bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perfect representation of a convex 2D contour with only one Fourier component
Authors: Harbitz Alf 
Published: September 2020

AbstractShape analysis of a closed 2D contour is an important topic within biological shape analysis, where Fourier methods to reproduce the shape with a limited number of parameters have been and still are of vital importance. An example is within marine management research on fish, where shape analysis of otolith (earstone) contours is performed for species identification as well as for stock discrimination purposes. In both cases, it is expected that the fewer parameters that are needed in a method to reproduce the contour sufficiently good, the better. This contribution outlines how a convex contour of any shape can be represented to any wanted accuracy by only one Fourier component. The key idea is to allow a flexible choice of a predetermined number ofx-values along anx-axis that goes through the two most distant points of the contour. They-variable along the perpendiculary-axis is then monotonically transformed to az-variable so that the minium and maximumz-values on the contour have the same distance from thex-axis. Thex-values of the contour points are now chosen so that the correspondingz-values on the contour follows a perfect sinusoid if thex-values were equidistant. The method is illustrated by application to lasso contours of Norwegian Coastal Cod (NCC) and North East Arctic Cod (NEAC) otolith images, where the average newx-positions for the individual otolith contours were applied to all otoliths. The results show that a considerably better fit to the original individual otolith contours were obtained by applying the invers FFT to the newy-values than by the frequently applied 2D EFDs (Elliptical Fourier Descriptors) approach, for the same number,m< 11, of frequency components. A promising classification result was also obtained by the linear Fisher discrimination method and cross validation applied to the individualx-values for the NCC and NEAC otoliths, with 82% score for NCC and 80% score for NEAC with sample sizes 367 and 240, respectively.
Computational modelling of chromosome re-replication in mutant strains of fission yeast
Authors: Novák Béla , Tyson John J 
Published: September 2020

AbstractTypically cells replicate their genome only once per division cycle, but under some circumstances, both natural and unnatural, cells synthesize an overabundance of DNA, either in a disorganized fashion (‘over-replication’) or by a systematic doubling of chromosome number (‘endoreplication’). These variations on the theme of DNA replication and division have been studied in strains of fission yeast,Schizosaccharomyces pombe, carrying mutations that interfere with the function of mitotic cyclin-dependent kinase (Cdk1:Cdc13) without impeding the roles of DNA-replication licensing factor (Cdc18) and S-phase cyclin-dependent kinase (Cdk1:Cig2). Some of these mutations support endoreplication, and some over-replication. In this paper, we propose a dynamical model of the interactions among the proteins governing DNA replication and cell division in fission yeast. By computational simulations of the mathematical model, we account for the observed phenotypes of these re-replicating mutants, and by theoretical analysis of the dynamical system, we provide insight into the molecular distinctions between over-replicating and endoreplicating cells. In case of induced over-production of regulatory proteins, our model predicts that cells first switch from normal mitotic cell cycles to growth-controlled endoreplication, and ultimately to disorganized over-replication, parallel to the slow increase of protein to very high levels.
Precision design of single and multi-heme de novo proteins
Authors: Hutchins George H. , Noble Claire E. M. , Blackburn Hector , Hardy Ben , Landau Charles , Parnell Alice E. , Yadav Sathish , Williams Christopher , Race Paul R. , Oliveira A. Sofia F. , Crump Matthew P. , Berger-Schaffitzel Christiane , Mulholland Adrian J. , Anderson J. L. Ross 
Published: September 2020

AbstractThede novodesign of simplified porphyrin-binding helical bundles is a versatile approach for the construction of valuable biomolecular tools to both understand and enhance protein functions such as electron transfer, oxygen binding and catalysis. However, the methods utilised to design such proteins by packing hydrophobic side chains into a buried binding pocket for ligands such as heme have typically created highly flexible, molten globule-like structures, which are not amenable to structural determination, hindering precise engineering of subsequent designs. Here we report the crystal structure of ade novotwo-heme binding “maquette” protein, 4D2, derived from the previously designed D2 peptide, offering new opportunities for computational design and re-engineering. The 4D2 structure was used as a basis to create a range of heme binding proteins which retain the architecture and stability of the initial crystal structure. A well-structured single-heme binding variant was constructed by computational sequence redesign of the hydrophobic protein core, assessed by NMR, and utilised for experimental validation of computational redox prediction and design. The structure was also extended into a four-heme binding helical bundle resembling a molecular wire. Despite a molecular weight of only 24kDa, imaging by CryoEM illustrated a remarkable level of detail in this structure, indicating the positioning of both the secondary structure and the heme cofactors. The design and determination of atomic-level resolution in suchde novoproteins is an invaluable resource for the continued development of novel and functional protein tools.
An evolutionary portrait of the progenitor SARS-CoV-2 and its dominant offshoots in COVID-19 pandemic
Authors: Kumar Sudhir , Tao Qiqing , Weaver Steven , Sanderford Maxwell , Caraballo-Ortiz Marcos A. , Sharma Sudip , Pond Sergei L. K. , Miura Sayaka 
Published: September 2020

AbstractWe report the likely most recent common ancestor of SARS-CoV-2 – the coronavirus that causes COVID-19. This progenitor SARS-CoV-2 genome was recovered through a novel application and advancement of computational methods initially developed to reconstruct the mutational history of tumor cells in a patient. The progenitor differs from the earliest coronaviruses sampled in China by three variants, implying that none of the earliest patients represent the index case or gave rise to all the human infections. However, multiple coronavirus infections in China and the USA harbored the progenitor genetic fingerprint in January 2020 and later, suggesting that the progenitor was spreading worldwide as soon as weeks after the first reported cases of COVID-19. Mutations of the progenitor and its offshoots have produced many dominant coronavirus strains, which have spread episodically over time. Fingerprinting based on common mutations reveals that the same coronavirus lineage has dominated North America for most of the pandemic. There have been multiple replacements of predominant coronavirus strains in Europe and Asia and the continued presence of multiple high-frequency strains in Asia and North America. We provide a continually updating dashboard of global evolution and spatiotemporal trends of SARS-CoV-2 spread (http://sars2evo.datamonkey.org/).
Hamster and ferret experimental infection with intranasal low dose of a single strain of SARS-CoV-2
Authors: Monchatre-Leroy Elodie , Lesellier Sandrine , Wasniewski Marine , Picard-Meyer Evelyne , Richomme Céline , Boué Franck , Lacôte Sandra , Murri Séverine , Pulido Coralie , Vulin Johann , Salguero Francisco J , Gouilh Meriadeg Ar , Servat Alexandre , Marianneau Philippe 
Published: September 2020

AbstractUnderstanding the pathogenesis of the SARS-CoV-2 infection is key to develop preventive and therapeutic strategies against COVID-19, in the case of severe illness but also when the disease is mild. The use of appropriate experimental animal models remains central in the in-vivo exploration of the physiopathology of infection and antiviral strategies. This study describes SARS-CoV-2 intra-nasal infection in ferrets and hamsters with low doses of low-passage SARS-CoV-2 clinical French isolate UCN19, describing infection levels, excretion, immune responses and pathological patterns in both animal species. Individual infection with 103pfu SARS-CoV-2 induced a more severe disease in hamsters than in ferrets. Viral RNA was detected in the lungs of hamsters but not of ferrets and in the brain (olfactive and/or spinal bulbs) of both species. Overall, the clinical disease remained mild, with serological responses detected from 7 days and 10 days post inoculation in hamsters and ferrets respectively. Virus became undetectable and pathology resolved within 14 days. The kinetics and levels of infection can be used in ferrets and hamsters as experimental models for understanding the pathogenicity of SARS-CoV-2, and testing the protective effect of drugs.
Genome-wide SNP data unravel the ancestry and signatures of divergent selection in Ghurrah pigs of India
Authors: Mehrotra Arnav , Bhushan Bharat , Karthikeyan A , Singh Akansha , Panda Snehasmita , Bhati Meenu , Panigrahi Manjit , Dutt Triveni , Mishra Bishnu P. , Pausch Hubert , Kumar Amit 
Published: September 2020

SummaryThe evolution and domestication of pigs is a complex and ongoing process. Despite its rich biodiversity and proximity to the geographical origins ofSus scrofa domesticus, the place of Indian pigs in the global phylogeny is unclear. Using microarray-derived (porcine 60K SNP chip) genotypes of 11 Ghurrah pigs from North-Western India and a public dataset comprising 2113 pigs of 146 breeds, we determined the genomic ancestry of Ghurrah pigs and compared their genetic constitution to European and Asian breeds to ascertain signatures of divergent selection. Results showed that Ghurrah pigs contain genes of Asian and European ancestry with signs of inter-species introgression. Using Admixture LD – decay statistics, the European admixture event was dated to the recent past, coinciding with the start of cross-breeding efforts in India. The complex Asian ancestry pattern of the breed resembled that of wild boars of South – Central China and Thailand, possibly suggesting introgression from an Indian wild boar relative.FSTand XP – EHH comparisons with Asian breeds highlighted divergent selection in genomic regions associated with odontogenesis and skeletal muscle development. Comparisons with European commercial breeds revealed that genomic regions governing olfaction and response to sensory stimulation were under selection in Ghurrah pigs. QTL for meat and carcass traits also showed divergent selection between European breeds and Ghurrah pigs. Our results present the first genomic characterization of an Indian pig breed using dense microarray-derived genotypes and highlight the importance of further genomic characterization of Indian domestic and wild pigs.
Insights into the diversity and survival strategies of soil bacterial isolates from the Atacama Desert
Authors: Reverdy Alicyn , Hathaway Daniel , Jha Jessica , Michaels Gabriel , Sullivan Jeffrey , Mac-Adoo Daniela Diaz , Riquelme Carlos , Chai Yunrong , Godoy Veronica G. 
Published: September 2020

AbstractThe Atacama Desert, the driest and oldest desert in the world, is a hostile environment for life. Despite the inhospitable conditions, bacterial sequences detected in this location suggest rich bacterial life. This study tested the idea that certain bacteria would thrive in this location and that some of them could be cultivated permitting further characterization. Environmental surface soil samples from 1-5 cm deep were collected from 18 diverse locations within the Atacama Desert. To assess the bacterial taxa, diversity, and abundance, Illumina 16S rRNA gene sequencing was performed directly on soil samples. Bacteria were also cultured from the samples. We have a collection of 74 unique bacterial isolates after cultivation and confirmation by 16S rRNA gene sequencing. Pigmentation, biofilm formation, antibiotic production againstEscherichia coliMG1655 andStaphylococcus aureusHG003, and antibiotic resistance were assessed on these isolates. We found that approximately a third of the colonies produced pigments, 80% of isolates formed biofilms, many isolates had antibiotic activity againstE. coliand/orS. aureus,and many were resistant to commercial antibiotics. The functional characterization of these isolates gives us insight into the adaptive bacterial strategies in harsh environments and enables us to learn about their possible use in agriculture, healthcare, or biotechnology.Originality-Significant StatementThis study provides the first microbial diversity analysis from Atacama Desert soil, presents the cultivation and isolation of 74 unique bacterial isolates, many of which may be novel genera and species, and explores pigment production, antibiotic production and resistance, and unique biofilm development as bacterial survival strategies for living within extreme environments.
DeepEthogram: a machine learning pipeline for supervised behavior classification from raw pixels
Authors: Bohnslav James P. , Wimalasena Nivanthika K. , Clausing Kelsey J. , Yarmolinksy David , Cruz Tomás , Chiappe Eugenia , Orefice Lauren L. , Woolf Clifford J. , Harvey Christopher D. 
Published: September 2020

AbstractResearchers commonly acquire videos of animal behavior and quantify the prevalence of behaviors of interest to study nervous system function, the effects of gene mutations, and the efficacy of pharmacological therapies. This analysis is typically performed manually and is therefore immensely time consuming, often limited to a small number of behaviors, and variable across researchers. Here, we created DeepEthogram: software that takes raw pixel values of videos as input and uses machine learning to output an ethogram, the set of user-defined behaviors of interest present in each frame of a video. We used convolutional neural network models that compute motion in a video, extract features from motion and single frames, and classify these features into behaviors. These models classified behaviors with greater than 90% accuracy on single frames in videos of flies and mice, matching expert-level human performance. The models accurately predicted even extremely rare behaviors, required little training data, and generalized to new videos and subjects. DeepEthogram runs rapidly on common scientific computer hardware and has a graphical user interface that does not require programming by the end-user. We anticipate DeepEthogram will enable the rapid, automated, and reproducible assignment of behavior labels to every frame of a video, thus accelerating all those studies that quantify behaviors of interest.Code is available at:https://github.com/jbohnslav/deepethogram
COVID-19 mortality rate in Russia: forecasts and reality evaluation
Authors: Marina Lifshits , Natalia Neklyudova 
Published: September 2020

RelevanceCOVID-19 is an extremely dangerous disease that not only spreads quickly, but is also characterized by a high mortality rate. Therefore, predicting the number of deaths from the new coronavirus is an urgent task.Research objectiveThe aim of the study is to analyze the factors affecting COVID-19 mortality rate in various countries, to predict direct and indirect victims of the pandemic in the Russian Federation, and to estimate additional mortality during the pandemic based on the demographic data.Data and methodsThe main research method is econometric modeling. Comparison of various data was also applied. The authors’ calculations were based on data from the RSSS, the World Bank, as well as specialized sites with coronavirus statistics in Russia and in the world.ResultsA predictive estimation of the deceased number of people due to the pandemic in Russia was made. It is confirmed that the deaths proportion of the completed cases of the disease depends on the level of testing. It is shown that the revealed mortality of the disease depends on the proportion of completed cases, on the population age structure, and on how early the pandemic entered the country compared to the other countries. It is determined that the number of additional deaths due to the coronavirus is approximately 31 thousand people.ConclusionsThe analysis revealed that the relatively low proportion of COVID in Russia is the result of a special approach to the cause of death determination. The mortality rate in Russia in April 2020 was about 3% higher than in April 2019. The share of the deceased health workers in the total coronavirus mortality in the Russian Federation is higher than in the developed countries, which indicates an underestimation of the data on COVID-19 deaths in the Russian Federation, and the unsatisfactory quality of the Russian healthcare system. The number of direct and indirect victims of the pandemic in the Russian Federation at the end of July was approximately 43 thousand people.
Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning
Authors: Zhang Wanqiu , Claesen Marc , Moerman Thomas , Groseclose M. Reid , Waelkens Etienne , De Moor Bart , Verbeeck Nico 
Published: September 2020

AbstractComputational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction.The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coinedneural ion images, are used to cluster ion images based on spatial expressions.We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters.Additionally, we introduce the Relative Isotope Ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes.The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.
An early cortical progenitor-specific mechanism regulates thalamocortical innervation
Authors: Pal Suranjana , Dwivedi Deepanjali , Pramanik Tuli , Godbole Geeta , Iwasato Takuji , Jabaudon Denis , Bhalla Upinder S. , Tole Shubha 
Published: September 2020

AbstractThe cortical subplate is critical in regulating the entry of thalamocortical sensory afferents into the cortex. These afferents reach the subplate at embryonic day (E)15.5 in the mouse, but “wait” for several days, entering the cortical plate postnatally. We report that when transcription factor Lhx2 is lost in E11.5 cortical progenitors, which give rise to subplate neurons, thalamocortical afferents display premature, exuberant innervation of the E15.5 cortex. Embryonic mutant subplate neurons are correctly positioned below the cortical plate, but they display an altered transcriptome and immature electrophysiological properties during the waiting period. The sensory thalamus in these cortex-specificLhx2mutants displays atrophy, eventually leading to severe deficits in thalamocortical innervation. Strikingly, these phenotypes do not manifest if Lhx2 is lost in postmitotic subplate neurons. These results demonstrate a mechanism operating in subplate progenitors that has profound consequences on the growth of thalamocortical axons into the cortex.
The long-term erosion of repeat-purchase loyalty
Authors: Dawes John G. , Graham Charles , Trinh Giang 
Published: September 2020

PurposeThe study investigates the long-term erosion of repeat-purchase loyalty among consumers who purchase brands in a one-year base period.Design/methodology/approachThe study uses a five-year consumer panel of continuous reporters. We identify brand buyers in a base year, then calculate the proportion that fail to buy the brand in later years. We analyse the top 20 brands in 10 consumer goods categories.FindingsWe find pronounced erosion in repeat-buying over the long-term. The proportion of buyers from a base year that fail to buy in a later year increases steadily over time, from 57% in year 2 to 71.5% by year 5. Moreover, we identify brand and marketing mix factors linked to this over-time customer loss or erosion.Research limitations/implicationsThe study provides evidence that consumers’ propensity to buy particular brands changes over a period of years, even though those brands continue to exhibit a stable market share. This evidence provides a different interpretation than the literature to date, which has viewed purchase propensities as fixed.Practical implicationsThe study finds that store brands and niche brands exhibit lower levels of erosion in their buyer base; that a broad range is associated with lower erosion, and that high price promotion incidence is associated with lower erosion for manufacturer brands.Originality/valueLoyalty erosion has been reported before (Ehrenberg, 1988; East and Hammond, 1996) but only over short periods. This study examines the phenomenon over five years, confirms that the rate of erosion does diminish over time, and that it is related to category and brand characteristics, as well as marketing mix decisions.
Not just another internal service provider: how a firm’s marketing research function influences uses of market research information
Authors: Bode Christian , Geiger Ingmar 
Published: September 2020

Purpose
This paper aims to better understand a firm’s market research function’s (MRF) roles and influences on the different uses of market research information (MRI) (instrumental, conceptual and symbolic) in the firm.

Design/methodology/approach
This study uses a mixed-methods design: In a qualitative study among marketing managers (n = 9) and market researchers (n = 10); different roles of a firm’s MRF are scrutinized. The quantitative study among corporate users of MRI (n = 235) tests a conceptual model on the MRF-related determinants of MRI uses, using structural equation modeling.

Findings
When the MRF exhibits methodological, market and business expertise and interacts with market research users, these features indirectly influence instrumental, conceptual, and symbolic uses, through MRI quality perceived by the users. The users’ knowledge of market research methods, the function’s integrity, top management support, and decentralized decision-making have various beneficial influences on the three uses of MRI.

Research limitations/implications
Broadly speaking, this research expands the theoretical understanding of marketing-related organizational learning processes. It considers message, source, recipient, and context variables to explain changes in attitude and behavior, related to MRI uses.

Practical implications
Overall, corporations should use MRI more thoroughly. Treating the MRF as a strategic asset rather than just another internal service provider increases its value to the firm. Firms should thus bolster the MRF’s different roles, namely, internal colleague, service provider, customer representative, quality assurance, and knowledge broker.

Originality/value
This paper is the first to comprehensively analyze the MRF’s role in MRI generation and various uses. It identifies three expertise dimensions of the MRF to fully leverage the MRF’s value to the organization and empirically underscores theoretical writings on the MRF’s roles in the firm.