bluuebunny/crossref_metadata_2025_split huggingface dataset:
Differing effects of size and lifestyle on bone structure in mammals
Authors: Amson Eli , Bibi Faysal 
Published: August 2020

AbstractThe skeleton is involved in most aspects of vertebrate life history. Previous macroevolutionary analyses have shown that structural, historical, and functional factors influence the gross morphology of bone. The inner structure of bone has, however, received comparatively little attention. Here we address this gap in our understanding of vertebrate evolution by quantifying bone structure in appendicular and axial elements (humerus and mid-lumbar vertebra) across therian mammals (placentals + marsupials). Our sampling captures all transitions to aerial, fully aquatic, and subterranean lifestyles in extant mammal clades. We found that mammalian inner bone structure is highly disparate. We show that vertebral structure mostly correlates with body size, but not lifestyle, while the opposite is true for humeral structure. The latter also shows a high degree of convergence among the clades that have acquired specialised lifestyles. Our results suggest that radically different extrinsic constraints can apply to bone structure in different skeletal elements.
IP3R activity increases frequency of RyR-mediated sparks by elevating dyadic Ca2+
Authors: Chung Joshua , Tilūnaitė Agnė , Ladd David , Hunt Hilary , Soeller Christian , Crampin Edmund J. , Johnston Stuart , Roderick H. Llewelyn , Rajagopal Vijay 
Published: August 2020

AbstractCalcium (Ca2+) plays a critical role in the excitation contraction coupling (ECC) process that governs the contraction of cardiomyocytes during each heartbeat. While ryanodine receptors (RyRs) are the primary Ca2+channels responsible for mediating cell-wide Ca2+transients during ECC, Ca2+release via inositol 1,4,5-trisphosphate (IP3) receptors (IP3Rs) have been reported to elicit ECC-modulating effects. Recent studies suggest that the proximal localization of IP3Rs at dyads grants their ability to modify the occurrence of Ca2+sparks (elementary Ca2+release events that constitute ECC-associated Ca2+transients) which may underlie the modulatory effects on ECC. Here, we aim to uncover the mechanism by which IP3Rs affect Ca2+spark dynamics. To this end, we developed a mathematical model of the dyad that incorporates IP3Rs to reveal their impact on local Ca2+handling and corresponding Ca2+spark formation. Consistent with published experimental data, our model predicts that the propensity for Ca2+spark formation increases with IP3R activity. Our simulations support the hypothesis that IP3R activity elevates Ca2+within the dyad, sensitizing proximal RyRs for future release. However, this lowers Ca2+in the JSR available for release and thus results in Ca2+sparks with the same duration but lower amplitudes.
Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data
Authors: Munden Rhys , Börger Luca , Wilson Rory P. , Redcliffe James , Brown Rowan , Garel Mathieu , Potts Jonathan R. 
Published: August 2020

AbstractStep selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as “selecting” each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data is increasingly gathered at very high frequencies, often ≥1Hz, so it may be possible to exploit these data to perform more behaviourally-meaningful step selection analysis.Here, we present a technique to do this. We first use an existing algorithm to determine the turning-points in an animal’s movement path. We define a “step” to be a straight-line movement between successive turning-points. We then construct a generalised version of integrated SSA (iSSA), calledtime-varying iSSA(tiSSA), which deals with the fact that turning-points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free-ranging goats (Capra aegagrus hircus), comparing our results to those of regular iSSA with locations that are separated by a constant time-interval.Using (regular) iSSA with constant time-steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.By constructing a step selection technique that works between observed turning-points of animals, we enable step selection to be used on high-frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning-points can be viewed as decisions, our method places step selection analysis on a more behaviourally-meaningful footing compared to previous techniques.
A lateralised design for the interaction of visual memories and heading representations in navigating ants
Authors: Wystrach Antoine , Le Moël Florent , Clement Leo , Schwarz Sebastian 
Published: August 2020

AbstractThe navigational skills of ants, bees and wasps represent one of the most baffling examples of the powers of minuscule brains. Insects store long-term memories of the visual scenes they experience1, and they use compass cues to build a robust representation of directions2,3. We know reasonably well how long-term memories are formed, in a brain area called the Mushroom Bodies (MB)4–8, as well as how heading representations are formed in another brain area called the Central Complex (CX)9–12. However, how such memories and heading representations interact to produce powerful navigational behaviours remains unclear7,13,14. Here we combine behavioural experiments with computational modelling that is strictly based on connectomic data to provide a new perspective on how navigation might be orchestrated in these insects. Our results reveal a lateralised design, where signals about whether to turn left or right are segregated in the left and right hemispheres, respectively. Furthermore, we show that guidance is a two-stage process: the recognition of visual memories – presumably in the MBs – does not directly drive the motor command, but instead updates a “desired heading” – presumably in the CX – which in turn is used to control guidance using celestial compass information. Overall, this circuit enables ants to recognise views independently of their body orientation, and combines terrestrial and celestial cues in a way that produces exceptionally robust navigation.
A Patient-based Model to Investigate the Effect of Micro-structure Scars on the Optogenetic Defibrillation Success: A Computational Modelling Approach
Authors: Zafarghandi Elham Zakeri , Bahrami Fariba 
Published: August 2020

AbstractThis study investigates the role of the microstructure of real scars in the success of optogenetic defibrillation. To reduce the computational cost of high-order models (like Ten Tusscher Model, TTM) for a single cell as well as to take advantage of their ability to generate a more realistic output, we developed a low-order model of optogenetic cardiac tissue based on the modified Alieve-Panfilov single-cell model and estimated its parameters using a TTM. Two-dimensional electrophysiological cardiac tissue models were produced including different scar shapes that were extracted from Late Gadolinium-Enhanced (LGE) magnetic resonance imaging data set of 10 patients with non-ischemic dilated cardiomyopathy. The scar shapes were classified based on four criteria: transmurality, relative area, scar entropy, and interface length. Scar with the highest 25% of the relative area showed 25% of successful cases, this ratio is 27%, and 25% for a scar with the most top 25% of entropy, and transmurality, respectively. In comparison, the proportions are 61.54%, 44.44%, and 61.76%, for the lowest 25% of the area, entropy, and transmurality. We also investigated the efficacy of various methods for light-sensitive cells’ distribution within the cardiac tissue with scar. Four types of distributions were defined. Defibrillation within tissues with 0.1 light-sensitive out of all cells was 15 to 25% more successful than their counterparts with 0.05 light-sensitive cells. Lastly, we examined the effect of an earlier stimulation on the success probability of defibrillation. Our results indicated that inducing 0.5 msec earlier resulted in a roughly 15% rise in successful cases.
Maternal high fat diet alters lactation-specific miRNA expression and programs the DNA methylome in the amygdala of female offspring
Authors: Abuaish Sameera , Wijenayake Sanoji , de Vega Wilfred C. , Lum Christine M.W. , Sasaki Aya , McGowan Patrick O. 
Published: August 2020

AbstractAdverse maternal diets high in saturated fats are associated with impaired neurodevelopment and epigenetic modifications in offspring. Maternal milk, the primary source of early life nutrition in mammals, contains lactation-specific microRNAs (miRNAs). Lactation-specific miRNAs have been found in various offspring tissues in early life, including the brain. We examined the effects of maternal high saturated fat diet (mHFD) on lactation-specific miRNAs that inhibit DNA methyltransferases (DNMTs), enzymes that catalyze DNA methylation modifications, in the amygdala of female offspring during early life and adulthood. Offspring exposed to mHFD showed reduced miR-148/152 and miR-21 transcripts in stomach milk and amygdala in the first week of life. This was associated with increased DNMT1 expression, DNMT activity, and global DNA methylation in the amygdala. In addition, persistent DNA methylation modifications from early life to adulthood were observed in pathways involved in neurodevelopment as well as genes regulating the DNMT machinery and protein function in mHFD offspring. The findings indicate a novel link between exogenous, lactation-specific miRNAs and developmental programming of the neural DNA methylome in offspring.
A Statistical Framework for QTL Hotspot Detection
Authors: Wu Po-Ya , Yang Man-Hsia , Kao Chen-Hung 
Published: August 2020

ABSTRACTQuantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These detection methods attempt to address some of the concerns, including the correlation structure among traits, the magnitude of LOD scores within a hotspot and computational cost, that arise during the process of QTL hotspot detection. In this article, we describe a statistical framework that can handle both types of data as well as address all the concerns at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computation cost, and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top γn,αprofile, are introduced into the framework. The trait grouping attempts to group the closely linked or pleiotropic traits together to take care of the true linkages and cope with the underestimation of hotspot thresholds due to non-genetic correlations (arising from ignoring the correlation structure among traits), so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top γn,αprofile is designed to outline the LOD-score pattern of a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD score distributions. Real examples, numerical analysis and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.
Inhibitory control and the structural parcellation of the right inferior frontal gyrus
Authors: Boen Rune , Raud Liisa , Huster Rene J. 
Published: August 2020

AbstractThe right inferior frontal gyrus (rIFG) has most strongly, although not exclusively, been associated with response inhibition, not least based on covariations of behavioral performance measures and local grey matter characteristics. However, the white matter microstructure of the rIFG as well as its connectivity has been less in focus, especially when it comes to the consideration of potential subdivisions within this area. The present study reconstructed the structural connections of the three main subregions of the rIFG (i.e. pars opercularis, pars triangularis and pars orbitalis) using diffusion tensor imaging, and further assessed their associations with behavioral measures of inhibitory control. The results revealed a marked heterogeneity of the three subregions with respect to the pattern and extent of their connections, with the pars orbitalis showing the most widespread inter-regional connectivity, while the pars opercularis showed the least amount of connections. When relating behavioral performance measures of a stop signal task to brain structure, the data indicated a differential association of dorsal and ventral opercular connectivity with the go reaction time and the stopping accuracy, respectively.
Microbiota in milk from healthy and mastitis cows varies greatly in diversity, species richness and composition, as revealed by PacBio sequencing
Authors: Ma Teng , Shen Lingling , Wen Qiannan , lv Ruirui , Hou Qiangchuan , Kwok Lai Yu , Sun Zhihong , Zhang Heping 
Published: August 2020

AbstractMastitis is the most economically important disease of dairy cows. This study used PacBio single-molecule real-time sequencing technology to sequence the full-length of the l6S rRNA from the microbiota in 27 milk samples (18 from mastitis and 9 from healthy cows; the cows were at different stages of lactation). We observed that healthy or late stage milk microbiota had significantly higher microbial diversity and richness. The community composition of the microbiota from different groups also varied greatly. In milk from healthy cows the microbiota was predominantly comprised ofLactococcus lactis, Acinetobacter johnsoniiandBacteroides dorei, while from mastitis cows it was predominantly comprised ofBacillus cereus, Clostridium cadaverisandStreptococcus suis. The prevalence ofLa. lactisandB. cereusin milk from healthy and mastitis cows was confirmed by digital droplets PCR. Differences in milk microbiota composition could suggest an important role for these microbes in protecting the host from mastitis. Based on the milk microbiota profiles, the Udder Health Index was constructed to predict the risk of bovine mastitis. Application of this predictive model could aid early identification and prevention of mastitis in dairy cows, though the model requires further optimisation using a larger dataset.
Effective variant filtering and expected candidate variant yield in studies of rare human disease
Authors: Pedersen Brent S. , Brown Joe M. , Dashnow Harriet , Wallace Amelia D. , Velinder Matt , Tvrdik Tatiana , Mao Rong , Best D. Hunter , Bayrak-Toydemir Pinar , Quinlan Aaron R. 
Published: August 2020

ABSTRACTIn studies of families with rare disease, it is common to screen forde novomutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we derive effective guidelines for variant filtering and report the expected number of candidates forde novodominant and recessive modes of inheritance. The filters are applied to common attributes, including genotype quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield approximately 10 candidate SNP and INDEL variants per exome, and 19 per genome. For whole genomes, this includes an average of threede novo, ten compound-heterozygotes, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. Theslivarsoftware we developed to establish and rapidly apply these filters to VCF files is available athttps://github.com/brentp/slivarunder an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.
glmGamPoi: Fitting Gamma-Poisson Generalized Linear Models on Single Cell Count Data
Authors: Ahlmann-Eltze Constantin , Huber Wolfgang 
Published: August 2020

AbstractMotivationThe Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts (Grünet al., 2014; Towneset al., 2019; Svensson, 2020; Silvermanet al., 2018; Hafemeister and Satija, 2019) and an essential building block for analysis approaches including differential expression analysis (Robinsonet al., 2010; McCarthyet al., 2012; Anders and Huber, 2010; Loveet al., 2014), principal component analysis (Towneset al., 2019) and factor analysis (Rissoet al., 2018). Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which typically comprise thousands or millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation.ResultsWe present a newRpackage for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously.AvailabilityThe packageglmGamPoiis available from Bioconductor (since release 3.11) for Windows, macOS, and Linux, and source code is available on GitHub under a GPL-3 license. The scripts to reproduce the results of this paper are available on GitHub as well.Contactconstantin.ahlmann@embl.de
Scarless engineering of theDrosophilagenome near any site-specific integration site
Authors: Feng Siqian , Lu Shan , Grueber Wesley B. , Mann Richard S. 
Published: August 2020

AbstractWe describe a simple and efficient technique that allows scarless engineering ofDrosophilagenomic sequences near any landing site containing an inverted attP cassette, such as aMiMICinsertion. This 2-step method combines phiC31 integrase mediated site-specific integration and homing nuclease-mediated resolution of local duplications, efficiently converting the original landing site allele to modified alleles that only have the desired change(s). Dominant markers incorporated into this method allow correct individual flies to be efficiently identified at each step. In principle, single attP sites and FRT sites are also valid landing sites. Given the large and increasing number of landing site lines available in the fly community, this method provides an easy and fast way to efficiently edit the majority of theDrosophilagenome in a scarless manner. This technique should also be applicable to other species.
Transient non-specific DNA binding dominates the target search of bacterial DNA-binding proteins
Authors: Stracy Mathew , Schweizer Jakob , Sherratt David J , Kapanidis Achillefs N , Uphoff Stephan , Lesterlin Christian 
Published: August 2020

ABSTRACTDespite their diverse biochemical characteristics and functions, all DNA-binding proteins share the ability to accurately locate their target sites among the vast excess of non-target DNA. Towards identifying universal mechanisms of the target search, we used single-molecule tracking of 11 diverse DNA-binding proteins in living Escherichia coli. The mobility of these proteins during the target search was dictated by DNA interactions, rather than by their molecular weights. By generating cells devoid of all chromosomal DNA, we discovered that the nucleoid does not pose a physical barrier for protein diffusion, but significantly slows the motion of DNA-binding proteins through frequent short-lived DNA interactions. The representative DNA-binding proteins (irrespective of their size, concentration, or function) spend the majority (58-99%) of their search time bound to DNA and occupy as much as ∼30% of the chromosomal DNA at any time. Chromosome-crowding likely has important implications for the function of all DNA-binding proteins.
Genetic testing of dogs predicts problem behaviors in clinical and nonclinical samples
Authors: Zapata Isain , Lilly M. Leanne , Herron Meghan E. , Serpell James A. , Alvarez Carlos E. 
Published: August 2020

AbstractVery little is known about the etiology of personality and psychiatric disorders. Because the core neurobiology of many such traits is evolutionarily conserved, dogs present a powerful model. We previously reported genome scans of breed averages of ten traits related to fear, anxiety, aggression and social behavior in multiple cohorts of pedigree dogs. As a second phase of that discovery, here we tested the ability of markers at 13 of those loci to predict canine behavior in a community sample of 397 pedigree and mixed-breed dogs with individual-level genotype and phenotype data. We found support for all markers and loci. By including 122 dogs with veterinary behavioral diagnoses in our cohort, we were able to identify eight loci associated with those diagnoses. Logistic regression models showed subsets of those loci could predict behavioral diagnoses. We corroborated our previous findings that small body size is associated with many problem behaviors and large body size is associated with increased trainability. Children in the home were associated with anxiety traits; illness and other animals in the home with coprophagia; working-dog status with increased energy and separation-related problems; and competitive dogs with increased aggression directed at familiar dogs, but reduced fear directed at humans and unfamiliar dogs. Compared to other dogs, Pit Bull-type dogs were not defined by a set of our markers and were not more aggressive; but they were strongly associated with pulling on the leash. Using severity-threshold models, Pit Bull-type dogs showed reduced risk of owner-directed aggression (75thquantile) and increased risk of dog-directed fear (95thquantile). Our findings have broad utility, including for clinical and breeding purposes, but we caution that thorough understanding is necessary for their interpretation and use.