bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learnt image processing to predict weight and size of rice kernels
Authors: Singh Samrendra K , Vidyarthi Sriram K , Tiwari Rakhee 
Published: August 2019

AbstractAccurate measurement of rice kernel sizes after milling is critical to design, develop and optimize rice milling operations. The size and mass of the individual rice kernels are important parameters typically associated with rice quality attributes, particularly head rice yield. In this study, we propose a novel methodology that combines image processing and machine learning (ML) ensemble to accurately measure the size and mass of several rice kernels simultaneously. We have developed an image processing algorithm with the help of recursive method to identify the individual rice kernels from an image and estimate the size of the kernels based on the pixels a kernel occupies. The number of pixels representing a rice kernel has been used as its digital fingerprint in order to predict its size and mass. We have employed a number of popular machine learning models to build a stacked ensemble model (SEM), which can predict the mass of the individual rice kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of our image processing and SEM are quantified using uncertainty quantification. The uncertainty quantification showed 3.6%, 2.5%, and 2.4% for mean errors in estimating the kernel length of small-grain (Calhikari-202), medium-grain (Jupiter), and long-grain (CL153) rice, respectively. Similarly, mean errors associated with predicting the 1000 grain weight are 4.1%, 2.9%, and 4.3% for Calhikari-202, Jupiter, and CL153, respectively. Use of the developed algorithm in rice imaging analyzers could facilitate head rice yield quantifications and promote quicker rice quality appraisals.
Resurrection of a Global, Metagenomically Defined Microvirus
Authors: Kirchberger Paul C. , Ochman Howard 
Published: August 2019

AbstractThe Gokushovirinae (family Microviridae) are a group of single-stranded, circular DNA bacteriophages that have been detected in metagenomic datasets from every ecosystem on the planet. Despite their abundance, little is known about their biology or their bacterial hosts: isolates are exceedingly rare, known only from a very small number of obligate intracellular bacteria. By synthesizing circularized phage genomes from prophages embedded in diverse enteric bacteria, we produced viable gokushovirus phage particles that could reliably infect E. coli, thereby allowing experimental analysis of its life cycle and growth characteristics. Revived phages integrate into host genomes by hijacking a phylogenetically conserved chromosome-dimer resolution system, in a manner reminiscent of cholera phage CTX. Sequence motifs required for lysogeny are detectable in other metagenomically defined gokushoviruses, but we show that even partial motifs enable phages to persist in a state of pseudolysogeny by continuously producing viral progeny inside hosts without leading to collapse of their host culture. This ability to employ multiple, disparate survival strategies is likely key to the long-term persistence and global distribution of Gokushovirinae. The capacity to harness gokushoviruses as an experimentally tractable model system thus substantially changes our knowledge of the nature and biology of these ubiquitous phages.
GABA levels in ventral visual cortex decline with age and are associated with neural distinctiveness
Authors: Chamberlain Jordan D. , Gagnon Holly , Lalwani Poortata , Cassady Kaitlin E. , Simmonite Molly , Seidler Rachael D. , Taylor Stephan F. , Weissman Daniel H. , Park Denise C. , Polk Thad A. 
Published: August 2019

AbstractAge-related neural dedifferentiation – a decline in the distinctiveness of neural representations in the aging brain–has been associated with age-related declines in cognitive abilities. But why does neural distinctiveness decline with age? Based on prior work in non-human primates and more recent work in humans, we hypothesized that the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) declines with age and is associated with neural dedifferentiation in older adults. To test this hypothesis, we used magnetic resonance spectroscopy (MRS) to measure GABA and functional MRI (fMRI) to measure neural distinctiveness in the ventral visual cortex in a set of older and younger participants. Relative to younger adults, older adults exhibited lower GABA levels and less distinct activation patterns for faces and houses in the ventral visual cortex. Furthermore, individual differences in GABA within older adults positively predicted individual differences in neural distinctiveness. These results provide novel support for the view that age-related reductions of GABA contribute to age-related reductions in neural distinctiveness (i.e., neural dedifferentiation) in the human ventral visual cortex.
Unifying ecosystem resistance, resilience, and recovery from extreme stress into a single statistical framework
Authors: Lemoine Nathan P. 
Published: August 2019

AbstractNatural communities and ecosystems are currently experiencing unprecedented rates of environmental and biotic change. While gradual shifts in average conditions, such as rising mean air temperatures, can significantly alter ecosystem function, ecologists recently acknowledged that the most damaging consequences of global change will probably emanate from both a higher prevalence and increased intensity of extreme climatic stress events. Given the potential ecological and societal ramifications of more frequent disturbances, it is imperative that we identify which ecosystems are most vulnerable to global change by accurately quantifying ecosystem responses to extreme stress. Unfortunately, the lack of a standardized method for estimating ecosystem sensitivity to drought makes drawing general conclusions difficult. There is a need for estimates of resistance/resilience/legacy effects that are free of observation error, not biased by stochasticity in production or rainfall, and standardizes stress magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all three components of ecosystem response to stress using standardized language (resistance, resilience, recovery, and legacy effects) while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of stress, estimate legacy effects, and obtain a standardized estimate of ecosystem resistance and resilience to drought free from observation error and stochastic processes inherent in raw data. This method will enable researchers to rigorously compare resistance and resilience among locations using long-term time series, thereby improving our knowledge of ecosystem responses to extreme stress.
Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging
Authors: Kimbrough Adam , Smith Lauren C. , Kallupi Marsida , Simpson Sierra , Collazo Andres , George Olivier 
Published: August 2019

AbstractNumerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.Significance StatementA key aspect of treating drug abuse is understanding similarities and differences of how drugs of abuse affect the brain. In the present study we examined how the brain is altered during withdrawal from psychostimulants. We found that each drug produced a unique pattern of activity in the brain, but that brains in withdrawal from cocaine and methamphetamine shared similar features. Interestingly, we found the major common link between withdrawal from all psychostimulants, when compared to controls, was a shift in the broad organization of the brain in the form of reduced modularity. Reduced modularity has been shown in several brain disorders, including traumatic brain injury, and dementia, and may be the common link between drugs of abuse.
Zygomorphic flowers have fewer potential pollinator species
Authors: Yoder Jeremy B. , Gomez Giancarlo , Carlson Colin J. 
Published: August 2019

ABSTRACTBotanists have long identified bilaterally symmetrical (zygomorphic) flowers with more specialized pollination interactions than radially symmetrical (actinomorphic) flowers. Zygomorphic flowers facilitate more precise contact with pollinators, guide pollinator behaviour, and exclude less effective pollinators. However, whether zygomorphic flowers are actually visited by a smaller subset of available pollinator species has not been broadly evaluated. We compiled 53,609 floral visitation records in 159 communities and classified the plants’ floral symmetry. Globally and within individual communities, plants with zygomorphic flowers are indeed visited by fewer species. At the same time, zygomorphic flowers share a somewhat larger proportion of their visitor species with other co-occurring plants, and have particularly high sharing with co-occurring plants that also have zygomorphic flowers. Visitation sub-networks for zygomorphic species also show differences that may arise from reduced visitor diversity, including greater connectance, greater web asymmetry, and lower coextinction robustness of both plants and visitor species — but these changes do not necessarily translate to whole plant-visitor communities. These results provide context for widely documented associations between zygomorphy and diversification and imply that species with zygomorphic flowers may face greater risk of extinction due to pollinator loss.
Convergent genomic signatures of high altitude adaptation among domestic mammals
Authors: Wu Dong-Dong , Yang Cui-Ping , Wang Ming-Shan , Dong Kun-Zhe , Yan Da-Wei , Hao Zi-Qian , Fan Song-Qing , Chu Shu-Zhou , Shen Qiu-Shuo , Jiang Li-Ping , Li Yan , Zeng Lin , Liu He-Qun , Xie Hai-Bing , Ma Yun-Fei , Kong Xiao-Yan , Yang Shu-Li , Dong Xin-Xing , Koshkoiyeh Ali Esmailizadeh , Irwin David M , Xiao Xiao , Li Ming , Dong Yang , Wang Wen , Shi Peng , Li Hai-Peng , Ma Yue-Hui , Gou Xiao , Chen Yong-Bin , Zhang Ya-Ping 
Published: August 2019

AbstractAbundant and diverse domestic mammals living on the Tibetan Plateau provide useful materials for investigating adaptive evolution and genetic convergence. Here, we utilized 327 genomes from horses, sheep, goats, cattle, pigs and dogs living at both high and low altitudes, including 73 genomes generated for this study, to disentangle the genetic mechanisms underlying local adaptation of domestic mammals. Although molecular convergence is comparatively rare at the DNA sequence level, we found convergent signature of positive selection at the gene level, particularlyEPAS1gene in these Tibetan domestic mammals. We also reported a potential function in response to hypoxia for the geneC10orf67, which underwent positive selection in three of the domestic mammals. Our data provides insight into adaptive evolution of high-altitude domestic mammals, and should facilitate the search for additional novel genes involved in the hypoxia response pathway.
Neocortical activity tracks syllable and phrasal structure of self-produced speech during reading aloud
Authors: Bourguignon Mathieu , Molinaro Nicola , Lizarazu Mikel , Taulu Samu , Jousmäki Veikko , Lallier Marie , Carreiras Manuel , Tiège Xavier De 
Published: August 2019

AbstractTo gain novel insights into how the human brain processes self-produced auditory information during reading aloud, we investigated the coupling between neuromagnetic activity and the temporal envelope of the heard speech sounds (i.e., speech brain tracking) in a group of adults who 1) read a text aloud, 2) listened to a recording of their own speech (i.e., playback), and 3) listened to another speech recording. Coherence analyses revealed that, during reading aloud, the reader’s brain tracked the slow temporal fluctuations of the speech output. Specifically, auditory cortices tracked phrasal structure (<1 Hz) but to a lesser extent than during the two speech listening conditions. Also, the tracking of syllable structure (4–8 Hz) occurred at parietal opercula during reading aloud and at auditory cortices during listening. Directionality analyses based on renormalized partial directed coherence revealed that speech brain tracking at <1 Hz and 4–8 Hz is dominated by speech-to-brain directional coupling during both reading aloud and listening, meaning that speech brain tracking mainly entails auditory feedback processing. Nevertheless, brain-to-speech directional coupling at 4– 8 Hz was enhanced during reading aloud compared with listening, likely reflecting speech monitoring before production. Altogether, these data bring novel insights into how auditory verbal information is tracked by the human brain during perception and self-generation of connected speech.HighlightsThe brain tracks phrasal and syllabic rhythmicity of self-produced (read) speech.Tracking of phrasal structures is attenuated during reading compared with listening.Speech rhythmicity mainly drives brain activity during reading and listening.Brain activity drives syllabic rhythmicity more during reading than listening.
In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics
Authors: Wandy Joe , Davies Vinny , van der Hooft Justin J.J. , Weidt Stefan , Daly Rónán , Rogers Simon 
Published: August 2019

AbstractLiquid-Chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimise strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in-silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data in-silico to allow the testing of different fragmentation strategies. It allows the comparison of different fragmentation strategies on real data, with the resulting scan results extractable as mzML files. To demonstrate its utility, we show how our proposed framework can be used to take the output of a real tandem mass spectrometry analysis and examine the effect of varying parameters in Top-N Data Dependent Acquisition protocol. We also demonstrate how ViMMS can be used to compare a recently published Data-set-Dependent Acquisition strategy with a standard Top-N strategy. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular experiment.
Using target capture to address conservation challenges: population-level tracking of a globally-traded herbal medicine
Authors: Manzanilla Vincent , Teixidor-Toneu Irene , Martin Gary J. , Hollingsworth Peter M. , de Boer Hugo J. , Kool Anneleen 
Published: August 2019

AbstractThe promotion of responsible and sustainable trade in biological resources is widely proposed as one solution to mitigate currently high levels of global biodiversity loss. Various molecular identification methods have been proposed as appropriate tools for monitoring global supply chains of commercialized animals and plants. We demonstrate the efficacy of target capture genomic barcoding in identifying and establishing the geographic origin of samples traded asAnacyclus pyrethrum, a medicinal plant assessed as globally vulnerable in the IUCN Red List of Threatened Species. Samples collected from national and international supply chains were identified through target capture sequencing of 443 low-copy nuclear makers and compared to results derived from genome skimming of plastome, standard plastid barcoding regions and ITS. Both target capture and genome skimming provided approximately 3.4 million reads per sample, but target capture largely outperformed standard plant DNA barcodes and entire plastid genome sequences. Despite the difficulty of distinguishing among closely related species and infraspecific taxa ofAnacyclususing conventional taxonomic methods, we succeeded in identifying 89 of 110 analysed samples to subspecies level without ambiguity through target capture. Of the remaining samples, we determined that eleven contained plant material from other genera and families and ten were unidentifiable regardless of the method used. Furthermore, we were able to discern the geographical origin ofAnacyclussamples collected in Moroccan, Indian and Sri Lankan markets, differentiating between plant materials originally harvested from diverse populations in Algeria and Morocco. With a recent drop in the cost of analysing samples, target capture offers the potential to routinely identify commercialized plant species and determine their geographic origin. It promises to play an important role in monitoring and regulation of plant species in trade, supporting biodiversity conservation efforts, and in ensuring that plant products are unadulterated, contributing to consumer protection.
Modelling the niches of wild and domesticated Ungulate species using deep learning
Authors: Rademaker Mark , Hogeweg Laurens , Vos Rutger 
Published: August 2019

AbstractKnowledge of global biodiversity remains limited by geographic and taxonomic sampling biases. The scarcity of species data restricts our understanding of the underlying environmental factors shaping distributions, and the ability to draw comparisons among species. Species distribution models (SDMs) were developed in the early 2000s to address this issue. Although SDMs based on single layered Neural Networks have been experimented with in the past, these performed poorly. However, the past two decades have seen a strong increase in the use of Deep Learning (DL) approaches, such as Deep Neural Networks (DNNs). Despite the large improvement in predictive capacity DNNs provide over shallow networks, to our knowledge these have not yet been applied to SDM. The aim of this research was to provide a proof of concept of a DL-SDM1. We used a pre-existing dataset of the world’s ungulates and abiotic environmental predictors that had recently been used in MaxEnt SDM, to allow for a direct comparison of performance between both methods. Our DL-SDM consisted of a binary classification DNN containing 4 hidden layers and drop-out regularization between each layer. Performance of the DL-SDM was similar to MaxEnt for species with relatively large sample sizes and worse for species with relatively low sample sizes. Increasing the number of occurrences further improved DL-SDM performance for species that already had relatively high sample sizes. We then tried to further improve performance by altering the sampling procedure of negative instances and increasing the number of environmental predictors, including species interactions. This led to a large increase in model performance across the range of sample sizes in the species datasets. We conclude that DL-SDMs provide a suitable alternative to traditional SDMs such as MaxEnt and have the advantage of being both able to directly include species interactions, as well as being able to handle correlated input features. Further improvements to the model would include increasing its scalability by turning it into a multi-classification model, as well as developing a more user friendly DL-SDM Python package.
Robust Hi-C chromatin loop maps in human neurogenesis and brain tissues at high-resolution
Authors: Lu Leina , Liu Xiaoxiao , Huang Wei-Kai , Giusti-Rodríguez Paola , Cui Jian , Zhang Shanshan , Xu Wanying , Wen Zhexing , Ma Shufeng , Rosen Jonathan D , Xu Zheng , Bartels Cynthia , Kawaguchi Riki , Hu Ming , Scacheri Peter , Rong Zhili , Li Yun , Sullivan Patrick F , Song Hongjun , Ming Guo-li , Li Yan , Jin Fulai 
Published: August 2019

AbstractGenome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input “easy Hi-C” (eHi-C) protocol to map the 3D genome architecture in neurogenesis and brain tissues, and also developed an improved Hi-C bias-correction pipeline (HiCorr) enabling better identification of enhancer loops or aggregates at sub-TAD level. We compared ultra-deep 3D genome maps from 10 human tissue- or cell types, with a focus on stem cells and neural development. We found several large loci in skin-derived human iPSC lines showing recurrent 3D compartmental memory of somatic heterochromatin. Chromatin loop interactions, but not genome compartments, are hallmarks of neural differentiation. Interestingly, we observed many cell type- or differentiation-specific enhancer aggregates spanning large neighborhoods, supporting a phase-separation mechanism that stabilizes enhancer contacts during development. Finally, we demonstrated that chromatin loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.HighlightsLow input “easy Hi-C” protocol compatible with 50-100K cellsImproved Hi-C bias correction allows direct observation and accurate identification of sub-TAD chromatin loops and enhancer aggregatesRecurrent architectural memory of somatic heterochromatin at compartment level in skin-derived hiPSCsChromatin loop, but not genome compartment, marks neural differentiationChromatin loop outperforms eQTL in defining brain GWAS target genes