China due to the rapid growth in population, particularly in the cities of the Zhujiang Deita. However, there have been few attempts to study the co-evolution of land use/land cover change and ecosystem service value (ESV) in the main urban area of Guangzhou. Therefore, based on Landsat TM/OLI images from 1987, 1993, 1999, 2005, 2011 and 2017, the weight vector AdaBoost (WV AdaBoost) multi-classification algorithm was utilized to extract LULC data sets, and the spatiotemporal patterns of LULC over these periods were studied. The ESV was estimated and the driving force was analysed. The effect of LULC dynamics on the ESV was evaluated. The results showed that great changes have taken place in LULC in the main urban area of Guangzhou from 1987 to 2017, of which the most significant was the large-scale expansion of the built-up area that occurred through degradation of the forest and cultivated land. The proportion of forest and cultivated land decreased from 43.12% and 34.23% to 25.88% and 12.59%, respectively. The results between periods revealed a decrease in total ESVs from 5.63 × 109 yuan in 1987 to 5.27, 4.16, 4.62, 3.76 and 4.47 × 109 yuan in 1993, 1999, 2005, 2011 and 2017, respectively. In total, ESVs decreased by 1.16 billion yuan (20.61%) from 1987 to 2017. Water supply, food production, nutrient cycling and gas regulation were the four principal ecosystem service functions that affected the total ESVs. Forest, water body and cultivated land areas played a key role in ecosystem services. Therefore, we advocate that when protecting natural ecosystems in the future land use management in Guangzhou should be prioritized.
The ideological divide in confidence in science and participation in medical research
Authors: Gabel Matthew , Gooblar Jonathan , Roe Catherine M. , Morris John C. 
Published: February 2021

AbstractIn the United States, the wide ideological divergence in public confidence in science poses a potentially significant problem for the scientific enterprise. We examine the behavioral consequences of this ideological divide for Americans’ contributions to medical research. Based on a mass survey of American adults, we find that engagement in a wide range of medical research activities is a function of a latent propensity to participate. The propensity is systematically higher among liberals than among conservatives. A substantial part of this ideological divide is due to conservative Americans’ lower confidence in science. These findings raise important issues for the recruitment of subjects for medical studies and the generalizability of results from such studies.
Fecundity determines the outcome of founding queen associations in ants
Authors: Teggers Eva-Maria , Deegener Falk , Libbrecht Romain 
Published: February 2021

AbstractAnimal cooperation evolved because of its benefits to the cooperators. Pleometrosis in ants—the cooperation of queens to found a colony—benefits colony growth, but also incurs costs for some of the cooperators because only one queen usually survives the association. While several traits in queens influence queen survival, they tend to be confounded and it is unclear which factor specifically determines the outcome of pleometrosis. In this study, we used the ant Lasius niger to monitor offspring production in colonies founded by one or two queens. Then, we experimentally paired queens that differed in fecundity but not in size, and vice versa, to disentangle the effect of these factors on queen survival. Finally, we investigated how fecundity and size differed between queens depending on whether they were chosen as pleometrotic partners. Our results indicate that pleometrosis increased and accelerated worker production via a nutritional boost to the larvae. The most fecund queens more frequently survived the associations, even when controlling for size and worker parentage, and queens selected as pleometrotic partners were less fecund. Our results are consistent with fecundity being central to the onset and outcome of pleometrosis, a classic example of cooperation among unrelated animals.
Explainable drug sensitivity prediction through cancer pathway enrichment
Authors: Tang Yi-Ching , Gottlieb Assaf 
Published: February 2021

AbstractComputational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.
Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery
Authors: Lee Cheng-chia , Lee Wei-Kai , Wu Chih-Chun , Lu Chia-Feng , Yang Huai-Che , Chen Yu-Wei , Chung Wen-Yuh , Hu Yong-Sin , Wu Hsiu-Mei , Wu Yu-Te , Guo Wan-Yuo 
Published: February 2021

AbstractArtificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.
Development of a robust protocol for the characterization of the pulmonary microbiota
Authors: Dumont-Leblond Nathan , Veillette Marc , Racine Christine , Joubert Philippe , Duchaine Caroline 
Published: February 2021

AbstractThe lack of methodological standardization diminishes the validity of results obtained and the conclusions drawn when studying the lung microbiota. We report the validation of a complete 16S rRNA gene amplicon sequencing workflow, from patient recruitment to bioinformatics, tailored to the constrains of the pulmonary environment. We minimize the impact of contaminants and establish negative controls to track and account for them at every step. Enzymatic and mechanical homogenization combined to commercially available extraction kits allow for a fast and reliable extraction of bacterial DNA. The DNA extraction kits have a significant impact on the bacterial composition of the controls. The bacterial signatures of extracted cancerous and healthy human tissues from 5 patients are highly distinguishable from methodological controls. Our work expands our understanding of low microbial burdened environments analysis. This article is to be a starting point towards methodological standardization and the implementation of proper sampling procedures in the study of lung microbiota.
Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
Authors: Maloca Peter M. , Müller Philipp L. , Lee Aaron Y. , Tufail Adnan , Balaskas Konstantinos , Niklaus Stephanie , Kaiser Pascal , Suter Susanne , Zarranz-Ventura Javier , Egan Catherine , Scholl Hendrik P. N. , Schnitzer Tobias K. , Singer Thomas , Hasler Pascal W. , Denk Nora 
Published: February 2021

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.