bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anatomical drivers of stomatal conductance in sorghum lines with different leaf widths grown under different temperatures
Authors: Al-Salman Yazen , Cano Francisco Javier , Pan Ling , Koller Fiona , Piñeiro Juan , Jordan David , Ghannoum Oula 
Published: October 2022

SummaryImprovements in leaf water use efficiency (iWUE) can maintain crop productivity in water limited environments under rising temperatures. We investigated the leaf anatomical traits which underpin our recently identified link between leaf width (LW) andiWUE.Ten sorghum lines with varyingLWwere grown under three temperatures to expand the range of variation of bothLWand gas exchange rates. Leaf gas exchange, surface morphology and cross-sectional anatomy were measured and analysed using structural equations modelling.Narrower leaves had lower stomatal conductance (gs) and higheriWUEacross growth temperatures. They also had smaller intercellular airspaces, stomatal size, percentage of open stomatal aperture relative to maximum, hydraulic pathway, mesophyll thickness, and leaf mass per area. Structural modelling revealed a developmental association among leaf anatomical traits that underpinnedgsvariation in sorghum.Growing temperature andLWboth impacted leaf gas exchange rates, but onlyLWdirectly impacted leaf anatomy. Wider leaves may be more productive under well-watered conditions, but consume more water for growth and development, which is detrimental under water stress.HighlightCoordination between leaf width and leaf anatomy underpins stomatal conductance variation in sorghum grown under different temperatures.
Microbial nanowires with genetically modified peptide ligands to sustainably fabricate electronic sensing devices
Authors: Lekbach Yassir , Ueki Toshiyuki , Liu Xiaomeng , Woodard Trevor , Yao Jun , Lovley Derek R. 
Published: October 2022

AbstractNanowires have substantial potential as the sensor component in electronic sensing devices. However, surface functionalization of traditional nanowire and nanotube materials with short peptides that increase sensor selectivity and sensitivity requires complex chemistries with toxic reagents. In contrast, microorganisms can assemble pilin monomers into protein nanowires with intrinsic conductivity from renewable feedstocks, yielding an electronic material that is robust and stable in applications, but also biodegradable. Here we report that the sensitivity and selectivity of protein nanowire-based sensors can be modified with a simple plug and play genetic approach in which a short peptide sequence, designed to bind the analyte of interest, is incorporated into the pilin protein that is microbially assembled into nanowires. We employed a scalableEscherichia colichassis to fabricate protein nanowires that displayed either a peptide previously demonstrated to effectively bind ammonia, or a peptide known to bind acetic acid. Sensors comprised of thin films of the nanowires amended with the ammonia-specific peptide had a ca. 100-fold greater response to ammonia than sensors made with unmodified protein nanowires. Protein nanowires with the peptide that binds acetic acid yielded a 4-fold higher response than nanowires without the peptide. The results demonstrate that protein nanowires with enhanced sensor response for analytes of interest can be fabricated with a flexible genetic strategy that sustainably eliminates the energy, environmental, and health concerns associated with other common nanomaterials.
Elasmobranch genome sequencing reveals evolutionary trends of vertebrate karyotype organization
Authors: Yamaguchi Kazuaki , Uno Yoshinobu , Kadota Mitsutaka , Nishimura Osamu , Nozu Ryo , Murakumo Kiyomi , Matsumoto Rui , Sato Keiichi , Kuraku Shigehiro 
Published: October 2022

AbstractGenomic studies of vertebrate chromosome evolution have long been hindered by the scarcity of chromosome-scale DNA sequences of some key taxa. One of those limiting taxa has been the elasmobranchs (sharks and rays), which harbor species often with numerous chromosomes and enlarged genomes. Here, we report the chromosome-scale genome assembly for the zebra sharkStegostoma tigrinum, an endangered species that has the smallest genome sequenced to date among sharks (3.71 Gb), as well as for the whale sharkRhincodon typus. Our analysis employing a male–female comparison identified an X chromosome, the first genomically characterized shark sex chromosome. The X chromosome harbors a Hox C cluster whose intact linkage has not been shown for an elasmobranch fish. The sequenced shark genomes exhibit a gradualism of chromosome length with remarkable length-dependent characteristics—shorter chromosomes tend to have higher GC content, gene density, synonymous substitution rate, and simple tandem repeat content as well as smaller gene length, which resemble the edges of longer chromosomes. This pattern of intragenomic heterogeneity, previously recognized as peculiar to species with so-called microchromosomes, occurs in more vertebrates including elasmobranchs. We challenge the traditional binary classification of karyotypes as with and without microchromosomes, as even without microchromosomes, shorter chromosomes tend to have higher contents of GC and simple tandem repeats and harbor shorter and more rapid-evolving genes. Such characteristics also appear on the edges of longer chromosomes. Our investigation of elasmobranch karyotypes underpins their unique characteristics and provides clues for understanding how vertebrate karyotypes accommodate intragenomic heterogeneity to realize a complex readout.
Dietary restriction fails to extend life in stressful environments
Authors: Zajitschek Felix , Zajitschek Susanne R.K. , Vasconcelos Ana C.O. , Bonduriansky Russell 
Published: October 2022

ABSTRACTModerate dietary restriction often prolongs life in laboratory animals, and this response has been interpreted as an adaptive strategy that promotes survival during famine. However, dietary restriction can also increase frailty, and it therefore remains unclear whether restricted diets prolong life under stressful conditions like those experienced by wild animals. We manipulated adult dietary protein ofDrosophila melanogasteracross a gradient of ambient temperature. We found that protein restriction increased longevity of both sexes at benign ambient temperatures (25-27°C), but failed to extend or even reduced longevity of flies maintained in cold (21-23°C) or hot (29°C) conditions. Protein restriction also generally reduced reproductive performance, and did not consistently enhance performance of F1, F2 or F3 descendants. Our results challenge the long-held idea that extended longevity of diet-restricted laboratory animals represents an adaptive survival strategy in natural populations, and suggest instead that this response is an artefact of benign laboratory conditions.
Loss of Linker Histone H1 in the Maternal Genome Influences DEMETER-Mediated Demethylation and Affects the Endosperm DNA Methylation Landscape
Authors: Han Qiang , Hung Yu-Hung , Zhang Changqing , Bartels Arthur , Rea Matthew , Yang Hanwen , Park Christine , Zhang Xiang-Qian , Fischer Robert L , Xiao Wenyan , Hsieh Tzung-Fu 
Published: October 2022

AbstractTheArabidopsisDEMETER (DME) DNA glycosylase demethylates the central cell genome prior to fertilization. This epigenetic reconfiguration of the female gamete companion cell establishes gene imprinting in the endosperm and is essential for seed viability. DME demethylates small and genic-flanking transposons as well as intergenic and heterochromatin sequences, but how DME is recruited to these target loci remains unknown. H1.2 was identified as a DME-interacting protein in a yeast two-hybrid screen, and maternal genome H1 loss affects DNA methylation and expression of selected imprinted genes in the endosperm. Yet, the extent to which how H1 influences DME demethylation and gene imprinting in theArabidopsisendosperm has not been investigated. Here, we showed that unlike in the vegetative cell, both canonical histone H1 variants are present in the central cell. Our endosperm methylome analysis revealed that without the maternal linker histones, DME-mediated demethylation is facilitated, particularly in the heterochromatin regions, indicating that H1-containing nucleosomes are barriers for DME demethylation. Loss of H1 in the maternal genome has a very limited effect on gene transcription or gene imprinting regulation in the endosperm; however, it variably influences euchromatin TE methylation and causes a slight hypermethylation and a reduced expression in selected imprinted genes. We conclude that loss of maternal H1 indirectly influences DME-mediated demethylation and endosperm DNA methylation landscape but does not appear to affect endosperm gene transcription and overall imprinting regulation.
Atomistic Insights Into The Mechanism of Dual Affinity Switching In Plant Nitrate Transporter NRT1.1
Authors: Selvam Balaji , Feng Jiangyan , Shukla Diwakar 
Published: October 2022

ABSTRACTImproving nitrogen use efficiency is critical to enhancing agricultural productivity and to mitigate environmental pollution. To overcome the fluctuations in soil nitrate concentration, plants have evolved an elaborate nitrate transporting mechanism that switches between high and low affinity. In plants, NRT1.1, a root-associated nitrate transporter, switches its affinity upon phosphorylation at Thr101. However, the molecular basis of this unique functional behavior known as dual-affinity switching remains elusive. Crystal structures of the NRT1.1 nitrate transporter have provided evidence for the two competing hypotheses to explain the origin of dual-affinity switching. It is not known how the interplay between transporter phosphorylation and dimerization regulates the affinity switching. To reconcile the different hypotheses, we have performed extensive simulations of nitrate transporter in conjunction with Markov state models to elucidate the molecular origin for a dual-affinity switching mechanism. Simulations of monomeric transporter reveal that phosphorylation stabilizes the outward-facing state and accelerates dynamical transitions for facilitating transport. On the other hand, phosphorylation of the transporter dimer decouples dynamic motions of dimer into independent monomers and thus facilitates substrate transport. Therefore, the phosphorylation-induced enhancement of substrate transport and dimer decoupling not only reconcile the competing experimental results but also provide an atomistic view of how nitrate transport is regulated in plants.
Estimating LFT and qPCR test-sensitivity over time since infection from a human challenge study
Authors: Davis Emma L , Hollingsworth T Deirdre 
Published: October 2022

AbstractTesting has been central to global policy throughout the SARS-CoV-2 pandemic. Understanding how test sensitivity changes after exposure is crucial for the interpretation of test outcomes and the design of testing-based interventions. Using data from a human challenge study, we derive temporal test sensitivity profiles for lateral flow tests (LFT), quantitative polymerase chain reaction (qPCR) tests and viable virus, measured by focus-forming assay (FFA). The median time to detectability was 2 days (throat swab) and 3 days (nasal) for qPCR and 4 days for LFT (both swabs), and there was strong positive correlation between first LFT positive and first FFA positive for both throat (p=0.00019) and nasal (p=0.00032) swabs, supporting the use of LFTs as a method of detecting infectiousness. Peak LFT sensitivity was 82.4% (67.0%-91.8%) for throat samples, occurring 6 days post-exposure and 93.3% (85.1%-98.0%) for nasal, 7 days post-exposure. These temporal profiles provide quantification of the mean behaviour of these tests and individual-based variability and could inform a framework for investigating future testing-based interventions.
Towards 3D Deep Learning for neuropsychiatry: predicting Autism diagnosis using an interpretable Deep Learning pipeline applied to minimally processed structural MRI data
Authors: Garcia Mélanie , Kelly Clare 
Published: October 2022

1AbstractBy capitalizing on the power of multivariate analyses of large datasets, predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions. While Deep Learning offers a particularly promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D Deep Learning applied to minimally processed structural MRI scans. We trained 3D Deep Learning models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous Machine Learning studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template, thus minimizing the time required to generate predictions. Further, our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, such as in a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism, and to enable the extension of our method to other neuropsychiatric conditions.
Cumulative COVID-19 incidence indicators should account for the population-at-risk dynamics
Authors: Masjuan Pere , Trias-Llimós Sergi , Rojas Camilo , Prakash Arul , Gullón Pedro , Tobias Aurelio , López-Gay Antonio 
Published: October 2022

AbstractRisk estimators for COVID-19 propagation based on the incidence rate of new cases can be misleading as they usually fail to account for the fraction of population immunized by infection or vaccination.This misconception yields different incidence rates, as we illustrate using the daily number of COVID-19 reported cases in Spain during the pre-vaccine period, between 15/01/2020 and 11/07/2021. An increase in the incidence rate of about 7% is found when properly accounting for the population at risk. Our results demonstrate that accounting for dynamic changes to the immunized fraction of the population is necessary for accurate risk estimation. We hope that our findings can lead to more effective strategies for pandemic response.
Challenges and advances in measuring phenotypic convergence
Authors: Grossnickle David M. , Brightly William H. , Weaver Lucas N. , Stanchak Kathryn E. , Roston Rachel A. , Pevsner Spencer K. , Stayton C. Tristan , Polly P. David , Law Chris J. 
Published: October 2022

ABSTRACTTests of phenotypic convergence can provide evidence of adaptive evolution, and the popularity of such studies has grown in recent years due to the development of novel, quantitative methods for identifying and measuring convergence. These methods include the commonly appliedC1–C4 measures of Stayton (2015), which measure morphological distances between lineages, and Ornstein-Uhlenbeck (OU) evolutionary model-fitting analyses, which test whether lineages convergently evolved toward adaptive peaks. We test the performance ofC-measures and other convergence measures under various evolutionary scenarios and reveal a critical issue withC-measures: they often misidentify divergent lineages as convergent. We address this issue by developing novel convergence measures—Ct1–Ct4-measures—that measure distances between lineages at specific points in time, minimizing the possibility of misidentifying divergent taxa as convergent.Ct-measures are most appropriate when focal lineages are of the same or similar geologic ages (e.g., extant taxa), meaning that the lineages’ evolutionary histories include considerable overlap in time. BeyondC-measures, we find that all convergence measures are influenced by the position of focal taxa in phenotypic space, with morphological outliers often statistically more likely to be measured as strongly convergent by chance. Further, we mimic scenarios in which researchers assess convergence using OU models witha prioriregime assignments (e.g., classifying taxa by ecological traits), and we find that multiple-regime OU models with phenotypically divergent lineages assigned to a shared selective regime often outperform simpler models. This highlights that model support for these multiple-regime OU models should not be assumed to always reflect convergence among focal lineages of a shared regime. Our newCt1–Ct4-measures provide researchers with an improved comparative tool, but we emphasize that all available convergence measures are imperfect, and researchers should recognize the limitations of these methods and use multiple lines of evidence when inferring and measuring convergence.
Spatial Transcriptomics of Intraductal Papillary Mucinous Neoplasms of The Pancreas Identifies NKX6-2 as a Driver of Gastric Differentiation and Indolent Biological Potential
Authors: Sans Marta , Makino Yuki , Min Jimin , Rajapakshe Kimal I. , Yip-Schneider Michele , Schmidt C Max , Hurd Mark W. , Burks Jared K. , Gomez Javier A. , Thege Fredrik I. , Fahrmann Johannes , Wolff Robert A. , Kim Michael P. , Guerrero Paola A. , Maitra Anirban 
Published: October 2022

AbstractIntraductal Papillary Mucinous Neoplasms (IPMNs) of the pancreas arebona fideprecursor lesions of pancreatic ductal adenocarcinoma (PDAC). The most common subtype of IPMNs harbor a gastric foveolar-type epithelium, and these low-grade mucinous neoplasms are harbingers of IPMNs with high-grade dysplasia and cancer. The molecular underpinning of gastric differentiation in IPMNs is unknown, although identifying drivers of this indolent phenotype might enable opportunities for intercepting progression to high-grade IPMN and cancer. We conducted spatial transcriptomics on a cohort of IPMNs, followed by orthogonal and cross species validation studies, which established the transcription factor NKX6-2 as a key determinant of gastric cell identity in low-grade IPMNs. Loss ofNKX6-2expression is a consistent feature of IPMN progression, while re-expression ofNKX6-2in murine IPMN lines recapitulates the aforementioned gastric transcriptional program and glandular morphology. Our study identifies NKX6-2 as a previously unknown transcription factor driving indolent gastric differentiation in IPMN pathogenesis.SignificanceImproved understanding of the molecular features driving IPMN development and differentiation is critical to prevent cancer progression and enhance risk stratification. Our study employed spatial profiling technologies to characterize the epithelium and microenvironment of IPMN, which revealed a previously unknown link betweenNKX6-2expression and gastric differentiation in IPMNs, the latter associated with an indolent biological potential.
Phase transition in atomistic simulations of model membrane with thylakoid lipids of red algae
Authors: Rathod Arun K. , Chavda Dhruvil , Manna Moutusi 
Published: October 2022

AbstractMarine algae are diverse photosynthetic organisms, profoundly rich in bioactive compounds. Temperature is a major factor in algal cultivation and biomass production. At the cellular level, the change of temperature is reflected in oscillating algal lipid/fatty acid profile and inhibition of photosynthetic activities. The function of thylakoid membrane system is intimately dependent on its lipid matrix, however the molecular organization of these lipid membranes and particularly their adaptive arrangements under temperature stress remain largely unexplored. The present work employing extensive atomistic simulations provides the first atomistic view of the phase transition and domain coexistence in model membrane composed of thylakoid lipids of a marine alga, between 10-40 °C. The model membrane undergoes a transition from a gel-like phase at 10-15 °C to a homogeneous liquid-disordered phase at 40 °C. Clear evidences of spontaneous phase separation into coexisting nanoscale domains are detected at intermediate temperatures. Particularly at 25-30 °C, we identified the formation of a stable rippled phase, where the gel-like domains rich in saturated and nearly hexagonally packed lipids separated from fluid-like domains enriched in lipids containing polyunsaturated chains. Cholesterol impairs the phase transition and the emergence of domains, and induces a fairly uniform liquid-ordered phase in the membrane over the temperatures studied. The results have implications in understanding the role of lipids in temperature adaptation in algal.
Genome sequencing of the Monkeypox virus 2022 outbreak with Amplicon-based Oxford Nanopore MinION sequencing
Authors: Brinkmann Annika , Pape Katharina , Uddin Steven , Woelk Niklas , Förster Sophie , Jessen Heiko , Michel Janine , Kohl Claudia , Schaade Lars , Nitsche Andreas 
Published: October 2022

AbstractWe present an amplicon-based assay for MinION Nanopore sequencing of monkeypox virus genomes from clinical specimens, obtaining high-quality results for up to 99 % of the genomes, even for samples with high Ct values of up to 30 which are not suitable for shotgun sequencing.
Automated neuronal reconstruction with super-multicolour fluorescence imaging
Authors: Leiwe Marcus N. , Fujimoto Satoshi , Baba Toshikazu , Moriyasu Daichi , Saha Biswanath , Sakaguchi Richi , Inagaki Shigenori , Imai Takeshi 
Published: October 2022

ABSTRACTFluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we established stochastic “super-multicolour” labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron was labelled with a unique combination of fluorescent proteins, which were then imaged and separated by linear unmixing. We also established an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder). To classify colour combinations, we used a newly developed unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space were clustered based on a given threshold distance. Our new strategy allows for the reconstruction of neurites for up to hundreds of neurons at a millimetre scale without manual tracing.