bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building blocks of protein structures – Physics meets Biology
Authors: Škrbić Tatjana , Maritan Amos , Giacometti Achille , Rose George D. , Banavar Jayanth R. 
Published: November 2020

AbstractThe native state structures of globular proteins are stable and well-packed indicating that self-interactions are favored over protein-solvent interactions under folding conditions. We use this as a guiding principle to derive the geometry of the building blocks of protein structures – α-helices and strands assembled into β-sheets – with no adjustable parameters, no amino acid sequence information, and no chemistry. There is an almost perfect fit between the dictates of mathematics and physics and the rules of quantum chemistry. Our theory establishes an energy landscape that channels protein evolution by providing sequence-independent platforms for elaborating sequence-dependent functional diversity. Our work highlights the vital role of discreteness in life and has implications for the creation of artificial life and on the nature of life elsewhere in the cosmos.
RVAgene: Generative modeling of gene expression time series data
Authors: Mitra Raktim , MacLean Adam L. 
Published: November 2020

AbstractMethods to model dynamic changes in gene expression at a genome-wide level are not currently sufficient for large (temporally rich or single-cell) datasets. Variational autoencoders offer means to characterize large datasets and have been used effectively to characterize features of single-cell datasets. Here we extend these methods for use with gene expression time series data. We present RVAgene: a recurrent variational autoencoder to model gene expression dynamics. RVAgene learns to accurately and efficiently reconstruct temporal gene profiles. It also learns a low dimensional representation of the data via a recurrent encoder network that can be used for biological feature discovery, and can generate new gene expression data by sampling from the latent space. We test RVAgene on simulated and real biological datasets, including embryonic stem cell differentiation and kidney injury response dynamics. In all cases, RVAgene accurately reconstructed complex gene expression temporal profiles. Via cross validation, we show that a low-error latent space representation can be learnt using only a fraction of the data. Through clustering and gene ontology term enrichment analysis on the latent space, we demonstrate the potential of RVAgene for unsupervised discovery. In particular, RVAgene identifies new programs of shared gene regulation ofLoxfamily genes in response to kidney injury.
A reduced level of consciousness affects non-conscious processes
Authors: Fontan A. , Lindgren L. , Pedale T. , Brorsson C. , Bergström F. , Eriksson J. 
Published: November 2020

AbstractBeing conscious is a profound aspect of human existence, and understanding its function and its inception is considered one of the truly grand scientific challenges. However, the nature of consciousness remains enigmatic, to a large part because “being conscious” can refer to both the content (phenomenology) and the level (arousal) of consciousness, and how these different aspects are related remains unclear. To empirically assess the relation between level and content of consciousness, we manipulated these two aspects by presenting stimuli consciously or non-consciously and by using Propofol sedation, while brain activity was measured using fMRI. We observed that sedation greatly affected non-conscious processes, which starkly contrasts the notion that anesthetics selectively reduce consciousness. Our findings reveal that level and content of consciousness are separate phenomena, and imply that one may need to reconsider what “being conscious” means.
Hippocampal-cortical encoding activity predicts the precision of episodic memory
Authors: Korkki Saana M. , Richter Franziska R. , Simons Jon S. 
Published: November 2020

AbstractOur recollections of past experiences can vary both in the number of specific event details accessible from memory and the precision with which such details are reconstructed. Prior neuroimaging evidence suggests the success and precision of episodic recollection to rely on distinct neural substrates during memoryretrieval. In contrast, the specificencodingmechanisms supporting later memory precision, and whether they differ from those underlying successful memory formation in general, are currently unknown. Here, we combined continuous measures of memory retrieval with model-based analyses of behavioural and neuroimaging data to tease apart the encoding correlates of successful memory formation and mnemonic precision. In the MRI scanner, participants encoded object-scene displays, and later reconstructed features of studied objects using a continuous scale. We observed overlapping encoding activity in inferior prefrontal and posterior perceptual regions to predict both which object features were later remembered versus forgotten, and the precision with which they were reconstructed from memory. In contrast, hippocampal encoding activity significantly predicted the precision, but not overall success, of subsequent memory retrieval. The current results identify a hippocampal-cortical encoding basis for episodic memory precision, and suggest a contribution of shared cortical encoding mechanisms to the formation of both accessible and precise memory representations.
Quantifying the contribution of dominance effects to complex trait variation in biobank-scale data
Authors: Pazokitoroudi Ali , Chiu Alec M. , Burch Kathryn S. , Pasaniuc Bogdan , Sankararaman Sriram 
Published: November 2020

AbstractThe proportion of variation in complex traits that can be attributed to non-additive genetic effects has been a topic of intense debate. The availability of Biobank-scale datasets of genotype and trait data from unrelated individuals opens up the possibility of obtaining precise estimates of the contribution of non-additive genetic effects. We present an efficient method that can partition the variation in complex traits into variance that can be attributed to additive (additive heritability) and dominance (dominance heritability) effects across all genotyped SNPs in a large collection of unrelated individuals. Over a wide range of genetic architectures, our method yields unbiased estimates of heritability. We applied our method, in turn, to array genotypes as well as imputed genotypes (at common SNPs with minor allele frequency, MAF > 1%) and 50 quantitative traits measured in 291, 273 unrelated white British individuals in the UK Biobank. Averaged across these 50 traits, we find that additive heritability on array SNPs is 21.86% while dominance heritability is 0.13% (about 0.48% of the additive heritability) with qualitatively similar results for imputed genotypes. We find no evidence for dominance heritability (accounting for the number of traits tested) and estimate that dominance heritability is unlikely to exceed 1% for the traits analyzed. Our analyses indicate a limited contribution of dominance heritability to complex trait variation.
Root system influence on high dimensional leaf phenotypes over the grapevine growing season
Authors: Harris Zachary N. , Klein Laura L. , Awale Mani , Swift Joel F. , Migicovsky Zoë , Bhakta Niyati , Frawley Emma , Chitwood Daniel H. , Fennell Anne , Kovacs Laszlo G. , Kwasniewski Misha , Londo Jason P. , Ma Qin , Miller Allison J. 
Published: November 2020

SummaryIn many perennial crops, grafting the root system of one individual to the shoot system of another individual has become an integral part of propagation performed at industrial scales to enhance pest, disease, and stress tolerance and to regulate yield and vigor. Grafted plants offer important experimental systems for understanding the extent and seasonality of root system effects on shoot system biology.Using an experimental vineyard where a common scion ‘Chambourcin’ is growing ungrafted and grafted to three different rootstocks, we explore associations between root system genotype and leaf phenotypes in grafted grapevines across a growing season. We quantified five high-dimensional leaf phenotyping modalities: ionomics, metabolomics, transcriptomics, morphometrics, and physiology and show that rootstock influence is subtle but ubiquitous across modalities.We find strong signatures of rootstock influence on the leaf ionome, with unique signatures detected at each phenological stage. Moreover, all phenotypes and patterns of phenotypic covariation were highly dynamic across the season.These findings expand upon previously identified patterns to suggest that the influence of root system on shoot system phenotypes is complex and broad understanding necessitates volumes of high-dimensional, multi-scale data previously unmet.
Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge
Authors: Gong Weikang , Beckmann Christian F. , Vedaldi Andrea , Smith Stephen M. , Peng Han 
Published: November 2020

AbstractBrain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject’s predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject’s age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal.
Prediction of the 4D Chromosome Structure From Time-Series Hi-C Data
Authors: Highsmith Max , Cheng Jianlin 
Published: November 2020

AbstractChromatin conformation plays an important role in a variety of genomic processes. Hi-C Data is frequently used to analyse structural features of chromatin such as AB compartments, topologically associated domains, and 3D structural models. Recently the genomics community has displayed growing interest in chromatin dynamics over time. Here we present 4DMax, a novel method which uses time-series Hi-C data to predict dynamic chromosome conformation. Using both synthetic data and real time-series Hi-C data from processes such as induced pluripotent stem cell reprogramming and cardiomyocyte differentiation, we construct fluid four dimensional models of individual chromosomes. These predicted 4D models effectively interpolate chromatin position across time, permitting prediction of unknown Hi-C contact maps at intermittent time points. Our results demonstrate that 4DMax correctly recovers higher order features of chromatin such as AB compartments and topologically associated domains, even at time points where Hi-C data is not made available to the algorithm. Use of 4DMax may alleviate the cost of expensive Hi-C experiments by interpolating intermediary timepoints while also providing valuable visualization of dynamic chromatin changes.
Phenotypic and environmental predictors of reproductive success in painted turtles
Authors: Judson Jessica M. , Hoekstra Luke A. , Holden Kaitlyn G. , Janzen Fredric J. 
Published: November 2020

ABSTRACTSexual selection is often assumed to elicit sexually dimorphic traits. However, most work on this assumption in tetrapod vertebrates has focused on birds. In this field experiment, we assessed relationships between both sexually dimorphic (body size, claw length) and non-dimorphic traits (forelimb stripe color, baseline corticosterone concentrations) and reproductive success in adult painted turtles to explicate the roles of these phenotypes in mate choice and the evolution of sexual dimorphism. We also modified adult sex ratios in experimental ponds to elucidate the role of biased sex ratios on reproductive success, which is a timely test of the potential threat of biased sex ratios on population persistence in a species with temperature-dependent sex determination. We found no strong influence of male phenotypes on male siring success, but female body size and baseline corticosterone concentrations predicted female clutch sizes. We find weak evidence that adult sex ratio influences male siring success, with a male-biased sex ratio producing lower male siring success than a female-biased sex ratio. This study offers evidence that female mate choice may not be an important selective force on male phenotypes, but that instead selection occurs on female phenotypes, particularly body size and corticosterone concentrations. Further, biased adult sex ratios can influence reproductive success of both sexes. Finally, the use of Kompetitive Allele Specific PCR (KASP) was highly successful in parentage analysis, which adds reptiles to the growing list of taxa successfully genotyped with this new technology.Lay SummaryFemale painted turtles aren’t choosy about traits of their mates. In a field experiment, we find that male traits do not predict male fitness, but key female traits (body size and stress levels) do predict female reproductive success. Further, we find weak evidence that adult sex ratio influences individual fitness in this species with environmental sex determination. Ultimately, we reject the long-assumed importance of female mate choice in this freshwater turtle.
Precise Estimation of In Vivo Protein Turnover Rates
Authors: O’Brien Jonathon J. , Narayan Vikram , Wong Yao , Seitzer Phillip , Sandoval Celeste M. , Haste Nicole , Smith Megan , Rad Ramin , Gaun Aleksandr , Baker Adam , Kukurugya Matthew , Martin-McNulty Baby , Zhang Chunlian , Kolumam Ganesh , Sidrauski Carmela , Jojic Vladimir , McAllister Fiona , Bennett Bryson , Buffenstein Rochelle 
Published: November 2020

AbstractIsotopic labeling with deuterium oxide (D2O) is a common technique for estimatingin vivoprotein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity.
Conflict over fertilization underlies the transient evolution of reinforcement
Authors: Rushworth Catherine A. , Wardlaw Alison M. , Ross-Ibarra Jeffrey , Brandvain Yaniv 
Published: November 2020

ABSTRACTWhen two species meet in secondary contact, the production of low fitness hybrids may be prevented by the adaptive evolution of increased prezygotic isolation, a process known as reinforcement. Theoretical challenges to the evolution of reinforcement are generally cast as a coordination problem, i.e. “how can LD between trait and preference be maintained in the face of recombination?” However, the evolution of reinforcement also poses a potential conflict between mates. For example, the opportunity costs to hybridization may differ between the sexes or species. This is particularly likely for reinforcement based on postmating prezygotic (PMPZ) incompatibilities, as the ability to fertilize both conspecific and heterospecific eggs is beneficial to male gametes, but heterospecific mating may incur a cost for female gametes. We develop a population genetic model of interspecific conflict over reinforcement inspired by “gametophytic factors”, which act as PMPZ barriers amongZea mayssubspecies. We demonstrate that this conflict results in the transient evolution of reinforcement—after females adaptively evolve to reject gametes lacking a signal common in conspecific gametes, this gamete signal adaptively introgresses into the other population. Ultimately the male gamete signal fixes in both species, and isolation returns to pre-reinforcement levels. We interpret geographic patterns of isolation amongZ. mayssubspecies in light of these findings, and suggest when and how this conflict can be resolved. Our results suggest that sexual conflict over fertilization may pose an understudied obstacle to the evolution of reinforcement.
The SpliZ generalizes “Percent Spliced In” to reveal regulated splicing at single-cell resolution
Authors: Olivieri Julia Eve , Dehghannasiri Roozbeh , Salzman Julia 
Published: November 2020

AbstractTo date, detecting robust single-cell-regulated splicing is viewed as out of reach from droplet based technologies such as 10x Chromium. This prevents the discovery of single-cell-regulated splicing in rare cell types or those that are difficult or impossible to sequence deeply. Here, we introduce a novel, robust, and computationally efficient set of statistics, the Splicing Z Score (SpliZ) and SpliZVD, to detect regulated splicing in single cell RNA-seq including 10x Chromium. The SpliZ(VD) provides annotation-free detection of differentially regulated, complex alternative splicing events. The SpliZ generalizes and increases statistical power compared to the Percent Spliced In (PSI) and mathematically reduces to PSI for simple exon-skipping. We applied the SpliZ to primary human lung cells to discover hundreds of genes with new regulated cell-type-specific splicing. The SpliZ has wide application to enable biological discovery of genes predicted to have functionally significant splicing programs including those regulated in development.
Comparative genomic analyses provide clues to capsule switch inStreptococcus suis
Authors: Zhu Yinchu , Dong Wenyang , Ma Jiale , Zhang Yue , Zhong Xiaojun , Pan Zihao , Liu Guangjin , Wu Zongfu , Yao Huochun 
Published: November 2020

AbstractStreptococcus suis(S. suis) is a major bacterial pathogen in swine industry and also an emerging zoonotic agent.S. suisproduces an important extracellular component, capsular polysaccharides (CPS). Based on which, dozens of serotypes have been identified. Through virulence genotyping, we uncovered the relatedness between proportions of SS2, SS3 and SS7 strains despite their differences in serotypes. Multi-locus sequence typing (MLST) was used to characterize wholeS. suispopulation, revealing that there is capsule switch betweenS. suisstrains. Importantly, capsule switch occurred in SS2, 3 and 7 strains belonging to CC28 and CC29, which is phylogenetically distinct from the main CC1 SS2 lineage. To further explore capsule switch inS. suis, comparative genomic analyses were performed using availableS. suiscomplete genomes. Phylogenetic analyses suggested that SS2 strains can be divided into two clades (1 and 2), and those classified into clade 2 are colocalized with SS3 and SS7 strains, which is in accordance with above virulence genotyping and MLST analyses. Clade 2 SS2 strains presented high genetic similarity with SS3 and SS7 and shared common competence and defensive elements, but are significantly different from Clade 1 SS2 strains. Notably, although thecpslocus shared by Clade 1 and 2 SS2 strains is almost the same, a specific region incpslocus of strain NSUI002 (Clade 2 SS2) can be found in SS3cpslocus, but not in Clade 1 SS2 strain. These data indicated that SS2 strains appeared in CC28 and CC29 might acquirecpslocus through capsule switch, which could well explain the distinction of genetic lineages within SS2 population.
On the necessity of recurrent processing during object recognition: it depends on the need for scene segmentation
Authors: Seijdel Noor , Loke Jessica , van de Klundert Ron , van der Meer Matthew , Quispel Eva , van Gaal Simon , de Haan Edward H.F. , Scholte H. Steven 
Published: November 2020

AbstractWhile feed-forward activity may suffice for recognizing objects in isolation, additional visual operations that aid object recognition might be needed for real-world scenes. One such additional operation is figure-ground segmentation; extracting the relevant features and locations of the target object while ignoring irrelevant features. In this study of 60 participants, we show objects on backgrounds of increasing complexity to investigate whether recurrent computations are increasingly important for segmenting objects from more complex backgrounds. Three lines of evidence show that recurrent processing is critical for recognition of objects embedded in complex scenes. First, behavioral results indicated a greater reduction in performance after masking objects presented on more complex backgrounds; with the degree of impairment increasing with increasing background complexity. Second, electroencephalography (EEG) measurements showed clear differences in the evoked response potentials (ERPs) between conditions around 200ms - a time point beyond feed-forward activity and object decoding based on the EEG signal indicated later decoding onsets for objects embedded in more complex backgrounds. Third, Deep Convolutional Neural Network performance confirmed this interpretation; feed-forward and less deep networks showed a higher degree of impairment in recognition for objects in complex backgrounds compared to recurrent and deeper networks. Together, these results support the notion that recurrent computations drive figure-ground segmentation of objects in complex scenes.
Interactions between two QTLs for time to anthesis on spike development and fertility in wheat
Authors: Basavaraddi Priyanka A. , Savin Roxana , Wingen Luzie U , Bencivenga Stefano , Przewieslik-Allen Alexandra M. , Griffiths Simon , Slafer Gustavo A. 
Published: November 2020

AbstractEarliness per se (Eps) genes are reported to be important in fine-tuning flowering time in wheat independently of photoperiod (Ppd) and vernalisation (Vrn). UnlikePpdandVrngenes,Epshave relatively small effects and their physiological effect along with chromosomal position are not well defined. We evaluated eight lines derived from crossing Paragon and Baj (late and early flowering respectively), vernalisation insensitive, to study the detailed effects of two newly identified QTLs,Eps-7DandEps-2Band their interactions under field conditions. The effect of both QTLs were minor but their effect was modulated by the allelic status of the other. While the magnitude of effect of these QTLs on anthesis was similar, they are associated with very different profiles of pre-anthesis development which also depends on their interaction.Eps-7Daffected both duration before and after terminal spikelet while not affecting final leaf number (FLN) soEps-7D-earlyhad a faster rate of leaf appearance.Eps-2Bacted more specifically in the early reproductive phase and slightly altered FLN without affecting the leaf appearance rate. BothEps-7Dand2Baffected the spike fertility by altering the rate of floret development and mortality. The effect of the latter was very small but consistent in that the -lateallele tended to produced more fertile florets.