bluuebunny/crossref_metadata_2025_split huggingface dataset:
Root Responses to Heterogeneous Nitrate Availability are Mediated by trans-Zeatin in Arabidopsis Shoots
Authors: Poitout Arthur , Crabos Amandine , Petřík Ivan , Novák Ondrej , Krouk Gabriel , Lacombe Benoît , Ruffel Sandrine 
Published: January 2018

ABSTRACTPlants are subjected to variable nitrogen (N) availability including frequent spatial nitrate (NO3-) heterogeneity in soil. Thus, plants constantly adapt their genome expression and root physiology in order to optimize N acquisition from this heterogeneous source. These adaptations rely on a complex and long-distance root-shoot-root signaling network that is still largely unknown. Here, we used a combination of reverse genetics, transcriptomic analysis, NO3- uptake experiments and hormone profiling under conditions of homogeneous or heterogeneous NO3- availability to characterize the systemic signaling involved. We demonstrate the important role of the trans-zeatin form of cytokinin (CK) in shoots, in particular using a mutant altered for ABCG14-mediated trans-zeatin-translocation from the root to the shoot, in mediating: (i) rapid long distance N-demand signaling and (ii) long term functional adaptations to heterogeneous NO3- supply, including changes in NO3- transport capacity and root growth modifications. We also provide insights into the potential CK-dependent and independent shoot-to-root signals involved in root adaptation to heterogeneous N availability.
An Epiblast Stem Cell derived multipotent progenitor population for axial extension
Authors: Edri Shlomit , Hayward Penny , Baillie-Johnson Peter , Steventon Benjamin , Arias Alfonso Martinez 
Published: January 2018

AbstractThe Caudal Lateral Epiblast of mammalian embryos harbours bipotent progenitors that contribute to the spinal cord and the paraxial mesoderm in concert with the elongation of the body axis. These progenitors, called Neural Mesodermal Progenitors (NMPs) are identified as cells coexpressingSox2andT/Brachyury, a criterion used to derive NMP-like cells from Embryonic Stem Cells in vitro. However, these progenitors do not self renew, as embryonic NMPs do. Here we find that protocols that yield NMP-like cells in vitro first produce a multipotent population that, additional to NMPs, generates progenitors for the lateral plate and intermediate mesoderm. We show that Epiblast Stem Cells (EpiSCs) are an effective source for these multipotent progenitors that are further differentiated by a balance between BMP and Nodal signalling. Importantly, we show that NMP-like cells derived from EpiSCs self renew in vitro and exhibit a gene expression signature similar to that of their embryo counterparts.
Impacts of a millennium drought on butterfly faunal dynamics
Authors: Forister Matthew L. , Fordyce James A. , Nice Chris C. , Thorne James H. , Waetjen David P. , Shapiro Arthur M. 
Published: January 2018

AbstractClimate change is challenging plants and animals not only with increasing temperatures, but also with shortened intervals between extreme weather events. Relatively little is known about diverse assemblages of organisms responding to extreme weather, and even less is known about landscape and life history properties that might mitigate effects of extreme weather. We find that northern California butterflies were impacted by a millennium-scale drought differentially at low and high elevations. At low elevations, phenological shifts facilitated persistence and even recovery during drought, while at higher elevations a shortened flight season was associated with decreases in species richness. Phenological and faunal dynamics are predicted by temperature and precipitation, thus advancing the possibility of understanding and forecasting biological responses to extreme weather.
Uncovering temporal structure in hippocampal output patterns
Authors: Maboudi Kourosh , Ackermann Etienne , Pfeiffer Brad , Foster David , Diba Kamran , Kemere Caleb 
Published: January 2018

AbstractThe place cell activity of hippocampal pyramidal cells has been described as the cognitive map substrate of spatial memory. Replay is observed during hippocampal sharp-wave ripple-associated population burst events and is critical for consolidation and recall-guided behaviors. To present, population burst event (PBE) activity has been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with temporal replay sequences and that the latent states correspond to a spatial map of the environment. Moreover, we demonstrate the identification of hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to form a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.
High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays
Authors: Chung Jason E. , Joo Hannah R. , Lan Fan Jiang , Liu Daniel F. , Barnett Alex H. , Chen Supin , Geaghan-Breiner Charlotte , Karlsson Mattias P. , Karlsson Magnus , Lee Kye Y. , Liang Hexin , Magland Jeremy F. , Tooker Angela C. , Greengard Leslie F. , Tolosa Vanessa M. , Frank Loren M. 
Published: January 2018

AbstractThe brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.
The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology
Authors: Ge Tian , Chen Chia-Yen , Doyle Alysa E. , Vettermann Richard , Tuominen Lauri J. , Holt Daphne J. , Sabuncu Mert R. , Smoller Jordan W. 
Published: January 2018

AbstractIndividual differences in educational attainment are linked to differences in intelligence, and predict important social, economic and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses reveal the genetic overlap between cognitive ability and cortical thickness measurements in bilateral primary motor cortex and predominantly left superior temporal cortex and proximal regions. These findings may contribute to our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.
Inhalation frequency controls reformatting of mitral/tufted cell odor representations in the olfactory bulb
Authors: Díaz-Quesada Marta , Youngstrom Isaac A. , Tsuno Yusuke , Hansen Kyle R. , Economo Michael N. , Wachowiak Matt 
Published: January 2018

AbstractIn mammals olfactory sensation depends on inhalation, which controls activation of sensory neurons and temporal patterning of central activity. Odor representations by mitral and tufted (MT) cells, the main output from the olfactory bulb (OB), reflect sensory input as well as excitation and inhibition from OB circuits, which may change as sniff frequency increases. To test the impact of sampling frequency on MT cell odor responses, we obtained whole-cell recordings from MT cells in anesthetized male and female mice while varying inhalation frequency via tracheotomy, allowing comparison of inhalation-linked responses across cells. We characterized frequency effects on MT cell responses during inhalation of air and odorants using inhalation pulses and also ‘playback’ of sniffing recorded from awake mice. Inhalation-linked changes in membrane potential were well-predicted across frequency from linear convolution of 1 Hz responses and, as frequency increased, near-identical temporal responses could emerge from depolarizing, hyperpolarizing or multiphasic MT responses. However, net excitation was not well predicted from 1 Hz responses and varied substantially across MT cells, with some cells increasing and others decreasing in spike rate. As a result, sustained odorant sampling at higher frequencies led to increasing decorrelation of the MT cell population response pattern over time. Bulk activation of sensory inputs by optogenetic stimulation affected MT cells more uniformly across frequency, suggesting that frequency-dependent decorrelation emerges from odor-specific patterns of activity in the OB network. These results suggest that sampling behavior alone can reformat early sensory representations, possibly to optimize sensory perception during repeated sampling.Significance statementOlfactory sensation in mammals depends on inhalation, which increases in frequency during active sampling of olfactory stimuli. We asked how inhalation frequency can shape the neural coding of odor information by recording from projection neurons of the olfactory bulb while artificially varying odor sampling frequency in the anesthetized mouse. We found that sampling an odor at higher frequencies led to diverse changes in net responsiveness, as measured by action potential output, that were not predicted from low-frequency responses. These changes led to a reorganization of the pattern of neural activity evoked by a given odorant that occurred preferentially during sustained, high-frequency inhalation. These results point to a novel mechanism for modulating early sensory representations solely as a function of sampling behavior.
Breast Cancer Histopathological Image Classification: A Deep Learning Approach
Authors: Motlagh Mehdi Habibzadeh , Jannesari Mahboobeh , Aboulkheyr HamidReza , Khosravi Pegah , Elemento Olivier , Totonchi Mehdi , Hajirasouliha Iman 
Published: January 2018

AbstractBreast cancer remains the most common type of cancer and the leading cause of cancer-induced mortality among women with 2.4 million new cases diagnosed and 523,000 deaths per year. Historically, a diagnosis has been initially performed using clinical screening followed by histopathological analysis. Automated classification of cancers using histopathological images is a chciteallenging task of accurate detection of tumor sub-types. This process could be facilitated by machine learning approaches, which may be more reliable and economical compared to conventional methods.To prove this principle, we applied fine-tuned pre-trained deep neural networks. To test the approach we first classify different cancer types using 6, 402 tissue micro-arrays (TMAs) training samples. Our framework accurately detected on average 99.8% of the four cancer types including breast, bladder, lung and lymphoma using the ResNet V1 50 pre-trained model. Then, for classification of breast cancer sub-types this approach was applied to 7,909 images from the BreakHis database. In the next step, ResNet V1 152 classified benign and malignant breast cancers with an accuracy of 98.7%. In addition, ResNet V1 50 and ResNet V1 152 categorized either benign- (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) or malignant- (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) sub-types with 94.8% and 96.4% accuracy, respectively. The confusion matrices revealed high sensitivity values of 1, 0.995 and 0.993 for cancer types, as well as malignant- and benign sub-types respectively. The areas under the curve (AUC) scores were 0.996,0.973 and 0.996 for cancer types, malignant and benign sub-types, respectively. Overall, our results show negligible false negative (on average 3.7 samples) and false positive (on average 2 samples) results among different models. Availability: Source codes, guidelines and data sets are temporarily available on google drive upon request before moving to a permanent GitHub repository.
Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown
Authors: Werner Gijsbert DA , Cornelissen Johannes HC , Cornwell William K , Soudzilovskaia Nadejda A , Kattge Jens , West Stuart A , Kiers E Toby 
Published: January 2018

AbstractCooperative interactions among species, termed mutualisms, have played a crucial role in the evolution of life on Earth. However, despite key potential benefits to partners, there are many cases where two species cease to cooperate, and mutualisms break down. What factors drive the evolutionary breakdown of mutualism? We examined the pathways towards breakdowns of the mutualism between plants and arbuscular mycorrhizal (AM) fungi. Using a comparative approach, we identify ~25 independent cases of complete mutualism breakdown across global seed plants. We found that breakdown of cooperation was only stable when host plants either: (i) partner with other root symbionts or (ii) evolve alternative resource acquisition strategies. Our results suggest that key mutualistic services are only permanently lost if hosts evolve alternative symbioses or adaptations.Significance StatementCooperative interactions among species – mutualisms – are major sources of evolutionary innovation. However, despite their importance, two species that formerly cooperated sometimes cease their partnership. Why do mutualisms breakdown? We asked this question in the partnership between arbuscular mycorrhizal (AM) fungi and their plant hosts, one of the most ancient mutualisms. We analyse two potential trajectories towards evolutionary breakdown of their cooperation, symbiont switching and mutualism abandonment. We find evidence that plants stop interacting with AM fungi when they switch to other microbial mutualists or when they evolve alternative strategies to extract nutrients from the environment. Our results show vital cooperative interactions can be lost - but only if successful alternatives evolve.
Transient N-6-methyladensosine Transcriptome sequencing reveals a regulatory role of m6A in splicing efficiency
Authors: Louloupi Annita , Ntini Evgenia , Conrad Thomas , Ørom Ulf Andersson 
Published: January 2018

AbstractSplicing efficiency varies among transcripts, and tight control of splicing kinetics is crucial for coordinated gene expression. N-6-methyladenosine (m6A) is the most abundant RNA modification and is involved in regulation of RNA biogenesis and function. The impact of m6A on the regulation of RNA splicing kinetics has not been investigated. Here, we provide the first time-resolved high-resolution assessment of m6A on nascent RNA transcripts and unveil its importance for the control of RNA splicing kinetics. We identify that early co-transcriptional m6A deposition near splice junctions promotes fast splicing, while m6A modification of introns is associated with long, slowly processed introns and alternative splicing events. In conclusion, by directly comparing the processing dynamics of individual transcripts in the methylated versus unmethylated state on a transcriptome-wide scale we show that early m6A deposition marks transcripts for a fast-track processing.
Identifying associations in dense connectomes using structured kernel principal component regression
Authors: Gong Weikang , Cheng Fan , Rolls Edmund T. , Zhang Lingli , Grünewald Stefan , Feng Jianfeng 
Published: January 2018

AbstractA powerful and computationally efficient multivariate approach is proposed here, called structured kernel principal component regression (sKPCR), for the identification of associations in the voxel-level dense connectome. The method can identify voxel-phenotype associations based on the voxels’ whole-brain connectivity pattern, which is applicable to detect linear and non-linear signals for both volume-based and surface-based functional magnetic resonance imaging (fMRI) data. For each voxel, our approach first extracts signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations via a general linear model. The method derives its power by appropriately modelling the spatial structure of the data. Simulations based on dense connectome data have shown that our method can accurately control the false-positive rate, and it is more powerful than many state-of-the-art approaches, such as the connectivity-wise general linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). To demonstrate the utility of our approach in real data analysis, we apply these methods to identify voxel-wise difference between schizophrenic patients and healthy controls in two independent resting-state fMRI datasets. The findings of our approach have a better between-sites reproducibility, and a larger proportion of overlap with existing schizophrenia findings. Code for our approach can be downloaded from https://github.com/weikanggong/vBWAS.
RNA-binding proteins with mixed charge domains self-assemble and aggregate in Alzheimer’s Disease
Authors: Bishof Isaac , Dammer Eric B. , Duong Duc M. , Gearing Marla , Lah James J. , Levey Allan I. , Seyfried Nicholas T. 
Published: January 2018

ABSTRACTU1 small nuclear ribonucleoprotein 70 kDa (U1-70K) and other RNA binding proteins (RBPs) are mislocalized to cytoplasmic neurofibrillary Tau aggregates in Alzheimer’s disease (AD), yet understanding of the mechanisms that cause their aggregation is limited. Many RBPs that aggregate in neurodegenerative diseases self-assemble into RNA granules through intrinsically disordered low complexity (LC) domains. We report here that a LC domain within U1-70K of mixed charge, containing highly repetitive complementary repeats of basic (R/K) and acidic (D/E) residues, shares many of the same properties of the Q/N-rich LC domains found in the RBPs TDP-43 and FUS. These properties include the ability to self-assemble into oligomers, and to form nuclear granules. To analyze the functional roles of the U1-70K LC domains, we performed co-immunoprecipitation and quantitative mass spectrometry analysis of recombinant U1-70K and deletions lacking the C-terminal LC domain(s). A network-driven approach resolved functional classes of U1-70K interacting proteins that showed dependency on the U1-70K LC domain(s) for their interaction. This included structurally similar RBPs, such as LUC7L3 and RBM25, which require their respective mixed charge domains for reciprocal interactions with U1-70K and for participation in nuclear RNA granules. Strikingly, a significant proportion of RBPs with mixed charge domains have elevated insolubility in AD brain proteome compared to controls. Furthermore, we show that the mixed charge LC domain of U1-70K can interact with Tau from AD brain. These findings highlight mechanisms for mixed charge domains in stabilizing RBP interactions and in potentially mediating co-aggregation with pathological Tau isoforms in AD.
MiCoP: Microbial Community Profiling method for detecting viral and fungal organisms in metagenomic samples
Authors: LaPierre Nathan , Mangul Serghei , Alser Mohammed , Mandric Igor , Wu Nicholas C. , Koslicki David , Eskin Eleazar 
Published: January 2018

AbstractBackgroundHigh throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes.ResultsHere we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project.ConclusionsMiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation is publicly available on GitHub at: https://github.com/smangul1/MiCoP