bluuebunny/crossref_metadata_2025 huggingface dataset:
Imaging findings of IgG4-related kidney disease without extrarenal organ involvement
Authors: Zhang ShuiXia , Yang Qian
Published: August 2019

Rationale:

IgG4-related disease (IgG4-RD) is a systemic chronic inflammatory disorder that can affect almost every organ. IgG4-RD includes IgG4-related kidney disease (IgG4-RKD), but lesions affecting the kidney alone or first are very rare, and a complete understanding is lacking. Computed tomography (CT) and magnetic resonance imaging (MRI) findings can show the typical characteristics of IgG4-RKD and provide information for accurate and rapid diagnosis.

Patient concerns:

We report a case of a 60-year-old woman who was admitted to our hospital for dizziness and instability while walking, her bilateral eyelids were also slightly swollen. She had no medical history.

Diagnoses:

CT and MRI images of the patient revealed multiple local and diffuse patchy lesions in the bilateral renal parenchyma and mass-like tissue in the bilateral renal pelvis, accompanied by right hydronephrosis. A pathological examination of renal samples showed numerous lymphocyte and plasma cell infiltration. Immunohistochemistry demonstrated approximately 50% of the IgG-positive plasma cells to be IgG4+. The serum IgG level was obviously elevated, with both C3and C4 levels were reduced. The patient was diagnosed with IgG4-RKD.

Interventions:

The patient received corticosteroid therapy at another hospital.

Outcomes:

The bilateral kidney lesions were smaller on follow-up CT images.

Lessons:

IgG4-RKD exhibits some characteristic imaging features. Despite the relatively low incidence of IgG4-RKD, it should be included in differential diagnoses when images show multiple lesions in kidneys with mild and delayed enhancement and hypointensity on T2WI in middle-aged to elderly patients
Challenges and Strategies for Conducting Research With Grandparent–Grandchild Dyads
Authors: Song MinKyoung , Bonds Kalisha
Published: August 2019

Background

Given a marked increase in the number of grandparents who play a caregiving role for their grandchildren, there is a parallel urgency for more research involving grandparent–grandchild dyads.

Objective

The aim of this study was to describe methodological challenges we encountered when conducting grandparent–grandchild dyadic research, some of which were challenges specific to working with dyads where the grandparents were caregivers to the child participants and others, which were challenges of a sort that might be encountered in any research involving data collection with multigenerational family units and, in particular, multigenerational family units including older adults.

Methods

During and after an observational study conducted by the researchers involving grandparent–grandchild dyads, we explored challenges we encountered and strategies for improving our research outcomes.

Results

We found several challenges specific to grandparent–grandchild dyads, including complications with reaching legal guardians to obtain permission for child participants; collecting accelerometer data from grandchildren was difficult, particularly if the grandparents did not live with their grandchildren; and participants who had various preferences for data collection methods. We also found challenges that might be found with any family context dyadic research, particularly involving older adults and across multiple generations, including relatively low follow-up response rates from those grandparents who initially expressed interest and a need for an extended data collection period. We employed targeted strategies to overcome those obstacles, and in this article, we describe the outcomes of those strategies.

Discussion

Recommendations from our results include the following: (a) find a way to involve the parents and/or legal guardian as early as possible, (b) have multiple team members involved in recruitment/data collection, (c) be flexible about data collection methods, (d) use flexible scheduling, and (e) use multiple simultaneous recruitment activities/multiple advertisement channels. Additional strategies include enhancing children’s sense of control during the consent process, simplifying study procedures, considering usability when creating online data surveys, and identifying power dynamics within the families.

Conclusion

The insights we gained will be useful for informing future family context dyadic research, in particular, research involving grandparent–grandchild dyads.
Cell death during complete metamorphosis
Authors: Tettamanti Gianluca , Casartelli Morena
Published: August 2019

In insects that undergo complete metamorphosis, cell death is essential for reshaping or removing larval tissues and organs, thus contributing to formation of the adult's body structure. In the last few decades, the study of metamorphosis in Lepidoptera and Diptera has provided broad information on the tissue remodelling processes that occur during larva–pupa–adult transition and made it possible to unravel the underlying regulatory pathways. This review summarizes recent knowledge on cell death mechanisms in Lepidoptera and other holometabolous insects, highlighting similarities and differences withDrosophila melanogaster, and discusses the role of apoptosis and autophagy in this developmental setting.

This article is part of the theme issue ‘The evolution of complete metamorphosis'.
Leveraging multiple data types to estimate the true size of the Zika epidemic in the Americas
Authors: Moore Sean M. , Oidtman Rachel J. , Soda K. James , Siraj Amir S. , Reiner Robert C. , Barker Christopher M. , Perkins T. Alex
Published: August 2019

Since the first Zika virus (ZIKV) infection was confirmed in Brazil in May 2015, several hundred thousand cases have been reported across the Americas. This figure gives an incomplete picture of the epidemic, however, given that asymptomatic infections, imperfect surveillance, and variability in reporting rates imply that the incidence of infection was likely much higher. The infection attack rate (IAR)—defined as the proportion of the population that was infected over the course of the epidemic—has important implications for the longer-term epidemiology of Zika in the region, such as the timing, location, and likelihood of future outbreaks. To estimate the IAR and the total number of people infected, we leveraged multiple types of Zika case data from 15 countries and territories where subnational data were publicly available. Datasets included confirmed and suspected Zika cases in pregnant women and in the total population, Zika-associated Guillan-Barré syndrome cases, and cases of congenital Zika syndrome. We used a hierarchical Bayesian model with empirically-informed priors that leveraged the different case report types to simultaneously estimate national and subnational reporting rates, the fraction of symptomatic infections, and subnational IARs. In these 15 countries and territories, estimates of Zika IAR ranged from 0.084 (95% CrI: 0.067 − 0.096) in Peru to 0.361 (95% CrI: 0.214 − 0.514) in Ecuador, with significant subnational variability in IAR for every country. Totaling these infection estimates across these and 33 other countries and territories in the region, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas have been infected by ZIKV since 2015. These estimates represent the most extensive attempt to date to determine the size of the Zika epidemic in the Americas, and they offer an important baseline for assessing the risk of future Zika epidemics in this region.
Chromosome-level assemblies of multiple Arabidopsis genomes reveal hotspots of rearrangements with altered evolutionary dynamics
Authors: Jiao Wen-Biao , Schneeberger Korbinian
Published: August 2019

We report chromosome-level, reference-quality assemblies of sevenArabidopsis thalianaaccessions selected across the global range of this predominately ruderal plant. Each genome revealed between 13-17 Mb rearranged and 5-6 Mb novel sequence introducing copy-number changes in ∼5,000 genes, including ∼1,900 genes which are not part of the current reference annotation. Analyzing the collinearity between the genomes revealed ∼350 regions (4.1% of the euchromatin) where accession-specific tandem duplications destroyed the syntenic gene order between the genomes. Thesehotspots of rearrangementswere characterized by the loss of meiotic recombination in hybrids within these regions and the enrichment of genes implicated in biotic stress response. Together this suggests that hotspots of rearrangements are governed by altered evolutionary dynamics as compared to the rest of the genome, which are based on new mutations and not on the recombination of existing variation, and thereby enable a quick response to the ever-evolving challenges of biotic stress.
Cytoplasmic mRNA decay factors modulate RNA polymerase II processivity in 5’ and 3’ gene regions in yeast
Authors: Fischer Jonathan , Song Yun S. , Yosef Nir , di Iulio Julia , Churchman L. Stirling , Choder Mordechai
Published: August 2019

mRNA levels are determined by the balance between mRNA synthesis and decay. Factors that mediate both processes, including the 5’ to 3’ exonuclease Xrn1, are responsible for the cross talk between the two processes in a manner that buffers steady-state mRNA levels. However, these proteins’ roles in transcription remain elusive and controversial. Applying NET-seq to yeast cells, we show that Xrn1 functions mainly as a transcriptional activator and that its disruption manifests via the reduction of RNA polymerase II (Pol II) occupancy downstream of transcription start sites. We combine our data and novel mathematical modeling of transcription to suggest that transcription initiation and elongation of targeted genes is modulated by Xrn1. Furthermore, Pol II occupancy markedly increases near cleavage and polyadenylation sites in xrn1Δ cells while its activity decreases, a characteristic feature of backtracked Pol II. We also provide indirect evidence that Xrn1 is involved in transcription termination downstream of polyadenylation sites. Two additional decay factors, Dhh1 and Lsm1, seem to function similarly to Xrn1 in transcription, perhaps as a complex, while the decay factors Ccr4 and Rpb4 also perturb transcription in other ways. Interestingly, DFs are capable of differentiating between SAGA- and TFIID-dominated promoters. These two classes of genes respond differently to XRN1 deletion in mRNA synthesis and differentially utilize mRNA decay pathways, raising the possibility that one distinction between the two types of genes lies in the mechanism(s) that balance these processes.
Ultrafine mapping of chromosome conformation at hundred basepair resolution reveals regulatory genome architecture
Authors: Zhu Yizhou , Suh Yousin
Published: August 2019

The resolution limit of chromatin conformation capture methodologies (3Cs) has restrained their application in detection of fine-level chromatin structure mediated by cis-regulatory elements (CREs). Here we report two 3C-derived methods, Tri-4C and Tri-HiC, which utilize mult-restriction enzyme digestions for ultrafine mapping of targeted and genome-wide chromatin interaction, respectively, at up to one hundred basepair resolution. Tri-4C identified CRE loop interaction networks and quantifatively revealed their alterations underlying dynamic gene control. Tri-HiC uncovered global fine-gage regulatory interaction networks, identifying > 20-fold more enhancer:promoter (E:P) loops than in situ HiC. In addition to vasly improved identification of subkilobase-sized E:P loops, Tri-HiC also uncovered interaction stripes and contact domain insulation from promoters and enhancers, revealing their loop extrusion behaviors resembling the topologically-associated domain (TAD) boundaries. Tri-4C and Tri-HiC provide robust approaches to achieve the high resolution interactome maps required for characterizing fine-gage regulatory chromatin interactions in analysis of development, homeostasis and disease.
Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning
Authors: Huttunen Mikko J. , Hristu Radu , Dumitru Adrian , Costache Mariana , Stanciu Stefan G.
Published: August 2019

Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton Microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained Convolutional Neural Networks. Our results suggest that Deep Learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.
Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines
Authors: Dinga Richard , Penninx Brenda W.J.H. , Veltman Dick J. , Schmaal Lianne , Marquand Andre F.
Published: August 2019

Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the proportion of correctly classified samples. While simple and intuitive, other performance measures are often more appropriate with respect to many common goals of neuroimaging pattern recognition studies. In this paper, we will review alternative performance measures and focus on their interpretation and practical aspects of model evaluation. Specifically, we will focus on 4 families of performance measures: 1) categorical performance measures such as accuracy, 2) rank based performance measures such as the area under the curve, 3) probabilistic performance measures based on quadratic error such as Brier score, and 4) probabilistic performance measures based on information criteria such as logarithmic score. We will examine their statistical properties in various settings using simulated data and real neuroimaging data derived from public datasets. Results showed that accuracy had the worst performance with respect to statistical power, detecting model improvement, selecting informative features and reliability of results. Therefore in most cases, it should not be used to make statistical inference about model performance. Accuracy should also be avoided for evaluating utility of clinical models, because it does not take into account clinically relevant information, such as relative cost of false-positive and false-negative misclassification or calibration of probabilistic predictions. We recommend alternative evaluation criteria with respect to the goals of a specific machine learning model.
Multidimensional Data Organization and Random Access in Large-Scale DNA Storage Systems
Authors: Song Xin , Shah Shalin , Reif John
Published: August 2019

With impressive density and coding capacity, DNA offers a promising solution for building long-lasting data archival storage systems. In recent implementations, data retrieval such as random access typically relies on a large library of non-interacting PCR primers. While several algorithms automate the primer design process, the capacity and scalability of DNA-based storage systems are still fundamentally limited by the availability of experimentally validated orthogonal primers. In this work, we combine the nested and semi-nested PCR techniques to virtually enforce multidimensional data organization in large DNA storage systems. The strategy effectively pushes the limit of DNA storage capacity and reduces the number of primers needed for efficient random access from very large address space. Specifically, our design requires k * n unique primers to index nk data entries, where k specifies the number of dimensions and n indicates the number of data entries stored in each dimension. We strategically leverage forward/reverse primer pairs from the same or different address layers to virtually specify and maintain data retrievals in the form of rows, columns, tables, and blocks with respect to the original storage pool. This architecture enables various random-access patterns that could be tailored to preserve the underlying data structures and relations (e.g., files and folders) within the storage content. With just one or two rounds of PCR, specific data subsets or individual datum from the large multidimensional storage can be selectively enriched for simple extraction by gel electrophoresis or readout via sequencing.

Abstract Figure
Machine learnt image processing to predict weight and size of rice kernels
Authors: Singh Samrendra K , Vidyarthi Sriram K , Tiwari Rakhee
Published: August 2019

Accurate 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

The 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

Age-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

Natural 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

Numerous 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 Statement

A 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

Botanists 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

Abundant 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

To 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.

Highlights

The 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

Liquid-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

Abstract

The 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

Knowledge 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

Genome-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.

Highlights

Low input “easy Hi-C” protocol compatible with 50-100K cells

Improved Hi-C bias correction allows direct observation and accurate identification of sub-TAD chromatin loops and enhancer aggregates

Recurrent architectural memory of somatic heterochromatin at compartment level in skin-derived hiPSCs

Chromatin loop, but not genome compartment, marks neural differentiation

Chromatin loop outperforms eQTL in defining brain GWAS target genes
Use of polygenic risk scores and other molecular markers to enhance cardiovascular risk prediction: prospective cohort study and modelling analysis
Authors: Sun Luanluan , Pennells Lisa , Kaptoge Stephen , Nelson Christopher P , Abraham Gad , Arnold Matthew , Bell Steven , Bolton Thomas , Burgess Stephen , Dudbridge Frank , Guo Qi , Ritchie Scott C , Sofianopoulou Eleni , Stevens David , Thompson John R , Butterworth Adam S , Wood Angela , Danesh John , Samani Nilesh J , Inouye Michael , Angelantonio Emanuele Di
Published: August 2019

Background

There is debate about the value of adding information on genetic and other molecular markers to conventional cardiovascular disease (CVD) risk predictors.

Methods

Using data on 306,654 individuals without a history of CVD from UK Biobank, we calculated measures of risk-discrimination and reclassification upon addition of polygenic risk scores (PRS) and a panel of 27 clinical biochemistry markers to a conventional risk prediction model (i.e., including age, sex, systolic blood pressure, smoking status, history of diabetes, total cholesterol and HDL cholesterol). We then modelled implications of initiating guideline-recommended statin therapy after the assessment of molecular markers for a UK primary-care setting.

Findings

The C-index was 0.710 (95% CI, 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. The C-index increased by similar amounts when adding information on PRS or biochemistry markers (0.011 and 0.014, respectively; P<0.001), and it increased still further (0.022; P<0.001) when information on both was combined. Among cases and controls, continuous net reclassification improvements were about 12% and 19%, respectively, when both PRS and biochemistry markers were added. If PRS and biochemistry markers were to be assessed in the entire primary care population aged 40-75, then it could help prevent one additional CVD event for every 893 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5-10%) 10-year CVD risk could help prevent one additional CVD event for every 233 individuals screened. This targeted strategy could help reclassify 16% of the intermediate-risk group to the high-risk (i.e., ≥10%) category, preventing 11% more CVD events than conventional risk prediction.

Interpretation

Adding information on both PRS and selected biochemistry markers moderately enhanced CVD predictive accuracy and could improve primary prevention of CVD. However, our modelling suggested that targeted assessment of molecular markers among individuals at intermediate-risk would be more efficient than blanket approaches.
Cellular and molecular mechanisms of photoreceptor tuning for prey capture in larval zebrafish
Authors: Yoshimatsu Takeshi , Schröder Cornelius , Nevala Noora E , Berens Philipp , Baden Tom
Published: August 2019

In the eye, the function of same-type photoreceptors must be regionally adjusted to process a highly asymmetrical natural visual world. Here we show that UV-cones in the larval zebrafisharea temporalisare specifically tuned for UV-bright prey capture in their upper frontal visual field, which uses the signal from a single cone at a time. For this, UV-detection efficiency is regionally boosted 42-fold. Next,in vivo2-photon imaging, transcriptomics and computational modelling reveal that these cones use an elevated baseline of synaptic calcium to facilitate the encoding of bright objects, which in turn results from expressional tuning of phototransduction genes. Finally, this signal is further accentuated at the level of glutamate release driving retinal networks. These regional differences tally with variations between peripheral and foveal cones in primates and hint at a common mechanistic origin. Together, our results highlight a rich mechanistic toolkit for the tuning of neurons.
Energy flux controls tetraether lipid cyclization in Sulfolobus acidocaldarius
Authors: Zhou Alice , Chiu Beverly K. , Weber Yuki , Elling Felix J. , Cobban Alec B. , Pearson Ann , Leavitt William D.
Published: August 2019

Microorganisms regulate the composition of their membranes in response to environmental cues. Many archaea maintain the fluidity and permeability of their membranes by adjusting the number of cyclic moieties within the cores of their glycerol dibiphytanyl glycerol tetraether (GDGT) lipids. Cyclized GDGTs increase membrane packing and stability, which has been shown to help cells survive shifts in temperature and pH. However, the extent of this cyclization also varies with growth phase and electron acceptor or donor limitation. These observations indicate a relationship between energy metabolism and membrane composition. Here we show that the average degree of GDGT cyclization increases with doubling time in continuous cultures of the thermoacidophile Sulfolobus acidocaldarius (DSM 639). This is consistent with the behavior of a mesoneutrophile, Nitrosopumilus maritimus SCM1. Together, these results demonstrate that archaeal GDGT distributions can shift in response to electron donor flux and energy availability, independent of pH or temperature. Paleoenvironmental reconstructions based on GDGTs thus capture the energy available to microbes, which encompasses fluctuations in temperature and pH, as well as electron donor and acceptor availability. The ability of Archaea to adjust membrane composition and packing may be an important strategy that enables survival during episodes of energy stress.

Significance Statement

Microbial lipid membranes protect and isolate a cell from its environment while regulating the flow of energy and nutrients to metabolic reaction centers within. We demonstrate that membrane lipids change as a function of energy flux using a well-studied archaeon that thrives in acidic hot springs and observe an increase in membrane packing as energy becomes more limited. These observations are consistent with chemostat experiments utilizing a low temperature, neutral pH, marine archaeon. This strategy appears to regulate membrane homeostasis is common across GDGT-producing lineages, demonstrating that diverse taxa adjust membrane composition in response to chronic energy stress.